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from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""MMBTForClassification""", """MMBTModel""", """ModalEmbeddings"""]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
a_ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = ["""pixel_values"""]
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = True , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
__lowerCamelCase = size if size is not None else {'''shortest_edge''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
__lowerCamelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase , param_name='''crop_size''' )
__lowerCamelCase = do_resize
__lowerCamelCase = size
__lowerCamelCase = resample
__lowerCamelCase = do_center_crop
__lowerCamelCase = crop_size
__lowerCamelCase = do_rescale
__lowerCamelCase = rescale_factor
__lowerCamelCase = do_normalize
__lowerCamelCase = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__lowerCamelCase = image_std if image_std is not None else OPENAI_CLIP_STD
__lowerCamelCase = do_convert_rgb
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
__lowerCamelCase = get_resize_output_image_size(__UpperCAmelCase , size=size['''shortest_edge'''] , default_to_square=__UpperCAmelCase )
return resize(__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(__UpperCAmelCase , size=(size['''height'''], size['''width''']) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = do_resize if do_resize is not None else self.do_resize
__lowerCamelCase = size if size is not None else self.size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = resample if resample is not None else self.resample
__lowerCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowerCamelCase = crop_size if crop_size is not None else self.crop_size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''crop_size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = do_rescale if do_rescale is not None else self.do_rescale
__lowerCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCamelCase = do_normalize if do_normalize is not None else self.do_normalize
__lowerCamelCase = image_mean if image_mean is not None else self.image_mean
__lowerCamelCase = image_std if image_std is not None else self.image_std
__lowerCamelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__lowerCamelCase = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__lowerCamelCase = [convert_to_rgb(__UpperCAmelCase ) for image in images]
# All transformations expect numpy arrays.
__lowerCamelCase = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
__lowerCamelCase = [self.resize(image=__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase ) for image in images]
if do_center_crop:
__lowerCamelCase = [self.center_crop(image=__UpperCAmelCase , size=__UpperCAmelCase ) for image in images]
if do_rescale:
__lowerCamelCase = [self.rescale(image=__UpperCAmelCase , scale=__UpperCAmelCase ) for image in images]
if do_normalize:
__lowerCamelCase = [self.normalize(image=__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase ) for image in images]
__lowerCamelCase = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
__lowerCamelCase = {'''pixel_values''': images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase )
| 330 | 1 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def a__ ( _UpperCamelCase : Optional[int] ):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class __lowerCAmelCase ( nn.Module ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
super().__init__()
__lowerCamelCase = module
__lowerCamelCase = nn.Sequential(
nn.Linear(module.in_features , __UpperCAmelCase , bias=__UpperCAmelCase ) , nn.Linear(__UpperCAmelCase , module.out_features , bias=__UpperCAmelCase ) , )
__lowerCamelCase = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=__UpperCAmelCase )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def lowerCamelCase ( self , __UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return self.module(__UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ) + self.adapter(__UpperCAmelCase )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __lowerCAmelCase ( unittest.TestCase ):
# We keep the constants inside the init function and model loading inside setUp function
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
# Therefore here we use only bloom-1b3 to test our module
lowerCAmelCase__ = """bigscience/bloom-1b7"""
# Constant values
lowerCAmelCase__ = 2.1_09_65_95_52_69_25_74
lowerCAmelCase__ = """Hello my name is"""
lowerCAmelCase__ = set()
EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I""" )
EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n""" )
EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University""" )
lowerCAmelCase__ = 1_0
def lowerCamelCase ( self ):
'''simple docstring'''
# Models and tokenizer
__lowerCamelCase = AutoTokenizer.from_pretrained(self.model_name )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# Models and tokenizer
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='''auto''' )
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
def lowerCamelCase ( self ):
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_abit.config
self.assertTrue(hasattr(__UpperCAmelCase , '''quantization_config''' ) )
__lowerCamelCase = config.to_dict()
__lowerCamelCase = config.to_diff_dict()
__lowerCamelCase = config.to_json_string()
def lowerCamelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
__lowerCamelCase = self.model_fpaa.get_memory_footprint()
__lowerCamelCase = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
__lowerCamelCase = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def lowerCamelCase ( self ):
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(__UpperCAmelCase , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = self.model_abit.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BitsAndBytesConfig()
__lowerCamelCase = True
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = model_abit_from_config.generate(
input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def lowerCamelCase ( self ):
'''simple docstring'''
with self.assertRaises(__UpperCAmelCase ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BitsAndBytesConfig()
with self.assertRaises(__UpperCAmelCase ):
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__UpperCAmelCase , load_in_abit=__UpperCAmelCase , device_map='''auto''' , bnb_abit_quant_type='''nf4''' , )
def lowerCamelCase ( self ):
'''simple docstring'''
with self.assertRaises(__UpperCAmelCase ):
# Tries with `str`
self.model_abit.to('''cpu''' )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.to(torch.device('''cuda:0''' ) )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = self.model_fpaa.to(torch.floataa )
__lowerCamelCase = self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.to('''cpu''' )
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.half()
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.float()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''t5-small''' , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __lowerCAmelCase ( unittest.TestCase ):
@classmethod
def lowerCamelCase ( cls ):
'''simple docstring'''
__lowerCamelCase = '''t5-small'''
__lowerCamelCase = '''google/flan-t5-small''' # flan-t5 uses dense-act instead of dense-relu-dense
__lowerCamelCase = AutoTokenizer.from_pretrained(cls.model_name )
__lowerCamelCase = '''Translate in German: Hello, my dog is cute'''
def lowerCamelCase ( self ):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
from transformers import TaForConditionalGeneration
__lowerCamelCase = TaForConditionalGeneration._keep_in_fpaa_modules
__lowerCamelCase = None
# test with `t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
# test with `flan-t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
__lowerCamelCase = modules
def lowerCamelCase ( self ):
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
# test with `flan-t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# model_name
__lowerCamelCase = '''bigscience/bloom-560m'''
__lowerCamelCase = '''t5-small'''
# Different types of model
__lowerCamelCase = AutoModel.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# Sequence classification model
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# CausalLM model
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# Seq2seq model
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
def lowerCamelCase ( self ):
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''text-generation''' , model=self.model_name , model_kwargs={'''device_map''': '''auto''', '''load_in_4bit''': True, '''torch_dtype''': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
__lowerCamelCase = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]['''generated_text'''] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=__UpperCAmelCase , device_map='''balanced''' )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
# Second real batch
__lowerCamelCase = model_parallel.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-350m'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
if version.parse(importlib.metadata.version('''bitsandbytes''' ) ) < version.parse('''0.37.0''' ):
return
# Step 1: freeze all parameters
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
__lowerCamelCase = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
__lowerCamelCase = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(__UpperCAmelCase ) ):
__lowerCamelCase = LoRALayer(module.q_proj , rank=16 )
__lowerCamelCase = LoRALayer(module.k_proj , rank=16 )
__lowerCamelCase = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
__lowerCamelCase = self.tokenizer('''Test batch ''' , return_tensors='''pt''' ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
__lowerCamelCase = model.forward(**__UpperCAmelCase )
out.logits.norm().backward()
for module in model.modules():
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(__UpperCAmelCase , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """gpt2-xl"""
lowerCAmelCase__ = 3.31_91_85_48_54_15_21_87
| 330 |
from __future__ import annotations
from typing import Generic, TypeVar
a_ = TypeVar("""T""")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = data
__lowerCamelCase = self
__lowerCamelCase = 0
class __lowerCAmelCase ( Generic[T] ):
def __init__( self ):
'''simple docstring'''
# map from node name to the node object
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# create a new set with x as its member
__lowerCamelCase = DisjointSetTreeNode(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# find the set x belongs to (with path-compression)
__lowerCamelCase = self.map[data]
if elem_ref != elem_ref.parent:
__lowerCamelCase = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# helper function for union operation
if nodea.rank > nodea.rank:
__lowerCamelCase = nodea
else:
__lowerCamelCase = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# merge 2 disjoint sets
self.link(self.find_set(__UpperCAmelCase ) , self.find_set(__UpperCAmelCase ) )
class __lowerCAmelCase ( Generic[T] ):
def __init__( self ):
'''simple docstring'''
# connections: map from the node to the neighbouring nodes (with weights)
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# add a node ONLY if its not present in the graph
if node not in self.connections:
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# add an edge with the given weight
self.add_node(__UpperCAmelCase )
self.add_node(__UpperCAmelCase )
__lowerCamelCase = weight
__lowerCamelCase = weight
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda __UpperCAmelCase : x[2] )
# creating the disjoint set
__lowerCamelCase = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(__UpperCAmelCase )
# MST generation
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = edges[index]
index += 1
__lowerCamelCase = disjoint_set.find_set(__UpperCAmelCase )
__lowerCamelCase = disjoint_set.find_set(__UpperCAmelCase )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
disjoint_set.union(__UpperCAmelCase , __UpperCAmelCase )
return graph
| 330 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = tempfile.mkdtemp()
# fmt: off
__lowerCamelCase = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__lowerCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__lowerCamelCase = {
'''do_resize''': True,
'''size''': {'''height''': 18, '''width''': 18},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__lowerCamelCase = os.path.join(self.tmpdirname , __UpperCAmelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__lowerCamelCase = [Image.fromarray(np.moveaxis(__UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = self.get_image_processor()
__lowerCamelCase = VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
__lowerCamelCase = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__lowerCamelCase = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__lowerCamelCase = self.get_image_processor(do_normalize=__UpperCAmelCase , padding_value=1.0 )
__lowerCamelCase = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_image_processor()
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
__lowerCamelCase = self.prepare_image_inputs()
__lowerCamelCase = image_processor(__UpperCAmelCase , return_tensors='''np''' )
__lowerCamelCase = processor(images=__UpperCAmelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_image_processor()
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
__lowerCamelCase = '''lower newer'''
__lowerCamelCase = processor(text=__UpperCAmelCase )
__lowerCamelCase = tokenizer(__UpperCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_image_processor()
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
__lowerCamelCase = '''lower newer'''
__lowerCamelCase = self.prepare_image_inputs()
__lowerCamelCase = processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(__UpperCAmelCase ):
processor()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_image_processor()
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
__lowerCamelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__lowerCamelCase = processor.batch_decode(__UpperCAmelCase )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_image_processor()
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
__lowerCamelCase = '''lower newer'''
__lowerCamelCase = self.prepare_image_inputs()
__lowerCamelCase = processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 330 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=2 , __UpperCAmelCase=8 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=16 , __UpperCAmelCase=5 , __UpperCAmelCase=2 , __UpperCAmelCase=36 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=512 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_input_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase ( self ):
'''simple docstring'''
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_config()
__lowerCamelCase = 300
return config
def lowerCamelCase ( self ):
'''simple docstring'''
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = self.prepare_config_and_inputs()
__lowerCamelCase = True
__lowerCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = True
__lowerCamelCase = MraModel(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , )
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , )
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraForQuestionAnswering(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MraForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MraForTokenClassification(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_choices
__lowerCamelCase = MraForMultipleChoice(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = ()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCamelCase = type
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = MraModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@unittest.skip(reason='''MRA does not output attentions''' )
def lowerCamelCase ( self ):
'''simple docstring'''
return
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowerCamelCase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowerCamelCase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = 50265
__lowerCamelCase = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' )
__lowerCamelCase = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = 50265
__lowerCamelCase = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 330 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowerCAmelCase ( lowerCAmelCase__ ):
@slow
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' , '''prajjwal1/bert-tiny''' )
__lowerCamelCase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
__lowerCamelCase = bertabert.config.encoder.vocab_size
__lowerCamelCase = tokenizer.sep_token_id
__lowerCamelCase = tokenizer.cls_token_id
__lowerCamelCase = 128
__lowerCamelCase = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''train[:1%]''' )
__lowerCamelCase = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''validation[:1%]''' )
__lowerCamelCase = train_dataset.select(range(32 ) )
__lowerCamelCase = val_dataset.select(range(16 ) )
__lowerCamelCase = 4
def _map_to_encoder_decoder_inputs(__UpperCAmelCase ):
# Tokenizer will automatically set [BOS] <text> [EOS]
__lowerCamelCase = tokenizer(batch['''article'''] , padding='''max_length''' , truncation=__UpperCAmelCase , max_length=512 )
__lowerCamelCase = tokenizer(batch['''highlights'''] , padding='''max_length''' , truncation=__UpperCAmelCase , max_length=128 )
__lowerCamelCase = inputs.input_ids
__lowerCamelCase = inputs.attention_mask
__lowerCamelCase = outputs.input_ids
__lowerCamelCase = outputs.input_ids.copy()
__lowerCamelCase = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
]
__lowerCamelCase = outputs.attention_mask
assert all(len(__UpperCAmelCase ) == 512 for x in inputs.input_ids )
assert all(len(__UpperCAmelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(__UpperCAmelCase ):
__lowerCamelCase = pred.label_ids
__lowerCamelCase = pred.predictions
# all unnecessary tokens are removed
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = sum([int(pred_str[i] == label_str[i] ) for i in range(len(__UpperCAmelCase ) )] ) / len(__UpperCAmelCase )
return {"accuracy": accuracy}
# map train dataset
__lowerCamelCase = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=__UpperCAmelCase , batch_size=__UpperCAmelCase , remove_columns=['''article''', '''highlights'''] , )
train_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
# same for validation dataset
__lowerCamelCase = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=__UpperCAmelCase , batch_size=__UpperCAmelCase , remove_columns=['''article''', '''highlights'''] , )
val_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
__lowerCamelCase = self.get_auto_remove_tmp_dir()
__lowerCamelCase = SeqaSeqTrainingArguments(
output_dir=__UpperCAmelCase , per_device_train_batch_size=__UpperCAmelCase , per_device_eval_batch_size=__UpperCAmelCase , predict_with_generate=__UpperCAmelCase , evaluation_strategy='''steps''' , do_train=__UpperCAmelCase , do_eval=__UpperCAmelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
__lowerCamelCase = SeqaSeqTrainer(
model=__UpperCAmelCase , args=__UpperCAmelCase , compute_metrics=_compute_metrics , train_dataset=__UpperCAmelCase , eval_dataset=__UpperCAmelCase , tokenizer=__UpperCAmelCase , )
# start training
trainer.train()
| 330 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""EncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""TFEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""FlaxEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
a_ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
a_ = {
"""vocab_file""": {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
},
"""tokenizer_file""": {
"""unc-nlp/lxmert-base-uncased""": (
"""https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
),
},
}
a_ = {
"""unc-nlp/lxmert-base-uncased""": 512,
}
a_ = {
"""unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = LxmertTokenizer
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase="[UNK]" , __UpperCAmelCase="[SEP]" , __UpperCAmelCase="[PAD]" , __UpperCAmelCase="[CLS]" , __UpperCAmelCase="[MASK]" , __UpperCAmelCase=True , __UpperCAmelCase=None , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , tokenize_chinese_chars=__UpperCAmelCase , strip_accents=__UpperCAmelCase , **__UpperCAmelCase , )
__lowerCamelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __UpperCAmelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __UpperCAmelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __UpperCAmelCase ) != tokenize_chinese_chars
):
__lowerCamelCase = getattr(__UpperCAmelCase , normalizer_state.pop('''type''' ) )
__lowerCamelCase = do_lower_case
__lowerCamelCase = strip_accents
__lowerCamelCase = tokenize_chinese_chars
__lowerCamelCase = normalizer_class(**__UpperCAmelCase )
__lowerCamelCase = do_lower_case
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__lowerCamelCase = [self.sep_token_id]
__lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__lowerCamelCase = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
| 330 |
from string import ascii_lowercase, ascii_uppercase
def a__ ( _UpperCamelCase : str ):
if not sentence:
return ""
__lowerCamelCase = dict(zip(_UpperCamelCase ,_UpperCamelCase ) )
return lower_to_upper.get(sentence[0] ,sentence[0] ) + sentence[1:]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 330 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_timm_backbone""": ["""TimmBackboneConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""TimmBackbone"""]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowerCAmelCase ( lowerCAmelCase__ ):
@slow
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' , '''prajjwal1/bert-tiny''' )
__lowerCamelCase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
__lowerCamelCase = bertabert.config.encoder.vocab_size
__lowerCamelCase = tokenizer.sep_token_id
__lowerCamelCase = tokenizer.cls_token_id
__lowerCamelCase = 128
__lowerCamelCase = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''train[:1%]''' )
__lowerCamelCase = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''validation[:1%]''' )
__lowerCamelCase = train_dataset.select(range(32 ) )
__lowerCamelCase = val_dataset.select(range(16 ) )
__lowerCamelCase = 4
def _map_to_encoder_decoder_inputs(__UpperCAmelCase ):
# Tokenizer will automatically set [BOS] <text> [EOS]
__lowerCamelCase = tokenizer(batch['''article'''] , padding='''max_length''' , truncation=__UpperCAmelCase , max_length=512 )
__lowerCamelCase = tokenizer(batch['''highlights'''] , padding='''max_length''' , truncation=__UpperCAmelCase , max_length=128 )
__lowerCamelCase = inputs.input_ids
__lowerCamelCase = inputs.attention_mask
__lowerCamelCase = outputs.input_ids
__lowerCamelCase = outputs.input_ids.copy()
__lowerCamelCase = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
]
__lowerCamelCase = outputs.attention_mask
assert all(len(__UpperCAmelCase ) == 512 for x in inputs.input_ids )
assert all(len(__UpperCAmelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(__UpperCAmelCase ):
__lowerCamelCase = pred.label_ids
__lowerCamelCase = pred.predictions
# all unnecessary tokens are removed
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = sum([int(pred_str[i] == label_str[i] ) for i in range(len(__UpperCAmelCase ) )] ) / len(__UpperCAmelCase )
return {"accuracy": accuracy}
# map train dataset
__lowerCamelCase = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=__UpperCAmelCase , batch_size=__UpperCAmelCase , remove_columns=['''article''', '''highlights'''] , )
train_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
# same for validation dataset
__lowerCamelCase = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=__UpperCAmelCase , batch_size=__UpperCAmelCase , remove_columns=['''article''', '''highlights'''] , )
val_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
__lowerCamelCase = self.get_auto_remove_tmp_dir()
__lowerCamelCase = SeqaSeqTrainingArguments(
output_dir=__UpperCAmelCase , per_device_train_batch_size=__UpperCAmelCase , per_device_eval_batch_size=__UpperCAmelCase , predict_with_generate=__UpperCAmelCase , evaluation_strategy='''steps''' , do_train=__UpperCAmelCase , do_eval=__UpperCAmelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
__lowerCamelCase = SeqaSeqTrainer(
model=__UpperCAmelCase , args=__UpperCAmelCase , compute_metrics=_compute_metrics , train_dataset=__UpperCAmelCase , eval_dataset=__UpperCAmelCase , tokenizer=__UpperCAmelCase , )
# start training
trainer.train()
| 330 | 1 |
import unittest
from knapsack import greedy_knapsack as kp
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = [10, 20, 30, 40, 50, 60]
__lowerCamelCase = [2, 4, 6, 8, 10, 12]
__lowerCamelCase = 100
self.assertEqual(kp.calc_profit(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) , 210 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.assertRaisesRegex(__UpperCAmelCase , '''max_weight must greater than zero.''' )
def lowerCamelCase ( self ):
'''simple docstring'''
self.assertRaisesRegex(__UpperCAmelCase , '''Weight can not be negative.''' )
def lowerCamelCase ( self ):
'''simple docstring'''
self.assertRaisesRegex(__UpperCAmelCase , '''Profit can not be negative.''' )
def lowerCamelCase ( self ):
'''simple docstring'''
self.assertRaisesRegex(__UpperCAmelCase , '''max_weight must greater than zero.''' )
def lowerCamelCase ( self ):
'''simple docstring'''
self.assertRaisesRegex(
__UpperCAmelCase , '''The length of profit and weight must be same.''' )
if __name__ == "__main__":
unittest.main()
| 330 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_timm_backbone""": ["""TimmBackboneConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""TimmBackbone"""]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
a_ = """\
Text data.
Second line of data."""
a_ = """file"""
@pytest.fixture(scope='''session''' )
def a__ ( _UpperCamelCase : str ):
__lowerCamelCase = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
__lowerCamelCase = bytes(_UpperCamelCase ,'''utf-8''' )
with zstd.open(_UpperCamelCase ,'''wb''' ) as f:
f.write(_UpperCamelCase )
return path
@pytest.fixture
def a__ ( _UpperCamelCase : Dict ):
with open(os.path.join(tmpfs.local_root_dir ,_UpperCamelCase ) ,'''w''' ) as f:
f.write(_UpperCamelCase )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' ,['''gzip''', '''xz''', '''zstd'''] )
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : Optional[int] ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : int ,_UpperCamelCase : List[str] ,_UpperCamelCase : Tuple ):
__lowerCamelCase = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
__lowerCamelCase = input_paths[compression_format]
__lowerCamelCase = tmp_path / '''cache'''
__lowerCamelCase = DownloadConfig(cache_dir=_UpperCamelCase ,extract_compressed_file=_UpperCamelCase )
__lowerCamelCase = cached_path(_UpperCamelCase ,download_config=_UpperCamelCase )
with open(_UpperCamelCase ) as f:
__lowerCamelCase = f.read()
with open(_UpperCamelCase ) as f:
__lowerCamelCase = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' ,[True, False] )
@pytest.mark.parametrize('''default_cache_dir''' ,[True, False] )
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : int ,_UpperCamelCase : List[str] ,_UpperCamelCase : str ):
__lowerCamelCase = '''custom_cache'''
__lowerCamelCase = '''custom_extracted_dir'''
__lowerCamelCase = tmp_path / '''custom_extracted_path'''
if default_extracted:
__lowerCamelCase = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' ,_UpperCamelCase )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' ,str(_UpperCamelCase ) )
__lowerCamelCase = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
__lowerCamelCase = xz_file
__lowerCamelCase = (
DownloadConfig(extract_compressed_file=_UpperCamelCase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir ,extract_compressed_file=_UpperCamelCase )
)
__lowerCamelCase = cached_path(_UpperCamelCase ,download_config=_UpperCamelCase )
assert Path(_UpperCamelCase ).parent.parts[-2:] == expected
def a__ ( _UpperCamelCase : Tuple ):
# absolute path
__lowerCamelCase = str(Path(_UpperCamelCase ).resolve() )
assert cached_path(_UpperCamelCase ) == text_file
# relative path
__lowerCamelCase = str(Path(_UpperCamelCase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_UpperCamelCase ) == text_file
def a__ ( _UpperCamelCase : Union[str, Any] ):
# absolute path
__lowerCamelCase = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(_UpperCamelCase ):
cached_path(_UpperCamelCase )
# relative path
__lowerCamelCase = '''./__missing_file__.txt'''
with pytest.raises(_UpperCamelCase ):
cached_path(_UpperCamelCase )
def a__ ( _UpperCamelCase : Dict ):
__lowerCamelCase = get_from_cache(F"""tmp://{tmpfs_file}""" )
with open(_UpperCamelCase ) as f:
__lowerCamelCase = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' ,_UpperCamelCase )
def a__ ( ):
with pytest.raises(_UpperCamelCase ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' ,_UpperCamelCase )
def a__ ( _UpperCamelCase : Optional[int] ):
__lowerCamelCase = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_UpperCamelCase ):
http_get('''https://huggingface.co''' ,temp_file=_UpperCamelCase )
with pytest.raises(_UpperCamelCase ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' ,_UpperCamelCase )
def a__ ( _UpperCamelCase : Optional[int] ):
__lowerCamelCase = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_UpperCamelCase ):
ftp_get('''ftp://huggingface.co''' ,temp_file=_UpperCamelCase )
with pytest.raises(_UpperCamelCase ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' ,_UpperCamelCase )
def a__ ( _UpperCamelCase : Dict ):
__lowerCamelCase = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_UpperCamelCase ):
fsspec_get('''s3://huggingface.co''' ,temp_file=_UpperCamelCase )
with pytest.raises(_UpperCamelCase ):
fsspec_head('''s3://huggingface.co''' )
| 330 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase=0.01 , __UpperCAmelCase=1000 ):
'''simple docstring'''
__lowerCamelCase = p_stop
__lowerCamelCase = max_length
def __iter__( self ):
'''simple docstring'''
__lowerCamelCase = 0
__lowerCamelCase = False
while not stop and count < self.max_length:
yield count
count += 1
__lowerCamelCase = random.random() < self.p_stop
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=True ):
'''simple docstring'''
__lowerCamelCase = [
BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
for i in range(2 )
]
__lowerCamelCase = [list(__UpperCAmelCase ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(__UpperCAmelCase ) for shard in batch_sampler_shards] , [len(__UpperCAmelCase ) for e in expected] )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of total batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of total batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
__lowerCamelCase = [BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , even_batches=__UpperCAmelCase ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=2 , __UpperCAmelCase=False ):
'''simple docstring'''
random.seed(__UpperCAmelCase )
__lowerCamelCase = list(__UpperCAmelCase )
__lowerCamelCase = [
IterableDatasetShard(
__UpperCAmelCase , batch_size=__UpperCAmelCase , drop_last=__UpperCAmelCase , num_processes=__UpperCAmelCase , process_index=__UpperCAmelCase , split_batches=__UpperCAmelCase , )
for i in range(__UpperCAmelCase )
]
__lowerCamelCase = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(__UpperCAmelCase )
iterable_dataset_lists.append(list(__UpperCAmelCase ) )
__lowerCamelCase = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
__lowerCamelCase = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
self.assertTrue(len(__UpperCAmelCase ) % shard_batch_size == 0 )
__lowerCamelCase = []
for idx in range(0 , len(__UpperCAmelCase ) , __UpperCAmelCase ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(__UpperCAmelCase ) < len(__UpperCAmelCase ):
reference += reference
self.assertListEqual(__UpperCAmelCase , reference[: len(__UpperCAmelCase )] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 42
__lowerCamelCase = RandomIterableDataset()
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
# Edge case with a very small dataset
__lowerCamelCase = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BatchSampler(range(16 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = SkipBatchSampler(__UpperCAmelCase , 2 )
self.assertListEqual(list(__UpperCAmelCase ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DataLoader(list(range(16 ) ) , batch_size=4 )
__lowerCamelCase = skip_first_batches(__UpperCAmelCase , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def lowerCamelCase ( self ):
'''simple docstring'''
Accelerator()
__lowerCamelCase = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 330 | 1 |
from __future__ import annotations
import os
from typing import Any
import requests
a_ = """https://api.github.com"""
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
a_ = BASE_URL + """/user"""
# https://github.com/settings/tokens
a_ = os.environ.get("""USER_TOKEN""", """""")
def a__ ( _UpperCamelCase : str ):
__lowerCamelCase = {
'''Authorization''': F"""token {auth_token}""",
'''Accept''': '''application/vnd.github.v3+json''',
}
return requests.get(_UpperCamelCase ,headers=_UpperCamelCase ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(f"{key}: {value}")
else:
raise ValueError("""'USER_TOKEN' field cannot be empty.""")
| 330 |
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 330 | 1 |
from __future__ import annotations
def a__ ( _UpperCamelCase : list[int] ):
if not nums:
return 0
__lowerCamelCase = nums[0]
__lowerCamelCase = 0
for num in nums[1:]:
__lowerCamelCase ,__lowerCamelCase = (
max_excluding + num,
max(_UpperCamelCase ,_UpperCamelCase ),
)
return max(_UpperCamelCase ,_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 330 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
a_ = 16
a_ = 32
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : int = 16 ):
__lowerCamelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' )
def tokenize_function(_UpperCamelCase : Optional[Any] ):
# max_length=None => use the model max length (it's actually the default)
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__lowerCamelCase = datasets.map(
_UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowerCamelCase = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__lowerCamelCase = 16
elif accelerator.mixed_precision != "no":
__lowerCamelCase = 8
else:
__lowerCamelCase = None
return tokenizer.pad(
_UpperCamelCase ,padding='''longest''' ,max_length=_UpperCamelCase ,pad_to_multiple_of=_UpperCamelCase ,return_tensors='''pt''' ,)
# Instantiate dataloaders.
__lowerCamelCase = DataLoader(
tokenized_datasets['''train'''] ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=_UpperCamelCase )
__lowerCamelCase = DataLoader(
tokenized_datasets['''validation'''] ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=_UpperCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
a_ = mocked_dataloaders # noqa: F811
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ):
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,_UpperCamelCase ) == "1":
__lowerCamelCase = 2
# Initialize accelerator
__lowerCamelCase = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCamelCase = config['''lr''']
__lowerCamelCase = int(config['''num_epochs'''] )
__lowerCamelCase = int(config['''seed'''] )
__lowerCamelCase = int(config['''batch_size'''] )
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=_UpperCamelCase )
def inner_training_loop(_UpperCamelCase : Union[str, Any] ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(_UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,return_dict=_UpperCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowerCamelCase = model.to(accelerator.device )
# Instantiate optimizer
__lowerCamelCase = AdamW(params=model.parameters() ,lr=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = get_dataloaders(_UpperCamelCase ,_UpperCamelCase )
# Instantiate scheduler
__lowerCamelCase = get_linear_schedule_with_warmup(
optimizer=_UpperCamelCase ,num_warmup_steps=1_00 ,num_training_steps=(len(_UpperCamelCase ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(
_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
# Now we train the model
for epoch in range(_UpperCamelCase ):
model.train()
for step, batch in enumerate(_UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.loss
accelerator.backward(_UpperCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_UpperCamelCase ,references=_UpperCamelCase ,)
__lowerCamelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" ,_UpperCamelCase )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def a__ ( ):
__lowerCamelCase = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' ,type=_UpperCamelCase ,default=_UpperCamelCase ,choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' ,)
parser.add_argument('''--cpu''' ,action='''store_true''' ,help='''If passed, will train on the CPU.''' )
__lowerCamelCase = parser.parse_args()
__lowerCamelCase = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_UpperCamelCase ,_UpperCamelCase )
if __name__ == "__main__":
main()
| 330 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
a_ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = ["""pixel_values"""]
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = True , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
__lowerCamelCase = size if size is not None else {'''shortest_edge''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
__lowerCamelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase , param_name='''crop_size''' )
__lowerCamelCase = do_resize
__lowerCamelCase = size
__lowerCamelCase = resample
__lowerCamelCase = do_center_crop
__lowerCamelCase = crop_size
__lowerCamelCase = do_rescale
__lowerCamelCase = rescale_factor
__lowerCamelCase = do_normalize
__lowerCamelCase = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__lowerCamelCase = image_std if image_std is not None else OPENAI_CLIP_STD
__lowerCamelCase = do_convert_rgb
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
__lowerCamelCase = get_resize_output_image_size(__UpperCAmelCase , size=size['''shortest_edge'''] , default_to_square=__UpperCAmelCase )
return resize(__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(__UpperCAmelCase , size=(size['''height'''], size['''width''']) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = do_resize if do_resize is not None else self.do_resize
__lowerCamelCase = size if size is not None else self.size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = resample if resample is not None else self.resample
__lowerCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowerCamelCase = crop_size if crop_size is not None else self.crop_size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''crop_size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = do_rescale if do_rescale is not None else self.do_rescale
__lowerCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCamelCase = do_normalize if do_normalize is not None else self.do_normalize
__lowerCamelCase = image_mean if image_mean is not None else self.image_mean
__lowerCamelCase = image_std if image_std is not None else self.image_std
__lowerCamelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__lowerCamelCase = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__lowerCamelCase = [convert_to_rgb(__UpperCAmelCase ) for image in images]
# All transformations expect numpy arrays.
__lowerCamelCase = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
__lowerCamelCase = [self.resize(image=__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase ) for image in images]
if do_center_crop:
__lowerCamelCase = [self.center_crop(image=__UpperCAmelCase , size=__UpperCAmelCase ) for image in images]
if do_rescale:
__lowerCamelCase = [self.rescale(image=__UpperCAmelCase , scale=__UpperCAmelCase ) for image in images]
if do_normalize:
__lowerCamelCase = [self.normalize(image=__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase ) for image in images]
__lowerCamelCase = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
__lowerCamelCase = {'''pixel_values''': images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase )
| 330 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
a_ = None
try:
import msvcrt
except ImportError:
a_ = None
try:
import fcntl
except ImportError:
a_ = None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
a_ = OSError
# Data
# ------------------------------------------------
a_ = [
"""Timeout""",
"""BaseFileLock""",
"""WindowsFileLock""",
"""UnixFileLock""",
"""SoftFileLock""",
"""FileLock""",
]
a_ = """3.0.12"""
a_ = None
def a__ ( ):
global _logger
__lowerCamelCase = _logger or logging.getLogger(__name__ )
return _logger
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = lock_file
return None
def __str__( self ):
'''simple docstring'''
__lowerCamelCase = F"""The file lock '{self.lock_file}' could not be acquired."""
return temp
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = lock
return None
def __enter__( self ):
'''simple docstring'''
return self.lock
def __exit__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.lock.release()
return None
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = max_filename_length if max_filename_length is not None else 255
# Hash the filename if it's too long
__lowerCamelCase = self.hash_filename_if_too_long(__UpperCAmelCase , __UpperCAmelCase )
# The path to the lock file.
__lowerCamelCase = lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
__lowerCamelCase = None
# The default timeout value.
__lowerCamelCase = timeout
# We use this lock primarily for the lock counter.
__lowerCamelCase = threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
__lowerCamelCase = 0
return None
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._lock_file
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._timeout
@timeout.setter
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = float(__UpperCAmelCase )
return None
def lowerCamelCase ( self ):
'''simple docstring'''
raise NotImplementedError()
def lowerCamelCase ( self ):
'''simple docstring'''
raise NotImplementedError()
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._lock_file_fd is not None
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=0.05 ):
'''simple docstring'''
# Use the default timeout, if no timeout is provided.
if timeout is None:
__lowerCamelCase = self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
__lowerCamelCase = id(self )
__lowerCamelCase = self._lock_file
__lowerCamelCase = time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(F"""Attempting to acquire lock {lock_id} on {lock_filename}""" )
self._acquire()
if self.is_locked:
logger().debug(F"""Lock {lock_id} acquired on {lock_filename}""" )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(F"""Timeout on acquiring lock {lock_id} on {lock_filename}""" )
raise Timeout(self._lock_file )
else:
logger().debug(
F"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...""" )
time.sleep(__UpperCAmelCase )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
__lowerCamelCase = max(0 , self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def lowerCamelCase ( self , __UpperCAmelCase=False ):
'''simple docstring'''
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
__lowerCamelCase = id(self )
__lowerCamelCase = self._lock_file
logger().debug(F"""Attempting to release lock {lock_id} on {lock_filename}""" )
self._release()
__lowerCamelCase = 0
logger().debug(F"""Lock {lock_id} released on {lock_filename}""" )
return None
def __enter__( self ):
'''simple docstring'''
self.acquire()
return self
def __exit__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.release()
return None
def __del__( self ):
'''simple docstring'''
self.release(force=__UpperCAmelCase )
return None
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = os.path.basename(__UpperCAmelCase )
if len(__UpperCAmelCase ) > max_length and max_length > 0:
__lowerCamelCase = os.path.dirname(__UpperCAmelCase )
__lowerCamelCase = str(hash(__UpperCAmelCase ) )
__lowerCamelCase = filename[: max_length - len(__UpperCAmelCase ) - 8] + '''...''' + hashed_filename + '''.lock'''
return os.path.join(__UpperCAmelCase , __UpperCAmelCase )
else:
return path
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
from .file_utils import relative_to_absolute_path
super().__init__(__UpperCAmelCase , timeout=__UpperCAmelCase , max_filename_length=__UpperCAmelCase )
__lowerCamelCase = '''\\\\?\\''' + relative_to_absolute_path(self.lock_file )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
except OSError:
pass
else:
try:
msvcrt.locking(__UpperCAmelCase , msvcrt.LK_NBLCK , 1 )
except OSError:
os.close(__UpperCAmelCase )
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self._lock_file_fd
__lowerCamelCase = None
msvcrt.locking(__UpperCAmelCase , msvcrt.LK_UNLCK , 1 )
os.close(__UpperCAmelCase )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = os.statvfs(os.path.dirname(__UpperCAmelCase ) ).f_namemax
super().__init__(__UpperCAmelCase , timeout=__UpperCAmelCase , max_filename_length=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_RDWR | os.O_CREAT | os.O_TRUNC
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
try:
fcntl.flock(__UpperCAmelCase , fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(__UpperCAmelCase )
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
# Do not remove the lockfile:
#
# https://github.com/benediktschmitt/py-filelock/issues/31
# https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
__lowerCamelCase = self._lock_file_fd
__lowerCamelCase = None
fcntl.flock(__UpperCAmelCase , fcntl.LOCK_UN )
os.close(__UpperCAmelCase )
return None
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
except OSError:
pass
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
os.close(self._lock_file_fd )
__lowerCamelCase = None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
a_ = None
if msvcrt:
a_ = WindowsFileLock
elif fcntl:
a_ = UnixFileLock
else:
a_ = SoftFileLock
if warnings is not None:
warnings.warn("""only soft file lock is available""")
| 330 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
a_ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = ["""pixel_values"""]
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = True , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
__lowerCamelCase = size if size is not None else {'''shortest_edge''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
__lowerCamelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase , param_name='''crop_size''' )
__lowerCamelCase = do_resize
__lowerCamelCase = size
__lowerCamelCase = resample
__lowerCamelCase = do_center_crop
__lowerCamelCase = crop_size
__lowerCamelCase = do_rescale
__lowerCamelCase = rescale_factor
__lowerCamelCase = do_normalize
__lowerCamelCase = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__lowerCamelCase = image_std if image_std is not None else OPENAI_CLIP_STD
__lowerCamelCase = do_convert_rgb
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
__lowerCamelCase = get_resize_output_image_size(__UpperCAmelCase , size=size['''shortest_edge'''] , default_to_square=__UpperCAmelCase )
return resize(__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(__UpperCAmelCase , size=(size['''height'''], size['''width''']) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = do_resize if do_resize is not None else self.do_resize
__lowerCamelCase = size if size is not None else self.size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = resample if resample is not None else self.resample
__lowerCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowerCamelCase = crop_size if crop_size is not None else self.crop_size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''crop_size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = do_rescale if do_rescale is not None else self.do_rescale
__lowerCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCamelCase = do_normalize if do_normalize is not None else self.do_normalize
__lowerCamelCase = image_mean if image_mean is not None else self.image_mean
__lowerCamelCase = image_std if image_std is not None else self.image_std
__lowerCamelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__lowerCamelCase = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__lowerCamelCase = [convert_to_rgb(__UpperCAmelCase ) for image in images]
# All transformations expect numpy arrays.
__lowerCamelCase = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
__lowerCamelCase = [self.resize(image=__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase ) for image in images]
if do_center_crop:
__lowerCamelCase = [self.center_crop(image=__UpperCAmelCase , size=__UpperCAmelCase ) for image in images]
if do_rescale:
__lowerCamelCase = [self.rescale(image=__UpperCAmelCase , scale=__UpperCAmelCase ) for image in images]
if do_normalize:
__lowerCamelCase = [self.normalize(image=__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase ) for image in images]
__lowerCamelCase = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
__lowerCamelCase = {'''pixel_values''': images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase )
| 330 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=10 , __UpperCAmelCase=3 , __UpperCAmelCase=2 , __UpperCAmelCase=2 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=10 , __UpperCAmelCase=0.02 , __UpperCAmelCase="divided_space_time" , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = image_size
__lowerCamelCase = num_channels
__lowerCamelCase = patch_size
__lowerCamelCase = num_frames
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = attention_type
__lowerCamelCase = initializer_range
__lowerCamelCase = scope
__lowerCamelCase = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
__lowerCamelCase = (image_size // patch_size) ** 2
__lowerCamelCase = (num_frames) * self.num_patches_per_frame + 1
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
__lowerCamelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , attention_type=self.attention_type , )
__lowerCamelCase = self.num_labels
return config
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TimesformerModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase )
# verify the logits shape
__lowerCamelCase = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = config_and_inputs
__lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
lowerCAmelCase__ = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerModelTester(self )
__lowerCamelCase = ConfigTester(
self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(__UpperCAmelCase )
if return_labels:
if model_class in get_values(__UpperCAmelCase ):
__lowerCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__UpperCAmelCase )
return inputs_dict
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(__UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCAmelCase , nn.Linear ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(__UpperCAmelCase )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = TimesformerModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
if not self.has_attentions:
pass
else:
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = True
for model_class in self.all_model_classes:
__lowerCamelCase = self.model_tester.seq_length
__lowerCamelCase = self.model_tester.num_frames
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
__lowerCamelCase = len(__UpperCAmelCase )
# Check attention is always last and order is fine
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
self.assertEqual(out_len + 1 , len(__UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def lowerCamelCase ( self ):
'''simple docstring'''
def check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.hidden_states
__lowerCamelCase = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
__lowerCamelCase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def a__ ( ):
__lowerCamelCase = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' ,filename='''eating_spaghetti.npy''' ,repo_type='''dataset''' )
__lowerCamelCase = np.load(_UpperCamelCase )
return list(_UpperCamelCase )
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
__UpperCAmelCase )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_video()
__lowerCamelCase = image_processor(video[:8] , return_tensors='''pt''' ).to(__UpperCAmelCase )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(**__UpperCAmelCase )
# verify the logits
__lowerCamelCase = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor([-0.3_016, -0.7_713, -0.4_205] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 330 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def a__ ( _UpperCamelCase : Optional[Any] ):
__lowerCamelCase = 3_84
__lowerCamelCase = 7
if "tiny" in model_name:
__lowerCamelCase = 96
__lowerCamelCase = (2, 2, 6, 2)
__lowerCamelCase = (3, 6, 12, 24)
elif "small" in model_name:
__lowerCamelCase = 96
__lowerCamelCase = (2, 2, 18, 2)
__lowerCamelCase = (3, 6, 12, 24)
elif "base" in model_name:
__lowerCamelCase = 1_28
__lowerCamelCase = (2, 2, 18, 2)
__lowerCamelCase = (4, 8, 16, 32)
__lowerCamelCase = 12
__lowerCamelCase = 5_12
elif "large" in model_name:
__lowerCamelCase = 1_92
__lowerCamelCase = (2, 2, 18, 2)
__lowerCamelCase = (6, 12, 24, 48)
__lowerCamelCase = 12
__lowerCamelCase = 7_68
# set label information
__lowerCamelCase = 1_50
__lowerCamelCase = '''huggingface/label-files'''
__lowerCamelCase = '''ade20k-id2label.json'''
__lowerCamelCase = json.load(open(hf_hub_download(_UpperCamelCase ,_UpperCamelCase ,repo_type='''dataset''' ) ,'''r''' ) )
__lowerCamelCase = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
__lowerCamelCase = {v: k for k, v in idalabel.items()}
__lowerCamelCase = SwinConfig(
embed_dim=_UpperCamelCase ,depths=_UpperCamelCase ,num_heads=_UpperCamelCase ,window_size=_UpperCamelCase ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] ,)
__lowerCamelCase = UperNetConfig(
backbone_config=_UpperCamelCase ,auxiliary_in_channels=_UpperCamelCase ,num_labels=_UpperCamelCase ,idalabel=_UpperCamelCase ,labelaid=_UpperCamelCase ,)
return config
def a__ ( _UpperCamelCase : List[Any] ):
__lowerCamelCase = []
# fmt: off
# stem
rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''') )
rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((F"""backbone.stages.{i}.downsample.reduction.weight""", F"""backbone.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((F"""backbone.stages.{i}.downsample.norm.weight""", F"""backbone.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((F"""backbone.stages.{i}.downsample.norm.bias""", F"""backbone.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""backbone.hidden_states_norms.stage{i+1}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""backbone.hidden_states_norms.stage{i+1}.bias""") )
# decode head
rename_keys.extend(
[
('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
] )
# fmt: on
return rename_keys
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : List[str] ,_UpperCamelCase : Optional[Any] ):
__lowerCamelCase = dct.pop(_UpperCamelCase )
__lowerCamelCase = val
def a__ ( _UpperCamelCase : Optional[int] ,_UpperCamelCase : Dict ):
__lowerCamelCase = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
__lowerCamelCase = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
__lowerCamelCase = state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight""" )
__lowerCamelCase = state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
__lowerCamelCase = in_proj_weight[:dim, :]
__lowerCamelCase = in_proj_bias[: dim]
__lowerCamelCase = in_proj_weight[
dim : dim * 2, :
]
__lowerCamelCase = in_proj_bias[
dim : dim * 2
]
__lowerCamelCase = in_proj_weight[
-dim :, :
]
__lowerCamelCase = in_proj_bias[-dim :]
# fmt: on
def a__ ( _UpperCamelCase : Optional[Any] ):
__lowerCamelCase ,__lowerCamelCase = x.shape
__lowerCamelCase = x.reshape(_UpperCamelCase ,4 ,in_channel // 4 )
__lowerCamelCase = x[:, [0, 2, 1, 3], :].transpose(1 ,2 ).reshape(_UpperCamelCase ,_UpperCamelCase )
return x
def a__ ( _UpperCamelCase : Optional[Any] ):
__lowerCamelCase ,__lowerCamelCase = x.shape
__lowerCamelCase = x.reshape(_UpperCamelCase ,in_channel // 4 ,4 )
__lowerCamelCase = x[:, :, [0, 2, 1, 3]].transpose(1 ,2 ).reshape(_UpperCamelCase ,_UpperCamelCase )
return x
def a__ ( _UpperCamelCase : Any ):
__lowerCamelCase = x.shape[0]
__lowerCamelCase = x.reshape(4 ,in_channel // 4 )
__lowerCamelCase = x[[0, 2, 1, 3], :].transpose(0 ,1 ).reshape(_UpperCamelCase )
return x
def a__ ( _UpperCamelCase : Tuple ):
__lowerCamelCase = x.shape[0]
__lowerCamelCase = x.reshape(in_channel // 4 ,4 )
__lowerCamelCase = x[:, [0, 2, 1, 3]].transpose(0 ,1 ).reshape(_UpperCamelCase )
return x
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : Dict ,_UpperCamelCase : str ):
__lowerCamelCase = {
'''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
'''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
'''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
'''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
}
__lowerCamelCase = model_name_to_url[model_name]
__lowerCamelCase = torch.hub.load_state_dict_from_url(_UpperCamelCase ,map_location='''cpu''' ,file_name=_UpperCamelCase )[
'''state_dict'''
]
for name, param in state_dict.items():
print(_UpperCamelCase ,param.shape )
__lowerCamelCase = get_upernet_config(_UpperCamelCase )
__lowerCamelCase = UperNetForSemanticSegmentation(_UpperCamelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
__lowerCamelCase = state_dict.pop(_UpperCamelCase )
if "bn" in key:
__lowerCamelCase = key.replace('''bn''' ,'''batch_norm''' )
__lowerCamelCase = val
# rename keys
__lowerCamelCase = create_rename_keys(_UpperCamelCase )
for src, dest in rename_keys:
rename_key(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
read_in_q_k_v(_UpperCamelCase ,config.backbone_config )
# fix downsample parameters
for key, value in state_dict.items():
if "downsample" in key:
if "reduction" in key:
__lowerCamelCase = reverse_correct_unfold_reduction_order(_UpperCamelCase )
if "norm" in key:
__lowerCamelCase = reverse_correct_unfold_norm_order(_UpperCamelCase )
model.load_state_dict(_UpperCamelCase )
# verify on image
__lowerCamelCase = '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
__lowerCamelCase = Image.open(requests.get(_UpperCamelCase ,stream=_UpperCamelCase ).raw ).convert('''RGB''' )
__lowerCamelCase = SegformerImageProcessor()
__lowerCamelCase = processor(_UpperCamelCase ,return_tensors='''pt''' ).pixel_values
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase )
__lowerCamelCase = outputs.logits
print(logits.shape )
print('''First values of logits:''' ,logits[0, 0, :3, :3] )
# assert values
if model_name == "upernet-swin-tiny":
__lowerCamelCase = torch.tensor(
[[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]] )
elif model_name == "upernet-swin-small":
__lowerCamelCase = torch.tensor(
[[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]] )
elif model_name == "upernet-swin-base":
__lowerCamelCase = torch.tensor(
[[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]] )
elif model_name == "upernet-swin-large":
__lowerCamelCase = torch.tensor(
[[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]] )
print('''Logits:''' ,outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] ,_UpperCamelCase ,atol=1e-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_UpperCamelCase )
print(F"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(_UpperCamelCase )
if push_to_hub:
print(F"""Pushing model and processor for {model_name} to hub""" )
model.push_to_hub(F"""openmmlab/{model_name}""" )
processor.push_to_hub(F"""openmmlab/{model_name}""" )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""upernet-swin-tiny""",
type=str,
choices=[f"upernet-swin-{size}" for size in ["""tiny""", """small""", """base""", """large"""]],
help="""Name of the Swin + UperNet model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
a_ = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 330 |
def a__ ( _UpperCamelCase : int ):
if not isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = F"""Input value of [number={number}] must be an integer"""
raise TypeError(_UpperCamelCase )
if number < 0:
return False
__lowerCamelCase = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 330 | 1 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=2 , __UpperCAmelCase=8 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=16 , __UpperCAmelCase=5 , __UpperCAmelCase=2 , __UpperCAmelCase=36 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=512 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_input_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase ( self ):
'''simple docstring'''
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_config()
__lowerCamelCase = 300
return config
def lowerCamelCase ( self ):
'''simple docstring'''
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = self.prepare_config_and_inputs()
__lowerCamelCase = True
__lowerCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = True
__lowerCamelCase = MraModel(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , )
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , )
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraForQuestionAnswering(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MraForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MraForTokenClassification(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_choices
__lowerCamelCase = MraForMultipleChoice(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = ()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCamelCase = type
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = MraModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@unittest.skip(reason='''MRA does not output attentions''' )
def lowerCamelCase ( self ):
'''simple docstring'''
return
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowerCamelCase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowerCamelCase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = 50265
__lowerCamelCase = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' )
__lowerCamelCase = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = 50265
__lowerCamelCase = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 330 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
return F"""gaussian_noise_s={seed}_shape={"_".join([str(__UpperCAmelCase ) for s in shape] )}.npy"""
def lowerCamelCase ( self ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowerCamelCase ( self , __UpperCAmelCase=0 , __UpperCAmelCase=(4, 4, 64, 64) , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = jnp.bfloataa if fpaa else jnp.floataa
__lowerCamelCase = jnp.array(load_hf_numpy(self.get_file_format(__UpperCAmelCase , __UpperCAmelCase ) ) , dtype=__UpperCAmelCase )
return image
def lowerCamelCase ( self , __UpperCAmelCase=False , __UpperCAmelCase="CompVis/stable-diffusion-v1-4" ):
'''simple docstring'''
__lowerCamelCase = jnp.bfloataa if fpaa else jnp.floataa
__lowerCamelCase = '''bf16''' if fpaa else None
__lowerCamelCase ,__lowerCamelCase = FlaxUNetaDConditionModel.from_pretrained(
__UpperCAmelCase , subfolder='''unet''' , dtype=__UpperCAmelCase , revision=__UpperCAmelCase )
return model, params
def lowerCamelCase ( self , __UpperCAmelCase=0 , __UpperCAmelCase=(4, 77, 768) , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = jnp.bfloataa if fpaa else jnp.floataa
__lowerCamelCase = jnp.array(load_hf_numpy(self.get_file_format(__UpperCAmelCase , __UpperCAmelCase ) ) , dtype=__UpperCAmelCase )
return hidden_states
@parameterized.expand(
[
# fmt: off
[83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
[17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
[8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
[3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
# fmt: on
] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4''' , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_latents(__UpperCAmelCase , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_encoder_hidden_states(__UpperCAmelCase , fpaa=__UpperCAmelCase )
__lowerCamelCase = model.apply(
{'''params''': params} , __UpperCAmelCase , jnp.array(__UpperCAmelCase , dtype=jnp.intaa ) , encoder_hidden_states=__UpperCAmelCase , ).sample
assert sample.shape == latents.shape
__lowerCamelCase = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
__lowerCamelCase = jnp.array(__UpperCAmelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
assert jnp.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
[17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
[8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
[3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
# fmt: on
] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.get_unet_model(model_id='''stabilityai/stable-diffusion-2''' , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_latents(__UpperCAmelCase , shape=(4, 4, 96, 96) , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_encoder_hidden_states(__UpperCAmelCase , shape=(4, 77, 1024) , fpaa=__UpperCAmelCase )
__lowerCamelCase = model.apply(
{'''params''': params} , __UpperCAmelCase , jnp.array(__UpperCAmelCase , dtype=jnp.intaa ) , encoder_hidden_states=__UpperCAmelCase , ).sample
assert sample.shape == latents.shape
__lowerCamelCase = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
__lowerCamelCase = jnp.array(__UpperCAmelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
assert jnp.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-2 )
| 330 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
a_ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
warnings.warn(
'''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use YolosImageProcessor instead.''' , __UpperCAmelCase , )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
| 330 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""MMBTForClassification""", """MMBTModel""", """ModalEmbeddings"""]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a_ = logging.getLogger(__name__)
def a__ ( _UpperCamelCase : List[str] ,_UpperCamelCase : Tuple ):
__lowerCamelCase = np.argmax(_UpperCamelCase ,axis=1 )
return np.sum(outputs == labels )
def a__ ( _UpperCamelCase : Optional[Any] ):
with open(_UpperCamelCase ,encoding='''utf_8''' ) as f:
__lowerCamelCase = csv.reader(_UpperCamelCase )
__lowerCamelCase = []
next(_UpperCamelCase ) # skip the first line
for line in tqdm(_UpperCamelCase ):
output.append((''' '''.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : Tuple ,_UpperCamelCase : str ,_UpperCamelCase : List[str] ,_UpperCamelCase : str ,_UpperCamelCase : Any ):
__lowerCamelCase = []
for dataset in encoded_datasets:
__lowerCamelCase = len(_UpperCamelCase )
__lowerCamelCase = np.zeros((n_batch, 2, input_len) ,dtype=np.intaa )
__lowerCamelCase = np.zeros((n_batch, 2) ,dtype=np.intaa )
__lowerCamelCase = np.full((n_batch, 2, input_len) ,fill_value=-1_00 ,dtype=np.intaa )
__lowerCamelCase = np.zeros((n_batch,) ,dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(_UpperCamelCase ):
__lowerCamelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__lowerCamelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__lowerCamelCase = with_conta
__lowerCamelCase = with_conta
__lowerCamelCase = len(_UpperCamelCase ) - 1
__lowerCamelCase = len(_UpperCamelCase ) - 1
__lowerCamelCase = with_conta
__lowerCamelCase = with_conta
__lowerCamelCase = mc_label
__lowerCamelCase = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(_UpperCamelCase ) for t in all_inputs ) )
return tensor_datasets
def a__ ( ):
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''--model_name''' ,type=_UpperCamelCase ,default='''openai-gpt''' ,help='''pretrained model name''' )
parser.add_argument('''--do_train''' ,action='''store_true''' ,help='''Whether to run training.''' )
parser.add_argument('''--do_eval''' ,action='''store_true''' ,help='''Whether to run eval on the dev set.''' )
parser.add_argument(
'''--output_dir''' ,default=_UpperCamelCase ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''The output directory where the model predictions and checkpoints will be written.''' ,)
parser.add_argument('''--train_dataset''' ,type=_UpperCamelCase ,default='''''' )
parser.add_argument('''--eval_dataset''' ,type=_UpperCamelCase ,default='''''' )
parser.add_argument('''--seed''' ,type=_UpperCamelCase ,default=42 )
parser.add_argument('''--num_train_epochs''' ,type=_UpperCamelCase ,default=3 )
parser.add_argument('''--train_batch_size''' ,type=_UpperCamelCase ,default=8 )
parser.add_argument('''--eval_batch_size''' ,type=_UpperCamelCase ,default=16 )
parser.add_argument('''--adam_epsilon''' ,default=1e-8 ,type=_UpperCamelCase ,help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--max_grad_norm''' ,type=_UpperCamelCase ,default=1 )
parser.add_argument(
'''--max_steps''' ,default=-1 ,type=_UpperCamelCase ,help=(
'''If > 0: set total number of training steps to perform. Override num_train_epochs.'''
) ,)
parser.add_argument(
'''--gradient_accumulation_steps''' ,type=_UpperCamelCase ,default=1 ,help='''Number of updates steps to accumulate before performing a backward/update pass.''' ,)
parser.add_argument('''--learning_rate''' ,type=_UpperCamelCase ,default=6.25e-5 )
parser.add_argument('''--warmup_steps''' ,default=0 ,type=_UpperCamelCase ,help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--lr_schedule''' ,type=_UpperCamelCase ,default='''warmup_linear''' )
parser.add_argument('''--weight_decay''' ,type=_UpperCamelCase ,default=0.01 )
parser.add_argument('''--lm_coef''' ,type=_UpperCamelCase ,default=0.9 )
parser.add_argument('''--n_valid''' ,type=_UpperCamelCase ,default=3_74 )
parser.add_argument('''--server_ip''' ,type=_UpperCamelCase ,default='''''' ,help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' ,type=_UpperCamelCase ,default='''''' ,help='''Can be used for distant debugging.''' )
__lowerCamelCase = parser.parse_args()
print(_UpperCamelCase )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,redirect_output=_UpperCamelCase )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
__lowerCamelCase = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
__lowerCamelCase = torch.cuda.device_count()
logger.info('''device: {}, n_gpu {}'''.format(_UpperCamelCase ,_UpperCamelCase ) )
if not args.do_train and not args.do_eval:
raise ValueError('''At least one of `do_train` or `do_eval` must be True.''' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
__lowerCamelCase = ['''_start_''', '''_delimiter_''', '''_classify_''']
__lowerCamelCase = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(_UpperCamelCase )
__lowerCamelCase = tokenizer.convert_tokens_to_ids(_UpperCamelCase )
__lowerCamelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(_UpperCamelCase ) )
model.to(_UpperCamelCase )
# Load and encode the datasets
def tokenize_and_encode(_UpperCamelCase : Tuple ):
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_UpperCamelCase ) )
elif isinstance(_UpperCamelCase ,_UpperCamelCase ):
return obj
return [tokenize_and_encode(_UpperCamelCase ) for o in obj]
logger.info('''Encoding dataset...''' )
__lowerCamelCase = load_rocstories_dataset(args.train_dataset )
__lowerCamelCase = load_rocstories_dataset(args.eval_dataset )
__lowerCamelCase = (train_dataset, eval_dataset)
__lowerCamelCase = tokenize_and_encode(_UpperCamelCase )
# Compute the max input length for the Transformer
__lowerCamelCase = model.config.n_positions // 2 - 2
__lowerCamelCase = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) ,len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
__lowerCamelCase = min(_UpperCamelCase ,model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
__lowerCamelCase = pre_process_datasets(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,*_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = tensor_datasets[0], tensor_datasets[1]
__lowerCamelCase = TensorDataset(*_UpperCamelCase )
__lowerCamelCase = RandomSampler(_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.train_batch_size )
__lowerCamelCase = TensorDataset(*_UpperCamelCase )
__lowerCamelCase = SequentialSampler(_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
__lowerCamelCase = args.max_steps
__lowerCamelCase = args.max_steps // (len(_UpperCamelCase ) // args.gradient_accumulation_steps) + 1
else:
__lowerCamelCase = len(_UpperCamelCase ) // args.gradient_accumulation_steps * args.num_train_epochs
__lowerCamelCase = list(model.named_parameters() )
__lowerCamelCase = ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
__lowerCamelCase = [
{
'''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'''weight_decay''': args.weight_decay,
},
{'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], '''weight_decay''': 0.0},
]
__lowerCamelCase = AdamW(_UpperCamelCase ,lr=args.learning_rate ,eps=args.adam_epsilon )
__lowerCamelCase = get_linear_schedule_with_warmup(
_UpperCamelCase ,num_warmup_steps=args.warmup_steps ,num_training_steps=_UpperCamelCase )
if args.do_train:
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) ,desc='''Epoch''' ):
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = tqdm(_UpperCamelCase ,desc='''Training''' )
for step, batch in enumerate(_UpperCamelCase ):
__lowerCamelCase = tuple(t.to(_UpperCamelCase ) for t in batch )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = batch
__lowerCamelCase = model(_UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase )
__lowerCamelCase = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
__lowerCamelCase = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
__lowerCamelCase = '''Training loss: {:.2e} lr: {:.2e}'''.format(_UpperCamelCase ,scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
__lowerCamelCase = model.module if hasattr(_UpperCamelCase ,'''module''' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
__lowerCamelCase = os.path.join(args.output_dir ,_UpperCamelCase )
__lowerCamelCase = os.path.join(args.output_dir ,_UpperCamelCase )
torch.save(model_to_save.state_dict() ,_UpperCamelCase )
model_to_save.config.to_json_file(_UpperCamelCase )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
__lowerCamelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
__lowerCamelCase = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(_UpperCamelCase )
if args.do_eval:
model.eval()
__lowerCamelCase ,__lowerCamelCase = 0, 0
__lowerCamelCase ,__lowerCamelCase = 0, 0
for batch in tqdm(_UpperCamelCase ,desc='''Evaluating''' ):
__lowerCamelCase = tuple(t.to(_UpperCamelCase ) for t in batch )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = batch
with torch.no_grad():
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = model(
_UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase )
__lowerCamelCase = mc_logits.detach().cpu().numpy()
__lowerCamelCase = mc_labels.to('''cpu''' ).numpy()
__lowerCamelCase = accuracy(_UpperCamelCase ,_UpperCamelCase )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
__lowerCamelCase = eval_loss / nb_eval_steps
__lowerCamelCase = eval_accuracy / nb_eval_examples
__lowerCamelCase = tr_loss / nb_tr_steps if args.do_train else None
__lowerCamelCase = {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}
__lowerCamelCase = os.path.join(args.output_dir ,'''eval_results.txt''' )
with open(_UpperCamelCase ,'''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key in sorted(result.keys() ):
logger.info(''' %s = %s''' ,_UpperCamelCase ,str(result[key] ) )
writer.write('''%s = %s\n''' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 330 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def a__ ( _UpperCamelCase : Optional[int] ):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class __lowerCAmelCase ( nn.Module ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
super().__init__()
__lowerCamelCase = module
__lowerCamelCase = nn.Sequential(
nn.Linear(module.in_features , __UpperCAmelCase , bias=__UpperCAmelCase ) , nn.Linear(__UpperCAmelCase , module.out_features , bias=__UpperCAmelCase ) , )
__lowerCamelCase = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=__UpperCAmelCase )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def lowerCamelCase ( self , __UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return self.module(__UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ) + self.adapter(__UpperCAmelCase )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __lowerCAmelCase ( unittest.TestCase ):
# We keep the constants inside the init function and model loading inside setUp function
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
# Therefore here we use only bloom-1b3 to test our module
lowerCAmelCase__ = """bigscience/bloom-1b7"""
# Constant values
lowerCAmelCase__ = 2.1_09_65_95_52_69_25_74
lowerCAmelCase__ = """Hello my name is"""
lowerCAmelCase__ = set()
EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I""" )
EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n""" )
EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University""" )
lowerCAmelCase__ = 1_0
def lowerCamelCase ( self ):
'''simple docstring'''
# Models and tokenizer
__lowerCamelCase = AutoTokenizer.from_pretrained(self.model_name )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# Models and tokenizer
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='''auto''' )
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
def lowerCamelCase ( self ):
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_abit.config
self.assertTrue(hasattr(__UpperCAmelCase , '''quantization_config''' ) )
__lowerCamelCase = config.to_dict()
__lowerCamelCase = config.to_diff_dict()
__lowerCamelCase = config.to_json_string()
def lowerCamelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
__lowerCamelCase = self.model_fpaa.get_memory_footprint()
__lowerCamelCase = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
__lowerCamelCase = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def lowerCamelCase ( self ):
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(__UpperCAmelCase , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = self.model_abit.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BitsAndBytesConfig()
__lowerCamelCase = True
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = model_abit_from_config.generate(
input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def lowerCamelCase ( self ):
'''simple docstring'''
with self.assertRaises(__UpperCAmelCase ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BitsAndBytesConfig()
with self.assertRaises(__UpperCAmelCase ):
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__UpperCAmelCase , load_in_abit=__UpperCAmelCase , device_map='''auto''' , bnb_abit_quant_type='''nf4''' , )
def lowerCamelCase ( self ):
'''simple docstring'''
with self.assertRaises(__UpperCAmelCase ):
# Tries with `str`
self.model_abit.to('''cpu''' )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.to(torch.device('''cuda:0''' ) )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = self.model_fpaa.to(torch.floataa )
__lowerCamelCase = self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.to('''cpu''' )
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.half()
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.float()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''t5-small''' , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __lowerCAmelCase ( unittest.TestCase ):
@classmethod
def lowerCamelCase ( cls ):
'''simple docstring'''
__lowerCamelCase = '''t5-small'''
__lowerCamelCase = '''google/flan-t5-small''' # flan-t5 uses dense-act instead of dense-relu-dense
__lowerCamelCase = AutoTokenizer.from_pretrained(cls.model_name )
__lowerCamelCase = '''Translate in German: Hello, my dog is cute'''
def lowerCamelCase ( self ):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
from transformers import TaForConditionalGeneration
__lowerCamelCase = TaForConditionalGeneration._keep_in_fpaa_modules
__lowerCamelCase = None
# test with `t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
# test with `flan-t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
__lowerCamelCase = modules
def lowerCamelCase ( self ):
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
# test with `flan-t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# model_name
__lowerCamelCase = '''bigscience/bloom-560m'''
__lowerCamelCase = '''t5-small'''
# Different types of model
__lowerCamelCase = AutoModel.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# Sequence classification model
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# CausalLM model
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# Seq2seq model
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
def lowerCamelCase ( self ):
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''text-generation''' , model=self.model_name , model_kwargs={'''device_map''': '''auto''', '''load_in_4bit''': True, '''torch_dtype''': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
__lowerCamelCase = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]['''generated_text'''] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=__UpperCAmelCase , device_map='''balanced''' )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
# Second real batch
__lowerCamelCase = model_parallel.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-350m'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
if version.parse(importlib.metadata.version('''bitsandbytes''' ) ) < version.parse('''0.37.0''' ):
return
# Step 1: freeze all parameters
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
__lowerCamelCase = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
__lowerCamelCase = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(__UpperCAmelCase ) ):
__lowerCamelCase = LoRALayer(module.q_proj , rank=16 )
__lowerCamelCase = LoRALayer(module.k_proj , rank=16 )
__lowerCamelCase = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
__lowerCamelCase = self.tokenizer('''Test batch ''' , return_tensors='''pt''' ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
__lowerCamelCase = model.forward(**__UpperCAmelCase )
out.logits.norm().backward()
for module in model.modules():
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(__UpperCAmelCase , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """gpt2-xl"""
lowerCAmelCase__ = 3.31_91_85_48_54_15_21_87
| 330 | 1 |
from __future__ import annotations
import os
from collections.abc import Mapping
a_ = tuple[int, int]
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = vertices
__lowerCamelCase = {
(min(__UpperCAmelCase ), max(__UpperCAmelCase )): weight for edge, weight in edges.items()
}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
__lowerCamelCase = weight
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = Graph({min(self.vertices )} , {} )
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
while len(subgraph.vertices ) < len(self.vertices ):
__lowerCamelCase = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
__lowerCamelCase = edge
__lowerCamelCase = weight
subgraph.add_edge(__UpperCAmelCase , __UpperCAmelCase )
return subgraph
def a__ ( _UpperCamelCase : str = "p107_network.txt" ):
__lowerCamelCase = os.path.abspath(os.path.dirname(_UpperCamelCase ) )
__lowerCamelCase = os.path.join(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = {}
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
with open(_UpperCamelCase ) as f:
__lowerCamelCase = f.read().strip().split('''\n''' )
__lowerCamelCase = [line.split(''',''' ) for line in data]
for edgea in range(1 ,len(_UpperCamelCase ) ):
for edgea in range(_UpperCamelCase ):
if adjaceny_matrix[edgea][edgea] != "-":
__lowerCamelCase = int(adjaceny_matrix[edgea][edgea] )
__lowerCamelCase = Graph(set(range(len(_UpperCamelCase ) ) ) ,_UpperCamelCase )
__lowerCamelCase = graph.prims_algorithm()
__lowerCamelCase = sum(graph.edges.values() )
__lowerCamelCase = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(f"{solution() = }")
| 330 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = 42
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
lowerCAmelCase__ = True
@register_to_config
def __init__( self , __UpperCAmelCase = 3 , __UpperCAmelCase = 3 , __UpperCAmelCase = ("DownEncoderBlock2D",) , __UpperCAmelCase = ("UpDecoderBlock2D",) , __UpperCAmelCase = (64,) , __UpperCAmelCase = 1 , __UpperCAmelCase = "silu" , __UpperCAmelCase = 4 , __UpperCAmelCase = 32 , __UpperCAmelCase = 32 , __UpperCAmelCase = 0.18_215 , ):
'''simple docstring'''
super().__init__()
# pass init params to Encoder
__lowerCamelCase = Encoder(
in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , down_block_types=__UpperCAmelCase , block_out_channels=__UpperCAmelCase , layers_per_block=__UpperCAmelCase , act_fn=__UpperCAmelCase , norm_num_groups=__UpperCAmelCase , double_z=__UpperCAmelCase , )
# pass init params to Decoder
__lowerCamelCase = Decoder(
in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , up_block_types=__UpperCAmelCase , block_out_channels=__UpperCAmelCase , layers_per_block=__UpperCAmelCase , norm_num_groups=__UpperCAmelCase , act_fn=__UpperCAmelCase , )
__lowerCamelCase = nn.Convad(2 * latent_channels , 2 * latent_channels , 1 )
__lowerCamelCase = nn.Convad(__UpperCAmelCase , __UpperCAmelCase , 1 )
__lowerCamelCase = False
__lowerCamelCase = False
# only relevant if vae tiling is enabled
__lowerCamelCase = self.config.sample_size
__lowerCamelCase = (
self.config.sample_size[0]
if isinstance(self.config.sample_size , (list, tuple) )
else self.config.sample_size
)
__lowerCamelCase = int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
__lowerCamelCase = 0.25
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
if isinstance(__UpperCAmelCase , (Encoder, Decoder) ):
__lowerCamelCase = value
def lowerCamelCase ( self , __UpperCAmelCase = True ):
'''simple docstring'''
__lowerCamelCase = use_tiling
def lowerCamelCase ( self ):
'''simple docstring'''
self.enable_tiling(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = True
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = {}
def fn_recursive_add_processors(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
if hasattr(__UpperCAmelCase , '''set_processor''' ):
__lowerCamelCase = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(F"""{name}.{sub_name}""" , __UpperCAmelCase , __UpperCAmelCase )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
return processors
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = len(self.attn_processors.keys() )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(__UpperCAmelCase ) != count:
raise ValueError(
F"""A dict of processors was passed, but the number of processors {len(__UpperCAmelCase )} does not match the"""
F""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" )
def fn_recursive_attn_processor(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
if hasattr(__UpperCAmelCase , '''set_processor''' ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
module.set_processor(__UpperCAmelCase )
else:
module.set_processor(processor.pop(F"""{name}.processor""" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(F"""{name}.{sub_name}""" , __UpperCAmelCase , __UpperCAmelCase )
for name, module in self.named_children():
fn_recursive_attn_processor(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(__UpperCAmelCase , return_dict=__UpperCAmelCase )
if self.use_slicing and x.shape[0] > 1:
__lowerCamelCase = [self.encoder(__UpperCAmelCase ) for x_slice in x.split(1 )]
__lowerCamelCase = torch.cat(__UpperCAmelCase )
else:
__lowerCamelCase = self.encoder(__UpperCAmelCase )
__lowerCamelCase = self.quant_conv(__UpperCAmelCase )
__lowerCamelCase = DiagonalGaussianDistribution(__UpperCAmelCase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.tiled_decode(__UpperCAmelCase , return_dict=__UpperCAmelCase )
__lowerCamelCase = self.post_quant_conv(__UpperCAmelCase )
__lowerCamelCase = self.decoder(__UpperCAmelCase )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__UpperCAmelCase )
@apply_forward_hook
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
if self.use_slicing and z.shape[0] > 1:
__lowerCamelCase = [self._decode(__UpperCAmelCase ).sample for z_slice in z.split(1 )]
__lowerCamelCase = torch.cat(__UpperCAmelCase )
else:
__lowerCamelCase = self._decode(__UpperCAmelCase ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = min(a.shape[2] , b.shape[2] , __UpperCAmelCase )
for y in range(__UpperCAmelCase ):
__lowerCamelCase = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = min(a.shape[3] , b.shape[3] , __UpperCAmelCase )
for x in range(__UpperCAmelCase ):
__lowerCamelCase = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
__lowerCamelCase = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
__lowerCamelCase = int(self.tile_latent_min_size * self.tile_overlap_factor )
__lowerCamelCase = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
__lowerCamelCase = []
for i in range(0 , x.shape[2] , __UpperCAmelCase ):
__lowerCamelCase = []
for j in range(0 , x.shape[3] , __UpperCAmelCase ):
__lowerCamelCase = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
__lowerCamelCase = self.encoder(__UpperCAmelCase )
__lowerCamelCase = self.quant_conv(__UpperCAmelCase )
row.append(__UpperCAmelCase )
rows.append(__UpperCAmelCase )
__lowerCamelCase = []
for i, row in enumerate(__UpperCAmelCase ):
__lowerCamelCase = []
for j, tile in enumerate(__UpperCAmelCase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
__lowerCamelCase = self.blend_v(rows[i - 1][j] , __UpperCAmelCase , __UpperCAmelCase )
if j > 0:
__lowerCamelCase = self.blend_h(row[j - 1] , __UpperCAmelCase , __UpperCAmelCase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__UpperCAmelCase , dim=3 ) )
__lowerCamelCase = torch.cat(__UpperCAmelCase , dim=2 )
__lowerCamelCase = DiagonalGaussianDistribution(__UpperCAmelCase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
__lowerCamelCase = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
__lowerCamelCase = int(self.tile_sample_min_size * self.tile_overlap_factor )
__lowerCamelCase = self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
__lowerCamelCase = []
for i in range(0 , z.shape[2] , __UpperCAmelCase ):
__lowerCamelCase = []
for j in range(0 , z.shape[3] , __UpperCAmelCase ):
__lowerCamelCase = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
__lowerCamelCase = self.post_quant_conv(__UpperCAmelCase )
__lowerCamelCase = self.decoder(__UpperCAmelCase )
row.append(__UpperCAmelCase )
rows.append(__UpperCAmelCase )
__lowerCamelCase = []
for i, row in enumerate(__UpperCAmelCase ):
__lowerCamelCase = []
for j, tile in enumerate(__UpperCAmelCase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
__lowerCamelCase = self.blend_v(rows[i - 1][j] , __UpperCAmelCase , __UpperCAmelCase )
if j > 0:
__lowerCamelCase = self.blend_h(row[j - 1] , __UpperCAmelCase , __UpperCAmelCase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__UpperCAmelCase , dim=3 ) )
__lowerCamelCase = torch.cat(__UpperCAmelCase , dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False , __UpperCAmelCase = True , __UpperCAmelCase = None , ):
'''simple docstring'''
__lowerCamelCase = sample
__lowerCamelCase = self.encode(__UpperCAmelCase ).latent_dist
if sample_posterior:
__lowerCamelCase = posterior.sample(generator=__UpperCAmelCase )
else:
__lowerCamelCase = posterior.mode()
__lowerCamelCase = self.decode(__UpperCAmelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=__UpperCAmelCase )
| 330 | 1 |
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
__lowerCamelCase = [
[-1 for i in range(total + 1 )] for j in range(2 ** len(__UpperCAmelCase ) )
]
__lowerCamelCase = defaultdict(__UpperCAmelCase ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
__lowerCamelCase = (1 << len(__UpperCAmelCase )) - 1
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# if mask == self.finalmask all persons are distributed tasks, return 1
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
__lowerCamelCase = self.count_ways_until(__UpperCAmelCase , task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1 )
# save the value.
__lowerCamelCase = total_ways_util
return self.dp[mask][task_no]
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# Store the list of persons for each task
for i in range(len(__UpperCAmelCase ) ):
for j in task_performed[i]:
self.task[j].append(__UpperCAmelCase )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0 , 1 )
if __name__ == "__main__":
a_ = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
a_ = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
| 330 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
a_ = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""kernel""", """weight"""),
("""beta""", """bias"""),
("""gamma""", """weight"""),
("""pegasus""", """model"""),
]
a_ = [
(""".output.dense""", """.fc2"""),
("""intermediate.LayerNorm""", """final_layer_norm"""),
("""intermediate.dense""", """fc1"""),
]
a_ = (
INIT_COMMON
+ [
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.out_proj"""),
("""attention.self""", """self_attn"""),
("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
("""attention.encdec""", """encoder_attn"""),
("""key""", """k_proj"""),
("""value""", """v_proj"""),
("""query""", """q_proj"""),
("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
]
+ END_COMMON
)
a_ = (
INIT_COMMON
+ [
("""embeddings.word_embeddings""", """shared.weight"""),
("""embeddings.position_embeddings""", """embed_positions.weight"""),
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.output"""),
("""attention.self""", """self_attn.self"""),
("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
]
+ END_COMMON
)
a_ = [
"""encdec/key/bias""",
"""encdec/query/bias""",
"""encdec/value/bias""",
"""self/key/bias""",
"""self/query/bias""",
"""self/value/bias""",
"""encdec_output/dense/bias""",
"""attention/output/dense/bias""",
]
def a__ ( _UpperCamelCase : Optional[int] ,_UpperCamelCase : Optional[Any] ):
for tf_name, hf_name in patterns:
__lowerCamelCase = k.replace(_UpperCamelCase ,_UpperCamelCase )
return k
def a__ ( _UpperCamelCase : dict ,_UpperCamelCase : dict ):
__lowerCamelCase = BigBirdPegasusConfig(**_UpperCamelCase )
__lowerCamelCase = BigBirdPegasusForConditionalGeneration(_UpperCamelCase )
__lowerCamelCase = torch_model.state_dict()
__lowerCamelCase = {}
# separating decoder weights
__lowerCamelCase = {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
__lowerCamelCase = {k: tf_weights[k] for k in tf_weights if not k.startswith('''pegasus/decoder''' )}
for k, v in tqdm(decoder_weights.items() ,'''tf -> hf conversion''' ):
__lowerCamelCase = [k.endswith(_UpperCamelCase ) for ending in KEYS_TO_IGNORE]
if any(_UpperCamelCase ):
continue
__lowerCamelCase = DECODER_PATTERNS
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase ,_UpperCamelCase )
if new_k not in state_dict:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
__lowerCamelCase = v.T
__lowerCamelCase = torch.from_numpy(_UpperCamelCase )
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items() ,'''tf -> hf conversion''' ):
__lowerCamelCase = [k.endswith(_UpperCamelCase ) for ending in KEYS_TO_IGNORE]
if any(_UpperCamelCase ):
continue
__lowerCamelCase = REMAINING_PATTERNS
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase ,_UpperCamelCase )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
__lowerCamelCase = v.T
__lowerCamelCase = torch.from_numpy(_UpperCamelCase )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
__lowerCamelCase = mapping['''model.embed_positions.weight''']
__lowerCamelCase = mapping.pop('''model.embed_positions.weight''' )
__lowerCamelCase ,__lowerCamelCase = torch_model.load_state_dict(_UpperCamelCase ,strict=_UpperCamelCase )
__lowerCamelCase = [
k
for k in missing
if k
not in [
'''final_logits_bias''',
'''model.encoder.embed_tokens.weight''',
'''model.decoder.embed_tokens.weight''',
'''lm_head.weight''',
]
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = tf.train.list_variables(_UpperCamelCase )
__lowerCamelCase = {}
__lowerCamelCase = ['''global_step''']
for name, shape in tqdm(_UpperCamelCase ,desc='''converting tf checkpoint to dict''' ):
__lowerCamelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
__lowerCamelCase = tf.train.load_variable(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = array
return tf_weights
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : dict ):
__lowerCamelCase = get_tf_weights_as_numpy(_UpperCamelCase )
__lowerCamelCase = convert_bigbird_pegasus(_UpperCamelCase ,_UpperCamelCase )
torch_model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
a_ = parser.parse_args()
a_ = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 330 | 1 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
a_ = """bert-base-cased"""
a_ = """google/pegasus-xsum"""
a_ = [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a_ = ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a_ = """patrickvonplaten/t5-tiny-random"""
a_ = """sshleifer/bart-tiny-random"""
a_ = """sshleifer/tiny-mbart"""
a_ = """sshleifer/tiny-marian-en-de"""
def a__ ( _UpperCamelCase : Path ,_UpperCamelCase : list ):
__lowerCamelCase = '''\n'''.join(_UpperCamelCase )
Path(_UpperCamelCase ).open('''w''' ).writelines(_UpperCamelCase )
def a__ ( _UpperCamelCase : Tuple ):
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(_UpperCamelCase ,F"""{split}.source""" ) ,_UpperCamelCase )
_dump_articles(os.path.join(_UpperCamelCase ,F"""{split}.target""" ) ,_UpperCamelCase )
return tmp_dir
class __lowerCAmelCase ( lowerCAmelCase__ ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__lowerCamelCase = max(len(tokenizer.encode(__UpperCAmelCase ) ) for a in ARTICLES )
__lowerCamelCase = max(len(tokenizer.encode(__UpperCAmelCase ) ) for a in SUMMARIES )
__lowerCamelCase = 4
__lowerCamelCase = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
__lowerCamelCase ,__lowerCamelCase = '''ro_RO''', '''de_DE''' # ignored for all but mbart, but never causes error.
__lowerCamelCase = SeqaSeqDataset(
__UpperCAmelCase , data_dir=__UpperCAmelCase , type_path='''train''' , max_source_length=__UpperCAmelCase , max_target_length=__UpperCAmelCase , src_lang=__UpperCAmelCase , tgt_lang=__UpperCAmelCase , )
__lowerCamelCase = DataLoader(__UpperCAmelCase , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(__UpperCAmelCase , __UpperCAmelCase )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
__lowerCamelCase = shift_tokens_right(batch['''labels'''] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__lowerCamelCase = max(len(tokenizer.encode(__UpperCAmelCase ) ) for a in ARTICLES )
__lowerCamelCase = max(len(tokenizer.encode(__UpperCAmelCase ) ) for a in SUMMARIES )
__lowerCamelCase = 4
__lowerCamelCase = LegacySeqaSeqDataset(
__UpperCAmelCase , data_dir=__UpperCAmelCase , type_path='''train''' , max_source_length=20 , max_target_length=__UpperCAmelCase , )
__lowerCamelCase = DataLoader(__UpperCAmelCase , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''' )
__lowerCamelCase = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
__lowerCamelCase = tmp_dir.joinpath('''train.source''' ).open().readlines()
__lowerCamelCase = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(__UpperCAmelCase , __UpperCAmelCase , 128 , __UpperCAmelCase )
__lowerCamelCase = {x.name for x in tmp_dir.iterdir()}
__lowerCamelCase = {x.name for x in save_dir.iterdir()}
__lowerCamelCase = save_dir.joinpath('''train.source''' ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(__UpperCAmelCase ) < len(__UpperCAmelCase )
assert len(__UpperCAmelCase ) == 1
assert len(packed_examples[0] ) == sum(len(__UpperCAmelCase ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason='''This test requires fairseq''' )
def lowerCamelCase ( self ):
'''simple docstring'''
if not FAIRSEQ_AVAILABLE:
return
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = self._get_dataset(max_len=64 )
__lowerCamelCase = 64
__lowerCamelCase = ds.make_dynamic_sampler(__UpperCAmelCase , required_batch_size_multiple=__UpperCAmelCase )
__lowerCamelCase = [len(__UpperCAmelCase ) for x in batch_sampler]
assert len(set(__UpperCAmelCase ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(__UpperCAmelCase ) == len(__UpperCAmelCase ) # no dropped or added examples
__lowerCamelCase = DataLoader(__UpperCAmelCase , batch_sampler=__UpperCAmelCase , collate_fn=ds.collate_fn , num_workers=2 )
__lowerCamelCase = []
__lowerCamelCase = []
for batch in data_loader:
__lowerCamelCase = batch['''input_ids'''].shape
__lowerCamelCase = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
__lowerCamelCase = np.product(batch['''input_ids'''].shape )
num_src_per_batch.append(__UpperCAmelCase )
if num_src_tokens > (max_tokens * 1.1):
failures.append(__UpperCAmelCase )
assert num_src_per_batch[0] == max(__UpperCAmelCase )
if failures:
raise AssertionError(F"""too many tokens in {len(__UpperCAmelCase )} batches""" )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = self._get_dataset(max_len=512 )
__lowerCamelCase = 2
__lowerCamelCase = ds.make_sortish_sampler(__UpperCAmelCase , shuffle=__UpperCAmelCase )
__lowerCamelCase = DataLoader(__UpperCAmelCase , batch_size=__UpperCAmelCase , collate_fn=ds.collate_fn , num_workers=2 )
__lowerCamelCase = DataLoader(__UpperCAmelCase , batch_size=__UpperCAmelCase , collate_fn=ds.collate_fn , num_workers=2 , sampler=__UpperCAmelCase )
__lowerCamelCase = tokenizer.pad_token_id
def count_pad_tokens(__UpperCAmelCase , __UpperCAmelCase="input_ids" ):
return [batch[k].eq(__UpperCAmelCase ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(__UpperCAmelCase , k='''labels''' ) ) < sum(count_pad_tokens(__UpperCAmelCase , k='''labels''' ) )
assert sum(count_pad_tokens(__UpperCAmelCase ) ) < sum(count_pad_tokens(__UpperCAmelCase ) )
assert len(__UpperCAmelCase ) == len(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase=1000 , __UpperCAmelCase=128 ):
'''simple docstring'''
if os.getenv('''USE_REAL_DATA''' , __UpperCAmelCase ):
__lowerCamelCase = '''examples/seq2seq/wmt_en_ro'''
__lowerCamelCase = max_len * 2 * 64
if not Path(__UpperCAmelCase ).joinpath('''train.len''' ).exists():
save_len_file(__UpperCAmelCase , __UpperCAmelCase )
else:
__lowerCamelCase = '''examples/seq2seq/test_data/wmt_en_ro'''
__lowerCamelCase = max_len * 4
save_len_file(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = AutoTokenizer.from_pretrained(__UpperCAmelCase )
__lowerCamelCase = SeqaSeqDataset(
__UpperCAmelCase , data_dir=__UpperCAmelCase , type_path='''train''' , max_source_length=__UpperCAmelCase , max_target_length=__UpperCAmelCase , n_obs=__UpperCAmelCase , )
return ds, max_tokens, tokenizer
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = self._get_dataset()
__lowerCamelCase = set(DistributedSortishSampler(__UpperCAmelCase , 256 , num_replicas=2 , rank=0 , add_extra_examples=__UpperCAmelCase ) )
__lowerCamelCase = set(DistributedSortishSampler(__UpperCAmelCase , 256 , num_replicas=2 , rank=1 , add_extra_examples=__UpperCAmelCase ) )
assert idsa.intersection(__UpperCAmelCase ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = AutoTokenizer.from_pretrained(__UpperCAmelCase , use_fast=__UpperCAmelCase )
if tok_name == MBART_TINY:
__lowerCamelCase = SeqaSeqDataset(
__UpperCAmelCase , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , src_lang='''EN''' , tgt_lang='''FR''' , )
__lowerCamelCase = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
__lowerCamelCase = SeqaSeqDataset(
__UpperCAmelCase , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , )
__lowerCamelCase = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(__UpperCAmelCase ) == 1 if tok_name == BART_TINY else len(__UpperCAmelCase ) == 0
| 330 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
a_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase=None , __UpperCAmelCase=None ):
'''simple docstring'''
if not conversation_id:
__lowerCamelCase = uuid.uuida()
if past_user_inputs is None:
__lowerCamelCase = []
if generated_responses is None:
__lowerCamelCase = []
__lowerCamelCase = conversation_id
__lowerCamelCase = past_user_inputs
__lowerCamelCase = generated_responses
__lowerCamelCase = text
def __eq__( self , __UpperCAmelCase ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False ):
'''simple docstring'''
if self.new_user_input:
if overwrite:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
F"""with: \"{text}\".""" )
__lowerCamelCase = text
else:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input""" )
else:
__lowerCamelCase = text
def lowerCamelCase ( self ):
'''simple docstring'''
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
__lowerCamelCase = None
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
self.generated_responses.append(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
'''simple docstring'''
__lowerCamelCase = F"""Conversation id: {self.uuid} \n"""
for is_user, text in self.iter_texts():
__lowerCamelCase = '''user''' if is_user else '''bot'''
output += F"""{name} >> {text} \n"""
return output
@add_end_docstrings(
lowerCAmelCase__ , r"""
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
""" , )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
if self.tokenizer.pad_token_id is None:
__lowerCamelCase = self.tokenizer.eos_token
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = {}
__lowerCamelCase = {}
__lowerCamelCase = {}
if min_length_for_response is not None:
__lowerCamelCase = min_length_for_response
if minimum_tokens is not None:
__lowerCamelCase = minimum_tokens
if "max_length" in generate_kwargs:
__lowerCamelCase = generate_kwargs['''max_length''']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
__lowerCamelCase = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(__UpperCAmelCase )
return preprocess_params, forward_params, postprocess_params
def __call__( self , __UpperCAmelCase , __UpperCAmelCase=0 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = super().__call__(__UpperCAmelCase , num_workers=__UpperCAmelCase , **__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(__UpperCAmelCase ) == 1:
return outputs[0]
return outputs
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=32 ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise ValueError('''ConversationalPipeline, expects Conversation as inputs''' )
if conversation.new_user_input is None:
raise ValueError(
F"""Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. """
'''Add user inputs with the conversation\'s `add_user_input` method''' )
if hasattr(self.tokenizer , '''_build_conversation_input_ids''' ):
__lowerCamelCase = self.tokenizer._build_conversation_input_ids(__UpperCAmelCase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
__lowerCamelCase = self._legacy_parse_and_tokenize(__UpperCAmelCase )
if self.framework == "pt":
__lowerCamelCase = torch.LongTensor([input_ids] )
elif self.framework == "tf":
__lowerCamelCase = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=10 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = generate_kwargs.get('''max_length''' , self.model.config.max_length )
__lowerCamelCase = model_inputs['''input_ids'''].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})""" )
__lowerCamelCase = max_length - minimum_tokens
__lowerCamelCase = model_inputs['''input_ids'''][:, -trim:]
if "attention_mask" in model_inputs:
__lowerCamelCase = model_inputs['''attention_mask'''][:, -trim:]
__lowerCamelCase = model_inputs.pop('''conversation''' )
__lowerCamelCase = max_length
__lowerCamelCase = self.model.generate(**__UpperCAmelCase , **__UpperCAmelCase )
if self.model.config.is_encoder_decoder:
__lowerCamelCase = 1
else:
__lowerCamelCase = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=True ):
'''simple docstring'''
__lowerCamelCase = model_outputs['''output_ids''']
__lowerCamelCase = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase , )
__lowerCamelCase = model_outputs['''conversation''']
conversation.mark_processed()
conversation.append_response(__UpperCAmelCase )
return conversation
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.tokenizer.eos_token_id
__lowerCamelCase = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) )
if len(__UpperCAmelCase ) > self.tokenizer.model_max_length:
__lowerCamelCase = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 330 | 1 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCAmelCase__ = HfApi()
UpperCAmelCase__ = {}
# fmt: off
UpperCAmelCase__ = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
UpperCAmelCase__ = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
UpperCAmelCase__ = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
UpperCAmelCase__ = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
UpperCAmelCase__ = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
UpperCAmelCase__ = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
UpperCAmelCase__ = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
UpperCAmelCase__ = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
UpperCAmelCase__ = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
UpperCAmelCase__ = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
UpperCAmelCase__ = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
UpperCAmelCase__ = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
UpperCAmelCase__ = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
UpperCAmelCase__ = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
UpperCAmelCase__ = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
UpperCAmelCase__ = api.list_models(filter="diffusers")
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
UpperCAmelCase__ = "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]
print(f"""Started running {mod.modelId}!!!""")
if mod.modelId.startswith("CompVis"):
UpperCAmelCase__ = UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
else:
UpperCAmelCase__ = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
UpperCAmelCase__ = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
UpperCAmelCase__ = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
UpperCAmelCase__ = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
)
print(f"""{mod.modelId} has passed successfully!!!""")
| 0 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
a_ = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["""memory_attention""", """encoder_attn"""],
["""attention""", """attn"""],
["""/""", """."""],
[""".LayerNorm.gamma""", """_layer_norm.weight"""],
[""".LayerNorm.beta""", """_layer_norm.bias"""],
["""r.layer_""", """r.layers."""],
["""output_proj""", """out_proj"""],
["""ffn.dense_1.""", """fc2."""],
["""ffn.dense.""", """fc1."""],
["""ffn_layer_norm""", """final_layer_norm"""],
["""kernel""", """weight"""],
["""encoder_layer_norm.""", """encoder.layer_norm."""],
["""decoder_layer_norm.""", """decoder.layer_norm."""],
["""embeddings.weights""", """shared.weight"""],
]
def a__ ( _UpperCamelCase : int ):
for pegasus_name, hf_name in PATTERNS:
__lowerCamelCase = k.replace(_UpperCamelCase ,_UpperCamelCase )
return k
def a__ ( _UpperCamelCase : dict ,_UpperCamelCase : dict ):
__lowerCamelCase = DEFAULTS.copy()
cfg_kwargs.update(_UpperCamelCase )
__lowerCamelCase = PegasusConfig(**_UpperCamelCase )
__lowerCamelCase = PegasusForConditionalGeneration(_UpperCamelCase )
__lowerCamelCase = torch_model.model.state_dict()
__lowerCamelCase = {}
for k, v in tf_weights.items():
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase )
if new_k not in sd:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if "dense" in k or "proj" in new_k:
__lowerCamelCase = v.T
__lowerCamelCase = torch.tensor(_UpperCamelCase ,dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
# make sure embedding.padding_idx is respected
__lowerCamelCase = torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1] )
__lowerCamelCase = mapping['''shared.weight''']
__lowerCamelCase = mapping['''shared.weight''']
__lowerCamelCase = {k: torch.zeros_like(_UpperCamelCase ) for k, v in sd.items() if k.endswith('''bias''' ) and k not in mapping}
mapping.update(**_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = torch_model.model.load_state_dict(_UpperCamelCase ,strict=_UpperCamelCase )
__lowerCamelCase = [
k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def a__ ( _UpperCamelCase : str="./ckpt/aeslc/model.ckpt-32000" ):
__lowerCamelCase = tf.train.list_variables(_UpperCamelCase )
__lowerCamelCase = {}
__lowerCamelCase = ['''Adafactor''', '''global_step''']
for name, shape in tqdm(_UpperCamelCase ,desc='''converting tf checkpoint to dict''' ):
__lowerCamelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
__lowerCamelCase = tf.train.load_variable(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = array
return tf_weights
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ):
# save tokenizer first
__lowerCamelCase = Path(_UpperCamelCase ).parent.name
__lowerCamelCase = task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
__lowerCamelCase = PegasusTokenizer.from_pretrained('''sshleifer/pegasus''' ,model_max_length=_UpperCamelCase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(_UpperCamelCase )
# convert model
__lowerCamelCase = get_tf_weights_as_numpy(_UpperCamelCase )
__lowerCamelCase = task_specific_params[F"""summarization_{dataset}"""]
if dataset == "large":
__lowerCamelCase = task_specific_params
__lowerCamelCase = convert_pegasus(_UpperCamelCase ,_UpperCamelCase )
torch_model.save_pretrained(_UpperCamelCase )
__lowerCamelCase = torch_model.state_dict()
sd.pop('''model.decoder.embed_positions.weight''' )
sd.pop('''model.encoder.embed_positions.weight''' )
torch.save(_UpperCamelCase ,Path(_UpperCamelCase ) / '''pytorch_model.bin''' )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
a_ = parser.parse_args()
if args.save_dir is None:
a_ = Path(args.tf_ckpt_path).parent.name
a_ = os.path.join("""pegasus""", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 330 | 0 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE_: Optional[Any] =[
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ = Github(os.environ["GITHUB_TOKEN"] )
UpperCAmelCase_ = g.get_repo("huggingface/transformers" )
UpperCAmelCase_ = repo.get_issues(state="open" )
for issue in open_issues:
UpperCAmelCase_ = sorted([comment for comment in issue.get_comments()] , key=lambda snake_case_ : i.created_at , reverse=snake_case_ )
UpperCAmelCase_ = comments[0] if len(snake_case_ ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state="closed" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
"This issue has been automatically marked as stale because it has not had "
"recent activity. If you think this still needs to be addressed "
"please comment on this thread.\n\nPlease note that issues that do not follow the "
"[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
if __name__ == "__main__":
main()
| 1 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
a_ = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : Tuple ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Dict ):
for attribute in key.split('''.''' ):
__lowerCamelCase = getattr(_UpperCamelCase ,_UpperCamelCase )
if weight_type is not None:
__lowerCamelCase = getattr(_UpperCamelCase ,_UpperCamelCase ).shape
else:
__lowerCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__lowerCamelCase = value
elif weight_type == "weight_g":
__lowerCamelCase = value
elif weight_type == "weight_v":
__lowerCamelCase = value
elif weight_type == "bias":
__lowerCamelCase = value
else:
__lowerCamelCase = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def a__ ( _UpperCamelCase : Any ,_UpperCamelCase : Any ):
__lowerCamelCase = []
__lowerCamelCase = fairseq_model.state_dict()
__lowerCamelCase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowerCamelCase = False
if "conv_layers" in name:
load_conv_layer(
_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,hf_model.config.feat_extract_norm == '''group''' ,)
__lowerCamelCase = True
else:
for key, mapped_key in MAPPING.items():
__lowerCamelCase = '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
# special case since naming is very similar
continue
__lowerCamelCase = True
if "*" in mapped_key:
__lowerCamelCase = name.split(_UpperCamelCase )[0].split('''.''' )[-2]
__lowerCamelCase = mapped_key.replace('''*''' ,_UpperCamelCase )
if "weight_g" in name:
__lowerCamelCase = '''weight_g'''
elif "weight_v" in name:
__lowerCamelCase = '''weight_v'''
elif "bias" in name:
__lowerCamelCase = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCamelCase = '''weight'''
else:
__lowerCamelCase = None
set_recursively(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
continue
if not is_used:
unused_weights.append(_UpperCamelCase )
logger.warning(F"""Unused weights: {unused_weights}""" )
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Dict ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = full_name.split('''conv_layers.''' )[-1]
__lowerCamelCase = name.split('''.''' )
__lowerCamelCase = int(items[0] )
__lowerCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_UpperCamelCase )
@torch.no_grad()
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : List[str] ,_UpperCamelCase : Tuple=None ,_UpperCamelCase : Tuple=None ,_UpperCamelCase : List[Any]=True ):
if config_path is not None:
__lowerCamelCase = UniSpeechSatConfig.from_pretrained(_UpperCamelCase )
else:
__lowerCamelCase = UniSpeechSatConfig()
__lowerCamelCase = ''''''
if is_finetuned:
__lowerCamelCase = UniSpeechSatForCTC(_UpperCamelCase )
else:
__lowerCamelCase = UniSpeechSatForPreTraining(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
__lowerCamelCase = model[0].eval()
recursively_load_weights(_UpperCamelCase ,_UpperCamelCase )
hf_wavavec.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
a_ = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 330 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
lowerCamelCase : Optional[Any] = logging.getLogger(__name__)
@dataclass(frozen=lowercase_ )
class __lowerCAmelCase :
'''simple docstring'''
lowerCAmelCase__ : str
lowerCAmelCase__ : str
lowerCAmelCase__ : Optional[str] = None
lowerCAmelCase__ : Optional[str] = None
lowerCAmelCase__ : Optional[str] = None
@dataclass(frozen=lowercase_ )
class __lowerCAmelCase :
'''simple docstring'''
lowerCAmelCase__ : List[int]
lowerCAmelCase__ : Optional[List[int]] = None
lowerCAmelCase__ : Optional[List[int]] = None
lowerCAmelCase__ : Optional[Union[int, float]] = None
lowerCAmelCase__ : Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
lowerCAmelCase__ : List[InputFeatures]
def __init__(self : Optional[int] , UpperCamelCase : str , UpperCamelCase : PreTrainedTokenizer , UpperCamelCase : str , UpperCamelCase : Optional[int] = None , UpperCamelCase : List[Any]=False , UpperCamelCase : bool = False , ):
'''simple docstring'''
lowercase__ = hans_processors[task]()
lowercase__ = os.path.join(
UpperCamelCase , '''cached_{}_{}_{}_{}'''.format(
'''dev''' if evaluate else '''train''' , tokenizer.__class__.__name__ , str(UpperCamelCase ) , UpperCamelCase , ) , )
lowercase__ = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowercase__ ,lowercase__ = label_list[2], label_list[1]
lowercase__ = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowercase__ = cached_features_file + '''.lock'''
with FileLock(UpperCamelCase ):
if os.path.exists(UpperCamelCase ) and not overwrite_cache:
logger.info(f"Loading features from cached file {cached_features_file}" )
lowercase__ = torch.load(UpperCamelCase )
else:
logger.info(f"Creating features from dataset file at {data_dir}" )
lowercase__ = (
processor.get_dev_examples(UpperCamelCase ) if evaluate else processor.get_train_examples(UpperCamelCase )
)
logger.info('''Training examples: %s''' , len(UpperCamelCase ) )
lowercase__ = hans_convert_examples_to_features(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
logger.info('''Saving features into cached file %s''' , UpperCamelCase )
torch.save(self.features , UpperCamelCase )
def __len__(self : Optional[int] ):
'''simple docstring'''
return len(self.features )
def __getitem__(self : Tuple , UpperCamelCase : str ):
'''simple docstring'''
return self.features[i]
def UpperCamelCase__ (self : str ):
'''simple docstring'''
return self.label_list
if is_tf_available():
import tensorflow as tf
class __lowerCAmelCase :
'''simple docstring'''
lowerCAmelCase__ : List[InputFeatures]
def __init__(self : List[Any] , UpperCamelCase : str , UpperCamelCase : PreTrainedTokenizer , UpperCamelCase : str , UpperCamelCase : Optional[int] = 128 , UpperCamelCase : Union[str, Any]=False , UpperCamelCase : bool = False , ):
'''simple docstring'''
lowercase__ = hans_processors[task]()
lowercase__ = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowercase__ ,lowercase__ = label_list[2], label_list[1]
lowercase__ = label_list
lowercase__ = processor.get_dev_examples(UpperCamelCase ) if evaluate else processor.get_train_examples(UpperCamelCase )
lowercase__ = hans_convert_examples_to_features(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='''convert examples to features''' ):
if ex_index % 10000 == 0:
logger.info('''Writing example %d of %d''' % (ex_index, len(UpperCamelCase )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
lowercase__ = tf.data.Dataset.from_generator(
UpperCamelCase , (
{
'''example_id''': tf.intaa,
'''input_ids''': tf.intaa,
'''attention_mask''': tf.intaa,
'''token_type_ids''': tf.intaa,
},
tf.intaa,
) , (
{
'''example_id''': tf.TensorShape([] ),
'''input_ids''': tf.TensorShape([None, None] ),
'''attention_mask''': tf.TensorShape([None, None] ),
'''token_type_ids''': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def UpperCamelCase__ (self : Optional[int] ):
'''simple docstring'''
return self.dataset
def __len__(self : List[Any] ):
'''simple docstring'''
return len(self.features )
def __getitem__(self : Union[str, Any] , UpperCamelCase : Tuple ):
'''simple docstring'''
return self.features[i]
def UpperCamelCase__ (self : Union[str, Any] ):
'''simple docstring'''
return self.label_list
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
def UpperCamelCase__ (self : Optional[int] , UpperCamelCase : Any ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase , '''heuristics_train_set.txt''' ) ) , '''train''' )
def UpperCamelCase__ (self : Optional[int] , UpperCamelCase : Optional[Any] ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase , '''heuristics_evaluation_set.txt''' ) ) , '''dev''' )
def UpperCamelCase__ (self : Dict ):
'''simple docstring'''
return ["contradiction", "entailment", "neutral"]
def UpperCamelCase__ (self : Optional[Any] , UpperCamelCase : int , UpperCamelCase : Optional[int] ):
'''simple docstring'''
lowercase__ = []
for i, line in enumerate(UpperCamelCase ):
if i == 0:
continue
lowercase__ = '''%s-%s''' % (set_type, line[0])
lowercase__ = line[5]
lowercase__ = line[6]
lowercase__ = line[7][2:] if line[7].startswith('''ex''' ) else line[7]
lowercase__ = line[0]
examples.append(InputExample(guid=UpperCamelCase , text_a=UpperCamelCase , text_b=UpperCamelCase , label=UpperCamelCase , pairID=UpperCamelCase ) )
return examples
def _SCREAMING_SNAKE_CASE (A , A , A , A , ) -> int:
"""simple docstring"""
lowercase__ = {label: i for i, label in enumerate(A )}
lowercase__ = []
for ex_index, example in tqdm.tqdm(enumerate(A ) , desc='''convert examples to features''' ):
if ex_index % 10_000 == 0:
logger.info('''Writing example %d''' % (ex_index) )
lowercase__ = tokenizer(
example.text_a , example.text_b , add_special_tokens=A , max_length=A , padding='''max_length''' , truncation=A , return_overflowing_tokens=A , )
lowercase__ = label_map[example.label] if example.label in label_map else 0
lowercase__ = int(example.pairID )
features.append(InputFeatures(**A , label=A , pairID=A ) )
for i, example in enumerate(examples[:5] ):
logger.info('''*** Example ***''' )
logger.info(f"guid: {example}" )
logger.info(f"features: {features[i]}" )
return features
lowerCamelCase : Tuple = {
'hans': 3,
}
lowerCamelCase : str = {
'hans': HansProcessor,
}
| 2 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
a_ = logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase__ )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
requires_backends(self , '''vision''' )
self.check_model_type(__UpperCAmelCase )
def __call__( self , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return super().__call__(__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return {}, {}, {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = load_image(__UpperCAmelCase )
__lowerCamelCase = image.size
__lowerCamelCase = self.image_processor(images=__UpperCAmelCase , return_tensors=self.framework )
return model_inputs
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.model(**__UpperCAmelCase )
return model_outputs
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = model_outputs.predicted_depth
__lowerCamelCase = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode='''bicubic''' , align_corners=__UpperCAmelCase )
__lowerCamelCase = prediction.squeeze().cpu().numpy()
__lowerCamelCase = (output * 255 / np.max(__UpperCAmelCase )).astype('''uint8''' )
__lowerCamelCase = Image.fromarray(__UpperCAmelCase )
__lowerCamelCase = {}
__lowerCamelCase = predicted_depth
__lowerCamelCase = depth
return output_dict
| 330 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class A ( unittest.TestCase ):
@slow
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(SCREAMING_SNAKE_CASE ):
A : int = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
A : List[str] = FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@slow
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(SCREAMING_SNAKE_CASE ):
A : Any = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
A : Any = FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@slow
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
for model_name in ["bert-base-cased", "bert-large-uncased"]:
A : Optional[int] = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
A : List[str] = FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE )
A : Optional[Any] = tokenizer('''Do you support jax jitted function?''' , return_tensors=TensorType.JAX )
@jax.jit
def eval(**SCREAMING_SNAKE_CASE ):
return model(**SCREAMING_SNAKE_CASE )
eval(**SCREAMING_SNAKE_CASE ).block_until_ready()
@slow
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
for model_name in ["roberta-base", "roberta-large"]:
A : List[str] = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
A : Union[str, Any] = FlaxRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE )
A : int = tokenizer('''Do you support jax jitted function?''' , return_tensors=TensorType.JAX )
@jax.jit
def eval(**SCREAMING_SNAKE_CASE ):
return model(**SCREAMING_SNAKE_CASE )
eval(**SCREAMING_SNAKE_CASE ).block_until_ready()
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
with self.assertRaisesRegex(
SCREAMING_SNAKE_CASE , '''bert-base is not a local folder and is not a valid model identifier''' ):
A : List[Any] = FlaxAutoModel.from_pretrained('''bert-base''' )
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
with self.assertRaisesRegex(
SCREAMING_SNAKE_CASE , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
A : Optional[int] = FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE , revision='''aaaaaa''' )
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
with self.assertRaisesRegex(
SCREAMING_SNAKE_CASE , '''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack''' , ):
A : List[str] = FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model''' )
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
with self.assertRaisesRegex(SCREAMING_SNAKE_CASE , '''Use `from_pt=True` to load this model''' ):
A : Any = FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only''' )
| 3 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
a_ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = ["""pixel_values"""]
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = True , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
__lowerCamelCase = size if size is not None else {'''shortest_edge''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
__lowerCamelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase , param_name='''crop_size''' )
__lowerCamelCase = do_resize
__lowerCamelCase = size
__lowerCamelCase = resample
__lowerCamelCase = do_center_crop
__lowerCamelCase = crop_size
__lowerCamelCase = do_rescale
__lowerCamelCase = rescale_factor
__lowerCamelCase = do_normalize
__lowerCamelCase = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__lowerCamelCase = image_std if image_std is not None else OPENAI_CLIP_STD
__lowerCamelCase = do_convert_rgb
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
__lowerCamelCase = get_resize_output_image_size(__UpperCAmelCase , size=size['''shortest_edge'''] , default_to_square=__UpperCAmelCase )
return resize(__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(__UpperCAmelCase , size=(size['''height'''], size['''width''']) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = do_resize if do_resize is not None else self.do_resize
__lowerCamelCase = size if size is not None else self.size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = resample if resample is not None else self.resample
__lowerCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowerCamelCase = crop_size if crop_size is not None else self.crop_size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''crop_size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = do_rescale if do_rescale is not None else self.do_rescale
__lowerCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCamelCase = do_normalize if do_normalize is not None else self.do_normalize
__lowerCamelCase = image_mean if image_mean is not None else self.image_mean
__lowerCamelCase = image_std if image_std is not None else self.image_std
__lowerCamelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__lowerCamelCase = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__lowerCamelCase = [convert_to_rgb(__UpperCAmelCase ) for image in images]
# All transformations expect numpy arrays.
__lowerCamelCase = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
__lowerCamelCase = [self.resize(image=__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase ) for image in images]
if do_center_crop:
__lowerCamelCase = [self.center_crop(image=__UpperCAmelCase , size=__UpperCAmelCase ) for image in images]
if do_rescale:
__lowerCamelCase = [self.rescale(image=__UpperCAmelCase , scale=__UpperCAmelCase ) for image in images]
if do_normalize:
__lowerCamelCase = [self.normalize(image=__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase ) for image in images]
__lowerCamelCase = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
__lowerCamelCase = {'''pixel_values''': images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase )
| 330 | 0 |
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( __lowercase ):
lowerCamelCase : str = (KDPMaDiscreteScheduler,)
lowerCamelCase : Optional[int] = 10
def __UpperCAmelCase ( self : List[Any] , **UpperCAmelCase__ : Optional[int] ) -> Any:
lowerCAmelCase = {
'num_train_timesteps': 1_1_0_0,
'beta_start': 0.0_001,
'beta_end': 0.02,
'beta_schedule': 'linear',
}
config.update(**UpperCAmelCase__ )
return config
def __UpperCAmelCase ( self : List[str] ) -> Tuple:
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase__ )
def __UpperCAmelCase ( self : Dict ) -> Optional[Any]:
for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] , [0.0_002, 0.002, 0.02] ):
self.check_over_configs(beta_start=UpperCAmelCase__ , beta_end=UpperCAmelCase__ )
def __UpperCAmelCase ( self : Optional[Any] ) -> int:
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=UpperCAmelCase__ )
def __UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase__ )
def __UpperCAmelCase ( self : int ) -> Dict:
lowerCAmelCase = self.scheduler_classes[0]
lowerCAmelCase = self.get_scheduler_config(prediction_type='v_prediction' )
lowerCAmelCase = scheduler_class(**UpperCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase = self.dummy_model()
lowerCAmelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase = sample.to(UpperCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase = scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = model(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = output.prev_sample
lowerCAmelCase = torch.sum(torch.abs(UpperCAmelCase__ ) )
lowerCAmelCase = torch.mean(torch.abs(UpperCAmelCase__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.69_34E-07 ) < 1E-2
assert abs(result_mean.item() - 6.11_12E-10 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07 ) < 1E-2
assert abs(result_mean.item() - 0.0_002 ) < 1E-3
def __UpperCAmelCase ( self : Tuple ) -> Dict:
if torch_device == "mps":
return
lowerCAmelCase = self.scheduler_classes[0]
lowerCAmelCase = self.get_scheduler_config()
lowerCAmelCase = scheduler_class(**UpperCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase = self.dummy_model()
lowerCAmelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase = sample.to(UpperCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase = scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = model(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = output.prev_sample
lowerCAmelCase = torch.sum(torch.abs(UpperCAmelCase__ ) )
lowerCAmelCase = torch.mean(torch.abs(UpperCAmelCase__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
def __UpperCAmelCase ( self : List[Any] ) -> Optional[Any]:
if torch_device == "mps":
return
lowerCAmelCase = self.scheduler_classes[0]
lowerCAmelCase = self.get_scheduler_config()
lowerCAmelCase = scheduler_class(**UpperCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=UpperCAmelCase__ )
lowerCAmelCase = self.dummy_model()
lowerCAmelCase = self.dummy_sample_deter.to(UpperCAmelCase__ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
lowerCAmelCase = scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = model(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = output.prev_sample
lowerCAmelCase = torch.sum(torch.abs(UpperCAmelCase__ ) )
lowerCAmelCase = torch.mean(torch.abs(UpperCAmelCase__ ) )
if str(UpperCAmelCase__ ).startswith('cpu' ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
| 4 |
from __future__ import annotations
from typing import Generic, TypeVar
a_ = TypeVar("""T""")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = data
__lowerCamelCase = self
__lowerCamelCase = 0
class __lowerCAmelCase ( Generic[T] ):
def __init__( self ):
'''simple docstring'''
# map from node name to the node object
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# create a new set with x as its member
__lowerCamelCase = DisjointSetTreeNode(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# find the set x belongs to (with path-compression)
__lowerCamelCase = self.map[data]
if elem_ref != elem_ref.parent:
__lowerCamelCase = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# helper function for union operation
if nodea.rank > nodea.rank:
__lowerCamelCase = nodea
else:
__lowerCamelCase = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# merge 2 disjoint sets
self.link(self.find_set(__UpperCAmelCase ) , self.find_set(__UpperCAmelCase ) )
class __lowerCAmelCase ( Generic[T] ):
def __init__( self ):
'''simple docstring'''
# connections: map from the node to the neighbouring nodes (with weights)
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# add a node ONLY if its not present in the graph
if node not in self.connections:
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# add an edge with the given weight
self.add_node(__UpperCAmelCase )
self.add_node(__UpperCAmelCase )
__lowerCamelCase = weight
__lowerCamelCase = weight
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda __UpperCAmelCase : x[2] )
# creating the disjoint set
__lowerCamelCase = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(__UpperCAmelCase )
# MST generation
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = edges[index]
index += 1
__lowerCamelCase = disjoint_set.find_set(__UpperCAmelCase )
__lowerCamelCase = disjoint_set.find_set(__UpperCAmelCase )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
disjoint_set.union(__UpperCAmelCase , __UpperCAmelCase )
return graph
| 330 | 0 |
# flake8: noqa
# Lint as: python3
UpperCAmelCase__ = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 5 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=2 , __UpperCAmelCase=8 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=16 , __UpperCAmelCase=5 , __UpperCAmelCase=2 , __UpperCAmelCase=36 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=512 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_input_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase ( self ):
'''simple docstring'''
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_config()
__lowerCamelCase = 300
return config
def lowerCamelCase ( self ):
'''simple docstring'''
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = self.prepare_config_and_inputs()
__lowerCamelCase = True
__lowerCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = True
__lowerCamelCase = MraModel(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , )
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , )
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraForQuestionAnswering(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MraForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MraForTokenClassification(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_choices
__lowerCamelCase = MraForMultipleChoice(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = ()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCamelCase = type
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = MraModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@unittest.skip(reason='''MRA does not output attentions''' )
def lowerCamelCase ( self ):
'''simple docstring'''
return
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowerCamelCase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowerCamelCase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = 50265
__lowerCamelCase = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' )
__lowerCamelCase = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = 50265
__lowerCamelCase = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 330 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
A : List[str] = None
A : List[str] = logging.get_logger(__name__)
A : int = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
A : Optional[Any] = {
'vocab_file': {
'facebook/mbart-large-en-ro': (
'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
),
'facebook/mbart-large-cc25': (
'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
),
},
'tokenizer_file': {
'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
},
}
A : List[str] = {
'facebook/mbart-large-en-ro': 1_0_2_4,
'facebook/mbart-large-cc25': 1_0_2_4,
}
# fmt: off
A : Optional[int] = ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']
class __A( a ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = ['''input_ids''', '''attention_mask''']
snake_case_ = MBartTokenizer
snake_case_ = []
snake_case_ = []
def __init__( self , _snake_case=None , _snake_case=None , _snake_case="<s>" , _snake_case="</s>" , _snake_case="</s>" , _snake_case="<s>" , _snake_case="<unk>" , _snake_case="<pad>" , _snake_case="<mask>" , _snake_case=None , _snake_case=None , _snake_case=None , **_snake_case , ) -> Tuple:
'''simple docstring'''
__a = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else mask_token
super().__init__(
vocab_file=_snake_case , tokenizer_file=_snake_case , bos_token=_snake_case , eos_token=_snake_case , sep_token=_snake_case , cls_token=_snake_case , unk_token=_snake_case , pad_token=_snake_case , mask_token=_snake_case , src_lang=_snake_case , tgt_lang=_snake_case , additional_special_tokens=_snake_case , **_snake_case , )
__a = vocab_file
__a = False if not self.vocab_file else True
__a = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
__a = {
lang_code: self.convert_tokens_to_ids(_snake_case ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
__a = src_lang if src_lang is not None else '''en_XX'''
__a = self.convert_tokens_to_ids(self._src_lang )
__a = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
return self._src_lang
@src_lang.setter
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> None:
'''simple docstring'''
__a = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None ) -> List[int]:
'''simple docstring'''
__a = [self.sep_token_id]
__a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , **_snake_case ) -> Any:
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
__a = src_lang
__a = self(_snake_case , add_special_tokens=_snake_case , return_tensors=_snake_case , **_snake_case )
__a = self.convert_tokens_to_ids(_snake_case )
__a = tgt_lang_id
return inputs
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = "en_XX" , _snake_case = None , _snake_case = "ro_RO" , **_snake_case , ) -> BatchEncoding:
'''simple docstring'''
__a = src_lang
__a = tgt_lang
return super().prepare_seqaseq_batch(_snake_case , _snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
return self.set_src_lang_special_tokens(self.src_lang )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> None:
'''simple docstring'''
__a = self.convert_tokens_to_ids(_snake_case )
__a = []
__a = [self.eos_token_id, self.cur_lang_code]
__a = self.convert_ids_to_tokens(self.prefix_tokens )
__a = self.convert_ids_to_tokens(self.suffix_tokens )
__a = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> None:
'''simple docstring'''
__a = self.convert_tokens_to_ids(_snake_case )
__a = []
__a = [self.eos_token_id, self.cur_lang_code]
__a = self.convert_ids_to_tokens(self.prefix_tokens )
__a = self.convert_ids_to_tokens(self.suffix_tokens )
__a = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(_snake_case ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
return
__a = os.path.join(
_snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_snake_case ):
copyfile(self.vocab_file , _snake_case )
return (out_vocab_file,) | 6 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""EncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""TFEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""FlaxEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : list ) -> float:
'''simple docstring'''
A__ = 0
while len(SCREAMING_SNAKE_CASE__ ) > 1:
A__ = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
A__ = files.index(min(SCREAMING_SNAKE_CASE__ ) )
temp += files[min_index]
files.pop(SCREAMING_SNAKE_CASE__ )
files.append(SCREAMING_SNAKE_CASE__ )
optimal_merge_cost += temp
return optimal_merge_cost
if __name__ == "__main__":
import doctest
doctest.testmod()
| 7 |
from string import ascii_lowercase, ascii_uppercase
def a__ ( _UpperCamelCase : str ):
if not sentence:
return ""
__lowerCamelCase = dict(zip(_UpperCamelCase ,_UpperCamelCase ) )
return lower_to_upper.get(sentence[0] ,sentence[0] ) + sentence[1:]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 330 | 0 |
import operator as op
lowerCAmelCase_ = '''scaler.pt'''
lowerCAmelCase_ = '''pytorch_model'''
lowerCAmelCase_ = '''random_states'''
lowerCAmelCase_ = '''optimizer'''
lowerCAmelCase_ = '''scheduler'''
lowerCAmelCase_ = '''pytorch_model.bin'''
lowerCAmelCase_ = '''pytorch_model.bin.index.json'''
lowerCAmelCase_ = '''model.safetensors'''
lowerCAmelCase_ = '''model.safetensors.index.json'''
lowerCAmelCase_ = '''1.10.2'''
lowerCAmelCase_ = '''py38'''
lowerCAmelCase_ = '''4.17.0'''
lowerCAmelCase_ = ['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
lowerCAmelCase_ = ['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
lowerCAmelCase_ = ['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
lowerCAmelCase_ = ['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
lowerCAmelCase_ = ['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
lowerCAmelCase_ = '''2.0.1'''
lowerCAmelCase_ = ['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
lowerCAmelCase_ = ['''default''', '''reduce-overhead''', '''max-autotune''']
lowerCAmelCase_ = {'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}
# These are the args for `torch.distributed.launch` for pytorch < 1.9
lowerCAmelCase_ = [
'''nnodes''',
'''nproc_per_node''',
'''rdzv_backend''',
'''rdzv_endpoint''',
'''rdzv_id''',
'''rdzv_conf''',
'''standalone''',
'''max_restarts''',
'''monitor_interval''',
'''start_method''',
'''role''',
'''module''',
'''m''',
'''no_python''',
'''run_path''',
'''log_dir''',
'''r''',
'''redirects''',
'''t''',
'''tee''',
'''node_rank''',
'''master_addr''',
'''master_port''',
]
lowerCAmelCase_ = ['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
lowerCAmelCase_ = ['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP'''] | 8 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowerCAmelCase ( lowerCAmelCase__ ):
@slow
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' , '''prajjwal1/bert-tiny''' )
__lowerCamelCase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
__lowerCamelCase = bertabert.config.encoder.vocab_size
__lowerCamelCase = tokenizer.sep_token_id
__lowerCamelCase = tokenizer.cls_token_id
__lowerCamelCase = 128
__lowerCamelCase = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''train[:1%]''' )
__lowerCamelCase = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''validation[:1%]''' )
__lowerCamelCase = train_dataset.select(range(32 ) )
__lowerCamelCase = val_dataset.select(range(16 ) )
__lowerCamelCase = 4
def _map_to_encoder_decoder_inputs(__UpperCAmelCase ):
# Tokenizer will automatically set [BOS] <text> [EOS]
__lowerCamelCase = tokenizer(batch['''article'''] , padding='''max_length''' , truncation=__UpperCAmelCase , max_length=512 )
__lowerCamelCase = tokenizer(batch['''highlights'''] , padding='''max_length''' , truncation=__UpperCAmelCase , max_length=128 )
__lowerCamelCase = inputs.input_ids
__lowerCamelCase = inputs.attention_mask
__lowerCamelCase = outputs.input_ids
__lowerCamelCase = outputs.input_ids.copy()
__lowerCamelCase = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
]
__lowerCamelCase = outputs.attention_mask
assert all(len(__UpperCAmelCase ) == 512 for x in inputs.input_ids )
assert all(len(__UpperCAmelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(__UpperCAmelCase ):
__lowerCamelCase = pred.label_ids
__lowerCamelCase = pred.predictions
# all unnecessary tokens are removed
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = sum([int(pred_str[i] == label_str[i] ) for i in range(len(__UpperCAmelCase ) )] ) / len(__UpperCAmelCase )
return {"accuracy": accuracy}
# map train dataset
__lowerCamelCase = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=__UpperCAmelCase , batch_size=__UpperCAmelCase , remove_columns=['''article''', '''highlights'''] , )
train_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
# same for validation dataset
__lowerCamelCase = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=__UpperCAmelCase , batch_size=__UpperCAmelCase , remove_columns=['''article''', '''highlights'''] , )
val_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
__lowerCamelCase = self.get_auto_remove_tmp_dir()
__lowerCamelCase = SeqaSeqTrainingArguments(
output_dir=__UpperCAmelCase , per_device_train_batch_size=__UpperCAmelCase , per_device_eval_batch_size=__UpperCAmelCase , predict_with_generate=__UpperCAmelCase , evaluation_strategy='''steps''' , do_train=__UpperCAmelCase , do_eval=__UpperCAmelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
__lowerCamelCase = SeqaSeqTrainer(
model=__UpperCAmelCase , args=__UpperCAmelCase , compute_metrics=_compute_metrics , train_dataset=__UpperCAmelCase , eval_dataset=__UpperCAmelCase , tokenizer=__UpperCAmelCase , )
# start training
trainer.train()
| 330 | 0 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
__lowerCAmelCase : Dict =logging.get_logger(__name__)
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ['''audio_values''', '''audio_mask''']
def __init__( self :Optional[Any] , lowerCAmelCase__ :str=2_048 , lowerCAmelCase__ :str=1 , lowerCAmelCase__ :List[Any]=[16, 16] , lowerCAmelCase__ :List[str]=128 , lowerCAmelCase__ :Dict=44_100 , lowerCAmelCase__ :Tuple=86 , lowerCAmelCase__ :List[str]=2_048 , lowerCAmelCase__ :Union[str, Any]=0.0 , **lowerCAmelCase__ :int , ) -> str:
super().__init__(
feature_size=lowerCAmelCase__ , sampling_rate=lowerCAmelCase__ , padding_value=lowerCAmelCase__ , **lowerCAmelCase__ , )
__SCREAMING_SNAKE_CASE : Union[str, Any] = spectrogram_length
__SCREAMING_SNAKE_CASE : Dict = num_channels
__SCREAMING_SNAKE_CASE : List[Any] = patch_size
__SCREAMING_SNAKE_CASE : Optional[int] = feature_size // self.patch_size[1]
__SCREAMING_SNAKE_CASE : Optional[Any] = n_fft
__SCREAMING_SNAKE_CASE : int = sampling_rate // hop_length_to_sampling_rate
__SCREAMING_SNAKE_CASE : Optional[int] = sampling_rate
__SCREAMING_SNAKE_CASE : Tuple = padding_value
__SCREAMING_SNAKE_CASE : Dict = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=lowerCAmelCase__ , min_frequency=0.0 , max_frequency=2_2050.0 , sampling_rate=lowerCAmelCase__ , norm='''slaney''' , mel_scale='''slaney''' , ).T
def __magic_name__( self :Union[str, Any] , lowerCAmelCase__ :np.array ) -> np.ndarray:
__SCREAMING_SNAKE_CASE : Union[str, Any] = spectrogram(
lowerCAmelCase__ , window_function(self.n_fft , '''hann''' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel='''dB''' , db_range=80.0 , )
__SCREAMING_SNAKE_CASE : Tuple = log_spec[:, :-1]
__SCREAMING_SNAKE_CASE : Tuple = log_spec - 20.0
__SCREAMING_SNAKE_CASE : List[str] = np.clip(log_spec / 40.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self :Dict , lowerCAmelCase__ :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , lowerCAmelCase__ :Optional[Union[str, TensorType]] = None , lowerCAmelCase__ :Optional[bool] = True , lowerCAmelCase__ :Optional[int] = None , lowerCAmelCase__ :bool = False , lowerCAmelCase__ :bool = False , **lowerCAmelCase__ :Union[str, Any] , ) -> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
'''This feature extractor is set to support sampling rate'''
f''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
f''' with {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
__SCREAMING_SNAKE_CASE : Any = isinstance(lowerCAmelCase__ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' )
__SCREAMING_SNAKE_CASE : str = is_batched_numpy or (
isinstance(lowerCAmelCase__ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase__ , np.ndarray ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(lowerCAmelCase__ , dtype=np.floataa )
elif isinstance(lowerCAmelCase__ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__SCREAMING_SNAKE_CASE : Optional[Any] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__SCREAMING_SNAKE_CASE : str = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
__SCREAMING_SNAKE_CASE : Optional[Any] = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = [np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
__SCREAMING_SNAKE_CASE : Tuple = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
__SCREAMING_SNAKE_CASE : Dict = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
__SCREAMING_SNAKE_CASE : Optional[int] = np.array(lowerCAmelCase__ ).astype(np.floataa )
# convert into correct format for padding
__SCREAMING_SNAKE_CASE : Optional[int] = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
__SCREAMING_SNAKE_CASE : List[str] = np.ones([len(lowerCAmelCase__ ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
__SCREAMING_SNAKE_CASE : Union[str, Any] = padded_audio_features * self.padding_value
for i in range(len(lowerCAmelCase__ ) ):
__SCREAMING_SNAKE_CASE : Dict = audio_features[i]
__SCREAMING_SNAKE_CASE : str = feature
# return as BatchFeature
if return_attention_mask:
__SCREAMING_SNAKE_CASE : Dict = {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
else:
__SCREAMING_SNAKE_CASE : List[str] = {'''audio_values''': padded_audio_features}
__SCREAMING_SNAKE_CASE : Any = BatchFeature(data=lowerCAmelCase__ , tensor_type=lowerCAmelCase__ )
return encoded_inputs
| 9 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_timm_backbone""": ["""TimmBackboneConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""TimmBackbone"""]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConfig",
"Pix2StructVisionConfig",
],
"processing_pix2struct": ["Pix2StructProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["Pix2StructImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
"Pix2StructPreTrainedModel",
"Pix2StructForConditionalGeneration",
"Pix2StructVisionModel",
"Pix2StructTextModel",
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 10 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase=0.01 , __UpperCAmelCase=1000 ):
'''simple docstring'''
__lowerCamelCase = p_stop
__lowerCamelCase = max_length
def __iter__( self ):
'''simple docstring'''
__lowerCamelCase = 0
__lowerCamelCase = False
while not stop and count < self.max_length:
yield count
count += 1
__lowerCamelCase = random.random() < self.p_stop
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=True ):
'''simple docstring'''
__lowerCamelCase = [
BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
for i in range(2 )
]
__lowerCamelCase = [list(__UpperCAmelCase ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(__UpperCAmelCase ) for shard in batch_sampler_shards] , [len(__UpperCAmelCase ) for e in expected] )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of total batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of total batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
__lowerCamelCase = [BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , even_batches=__UpperCAmelCase ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=2 , __UpperCAmelCase=False ):
'''simple docstring'''
random.seed(__UpperCAmelCase )
__lowerCamelCase = list(__UpperCAmelCase )
__lowerCamelCase = [
IterableDatasetShard(
__UpperCAmelCase , batch_size=__UpperCAmelCase , drop_last=__UpperCAmelCase , num_processes=__UpperCAmelCase , process_index=__UpperCAmelCase , split_batches=__UpperCAmelCase , )
for i in range(__UpperCAmelCase )
]
__lowerCamelCase = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(__UpperCAmelCase )
iterable_dataset_lists.append(list(__UpperCAmelCase ) )
__lowerCamelCase = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
__lowerCamelCase = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
self.assertTrue(len(__UpperCAmelCase ) % shard_batch_size == 0 )
__lowerCamelCase = []
for idx in range(0 , len(__UpperCAmelCase ) , __UpperCAmelCase ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(__UpperCAmelCase ) < len(__UpperCAmelCase ):
reference += reference
self.assertListEqual(__UpperCAmelCase , reference[: len(__UpperCAmelCase )] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 42
__lowerCamelCase = RandomIterableDataset()
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
# Edge case with a very small dataset
__lowerCamelCase = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BatchSampler(range(16 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = SkipBatchSampler(__UpperCAmelCase , 2 )
self.assertListEqual(list(__UpperCAmelCase ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DataLoader(list(range(16 ) ) , batch_size=4 )
__lowerCamelCase = skip_first_batches(__UpperCAmelCase , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def lowerCamelCase ( self ):
'''simple docstring'''
Accelerator()
__lowerCamelCase = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 330 | 0 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
lowerCAmelCase__ = logging.getLogger(__name__)
class lowerCAmelCase__ ( a):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = "sequence-classification"
def __init__( self , __lowerCamelCase) -> List[Any]:
if type(__lowerCamelCase) == dict:
_A : Tuple = Namespace(**__lowerCamelCase)
_A : Tuple = glue_output_modes[hparams.task]
_A : Union[str, Any] = glue_tasks_num_labels[hparams.task]
super().__init__(__lowerCamelCase , __lowerCamelCase , self.mode)
def _lowerCamelCase ( self , **__lowerCamelCase) -> List[str]:
return self.model(**__lowerCamelCase)
def _lowerCamelCase ( self , __lowerCamelCase , __lowerCamelCase) -> str:
_A : List[Any] = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A : Union[str, Any] = batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None
_A : Union[str, Any] = self(**__lowerCamelCase)
_A : str = outputs[0]
_A : Optional[Any] = self.trainer.lr_schedulers[0]["scheduler"]
_A : Tuple = {"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def _lowerCamelCase ( self) -> Dict:
_A : Tuple = self.hparams
_A : Dict = processors[args.task]()
_A : str = processor.get_labels()
for mode in ["train", "dev"]:
_A : Optional[int] = self._feature_file(__lowerCamelCase)
if os.path.exists(__lowerCamelCase) and not args.overwrite_cache:
logger.info("Loading features from cached file %s" , __lowerCamelCase)
else:
logger.info("Creating features from dataset file at %s" , args.data_dir)
_A : Any = (
processor.get_dev_examples(args.data_dir)
if mode == "dev"
else processor.get_train_examples(args.data_dir)
)
_A : Union[str, Any] = convert_examples_to_features(
__lowerCamelCase , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info("Saving features into cached file %s" , __lowerCamelCase)
torch.save(__lowerCamelCase , __lowerCamelCase)
def _lowerCamelCase ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = False) -> DataLoader:
_A : Dict = "dev" if mode == "test" else mode
_A : Optional[int] = self._feature_file(__lowerCamelCase)
logger.info("Loading features from cached file %s" , __lowerCamelCase)
_A : str = torch.load(__lowerCamelCase)
_A : Union[str, Any] = torch.tensor([f.input_ids for f in features] , dtype=torch.long)
_A : Dict = torch.tensor([f.attention_mask for f in features] , dtype=torch.long)
_A : Tuple = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long)
if self.hparams.glue_output_mode == "classification":
_A : Optional[Any] = torch.tensor([f.label for f in features] , dtype=torch.long)
elif self.hparams.glue_output_mode == "regression":
_A : int = torch.tensor([f.label for f in features] , dtype=torch.float)
return DataLoader(
TensorDataset(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase) , batch_size=__lowerCamelCase , shuffle=__lowerCamelCase , )
def _lowerCamelCase ( self , __lowerCamelCase , __lowerCamelCase) -> Any:
_A : Dict = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A : Union[str, Any] = batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None
_A : Union[str, Any] = self(**__lowerCamelCase)
_A , _A : Union[str, Any] = outputs[:2]
_A : Any = logits.detach().cpu().numpy()
_A : int = inputs["labels"].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _lowerCamelCase ( self , __lowerCamelCase) -> tuple:
_A : int = torch.stack([x["val_loss"] for x in outputs]).mean().detach().cpu().item()
_A : Optional[Any] = np.concatenate([x["pred"] for x in outputs] , axis=0)
if self.hparams.glue_output_mode == "classification":
_A : int = np.argmax(__lowerCamelCase , axis=1)
elif self.hparams.glue_output_mode == "regression":
_A : Tuple = np.squeeze(__lowerCamelCase)
_A : Any = np.concatenate([x["target"] for x in outputs] , axis=0)
_A : Optional[Any] = [[] for _ in range(out_label_ids.shape[0])]
_A : Union[str, Any] = [[] for _ in range(out_label_ids.shape[0])]
_A : str = {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task , __lowerCamelCase , __lowerCamelCase)}
_A : List[Any] = dict(results.items())
_A : str = results
return ret, preds_list, out_label_list
def _lowerCamelCase ( self , __lowerCamelCase) -> dict:
_A , _A , _A : Tuple = self._eval_end(__lowerCamelCase)
_A : List[Any] = ret["log"]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _lowerCamelCase ( self , __lowerCamelCase) -> dict:
_A , _A , _A : int = self._eval_end(__lowerCamelCase)
_A : Any = ret["log"]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase) -> List[str]:
BaseTransformer.add_model_specific_args(__lowerCamelCase , __lowerCamelCase)
parser.add_argument(
"--max_seq_length" , default=1_2_8 , type=__lowerCamelCase , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument(
"--task" , default="" , type=__lowerCamelCase , required=__lowerCamelCase , help="The GLUE task to run" , )
parser.add_argument(
"--gpus" , default=0 , type=__lowerCamelCase , help="The number of GPUs allocated for this, it is by default 0 meaning none" , )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets")
return parser
def _UpperCAmelCase ():
_A : Optional[Any] = argparse.ArgumentParser()
add_generic_args(UpperCamelCase__ , os.getcwd() )
_A : Optional[int] = GLUETransformer.add_model_specific_args(UpperCamelCase__ , os.getcwd() )
_A : List[str] = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_A : Any = os.path.join(
"./results" , f"{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}" , )
os.makedirs(args.output_dir )
_A : Union[str, Any] = GLUETransformer(UpperCamelCase__ )
_A : Optional[int] = generic_train(UpperCamelCase__ , UpperCamelCase__ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_A : Optional[Any] = sorted(glob.glob(os.path.join(args.output_dir , "checkpoint-epoch=*.ckpt" ) , recursive=UpperCamelCase__ ) )
_A : Dict = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(UpperCamelCase__ )
if __name__ == "__main__":
main()
| 11 |
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 330 | 0 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
UpperCAmelCase_ = 50_000
UpperCAmelCase_ = 5_000
UpperCAmelCase_ , UpperCAmelCase_ = os.path.split(__file__)
UpperCAmelCase_ = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def lowerCamelCase__ ( A__ : datasets.Dataset , A__ : Optional[int] ):
'''simple docstring'''
for i in range(A__ ):
__lowerCamelCase = dataset[i]
@get_duration
def lowerCamelCase__ ( A__ : datasets.Dataset , A__ : Union[str, Any] , A__ : str ):
'''simple docstring'''
for i in range(0 , len(A__ ) , A__ ):
__lowerCamelCase = dataset[i : i + batch_size]
@get_duration
def lowerCamelCase__ ( A__ : datasets.Dataset , A__ : Optional[int] , A__ : Union[str, Any] ):
'''simple docstring'''
with dataset.formatted_as(type=A__ ):
for i in range(A__ ):
__lowerCamelCase = dataset[i]
@get_duration
def lowerCamelCase__ ( A__ : datasets.Dataset , A__ : int , A__ : Optional[int] , A__ : Any ):
'''simple docstring'''
with dataset.formatted_as(type=A__ ):
for i in range(0 , A__ , A__ ):
__lowerCamelCase = dataset[i : i + batch_size]
def lowerCamelCase__ ( ):
'''simple docstring'''
__lowerCamelCase = {"""num examples""": SPEED_TEST_N_EXAMPLES}
__lowerCamelCase = [
(read, {"""length""": SMALL_TEST}),
(read, {"""length""": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
(read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1000}),
]
__lowerCamelCase = [
(read, {"""length""": SMALL_TEST}),
(read, {"""length""": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
(read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1000}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print("""generating dataset""" )
__lowerCamelCase = datasets.Features(
{"""list""": datasets.Sequence(datasets.Value("""float32""" ) ), """numbers""": datasets.Value("""float32""" )} )
__lowerCamelCase = generate_example_dataset(
os.path.join(A__ , """dataset.arrow""" ) , A__ , num_examples=A__ , seq_shapes={"""list""": (100,)} , )
print("""first set of iterations""" )
for func, kwargs in functions:
print(func.__name__ , str(A__ ) )
__lowerCamelCase = func(A__ , **A__ )
print("""shuffling dataset""" )
__lowerCamelCase = dataset.shuffle()
print("""Second set of iterations (after shuffling""" )
for func, kwargs in functions_shuffled:
print("""shuffled """ , func.__name__ , str(A__ ) )
__lowerCamelCase = func(
A__ , **A__ )
with open(A__ , """wb""" ) as f:
f.write(json.dumps(A__ ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 12 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
a_ = 16
a_ = 32
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : int = 16 ):
__lowerCamelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' )
def tokenize_function(_UpperCamelCase : Optional[Any] ):
# max_length=None => use the model max length (it's actually the default)
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__lowerCamelCase = datasets.map(
_UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowerCamelCase = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__lowerCamelCase = 16
elif accelerator.mixed_precision != "no":
__lowerCamelCase = 8
else:
__lowerCamelCase = None
return tokenizer.pad(
_UpperCamelCase ,padding='''longest''' ,max_length=_UpperCamelCase ,pad_to_multiple_of=_UpperCamelCase ,return_tensors='''pt''' ,)
# Instantiate dataloaders.
__lowerCamelCase = DataLoader(
tokenized_datasets['''train'''] ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=_UpperCamelCase )
__lowerCamelCase = DataLoader(
tokenized_datasets['''validation'''] ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=_UpperCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
a_ = mocked_dataloaders # noqa: F811
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ):
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,_UpperCamelCase ) == "1":
__lowerCamelCase = 2
# Initialize accelerator
__lowerCamelCase = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCamelCase = config['''lr''']
__lowerCamelCase = int(config['''num_epochs'''] )
__lowerCamelCase = int(config['''seed'''] )
__lowerCamelCase = int(config['''batch_size'''] )
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=_UpperCamelCase )
def inner_training_loop(_UpperCamelCase : Union[str, Any] ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(_UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,return_dict=_UpperCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowerCamelCase = model.to(accelerator.device )
# Instantiate optimizer
__lowerCamelCase = AdamW(params=model.parameters() ,lr=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = get_dataloaders(_UpperCamelCase ,_UpperCamelCase )
# Instantiate scheduler
__lowerCamelCase = get_linear_schedule_with_warmup(
optimizer=_UpperCamelCase ,num_warmup_steps=1_00 ,num_training_steps=(len(_UpperCamelCase ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(
_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
# Now we train the model
for epoch in range(_UpperCamelCase ):
model.train()
for step, batch in enumerate(_UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.loss
accelerator.backward(_UpperCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_UpperCamelCase ,references=_UpperCamelCase ,)
__lowerCamelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" ,_UpperCamelCase )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def a__ ( ):
__lowerCamelCase = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' ,type=_UpperCamelCase ,default=_UpperCamelCase ,choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' ,)
parser.add_argument('''--cpu''' ,action='''store_true''' ,help='''If passed, will train on the CPU.''' )
__lowerCamelCase = parser.parse_args()
__lowerCamelCase = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_UpperCamelCase ,_UpperCamelCase )
if __name__ == "__main__":
main()
| 330 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[Any] = image.size
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: str = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
SCREAMING_SNAKE_CASE_: Tuple = image.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] )
SCREAMING_SNAKE_CASE_: str = np.array(_UpperCAmelCase ).astype(np.floataa ) / 2_5_5.0
SCREAMING_SNAKE_CASE_: Optional[Any] = image[None].transpose(0 , 3 , 1 , 2 )
SCREAMING_SNAKE_CASE_: str = torch.from_numpy(_UpperCAmelCase )
return 2.0 * image - 1.0
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase__ : VQModel , lowerCAmelCase__ : UNetaDModel , lowerCAmelCase__ : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] , ):
super().__init__()
self.register_modules(vqvae=lowerCAmelCase__ , unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__)
@torch.no_grad()
def __call__( self : str , lowerCAmelCase__ : Union[torch.Tensor, PIL.Image.Image] = None , lowerCAmelCase__ : Optional[int] = 1 , lowerCAmelCase__ : Optional[int] = 100 , lowerCAmelCase__ : Optional[float] = 0.0 , lowerCAmelCase__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCAmelCase__ : Optional[str] = "pil" , lowerCAmelCase__ : bool = True , ):
if isinstance(lowerCAmelCase__ , PIL.Image.Image):
SCREAMING_SNAKE_CASE_: List[str] = 1
elif isinstance(lowerCAmelCase__ , torch.Tensor):
SCREAMING_SNAKE_CASE_: str = image.shape[0]
else:
raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCAmelCase__)}")
if isinstance(lowerCAmelCase__ , PIL.Image.Image):
SCREAMING_SNAKE_CASE_: Dict = preprocess(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
SCREAMING_SNAKE_CASE_: List[str] = (batch_size, self.unet.config.in_channels // 2, height, width)
SCREAMING_SNAKE_CASE_: List[str] = next(self.unet.parameters()).dtype
SCREAMING_SNAKE_CASE_: Dict = randn_tensor(lowerCAmelCase__ , generator=lowerCAmelCase__ , device=self.device , dtype=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: str = image.to(device=self.device , dtype=lowerCAmelCase__)
# set timesteps and move to the correct device
self.scheduler.set_timesteps(lowerCAmelCase__ , device=self.device)
SCREAMING_SNAKE_CASE_: List[str] = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
SCREAMING_SNAKE_CASE_: List[str] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
SCREAMING_SNAKE_CASE_: List[str] = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
SCREAMING_SNAKE_CASE_: Optional[Any] = {}
if accepts_eta:
SCREAMING_SNAKE_CASE_: int = eta
for t in self.progress_bar(lowerCAmelCase__):
# concat latents and low resolution image in the channel dimension.
SCREAMING_SNAKE_CASE_: Optional[Any] = torch.cat([latents, image] , dim=1)
SCREAMING_SNAKE_CASE_: List[Any] = self.scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__)
# predict the noise residual
SCREAMING_SNAKE_CASE_: Union[str, Any] = self.unet(lowerCAmelCase__ , lowerCAmelCase__).sample
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE_: int = self.scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__).prev_sample
# decode the image latents with the VQVAE
SCREAMING_SNAKE_CASE_: int = self.vqvae.decode(lowerCAmelCase__).sample
SCREAMING_SNAKE_CASE_: Optional[Any] = torch.clamp(lowerCAmelCase__ , -1.0 , 1.0)
SCREAMING_SNAKE_CASE_: Optional[int] = image / 2 + 0.5
SCREAMING_SNAKE_CASE_: Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE_: Dict = self.numpy_to_pil(lowerCAmelCase__)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCAmelCase__)
| 13 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
a_ = None
try:
import msvcrt
except ImportError:
a_ = None
try:
import fcntl
except ImportError:
a_ = None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
a_ = OSError
# Data
# ------------------------------------------------
a_ = [
"""Timeout""",
"""BaseFileLock""",
"""WindowsFileLock""",
"""UnixFileLock""",
"""SoftFileLock""",
"""FileLock""",
]
a_ = """3.0.12"""
a_ = None
def a__ ( ):
global _logger
__lowerCamelCase = _logger or logging.getLogger(__name__ )
return _logger
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = lock_file
return None
def __str__( self ):
'''simple docstring'''
__lowerCamelCase = F"""The file lock '{self.lock_file}' could not be acquired."""
return temp
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = lock
return None
def __enter__( self ):
'''simple docstring'''
return self.lock
def __exit__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.lock.release()
return None
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = max_filename_length if max_filename_length is not None else 255
# Hash the filename if it's too long
__lowerCamelCase = self.hash_filename_if_too_long(__UpperCAmelCase , __UpperCAmelCase )
# The path to the lock file.
__lowerCamelCase = lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
__lowerCamelCase = None
# The default timeout value.
__lowerCamelCase = timeout
# We use this lock primarily for the lock counter.
__lowerCamelCase = threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
__lowerCamelCase = 0
return None
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._lock_file
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._timeout
@timeout.setter
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = float(__UpperCAmelCase )
return None
def lowerCamelCase ( self ):
'''simple docstring'''
raise NotImplementedError()
def lowerCamelCase ( self ):
'''simple docstring'''
raise NotImplementedError()
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._lock_file_fd is not None
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=0.05 ):
'''simple docstring'''
# Use the default timeout, if no timeout is provided.
if timeout is None:
__lowerCamelCase = self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
__lowerCamelCase = id(self )
__lowerCamelCase = self._lock_file
__lowerCamelCase = time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(F"""Attempting to acquire lock {lock_id} on {lock_filename}""" )
self._acquire()
if self.is_locked:
logger().debug(F"""Lock {lock_id} acquired on {lock_filename}""" )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(F"""Timeout on acquiring lock {lock_id} on {lock_filename}""" )
raise Timeout(self._lock_file )
else:
logger().debug(
F"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...""" )
time.sleep(__UpperCAmelCase )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
__lowerCamelCase = max(0 , self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def lowerCamelCase ( self , __UpperCAmelCase=False ):
'''simple docstring'''
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
__lowerCamelCase = id(self )
__lowerCamelCase = self._lock_file
logger().debug(F"""Attempting to release lock {lock_id} on {lock_filename}""" )
self._release()
__lowerCamelCase = 0
logger().debug(F"""Lock {lock_id} released on {lock_filename}""" )
return None
def __enter__( self ):
'''simple docstring'''
self.acquire()
return self
def __exit__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.release()
return None
def __del__( self ):
'''simple docstring'''
self.release(force=__UpperCAmelCase )
return None
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = os.path.basename(__UpperCAmelCase )
if len(__UpperCAmelCase ) > max_length and max_length > 0:
__lowerCamelCase = os.path.dirname(__UpperCAmelCase )
__lowerCamelCase = str(hash(__UpperCAmelCase ) )
__lowerCamelCase = filename[: max_length - len(__UpperCAmelCase ) - 8] + '''...''' + hashed_filename + '''.lock'''
return os.path.join(__UpperCAmelCase , __UpperCAmelCase )
else:
return path
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
from .file_utils import relative_to_absolute_path
super().__init__(__UpperCAmelCase , timeout=__UpperCAmelCase , max_filename_length=__UpperCAmelCase )
__lowerCamelCase = '''\\\\?\\''' + relative_to_absolute_path(self.lock_file )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
except OSError:
pass
else:
try:
msvcrt.locking(__UpperCAmelCase , msvcrt.LK_NBLCK , 1 )
except OSError:
os.close(__UpperCAmelCase )
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self._lock_file_fd
__lowerCamelCase = None
msvcrt.locking(__UpperCAmelCase , msvcrt.LK_UNLCK , 1 )
os.close(__UpperCAmelCase )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = os.statvfs(os.path.dirname(__UpperCAmelCase ) ).f_namemax
super().__init__(__UpperCAmelCase , timeout=__UpperCAmelCase , max_filename_length=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_RDWR | os.O_CREAT | os.O_TRUNC
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
try:
fcntl.flock(__UpperCAmelCase , fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(__UpperCAmelCase )
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
# Do not remove the lockfile:
#
# https://github.com/benediktschmitt/py-filelock/issues/31
# https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
__lowerCamelCase = self._lock_file_fd
__lowerCamelCase = None
fcntl.flock(__UpperCAmelCase , fcntl.LOCK_UN )
os.close(__UpperCAmelCase )
return None
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
except OSError:
pass
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
os.close(self._lock_file_fd )
__lowerCamelCase = None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
a_ = None
if msvcrt:
a_ = WindowsFileLock
elif fcntl:
a_ = UnixFileLock
else:
a_ = SoftFileLock
if warnings is not None:
warnings.warn("""only soft file lock is available""")
| 330 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
"""shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
# See all Nat models at https://huggingface.co/models?filter=nat
}
class UpperCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''nat'''
UpperCAmelCase__ = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Dict , UpperCAmelCase__ : Optional[Any]=4 , UpperCAmelCase__ : Union[str, Any]=3 , UpperCAmelCase__ : Optional[int]=64 , UpperCAmelCase__ : Dict=[3, 4, 6, 5] , UpperCAmelCase__ : Dict=[2, 4, 8, 16] , UpperCAmelCase__ : int=7 , UpperCAmelCase__ : List[Any]=3.0 , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : List[Any]=0.0 , UpperCAmelCase__ : str=0.0 , UpperCAmelCase__ : Dict=0.1 , UpperCAmelCase__ : Union[str, Any]="gelu" , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : Optional[Any]=1e-5 , UpperCAmelCase__ : List[Any]=0.0 , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Dict=None , **UpperCAmelCase__ : Any , ) ->str:
'''simple docstring'''
super().__init__(**UpperCAmelCase__)
A__ = patch_size
A__ = num_channels
A__ = embed_dim
A__ = depths
A__ = len(UpperCAmelCase__)
A__ = num_heads
A__ = kernel_size
A__ = mlp_ratio
A__ = qkv_bias
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = drop_path_rate
A__ = hidden_act
A__ = layer_norm_eps
A__ = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
A__ = int(embed_dim * 2 ** (len(UpperCAmelCase__) - 1))
A__ = layer_scale_init_value
A__ = ['''stem'''] + [f"""stage{idx}""" for idx in range(1 , len(UpperCAmelCase__) + 1)]
A__ , A__ = get_aligned_output_features_output_indices(
out_features=UpperCAmelCase__ , out_indices=UpperCAmelCase__ , stage_names=self.stage_names)
| 14 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=10 , __UpperCAmelCase=3 , __UpperCAmelCase=2 , __UpperCAmelCase=2 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=10 , __UpperCAmelCase=0.02 , __UpperCAmelCase="divided_space_time" , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = image_size
__lowerCamelCase = num_channels
__lowerCamelCase = patch_size
__lowerCamelCase = num_frames
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = attention_type
__lowerCamelCase = initializer_range
__lowerCamelCase = scope
__lowerCamelCase = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
__lowerCamelCase = (image_size // patch_size) ** 2
__lowerCamelCase = (num_frames) * self.num_patches_per_frame + 1
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
__lowerCamelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , attention_type=self.attention_type , )
__lowerCamelCase = self.num_labels
return config
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TimesformerModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase )
# verify the logits shape
__lowerCamelCase = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = config_and_inputs
__lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
lowerCAmelCase__ = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerModelTester(self )
__lowerCamelCase = ConfigTester(
self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(__UpperCAmelCase )
if return_labels:
if model_class in get_values(__UpperCAmelCase ):
__lowerCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__UpperCAmelCase )
return inputs_dict
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(__UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCAmelCase , nn.Linear ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(__UpperCAmelCase )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = TimesformerModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
if not self.has_attentions:
pass
else:
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = True
for model_class in self.all_model_classes:
__lowerCamelCase = self.model_tester.seq_length
__lowerCamelCase = self.model_tester.num_frames
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
__lowerCamelCase = len(__UpperCAmelCase )
# Check attention is always last and order is fine
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
self.assertEqual(out_len + 1 , len(__UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def lowerCamelCase ( self ):
'''simple docstring'''
def check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.hidden_states
__lowerCamelCase = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
__lowerCamelCase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def a__ ( ):
__lowerCamelCase = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' ,filename='''eating_spaghetti.npy''' ,repo_type='''dataset''' )
__lowerCamelCase = np.load(_UpperCamelCase )
return list(_UpperCamelCase )
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
__UpperCAmelCase )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_video()
__lowerCamelCase = image_processor(video[:8] , return_tensors='''pt''' ).to(__UpperCAmelCase )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(**__UpperCAmelCase )
# verify the logits
__lowerCamelCase = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor([-0.3_016, -0.7_713, -0.4_205] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 330 | 0 |
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : int ,A : int ):
__A = n
__A = [None] * self.n
__A = 0 # index of the first element
__A = 0
__A = 0
def __len__( self : Tuple ):
return self.size
def UpperCamelCase_ ( self : int ):
return self.size == 0
def UpperCamelCase_ ( self : str ):
return False if self.is_empty() else self.array[self.front]
def UpperCamelCase_ ( self : Union[str, Any] ,A : Dict ):
if self.size >= self.n:
raise Exception("QUEUE IS FULL" )
__A = data
__A = (self.rear + 1) % self.n
self.size += 1
return self
def UpperCamelCase_ ( self : Union[str, Any] ):
if self.size == 0:
raise Exception("UNDERFLOW" )
__A = self.array[self.front]
__A = None
__A = (self.front + 1) % self.n
self.size -= 1
return temp
| 15 |
def a__ ( _UpperCamelCase : int ):
if not isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = F"""Input value of [number={number}] must be an integer"""
raise TypeError(_UpperCamelCase )
if number < 0:
return False
__lowerCamelCase = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 330 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase = 1_00 ) -> int:
lowercase__ : Optional[Any] = n * (n + 1) * (2 * n + 1) / 6
lowercase__ : List[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 16 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
return F"""gaussian_noise_s={seed}_shape={"_".join([str(__UpperCAmelCase ) for s in shape] )}.npy"""
def lowerCamelCase ( self ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowerCamelCase ( self , __UpperCAmelCase=0 , __UpperCAmelCase=(4, 4, 64, 64) , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = jnp.bfloataa if fpaa else jnp.floataa
__lowerCamelCase = jnp.array(load_hf_numpy(self.get_file_format(__UpperCAmelCase , __UpperCAmelCase ) ) , dtype=__UpperCAmelCase )
return image
def lowerCamelCase ( self , __UpperCAmelCase=False , __UpperCAmelCase="CompVis/stable-diffusion-v1-4" ):
'''simple docstring'''
__lowerCamelCase = jnp.bfloataa if fpaa else jnp.floataa
__lowerCamelCase = '''bf16''' if fpaa else None
__lowerCamelCase ,__lowerCamelCase = FlaxUNetaDConditionModel.from_pretrained(
__UpperCAmelCase , subfolder='''unet''' , dtype=__UpperCAmelCase , revision=__UpperCAmelCase )
return model, params
def lowerCamelCase ( self , __UpperCAmelCase=0 , __UpperCAmelCase=(4, 77, 768) , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = jnp.bfloataa if fpaa else jnp.floataa
__lowerCamelCase = jnp.array(load_hf_numpy(self.get_file_format(__UpperCAmelCase , __UpperCAmelCase ) ) , dtype=__UpperCAmelCase )
return hidden_states
@parameterized.expand(
[
# fmt: off
[83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
[17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
[8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
[3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
# fmt: on
] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4''' , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_latents(__UpperCAmelCase , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_encoder_hidden_states(__UpperCAmelCase , fpaa=__UpperCAmelCase )
__lowerCamelCase = model.apply(
{'''params''': params} , __UpperCAmelCase , jnp.array(__UpperCAmelCase , dtype=jnp.intaa ) , encoder_hidden_states=__UpperCAmelCase , ).sample
assert sample.shape == latents.shape
__lowerCamelCase = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
__lowerCamelCase = jnp.array(__UpperCAmelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
assert jnp.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
[17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
[8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
[3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
# fmt: on
] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.get_unet_model(model_id='''stabilityai/stable-diffusion-2''' , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_latents(__UpperCAmelCase , shape=(4, 4, 96, 96) , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_encoder_hidden_states(__UpperCAmelCase , shape=(4, 77, 1024) , fpaa=__UpperCAmelCase )
__lowerCamelCase = model.apply(
{'''params''': params} , __UpperCAmelCase , jnp.array(__UpperCAmelCase , dtype=jnp.intaa ) , encoder_hidden_states=__UpperCAmelCase , ).sample
assert sample.shape == latents.shape
__lowerCamelCase = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
__lowerCamelCase = jnp.array(__UpperCAmelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
assert jnp.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-2 )
| 330 | 0 |
"""simple docstring"""
from math import factorial
_a = {str(d): factorial(d) for d in range(10)}
def _A ( UpperCamelCase_ : int) -> int:
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(UpperCamelCase_))
def _A ( ) -> int:
'''simple docstring'''
__lowercase = 7 * factorial(9) + 1
return sum(i for i in range(3, UpperCamelCase_) if sum_of_digit_factorial(UpperCamelCase_) == i)
if __name__ == "__main__":
print(F"{solution() = }")
| 17 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""MMBTForClassification""", """MMBTModel""", """ModalEmbeddings"""]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
__lowerCamelCase : Tuple = logging.get_logger(__name__)
__lowerCamelCase : Any = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__lowerCamelCase : List[str] = {
'''vocab_file''': {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-encoder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-scorer''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-openqa''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'''
),
'''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt''',
'''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt''',
'''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt''',
'''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt''',
},
'''tokenizer_file''': {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'''
),
'''google/realm-cc-news-pretrained-encoder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'''
),
'''google/realm-cc-news-pretrained-scorer''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'''
),
'''google/realm-cc-news-pretrained-openqa''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'''
),
'''google/realm-orqa-nq-openqa''': (
'''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-nq-reader''': (
'''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-wq-openqa''': (
'''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-wq-reader''': (
'''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'''
),
},
}
__lowerCamelCase : Union[str, Any] = {
'''google/realm-cc-news-pretrained-embedder''': 5_12,
'''google/realm-cc-news-pretrained-encoder''': 5_12,
'''google/realm-cc-news-pretrained-scorer''': 5_12,
'''google/realm-cc-news-pretrained-openqa''': 5_12,
'''google/realm-orqa-nq-openqa''': 5_12,
'''google/realm-orqa-nq-reader''': 5_12,
'''google/realm-orqa-wq-openqa''': 5_12,
'''google/realm-orqa-wq-reader''': 5_12,
}
__lowerCamelCase : Optional[Any] = {
'''google/realm-cc-news-pretrained-embedder''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-encoder''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-scorer''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-nq-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-nq-reader''': {'''do_lower_case''': True},
'''google/realm-orqa-wq-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-wq-reader''': {'''do_lower_case''': True},
}
class a__ ( A__ ):
A = VOCAB_FILES_NAMES
A = PRETRAINED_VOCAB_FILES_MAP
A = PRETRAINED_INIT_CONFIGURATION
A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A = RealmTokenizer
def __init__( self : Any,_A : Tuple=None,_A : Union[str, Any]=None,_A : str=True,_A : Tuple="[UNK]",_A : List[str]="[SEP]",_A : List[str]="[PAD]",_A : int="[CLS]",_A : Dict="[MASK]",_A : str=True,_A : int=None,**_A : List[str],):
"""simple docstring"""
super().__init__(
_A,tokenizer_file=_A,do_lower_case=_A,unk_token=_A,sep_token=_A,pad_token=_A,cls_token=_A,mask_token=_A,tokenize_chinese_chars=_A,strip_accents=_A,**_A,)
SCREAMING_SNAKE_CASE_ : int = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase",_A ) != do_lower_case
or normalizer_state.get("strip_accents",_A ) != strip_accents
or normalizer_state.get("handle_chinese_chars",_A ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE_ : Optional[Any] = getattr(_A,normalizer_state.pop("type" ) )
SCREAMING_SNAKE_CASE_ : int = do_lower_case
SCREAMING_SNAKE_CASE_ : Union[str, Any] = strip_accents
SCREAMING_SNAKE_CASE_ : int = tokenize_chinese_chars
SCREAMING_SNAKE_CASE_ : str = normalizer_class(**_A )
SCREAMING_SNAKE_CASE_ : Tuple = do_lower_case
def __UpperCamelCase ( self : Tuple,_A : List[Any],**_A : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = PaddingStrategy.MAX_LENGTH
SCREAMING_SNAKE_CASE_ : Dict = text
SCREAMING_SNAKE_CASE_ : List[str] = kwargs.pop("text_pair",_A )
SCREAMING_SNAKE_CASE_ : Optional[int] = kwargs.pop("return_tensors",_A )
SCREAMING_SNAKE_CASE_ : int = {
"input_ids": [],
"attention_mask": [],
"token_type_ids": [],
}
for idx, candidate_text in enumerate(_A ):
if batch_text_pair is not None:
SCREAMING_SNAKE_CASE_ : Optional[Any] = batch_text_pair[idx]
else:
SCREAMING_SNAKE_CASE_ : Optional[int] = None
SCREAMING_SNAKE_CASE_ : Optional[int] = super().__call__(_A,_A,return_tensors=_A,**_A )
SCREAMING_SNAKE_CASE_ : Optional[int] = encoded_candidates.get("input_ids" )
SCREAMING_SNAKE_CASE_ : Optional[Any] = encoded_candidates.get("attention_mask" )
SCREAMING_SNAKE_CASE_ : Optional[int] = encoded_candidates.get("token_type_ids" )
if encoded_input_ids is not None:
output_data["input_ids"].append(_A )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(_A )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(_A )
SCREAMING_SNAKE_CASE_ : Any = {key: item for key, item in output_data.items() if len(_A ) != 0}
return BatchEncoding(_A,tensor_type=_A )
def __UpperCamelCase ( self : Dict,_A : Optional[Any],_A : Union[str, Any]=None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __UpperCamelCase ( self : Optional[int],_A : List[int],_A : Optional[List[int]] = None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = [self.sep_token_id]
SCREAMING_SNAKE_CASE_ : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __UpperCamelCase ( self : Any,_A : str,_A : Optional[str] = None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = self._tokenizer.model.save(_A,name=_A )
return tuple(_A )
| 18 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def a__ ( _UpperCamelCase : Optional[int] ):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class __lowerCAmelCase ( nn.Module ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
super().__init__()
__lowerCamelCase = module
__lowerCamelCase = nn.Sequential(
nn.Linear(module.in_features , __UpperCAmelCase , bias=__UpperCAmelCase ) , nn.Linear(__UpperCAmelCase , module.out_features , bias=__UpperCAmelCase ) , )
__lowerCamelCase = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=__UpperCAmelCase )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def lowerCamelCase ( self , __UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return self.module(__UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ) + self.adapter(__UpperCAmelCase )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __lowerCAmelCase ( unittest.TestCase ):
# We keep the constants inside the init function and model loading inside setUp function
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
# Therefore here we use only bloom-1b3 to test our module
lowerCAmelCase__ = """bigscience/bloom-1b7"""
# Constant values
lowerCAmelCase__ = 2.1_09_65_95_52_69_25_74
lowerCAmelCase__ = """Hello my name is"""
lowerCAmelCase__ = set()
EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I""" )
EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n""" )
EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University""" )
lowerCAmelCase__ = 1_0
def lowerCamelCase ( self ):
'''simple docstring'''
# Models and tokenizer
__lowerCamelCase = AutoTokenizer.from_pretrained(self.model_name )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# Models and tokenizer
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='''auto''' )
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
def lowerCamelCase ( self ):
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_abit.config
self.assertTrue(hasattr(__UpperCAmelCase , '''quantization_config''' ) )
__lowerCamelCase = config.to_dict()
__lowerCamelCase = config.to_diff_dict()
__lowerCamelCase = config.to_json_string()
def lowerCamelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
__lowerCamelCase = self.model_fpaa.get_memory_footprint()
__lowerCamelCase = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
__lowerCamelCase = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def lowerCamelCase ( self ):
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(__UpperCAmelCase , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = self.model_abit.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BitsAndBytesConfig()
__lowerCamelCase = True
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = model_abit_from_config.generate(
input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def lowerCamelCase ( self ):
'''simple docstring'''
with self.assertRaises(__UpperCAmelCase ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BitsAndBytesConfig()
with self.assertRaises(__UpperCAmelCase ):
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__UpperCAmelCase , load_in_abit=__UpperCAmelCase , device_map='''auto''' , bnb_abit_quant_type='''nf4''' , )
def lowerCamelCase ( self ):
'''simple docstring'''
with self.assertRaises(__UpperCAmelCase ):
# Tries with `str`
self.model_abit.to('''cpu''' )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.to(torch.device('''cuda:0''' ) )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = self.model_fpaa.to(torch.floataa )
__lowerCamelCase = self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.to('''cpu''' )
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.half()
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.float()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''t5-small''' , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __lowerCAmelCase ( unittest.TestCase ):
@classmethod
def lowerCamelCase ( cls ):
'''simple docstring'''
__lowerCamelCase = '''t5-small'''
__lowerCamelCase = '''google/flan-t5-small''' # flan-t5 uses dense-act instead of dense-relu-dense
__lowerCamelCase = AutoTokenizer.from_pretrained(cls.model_name )
__lowerCamelCase = '''Translate in German: Hello, my dog is cute'''
def lowerCamelCase ( self ):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
from transformers import TaForConditionalGeneration
__lowerCamelCase = TaForConditionalGeneration._keep_in_fpaa_modules
__lowerCamelCase = None
# test with `t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
# test with `flan-t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
__lowerCamelCase = modules
def lowerCamelCase ( self ):
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
# test with `flan-t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# model_name
__lowerCamelCase = '''bigscience/bloom-560m'''
__lowerCamelCase = '''t5-small'''
# Different types of model
__lowerCamelCase = AutoModel.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# Sequence classification model
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# CausalLM model
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# Seq2seq model
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
def lowerCamelCase ( self ):
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''text-generation''' , model=self.model_name , model_kwargs={'''device_map''': '''auto''', '''load_in_4bit''': True, '''torch_dtype''': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
__lowerCamelCase = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]['''generated_text'''] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=__UpperCAmelCase , device_map='''balanced''' )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
# Second real batch
__lowerCamelCase = model_parallel.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-350m'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
if version.parse(importlib.metadata.version('''bitsandbytes''' ) ) < version.parse('''0.37.0''' ):
return
# Step 1: freeze all parameters
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
__lowerCamelCase = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
__lowerCamelCase = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(__UpperCAmelCase ) ):
__lowerCamelCase = LoRALayer(module.q_proj , rank=16 )
__lowerCamelCase = LoRALayer(module.k_proj , rank=16 )
__lowerCamelCase = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
__lowerCamelCase = self.tokenizer('''Test batch ''' , return_tensors='''pt''' ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
__lowerCamelCase = model.forward(**__UpperCAmelCase )
out.logits.norm().backward()
for module in model.modules():
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(__UpperCAmelCase , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """gpt2-xl"""
lowerCAmelCase__ = 3.31_91_85_48_54_15_21_87
| 330 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=True , lowercase=True , lowercase=99 , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=512 , lowercase=16 , lowercase=2 , lowercase=0.0_2 , lowercase=3 , lowercase=4 , lowercase=None , ) -> List[str]:
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = seq_length
lowerCamelCase_ = is_training
lowerCamelCase_ = use_token_type_ids
lowerCamelCase_ = use_labels
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = type_vocab_size
lowerCamelCase_ = type_sequence_label_size
lowerCamelCase_ = initializer_range
lowerCamelCase_ = num_labels
lowerCamelCase_ = num_choices
lowerCamelCase_ = scope
lowerCamelCase_ = self.vocab_size - 1
def SCREAMING_SNAKE_CASE_( self ) -> Any:
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = None
if self.use_token_type_ids:
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCamelCase_ = None
lowerCamelCase_ = None
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_choices )
lowerCamelCase_ = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
lowerCamelCase_ = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase , lowercase , *lowercase ) -> Dict:
lowerCamelCase_ = OpenAIGPTModel(config=lowercase )
model.to(lowercase )
model.eval()
lowerCamelCase_ = model(lowercase , token_type_ids=lowercase , head_mask=lowercase )
lowerCamelCase_ = model(lowercase , token_type_ids=lowercase )
lowerCamelCase_ = model(lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase , lowercase , *lowercase ) -> int:
lowerCamelCase_ = OpenAIGPTLMHeadModel(lowercase )
model.to(lowercase )
model.eval()
lowerCamelCase_ = model(lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase , lowercase , *lowercase ) -> Dict:
lowerCamelCase_ = OpenAIGPTDoubleHeadsModel(lowercase )
model.to(lowercase )
model.eval()
lowerCamelCase_ = model(lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase , lowercase , *lowercase ) -> int:
lowerCamelCase_ = self.num_labels
lowerCamelCase_ = OpenAIGPTForSequenceClassification(lowercase )
model.to(lowercase )
model.eval()
lowerCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase_ = model(lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_( self ) -> Union[str, Any]:
lowerCamelCase_ = self.prepare_config_and_inputs()
(
(
lowerCamelCase_
) , (
lowerCamelCase_
) , (
lowerCamelCase_
) , (
lowerCamelCase_
) , (
lowerCamelCase_
) , (
lowerCamelCase_
) , (
lowerCamelCase_
) ,
) = config_and_inputs
lowerCamelCase_ = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
lowerCAmelCase__ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
lowerCAmelCase__ = (
{
'feature-extraction': OpenAIGPTModel,
'text-classification': OpenAIGPTForSequenceClassification,
'text-generation': OpenAIGPTLMHeadModel,
'zero-shot': OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase , lowercase , lowercase ) -> int:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase=False ) -> Any:
lowerCamelCase_ = super()._prepare_for_class(lowercase , lowercase , return_labels=lowercase )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowerCamelCase_ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=lowercase , )
lowerCamelCase_ = inputs_dict["labels"]
lowerCamelCase_ = inputs_dict["labels"]
lowerCamelCase_ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=lowercase , )
lowerCamelCase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowercase )
return inputs_dict
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
lowerCamelCase_ = OpenAIGPTModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=lowercase , n_embd=37 )
def SCREAMING_SNAKE_CASE_( self ) -> Any:
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_( self ) -> Union[str, Any]:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> str:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase )
@slow
def SCREAMING_SNAKE_CASE_( self ) -> Any:
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = OpenAIGPTModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_( self ) -> str:
lowerCamelCase_ = OpenAIGPTLMHeadModel.from_pretrained("openai-gpt" )
model.to(lowercase )
lowerCamelCase_ = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=lowercase ) # the president is
lowerCamelCase_ = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
lowerCamelCase_ = model.generate(lowercase , do_sample=lowercase )
self.assertListEqual(output_ids[0].tolist() , lowercase )
| 19 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = 42
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
lowerCAmelCase__ = True
@register_to_config
def __init__( self , __UpperCAmelCase = 3 , __UpperCAmelCase = 3 , __UpperCAmelCase = ("DownEncoderBlock2D",) , __UpperCAmelCase = ("UpDecoderBlock2D",) , __UpperCAmelCase = (64,) , __UpperCAmelCase = 1 , __UpperCAmelCase = "silu" , __UpperCAmelCase = 4 , __UpperCAmelCase = 32 , __UpperCAmelCase = 32 , __UpperCAmelCase = 0.18_215 , ):
'''simple docstring'''
super().__init__()
# pass init params to Encoder
__lowerCamelCase = Encoder(
in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , down_block_types=__UpperCAmelCase , block_out_channels=__UpperCAmelCase , layers_per_block=__UpperCAmelCase , act_fn=__UpperCAmelCase , norm_num_groups=__UpperCAmelCase , double_z=__UpperCAmelCase , )
# pass init params to Decoder
__lowerCamelCase = Decoder(
in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , up_block_types=__UpperCAmelCase , block_out_channels=__UpperCAmelCase , layers_per_block=__UpperCAmelCase , norm_num_groups=__UpperCAmelCase , act_fn=__UpperCAmelCase , )
__lowerCamelCase = nn.Convad(2 * latent_channels , 2 * latent_channels , 1 )
__lowerCamelCase = nn.Convad(__UpperCAmelCase , __UpperCAmelCase , 1 )
__lowerCamelCase = False
__lowerCamelCase = False
# only relevant if vae tiling is enabled
__lowerCamelCase = self.config.sample_size
__lowerCamelCase = (
self.config.sample_size[0]
if isinstance(self.config.sample_size , (list, tuple) )
else self.config.sample_size
)
__lowerCamelCase = int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
__lowerCamelCase = 0.25
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
if isinstance(__UpperCAmelCase , (Encoder, Decoder) ):
__lowerCamelCase = value
def lowerCamelCase ( self , __UpperCAmelCase = True ):
'''simple docstring'''
__lowerCamelCase = use_tiling
def lowerCamelCase ( self ):
'''simple docstring'''
self.enable_tiling(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = True
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = {}
def fn_recursive_add_processors(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
if hasattr(__UpperCAmelCase , '''set_processor''' ):
__lowerCamelCase = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(F"""{name}.{sub_name}""" , __UpperCAmelCase , __UpperCAmelCase )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
return processors
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = len(self.attn_processors.keys() )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(__UpperCAmelCase ) != count:
raise ValueError(
F"""A dict of processors was passed, but the number of processors {len(__UpperCAmelCase )} does not match the"""
F""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" )
def fn_recursive_attn_processor(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
if hasattr(__UpperCAmelCase , '''set_processor''' ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
module.set_processor(__UpperCAmelCase )
else:
module.set_processor(processor.pop(F"""{name}.processor""" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(F"""{name}.{sub_name}""" , __UpperCAmelCase , __UpperCAmelCase )
for name, module in self.named_children():
fn_recursive_attn_processor(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(__UpperCAmelCase , return_dict=__UpperCAmelCase )
if self.use_slicing and x.shape[0] > 1:
__lowerCamelCase = [self.encoder(__UpperCAmelCase ) for x_slice in x.split(1 )]
__lowerCamelCase = torch.cat(__UpperCAmelCase )
else:
__lowerCamelCase = self.encoder(__UpperCAmelCase )
__lowerCamelCase = self.quant_conv(__UpperCAmelCase )
__lowerCamelCase = DiagonalGaussianDistribution(__UpperCAmelCase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.tiled_decode(__UpperCAmelCase , return_dict=__UpperCAmelCase )
__lowerCamelCase = self.post_quant_conv(__UpperCAmelCase )
__lowerCamelCase = self.decoder(__UpperCAmelCase )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__UpperCAmelCase )
@apply_forward_hook
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
if self.use_slicing and z.shape[0] > 1:
__lowerCamelCase = [self._decode(__UpperCAmelCase ).sample for z_slice in z.split(1 )]
__lowerCamelCase = torch.cat(__UpperCAmelCase )
else:
__lowerCamelCase = self._decode(__UpperCAmelCase ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = min(a.shape[2] , b.shape[2] , __UpperCAmelCase )
for y in range(__UpperCAmelCase ):
__lowerCamelCase = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = min(a.shape[3] , b.shape[3] , __UpperCAmelCase )
for x in range(__UpperCAmelCase ):
__lowerCamelCase = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
__lowerCamelCase = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
__lowerCamelCase = int(self.tile_latent_min_size * self.tile_overlap_factor )
__lowerCamelCase = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
__lowerCamelCase = []
for i in range(0 , x.shape[2] , __UpperCAmelCase ):
__lowerCamelCase = []
for j in range(0 , x.shape[3] , __UpperCAmelCase ):
__lowerCamelCase = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
__lowerCamelCase = self.encoder(__UpperCAmelCase )
__lowerCamelCase = self.quant_conv(__UpperCAmelCase )
row.append(__UpperCAmelCase )
rows.append(__UpperCAmelCase )
__lowerCamelCase = []
for i, row in enumerate(__UpperCAmelCase ):
__lowerCamelCase = []
for j, tile in enumerate(__UpperCAmelCase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
__lowerCamelCase = self.blend_v(rows[i - 1][j] , __UpperCAmelCase , __UpperCAmelCase )
if j > 0:
__lowerCamelCase = self.blend_h(row[j - 1] , __UpperCAmelCase , __UpperCAmelCase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__UpperCAmelCase , dim=3 ) )
__lowerCamelCase = torch.cat(__UpperCAmelCase , dim=2 )
__lowerCamelCase = DiagonalGaussianDistribution(__UpperCAmelCase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
__lowerCamelCase = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
__lowerCamelCase = int(self.tile_sample_min_size * self.tile_overlap_factor )
__lowerCamelCase = self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
__lowerCamelCase = []
for i in range(0 , z.shape[2] , __UpperCAmelCase ):
__lowerCamelCase = []
for j in range(0 , z.shape[3] , __UpperCAmelCase ):
__lowerCamelCase = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
__lowerCamelCase = self.post_quant_conv(__UpperCAmelCase )
__lowerCamelCase = self.decoder(__UpperCAmelCase )
row.append(__UpperCAmelCase )
rows.append(__UpperCAmelCase )
__lowerCamelCase = []
for i, row in enumerate(__UpperCAmelCase ):
__lowerCamelCase = []
for j, tile in enumerate(__UpperCAmelCase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
__lowerCamelCase = self.blend_v(rows[i - 1][j] , __UpperCAmelCase , __UpperCAmelCase )
if j > 0:
__lowerCamelCase = self.blend_h(row[j - 1] , __UpperCAmelCase , __UpperCAmelCase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__UpperCAmelCase , dim=3 ) )
__lowerCamelCase = torch.cat(__UpperCAmelCase , dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False , __UpperCAmelCase = True , __UpperCAmelCase = None , ):
'''simple docstring'''
__lowerCamelCase = sample
__lowerCamelCase = self.encode(__UpperCAmelCase ).latent_dist
if sample_posterior:
__lowerCamelCase = posterior.sample(generator=__UpperCAmelCase )
else:
__lowerCamelCase = posterior.mode()
__lowerCamelCase = self.decode(__UpperCAmelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=__UpperCAmelCase )
| 330 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> float:
return round(float(moles / volume ) * nfactor )
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> float:
return round(float((moles * 0.0821 * temperature) / (volume) ) )
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> float:
return round(float((moles * 0.0821 * temperature) / (pressure) ) )
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> float:
return round(float((pressure * volume) / (0.0821 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 20 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
a_ = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""kernel""", """weight"""),
("""beta""", """bias"""),
("""gamma""", """weight"""),
("""pegasus""", """model"""),
]
a_ = [
(""".output.dense""", """.fc2"""),
("""intermediate.LayerNorm""", """final_layer_norm"""),
("""intermediate.dense""", """fc1"""),
]
a_ = (
INIT_COMMON
+ [
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.out_proj"""),
("""attention.self""", """self_attn"""),
("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
("""attention.encdec""", """encoder_attn"""),
("""key""", """k_proj"""),
("""value""", """v_proj"""),
("""query""", """q_proj"""),
("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
]
+ END_COMMON
)
a_ = (
INIT_COMMON
+ [
("""embeddings.word_embeddings""", """shared.weight"""),
("""embeddings.position_embeddings""", """embed_positions.weight"""),
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.output"""),
("""attention.self""", """self_attn.self"""),
("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
]
+ END_COMMON
)
a_ = [
"""encdec/key/bias""",
"""encdec/query/bias""",
"""encdec/value/bias""",
"""self/key/bias""",
"""self/query/bias""",
"""self/value/bias""",
"""encdec_output/dense/bias""",
"""attention/output/dense/bias""",
]
def a__ ( _UpperCamelCase : Optional[int] ,_UpperCamelCase : Optional[Any] ):
for tf_name, hf_name in patterns:
__lowerCamelCase = k.replace(_UpperCamelCase ,_UpperCamelCase )
return k
def a__ ( _UpperCamelCase : dict ,_UpperCamelCase : dict ):
__lowerCamelCase = BigBirdPegasusConfig(**_UpperCamelCase )
__lowerCamelCase = BigBirdPegasusForConditionalGeneration(_UpperCamelCase )
__lowerCamelCase = torch_model.state_dict()
__lowerCamelCase = {}
# separating decoder weights
__lowerCamelCase = {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
__lowerCamelCase = {k: tf_weights[k] for k in tf_weights if not k.startswith('''pegasus/decoder''' )}
for k, v in tqdm(decoder_weights.items() ,'''tf -> hf conversion''' ):
__lowerCamelCase = [k.endswith(_UpperCamelCase ) for ending in KEYS_TO_IGNORE]
if any(_UpperCamelCase ):
continue
__lowerCamelCase = DECODER_PATTERNS
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase ,_UpperCamelCase )
if new_k not in state_dict:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
__lowerCamelCase = v.T
__lowerCamelCase = torch.from_numpy(_UpperCamelCase )
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items() ,'''tf -> hf conversion''' ):
__lowerCamelCase = [k.endswith(_UpperCamelCase ) for ending in KEYS_TO_IGNORE]
if any(_UpperCamelCase ):
continue
__lowerCamelCase = REMAINING_PATTERNS
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase ,_UpperCamelCase )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
__lowerCamelCase = v.T
__lowerCamelCase = torch.from_numpy(_UpperCamelCase )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
__lowerCamelCase = mapping['''model.embed_positions.weight''']
__lowerCamelCase = mapping.pop('''model.embed_positions.weight''' )
__lowerCamelCase ,__lowerCamelCase = torch_model.load_state_dict(_UpperCamelCase ,strict=_UpperCamelCase )
__lowerCamelCase = [
k
for k in missing
if k
not in [
'''final_logits_bias''',
'''model.encoder.embed_tokens.weight''',
'''model.decoder.embed_tokens.weight''',
'''lm_head.weight''',
]
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = tf.train.list_variables(_UpperCamelCase )
__lowerCamelCase = {}
__lowerCamelCase = ['''global_step''']
for name, shape in tqdm(_UpperCamelCase ,desc='''converting tf checkpoint to dict''' ):
__lowerCamelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
__lowerCamelCase = tf.train.load_variable(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = array
return tf_weights
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : dict ):
__lowerCamelCase = get_tf_weights_as_numpy(_UpperCamelCase )
__lowerCamelCase = convert_bigbird_pegasus(_UpperCamelCase ,_UpperCamelCase )
torch_model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
a_ = parser.parse_args()
a_ = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 330 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE : str = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : List[Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
a_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase=None , __UpperCAmelCase=None ):
'''simple docstring'''
if not conversation_id:
__lowerCamelCase = uuid.uuida()
if past_user_inputs is None:
__lowerCamelCase = []
if generated_responses is None:
__lowerCamelCase = []
__lowerCamelCase = conversation_id
__lowerCamelCase = past_user_inputs
__lowerCamelCase = generated_responses
__lowerCamelCase = text
def __eq__( self , __UpperCAmelCase ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False ):
'''simple docstring'''
if self.new_user_input:
if overwrite:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
F"""with: \"{text}\".""" )
__lowerCamelCase = text
else:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input""" )
else:
__lowerCamelCase = text
def lowerCamelCase ( self ):
'''simple docstring'''
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
__lowerCamelCase = None
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
self.generated_responses.append(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
'''simple docstring'''
__lowerCamelCase = F"""Conversation id: {self.uuid} \n"""
for is_user, text in self.iter_texts():
__lowerCamelCase = '''user''' if is_user else '''bot'''
output += F"""{name} >> {text} \n"""
return output
@add_end_docstrings(
lowerCAmelCase__ , r"""
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
""" , )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
if self.tokenizer.pad_token_id is None:
__lowerCamelCase = self.tokenizer.eos_token
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = {}
__lowerCamelCase = {}
__lowerCamelCase = {}
if min_length_for_response is not None:
__lowerCamelCase = min_length_for_response
if minimum_tokens is not None:
__lowerCamelCase = minimum_tokens
if "max_length" in generate_kwargs:
__lowerCamelCase = generate_kwargs['''max_length''']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
__lowerCamelCase = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(__UpperCAmelCase )
return preprocess_params, forward_params, postprocess_params
def __call__( self , __UpperCAmelCase , __UpperCAmelCase=0 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = super().__call__(__UpperCAmelCase , num_workers=__UpperCAmelCase , **__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(__UpperCAmelCase ) == 1:
return outputs[0]
return outputs
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=32 ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise ValueError('''ConversationalPipeline, expects Conversation as inputs''' )
if conversation.new_user_input is None:
raise ValueError(
F"""Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. """
'''Add user inputs with the conversation\'s `add_user_input` method''' )
if hasattr(self.tokenizer , '''_build_conversation_input_ids''' ):
__lowerCamelCase = self.tokenizer._build_conversation_input_ids(__UpperCAmelCase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
__lowerCamelCase = self._legacy_parse_and_tokenize(__UpperCAmelCase )
if self.framework == "pt":
__lowerCamelCase = torch.LongTensor([input_ids] )
elif self.framework == "tf":
__lowerCamelCase = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=10 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = generate_kwargs.get('''max_length''' , self.model.config.max_length )
__lowerCamelCase = model_inputs['''input_ids'''].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})""" )
__lowerCamelCase = max_length - minimum_tokens
__lowerCamelCase = model_inputs['''input_ids'''][:, -trim:]
if "attention_mask" in model_inputs:
__lowerCamelCase = model_inputs['''attention_mask'''][:, -trim:]
__lowerCamelCase = model_inputs.pop('''conversation''' )
__lowerCamelCase = max_length
__lowerCamelCase = self.model.generate(**__UpperCAmelCase , **__UpperCAmelCase )
if self.model.config.is_encoder_decoder:
__lowerCamelCase = 1
else:
__lowerCamelCase = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=True ):
'''simple docstring'''
__lowerCamelCase = model_outputs['''output_ids''']
__lowerCamelCase = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase , )
__lowerCamelCase = model_outputs['''conversation''']
conversation.mark_processed()
conversation.append_response(__UpperCAmelCase )
return conversation
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.tokenizer.eos_token_id
__lowerCamelCase = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) )
if len(__UpperCAmelCase ) > self.tokenizer.model_max_length:
__lowerCamelCase = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 330 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE :str = {
'''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
'''tokenization_roformer''': ['''RoFormerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Optional[Any] = ['''RoFormerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :int = [
'''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RoFormerForCausalLM''',
'''RoFormerForMaskedLM''',
'''RoFormerForMultipleChoice''',
'''RoFormerForQuestionAnswering''',
'''RoFormerForSequenceClassification''',
'''RoFormerForTokenClassification''',
'''RoFormerLayer''',
'''RoFormerModel''',
'''RoFormerPreTrainedModel''',
'''load_tf_weights_in_roformer''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :List[Any] = [
'''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRoFormerForCausalLM''',
'''TFRoFormerForMaskedLM''',
'''TFRoFormerForMultipleChoice''',
'''TFRoFormerForQuestionAnswering''',
'''TFRoFormerForSequenceClassification''',
'''TFRoFormerForTokenClassification''',
'''TFRoFormerLayer''',
'''TFRoFormerModel''',
'''TFRoFormerPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :int = [
'''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FlaxRoFormerForMaskedLM''',
'''FlaxRoFormerForMultipleChoice''',
'''FlaxRoFormerForQuestionAnswering''',
'''FlaxRoFormerForSequenceClassification''',
'''FlaxRoFormerForTokenClassification''',
'''FlaxRoFormerModel''',
'''FlaxRoFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE :Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 22 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
a_ = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["""memory_attention""", """encoder_attn"""],
["""attention""", """attn"""],
["""/""", """."""],
[""".LayerNorm.gamma""", """_layer_norm.weight"""],
[""".LayerNorm.beta""", """_layer_norm.bias"""],
["""r.layer_""", """r.layers."""],
["""output_proj""", """out_proj"""],
["""ffn.dense_1.""", """fc2."""],
["""ffn.dense.""", """fc1."""],
["""ffn_layer_norm""", """final_layer_norm"""],
["""kernel""", """weight"""],
["""encoder_layer_norm.""", """encoder.layer_norm."""],
["""decoder_layer_norm.""", """decoder.layer_norm."""],
["""embeddings.weights""", """shared.weight"""],
]
def a__ ( _UpperCamelCase : int ):
for pegasus_name, hf_name in PATTERNS:
__lowerCamelCase = k.replace(_UpperCamelCase ,_UpperCamelCase )
return k
def a__ ( _UpperCamelCase : dict ,_UpperCamelCase : dict ):
__lowerCamelCase = DEFAULTS.copy()
cfg_kwargs.update(_UpperCamelCase )
__lowerCamelCase = PegasusConfig(**_UpperCamelCase )
__lowerCamelCase = PegasusForConditionalGeneration(_UpperCamelCase )
__lowerCamelCase = torch_model.model.state_dict()
__lowerCamelCase = {}
for k, v in tf_weights.items():
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase )
if new_k not in sd:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if "dense" in k or "proj" in new_k:
__lowerCamelCase = v.T
__lowerCamelCase = torch.tensor(_UpperCamelCase ,dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
# make sure embedding.padding_idx is respected
__lowerCamelCase = torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1] )
__lowerCamelCase = mapping['''shared.weight''']
__lowerCamelCase = mapping['''shared.weight''']
__lowerCamelCase = {k: torch.zeros_like(_UpperCamelCase ) for k, v in sd.items() if k.endswith('''bias''' ) and k not in mapping}
mapping.update(**_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = torch_model.model.load_state_dict(_UpperCamelCase ,strict=_UpperCamelCase )
__lowerCamelCase = [
k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def a__ ( _UpperCamelCase : str="./ckpt/aeslc/model.ckpt-32000" ):
__lowerCamelCase = tf.train.list_variables(_UpperCamelCase )
__lowerCamelCase = {}
__lowerCamelCase = ['''Adafactor''', '''global_step''']
for name, shape in tqdm(_UpperCamelCase ,desc='''converting tf checkpoint to dict''' ):
__lowerCamelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
__lowerCamelCase = tf.train.load_variable(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = array
return tf_weights
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ):
# save tokenizer first
__lowerCamelCase = Path(_UpperCamelCase ).parent.name
__lowerCamelCase = task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
__lowerCamelCase = PegasusTokenizer.from_pretrained('''sshleifer/pegasus''' ,model_max_length=_UpperCamelCase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(_UpperCamelCase )
# convert model
__lowerCamelCase = get_tf_weights_as_numpy(_UpperCamelCase )
__lowerCamelCase = task_specific_params[F"""summarization_{dataset}"""]
if dataset == "large":
__lowerCamelCase = task_specific_params
__lowerCamelCase = convert_pegasus(_UpperCamelCase ,_UpperCamelCase )
torch_model.save_pretrained(_UpperCamelCase )
__lowerCamelCase = torch_model.state_dict()
sd.pop('''model.decoder.embed_positions.weight''' )
sd.pop('''model.encoder.embed_positions.weight''' )
torch.save(_UpperCamelCase ,Path(_UpperCamelCase ) / '''pytorch_model.bin''' )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
a_ = parser.parse_args()
if args.save_dir is None:
a_ = Path(args.tf_ckpt_path).parent.name
a_ = os.path.join("""pegasus""", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 330 | 0 |
'''simple docstring'''
import cmath
import math
def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ) -> complex:
UpperCAmelCase : Tuple = math.radians(_lowerCAmelCase )
UpperCAmelCase : str = math.radians(_lowerCAmelCase )
# Convert voltage and current to rectangular form
UpperCAmelCase : Union[str, Any] = cmath.rect(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase : str = cmath.rect(_lowerCAmelCase , _lowerCAmelCase )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 23 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
a_ = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : Tuple ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Dict ):
for attribute in key.split('''.''' ):
__lowerCamelCase = getattr(_UpperCamelCase ,_UpperCamelCase )
if weight_type is not None:
__lowerCamelCase = getattr(_UpperCamelCase ,_UpperCamelCase ).shape
else:
__lowerCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__lowerCamelCase = value
elif weight_type == "weight_g":
__lowerCamelCase = value
elif weight_type == "weight_v":
__lowerCamelCase = value
elif weight_type == "bias":
__lowerCamelCase = value
else:
__lowerCamelCase = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def a__ ( _UpperCamelCase : Any ,_UpperCamelCase : Any ):
__lowerCamelCase = []
__lowerCamelCase = fairseq_model.state_dict()
__lowerCamelCase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowerCamelCase = False
if "conv_layers" in name:
load_conv_layer(
_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,hf_model.config.feat_extract_norm == '''group''' ,)
__lowerCamelCase = True
else:
for key, mapped_key in MAPPING.items():
__lowerCamelCase = '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
# special case since naming is very similar
continue
__lowerCamelCase = True
if "*" in mapped_key:
__lowerCamelCase = name.split(_UpperCamelCase )[0].split('''.''' )[-2]
__lowerCamelCase = mapped_key.replace('''*''' ,_UpperCamelCase )
if "weight_g" in name:
__lowerCamelCase = '''weight_g'''
elif "weight_v" in name:
__lowerCamelCase = '''weight_v'''
elif "bias" in name:
__lowerCamelCase = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCamelCase = '''weight'''
else:
__lowerCamelCase = None
set_recursively(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
continue
if not is_used:
unused_weights.append(_UpperCamelCase )
logger.warning(F"""Unused weights: {unused_weights}""" )
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Dict ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = full_name.split('''conv_layers.''' )[-1]
__lowerCamelCase = name.split('''.''' )
__lowerCamelCase = int(items[0] )
__lowerCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_UpperCamelCase )
@torch.no_grad()
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : List[str] ,_UpperCamelCase : Tuple=None ,_UpperCamelCase : Tuple=None ,_UpperCamelCase : List[Any]=True ):
if config_path is not None:
__lowerCamelCase = UniSpeechSatConfig.from_pretrained(_UpperCamelCase )
else:
__lowerCamelCase = UniSpeechSatConfig()
__lowerCamelCase = ''''''
if is_finetuned:
__lowerCamelCase = UniSpeechSatForCTC(_UpperCamelCase )
else:
__lowerCamelCase = UniSpeechSatForPreTraining(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
__lowerCamelCase = model[0].eval()
recursively_load_weights(_UpperCamelCase ,_UpperCamelCase )
hf_wavavec.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
a_ = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 330 | 0 |
from __future__ import annotations
from random import random
class SCREAMING_SNAKE_CASE__ :
def __init__(self : int , a__ : int | None = None ):
"""simple docstring"""
__snake_case = value
__snake_case = random()
__snake_case = None
__snake_case = None
def __repr__(self : List[Any] ):
"""simple docstring"""
from pprint import pformat
if self.left is None and self.right is None:
return f"""'{self.value}: {self.prior:.5}'"""
else:
return pformat(
{f"""{self.value}: {self.prior:.5}""": (self.left, self.right)} , indent=1 )
def __str__(self : int ):
"""simple docstring"""
__snake_case = str(self.value ) + ''' '''
__snake_case = str(self.left or '''''' )
__snake_case = str(self.right or '''''' )
return value + left + right
def lowerCamelCase__ ( snake_case_ : Node | None , snake_case_ : int ) -> tuple[Node | None, Node | None]:
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
__snake_case , __snake_case = split(root.left , snake_case_ )
return left, root
else:
__snake_case , __snake_case = split(root.right , snake_case_ )
return root, right
def lowerCamelCase__ ( snake_case_ : Node | None , snake_case_ : Node | None ) -> Node | None:
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
__snake_case = merge(left.right , snake_case_ )
return left
else:
__snake_case = merge(snake_case_ , right.left )
return right
def lowerCamelCase__ ( snake_case_ : Node | None , snake_case_ : int ) -> Node | None:
__snake_case = Node(snake_case_ )
__snake_case , __snake_case = split(snake_case_ , snake_case_ )
return merge(merge(snake_case_ , snake_case_ ) , snake_case_ )
def lowerCamelCase__ ( snake_case_ : Node | None , snake_case_ : int ) -> Node | None:
__snake_case , __snake_case = split(snake_case_ , value - 1 )
__snake_case , __snake_case = split(snake_case_ , snake_case_ )
return merge(snake_case_ , snake_case_ )
def lowerCamelCase__ ( snake_case_ : Node | None ) -> None:
if not root: # None
return
else:
inorder(root.left )
print(root.value , end=''',''' )
inorder(root.right )
def lowerCamelCase__ ( snake_case_ : Node | None , snake_case_ : str ) -> Node | None:
for arg in args.split():
if arg[0] == "+":
__snake_case = insert(snake_case_ , int(arg[1:] ) )
elif arg[0] == "-":
__snake_case = erase(snake_case_ , int(arg[1:] ) )
else:
print('''Unknown command''' )
return root
def lowerCamelCase__ ( ) -> None:
__snake_case = None
print(
'''enter numbers to create a tree, + value to add value into treap, '''
'''- value to erase all nodes with value. \'q\' to quit. ''' )
__snake_case = input()
while args != "q":
__snake_case = interact_treap(snake_case_ , snake_case_ )
print(snake_case_ )
__snake_case = input()
print('''good by!''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 24 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
a_ = logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase__ )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
requires_backends(self , '''vision''' )
self.check_model_type(__UpperCAmelCase )
def __call__( self , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return super().__call__(__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return {}, {}, {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = load_image(__UpperCAmelCase )
__lowerCamelCase = image.size
__lowerCamelCase = self.image_processor(images=__UpperCAmelCase , return_tensors=self.framework )
return model_inputs
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.model(**__UpperCAmelCase )
return model_outputs
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = model_outputs.predicted_depth
__lowerCamelCase = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode='''bicubic''' , align_corners=__UpperCAmelCase )
__lowerCamelCase = prediction.squeeze().cpu().numpy()
__lowerCamelCase = (output * 255 / np.max(__UpperCAmelCase )).astype('''uint8''' )
__lowerCamelCase = Image.fromarray(__UpperCAmelCase )
__lowerCamelCase = {}
__lowerCamelCase = predicted_depth
__lowerCamelCase = depth
return output_dict
| 330 | 0 |
"""simple docstring"""
import re
import string
import numpy as np
import datasets
UpperCAmelCase__ : List[Any] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
UpperCAmelCase__ : Optional[int] = '\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results["exact_match"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 33.3\n\n'
UpperCAmelCase__ : Any = '\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase_ (datasets.Metric ):
"""simple docstring"""
def __magic_name__ (self ) -> int:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , reference_urls=[] , )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , ) -> Optional[int]:
"""simple docstring"""
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
SCREAMING_SNAKE_CASE__ : Optional[int] = np.array([re.sub(SCREAMING_SNAKE_CASE__ , """""" , SCREAMING_SNAKE_CASE__ ) for x in predictions] )
SCREAMING_SNAKE_CASE__ : List[Any] = np.array([re.sub(SCREAMING_SNAKE_CASE__ , """""" , SCREAMING_SNAKE_CASE__ ) for x in references] )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = np.asarray(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.asarray(SCREAMING_SNAKE_CASE__ )
if ignore_case:
SCREAMING_SNAKE_CASE__ : Optional[int] = np.char.lower(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.char.lower(SCREAMING_SNAKE_CASE__ )
if ignore_punctuation:
SCREAMING_SNAKE_CASE__ : Optional[Any] = string.punctuation.maketrans("""""" , """""" , string.punctuation )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.char.translate(SCREAMING_SNAKE_CASE__ , table=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.char.translate(SCREAMING_SNAKE_CASE__ , table=SCREAMING_SNAKE_CASE__ )
if ignore_numbers:
SCREAMING_SNAKE_CASE__ : Optional[Any] = string.digits.maketrans("""""" , """""" , string.digits )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.char.translate(SCREAMING_SNAKE_CASE__ , table=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : str = np.char.translate(SCREAMING_SNAKE_CASE__ , table=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Tuple = predictions == references
return {"exact_match": np.mean(SCREAMING_SNAKE_CASE__ ) * 1_00}
| 25 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
a_ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = ["""pixel_values"""]
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = True , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
__lowerCamelCase = size if size is not None else {'''shortest_edge''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
__lowerCamelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase , param_name='''crop_size''' )
__lowerCamelCase = do_resize
__lowerCamelCase = size
__lowerCamelCase = resample
__lowerCamelCase = do_center_crop
__lowerCamelCase = crop_size
__lowerCamelCase = do_rescale
__lowerCamelCase = rescale_factor
__lowerCamelCase = do_normalize
__lowerCamelCase = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__lowerCamelCase = image_std if image_std is not None else OPENAI_CLIP_STD
__lowerCamelCase = do_convert_rgb
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
__lowerCamelCase = get_resize_output_image_size(__UpperCAmelCase , size=size['''shortest_edge'''] , default_to_square=__UpperCAmelCase )
return resize(__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(__UpperCAmelCase , size=(size['''height'''], size['''width''']) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = do_resize if do_resize is not None else self.do_resize
__lowerCamelCase = size if size is not None else self.size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = resample if resample is not None else self.resample
__lowerCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowerCamelCase = crop_size if crop_size is not None else self.crop_size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''crop_size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = do_rescale if do_rescale is not None else self.do_rescale
__lowerCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCamelCase = do_normalize if do_normalize is not None else self.do_normalize
__lowerCamelCase = image_mean if image_mean is not None else self.image_mean
__lowerCamelCase = image_std if image_std is not None else self.image_std
__lowerCamelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__lowerCamelCase = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__lowerCamelCase = [convert_to_rgb(__UpperCAmelCase ) for image in images]
# All transformations expect numpy arrays.
__lowerCamelCase = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
__lowerCamelCase = [self.resize(image=__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase ) for image in images]
if do_center_crop:
__lowerCamelCase = [self.center_crop(image=__UpperCAmelCase , size=__UpperCAmelCase ) for image in images]
if do_rescale:
__lowerCamelCase = [self.rescale(image=__UpperCAmelCase , scale=__UpperCAmelCase ) for image in images]
if do_normalize:
__lowerCamelCase = [self.normalize(image=__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase ) for image in images]
__lowerCamelCase = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
__lowerCamelCase = {'''pixel_values''': images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase )
| 330 | 0 |
import requests
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : List[str] = {"""Content-Type""": """application/json"""}
_A : str = requests.post(snake_case_,json={"""text""": message_body},headers=snake_case_ )
if response.status_code != 200:
_A : Union[str, Any] = (
"""Request to slack returned an error """
f'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(snake_case_ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message("<YOUR MESSAGE BODY>", "<SLACK CHANNEL URL>")
| 26 |
from __future__ import annotations
from typing import Generic, TypeVar
a_ = TypeVar("""T""")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = data
__lowerCamelCase = self
__lowerCamelCase = 0
class __lowerCAmelCase ( Generic[T] ):
def __init__( self ):
'''simple docstring'''
# map from node name to the node object
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# create a new set with x as its member
__lowerCamelCase = DisjointSetTreeNode(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# find the set x belongs to (with path-compression)
__lowerCamelCase = self.map[data]
if elem_ref != elem_ref.parent:
__lowerCamelCase = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# helper function for union operation
if nodea.rank > nodea.rank:
__lowerCamelCase = nodea
else:
__lowerCamelCase = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# merge 2 disjoint sets
self.link(self.find_set(__UpperCAmelCase ) , self.find_set(__UpperCAmelCase ) )
class __lowerCAmelCase ( Generic[T] ):
def __init__( self ):
'''simple docstring'''
# connections: map from the node to the neighbouring nodes (with weights)
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# add a node ONLY if its not present in the graph
if node not in self.connections:
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# add an edge with the given weight
self.add_node(__UpperCAmelCase )
self.add_node(__UpperCAmelCase )
__lowerCamelCase = weight
__lowerCamelCase = weight
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda __UpperCAmelCase : x[2] )
# creating the disjoint set
__lowerCamelCase = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(__UpperCAmelCase )
# MST generation
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = edges[index]
index += 1
__lowerCamelCase = disjoint_set.find_set(__UpperCAmelCase )
__lowerCamelCase = disjoint_set.find_set(__UpperCAmelCase )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
disjoint_set.union(__UpperCAmelCase , __UpperCAmelCase )
return graph
| 330 | 0 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict ):
__a : Optional[Any] = tmp_path / 'file.csv'
__a : Union[str, Any] = textwrap.dedent(
'\\n header1,header2\n 1,2\n 10,20\n ' )
with open(_SCREAMING_SNAKE_CASE , 'w' ) as f:
f.write(_SCREAMING_SNAKE_CASE )
return str(_SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Union[str, Any] ):
__a : str = tmp_path / 'malformed_file.csv'
__a : int = textwrap.dedent(
'\\n header1,header2\n 1,2\n 10,20,\n ' )
with open(_SCREAMING_SNAKE_CASE , 'w' ) as f:
f.write(_SCREAMING_SNAKE_CASE )
return str(_SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : List[str] ):
__a : Optional[Any] = tmp_path / 'csv_with_image.csv'
__a : Dict = textwrap.dedent(
F"""\
image
{image_file}
""" )
with open(_SCREAMING_SNAKE_CASE , 'w' ) as f:
f.write(_SCREAMING_SNAKE_CASE )
return str(_SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
__a : Union[str, Any] = tmp_path / 'csv_with_label.csv'
__a : Any = textwrap.dedent(
'\\n label\n good\n bad\n good\n ' )
with open(_SCREAMING_SNAKE_CASE , 'w' ) as f:
f.write(_SCREAMING_SNAKE_CASE )
return str(_SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] ):
__a : Dict = tmp_path / 'csv_with_int_list.csv'
__a : Tuple = textwrap.dedent(
'\\n int_list\n 1 2 3\n 4 5 6\n 7 8 9\n ' )
with open(_SCREAMING_SNAKE_CASE , 'w' ) as f:
f.write(_SCREAMING_SNAKE_CASE )
return str(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] ):
__a : int = Csv()
__a : str = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(_SCREAMING_SNAKE_CASE , match='Error tokenizing data' ):
for _ in generator:
pass
assert any(
record.levelname == 'ERROR'
and 'Failed to read file' in record.message
and os.path.basename(_SCREAMING_SNAKE_CASE ) in record.message
for record in caplog.records )
@require_pil
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
with open(_SCREAMING_SNAKE_CASE , encoding='utf-8' ) as f:
__a : Tuple = f.read().splitlines()[1]
__a : Tuple = Csv(encoding='utf-8' , features=Features({'image': Image()} ) )
__a : Any = csv._generate_tables([[csv_file_with_image]] )
__a : int = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('image' ).type == Image()()
__a : Any = pa_table.to_pydict()['image']
assert generated_content == [{"path": image_file, "bytes": None}]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Union[str, Any] ):
with open(_SCREAMING_SNAKE_CASE , encoding='utf-8' ) as f:
__a : Tuple = f.read().splitlines()[1:]
__a : Optional[int] = Csv(encoding='utf-8' , features=Features({'label': ClassLabel(names=['good', 'bad'] )} ) )
__a : List[str] = csv._generate_tables([[csv_file_with_label]] )
__a : Dict = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('label' ).type == ClassLabel(names=['good', 'bad'] )()
__a : int = pa_table.to_pydict()['label']
assert generated_content == [ClassLabel(names=['good', 'bad'] ).straint(_SCREAMING_SNAKE_CASE ) for label in labels]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[int] ):
__a : str = Csv(encoding='utf-8' , sep=',' , converters={'int_list': lambda _SCREAMING_SNAKE_CASE : [int(_SCREAMING_SNAKE_CASE ) for i in x.split()]} )
__a : Any = csv._generate_tables([[csv_file_with_int_list]] )
__a : Any = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field('int_list' ).type )
__a : Tuple = pa_table.to_pydict()['int_list']
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 27 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=2 , __UpperCAmelCase=8 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=16 , __UpperCAmelCase=5 , __UpperCAmelCase=2 , __UpperCAmelCase=36 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=512 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_input_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase ( self ):
'''simple docstring'''
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_config()
__lowerCamelCase = 300
return config
def lowerCamelCase ( self ):
'''simple docstring'''
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = self.prepare_config_and_inputs()
__lowerCamelCase = True
__lowerCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = True
__lowerCamelCase = MraModel(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , )
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , )
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraForQuestionAnswering(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MraForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MraForTokenClassification(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_choices
__lowerCamelCase = MraForMultipleChoice(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = ()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCamelCase = type
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = MraModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@unittest.skip(reason='''MRA does not output attentions''' )
def lowerCamelCase ( self ):
'''simple docstring'''
return
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowerCamelCase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowerCamelCase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = 50265
__lowerCamelCase = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' )
__lowerCamelCase = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = 50265
__lowerCamelCase = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 330 | 0 |
'''simple docstring'''
from __future__ import annotations
_lowerCamelCase : str = [True] * 100_0001
_lowerCamelCase : str = 2
while i * i <= 100_0000:
if seive[i]:
for j in range(i * i, 100_0001, i):
_lowerCamelCase : Optional[int] = False
i += 1
def __lowerCamelCase ( A__ ) -> bool:
"""simple docstring"""
return seive[n]
def __lowerCamelCase ( A__ ) -> bool:
"""simple docstring"""
return any(digit in '02468' for digit in str(A__ ) )
def __lowerCamelCase ( A__ = 1_000_000 ) -> list[int]:
"""simple docstring"""
UpperCamelCase = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(A__ ) and not contains_an_even_digit(A__ ):
UpperCamelCase = str(A__ )
UpperCamelCase = [int(str_num[j:] + str_num[:j] ) for j in range(len(A__ ) )]
if all(is_prime(A__ ) for i in list_nums ):
result.append(A__ )
return result
def __lowerCamelCase ( ) -> int:
"""simple docstring"""
return len(find_circular_primes() )
if __name__ == "__main__":
print(f'''{len(find_circular_primes()) = }''')
| 28 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""EncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""TFEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""FlaxEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class lowerCamelCase (_snake_case , _snake_case , unittest.TestCase ):
'''simple docstring'''
_snake_case : Tuple = IFInpaintingPipeline
_snake_case : List[str] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
_snake_case : Union[str, Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
_snake_case : Union[str, Any] = PipelineTesterMixin.required_optional_params - {'''latents'''}
def __UpperCAmelCase ( self ) -> Dict:
return self._get_dummy_components()
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase=0 ) -> int:
if str(_UpperCamelCase ).startswith('mps' ):
UpperCAmelCase_ : List[Any] = torch.manual_seed(_UpperCamelCase )
else:
UpperCAmelCase_ : Union[str, Any] = torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
UpperCAmelCase_ : Any = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(_UpperCamelCase ) ).to(_UpperCamelCase )
UpperCAmelCase_ : Any = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(_UpperCamelCase ) ).to(_UpperCamelCase )
UpperCAmelCase_ : List[str] = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def __UpperCAmelCase ( self ) -> Any:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def __UpperCAmelCase ( self ) -> List[Any]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def __UpperCAmelCase ( self ) -> Optional[int]:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def __UpperCAmelCase ( self ) -> Optional[int]:
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def __UpperCAmelCase ( self ) -> Dict:
self._test_save_load_local()
def __UpperCAmelCase ( self ) -> List[str]:
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 29 |
from string import ascii_lowercase, ascii_uppercase
def a__ ( _UpperCamelCase : str ):
if not sentence:
return ""
__lowerCamelCase = dict(zip(_UpperCamelCase ,_UpperCamelCase ) )
return lower_to_upper.get(sentence[0] ,sentence[0] ) + sentence[1:]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 330 | 0 |
import mpmath # for roots of unity
import numpy as np
class lowercase__:
"""simple docstring"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : str=None , SCREAMING_SNAKE_CASE_ : Tuple=None ) -> List[Any]:
# Input as list
lowercase_ = list(poly_a or [0] )[:]
lowercase_ = list(poly_b or [0] )[:]
# Remove leading zero coefficients
while self.polyA[-1] == 0:
self.polyA.pop()
lowercase_ = len(self.polyA )
while self.polyB[-1] == 0:
self.polyB.pop()
lowercase_ = len(self.polyB )
# Add 0 to make lengths equal a power of 2
lowercase_ = int(
2 ** np.ceil(np.loga(len(self.polyA ) + len(self.polyB ) - 1 ) ) )
while len(self.polyA ) < self.c_max_length:
self.polyA.append(0 )
while len(self.polyB ) < self.c_max_length:
self.polyB.append(0 )
# A complex root used for the fourier transform
lowercase_ = complex(mpmath.root(x=1 , n=self.c_max_length , k=1 ) )
# The product
lowercase_ = self.__multiply()
def _lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : str ) -> List[Any]:
lowercase_ = [[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
# Corner case
if len(SCREAMING_SNAKE_CASE_ ) <= 1:
return dft[0]
#
lowercase_ = self.c_max_length // 2
while next_ncol > 0:
lowercase_ = [[] for i in range(SCREAMING_SNAKE_CASE_ )]
lowercase_ = self.root**next_ncol
# First half of next step
lowercase_ = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(SCREAMING_SNAKE_CASE_ ):
new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j] )
current_root *= root
# Second half of next step
lowercase_ = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(SCREAMING_SNAKE_CASE_ ):
new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j] )
current_root *= root
# Update
lowercase_ = new_dft
lowercase_ = next_ncol // 2
return dft[0]
def _lowercase ( self : int ) -> Dict:
lowercase_ = self.__dft('''A''' )
lowercase_ = self.__dft('''B''' )
lowercase_ = [[dft_a[i] * dft_b[i] for i in range(self.c_max_length )]]
del dft_a
del dft_b
# Corner Case
if len(inverce_c[0] ) <= 1:
return inverce_c[0]
# Inverse DFT
lowercase_ = 2
while next_ncol <= self.c_max_length:
lowercase_ = [[] for i in range(SCREAMING_SNAKE_CASE_ )]
lowercase_ = self.root ** (next_ncol // 2)
lowercase_ = 1
# First half of next step
for j in range(self.c_max_length // next_ncol ):
for i in range(next_ncol // 2 ):
# Even positions
new_inverse_c[i].append(
(
inverce_c[i][j]
+ inverce_c[i][j + self.c_max_length // next_ncol]
)
/ 2 )
# Odd positions
new_inverse_c[i + next_ncol // 2].append(
(
inverce_c[i][j]
- inverce_c[i][j + self.c_max_length // next_ncol]
)
/ (2 * current_root) )
current_root *= root
# Update
lowercase_ = new_inverse_c
next_ncol *= 2
# Unpack
lowercase_ = [round(x[0].real , 8 ) + round(x[0].imag , 8 ) * 1J for x in inverce_c]
# Remove leading 0's
while inverce_c[-1] == 0:
inverce_c.pop()
return inverce_c
def __str__( self : str ) -> Optional[int]:
lowercase_ = '''A = ''' + ''' + '''.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A] ) )
lowercase_ = '''B = ''' + ''' + '''.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B] ) )
lowercase_ = '''A*B = ''' + ''' + '''.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.product ) )
return f'''{a}\n{b}\n{c}'''
# Unit tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowerCAmelCase ( lowerCAmelCase__ ):
@slow
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' , '''prajjwal1/bert-tiny''' )
__lowerCamelCase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
__lowerCamelCase = bertabert.config.encoder.vocab_size
__lowerCamelCase = tokenizer.sep_token_id
__lowerCamelCase = tokenizer.cls_token_id
__lowerCamelCase = 128
__lowerCamelCase = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''train[:1%]''' )
__lowerCamelCase = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''validation[:1%]''' )
__lowerCamelCase = train_dataset.select(range(32 ) )
__lowerCamelCase = val_dataset.select(range(16 ) )
__lowerCamelCase = 4
def _map_to_encoder_decoder_inputs(__UpperCAmelCase ):
# Tokenizer will automatically set [BOS] <text> [EOS]
__lowerCamelCase = tokenizer(batch['''article'''] , padding='''max_length''' , truncation=__UpperCAmelCase , max_length=512 )
__lowerCamelCase = tokenizer(batch['''highlights'''] , padding='''max_length''' , truncation=__UpperCAmelCase , max_length=128 )
__lowerCamelCase = inputs.input_ids
__lowerCamelCase = inputs.attention_mask
__lowerCamelCase = outputs.input_ids
__lowerCamelCase = outputs.input_ids.copy()
__lowerCamelCase = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
]
__lowerCamelCase = outputs.attention_mask
assert all(len(__UpperCAmelCase ) == 512 for x in inputs.input_ids )
assert all(len(__UpperCAmelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(__UpperCAmelCase ):
__lowerCamelCase = pred.label_ids
__lowerCamelCase = pred.predictions
# all unnecessary tokens are removed
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = sum([int(pred_str[i] == label_str[i] ) for i in range(len(__UpperCAmelCase ) )] ) / len(__UpperCAmelCase )
return {"accuracy": accuracy}
# map train dataset
__lowerCamelCase = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=__UpperCAmelCase , batch_size=__UpperCAmelCase , remove_columns=['''article''', '''highlights'''] , )
train_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
# same for validation dataset
__lowerCamelCase = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=__UpperCAmelCase , batch_size=__UpperCAmelCase , remove_columns=['''article''', '''highlights'''] , )
val_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
__lowerCamelCase = self.get_auto_remove_tmp_dir()
__lowerCamelCase = SeqaSeqTrainingArguments(
output_dir=__UpperCAmelCase , per_device_train_batch_size=__UpperCAmelCase , per_device_eval_batch_size=__UpperCAmelCase , predict_with_generate=__UpperCAmelCase , evaluation_strategy='''steps''' , do_train=__UpperCAmelCase , do_eval=__UpperCAmelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
__lowerCamelCase = SeqaSeqTrainer(
model=__UpperCAmelCase , args=__UpperCAmelCase , compute_metrics=_compute_metrics , train_dataset=__UpperCAmelCase , eval_dataset=__UpperCAmelCase , tokenizer=__UpperCAmelCase , )
# start training
trainer.train()
| 330 | 0 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
__SCREAMING_SNAKE_CASE : Any = imread(R"""digital_image_processing/image_data/lena_small.jpg""")
__SCREAMING_SNAKE_CASE : Tuple = cvtColor(img, COLOR_BGR2GRAY)
def UpperCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = cn.convert_to_negative(_UpperCAmelCase )
# assert negative_img array for at least one True
assert negative_img.any()
def UpperCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
with Image.open("digital_image_processing/image_data/lena_small.jpg" ) as img:
# Work around assertion for response
assert str(cc.change_contrast(_UpperCAmelCase , 110 ) ).startswith(
"<PIL.Image.Image image mode=RGB size=100x100 at" )
def UpperCamelCase_ ( ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : int = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def UpperCamelCase_ ( ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : str = imread("digital_image_processing/image_data/lena_small.jpg" , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
_UpperCAmelCase : str = canny.canny(_UpperCAmelCase )
# assert canny array for at least one True
assert canny_array.any()
def UpperCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
assert gg.gaussian_filter(_UpperCAmelCase , 5 , sigma=0.9 ).all()
def UpperCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]] )
_UpperCAmelCase : List[str] = conv.img_convolve(_UpperCAmelCase , _UpperCAmelCase ).astype(_UpperCAmelCase )
assert res.any()
def UpperCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
assert med.median_filter(_UpperCAmelCase , 3 ).any()
def UpperCamelCase_ ( ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase : Any = sob.sobel_filter(_UpperCAmelCase )
assert grad.any() and theta.any()
def UpperCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = sp.make_sepia(_UpperCAmelCase , 20 )
assert sepia.all()
def UpperCamelCase_ ( _UpperCAmelCase : str = "digital_image_processing/image_data/lena_small.jpg" ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Tuple = bs.Burkes(imread(_UpperCAmelCase , 1 ) , 120 )
burkes.process()
assert burkes.output_img.any()
def UpperCamelCase_ ( _UpperCAmelCase : str = "digital_image_processing/image_data/lena_small.jpg" , ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Tuple = rs.NearestNeighbour(imread(_UpperCAmelCase , 1 ) , 400 , 200 )
nn.process()
assert nn.output.any()
def UpperCamelCase_ ( ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : str = "digital_image_processing/image_data/lena.jpg"
# Reading the image and converting it to grayscale.
_UpperCAmelCase : Tuple = imread(_UpperCAmelCase , 0 )
# Test for get_neighbors_pixel function() return not None
_UpperCAmelCase : Any = 0
_UpperCAmelCase : str = 0
_UpperCAmelCase : Tuple = image[x_coordinate][y_coordinate]
_UpperCAmelCase : Dict = lbp.get_neighbors_pixel(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
_UpperCAmelCase : Dict = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
_UpperCAmelCase : List[str] = lbp.local_binary_value(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
assert lbp_image.any()
| 31 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_timm_backbone""": ["""TimmBackboneConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""TimmBackbone"""]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : str = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTextConfig',
'AltCLIPVisionConfig',
],
'processing_altclip': ['AltCLIPProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Any = [
'ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'AltCLIPPreTrainedModel',
'AltCLIPModel',
'AltCLIPTextModel',
'AltCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
UpperCAmelCase_ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 32 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase=0.01 , __UpperCAmelCase=1000 ):
'''simple docstring'''
__lowerCamelCase = p_stop
__lowerCamelCase = max_length
def __iter__( self ):
'''simple docstring'''
__lowerCamelCase = 0
__lowerCamelCase = False
while not stop and count < self.max_length:
yield count
count += 1
__lowerCamelCase = random.random() < self.p_stop
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=True ):
'''simple docstring'''
__lowerCamelCase = [
BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
for i in range(2 )
]
__lowerCamelCase = [list(__UpperCAmelCase ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(__UpperCAmelCase ) for shard in batch_sampler_shards] , [len(__UpperCAmelCase ) for e in expected] )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of total batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of total batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
__lowerCamelCase = [BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , even_batches=__UpperCAmelCase ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=2 , __UpperCAmelCase=False ):
'''simple docstring'''
random.seed(__UpperCAmelCase )
__lowerCamelCase = list(__UpperCAmelCase )
__lowerCamelCase = [
IterableDatasetShard(
__UpperCAmelCase , batch_size=__UpperCAmelCase , drop_last=__UpperCAmelCase , num_processes=__UpperCAmelCase , process_index=__UpperCAmelCase , split_batches=__UpperCAmelCase , )
for i in range(__UpperCAmelCase )
]
__lowerCamelCase = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(__UpperCAmelCase )
iterable_dataset_lists.append(list(__UpperCAmelCase ) )
__lowerCamelCase = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
__lowerCamelCase = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
self.assertTrue(len(__UpperCAmelCase ) % shard_batch_size == 0 )
__lowerCamelCase = []
for idx in range(0 , len(__UpperCAmelCase ) , __UpperCAmelCase ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(__UpperCAmelCase ) < len(__UpperCAmelCase ):
reference += reference
self.assertListEqual(__UpperCAmelCase , reference[: len(__UpperCAmelCase )] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 42
__lowerCamelCase = RandomIterableDataset()
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
# Edge case with a very small dataset
__lowerCamelCase = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BatchSampler(range(16 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = SkipBatchSampler(__UpperCAmelCase , 2 )
self.assertListEqual(list(__UpperCAmelCase ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DataLoader(list(range(16 ) ) , batch_size=4 )
__lowerCamelCase = skip_first_batches(__UpperCAmelCase , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def lowerCamelCase ( self ):
'''simple docstring'''
Accelerator()
__lowerCamelCase = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 330 | 0 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__A : Union[str, Any] = logging.get_logger(__name__)
# General docstring
__A : Tuple = '''MobileNetV1Config'''
# Base docstring
__A : Union[str, Any] = '''google/mobilenet_v1_1.0_224'''
__A : Union[str, Any] = [1, 1_024, 7, 7]
# Image classification docstring
__A : Optional[Any] = '''google/mobilenet_v1_1.0_224'''
__A : List[Any] = '''tabby, tabby cat'''
__A : Union[str, Any] = [
'''google/mobilenet_v1_1.0_224''',
'''google/mobilenet_v1_0.75_192''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowercase ( __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : Dict=None ):
lowercase_ : str = {}
if isinstance(__snake_case , __snake_case ):
lowercase_ : Union[str, Any] = model.mobilenet_va
else:
lowercase_ : Optional[Any] = model
lowercase_ : Union[str, Any] = '''MobilenetV1/Conv2d_0/'''
lowercase_ : Union[str, Any] = backbone.conv_stem.convolution.weight
lowercase_ : Optional[Any] = backbone.conv_stem.normalization.bias
lowercase_ : Union[str, Any] = backbone.conv_stem.normalization.weight
lowercase_ : Any = backbone.conv_stem.normalization.running_mean
lowercase_ : int = backbone.conv_stem.normalization.running_var
for i in range(1_3 ):
lowercase_ : Optional[int] = i + 1
lowercase_ : Union[str, Any] = i * 2
lowercase_ : Optional[Any] = backbone.layer[pt_index]
lowercase_ : Union[str, Any] = F'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
lowercase_ : str = pointer.convolution.weight
lowercase_ : int = pointer.normalization.bias
lowercase_ : Any = pointer.normalization.weight
lowercase_ : Dict = pointer.normalization.running_mean
lowercase_ : Union[str, Any] = pointer.normalization.running_var
lowercase_ : Any = backbone.layer[pt_index + 1]
lowercase_ : Union[str, Any] = F'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
lowercase_ : int = pointer.convolution.weight
lowercase_ : str = pointer.normalization.bias
lowercase_ : Tuple = pointer.normalization.weight
lowercase_ : Dict = pointer.normalization.running_mean
lowercase_ : Any = pointer.normalization.running_var
if isinstance(__snake_case , __snake_case ):
lowercase_ : Optional[Any] = '''MobilenetV1/Logits/Conv2d_1c_1x1/'''
lowercase_ : Any = model.classifier.weight
lowercase_ : Optional[int] = model.classifier.bias
return tf_to_pt_map
def lowercase ( __snake_case : Optional[int] , __snake_case : int , __snake_case : Dict ):
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
'''https://www.tensorflow.org/install/ for installation instructions.''' )
raise
# Load weights from TF model
lowercase_ : Tuple = tf.train.list_variables(__snake_case )
lowercase_ : int = {}
for name, shape in init_vars:
logger.info(F'''Loading TF weight {name} with shape {shape}''' )
lowercase_ : Optional[Any] = tf.train.load_variable(__snake_case , __snake_case )
lowercase_ : Optional[int] = array
# Build TF to PyTorch weights loading map
lowercase_ : Any = _build_tf_to_pytorch_map(__snake_case , __snake_case , __snake_case )
for name, pointer in tf_to_pt_map.items():
logger.info(F'''Importing {name}''' )
if name not in tf_weights:
logger.info(F'''{name} not in tf pre-trained weights, skipping''' )
continue
lowercase_ : Union[str, Any] = tf_weights[name]
if "depthwise_weights" in name:
logger.info('''Transposing depthwise''' )
lowercase_ : Any = np.transpose(__snake_case , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('''Transposing''' )
if len(pointer.shape ) == 2: # copying into linear layer
lowercase_ : Optional[int] = array.squeeze().transpose()
else:
lowercase_ : Optional[int] = np.transpose(__snake_case , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched''' )
logger.info(F'''Initialize PyTorch weight {name} {array.shape}''' )
lowercase_ : str = torch.from_numpy(__snake_case )
tf_weights.pop(__snake_case , __snake_case )
tf_weights.pop(name + '''/RMSProp''' , __snake_case )
tf_weights.pop(name + '''/RMSProp_1''' , __snake_case )
tf_weights.pop(name + '''/ExponentialMovingAverage''' , __snake_case )
logger.info(F'''Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}''' )
return model
def lowercase ( __snake_case : torch.Tensor , __snake_case : nn.Convad ):
lowercase_ , lowercase_ : Optional[int] = features.shape[-2:]
lowercase_ , lowercase_ : str = conv_layer.stride
lowercase_ , lowercase_ : Tuple = conv_layer.kernel_size
if in_height % stride_height == 0:
lowercase_ : Dict = max(kernel_height - stride_height , 0 )
else:
lowercase_ : List[Any] = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
lowercase_ : str = max(kernel_width - stride_width , 0 )
else:
lowercase_ : int = max(kernel_width - (in_width % stride_width) , 0 )
lowercase_ : int = pad_along_width // 2
lowercase_ : Union[str, Any] = pad_along_width - pad_left
lowercase_ : Tuple = pad_along_height // 2
lowercase_ : List[str] = pad_along_height - pad_top
lowercase_ : str = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(__snake_case , __snake_case , '''constant''' , 0.0 )
class _UpperCAmelCase ( nn.Module ):
def __init__( self : List[Any] , A : MobileNetVaConfig , A : int , A : int , A : int , A : Optional[int] = 1 , A : Optional[int] = 1 , A : bool = False , A : Optional[bool] = True , A : Optional[bool or str] = True , ) -> None:
super().__init__()
lowercase_ : int = config
if in_channels % groups != 0:
raise ValueError(F'''Input channels ({in_channels}) are not divisible by {groups} groups.''' )
if out_channels % groups != 0:
raise ValueError(F'''Output channels ({out_channels}) are not divisible by {groups} groups.''' )
lowercase_ : Tuple = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
lowercase_ : int = nn.Convad(
in_channels=A , out_channels=A , kernel_size=A , stride=A , padding=A , groups=A , bias=A , padding_mode='''zeros''' , )
if use_normalization:
lowercase_ : Optional[Any] = nn.BatchNormad(
num_features=A , eps=config.layer_norm_eps , momentum=0.9997 , affine=A , track_running_stats=A , )
else:
lowercase_ : Union[str, Any] = None
if use_activation:
if isinstance(A , A ):
lowercase_ : str = ACTaFN[use_activation]
elif isinstance(config.hidden_act , A ):
lowercase_ : Any = ACTaFN[config.hidden_act]
else:
lowercase_ : Tuple = config.hidden_act
else:
lowercase_ : Tuple = None
def A ( self : str , A : torch.Tensor ) -> torch.Tensor:
if self.config.tf_padding:
lowercase_ : List[Any] = apply_tf_padding(A , self.convolution )
lowercase_ : Optional[int] = self.convolution(A )
if self.normalization is not None:
lowercase_ : Union[str, Any] = self.normalization(A )
if self.activation is not None:
lowercase_ : Optional[int] = self.activation(A )
return features
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Optional[int] = MobileNetVaConfig
SCREAMING_SNAKE_CASE_ : int = load_tf_weights_in_mobilenet_va
SCREAMING_SNAKE_CASE_ : Optional[Any] = "mobilenet_v1"
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "pixel_values"
SCREAMING_SNAKE_CASE_ : List[str] = False
def A ( self : Any , A : Union[nn.Linear, nn.Convad] ) -> None:
if isinstance(A , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(A , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__A : Union[str, Any] = R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
__A : List[str] = R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , _A , )
class _UpperCAmelCase ( _A ):
def __init__( self : str , A : MobileNetVaConfig , A : bool = True ) -> int:
super().__init__(A )
lowercase_ : Union[str, Any] = config
lowercase_ : List[str] = 32
lowercase_ : str = max(int(depth * config.depth_multiplier ) , config.min_depth )
lowercase_ : Union[str, Any] = MobileNetVaConvLayer(
A , in_channels=config.num_channels , out_channels=A , kernel_size=3 , stride=2 , )
lowercase_ : Optional[Any] = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
lowercase_ : List[Any] = nn.ModuleList()
for i in range(13 ):
lowercase_ : Dict = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
lowercase_ : str = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
A , in_channels=A , out_channels=A , kernel_size=3 , stride=strides[i] , groups=A , ) )
self.layer.append(
MobileNetVaConvLayer(
A , in_channels=A , out_channels=A , kernel_size=1 , ) )
lowercase_ : int = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def A ( self : Any , A : Optional[Any] ) -> Optional[int]:
raise NotImplementedError
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=A , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def A ( self : List[Any] , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , A : Optional[bool] = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
lowercase_ : str = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('''You have to specify pixel_values''' )
lowercase_ : List[str] = self.conv_stem(A )
lowercase_ : Dict = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
lowercase_ : Optional[int] = layer_module(A )
if output_hidden_states:
lowercase_ : str = all_hidden_states + (hidden_states,)
lowercase_ : Tuple = hidden_states
if self.pooler is not None:
lowercase_ : Dict = torch.flatten(self.pooler(A ) , start_dim=1 )
else:
lowercase_ : Optional[Any] = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=A , pooler_output=A , hidden_states=A , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , _A , )
class _UpperCAmelCase ( _A ):
def __init__( self : List[str] , A : MobileNetVaConfig ) -> None:
super().__init__(A )
lowercase_ : int = config.num_labels
lowercase_ : List[str] = MobileNetVaModel(A )
lowercase_ : Union[str, Any] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
lowercase_ : Tuple = nn.Dropout(config.classifier_dropout_prob , inplace=A )
lowercase_ : int = nn.Linear(A , config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=A , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def A ( self : Optional[Any] , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
lowercase_ : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ : List[Any] = self.mobilenet_va(A , output_hidden_states=A , return_dict=A )
lowercase_ : Union[str, Any] = outputs.pooler_output if return_dict else outputs[1]
lowercase_ : Dict = self.classifier(self.dropout(A ) )
lowercase_ : int = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase_ : List[str] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase_ : Optional[Any] = '''single_label_classification'''
else:
lowercase_ : Tuple = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase_ : str = MSELoss()
if self.num_labels == 1:
lowercase_ : List[str] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowercase_ : List[str] = loss_fct(A , A )
elif self.config.problem_type == "single_label_classification":
lowercase_ : List[Any] = CrossEntropyLoss()
lowercase_ : str = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase_ : str = BCEWithLogitsLoss()
lowercase_ : List[Any] = loss_fct(A , A )
if not return_dict:
lowercase_ : Tuple = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=A , logits=A , hidden_states=outputs.hidden_states , )
| 33 |
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 330 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A ={
'configuration_layoutlmv3': [
'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
'LayoutLMv3Config',
'LayoutLMv3OnnxConfig',
],
'processing_layoutlmv3': ['LayoutLMv3Processor'],
'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =['LayoutLMv3TokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
'LayoutLMv3ForQuestionAnswering',
'LayoutLMv3ForSequenceClassification',
'LayoutLMv3ForTokenClassification',
'LayoutLMv3Model',
'LayoutLMv3PreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFLayoutLMv3ForQuestionAnswering',
'TFLayoutLMv3ForSequenceClassification',
'TFLayoutLMv3ForTokenClassification',
'TFLayoutLMv3Model',
'TFLayoutLMv3PreTrainedModel',
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =['LayoutLMv3FeatureExtractor']
A =['LayoutLMv3ImageProcessor']
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
A =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 34 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
a_ = 16
a_ = 32
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : int = 16 ):
__lowerCamelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' )
def tokenize_function(_UpperCamelCase : Optional[Any] ):
# max_length=None => use the model max length (it's actually the default)
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__lowerCamelCase = datasets.map(
_UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowerCamelCase = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__lowerCamelCase = 16
elif accelerator.mixed_precision != "no":
__lowerCamelCase = 8
else:
__lowerCamelCase = None
return tokenizer.pad(
_UpperCamelCase ,padding='''longest''' ,max_length=_UpperCamelCase ,pad_to_multiple_of=_UpperCamelCase ,return_tensors='''pt''' ,)
# Instantiate dataloaders.
__lowerCamelCase = DataLoader(
tokenized_datasets['''train'''] ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=_UpperCamelCase )
__lowerCamelCase = DataLoader(
tokenized_datasets['''validation'''] ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=_UpperCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
a_ = mocked_dataloaders # noqa: F811
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ):
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,_UpperCamelCase ) == "1":
__lowerCamelCase = 2
# Initialize accelerator
__lowerCamelCase = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCamelCase = config['''lr''']
__lowerCamelCase = int(config['''num_epochs'''] )
__lowerCamelCase = int(config['''seed'''] )
__lowerCamelCase = int(config['''batch_size'''] )
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=_UpperCamelCase )
def inner_training_loop(_UpperCamelCase : Union[str, Any] ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(_UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,return_dict=_UpperCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowerCamelCase = model.to(accelerator.device )
# Instantiate optimizer
__lowerCamelCase = AdamW(params=model.parameters() ,lr=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = get_dataloaders(_UpperCamelCase ,_UpperCamelCase )
# Instantiate scheduler
__lowerCamelCase = get_linear_schedule_with_warmup(
optimizer=_UpperCamelCase ,num_warmup_steps=1_00 ,num_training_steps=(len(_UpperCamelCase ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(
_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
# Now we train the model
for epoch in range(_UpperCamelCase ):
model.train()
for step, batch in enumerate(_UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.loss
accelerator.backward(_UpperCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_UpperCamelCase ,references=_UpperCamelCase ,)
__lowerCamelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" ,_UpperCamelCase )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def a__ ( ):
__lowerCamelCase = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' ,type=_UpperCamelCase ,default=_UpperCamelCase ,choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' ,)
parser.add_argument('''--cpu''' ,action='''store_true''' ,help='''If passed, will train on the CPU.''' )
__lowerCamelCase = parser.parse_args()
__lowerCamelCase = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_UpperCamelCase ,_UpperCamelCase )
if __name__ == "__main__":
main()
| 330 | 0 |
'''simple docstring'''
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = "align_text_model"
def __init__( self : Dict , snake_case_ : Any=30_522 , snake_case_ : Tuple=768 , snake_case_ : Any=12 , snake_case_ : Union[str, Any]=12 , snake_case_ : List[Any]=3_072 , snake_case_ : Dict="gelu" , snake_case_ : Optional[int]=0.1 , snake_case_ : str=0.1 , snake_case_ : List[str]=512 , snake_case_ : str=2 , snake_case_ : Optional[Any]=0.02 , snake_case_ : Optional[Any]=1E-1_2 , snake_case_ : List[Any]=0 , snake_case_ : Union[str, Any]="absolute" , snake_case_ : List[Any]=True , **snake_case_ : List[Any] , ):
super().__init__(**snake_case_ )
snake_case__ : str = vocab_size
snake_case__ : str = hidden_size
snake_case__ : Optional[Any] = num_hidden_layers
snake_case__ : Union[str, Any] = num_attention_heads
snake_case__ : Tuple = hidden_act
snake_case__ : int = intermediate_size
snake_case__ : Tuple = hidden_dropout_prob
snake_case__ : Dict = attention_probs_dropout_prob
snake_case__ : Tuple = max_position_embeddings
snake_case__ : Tuple = type_vocab_size
snake_case__ : Dict = initializer_range
snake_case__ : str = layer_norm_eps
snake_case__ : str = position_embedding_type
snake_case__ : Dict = use_cache
snake_case__ : Tuple = pad_token_id
@classmethod
def lowerCamelCase ( cls : Union[str, Any] , snake_case_ : Union[str, os.PathLike] , **snake_case_ : Optional[int] ):
cls._set_token_in_kwargs(snake_case_ )
snake_case__ , snake_case__ : List[str] = cls.get_config_dict(snake_case_ , **snake_case_ )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("""model_type""" ) == "align":
snake_case__ : Optional[Any] = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(snake_case_ , **snake_case_ )
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = "align_vision_model"
def __init__( self : Union[str, Any] , snake_case_ : int = 3 , snake_case_ : int = 600 , snake_case_ : float = 2.0 , snake_case_ : float = 3.1 , snake_case_ : int = 8 , snake_case_ : List[int] = [3, 3, 5, 3, 5, 5, 3] , snake_case_ : List[int] = [32, 16, 24, 40, 80, 112, 192] , snake_case_ : List[int] = [16, 24, 40, 80, 112, 192, 320] , snake_case_ : List[int] = [] , snake_case_ : List[int] = [1, 2, 2, 2, 1, 2, 1] , snake_case_ : List[int] = [1, 2, 2, 3, 3, 4, 1] , snake_case_ : List[int] = [1, 6, 6, 6, 6, 6, 6] , snake_case_ : float = 0.25 , snake_case_ : str = "swish" , snake_case_ : int = 2_560 , snake_case_ : str = "mean" , snake_case_ : float = 0.02 , snake_case_ : float = 0.001 , snake_case_ : float = 0.99 , snake_case_ : float = 0.2 , **snake_case_ : Optional[int] , ):
super().__init__(**snake_case_ )
snake_case__ : int = num_channels
snake_case__ : Optional[int] = image_size
snake_case__ : int = width_coefficient
snake_case__ : str = depth_coefficient
snake_case__ : Optional[int] = depth_divisor
snake_case__ : Any = kernel_sizes
snake_case__ : Optional[int] = in_channels
snake_case__ : Optional[Any] = out_channels
snake_case__ : Tuple = depthwise_padding
snake_case__ : Tuple = strides
snake_case__ : str = num_block_repeats
snake_case__ : Any = expand_ratios
snake_case__ : Any = squeeze_expansion_ratio
snake_case__ : Tuple = hidden_act
snake_case__ : int = hidden_dim
snake_case__ : int = pooling_type
snake_case__ : List[Any] = initializer_range
snake_case__ : Optional[Any] = batch_norm_eps
snake_case__ : Optional[int] = batch_norm_momentum
snake_case__ : int = drop_connect_rate
snake_case__ : int = sum(snake_case_ ) * 4
@classmethod
def lowerCamelCase ( cls : str , snake_case_ : Union[str, os.PathLike] , **snake_case_ : List[Any] ):
cls._set_token_in_kwargs(snake_case_ )
snake_case__ , snake_case__ : Optional[Any] = cls.get_config_dict(snake_case_ , **snake_case_ )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("""model_type""" ) == "align":
snake_case__ : Optional[Any] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(snake_case_ , **snake_case_ )
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = "align"
lowercase = True
def __init__( self : int , snake_case_ : List[Any]=None , snake_case_ : List[str]=None , snake_case_ : Tuple=640 , snake_case_ : List[Any]=1.0 , snake_case_ : List[str]=0.02 , **snake_case_ : Any , ):
super().__init__(**snake_case_ )
if text_config is None:
snake_case__ : Optional[int] = {}
logger.info("""text_config is None. Initializing the AlignTextConfig with default values.""" )
if vision_config is None:
snake_case__ : Optional[Any] = {}
logger.info("""vision_config is None. Initializing the AlignVisionConfig with default values.""" )
snake_case__ : List[str] = AlignTextConfig(**snake_case_ )
snake_case__ : Optional[Any] = AlignVisionConfig(**snake_case_ )
snake_case__ : Dict = projection_dim
snake_case__ : List[str] = temperature_init_value
snake_case__ : Tuple = initializer_range
@classmethod
def lowerCamelCase ( cls : Union[str, Any] , snake_case_ : AlignTextConfig , snake_case_ : AlignVisionConfig , **snake_case_ : Optional[int] ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **snake_case_ )
def lowerCamelCase ( self : List[Any] ):
snake_case__ : int = copy.deepcopy(self.__dict__ )
snake_case__ : Union[str, Any] = self.text_config.to_dict()
snake_case__ : List[str] = self.vision_config.to_dict()
snake_case__ : List[str] = self.__class__.model_type
return output
| 35 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
a_ = None
try:
import msvcrt
except ImportError:
a_ = None
try:
import fcntl
except ImportError:
a_ = None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
a_ = OSError
# Data
# ------------------------------------------------
a_ = [
"""Timeout""",
"""BaseFileLock""",
"""WindowsFileLock""",
"""UnixFileLock""",
"""SoftFileLock""",
"""FileLock""",
]
a_ = """3.0.12"""
a_ = None
def a__ ( ):
global _logger
__lowerCamelCase = _logger or logging.getLogger(__name__ )
return _logger
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = lock_file
return None
def __str__( self ):
'''simple docstring'''
__lowerCamelCase = F"""The file lock '{self.lock_file}' could not be acquired."""
return temp
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = lock
return None
def __enter__( self ):
'''simple docstring'''
return self.lock
def __exit__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.lock.release()
return None
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = max_filename_length if max_filename_length is not None else 255
# Hash the filename if it's too long
__lowerCamelCase = self.hash_filename_if_too_long(__UpperCAmelCase , __UpperCAmelCase )
# The path to the lock file.
__lowerCamelCase = lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
__lowerCamelCase = None
# The default timeout value.
__lowerCamelCase = timeout
# We use this lock primarily for the lock counter.
__lowerCamelCase = threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
__lowerCamelCase = 0
return None
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._lock_file
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._timeout
@timeout.setter
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = float(__UpperCAmelCase )
return None
def lowerCamelCase ( self ):
'''simple docstring'''
raise NotImplementedError()
def lowerCamelCase ( self ):
'''simple docstring'''
raise NotImplementedError()
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._lock_file_fd is not None
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=0.05 ):
'''simple docstring'''
# Use the default timeout, if no timeout is provided.
if timeout is None:
__lowerCamelCase = self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
__lowerCamelCase = id(self )
__lowerCamelCase = self._lock_file
__lowerCamelCase = time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(F"""Attempting to acquire lock {lock_id} on {lock_filename}""" )
self._acquire()
if self.is_locked:
logger().debug(F"""Lock {lock_id} acquired on {lock_filename}""" )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(F"""Timeout on acquiring lock {lock_id} on {lock_filename}""" )
raise Timeout(self._lock_file )
else:
logger().debug(
F"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...""" )
time.sleep(__UpperCAmelCase )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
__lowerCamelCase = max(0 , self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def lowerCamelCase ( self , __UpperCAmelCase=False ):
'''simple docstring'''
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
__lowerCamelCase = id(self )
__lowerCamelCase = self._lock_file
logger().debug(F"""Attempting to release lock {lock_id} on {lock_filename}""" )
self._release()
__lowerCamelCase = 0
logger().debug(F"""Lock {lock_id} released on {lock_filename}""" )
return None
def __enter__( self ):
'''simple docstring'''
self.acquire()
return self
def __exit__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.release()
return None
def __del__( self ):
'''simple docstring'''
self.release(force=__UpperCAmelCase )
return None
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = os.path.basename(__UpperCAmelCase )
if len(__UpperCAmelCase ) > max_length and max_length > 0:
__lowerCamelCase = os.path.dirname(__UpperCAmelCase )
__lowerCamelCase = str(hash(__UpperCAmelCase ) )
__lowerCamelCase = filename[: max_length - len(__UpperCAmelCase ) - 8] + '''...''' + hashed_filename + '''.lock'''
return os.path.join(__UpperCAmelCase , __UpperCAmelCase )
else:
return path
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
from .file_utils import relative_to_absolute_path
super().__init__(__UpperCAmelCase , timeout=__UpperCAmelCase , max_filename_length=__UpperCAmelCase )
__lowerCamelCase = '''\\\\?\\''' + relative_to_absolute_path(self.lock_file )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
except OSError:
pass
else:
try:
msvcrt.locking(__UpperCAmelCase , msvcrt.LK_NBLCK , 1 )
except OSError:
os.close(__UpperCAmelCase )
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self._lock_file_fd
__lowerCamelCase = None
msvcrt.locking(__UpperCAmelCase , msvcrt.LK_UNLCK , 1 )
os.close(__UpperCAmelCase )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = os.statvfs(os.path.dirname(__UpperCAmelCase ) ).f_namemax
super().__init__(__UpperCAmelCase , timeout=__UpperCAmelCase , max_filename_length=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_RDWR | os.O_CREAT | os.O_TRUNC
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
try:
fcntl.flock(__UpperCAmelCase , fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(__UpperCAmelCase )
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
# Do not remove the lockfile:
#
# https://github.com/benediktschmitt/py-filelock/issues/31
# https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
__lowerCamelCase = self._lock_file_fd
__lowerCamelCase = None
fcntl.flock(__UpperCAmelCase , fcntl.LOCK_UN )
os.close(__UpperCAmelCase )
return None
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
except OSError:
pass
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
os.close(self._lock_file_fd )
__lowerCamelCase = None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
a_ = None
if msvcrt:
a_ = WindowsFileLock
elif fcntl:
a_ = UnixFileLock
else:
a_ = SoftFileLock
if warnings is not None:
warnings.warn("""only soft file lock is available""")
| 330 | 0 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class UpperCAmelCase_ :
def __init__( self, __a, __a=13, __a=7, __a=True, __a=True, __a=True, __a=True, __a=99, __a=64, __a=5, __a=4, __a=37, __a="gelu", __a=0.1, __a=0.1, __a=512, __a=16, __a=2, __a=0.02, __a=3, __a=4, __a=None, ):
'''simple docstring'''
_lowerCAmelCase : Tuple = parent
_lowerCAmelCase : Optional[int] = batch_size
_lowerCAmelCase : Optional[int] = seq_length
_lowerCAmelCase : List[Any] = is_training
_lowerCAmelCase : Any = use_input_mask
_lowerCAmelCase : List[Any] = use_token_type_ids
_lowerCAmelCase : str = use_labels
_lowerCAmelCase : Optional[int] = vocab_size
_lowerCAmelCase : Optional[Any] = hidden_size
_lowerCAmelCase : int = num_hidden_layers
_lowerCAmelCase : str = num_attention_heads
_lowerCAmelCase : Any = intermediate_size
_lowerCAmelCase : Optional[Any] = hidden_act
_lowerCAmelCase : Optional[int] = hidden_dropout_prob
_lowerCAmelCase : List[Any] = attention_probs_dropout_prob
_lowerCAmelCase : List[Any] = max_position_embeddings
_lowerCAmelCase : Union[str, Any] = type_vocab_size
_lowerCAmelCase : str = type_sequence_label_size
_lowerCAmelCase : Optional[Any] = initializer_range
_lowerCAmelCase : Dict = num_labels
_lowerCAmelCase : Any = num_choices
_lowerCAmelCase : Optional[int] = scope
_lowerCAmelCase : List[str] = vocab_size - 1
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
_lowerCAmelCase : Union[str, Any] = None
if self.use_input_mask:
_lowerCAmelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length])
_lowerCAmelCase : Any = None
if self.use_labels:
_lowerCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
_lowerCAmelCase : Optional[Any] = self.get_config()
return config, input_ids, input_mask, token_labels
def snake_case__ ( self):
'''simple docstring'''
return GPTNeoXConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=__a, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, )
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] = self.prepare_config_and_inputs()
_lowerCAmelCase : str = True
return config, input_ids, input_mask, token_labels
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[str] = GPTNeoXModel(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Optional[int] = model(__a, attention_mask=__a)
_lowerCAmelCase : str = model(__a)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = True
_lowerCAmelCase : List[Any] = GPTNeoXModel(__a)
model.to(__a)
model.eval()
_lowerCAmelCase : List[Any] = model(__a, attention_mask=__a)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def snake_case__ ( self, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : int = GPTNeoXForCausalLM(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Tuple = model(__a, attention_mask=__a, labels=__a)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def snake_case__ ( self, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.num_labels
_lowerCAmelCase : Union[str, Any] = GPTNeoXForQuestionAnswering(__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Tuple = model(__a, attention_mask=__a)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def snake_case__ ( self, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : str = self.num_labels
_lowerCAmelCase : str = GPTNeoXForSequenceClassification(__a)
model.to(__a)
model.eval()
_lowerCAmelCase : List[str] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowerCAmelCase : Dict = model(__a, attention_mask=__a, labels=__a)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def snake_case__ ( self, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : str = self.num_labels
_lowerCAmelCase : Dict = GPTNeoXForTokenClassification(__a)
model.to(__a)
model.eval()
_lowerCAmelCase : str = model(__a, attention_mask=__a, labels=__a)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = True
_lowerCAmelCase : Dict = GPTNeoXForCausalLM(config=__a)
model.to(__a)
model.eval()
# first forward pass
_lowerCAmelCase : List[Any] = model(__a, attention_mask=__a, use_cache=__a)
_lowerCAmelCase : Optional[int] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_lowerCAmelCase : Optional[Any] = ids_tensor((self.batch_size, 3), config.vocab_size)
_lowerCAmelCase : Dict = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
_lowerCAmelCase : int = torch.cat([input_ids, next_tokens], dim=-1)
_lowerCAmelCase : str = torch.cat([input_mask, next_mask], dim=-1)
_lowerCAmelCase : List[str] = model(__a, attention_mask=__a, output_hidden_states=__a)
_lowerCAmelCase : List[str] = output_from_no_past["hidden_states"][0]
_lowerCAmelCase : List[Any] = model(
__a, attention_mask=__a, past_key_values=__a, output_hidden_states=__a, )["hidden_states"][0]
# select random slice
_lowerCAmelCase : int = ids_tensor((1,), output_from_past.shape[-1]).item()
_lowerCAmelCase : str = output_from_no_past[:, -3:, random_slice_idx].detach()
_lowerCAmelCase : Union[str, Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__a, __a, atol=1E-3))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] = config_and_inputs
_lowerCAmelCase : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( a , a , a , unittest.TestCase):
lowerCamelCase__ = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase__ = (GPTNeoXForCausalLM,) if is_torch_available() else ()
lowerCamelCase__ = (
{
'feature-extraction': GPTNeoXModel,
'question-answering': GPTNeoXForQuestionAnswering,
'text-classification': GPTNeoXForSequenceClassification,
'text-generation': GPTNeoXForCausalLM,
'token-classification': GPTNeoXForTokenClassification,
'zero-shot': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = GPTNeoXModelTester(self)
_lowerCAmelCase : Union[str, Any] = ConfigTester(self, config_class=__a, hidden_size=64, num_attention_heads=8)
def snake_case__ ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(__a, __a, __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(__a, __a, __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
_lowerCAmelCase : List[str] = None
self.model_tester.create_and_check_model_as_decoder(__a, __a, __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(__a, __a, __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a)
@unittest.skip(reason="Feed forward chunking is not implemented")
def snake_case__ ( self):
'''simple docstring'''
pass
@parameterized.expand([("linear",), ("dynamic",)])
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase : Optional[Any] = ids_tensor([1, 10], config.vocab_size)
_lowerCAmelCase : List[str] = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
set_seed(42) # Fixed seed at init time so the two models get the same random weights
_lowerCAmelCase : List[Any] = GPTNeoXModel(__a)
original_model.to(__a)
original_model.eval()
_lowerCAmelCase : Union[str, Any] = original_model(__a).last_hidden_state
_lowerCAmelCase : Union[str, Any] = original_model(__a).last_hidden_state
set_seed(42) # Fixed seed at init time so the two models get the same random weights
_lowerCAmelCase : int = {"type": scaling_type, "factor": 10.0}
_lowerCAmelCase : Optional[int] = GPTNeoXModel(__a)
scaled_model.to(__a)
scaled_model.eval()
_lowerCAmelCase : Optional[Any] = scaled_model(__a).last_hidden_state
_lowerCAmelCase : Optional[Any] = scaled_model(__a).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(__a, __a, atol=1E-5))
else:
self.assertFalse(torch.allclose(__a, __a, atol=1E-5))
# The output should be different for long inputs
self.assertFalse(torch.allclose(__a, __a, atol=1E-5))
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped")
for checkpointing in [True, False]:
_lowerCAmelCase : List[str] = GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped")
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(__a)
_lowerCAmelCase : str = tokenizer("My favorite food is", return_tensors="pt").to(__a)
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
_lowerCAmelCase : str = "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"
_lowerCAmelCase : Any = model.generate(**__a, do_sample=__a, max_new_tokens=20)
_lowerCAmelCase : str = tokenizer.batch_decode(__a)[0]
self.assertEqual(__a, __a)
| 36 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=10 , __UpperCAmelCase=3 , __UpperCAmelCase=2 , __UpperCAmelCase=2 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=10 , __UpperCAmelCase=0.02 , __UpperCAmelCase="divided_space_time" , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = image_size
__lowerCamelCase = num_channels
__lowerCamelCase = patch_size
__lowerCamelCase = num_frames
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = attention_type
__lowerCamelCase = initializer_range
__lowerCamelCase = scope
__lowerCamelCase = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
__lowerCamelCase = (image_size // patch_size) ** 2
__lowerCamelCase = (num_frames) * self.num_patches_per_frame + 1
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
__lowerCamelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , attention_type=self.attention_type , )
__lowerCamelCase = self.num_labels
return config
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TimesformerModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase )
# verify the logits shape
__lowerCamelCase = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = config_and_inputs
__lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
lowerCAmelCase__ = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerModelTester(self )
__lowerCamelCase = ConfigTester(
self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(__UpperCAmelCase )
if return_labels:
if model_class in get_values(__UpperCAmelCase ):
__lowerCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__UpperCAmelCase )
return inputs_dict
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(__UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCAmelCase , nn.Linear ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(__UpperCAmelCase )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = TimesformerModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
if not self.has_attentions:
pass
else:
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = True
for model_class in self.all_model_classes:
__lowerCamelCase = self.model_tester.seq_length
__lowerCamelCase = self.model_tester.num_frames
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
__lowerCamelCase = len(__UpperCAmelCase )
# Check attention is always last and order is fine
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
self.assertEqual(out_len + 1 , len(__UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def lowerCamelCase ( self ):
'''simple docstring'''
def check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.hidden_states
__lowerCamelCase = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
__lowerCamelCase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def a__ ( ):
__lowerCamelCase = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' ,filename='''eating_spaghetti.npy''' ,repo_type='''dataset''' )
__lowerCamelCase = np.load(_UpperCamelCase )
return list(_UpperCamelCase )
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
__UpperCAmelCase )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_video()
__lowerCamelCase = image_processor(video[:8] , return_tensors='''pt''' ).to(__UpperCAmelCase )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(**__UpperCAmelCase )
# verify the logits
__lowerCamelCase = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor([-0.3_016, -0.7_713, -0.4_205] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 330 | 0 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_lowerCAmelCase = logging.get_logger(__name__)
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ,__UpperCAmelCase = None ,__UpperCAmelCase = None ,__UpperCAmelCase=None ,__UpperCAmelCase=None ) -> str:
if not conversation_id:
lowerCAmelCase__ : List[str] = uuid.uuida()
if past_user_inputs is None:
lowerCAmelCase__ : List[Any] = []
if generated_responses is None:
lowerCAmelCase__ : str = []
lowerCAmelCase__ : uuid.UUID = conversation_id
lowerCAmelCase__ : List[str] = past_user_inputs
lowerCAmelCase__ : List[str] = generated_responses
lowerCAmelCase__ : Optional[str] = text
def __eq__( self ,__UpperCAmelCase ) -> Dict:
if not isinstance(__UpperCAmelCase ,__UpperCAmelCase ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase = False ) -> Optional[Any]:
if self.new_user_input:
if overwrite:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
F"""with: \"{text}\".""" )
lowerCAmelCase__ : Optional[int] = text
else:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input""" )
else:
lowerCAmelCase__ : Optional[Any] = text
def UpperCAmelCase_ ( self ) -> List[Any]:
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
lowerCAmelCase__ : Union[str, Any] = None
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> Tuple:
self.generated_responses.append(__UpperCAmelCase )
def UpperCAmelCase_ ( self ) -> List[str]:
for user_input, generated_response in zip(self.past_user_inputs ,self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ) -> Tuple:
lowerCAmelCase__ : Tuple = F"""Conversation id: {self.uuid} \n"""
for is_user, text in self.iter_texts():
lowerCAmelCase__ : Any = """user""" if is_user else """bot"""
output += F"""{name} >> {text} \n"""
return output
@add_end_docstrings(
SCREAMING_SNAKE_CASE_ , R'''
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
''' , )
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def __init__( self ,*__UpperCAmelCase ,**__UpperCAmelCase ) -> Tuple:
super().__init__(*__UpperCAmelCase ,**__UpperCAmelCase )
if self.tokenizer.pad_token_id is None:
lowerCAmelCase__ : Tuple = self.tokenizer.eos_token
def UpperCAmelCase_ ( self ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,**__UpperCAmelCase ) -> Optional[int]:
lowerCAmelCase__ : List[Any] = {}
lowerCAmelCase__ : Optional[int] = {}
lowerCAmelCase__ : List[str] = {}
if min_length_for_response is not None:
lowerCAmelCase__ : Any = min_length_for_response
if minimum_tokens is not None:
lowerCAmelCase__ : Optional[int] = minimum_tokens
if "max_length" in generate_kwargs:
lowerCAmelCase__ : Optional[Any] = generate_kwargs["""max_length"""]
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
lowerCAmelCase__ : int = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(__UpperCAmelCase )
return preprocess_params, forward_params, postprocess_params
def __call__( self ,__UpperCAmelCase ,__UpperCAmelCase=0 ,**__UpperCAmelCase ) -> List[str]:
lowerCAmelCase__ : Optional[int] = super().__call__(__UpperCAmelCase ,num_workers=__UpperCAmelCase ,**__UpperCAmelCase )
if isinstance(__UpperCAmelCase ,__UpperCAmelCase ) and len(__UpperCAmelCase ) == 1:
return outputs[0]
return outputs
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase=32 ) -> Dict[str, Any]:
if not isinstance(__UpperCAmelCase ,__UpperCAmelCase ):
raise ValueError("""ConversationalPipeline, expects Conversation as inputs""" )
if conversation.new_user_input is None:
raise ValueError(
F"""Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. """
"""Add user inputs with the conversation's `add_user_input` method""" )
if hasattr(self.tokenizer ,"""_build_conversation_input_ids""" ):
lowerCAmelCase__ : str = self.tokenizer._build_conversation_input_ids(__UpperCAmelCase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
lowerCAmelCase__ : List[Any] = self._legacy_parse_and_tokenize(__UpperCAmelCase )
if self.framework == "pt":
lowerCAmelCase__ : List[Any] = torch.LongTensor([input_ids] )
elif self.framework == "tf":
lowerCAmelCase__ : Dict = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase=10 ,**__UpperCAmelCase ) -> Dict:
lowerCAmelCase__ : Optional[Any] = generate_kwargs.get("""max_length""" ,self.model.config.max_length )
lowerCAmelCase__ : Optional[Any] = model_inputs["""input_ids"""].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})""" )
lowerCAmelCase__ : str = max_length - minimum_tokens
lowerCAmelCase__ : Union[str, Any] = model_inputs["""input_ids"""][:, -trim:]
if "attention_mask" in model_inputs:
lowerCAmelCase__ : Tuple = model_inputs["""attention_mask"""][:, -trim:]
lowerCAmelCase__ : str = model_inputs.pop("""conversation""" )
lowerCAmelCase__ : Union[str, Any] = max_length
lowerCAmelCase__ : Any = self.model.generate(**__UpperCAmelCase ,**__UpperCAmelCase )
if self.model.config.is_encoder_decoder:
lowerCAmelCase__ : int = 1
else:
lowerCAmelCase__ : int = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase=True ) -> List[str]:
lowerCAmelCase__ : Optional[int] = model_outputs["""output_ids"""]
lowerCAmelCase__ : Tuple = self.tokenizer.decode(
output_ids[0] ,skip_special_tokens=__UpperCAmelCase ,clean_up_tokenization_spaces=__UpperCAmelCase ,)
lowerCAmelCase__ : Union[str, Any] = model_outputs["""conversation"""]
conversation.mark_processed()
conversation.append_response(__UpperCAmelCase )
return conversation
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> Dict:
lowerCAmelCase__ : Dict = self.tokenizer.eos_token_id
lowerCAmelCase__ : int = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase ,add_special_tokens=__UpperCAmelCase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase ,add_special_tokens=__UpperCAmelCase ) )
if len(__UpperCAmelCase ) > self.tokenizer.model_max_length:
lowerCAmelCase__ : Optional[Any] = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 37 |
def a__ ( _UpperCamelCase : int ):
if not isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = F"""Input value of [number={number}] must be an integer"""
raise TypeError(_UpperCamelCase )
if number < 0:
return False
__lowerCamelCase = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 330 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase_ : Optional[Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class _SCREAMING_SNAKE_CASE :
snake_case__ : Optional[str] = field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""} )
snake_case__ : Optional[str] = field(
default=_a , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
snake_case__ : Optional[str] = field(
default=_a , metadata={"""help""": """The column name of the images in the files."""} )
snake_case__ : Optional[str] = field(default=_a , metadata={"""help""": """A folder containing the training data."""} )
snake_case__ : Optional[str] = field(default=_a , metadata={"""help""": """A folder containing the validation data."""} )
snake_case__ : Optional[float] = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
snake_case__ : Optional[int] = field(
default=_a , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
snake_case__ : Optional[int] = field(
default=_a , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def _A ( self : Dict ):
UpperCamelCase :Dict = {}
if self.train_dir is not None:
UpperCamelCase :Any = self.train_dir
if self.validation_dir is not None:
UpperCamelCase :List[Any] = self.validation_dir
UpperCamelCase :Optional[Any] = data_files if data_files else None
@dataclass
class _SCREAMING_SNAKE_CASE :
snake_case__ : str = field(
default=_a , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
snake_case__ : Optional[str] = field(
default=_a , metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
snake_case__ : Optional[str] = field(
default=_a , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
snake_case__ : Optional[str] = field(
default=_a , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
snake_case__ : str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
snake_case__ : str = field(default=_a , metadata={"""help""": """Name or path of preprocessor config."""} )
snake_case__ : bool = field(
default=_a , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
snake_case__ : float = field(
default=0.75 , metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""} )
snake_case__ : bool = field(
default=_a , metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )
@dataclass
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : float = field(
default=1e-3 , metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : List[str] ) -> Dict:
"""simple docstring"""
UpperCamelCase :Tuple = torch.stack([example["""pixel_values"""] for example in examples] )
return {"pixel_values": pixel_values}
def SCREAMING_SNAKE_CASE_ ( ) -> Tuple:
"""simple docstring"""
UpperCamelCase :Any = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase , UpperCamelCase , UpperCamelCase :Tuple = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase , UpperCamelCase , UpperCamelCase :str = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mae""" , __magic_name__ , __magic_name__ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
UpperCamelCase :List[str] = training_args.get_process_log_level()
logger.setLevel(__magic_name__ )
transformers.utils.logging.set_verbosity(__magic_name__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
UpperCamelCase :List[str] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCamelCase :Tuple = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset.
UpperCamelCase :Optional[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
UpperCamelCase :Optional[Any] = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __magic_name__ ) and data_args.train_val_split > 0.0:
UpperCamelCase :Any = ds["""train"""].train_test_split(data_args.train_val_split )
UpperCamelCase :Optional[Any] = split["""train"""]
UpperCamelCase :int = split["""test"""]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase :Tuple = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
UpperCamelCase :str = ViTMAEConfig.from_pretrained(model_args.config_name , **__magic_name__ )
elif model_args.model_name_or_path:
UpperCamelCase :List[Any] = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **__magic_name__ )
else:
UpperCamelCase :int = ViTMAEConfig()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(f"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(f"""New config: {config}""" )
# adapt config
config.update(
{
"""mask_ratio""": model_args.mask_ratio,
"""norm_pix_loss""": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
UpperCamelCase :Any = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **__magic_name__ )
elif model_args.model_name_or_path:
UpperCamelCase :List[str] = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **__magic_name__ )
else:
UpperCamelCase :List[str] = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
UpperCamelCase :List[Any] = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__magic_name__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
UpperCamelCase :str = ViTMAEForPreTraining(__magic_name__ )
if training_args.do_train:
UpperCamelCase :int = ds["""train"""].column_names
else:
UpperCamelCase :List[str] = ds["""validation"""].column_names
if data_args.image_column_name is not None:
UpperCamelCase :List[Any] = data_args.image_column_name
elif "image" in column_names:
UpperCamelCase :Any = """image"""
elif "img" in column_names:
UpperCamelCase :Union[str, Any] = """img"""
else:
UpperCamelCase :Optional[int] = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
UpperCamelCase :Union[str, Any] = image_processor.size["""shortest_edge"""]
else:
UpperCamelCase :List[str] = (image_processor.size["""height"""], image_processor.size["""width"""])
UpperCamelCase :Optional[int] = Compose(
[
Lambda(lambda __magic_name__ : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(__magic_name__ , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(__magic_name__ : Union[str, Any] ):
UpperCamelCase :int = [transforms(__magic_name__ ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
UpperCamelCase :List[str] = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(__magic_name__ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
UpperCamelCase :Dict = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(__magic_name__ )
# Compute absolute learning rate
UpperCamelCase :Optional[Any] = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
UpperCamelCase :Optional[int] = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
UpperCamelCase :str = Trainer(
model=__magic_name__ , args=__magic_name__ , train_dataset=ds["""train"""] if training_args.do_train else None , eval_dataset=ds["""validation"""] if training_args.do_eval else None , tokenizer=__magic_name__ , data_collator=__magic_name__ , )
# Training
if training_args.do_train:
UpperCamelCase :Optional[int] = None
if training_args.resume_from_checkpoint is not None:
UpperCamelCase :int = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCamelCase :Any = last_checkpoint
UpperCamelCase :int = trainer.train(resume_from_checkpoint=__magic_name__ )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
UpperCamelCase :List[str] = trainer.evaluate()
trainer.log_metrics("""eval""" , __magic_name__ )
trainer.save_metrics("""eval""" , __magic_name__ )
# Write model card and (optionally) push to hub
UpperCamelCase :Any = {
"""tasks""": """masked-auto-encoding""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-auto-encoding"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__magic_name__ )
else:
trainer.create_model_card(**__magic_name__ )
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int ) -> Union[str, Any]:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 38 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
return F"""gaussian_noise_s={seed}_shape={"_".join([str(__UpperCAmelCase ) for s in shape] )}.npy"""
def lowerCamelCase ( self ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowerCamelCase ( self , __UpperCAmelCase=0 , __UpperCAmelCase=(4, 4, 64, 64) , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = jnp.bfloataa if fpaa else jnp.floataa
__lowerCamelCase = jnp.array(load_hf_numpy(self.get_file_format(__UpperCAmelCase , __UpperCAmelCase ) ) , dtype=__UpperCAmelCase )
return image
def lowerCamelCase ( self , __UpperCAmelCase=False , __UpperCAmelCase="CompVis/stable-diffusion-v1-4" ):
'''simple docstring'''
__lowerCamelCase = jnp.bfloataa if fpaa else jnp.floataa
__lowerCamelCase = '''bf16''' if fpaa else None
__lowerCamelCase ,__lowerCamelCase = FlaxUNetaDConditionModel.from_pretrained(
__UpperCAmelCase , subfolder='''unet''' , dtype=__UpperCAmelCase , revision=__UpperCAmelCase )
return model, params
def lowerCamelCase ( self , __UpperCAmelCase=0 , __UpperCAmelCase=(4, 77, 768) , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = jnp.bfloataa if fpaa else jnp.floataa
__lowerCamelCase = jnp.array(load_hf_numpy(self.get_file_format(__UpperCAmelCase , __UpperCAmelCase ) ) , dtype=__UpperCAmelCase )
return hidden_states
@parameterized.expand(
[
# fmt: off
[83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
[17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
[8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
[3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
# fmt: on
] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4''' , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_latents(__UpperCAmelCase , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_encoder_hidden_states(__UpperCAmelCase , fpaa=__UpperCAmelCase )
__lowerCamelCase = model.apply(
{'''params''': params} , __UpperCAmelCase , jnp.array(__UpperCAmelCase , dtype=jnp.intaa ) , encoder_hidden_states=__UpperCAmelCase , ).sample
assert sample.shape == latents.shape
__lowerCamelCase = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
__lowerCamelCase = jnp.array(__UpperCAmelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
assert jnp.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
[17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
[8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
[3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
# fmt: on
] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.get_unet_model(model_id='''stabilityai/stable-diffusion-2''' , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_latents(__UpperCAmelCase , shape=(4, 4, 96, 96) , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_encoder_hidden_states(__UpperCAmelCase , shape=(4, 77, 1024) , fpaa=__UpperCAmelCase )
__lowerCamelCase = model.apply(
{'''params''': params} , __UpperCAmelCase , jnp.array(__UpperCAmelCase , dtype=jnp.intaa ) , encoder_hidden_states=__UpperCAmelCase , ).sample
assert sample.shape == latents.shape
__lowerCamelCase = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
__lowerCamelCase = jnp.array(__UpperCAmelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
assert jnp.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-2 )
| 330 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
UpperCamelCase__ = 42
class __lowerCamelCase ( snake_case__ , snake_case__):
"""simple docstring"""
@register_to_config
def __init__( self , UpperCAmelCase = 3 , UpperCAmelCase = 3 , UpperCAmelCase = ("DownEncoderBlock2D",) , UpperCAmelCase = ("UpDecoderBlock2D",) , UpperCAmelCase = (64,) , UpperCAmelCase = 1 , UpperCAmelCase = "silu" , UpperCAmelCase = 3 , UpperCAmelCase = 32 , UpperCAmelCase = 256 , UpperCAmelCase = 32 , UpperCAmelCase = None , UpperCAmelCase = 0.1_82_15 , UpperCAmelCase = "group" , ):
"""simple docstring"""
super().__init__()
# pass init params to Encoder
_UpperCAmelCase = Encoder(
in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , down_block_types=UpperCAmelCase , block_out_channels=UpperCAmelCase , layers_per_block=UpperCAmelCase , act_fn=UpperCAmelCase , norm_num_groups=UpperCAmelCase , double_z=UpperCAmelCase , )
_UpperCAmelCase = vq_embed_dim if vq_embed_dim is not None else latent_channels
_UpperCAmelCase = nn.Convad(UpperCAmelCase , UpperCAmelCase , 1 )
_UpperCAmelCase = VectorQuantizer(UpperCAmelCase , UpperCAmelCase , beta=0.25 , remap=UpperCAmelCase , sane_index_shape=UpperCAmelCase )
_UpperCAmelCase = nn.Convad(UpperCAmelCase , UpperCAmelCase , 1 )
# pass init params to Decoder
_UpperCAmelCase = Decoder(
in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , up_block_types=UpperCAmelCase , block_out_channels=UpperCAmelCase , layers_per_block=UpperCAmelCase , act_fn=UpperCAmelCase , norm_num_groups=UpperCAmelCase , norm_type=UpperCAmelCase , )
@apply_forward_hook
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase = True ):
"""simple docstring"""
_UpperCAmelCase = self.encoder(UpperCAmelCase )
_UpperCAmelCase = self.quant_conv(UpperCAmelCase )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=UpperCAmelCase )
@apply_forward_hook
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase = False , UpperCAmelCase = True ):
"""simple docstring"""
if not force_not_quantize:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.quantize(UpperCAmelCase )
else:
_UpperCAmelCase = h
_UpperCAmelCase = self.post_quant_conv(UpperCAmelCase )
_UpperCAmelCase = self.decoder(UpperCAmelCase , quant if self.config.norm_type == 'spatial' else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=UpperCAmelCase )
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase = True ):
"""simple docstring"""
_UpperCAmelCase = sample
_UpperCAmelCase = self.encode(UpperCAmelCase ).latents
_UpperCAmelCase = self.decode(UpperCAmelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=UpperCAmelCase )
| 39 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""MMBTForClassification""", """MMBTModel""", """ModalEmbeddings"""]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class _A ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Optional[Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : str=7 , __UpperCAmelCase : str=3 , __UpperCAmelCase : Tuple=18 , __UpperCAmelCase : Dict=30 , __UpperCAmelCase : Any=400 , __UpperCAmelCase : List[Any]=True , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Any=True , __UpperCAmelCase : Optional[Any]=None , __UpperCAmelCase : Tuple=True , __UpperCAmelCase : Tuple=[0.48_145_466, 0.4_578_275, 0.40_821_073] , __UpperCAmelCase : Optional[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711] , __UpperCAmelCase : Union[str, Any]=True , ):
a : int = size if size is not None else {"height": 224, "width": 224}
a : List[str] = crop_size if crop_size is not None else {"height": 18, "width": 18}
a : List[Any] = parent
a : Any = batch_size
a : str = num_channels
a : Optional[int] = image_size
a : Tuple = min_resolution
a : str = max_resolution
a : Dict = do_resize
a : Any = size
a : Dict = do_center_crop
a : List[str] = crop_size
a : str = do_normalize
a : Optional[int] = image_mean
a : Tuple = image_std
a : Any = do_convert_rgb
def __snake_case ( self : Union[str, Any]):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def __snake_case ( self : Dict , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : List[Any]=False , __UpperCAmelCase : Any=False):
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
a : str = []
for i in range(self.batch_size):
image_inputs.append(
np.random.randint(
255 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta))
else:
a : Tuple = []
for i in range(self.batch_size):
a , a : List[str] = np.random.choice(np.arange(self.min_resolution , self.max_resolution) , 2)
image_inputs.append(np.random.randint(255 , size=(self.num_channels, width, height) , dtype=np.uinta))
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
a : List[Any] = [Image.fromarray(np.moveaxis(__UpperCAmelCase , 0 , -1)) for x in image_inputs]
if torchify:
a : Optional[Any] = [torch.from_numpy(__UpperCAmelCase) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class _A ( _a ,unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase : Union[str, Any] = ChineseCLIPImageProcessor if is_vision_available() else None
def __snake_case ( self : List[str]):
a : Dict = ChineseCLIPImageProcessingTester(self , do_center_crop=__UpperCAmelCase)
@property
def __snake_case ( self : Union[str, Any]):
return self.image_processor_tester.prepare_image_processor_dict()
def __snake_case ( self : str):
a : Optional[int] = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(__UpperCAmelCase , "do_resize"))
self.assertTrue(hasattr(__UpperCAmelCase , "size"))
self.assertTrue(hasattr(__UpperCAmelCase , "do_center_crop"))
self.assertTrue(hasattr(__UpperCAmelCase , "center_crop"))
self.assertTrue(hasattr(__UpperCAmelCase , "do_normalize"))
self.assertTrue(hasattr(__UpperCAmelCase , "image_mean"))
self.assertTrue(hasattr(__UpperCAmelCase , "image_std"))
self.assertTrue(hasattr(__UpperCAmelCase , "do_convert_rgb"))
def __snake_case ( self : Any):
a : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {"height": 224, "width": 224})
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18})
a : int = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84)
self.assertEqual(image_processor.size , {"shortest_edge": 42})
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84})
def __snake_case ( self : str):
pass
def __snake_case ( self : Tuple):
# Initialize image_processing
a : Union[str, Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
a : Any = self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase)
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , Image.Image)
# Test not batched input
a : str = image_processing(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
a : Dict = image_processing(__UpperCAmelCase , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def __snake_case ( self : List[Any]):
# Initialize image_processing
a : str = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
a : Any = self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase , numpify=__UpperCAmelCase)
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , np.ndarray)
# Test not batched input
a : Optional[Any] = image_processing(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
a : Union[str, Any] = image_processing(__UpperCAmelCase , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def __snake_case ( self : List[str]):
# Initialize image_processing
a : str = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
a : Tuple = self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase , torchify=__UpperCAmelCase)
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , torch.Tensor)
# Test not batched input
a : List[str] = image_processing(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
a : str = image_processing(__UpperCAmelCase , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
@require_torch
@require_vision
class _A ( _a ,unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase : Optional[int] = ChineseCLIPImageProcessor if is_vision_available() else None
def __snake_case ( self : Union[str, Any]):
a : str = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=__UpperCAmelCase)
a : Dict = 3
@property
def __snake_case ( self : Optional[Any]):
return self.image_processor_tester.prepare_image_processor_dict()
def __snake_case ( self : Optional[int]):
a : Tuple = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(__UpperCAmelCase , "do_resize"))
self.assertTrue(hasattr(__UpperCAmelCase , "size"))
self.assertTrue(hasattr(__UpperCAmelCase , "do_center_crop"))
self.assertTrue(hasattr(__UpperCAmelCase , "center_crop"))
self.assertTrue(hasattr(__UpperCAmelCase , "do_normalize"))
self.assertTrue(hasattr(__UpperCAmelCase , "image_mean"))
self.assertTrue(hasattr(__UpperCAmelCase , "image_std"))
self.assertTrue(hasattr(__UpperCAmelCase , "do_convert_rgb"))
def __snake_case ( self : Any):
pass
def __snake_case ( self : Union[str, Any]):
# Initialize image_processing
a : List[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
a : str = self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase)
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , Image.Image)
# Test not batched input
a : Tuple = image_processing(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
a : Optional[Any] = image_processing(__UpperCAmelCase , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 40 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def a__ ( _UpperCamelCase : Optional[int] ):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class __lowerCAmelCase ( nn.Module ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
super().__init__()
__lowerCamelCase = module
__lowerCamelCase = nn.Sequential(
nn.Linear(module.in_features , __UpperCAmelCase , bias=__UpperCAmelCase ) , nn.Linear(__UpperCAmelCase , module.out_features , bias=__UpperCAmelCase ) , )
__lowerCamelCase = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=__UpperCAmelCase )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def lowerCamelCase ( self , __UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return self.module(__UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ) + self.adapter(__UpperCAmelCase )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __lowerCAmelCase ( unittest.TestCase ):
# We keep the constants inside the init function and model loading inside setUp function
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
# Therefore here we use only bloom-1b3 to test our module
lowerCAmelCase__ = """bigscience/bloom-1b7"""
# Constant values
lowerCAmelCase__ = 2.1_09_65_95_52_69_25_74
lowerCAmelCase__ = """Hello my name is"""
lowerCAmelCase__ = set()
EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I""" )
EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n""" )
EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University""" )
lowerCAmelCase__ = 1_0
def lowerCamelCase ( self ):
'''simple docstring'''
# Models and tokenizer
__lowerCamelCase = AutoTokenizer.from_pretrained(self.model_name )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# Models and tokenizer
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='''auto''' )
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
def lowerCamelCase ( self ):
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_abit.config
self.assertTrue(hasattr(__UpperCAmelCase , '''quantization_config''' ) )
__lowerCamelCase = config.to_dict()
__lowerCamelCase = config.to_diff_dict()
__lowerCamelCase = config.to_json_string()
def lowerCamelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
__lowerCamelCase = self.model_fpaa.get_memory_footprint()
__lowerCamelCase = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
__lowerCamelCase = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def lowerCamelCase ( self ):
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(__UpperCAmelCase , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = self.model_abit.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BitsAndBytesConfig()
__lowerCamelCase = True
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = model_abit_from_config.generate(
input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def lowerCamelCase ( self ):
'''simple docstring'''
with self.assertRaises(__UpperCAmelCase ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BitsAndBytesConfig()
with self.assertRaises(__UpperCAmelCase ):
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__UpperCAmelCase , load_in_abit=__UpperCAmelCase , device_map='''auto''' , bnb_abit_quant_type='''nf4''' , )
def lowerCamelCase ( self ):
'''simple docstring'''
with self.assertRaises(__UpperCAmelCase ):
# Tries with `str`
self.model_abit.to('''cpu''' )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.to(torch.device('''cuda:0''' ) )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = self.model_fpaa.to(torch.floataa )
__lowerCamelCase = self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.to('''cpu''' )
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.half()
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.float()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''t5-small''' , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __lowerCAmelCase ( unittest.TestCase ):
@classmethod
def lowerCamelCase ( cls ):
'''simple docstring'''
__lowerCamelCase = '''t5-small'''
__lowerCamelCase = '''google/flan-t5-small''' # flan-t5 uses dense-act instead of dense-relu-dense
__lowerCamelCase = AutoTokenizer.from_pretrained(cls.model_name )
__lowerCamelCase = '''Translate in German: Hello, my dog is cute'''
def lowerCamelCase ( self ):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
from transformers import TaForConditionalGeneration
__lowerCamelCase = TaForConditionalGeneration._keep_in_fpaa_modules
__lowerCamelCase = None
# test with `t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
# test with `flan-t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
__lowerCamelCase = modules
def lowerCamelCase ( self ):
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
# test with `flan-t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# model_name
__lowerCamelCase = '''bigscience/bloom-560m'''
__lowerCamelCase = '''t5-small'''
# Different types of model
__lowerCamelCase = AutoModel.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# Sequence classification model
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# CausalLM model
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# Seq2seq model
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
def lowerCamelCase ( self ):
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''text-generation''' , model=self.model_name , model_kwargs={'''device_map''': '''auto''', '''load_in_4bit''': True, '''torch_dtype''': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
__lowerCamelCase = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]['''generated_text'''] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=__UpperCAmelCase , device_map='''balanced''' )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
# Second real batch
__lowerCamelCase = model_parallel.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-350m'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
if version.parse(importlib.metadata.version('''bitsandbytes''' ) ) < version.parse('''0.37.0''' ):
return
# Step 1: freeze all parameters
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
__lowerCamelCase = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
__lowerCamelCase = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(__UpperCAmelCase ) ):
__lowerCamelCase = LoRALayer(module.q_proj , rank=16 )
__lowerCamelCase = LoRALayer(module.k_proj , rank=16 )
__lowerCamelCase = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
__lowerCamelCase = self.tokenizer('''Test batch ''' , return_tensors='''pt''' ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
__lowerCamelCase = model.forward(**__UpperCAmelCase )
out.logits.norm().backward()
for module in model.modules():
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(__UpperCAmelCase , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """gpt2-xl"""
lowerCAmelCase__ = 3.31_91_85_48_54_15_21_87
| 330 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> list:
if len(UpperCamelCase ) <= 1:
return [tuple(UpperCamelCase )]
lowerCamelCase__ : Union[str, Any] = []
def generate(UpperCamelCase , UpperCamelCase ):
lowerCamelCase__ : List[str] = [0] * n
res.append(tuple(UpperCamelCase ) )
lowerCamelCase__ : str = 0
while i < n:
if c[i] < i:
if i % 2 == 0:
lowerCamelCase__ , lowerCamelCase__ : Dict = arr[i], arr[0]
else:
lowerCamelCase__ , lowerCamelCase__ : Dict = arr[i], arr[c[i]]
res.append(tuple(UpperCamelCase ) )
c[i] += 1
lowerCamelCase__ : Optional[int] = 0
else:
lowerCamelCase__ : Dict = 0
i += 1
generate(len(UpperCamelCase ) , UpperCamelCase )
return res
if __name__ == "__main__":
_A : str =input('''Enter numbers separated by a comma:\n''').strip()
_A : Optional[int] =[int(item) for item in user_input.split(''',''')]
print(heaps(arr))
| 41 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = 42
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
lowerCAmelCase__ = True
@register_to_config
def __init__( self , __UpperCAmelCase = 3 , __UpperCAmelCase = 3 , __UpperCAmelCase = ("DownEncoderBlock2D",) , __UpperCAmelCase = ("UpDecoderBlock2D",) , __UpperCAmelCase = (64,) , __UpperCAmelCase = 1 , __UpperCAmelCase = "silu" , __UpperCAmelCase = 4 , __UpperCAmelCase = 32 , __UpperCAmelCase = 32 , __UpperCAmelCase = 0.18_215 , ):
'''simple docstring'''
super().__init__()
# pass init params to Encoder
__lowerCamelCase = Encoder(
in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , down_block_types=__UpperCAmelCase , block_out_channels=__UpperCAmelCase , layers_per_block=__UpperCAmelCase , act_fn=__UpperCAmelCase , norm_num_groups=__UpperCAmelCase , double_z=__UpperCAmelCase , )
# pass init params to Decoder
__lowerCamelCase = Decoder(
in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , up_block_types=__UpperCAmelCase , block_out_channels=__UpperCAmelCase , layers_per_block=__UpperCAmelCase , norm_num_groups=__UpperCAmelCase , act_fn=__UpperCAmelCase , )
__lowerCamelCase = nn.Convad(2 * latent_channels , 2 * latent_channels , 1 )
__lowerCamelCase = nn.Convad(__UpperCAmelCase , __UpperCAmelCase , 1 )
__lowerCamelCase = False
__lowerCamelCase = False
# only relevant if vae tiling is enabled
__lowerCamelCase = self.config.sample_size
__lowerCamelCase = (
self.config.sample_size[0]
if isinstance(self.config.sample_size , (list, tuple) )
else self.config.sample_size
)
__lowerCamelCase = int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
__lowerCamelCase = 0.25
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
if isinstance(__UpperCAmelCase , (Encoder, Decoder) ):
__lowerCamelCase = value
def lowerCamelCase ( self , __UpperCAmelCase = True ):
'''simple docstring'''
__lowerCamelCase = use_tiling
def lowerCamelCase ( self ):
'''simple docstring'''
self.enable_tiling(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = True
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = {}
def fn_recursive_add_processors(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
if hasattr(__UpperCAmelCase , '''set_processor''' ):
__lowerCamelCase = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(F"""{name}.{sub_name}""" , __UpperCAmelCase , __UpperCAmelCase )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
return processors
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = len(self.attn_processors.keys() )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(__UpperCAmelCase ) != count:
raise ValueError(
F"""A dict of processors was passed, but the number of processors {len(__UpperCAmelCase )} does not match the"""
F""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" )
def fn_recursive_attn_processor(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
if hasattr(__UpperCAmelCase , '''set_processor''' ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
module.set_processor(__UpperCAmelCase )
else:
module.set_processor(processor.pop(F"""{name}.processor""" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(F"""{name}.{sub_name}""" , __UpperCAmelCase , __UpperCAmelCase )
for name, module in self.named_children():
fn_recursive_attn_processor(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(__UpperCAmelCase , return_dict=__UpperCAmelCase )
if self.use_slicing and x.shape[0] > 1:
__lowerCamelCase = [self.encoder(__UpperCAmelCase ) for x_slice in x.split(1 )]
__lowerCamelCase = torch.cat(__UpperCAmelCase )
else:
__lowerCamelCase = self.encoder(__UpperCAmelCase )
__lowerCamelCase = self.quant_conv(__UpperCAmelCase )
__lowerCamelCase = DiagonalGaussianDistribution(__UpperCAmelCase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.tiled_decode(__UpperCAmelCase , return_dict=__UpperCAmelCase )
__lowerCamelCase = self.post_quant_conv(__UpperCAmelCase )
__lowerCamelCase = self.decoder(__UpperCAmelCase )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__UpperCAmelCase )
@apply_forward_hook
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
if self.use_slicing and z.shape[0] > 1:
__lowerCamelCase = [self._decode(__UpperCAmelCase ).sample for z_slice in z.split(1 )]
__lowerCamelCase = torch.cat(__UpperCAmelCase )
else:
__lowerCamelCase = self._decode(__UpperCAmelCase ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = min(a.shape[2] , b.shape[2] , __UpperCAmelCase )
for y in range(__UpperCAmelCase ):
__lowerCamelCase = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = min(a.shape[3] , b.shape[3] , __UpperCAmelCase )
for x in range(__UpperCAmelCase ):
__lowerCamelCase = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
__lowerCamelCase = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
__lowerCamelCase = int(self.tile_latent_min_size * self.tile_overlap_factor )
__lowerCamelCase = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
__lowerCamelCase = []
for i in range(0 , x.shape[2] , __UpperCAmelCase ):
__lowerCamelCase = []
for j in range(0 , x.shape[3] , __UpperCAmelCase ):
__lowerCamelCase = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
__lowerCamelCase = self.encoder(__UpperCAmelCase )
__lowerCamelCase = self.quant_conv(__UpperCAmelCase )
row.append(__UpperCAmelCase )
rows.append(__UpperCAmelCase )
__lowerCamelCase = []
for i, row in enumerate(__UpperCAmelCase ):
__lowerCamelCase = []
for j, tile in enumerate(__UpperCAmelCase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
__lowerCamelCase = self.blend_v(rows[i - 1][j] , __UpperCAmelCase , __UpperCAmelCase )
if j > 0:
__lowerCamelCase = self.blend_h(row[j - 1] , __UpperCAmelCase , __UpperCAmelCase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__UpperCAmelCase , dim=3 ) )
__lowerCamelCase = torch.cat(__UpperCAmelCase , dim=2 )
__lowerCamelCase = DiagonalGaussianDistribution(__UpperCAmelCase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
__lowerCamelCase = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
__lowerCamelCase = int(self.tile_sample_min_size * self.tile_overlap_factor )
__lowerCamelCase = self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
__lowerCamelCase = []
for i in range(0 , z.shape[2] , __UpperCAmelCase ):
__lowerCamelCase = []
for j in range(0 , z.shape[3] , __UpperCAmelCase ):
__lowerCamelCase = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
__lowerCamelCase = self.post_quant_conv(__UpperCAmelCase )
__lowerCamelCase = self.decoder(__UpperCAmelCase )
row.append(__UpperCAmelCase )
rows.append(__UpperCAmelCase )
__lowerCamelCase = []
for i, row in enumerate(__UpperCAmelCase ):
__lowerCamelCase = []
for j, tile in enumerate(__UpperCAmelCase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
__lowerCamelCase = self.blend_v(rows[i - 1][j] , __UpperCAmelCase , __UpperCAmelCase )
if j > 0:
__lowerCamelCase = self.blend_h(row[j - 1] , __UpperCAmelCase , __UpperCAmelCase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__UpperCAmelCase , dim=3 ) )
__lowerCamelCase = torch.cat(__UpperCAmelCase , dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False , __UpperCAmelCase = True , __UpperCAmelCase = None , ):
'''simple docstring'''
__lowerCamelCase = sample
__lowerCamelCase = self.encode(__UpperCAmelCase ).latent_dist
if sample_posterior:
__lowerCamelCase = posterior.sample(generator=__UpperCAmelCase )
else:
__lowerCamelCase = posterior.mode()
__lowerCamelCase = self.decode(__UpperCAmelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=__UpperCAmelCase )
| 330 | 0 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __A , __A = None , __A = None ) -> None:
if start is None:
_snake_case = 0
if end is None:
_snake_case = len(__A ) - 1
if start >= end:
return
_snake_case = (start + end) // 2
slowsort(__A , __A , __A )
slowsort(__A , mid + 1 , __A )
if sequence[end] < sequence[mid]:
_snake_case , _snake_case = sequence[mid], sequence[end]
slowsort(__A , __A , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 42 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
a_ = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""kernel""", """weight"""),
("""beta""", """bias"""),
("""gamma""", """weight"""),
("""pegasus""", """model"""),
]
a_ = [
(""".output.dense""", """.fc2"""),
("""intermediate.LayerNorm""", """final_layer_norm"""),
("""intermediate.dense""", """fc1"""),
]
a_ = (
INIT_COMMON
+ [
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.out_proj"""),
("""attention.self""", """self_attn"""),
("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
("""attention.encdec""", """encoder_attn"""),
("""key""", """k_proj"""),
("""value""", """v_proj"""),
("""query""", """q_proj"""),
("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
]
+ END_COMMON
)
a_ = (
INIT_COMMON
+ [
("""embeddings.word_embeddings""", """shared.weight"""),
("""embeddings.position_embeddings""", """embed_positions.weight"""),
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.output"""),
("""attention.self""", """self_attn.self"""),
("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
]
+ END_COMMON
)
a_ = [
"""encdec/key/bias""",
"""encdec/query/bias""",
"""encdec/value/bias""",
"""self/key/bias""",
"""self/query/bias""",
"""self/value/bias""",
"""encdec_output/dense/bias""",
"""attention/output/dense/bias""",
]
def a__ ( _UpperCamelCase : Optional[int] ,_UpperCamelCase : Optional[Any] ):
for tf_name, hf_name in patterns:
__lowerCamelCase = k.replace(_UpperCamelCase ,_UpperCamelCase )
return k
def a__ ( _UpperCamelCase : dict ,_UpperCamelCase : dict ):
__lowerCamelCase = BigBirdPegasusConfig(**_UpperCamelCase )
__lowerCamelCase = BigBirdPegasusForConditionalGeneration(_UpperCamelCase )
__lowerCamelCase = torch_model.state_dict()
__lowerCamelCase = {}
# separating decoder weights
__lowerCamelCase = {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
__lowerCamelCase = {k: tf_weights[k] for k in tf_weights if not k.startswith('''pegasus/decoder''' )}
for k, v in tqdm(decoder_weights.items() ,'''tf -> hf conversion''' ):
__lowerCamelCase = [k.endswith(_UpperCamelCase ) for ending in KEYS_TO_IGNORE]
if any(_UpperCamelCase ):
continue
__lowerCamelCase = DECODER_PATTERNS
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase ,_UpperCamelCase )
if new_k not in state_dict:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
__lowerCamelCase = v.T
__lowerCamelCase = torch.from_numpy(_UpperCamelCase )
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items() ,'''tf -> hf conversion''' ):
__lowerCamelCase = [k.endswith(_UpperCamelCase ) for ending in KEYS_TO_IGNORE]
if any(_UpperCamelCase ):
continue
__lowerCamelCase = REMAINING_PATTERNS
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase ,_UpperCamelCase )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
__lowerCamelCase = v.T
__lowerCamelCase = torch.from_numpy(_UpperCamelCase )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
__lowerCamelCase = mapping['''model.embed_positions.weight''']
__lowerCamelCase = mapping.pop('''model.embed_positions.weight''' )
__lowerCamelCase ,__lowerCamelCase = torch_model.load_state_dict(_UpperCamelCase ,strict=_UpperCamelCase )
__lowerCamelCase = [
k
for k in missing
if k
not in [
'''final_logits_bias''',
'''model.encoder.embed_tokens.weight''',
'''model.decoder.embed_tokens.weight''',
'''lm_head.weight''',
]
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = tf.train.list_variables(_UpperCamelCase )
__lowerCamelCase = {}
__lowerCamelCase = ['''global_step''']
for name, shape in tqdm(_UpperCamelCase ,desc='''converting tf checkpoint to dict''' ):
__lowerCamelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
__lowerCamelCase = tf.train.load_variable(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = array
return tf_weights
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : dict ):
__lowerCamelCase = get_tf_weights_as_numpy(_UpperCamelCase )
__lowerCamelCase = convert_bigbird_pegasus(_UpperCamelCase ,_UpperCamelCase )
torch_model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
a_ = parser.parse_args()
a_ = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 330 | 0 |
import random
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[Any] = a[left_index]
__UpperCamelCase :Any = left_index + 1
for j in range(left_index + 1 , SCREAMING_SNAKE_CASE ):
if a[j] < pivot:
__UpperCamelCase , __UpperCamelCase :str = a[i], a[j]
i += 1
__UpperCamelCase , __UpperCamelCase :Optional[int] = a[i - 1], a[left_index]
return i - 1
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if left < right:
__UpperCamelCase :int = random.randint(SCREAMING_SNAKE_CASE , right - 1 )
__UpperCamelCase , __UpperCamelCase :List[str] = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
__UpperCamelCase :Dict = partition(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
quick_sort_random(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) # recursive quicksort to the left of the pivot point
quick_sort_random(
SCREAMING_SNAKE_CASE , pivot_index + 1 , SCREAMING_SNAKE_CASE ) # recursive quicksort to the right of the pivot point
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Tuple = input('''Enter numbers separated by a comma:\n''' ).strip()
__UpperCamelCase :Union[str, Any] = [int(SCREAMING_SNAKE_CASE ) for item in user_input.split(''',''' )]
quick_sort_random(SCREAMING_SNAKE_CASE , 0 , len(SCREAMING_SNAKE_CASE ) )
print(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 43 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
a_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase=None , __UpperCAmelCase=None ):
'''simple docstring'''
if not conversation_id:
__lowerCamelCase = uuid.uuida()
if past_user_inputs is None:
__lowerCamelCase = []
if generated_responses is None:
__lowerCamelCase = []
__lowerCamelCase = conversation_id
__lowerCamelCase = past_user_inputs
__lowerCamelCase = generated_responses
__lowerCamelCase = text
def __eq__( self , __UpperCAmelCase ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False ):
'''simple docstring'''
if self.new_user_input:
if overwrite:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
F"""with: \"{text}\".""" )
__lowerCamelCase = text
else:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input""" )
else:
__lowerCamelCase = text
def lowerCamelCase ( self ):
'''simple docstring'''
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
__lowerCamelCase = None
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
self.generated_responses.append(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
'''simple docstring'''
__lowerCamelCase = F"""Conversation id: {self.uuid} \n"""
for is_user, text in self.iter_texts():
__lowerCamelCase = '''user''' if is_user else '''bot'''
output += F"""{name} >> {text} \n"""
return output
@add_end_docstrings(
lowerCAmelCase__ , r"""
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
""" , )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
if self.tokenizer.pad_token_id is None:
__lowerCamelCase = self.tokenizer.eos_token
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = {}
__lowerCamelCase = {}
__lowerCamelCase = {}
if min_length_for_response is not None:
__lowerCamelCase = min_length_for_response
if minimum_tokens is not None:
__lowerCamelCase = minimum_tokens
if "max_length" in generate_kwargs:
__lowerCamelCase = generate_kwargs['''max_length''']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
__lowerCamelCase = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(__UpperCAmelCase )
return preprocess_params, forward_params, postprocess_params
def __call__( self , __UpperCAmelCase , __UpperCAmelCase=0 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = super().__call__(__UpperCAmelCase , num_workers=__UpperCAmelCase , **__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(__UpperCAmelCase ) == 1:
return outputs[0]
return outputs
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=32 ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise ValueError('''ConversationalPipeline, expects Conversation as inputs''' )
if conversation.new_user_input is None:
raise ValueError(
F"""Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. """
'''Add user inputs with the conversation\'s `add_user_input` method''' )
if hasattr(self.tokenizer , '''_build_conversation_input_ids''' ):
__lowerCamelCase = self.tokenizer._build_conversation_input_ids(__UpperCAmelCase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
__lowerCamelCase = self._legacy_parse_and_tokenize(__UpperCAmelCase )
if self.framework == "pt":
__lowerCamelCase = torch.LongTensor([input_ids] )
elif self.framework == "tf":
__lowerCamelCase = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=10 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = generate_kwargs.get('''max_length''' , self.model.config.max_length )
__lowerCamelCase = model_inputs['''input_ids'''].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})""" )
__lowerCamelCase = max_length - minimum_tokens
__lowerCamelCase = model_inputs['''input_ids'''][:, -trim:]
if "attention_mask" in model_inputs:
__lowerCamelCase = model_inputs['''attention_mask'''][:, -trim:]
__lowerCamelCase = model_inputs.pop('''conversation''' )
__lowerCamelCase = max_length
__lowerCamelCase = self.model.generate(**__UpperCAmelCase , **__UpperCAmelCase )
if self.model.config.is_encoder_decoder:
__lowerCamelCase = 1
else:
__lowerCamelCase = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=True ):
'''simple docstring'''
__lowerCamelCase = model_outputs['''output_ids''']
__lowerCamelCase = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase , )
__lowerCamelCase = model_outputs['''conversation''']
conversation.mark_processed()
conversation.append_response(__UpperCAmelCase )
return conversation
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.tokenizer.eos_token_id
__lowerCamelCase = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) )
if len(__UpperCAmelCase ) > self.tokenizer.model_max_length:
__lowerCamelCase = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 330 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class __A ( unittest.TestCase ):
def __A ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def __A ( self ):
_lowerCAmelCase : List[str] = 1
_lowerCAmelCase : Tuple = 3
_lowerCAmelCase : Optional[int] = (32, 32)
_lowerCAmelCase : int = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(a__ )
return image
@property
def __A ( self ):
torch.manual_seed(0 )
_lowerCAmelCase : Dict = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
return model
@property
def __A ( self ):
torch.manual_seed(0 )
_lowerCAmelCase : Tuple = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
return model
@property
def __A ( self ):
torch.manual_seed(0 )
_lowerCAmelCase : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModel(a__ )
@property
def __A ( self ):
def extract(*a__ , **a__ ):
class __A :
def __init__( self ):
_lowerCAmelCase : Dict = torch.ones([0] )
def __A ( self , a__ ):
self.pixel_values.to(a__ )
return self
return Out()
return extract
def __A ( self ):
_lowerCAmelCase : int = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase : Optional[Any] = self.dummy_cond_unet
_lowerCAmelCase : Any = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="""scaled_linear""" , clip_sample=a__ , set_alpha_to_one=a__ , )
_lowerCAmelCase : str = self.dummy_vae
_lowerCAmelCase : Tuple = self.dummy_text_encoder
_lowerCAmelCase : Optional[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_lowerCAmelCase : Optional[int] = StableDiffusionPipeline(
unet=a__ , scheduler=a__ , vae=a__ , text_encoder=a__ , tokenizer=a__ , safety_checker=a__ , feature_extractor=self.dummy_extractor , )
_lowerCAmelCase : int = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : Optional[Any] = """A painting of a squirrel eating a burger"""
_lowerCAmelCase : List[Any] = torch.Generator(device=a__ ).manual_seed(0 )
_lowerCAmelCase : Union[str, Any] = sd_pipe([prompt] , generator=a__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" )
_lowerCAmelCase : Any = output.images
_lowerCAmelCase : str = torch.Generator(device=a__ ).manual_seed(0 )
_lowerCAmelCase : int = sd_pipe(
[prompt] , generator=a__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , return_dict=a__ , )[0]
_lowerCAmelCase : Tuple = image[0, -3:, -3:, -1]
_lowerCAmelCase : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCAmelCase : Dict = np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __A ( self ):
_lowerCAmelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase : int = self.dummy_cond_unet
_lowerCAmelCase : Optional[int] = PNDMScheduler(skip_prk_steps=a__ )
_lowerCAmelCase : List[str] = self.dummy_vae
_lowerCAmelCase : Any = self.dummy_text_encoder
_lowerCAmelCase : Any = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_lowerCAmelCase : str = StableDiffusionPipeline(
unet=a__ , scheduler=a__ , vae=a__ , text_encoder=a__ , tokenizer=a__ , safety_checker=a__ , feature_extractor=self.dummy_extractor , )
_lowerCAmelCase : Optional[Any] = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : str = """A painting of a squirrel eating a burger"""
_lowerCAmelCase : str = torch.Generator(device=a__ ).manual_seed(0 )
_lowerCAmelCase : Tuple = sd_pipe([prompt] , generator=a__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" )
_lowerCAmelCase : Tuple = output.images
_lowerCAmelCase : Dict = torch.Generator(device=a__ ).manual_seed(0 )
_lowerCAmelCase : List[str] = sd_pipe(
[prompt] , generator=a__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , return_dict=a__ , )[0]
_lowerCAmelCase : Any = image[0, -3:, -3:, -1]
_lowerCAmelCase : List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCAmelCase : Union[str, Any] = np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __A ( self ):
_lowerCAmelCase : List[str] = StableDiffusionPipeline.from_pretrained(
"""hf-internal-testing/tiny-stable-diffusion-lms-pipe""" , safety_checker=a__ )
assert isinstance(a__ , a__ )
assert isinstance(pipe.scheduler , a__ )
assert pipe.safety_checker is None
_lowerCAmelCase : Any = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(a__ )
_lowerCAmelCase : str = StableDiffusionPipeline.from_pretrained(a__ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
_lowerCAmelCase : Optional[Any] = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def __A ( self ):
_lowerCAmelCase : int = self.dummy_cond_unet
_lowerCAmelCase : str = PNDMScheduler(skip_prk_steps=a__ )
_lowerCAmelCase : Any = self.dummy_vae
_lowerCAmelCase : Dict = self.dummy_text_encoder
_lowerCAmelCase : Tuple = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# put models in fp16
_lowerCAmelCase : str = unet.half()
_lowerCAmelCase : List[str] = vae.half()
_lowerCAmelCase : Tuple = bert.half()
# make sure here that pndm scheduler skips prk
_lowerCAmelCase : Dict = StableDiffusionPipeline(
unet=a__ , scheduler=a__ , vae=a__ , text_encoder=a__ , tokenizer=a__ , safety_checker=a__ , feature_extractor=self.dummy_extractor , )
_lowerCAmelCase : int = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : int = """A painting of a squirrel eating a burger"""
_lowerCAmelCase : Dict = sd_pipe([prompt] , num_inference_steps=2 , output_type="""np""" ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class __A ( unittest.TestCase ):
def __A ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __A ( self ):
_lowerCAmelCase : List[str] = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" , safety_checker=a__ )
_lowerCAmelCase : List[Any] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_lowerCAmelCase : Optional[int] = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : Dict = (
"""portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
""" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
""" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
""" children from bahnhof zoo, detailed """
)
_lowerCAmelCase : List[Any] = 4003660346
_lowerCAmelCase : List[Any] = 7
# without safety guidance (sld_guidance_scale = 0)
_lowerCAmelCase : int = torch.manual_seed(a__ )
_lowerCAmelCase : Dict = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=0 , )
_lowerCAmelCase : int = output.images
_lowerCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_lowerCAmelCase : Dict = [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
# without safety guidance (strong configuration)
_lowerCAmelCase : Optional[int] = torch.manual_seed(a__ )
_lowerCAmelCase : List[str] = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_2_5 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_lowerCAmelCase : Optional[int] = output.images
_lowerCAmelCase : Tuple = image[0, -3:, -3:, -1]
_lowerCAmelCase : str = [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __A ( self ):
_lowerCAmelCase : Tuple = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" , safety_checker=a__ )
_lowerCAmelCase : List[str] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_lowerCAmelCase : Optional[Any] = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : List[str] = """padme amidala taking a bath artwork, safe for work, no nudity"""
_lowerCAmelCase : Tuple = 2734971755
_lowerCAmelCase : Union[str, Any] = 7
_lowerCAmelCase : Optional[int] = torch.manual_seed(a__ )
_lowerCAmelCase : Any = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=0 , )
_lowerCAmelCase : int = output.images
_lowerCAmelCase : int = image[0, -3:, -3:, -1]
_lowerCAmelCase : Dict = [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
_lowerCAmelCase : Optional[Any] = torch.manual_seed(a__ )
_lowerCAmelCase : Any = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_2_5 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_lowerCAmelCase : List[Any] = output.images
_lowerCAmelCase : List[Any] = image[0, -3:, -3:, -1]
_lowerCAmelCase : Optional[int] = [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __A ( self ):
_lowerCAmelCase : List[str] = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
_lowerCAmelCase : Any = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : Dict = (
"""the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
""" leyendecker"""
)
_lowerCAmelCase : int = 1044355234
_lowerCAmelCase : Tuple = 12
_lowerCAmelCase : int = torch.manual_seed(a__ )
_lowerCAmelCase : str = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=0 , )
_lowerCAmelCase : List[str] = output.images
_lowerCAmelCase : List[str] = image[0, -3:, -3:, -1]
_lowerCAmelCase : int = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-7
_lowerCAmelCase : Optional[int] = torch.manual_seed(a__ )
_lowerCAmelCase : List[Any] = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_2_5 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_lowerCAmelCase : List[str] = output.images
_lowerCAmelCase : Any = image[0, -3:, -3:, -1]
_lowerCAmelCase : List[str] = np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 44 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
a_ = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["""memory_attention""", """encoder_attn"""],
["""attention""", """attn"""],
["""/""", """."""],
[""".LayerNorm.gamma""", """_layer_norm.weight"""],
[""".LayerNorm.beta""", """_layer_norm.bias"""],
["""r.layer_""", """r.layers."""],
["""output_proj""", """out_proj"""],
["""ffn.dense_1.""", """fc2."""],
["""ffn.dense.""", """fc1."""],
["""ffn_layer_norm""", """final_layer_norm"""],
["""kernel""", """weight"""],
["""encoder_layer_norm.""", """encoder.layer_norm."""],
["""decoder_layer_norm.""", """decoder.layer_norm."""],
["""embeddings.weights""", """shared.weight"""],
]
def a__ ( _UpperCamelCase : int ):
for pegasus_name, hf_name in PATTERNS:
__lowerCamelCase = k.replace(_UpperCamelCase ,_UpperCamelCase )
return k
def a__ ( _UpperCamelCase : dict ,_UpperCamelCase : dict ):
__lowerCamelCase = DEFAULTS.copy()
cfg_kwargs.update(_UpperCamelCase )
__lowerCamelCase = PegasusConfig(**_UpperCamelCase )
__lowerCamelCase = PegasusForConditionalGeneration(_UpperCamelCase )
__lowerCamelCase = torch_model.model.state_dict()
__lowerCamelCase = {}
for k, v in tf_weights.items():
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase )
if new_k not in sd:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if "dense" in k or "proj" in new_k:
__lowerCamelCase = v.T
__lowerCamelCase = torch.tensor(_UpperCamelCase ,dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
# make sure embedding.padding_idx is respected
__lowerCamelCase = torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1] )
__lowerCamelCase = mapping['''shared.weight''']
__lowerCamelCase = mapping['''shared.weight''']
__lowerCamelCase = {k: torch.zeros_like(_UpperCamelCase ) for k, v in sd.items() if k.endswith('''bias''' ) and k not in mapping}
mapping.update(**_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = torch_model.model.load_state_dict(_UpperCamelCase ,strict=_UpperCamelCase )
__lowerCamelCase = [
k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def a__ ( _UpperCamelCase : str="./ckpt/aeslc/model.ckpt-32000" ):
__lowerCamelCase = tf.train.list_variables(_UpperCamelCase )
__lowerCamelCase = {}
__lowerCamelCase = ['''Adafactor''', '''global_step''']
for name, shape in tqdm(_UpperCamelCase ,desc='''converting tf checkpoint to dict''' ):
__lowerCamelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
__lowerCamelCase = tf.train.load_variable(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = array
return tf_weights
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ):
# save tokenizer first
__lowerCamelCase = Path(_UpperCamelCase ).parent.name
__lowerCamelCase = task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
__lowerCamelCase = PegasusTokenizer.from_pretrained('''sshleifer/pegasus''' ,model_max_length=_UpperCamelCase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(_UpperCamelCase )
# convert model
__lowerCamelCase = get_tf_weights_as_numpy(_UpperCamelCase )
__lowerCamelCase = task_specific_params[F"""summarization_{dataset}"""]
if dataset == "large":
__lowerCamelCase = task_specific_params
__lowerCamelCase = convert_pegasus(_UpperCamelCase ,_UpperCamelCase )
torch_model.save_pretrained(_UpperCamelCase )
__lowerCamelCase = torch_model.state_dict()
sd.pop('''model.decoder.embed_positions.weight''' )
sd.pop('''model.encoder.embed_positions.weight''' )
torch.save(_UpperCamelCase ,Path(_UpperCamelCase ) / '''pytorch_model.bin''' )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
a_ = parser.parse_args()
if args.save_dir is None:
a_ = Path(args.tf_ckpt_path).parent.name
a_ = os.path.join("""pegasus""", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 330 | 0 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
lowercase_ = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def lowercase ( lowerCAmelCase__ : Optional[int] ) -> Optional[Any]:
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def lowercase ( lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : str ) -> List[str]:
return max(metric_fn(lowerCAmelCase__ , lowerCAmelCase__ ) for gt in ground_truths )
def lowercase ( lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Dict ) -> int:
__a = [line.strip() for line in open(lowerCAmelCase__ , '''r''' ).readlines()]
__a = []
if args.gold_data_mode == "qa":
__a = pd.read_csv(lowerCAmelCase__ , sep='''\t''' , header=lowerCAmelCase__ )
for answer_list in data[1]:
__a = ast.literal_eval(lowerCAmelCase__ )
answers.append(lowerCAmelCase__ )
else:
__a = [line.strip() for line in open(lowerCAmelCase__ , '''r''' ).readlines()]
__a = [[reference] for reference in references]
__a = __a = __a = 0
for prediction, ground_truths in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
total += 1
em += metric_max_over_ground_truths(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
fa += metric_max_over_ground_truths(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
__a = 1_00.0 * em / total
__a = 1_00.0 * fa / total
logger.info(f'''F1: {fa:.2f}''' )
logger.info(f'''EM: {em:.2f}''' )
def lowercase ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Any ) -> Any:
__a = args.k
__a = [line.strip() for line in open(lowerCAmelCase__ , '''r''' ).readlines()]
__a = [line.strip() for line in open(lowerCAmelCase__ , '''r''' ).readlines()]
__a = __a = 0
for hypo, reference in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
__a = set(hypo.split('''\t''' )[:k] )
__a = set(reference.split('''\t''' ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
__a = 1_00.0 * em / total
logger.info(f'''Precision@{k}: {em: .2f}''' )
def lowercase ( lowerCAmelCase__ : List[str] , lowerCAmelCase__ : str , lowerCAmelCase__ : str ) -> Dict:
def strip_title(lowerCAmelCase__ : Tuple ):
if title.startswith('''"''' ):
__a = title[1:]
if title.endswith('''"''' ):
__a = title[:-1]
return title
__a = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
lowerCAmelCase__ , return_tensors='''pt''' , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , )['''input_ids'''].to(args.device )
__a = rag_model.rag.question_encoder(lowerCAmelCase__ )
__a = question_enc_outputs[0]
__a = rag_model.retriever(
lowerCAmelCase__ , question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors='''pt''' , )
__a = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
__a = []
for docs in all_docs:
__a = [strip_title(lowerCAmelCase__ ) for title in docs['''title''']]
provenance_strings.append('''\t'''.join(lowerCAmelCase__ ) )
return provenance_strings
def lowercase ( lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any ) -> Any:
with torch.no_grad():
__a = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
lowerCAmelCase__ , return_tensors='''pt''' , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ )
__a = inputs_dict.input_ids.to(args.device )
__a = inputs_dict.attention_mask.to(args.device )
__a = rag_model.generate( # rag_model overwrites generate
lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=lowerCAmelCase__ , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
__a = rag_model.retriever.generator_tokenizer.batch_decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )
if args.print_predictions:
for q, a in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
logger.info('''Q: {} - A: {}'''.format(lowerCAmelCase__ , lowerCAmelCase__ ) )
return answers
def lowercase ( ) -> Optional[Any]:
__a = argparse.ArgumentParser()
parser.add_argument(
'''--model_type''' , choices=['''rag_sequence''', '''rag_token''', '''bart'''] , type=lowerCAmelCase__ , help=(
'''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
''' model_name_or_path'''
) , )
parser.add_argument(
'''--index_name''' , default=lowerCAmelCase__ , choices=['''exact''', '''compressed''', '''legacy'''] , type=lowerCAmelCase__ , help='''RAG model retriever type''' , )
parser.add_argument(
'''--index_path''' , default=lowerCAmelCase__ , type=lowerCAmelCase__ , help='''Path to the retrieval index''' , )
parser.add_argument('''--n_docs''' , default=5 , type=lowerCAmelCase__ , help='''Number of retrieved docs''' )
parser.add_argument(
'''--model_name_or_path''' , default=lowerCAmelCase__ , type=lowerCAmelCase__ , required=lowerCAmelCase__ , help='''Path to pretrained checkpoints or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--eval_mode''' , choices=['''e2e''', '''retrieval'''] , default='''e2e''' , type=lowerCAmelCase__ , help=(
'''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
''' precision@k.'''
) , )
parser.add_argument('''--k''' , default=1 , type=lowerCAmelCase__ , help='''k for the precision@k calculation''' )
parser.add_argument(
'''--evaluation_set''' , default=lowerCAmelCase__ , type=lowerCAmelCase__ , required=lowerCAmelCase__ , help='''Path to a file containing evaluation samples''' , )
parser.add_argument(
'''--gold_data_path''' , default=lowerCAmelCase__ , type=lowerCAmelCase__ , required=lowerCAmelCase__ , help='''Path to a tab-separated file with gold samples''' , )
parser.add_argument(
'''--gold_data_mode''' , default='''qa''' , type=lowerCAmelCase__ , choices=['''qa''', '''ans'''] , help=(
'''Format of the gold data file'''
'''qa - a single line in the following format: question [tab] answer_list'''
'''ans - a single line of the gold file contains the expected answer string'''
) , )
parser.add_argument(
'''--predictions_path''' , type=lowerCAmelCase__ , default='''predictions.txt''' , help='''Name of the predictions file, to be stored in the checkpoints directory''' , )
parser.add_argument(
'''--eval_all_checkpoints''' , action='''store_true''' , help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number''' , )
parser.add_argument(
'''--eval_batch_size''' , default=8 , type=lowerCAmelCase__ , help='''Batch size per GPU/CPU for evaluation.''' , )
parser.add_argument(
'''--recalculate''' , help='''Recalculate predictions even if the prediction file exists''' , action='''store_true''' , )
parser.add_argument(
'''--num_beams''' , default=4 , type=lowerCAmelCase__ , help='''Number of beams to be used when generating answers''' , )
parser.add_argument('''--min_length''' , default=1 , type=lowerCAmelCase__ , help='''Min length of the generated answers''' )
parser.add_argument('''--max_length''' , default=50 , type=lowerCAmelCase__ , help='''Max length of the generated answers''' )
parser.add_argument(
'''--print_predictions''' , action='''store_true''' , help='''If True, prints predictions while evaluating.''' , )
parser.add_argument(
'''--print_docs''' , action='''store_true''' , help='''If True, prints docs retried while generating.''' , )
__a = parser.parse_args()
__a = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
return args
def lowercase ( lowerCAmelCase__ : Union[str, Any] ) -> str:
__a = {}
if args.model_type is None:
__a = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith('''rag''' ):
__a = RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
__a = args.n_docs
if args.index_name is not None:
__a = args.index_name
if args.index_path is not None:
__a = args.index_path
else:
__a = BartForConditionalGeneration
__a = (
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info('''Evaluate the following checkpoints: %s''' , lowerCAmelCase__ )
__a = get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
__a = evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path ) )
score_fn(lowerCAmelCase__ , args.predictions_path , args.gold_data_path )
continue
logger.info('''***** Running evaluation for {} *****'''.format(lowerCAmelCase__ ) )
logger.info(''' Batch size = %d''' , args.eval_batch_size )
logger.info(''' Predictions will be stored under {}'''.format(args.predictions_path ) )
if args.model_type.startswith('''rag''' ):
__a = RagRetriever.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
__a = model_class.from_pretrained(lowerCAmelCase__ , retriever=lowerCAmelCase__ , **lowerCAmelCase__ )
model.retriever.init_retrieval()
else:
__a = model_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
model.to(args.device )
with open(args.evaluation_set , '''r''' ) as eval_file, open(args.predictions_path , '''w''' ) as preds_file:
__a = []
for line in tqdm(lowerCAmelCase__ ):
questions.append(line.strip() )
if len(lowerCAmelCase__ ) == args.eval_batch_size:
__a = evaluate_batch_fn(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
preds_file.write('''\n'''.join(lowerCAmelCase__ ) + '''\n''' )
preds_file.flush()
__a = []
if len(lowerCAmelCase__ ) > 0:
__a = evaluate_batch_fn(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
preds_file.write('''\n'''.join(lowerCAmelCase__ ) )
preds_file.flush()
score_fn(lowerCAmelCase__ , args.predictions_path , args.gold_data_path )
if __name__ == "__main__":
lowercase_ = get_args()
main(args)
| 45 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
a_ = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : Tuple ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Dict ):
for attribute in key.split('''.''' ):
__lowerCamelCase = getattr(_UpperCamelCase ,_UpperCamelCase )
if weight_type is not None:
__lowerCamelCase = getattr(_UpperCamelCase ,_UpperCamelCase ).shape
else:
__lowerCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__lowerCamelCase = value
elif weight_type == "weight_g":
__lowerCamelCase = value
elif weight_type == "weight_v":
__lowerCamelCase = value
elif weight_type == "bias":
__lowerCamelCase = value
else:
__lowerCamelCase = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def a__ ( _UpperCamelCase : Any ,_UpperCamelCase : Any ):
__lowerCamelCase = []
__lowerCamelCase = fairseq_model.state_dict()
__lowerCamelCase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowerCamelCase = False
if "conv_layers" in name:
load_conv_layer(
_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,hf_model.config.feat_extract_norm == '''group''' ,)
__lowerCamelCase = True
else:
for key, mapped_key in MAPPING.items():
__lowerCamelCase = '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
# special case since naming is very similar
continue
__lowerCamelCase = True
if "*" in mapped_key:
__lowerCamelCase = name.split(_UpperCamelCase )[0].split('''.''' )[-2]
__lowerCamelCase = mapped_key.replace('''*''' ,_UpperCamelCase )
if "weight_g" in name:
__lowerCamelCase = '''weight_g'''
elif "weight_v" in name:
__lowerCamelCase = '''weight_v'''
elif "bias" in name:
__lowerCamelCase = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCamelCase = '''weight'''
else:
__lowerCamelCase = None
set_recursively(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
continue
if not is_used:
unused_weights.append(_UpperCamelCase )
logger.warning(F"""Unused weights: {unused_weights}""" )
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Dict ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = full_name.split('''conv_layers.''' )[-1]
__lowerCamelCase = name.split('''.''' )
__lowerCamelCase = int(items[0] )
__lowerCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_UpperCamelCase )
@torch.no_grad()
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : List[str] ,_UpperCamelCase : Tuple=None ,_UpperCamelCase : Tuple=None ,_UpperCamelCase : List[Any]=True ):
if config_path is not None:
__lowerCamelCase = UniSpeechSatConfig.from_pretrained(_UpperCamelCase )
else:
__lowerCamelCase = UniSpeechSatConfig()
__lowerCamelCase = ''''''
if is_finetuned:
__lowerCamelCase = UniSpeechSatForCTC(_UpperCamelCase )
else:
__lowerCamelCase = UniSpeechSatForPreTraining(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
__lowerCamelCase = model[0].eval()
recursively_load_weights(_UpperCamelCase ,_UpperCamelCase )
hf_wavavec.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
a_ = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 330 | 0 |
"""simple docstring"""
from manim import *
class lowercase ( _UpperCAmelCase ):
def _snake_case ( self ) -> Tuple:
lowerCAmelCase = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
lowerCAmelCase = [mem.copy() for i in range(6 )]
lowerCAmelCase = [mem.copy() for i in range(6 )]
lowerCAmelCase = VGroup(*lowercase ).arrange(lowercase , buff=0 )
lowerCAmelCase = VGroup(*lowercase ).arrange(lowercase , buff=0 )
lowerCAmelCase = VGroup(lowercase , lowercase ).arrange(lowercase , buff=0 )
lowerCAmelCase = Text("""CPU""" , font_size=24 )
lowerCAmelCase = Group(lowercase , lowercase ).arrange(lowercase , buff=0.5 , aligned_edge=lowercase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowercase )
lowerCAmelCase = [mem.copy() for i in range(1 )]
lowerCAmelCase = VGroup(*lowercase ).arrange(lowercase , buff=0 )
lowerCAmelCase = Text("""GPU""" , font_size=24 )
lowerCAmelCase = Group(lowercase , lowercase ).arrange(lowercase , buff=0.5 , aligned_edge=lowercase )
gpu.align_to(lowercase , lowercase )
gpu.set_x(gpu.get_x() - 1 )
self.add(lowercase )
lowerCAmelCase = [mem.copy() for i in range(6 )]
lowerCAmelCase = VGroup(*lowercase ).arrange(lowercase , buff=0 )
lowerCAmelCase = Text("""Model""" , font_size=24 )
lowerCAmelCase = Group(lowercase , lowercase ).arrange(lowercase , buff=0.5 , aligned_edge=lowercase )
model.move_to([3, -1.0, 0] )
self.play(
Create(lowercase , run_time=1 ) , Create(lowercase , run_time=1 ) , Create(lowercase , run_time=1 ) , )
lowerCAmelCase = MarkupText(
f'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.' , font_size=24 , )
lowerCAmelCase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowerCAmelCase = MarkupText(
f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(lowercase , run_time=2.5 ) , Write(lowercase ) , Write(lowercase ) )
self.add(lowercase )
lowerCAmelCase = []
lowerCAmelCase = []
lowerCAmelCase = []
for i, rect in enumerate(lowercase ):
lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(lowercase , opacity=0.7 )
cpu_target.move_to(lowercase )
cpu_target.generate_target()
lowerCAmelCase = 0.46 / 4
lowerCAmelCase = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=lowercase )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=lowercase , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=lowercase , buff=0.0 )
cpu_targs.append(lowercase )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(lowercase ) )
second_animations.append(MoveToTarget(lowercase , run_time=1.5 ) )
self.play(*lowercase )
self.play(*lowercase )
self.wait()
| 46 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
a_ = logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase__ )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
requires_backends(self , '''vision''' )
self.check_model_type(__UpperCAmelCase )
def __call__( self , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return super().__call__(__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return {}, {}, {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = load_image(__UpperCAmelCase )
__lowerCamelCase = image.size
__lowerCamelCase = self.image_processor(images=__UpperCAmelCase , return_tensors=self.framework )
return model_inputs
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.model(**__UpperCAmelCase )
return model_outputs
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = model_outputs.predicted_depth
__lowerCamelCase = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode='''bicubic''' , align_corners=__UpperCAmelCase )
__lowerCamelCase = prediction.squeeze().cpu().numpy()
__lowerCamelCase = (output * 255 / np.max(__UpperCAmelCase )).astype('''uint8''' )
__lowerCamelCase = Image.fromarray(__UpperCAmelCase )
__lowerCamelCase = {}
__lowerCamelCase = predicted_depth
__lowerCamelCase = depth
return output_dict
| 330 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class A__ ( unittest.TestCase ):
def __init__( self : List[str] , _a : List[Any] , _a : List[str]=7 , _a : Optional[int]=3 , _a : Tuple=10 , _a : Optional[Any]=18 , _a : int=30 , _a : Optional[int]=400 , _a : Dict=True , _a : Union[str, Any]=None , _a : List[Any]=True , _a : str=[0.5, 0.5, 0.5] , _a : List[Any]=[0.5, 0.5, 0.5] , _a : str=None , ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =size if size is not None else {'shortest_edge': 18}
_SCREAMING_SNAKE_CASE =crop_size if crop_size is not None else {'height': 18, 'width': 18}
_SCREAMING_SNAKE_CASE =parent
_SCREAMING_SNAKE_CASE =batch_size
_SCREAMING_SNAKE_CASE =num_channels
_SCREAMING_SNAKE_CASE =num_frames
_SCREAMING_SNAKE_CASE =image_size
_SCREAMING_SNAKE_CASE =min_resolution
_SCREAMING_SNAKE_CASE =max_resolution
_SCREAMING_SNAKE_CASE =do_resize
_SCREAMING_SNAKE_CASE =size
_SCREAMING_SNAKE_CASE =do_normalize
_SCREAMING_SNAKE_CASE =image_mean
_SCREAMING_SNAKE_CASE =image_std
_SCREAMING_SNAKE_CASE =crop_size
def A ( self : int ) -> Dict:
'''simple docstring'''
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class A__ ( A__ , unittest.TestCase ):
A__ = VivitImageProcessor if is_vision_available() else None
def A ( self : int ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =VivitImageProcessingTester(self )
@property
def A ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , 'image_mean' ) )
self.assertTrue(hasattr(_a , 'image_std' ) )
self.assertTrue(hasattr(_a , 'do_normalize' ) )
self.assertTrue(hasattr(_a , 'do_resize' ) )
self.assertTrue(hasattr(_a , 'do_center_crop' ) )
self.assertTrue(hasattr(_a , 'size' ) )
def A ( self : str ) -> Any:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18} )
self.assertEqual(image_processor.crop_size , {'height': 18, 'width': 18} )
_SCREAMING_SNAKE_CASE =self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'shortest_edge': 42} )
self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84} )
def A ( self : Any ) -> str:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
_SCREAMING_SNAKE_CASE =prepare_video_inputs(self.image_processor_tester , equal_resolution=_a )
for video in video_inputs:
self.assertIsInstance(_a , _a )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
_SCREAMING_SNAKE_CASE =image_processing(video_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_SCREAMING_SNAKE_CASE =image_processing(_a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def A ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_SCREAMING_SNAKE_CASE =prepare_video_inputs(self.image_processor_tester , equal_resolution=_a , numpify=_a )
for video in video_inputs:
self.assertIsInstance(_a , _a )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
_SCREAMING_SNAKE_CASE =image_processing(video_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_SCREAMING_SNAKE_CASE =image_processing(_a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def A ( self : Optional[int] ) -> Any:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_SCREAMING_SNAKE_CASE =prepare_video_inputs(self.image_processor_tester , equal_resolution=_a , torchify=_a )
for video in video_inputs:
self.assertIsInstance(_a , _a )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
_SCREAMING_SNAKE_CASE =image_processing(video_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_SCREAMING_SNAKE_CASE =image_processing(_a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 47 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
a_ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = ["""pixel_values"""]
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = True , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
__lowerCamelCase = size if size is not None else {'''shortest_edge''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
__lowerCamelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase , param_name='''crop_size''' )
__lowerCamelCase = do_resize
__lowerCamelCase = size
__lowerCamelCase = resample
__lowerCamelCase = do_center_crop
__lowerCamelCase = crop_size
__lowerCamelCase = do_rescale
__lowerCamelCase = rescale_factor
__lowerCamelCase = do_normalize
__lowerCamelCase = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__lowerCamelCase = image_std if image_std is not None else OPENAI_CLIP_STD
__lowerCamelCase = do_convert_rgb
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
__lowerCamelCase = get_resize_output_image_size(__UpperCAmelCase , size=size['''shortest_edge'''] , default_to_square=__UpperCAmelCase )
return resize(__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(__UpperCAmelCase , size=(size['''height'''], size['''width''']) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = do_resize if do_resize is not None else self.do_resize
__lowerCamelCase = size if size is not None else self.size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = resample if resample is not None else self.resample
__lowerCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowerCamelCase = crop_size if crop_size is not None else self.crop_size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''crop_size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = do_rescale if do_rescale is not None else self.do_rescale
__lowerCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCamelCase = do_normalize if do_normalize is not None else self.do_normalize
__lowerCamelCase = image_mean if image_mean is not None else self.image_mean
__lowerCamelCase = image_std if image_std is not None else self.image_std
__lowerCamelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__lowerCamelCase = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__lowerCamelCase = [convert_to_rgb(__UpperCAmelCase ) for image in images]
# All transformations expect numpy arrays.
__lowerCamelCase = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
__lowerCamelCase = [self.resize(image=__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase ) for image in images]
if do_center_crop:
__lowerCamelCase = [self.center_crop(image=__UpperCAmelCase , size=__UpperCAmelCase ) for image in images]
if do_rescale:
__lowerCamelCase = [self.rescale(image=__UpperCAmelCase , scale=__UpperCAmelCase ) for image in images]
if do_normalize:
__lowerCamelCase = [self.normalize(image=__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase ) for image in images]
__lowerCamelCase = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
__lowerCamelCase = {'''pixel_values''': images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase )
| 330 | 0 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def A ( _SCREAMING_SNAKE_CASE ) -> Dict:
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class UpperCamelCase__ (nn.Module ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
super().__init__()
lowerCamelCase : int = module
lowerCamelCase : str = nn.Sequential(
nn.Linear(module.in_features , UpperCamelCase__ , bias=UpperCamelCase__ ) , nn.Linear(UpperCamelCase__ , module.out_features , bias=UpperCamelCase__ ) , )
lowerCamelCase : Any = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=UpperCamelCase__ )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def _lowercase ( self , UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ ) -> int:
return self.module(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ ) + self.adapter(UpperCamelCase__ )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ : Union[str, Any] = """bigscience/bloom-1b7"""
# Constant values
lowerCamelCase_ : int = 2.1_09_65_95_52_69_25_74
lowerCamelCase_ : Union[str, Any] = """Hello my name is"""
lowerCamelCase_ : Dict = set()
EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I""" )
EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n""" )
EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University""" )
lowerCamelCase_ : Optional[Any] = 1_0
def _lowercase ( self ) -> List[str]:
# Models and tokenizer
lowerCamelCase : str = AutoTokenizer.from_pretrained(self.model_name )
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def _lowercase ( self ) -> Tuple:
super().setUp()
# Models and tokenizer
lowerCamelCase : Tuple = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map="auto" )
lowerCamelCase : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="auto" )
def _lowercase ( self ) -> Dict:
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self ) -> Optional[Any]:
lowerCamelCase : int = self.model_abit.config
self.assertTrue(hasattr(UpperCamelCase__ , "quantization_config" ) )
lowerCamelCase : Optional[Any] = config.to_dict()
lowerCamelCase : Tuple = config.to_diff_dict()
lowerCamelCase : Tuple = config.to_json_string()
def _lowercase ( self ) -> Any:
from bitsandbytes.nn import Paramsabit
lowerCamelCase : Union[str, Any] = self.model_fpaa.get_memory_footprint()
lowerCamelCase : str = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
lowerCamelCase : Tuple = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def _lowercase ( self ) -> List[Any]:
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(UpperCamelCase__ , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def _lowercase ( self ) -> List[Any]:
lowerCamelCase : Optional[Any] = self.tokenizer(self.input_text , return_tensors="pt" )
lowerCamelCase : int = self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=UpperCamelCase__ ) , self.EXPECTED_OUTPUTS )
def _lowercase ( self ) -> Any:
lowerCamelCase : Dict = BitsAndBytesConfig()
lowerCamelCase : Dict = True
lowerCamelCase : Optional[int] = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=UpperCamelCase__ , device_map="auto" )
lowerCamelCase : Any = self.tokenizer(self.input_text , return_tensors="pt" )
lowerCamelCase : Optional[int] = model_abit_from_config.generate(
input_ids=encoded_input["input_ids"].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=UpperCamelCase__ ) , self.EXPECTED_OUTPUTS )
def _lowercase ( self ) -> str:
with self.assertRaises(UpperCamelCase__ ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(UpperCamelCase__ )
def _lowercase ( self ) -> Any:
lowerCamelCase : Dict = BitsAndBytesConfig()
with self.assertRaises(UpperCamelCase__ ):
lowerCamelCase : Any = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=UpperCamelCase__ , load_in_abit=UpperCamelCase__ , device_map="auto" , bnb_abit_quant_type="nf4" , )
def _lowercase ( self ) -> Optional[int]:
with self.assertRaises(UpperCamelCase__ ):
# Tries with `str`
self.model_abit.to("cpu" )
with self.assertRaises(UpperCamelCase__ ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(UpperCamelCase__ ):
# Tries with a `device`
self.model_abit.to(torch.device("cuda:0" ) )
with self.assertRaises(UpperCamelCase__ ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(UpperCamelCase__ ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
lowerCamelCase : Tuple = self.tokenizer(self.input_text , return_tensors="pt" )
lowerCamelCase : Optional[int] = self.model_fpaa.to(torch.floataa )
lowerCamelCase : Dict = self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
lowerCamelCase : Union[str, Any] = self.model_fpaa.to("cpu" )
# Check this does not throw an error
lowerCamelCase : List[Any] = self.model_fpaa.half()
# Check this does not throw an error
lowerCamelCase : Optional[int] = self.model_fpaa.float()
def _lowercase ( self ) -> Optional[Any]:
lowerCamelCase : List[str] = AutoModelForSeqaSeqLM.from_pretrained("t5-small" , load_in_abit=UpperCamelCase__ , device_map="auto" )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
@classmethod
def _lowercase ( cls ) -> Union[str, Any]:
lowerCamelCase : str = "t5-small"
lowerCamelCase : Optional[int] = "google/flan-t5-small" # flan-t5 uses dense-act instead of dense-relu-dense
lowerCamelCase : int = AutoTokenizer.from_pretrained(cls.model_name )
lowerCamelCase : Tuple = "Translate in German: Hello, my dog is cute"
def _lowercase ( self ) -> List[Any]:
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self ) -> Optional[int]:
from transformers import TaForConditionalGeneration
lowerCamelCase : int = TaForConditionalGeneration._keep_in_fpaa_modules
lowerCamelCase : List[Any] = None
# test with `t5-small`
lowerCamelCase : Union[str, Any] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="auto" )
lowerCamelCase : str = self.tokenizer(self.input_text , return_tensors="pt" ).to(0 )
lowerCamelCase : List[Any] = model.generate(**UpperCamelCase__ )
# test with `flan-t5-small`
lowerCamelCase : str = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=UpperCamelCase__ , device_map="auto" )
lowerCamelCase : str = self.tokenizer(self.input_text , return_tensors="pt" ).to(0 )
lowerCamelCase : Union[str, Any] = model.generate(**UpperCamelCase__ )
lowerCamelCase : int = modules
def _lowercase ( self ) -> int:
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
lowerCamelCase : List[str] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="auto" )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
lowerCamelCase : str = self.tokenizer(self.input_text , return_tensors="pt" ).to(0 )
lowerCamelCase : Union[str, Any] = model.generate(**UpperCamelCase__ )
# test with `flan-t5-small`
lowerCamelCase : List[Any] = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=UpperCamelCase__ , device_map="auto" )
lowerCamelCase : str = self.tokenizer(self.input_text , return_tensors="pt" ).to(0 )
lowerCamelCase : Any = model.generate(**UpperCamelCase__ )
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def _lowercase ( self ) -> Optional[Any]:
super().setUp()
# model_name
lowerCamelCase : Tuple = "bigscience/bloom-560m"
lowerCamelCase : List[Any] = "t5-small"
# Different types of model
lowerCamelCase : Dict = AutoModel.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="auto" )
# Sequence classification model
lowerCamelCase : Any = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=UpperCamelCase__ , device_map="auto" )
# CausalLM model
lowerCamelCase : Optional[Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="auto" )
# Seq2seq model
lowerCamelCase : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=UpperCamelCase__ , device_map="auto" )
def _lowercase ( self ) -> List[str]:
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self ) -> int:
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def _lowercase ( self ) -> List[str]:
super().setUp()
def _lowercase ( self ) -> str:
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self ) -> List[str]:
lowerCamelCase : Any = pipeline(
"text-generation" , model=self.model_name , model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
lowerCamelCase : List[Any] = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]["generated_text"] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def _lowercase ( self ) -> Optional[Any]:
super().setUp()
def _lowercase ( self ) -> List[Any]:
lowerCamelCase : Optional[int] = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=UpperCamelCase__ , device_map="balanced" )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
lowerCamelCase : Dict = self.tokenizer(self.input_text , return_tensors="pt" )
# Second real batch
lowerCamelCase : Any = model_parallel.generate(input_ids=encoded_input["input_ids"].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=UpperCamelCase__ ) , self.EXPECTED_OUTPUTS )
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def _lowercase ( self ) -> Optional[Any]:
lowerCamelCase : Dict = "facebook/opt-350m"
super().setUp()
def _lowercase ( self ) -> Dict:
if version.parse(importlib.metadata.version("bitsandbytes" ) ) < version.parse("0.37.0" ):
return
# Step 1: freeze all parameters
lowerCamelCase : Optional[Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
lowerCamelCase : Any = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
lowerCamelCase : Optional[Any] = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(UpperCamelCase__ ) ):
lowerCamelCase : Dict = LoRALayer(module.q_proj , rank=16 )
lowerCamelCase : Any = LoRALayer(module.k_proj , rank=16 )
lowerCamelCase : Tuple = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
lowerCamelCase : int = self.tokenizer("Test batch " , return_tensors="pt" ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
lowerCamelCase : Tuple = model.forward(**UpperCamelCase__ )
out.logits.norm().backward()
for module in model.modules():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(UpperCamelCase__ , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : str = """gpt2-xl"""
lowerCamelCase_ : int = 3.31_91_85_48_54_15_21_87
| 48 |
from __future__ import annotations
from typing import Generic, TypeVar
a_ = TypeVar("""T""")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = data
__lowerCamelCase = self
__lowerCamelCase = 0
class __lowerCAmelCase ( Generic[T] ):
def __init__( self ):
'''simple docstring'''
# map from node name to the node object
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# create a new set with x as its member
__lowerCamelCase = DisjointSetTreeNode(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# find the set x belongs to (with path-compression)
__lowerCamelCase = self.map[data]
if elem_ref != elem_ref.parent:
__lowerCamelCase = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# helper function for union operation
if nodea.rank > nodea.rank:
__lowerCamelCase = nodea
else:
__lowerCamelCase = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# merge 2 disjoint sets
self.link(self.find_set(__UpperCAmelCase ) , self.find_set(__UpperCAmelCase ) )
class __lowerCAmelCase ( Generic[T] ):
def __init__( self ):
'''simple docstring'''
# connections: map from the node to the neighbouring nodes (with weights)
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# add a node ONLY if its not present in the graph
if node not in self.connections:
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# add an edge with the given weight
self.add_node(__UpperCAmelCase )
self.add_node(__UpperCAmelCase )
__lowerCamelCase = weight
__lowerCamelCase = weight
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda __UpperCAmelCase : x[2] )
# creating the disjoint set
__lowerCamelCase = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(__UpperCAmelCase )
# MST generation
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = edges[index]
index += 1
__lowerCamelCase = disjoint_set.find_set(__UpperCAmelCase )
__lowerCamelCase = disjoint_set.find_set(__UpperCAmelCase )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
disjoint_set.union(__UpperCAmelCase , __UpperCAmelCase )
return graph
| 330 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__snake_case :Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
def __snake_case ( _UpperCAmelCase ):
if not path:
return "pipe"
for ext in PipelineDataFormat.SUPPORTED_FORMATS:
if path.endswith(_UpperCAmelCase ):
return ext
raise Exception(
f'Unable to determine file format from file extension {path}. '
f'Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}' )
def __snake_case ( _UpperCAmelCase ):
__a = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
__a = try_infer_format_from_ext(args.input ) if args.format == '''infer''' else args.format
__a = PipelineDataFormat.from_str(
format=_UpperCAmelCase , output_path=args.output , input_path=args.input , column=args.column if args.column else nlp.default_input_names , overwrite=args.overwrite , )
return RunCommand(_UpperCAmelCase , _UpperCAmelCase )
class _A ( __UpperCAmelCase ):
def __init__( self : int , __SCREAMING_SNAKE_CASE : Pipeline , __SCREAMING_SNAKE_CASE : PipelineDataFormat):
'''simple docstring'''
__a = nlp
__a = reader
@staticmethod
def _lowerCamelCase ( __SCREAMING_SNAKE_CASE : ArgumentParser):
'''simple docstring'''
__a = parser.add_parser('''run''' , help='''Run a pipeline through the CLI''')
run_parser.add_argument('''--task''' , choices=get_supported_tasks() , help='''Task to run''')
run_parser.add_argument('''--input''' , type=__SCREAMING_SNAKE_CASE , help='''Path to the file to use for inference''')
run_parser.add_argument('''--output''' , type=__SCREAMING_SNAKE_CASE , help='''Path to the file that will be used post to write results.''')
run_parser.add_argument('''--model''' , type=__SCREAMING_SNAKE_CASE , help='''Name or path to the model to instantiate.''')
run_parser.add_argument('''--config''' , type=__SCREAMING_SNAKE_CASE , help='''Name or path to the model\'s config to instantiate.''')
run_parser.add_argument(
'''--tokenizer''' , type=__SCREAMING_SNAKE_CASE , help='''Name of the tokenizer to use. (default: same as the model name)''')
run_parser.add_argument(
'''--column''' , type=__SCREAMING_SNAKE_CASE , help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)''' , )
run_parser.add_argument(
'''--format''' , type=__SCREAMING_SNAKE_CASE , default='''infer''' , choices=PipelineDataFormat.SUPPORTED_FORMATS , help='''Input format to read from''' , )
run_parser.add_argument(
'''--device''' , type=__SCREAMING_SNAKE_CASE , default=-1 , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' , )
run_parser.add_argument('''--overwrite''' , action='''store_true''' , help='''Allow overwriting the output file.''')
run_parser.set_defaults(func=__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__a , __a = self._nlp, []
for entry in self._reader:
__a = nlp(**__SCREAMING_SNAKE_CASE) if self._reader.is_multi_columns else nlp(__SCREAMING_SNAKE_CASE)
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
outputs.append(__SCREAMING_SNAKE_CASE)
else:
outputs += output
# Saving data
if self._nlp.binary_output:
__a = self._reader.save_binary(__SCREAMING_SNAKE_CASE)
logger.warning(F'Current pipeline requires output to be in binary format, saving at {binary_path}')
else:
self._reader.save(__SCREAMING_SNAKE_CASE)
| 49 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=2 , __UpperCAmelCase=8 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=16 , __UpperCAmelCase=5 , __UpperCAmelCase=2 , __UpperCAmelCase=36 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=512 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_input_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase ( self ):
'''simple docstring'''
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_config()
__lowerCamelCase = 300
return config
def lowerCamelCase ( self ):
'''simple docstring'''
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = self.prepare_config_and_inputs()
__lowerCamelCase = True
__lowerCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = True
__lowerCamelCase = MraModel(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , )
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , )
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraForQuestionAnswering(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MraForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MraForTokenClassification(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_choices
__lowerCamelCase = MraForMultipleChoice(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = ()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCamelCase = type
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = MraModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@unittest.skip(reason='''MRA does not output attentions''' )
def lowerCamelCase ( self ):
'''simple docstring'''
return
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowerCamelCase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowerCamelCase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = 50265
__lowerCamelCase = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' )
__lowerCamelCase = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = 50265
__lowerCamelCase = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 330 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
_UpperCAmelCase : Any = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
_UpperCAmelCase : int = {
"""vocab_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
),
"""google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
},
"""tokenizer_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-openqa""": (
"""https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-reader""": (
"""https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-openqa""": (
"""https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-reader""": (
"""https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
),
},
}
_UpperCAmelCase : int = {
"""google/realm-cc-news-pretrained-embedder""": 5_12,
"""google/realm-cc-news-pretrained-encoder""": 5_12,
"""google/realm-cc-news-pretrained-scorer""": 5_12,
"""google/realm-cc-news-pretrained-openqa""": 5_12,
"""google/realm-orqa-nq-openqa""": 5_12,
"""google/realm-orqa-nq-reader""": 5_12,
"""google/realm-orqa-wq-openqa""": 5_12,
"""google/realm-orqa-wq-reader""": 5_12,
}
_UpperCAmelCase : Any = {
"""google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = VOCAB_FILES_NAMES
UpperCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ = PRETRAINED_INIT_CONFIGURATION
UpperCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ = RealmTokenizer
def __init__( self : Optional[int] , UpperCAmelCase : Tuple=None , UpperCAmelCase : Any=None , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Optional[Any]="[UNK]" , UpperCAmelCase : Any="[SEP]" , UpperCAmelCase : Tuple="[PAD]" , UpperCAmelCase : List[Any]="[CLS]" , UpperCAmelCase : Union[str, Any]="[MASK]" , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : Any=None , **UpperCAmelCase : Optional[int] , ) -> str:
super().__init__(
UpperCAmelCase , tokenizer_file=UpperCAmelCase , do_lower_case=UpperCAmelCase , unk_token=UpperCAmelCase , sep_token=UpperCAmelCase , pad_token=UpperCAmelCase , cls_token=UpperCAmelCase , mask_token=UpperCAmelCase , tokenize_chinese_chars=UpperCAmelCase , strip_accents=UpperCAmelCase , **UpperCAmelCase , )
lowerCamelCase__ : List[Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , UpperCAmelCase ) != do_lower_case
or normalizer_state.get('strip_accents' , UpperCAmelCase ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , UpperCAmelCase ) != tokenize_chinese_chars
):
lowerCamelCase__ : Optional[int] = getattr(UpperCAmelCase , normalizer_state.pop('type' ) )
lowerCamelCase__ : Optional[Any] = do_lower_case
lowerCamelCase__ : str = strip_accents
lowerCamelCase__ : Optional[Any] = tokenize_chinese_chars
lowerCamelCase__ : int = normalizer_class(**UpperCAmelCase )
lowerCamelCase__ : str = do_lower_case
def A_ ( self : Optional[int] , UpperCAmelCase : int , **UpperCAmelCase : int ) -> List[Any]:
lowerCamelCase__ : List[Any] = PaddingStrategy.MAX_LENGTH
lowerCamelCase__ : Optional[int] = text
lowerCamelCase__ : Dict = kwargs.pop('text_pair' , UpperCAmelCase )
lowerCamelCase__ : List[Any] = kwargs.pop('return_tensors' , UpperCAmelCase )
lowerCamelCase__ : List[Any] = {
'input_ids': [],
'attention_mask': [],
'token_type_ids': [],
}
for idx, candidate_text in enumerate(UpperCAmelCase ):
if batch_text_pair is not None:
lowerCamelCase__ : Tuple = batch_text_pair[idx]
else:
lowerCamelCase__ : Dict = None
lowerCamelCase__ : Optional[int] = super().__call__(UpperCAmelCase , UpperCAmelCase , return_tensors=UpperCAmelCase , **UpperCAmelCase )
lowerCamelCase__ : Any = encoded_candidates.get('input_ids' )
lowerCamelCase__ : Union[str, Any] = encoded_candidates.get('attention_mask' )
lowerCamelCase__ : Tuple = encoded_candidates.get('token_type_ids' )
if encoded_input_ids is not None:
output_data["input_ids"].append(UpperCAmelCase )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(UpperCAmelCase )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(UpperCAmelCase )
lowerCamelCase__ : int = {key: item for key, item in output_data.items() if len(UpperCAmelCase ) != 0}
return BatchEncoding(UpperCAmelCase , tensor_type=UpperCAmelCase )
def A_ ( self : int , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=None ) -> List[str]:
lowerCamelCase__ : Tuple = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A_ ( self : Tuple , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
lowerCamelCase__ : List[Any] = [self.sep_token_id]
lowerCamelCase__ : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def A_ ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
lowerCamelCase__ : int = self._tokenizer.model.save(UpperCAmelCase , name=UpperCAmelCase )
return tuple(UpperCAmelCase )
| 50 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""EncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""TFEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""FlaxEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 0 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __snake_case ( a ):
def __init__( self : str , _snake_case : NestedDataStructureLike[PathLike] , _snake_case : Optional[NamedSplit] = None , _snake_case : Optional[Features] = None , _snake_case : str = None , _snake_case : bool = False , _snake_case : bool = False , _snake_case : Optional[int] = None , **_snake_case : Optional[int] , ):
"""simple docstring"""
super().__init__(
_snake_case , split=_snake_case , features=_snake_case , cache_dir=_snake_case , keep_in_memory=_snake_case , streaming=_snake_case , num_proc=_snake_case , **_snake_case , )
UpperCAmelCase_ = path_or_paths if isinstance(_snake_case , _snake_case) else {self.split: path_or_paths}
UpperCAmelCase_ = Text(
cache_dir=_snake_case , data_files=_snake_case , features=_snake_case , **_snake_case , )
def lowerCamelCase ( self : int):
"""simple docstring"""
if self.streaming:
UpperCAmelCase_ = self.builder.as_streaming_dataset(split=self.split)
# Build regular (map-style) dataset
else:
UpperCAmelCase_ = None
UpperCAmelCase_ = None
UpperCAmelCase_ = None
UpperCAmelCase_ = None
self.builder.download_and_prepare(
download_config=_snake_case , download_mode=_snake_case , verification_mode=_snake_case , base_path=_snake_case , num_proc=self.num_proc , )
UpperCAmelCase_ = self.builder.as_dataset(
split=self.split , verification_mode=_snake_case , in_memory=self.keep_in_memory)
return dataset
| 51 |
from string import ascii_lowercase, ascii_uppercase
def a__ ( _UpperCamelCase : str ):
if not sentence:
return ""
__lowerCamelCase = dict(zip(_UpperCamelCase ,_UpperCamelCase ) )
return lower_to_upper.get(sentence[0] ,sentence[0] ) + sentence[1:]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 330 | 0 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class A__ ( __snake_case ):
def __init__( self , *A_ , A_=None , A_=None , **A_ ):
'''simple docstring'''
super().__init__(*A_ , **A_ )
UpperCamelCase : str = eval_examples
UpperCamelCase : int = post_process_function
def __UpperCamelCase( self , A_=None , A_=None , A_=None , A_ = "eval" ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = self.eval_dataset if eval_dataset is None else eval_dataset
UpperCamelCase : str = self.get_eval_dataloader(A_ )
UpperCamelCase : List[Any] = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
UpperCamelCase : Dict = self.compute_metrics
UpperCamelCase : Dict = None
UpperCamelCase : Optional[Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
UpperCamelCase : List[str] = time.time()
try:
UpperCamelCase : Optional[int] = eval_loop(
A_ , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A_ , metric_key_prefix=A_ , )
finally:
UpperCamelCase : Optional[Any] = compute_metrics
UpperCamelCase : Tuple = self.args.eval_batch_size * self.args.world_size
if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
A_ , A_ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
UpperCamelCase : Tuple = self.post_process_function(A_ , A_ , output.predictions )
UpperCamelCase : Tuple = self.compute_metrics(A_ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"""{metric_key_prefix}_""" ):
UpperCamelCase : Tuple = metrics.pop(A_ )
metrics.update(output.metrics )
else:
UpperCamelCase : Union[str, Any] = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(A_ )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
UpperCamelCase : Tuple = self.callback_handler.on_evaluate(self.args , self.state , self.control , A_ )
return metrics
def __UpperCamelCase( self , A_ , A_ , A_=None , A_ = "test" ):
'''simple docstring'''
UpperCamelCase : Any = self.get_test_dataloader(A_ )
# Temporarily disable metric computation, we will do it in the loop here.
UpperCamelCase : Any = self.compute_metrics
UpperCamelCase : List[Any] = None
UpperCamelCase : str = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
UpperCamelCase : Tuple = time.time()
try:
UpperCamelCase : List[Any] = eval_loop(
A_ , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A_ , metric_key_prefix=A_ , )
finally:
UpperCamelCase : List[str] = compute_metrics
UpperCamelCase : Union[str, Any] = self.args.eval_batch_size * self.args.world_size
if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
A_ , A_ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
UpperCamelCase : List[str] = self.post_process_function(A_ , A_ , output.predictions , "predict" )
UpperCamelCase : List[str] = self.compute_metrics(A_ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"""{metric_key_prefix}_""" ):
UpperCamelCase : str = metrics.pop(A_ )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=A_ )
| 52 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowerCAmelCase ( lowerCAmelCase__ ):
@slow
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' , '''prajjwal1/bert-tiny''' )
__lowerCamelCase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
__lowerCamelCase = bertabert.config.encoder.vocab_size
__lowerCamelCase = tokenizer.sep_token_id
__lowerCamelCase = tokenizer.cls_token_id
__lowerCamelCase = 128
__lowerCamelCase = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''train[:1%]''' )
__lowerCamelCase = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''validation[:1%]''' )
__lowerCamelCase = train_dataset.select(range(32 ) )
__lowerCamelCase = val_dataset.select(range(16 ) )
__lowerCamelCase = 4
def _map_to_encoder_decoder_inputs(__UpperCAmelCase ):
# Tokenizer will automatically set [BOS] <text> [EOS]
__lowerCamelCase = tokenizer(batch['''article'''] , padding='''max_length''' , truncation=__UpperCAmelCase , max_length=512 )
__lowerCamelCase = tokenizer(batch['''highlights'''] , padding='''max_length''' , truncation=__UpperCAmelCase , max_length=128 )
__lowerCamelCase = inputs.input_ids
__lowerCamelCase = inputs.attention_mask
__lowerCamelCase = outputs.input_ids
__lowerCamelCase = outputs.input_ids.copy()
__lowerCamelCase = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
]
__lowerCamelCase = outputs.attention_mask
assert all(len(__UpperCAmelCase ) == 512 for x in inputs.input_ids )
assert all(len(__UpperCAmelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(__UpperCAmelCase ):
__lowerCamelCase = pred.label_ids
__lowerCamelCase = pred.predictions
# all unnecessary tokens are removed
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = sum([int(pred_str[i] == label_str[i] ) for i in range(len(__UpperCAmelCase ) )] ) / len(__UpperCAmelCase )
return {"accuracy": accuracy}
# map train dataset
__lowerCamelCase = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=__UpperCAmelCase , batch_size=__UpperCAmelCase , remove_columns=['''article''', '''highlights'''] , )
train_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
# same for validation dataset
__lowerCamelCase = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=__UpperCAmelCase , batch_size=__UpperCAmelCase , remove_columns=['''article''', '''highlights'''] , )
val_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
__lowerCamelCase = self.get_auto_remove_tmp_dir()
__lowerCamelCase = SeqaSeqTrainingArguments(
output_dir=__UpperCAmelCase , per_device_train_batch_size=__UpperCAmelCase , per_device_eval_batch_size=__UpperCAmelCase , predict_with_generate=__UpperCAmelCase , evaluation_strategy='''steps''' , do_train=__UpperCAmelCase , do_eval=__UpperCAmelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
__lowerCamelCase = SeqaSeqTrainer(
model=__UpperCAmelCase , args=__UpperCAmelCase , compute_metrics=_compute_metrics , train_dataset=__UpperCAmelCase , eval_dataset=__UpperCAmelCase , tokenizer=__UpperCAmelCase , )
# start training
trainer.train()
| 330 | 0 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a__ : Optional[Any] =abspath(join(dirname(dirname(dirname(__file__))), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def lowercase__ ( __lowercase : List[str] ) -> Optional[int]:
"""simple docstring"""
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__lowercase )
def lowercase__ ( __lowercase : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
from transformers.testing_utils import pytest_terminal_summary_main
__UpperCamelCase = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(__lowercase , id=__lowercase )
| 53 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_timm_backbone""": ["""TimmBackboneConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""TimmBackbone"""]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 0 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = len(lowerCAmelCase_ )
for i in range(n - 1 ):
for j in range(i + 1 , lowerCAmelCase_ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if len(lowerCAmelCase_ ) <= 1:
return arr, 0
__SCREAMING_SNAKE_CASE = len(lowerCAmelCase_ ) // 2
__SCREAMING_SNAKE_CASE = arr[0:mid]
__SCREAMING_SNAKE_CASE = arr[mid:]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = count_inversions_recursive(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = count_inversions_recursive(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = _count_cross_inversions(lowerCAmelCase_ , lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = __SCREAMING_SNAKE_CASE = __SCREAMING_SNAKE_CASE = 0
while i < len(lowerCAmelCase_ ) and j < len(lowerCAmelCase_ ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(lowerCAmelCase_ ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(lowerCAmelCase_ ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def UpperCAmelCase__ ():
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
__SCREAMING_SNAKE_CASE = count_inversions_bf(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = count_inversions_recursive(lowerCAmelCase_ )
assert num_inversions_bf == num_inversions_recursive == 8
print("number of inversions = " , lowerCAmelCase_ )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
__SCREAMING_SNAKE_CASE = count_inversions_bf(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = count_inversions_recursive(lowerCAmelCase_ )
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = " , lowerCAmelCase_ )
# an empty list should also have zero inversions
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = count_inversions_bf(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = count_inversions_recursive(lowerCAmelCase_ )
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = " , lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 54 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase=0.01 , __UpperCAmelCase=1000 ):
'''simple docstring'''
__lowerCamelCase = p_stop
__lowerCamelCase = max_length
def __iter__( self ):
'''simple docstring'''
__lowerCamelCase = 0
__lowerCamelCase = False
while not stop and count < self.max_length:
yield count
count += 1
__lowerCamelCase = random.random() < self.p_stop
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=True ):
'''simple docstring'''
__lowerCamelCase = [
BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
for i in range(2 )
]
__lowerCamelCase = [list(__UpperCAmelCase ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(__UpperCAmelCase ) for shard in batch_sampler_shards] , [len(__UpperCAmelCase ) for e in expected] )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of total batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of total batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
__lowerCamelCase = [BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , even_batches=__UpperCAmelCase ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=2 , __UpperCAmelCase=False ):
'''simple docstring'''
random.seed(__UpperCAmelCase )
__lowerCamelCase = list(__UpperCAmelCase )
__lowerCamelCase = [
IterableDatasetShard(
__UpperCAmelCase , batch_size=__UpperCAmelCase , drop_last=__UpperCAmelCase , num_processes=__UpperCAmelCase , process_index=__UpperCAmelCase , split_batches=__UpperCAmelCase , )
for i in range(__UpperCAmelCase )
]
__lowerCamelCase = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(__UpperCAmelCase )
iterable_dataset_lists.append(list(__UpperCAmelCase ) )
__lowerCamelCase = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
__lowerCamelCase = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
self.assertTrue(len(__UpperCAmelCase ) % shard_batch_size == 0 )
__lowerCamelCase = []
for idx in range(0 , len(__UpperCAmelCase ) , __UpperCAmelCase ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(__UpperCAmelCase ) < len(__UpperCAmelCase ):
reference += reference
self.assertListEqual(__UpperCAmelCase , reference[: len(__UpperCAmelCase )] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 42
__lowerCamelCase = RandomIterableDataset()
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
# Edge case with a very small dataset
__lowerCamelCase = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BatchSampler(range(16 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = SkipBatchSampler(__UpperCAmelCase , 2 )
self.assertListEqual(list(__UpperCAmelCase ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DataLoader(list(range(16 ) ) , batch_size=4 )
__lowerCamelCase = skip_first_batches(__UpperCAmelCase , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def lowerCamelCase ( self ):
'''simple docstring'''
Accelerator()
__lowerCamelCase = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 330 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def __snake_case ( UpperCAmelCase_ : str="" ):
lowerCamelCase_ = tempfile.mkdtemp()
return os.path.join(UpperCAmelCase_ , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = torch.rand(12 , dtype=torch.floataa ) - 0.5
lowerCamelCase_ = AgentAudio(UpperCamelCase )
lowerCamelCase_ = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(UpperCamelCase , agent_type.to_raw() , atol=1e-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(UpperCamelCase ) )
# Ensure that the file contains the same value as the original tensor
lowerCamelCase_ ,lowerCamelCase_ = sf.read(UpperCamelCase )
self.assertTrue(torch.allclose(UpperCamelCase , torch.tensor(UpperCamelCase ) , atol=1e-4 ) )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = torch.rand(12 , dtype=torch.floataa ) - 0.5
lowerCamelCase_ = get_new_path(suffix=".wav" )
sf.write(UpperCamelCase , UpperCamelCase , 1_6000 )
lowerCamelCase_ = AgentAudio(UpperCamelCase )
self.assertTrue(torch.allclose(UpperCamelCase , agent_type.to_raw() , atol=1e-4 ) )
self.assertEqual(agent_type.to_string() , UpperCamelCase )
@require_vision
@require_torch
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = torch.randint(0 , 256 , (64, 64, 3) )
lowerCamelCase_ = AgentImage(UpperCamelCase )
lowerCamelCase_ = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(UpperCamelCase , agent_type._tensor , atol=1e-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(UpperCamelCase ) )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png"
lowerCamelCase_ = Image.open(UpperCamelCase )
lowerCamelCase_ = AgentImage(UpperCamelCase )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(UpperCamelCase ) )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png"
lowerCamelCase_ = Image.open(UpperCamelCase )
lowerCamelCase_ = AgentImage(UpperCamelCase )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(UpperCamelCase ) )
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = "Hey!"
lowerCamelCase_ = AgentText(UpperCamelCase )
self.assertEqual(UpperCamelCase , agent_type.to_string() )
self.assertEqual(UpperCamelCase , agent_type.to_raw() )
self.assertEqual(UpperCamelCase , UpperCamelCase )
| 55 |
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 330 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
if height >= 1:
move_tower(height - 1, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase )
move_disk(__UpperCAmelCase, __UpperCAmelCase )
move_tower(height - 1, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
print('''moving disk from''', __UpperCAmelCase, '''to''', __UpperCAmelCase )
def __magic_name__ ( ) -> List[Any]:
'''simple docstring'''
snake_case_ = int(input('''Height of hanoi: ''' ).strip() )
move_tower(__UpperCAmelCase, '''A''', '''B''', '''C''' )
if __name__ == "__main__":
main()
| 56 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
a_ = 16
a_ = 32
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : int = 16 ):
__lowerCamelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' )
def tokenize_function(_UpperCamelCase : Optional[Any] ):
# max_length=None => use the model max length (it's actually the default)
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__lowerCamelCase = datasets.map(
_UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowerCamelCase = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__lowerCamelCase = 16
elif accelerator.mixed_precision != "no":
__lowerCamelCase = 8
else:
__lowerCamelCase = None
return tokenizer.pad(
_UpperCamelCase ,padding='''longest''' ,max_length=_UpperCamelCase ,pad_to_multiple_of=_UpperCamelCase ,return_tensors='''pt''' ,)
# Instantiate dataloaders.
__lowerCamelCase = DataLoader(
tokenized_datasets['''train'''] ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=_UpperCamelCase )
__lowerCamelCase = DataLoader(
tokenized_datasets['''validation'''] ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=_UpperCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
a_ = mocked_dataloaders # noqa: F811
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ):
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,_UpperCamelCase ) == "1":
__lowerCamelCase = 2
# Initialize accelerator
__lowerCamelCase = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCamelCase = config['''lr''']
__lowerCamelCase = int(config['''num_epochs'''] )
__lowerCamelCase = int(config['''seed'''] )
__lowerCamelCase = int(config['''batch_size'''] )
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=_UpperCamelCase )
def inner_training_loop(_UpperCamelCase : Union[str, Any] ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(_UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,return_dict=_UpperCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowerCamelCase = model.to(accelerator.device )
# Instantiate optimizer
__lowerCamelCase = AdamW(params=model.parameters() ,lr=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = get_dataloaders(_UpperCamelCase ,_UpperCamelCase )
# Instantiate scheduler
__lowerCamelCase = get_linear_schedule_with_warmup(
optimizer=_UpperCamelCase ,num_warmup_steps=1_00 ,num_training_steps=(len(_UpperCamelCase ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(
_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
# Now we train the model
for epoch in range(_UpperCamelCase ):
model.train()
for step, batch in enumerate(_UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.loss
accelerator.backward(_UpperCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_UpperCamelCase ,references=_UpperCamelCase ,)
__lowerCamelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" ,_UpperCamelCase )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def a__ ( ):
__lowerCamelCase = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' ,type=_UpperCamelCase ,default=_UpperCamelCase ,choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' ,)
parser.add_argument('''--cpu''' ,action='''store_true''' ,help='''If passed, will train on the CPU.''' )
__lowerCamelCase = parser.parse_args()
__lowerCamelCase = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_UpperCamelCase ,_UpperCamelCase )
if __name__ == "__main__":
main()
| 330 | 0 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
__lowerCAmelCase = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(_UpperCamelCase )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
a_ = None
try:
import msvcrt
except ImportError:
a_ = None
try:
import fcntl
except ImportError:
a_ = None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
a_ = OSError
# Data
# ------------------------------------------------
a_ = [
"""Timeout""",
"""BaseFileLock""",
"""WindowsFileLock""",
"""UnixFileLock""",
"""SoftFileLock""",
"""FileLock""",
]
a_ = """3.0.12"""
a_ = None
def a__ ( ):
global _logger
__lowerCamelCase = _logger or logging.getLogger(__name__ )
return _logger
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = lock_file
return None
def __str__( self ):
'''simple docstring'''
__lowerCamelCase = F"""The file lock '{self.lock_file}' could not be acquired."""
return temp
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = lock
return None
def __enter__( self ):
'''simple docstring'''
return self.lock
def __exit__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.lock.release()
return None
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = max_filename_length if max_filename_length is not None else 255
# Hash the filename if it's too long
__lowerCamelCase = self.hash_filename_if_too_long(__UpperCAmelCase , __UpperCAmelCase )
# The path to the lock file.
__lowerCamelCase = lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
__lowerCamelCase = None
# The default timeout value.
__lowerCamelCase = timeout
# We use this lock primarily for the lock counter.
__lowerCamelCase = threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
__lowerCamelCase = 0
return None
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._lock_file
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._timeout
@timeout.setter
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = float(__UpperCAmelCase )
return None
def lowerCamelCase ( self ):
'''simple docstring'''
raise NotImplementedError()
def lowerCamelCase ( self ):
'''simple docstring'''
raise NotImplementedError()
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._lock_file_fd is not None
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=0.05 ):
'''simple docstring'''
# Use the default timeout, if no timeout is provided.
if timeout is None:
__lowerCamelCase = self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
__lowerCamelCase = id(self )
__lowerCamelCase = self._lock_file
__lowerCamelCase = time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(F"""Attempting to acquire lock {lock_id} on {lock_filename}""" )
self._acquire()
if self.is_locked:
logger().debug(F"""Lock {lock_id} acquired on {lock_filename}""" )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(F"""Timeout on acquiring lock {lock_id} on {lock_filename}""" )
raise Timeout(self._lock_file )
else:
logger().debug(
F"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...""" )
time.sleep(__UpperCAmelCase )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
__lowerCamelCase = max(0 , self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def lowerCamelCase ( self , __UpperCAmelCase=False ):
'''simple docstring'''
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
__lowerCamelCase = id(self )
__lowerCamelCase = self._lock_file
logger().debug(F"""Attempting to release lock {lock_id} on {lock_filename}""" )
self._release()
__lowerCamelCase = 0
logger().debug(F"""Lock {lock_id} released on {lock_filename}""" )
return None
def __enter__( self ):
'''simple docstring'''
self.acquire()
return self
def __exit__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.release()
return None
def __del__( self ):
'''simple docstring'''
self.release(force=__UpperCAmelCase )
return None
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = os.path.basename(__UpperCAmelCase )
if len(__UpperCAmelCase ) > max_length and max_length > 0:
__lowerCamelCase = os.path.dirname(__UpperCAmelCase )
__lowerCamelCase = str(hash(__UpperCAmelCase ) )
__lowerCamelCase = filename[: max_length - len(__UpperCAmelCase ) - 8] + '''...''' + hashed_filename + '''.lock'''
return os.path.join(__UpperCAmelCase , __UpperCAmelCase )
else:
return path
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
from .file_utils import relative_to_absolute_path
super().__init__(__UpperCAmelCase , timeout=__UpperCAmelCase , max_filename_length=__UpperCAmelCase )
__lowerCamelCase = '''\\\\?\\''' + relative_to_absolute_path(self.lock_file )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
except OSError:
pass
else:
try:
msvcrt.locking(__UpperCAmelCase , msvcrt.LK_NBLCK , 1 )
except OSError:
os.close(__UpperCAmelCase )
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self._lock_file_fd
__lowerCamelCase = None
msvcrt.locking(__UpperCAmelCase , msvcrt.LK_UNLCK , 1 )
os.close(__UpperCAmelCase )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = os.statvfs(os.path.dirname(__UpperCAmelCase ) ).f_namemax
super().__init__(__UpperCAmelCase , timeout=__UpperCAmelCase , max_filename_length=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_RDWR | os.O_CREAT | os.O_TRUNC
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
try:
fcntl.flock(__UpperCAmelCase , fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(__UpperCAmelCase )
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
# Do not remove the lockfile:
#
# https://github.com/benediktschmitt/py-filelock/issues/31
# https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
__lowerCamelCase = self._lock_file_fd
__lowerCamelCase = None
fcntl.flock(__UpperCAmelCase , fcntl.LOCK_UN )
os.close(__UpperCAmelCase )
return None
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
except OSError:
pass
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
os.close(self._lock_file_fd )
__lowerCamelCase = None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
a_ = None
if msvcrt:
a_ = WindowsFileLock
elif fcntl:
a_ = UnixFileLock
else:
a_ = SoftFileLock
if warnings is not None:
warnings.warn("""only soft file lock is available""")
| 330 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class a_ :
'''simple docstring'''
def __init__( self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> int:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = seq_length
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_input_mask
_SCREAMING_SNAKE_CASE = use_token_type_ids
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = type_vocab_size
_SCREAMING_SNAKE_CASE = type_sequence_label_size
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = num_labels
_SCREAMING_SNAKE_CASE = num_choices
_SCREAMING_SNAKE_CASE = scope
def snake_case_( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
_SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case_( self ) -> Any:
return NystromformerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
def snake_case_( self , A , A , A , A , A , A , A ) -> str:
_SCREAMING_SNAKE_CASE = NystromformerModel(config=A )
model.to(A )
model.eval()
_SCREAMING_SNAKE_CASE = model(A , attention_mask=A , token_type_ids=A )
_SCREAMING_SNAKE_CASE = model(A , token_type_ids=A )
_SCREAMING_SNAKE_CASE = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_( self , A , A , A , A , A , A , A ) -> str:
_SCREAMING_SNAKE_CASE = NystromformerForMaskedLM(config=A )
model.to(A )
model.eval()
_SCREAMING_SNAKE_CASE = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_( self , A , A , A , A , A , A , A ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = NystromformerForQuestionAnswering(config=A )
model.to(A )
model.eval()
_SCREAMING_SNAKE_CASE = model(
A , attention_mask=A , token_type_ids=A , start_positions=A , end_positions=A , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def snake_case_( self , A , A , A , A , A , A , A ) -> Any:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = NystromformerForSequenceClassification(A )
model.to(A )
model.eval()
_SCREAMING_SNAKE_CASE = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case_( self , A , A , A , A , A , A , A ) -> str:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = NystromformerForTokenClassification(config=A )
model.to(A )
model.eval()
_SCREAMING_SNAKE_CASE = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case_( self , A , A , A , A , A , A , A ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = NystromformerForMultipleChoice(config=A )
model.to(A )
model.eval()
_SCREAMING_SNAKE_CASE = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_SCREAMING_SNAKE_CASE = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_SCREAMING_SNAKE_CASE = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_SCREAMING_SNAKE_CASE = model(
A , attention_mask=A , token_type_ids=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case_( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class a_ ( snake_case_ , snake_case_ , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
UpperCamelCase = (
{
'''feature-extraction''': NystromformerModel,
'''fill-mask''': NystromformerForMaskedLM,
'''question-answering''': NystromformerForQuestionAnswering,
'''text-classification''': NystromformerForSequenceClassification,
'''token-classification''': NystromformerForTokenClassification,
'''zero-shot''': NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
def snake_case_( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = NystromformerModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A , hidden_size=37 )
def snake_case_( self ) -> int:
self.config_tester.run_common_tests()
def snake_case_( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def snake_case_( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_SCREAMING_SNAKE_CASE = type
self.model_tester.create_and_check_model(*A )
def snake_case_( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A )
def snake_case_( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A )
def snake_case_( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A )
def snake_case_( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A )
def snake_case_( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A )
@slow
def snake_case_( self ) -> int:
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE = NystromformerModel.from_pretrained(A )
self.assertIsNotNone(A )
@require_torch
class a_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case_( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = NystromformerModel.from_pretrained("""uw-madison/nystromformer-512""" )
_SCREAMING_SNAKE_CASE = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(A )[0]
_SCREAMING_SNAKE_CASE = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , A )
_SCREAMING_SNAKE_CASE = torch.tensor(
[[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A , atol=1e-4 ) )
@slow
def snake_case_( self ) -> int:
_SCREAMING_SNAKE_CASE = """the [MASK] of Belgium is Brussels"""
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512""" )
_SCREAMING_SNAKE_CASE = NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512""" )
_SCREAMING_SNAKE_CASE = tokenizer(A , return_tensors="""pt""" )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(encoding.input_ids ).logits
_SCREAMING_SNAKE_CASE = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(A ) , """capital""" )
| 58 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=10 , __UpperCAmelCase=3 , __UpperCAmelCase=2 , __UpperCAmelCase=2 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=10 , __UpperCAmelCase=0.02 , __UpperCAmelCase="divided_space_time" , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = image_size
__lowerCamelCase = num_channels
__lowerCamelCase = patch_size
__lowerCamelCase = num_frames
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = attention_type
__lowerCamelCase = initializer_range
__lowerCamelCase = scope
__lowerCamelCase = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
__lowerCamelCase = (image_size // patch_size) ** 2
__lowerCamelCase = (num_frames) * self.num_patches_per_frame + 1
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
__lowerCamelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , attention_type=self.attention_type , )
__lowerCamelCase = self.num_labels
return config
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TimesformerModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase )
# verify the logits shape
__lowerCamelCase = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = config_and_inputs
__lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
lowerCAmelCase__ = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerModelTester(self )
__lowerCamelCase = ConfigTester(
self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(__UpperCAmelCase )
if return_labels:
if model_class in get_values(__UpperCAmelCase ):
__lowerCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__UpperCAmelCase )
return inputs_dict
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(__UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCAmelCase , nn.Linear ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(__UpperCAmelCase )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = TimesformerModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
if not self.has_attentions:
pass
else:
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = True
for model_class in self.all_model_classes:
__lowerCamelCase = self.model_tester.seq_length
__lowerCamelCase = self.model_tester.num_frames
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
__lowerCamelCase = len(__UpperCAmelCase )
# Check attention is always last and order is fine
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
self.assertEqual(out_len + 1 , len(__UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def lowerCamelCase ( self ):
'''simple docstring'''
def check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.hidden_states
__lowerCamelCase = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
__lowerCamelCase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def a__ ( ):
__lowerCamelCase = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' ,filename='''eating_spaghetti.npy''' ,repo_type='''dataset''' )
__lowerCamelCase = np.load(_UpperCamelCase )
return list(_UpperCamelCase )
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
__UpperCAmelCase )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_video()
__lowerCamelCase = image_processor(video[:8] , return_tensors='''pt''' ).to(__UpperCAmelCase )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(**__UpperCAmelCase )
# verify the logits
__lowerCamelCase = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor([-0.3_016, -0.7_713, -0.4_205] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 330 | 0 |
import re
from filelock import FileLock
try:
import nltk
__lowerCamelCase = True
except (ImportError, ModuleNotFoundError):
__lowerCamelCase = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def UpperCamelCase ( __lowerCamelCase : str ):
re.sub("<n>" , "" , __lowerCamelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__lowerCamelCase ) )
| 59 |
def a__ ( _UpperCamelCase : int ):
if not isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = F"""Input value of [number={number}] must be an integer"""
raise TypeError(_UpperCamelCase )
if number < 0:
return False
__lowerCamelCase = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 330 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : str = "The quick brown fox jumps over the lazy dog" , ):
lowerCAmelCase : List[str] = set()
# Replace all the whitespace in our sentence
lowerCAmelCase : List[Any] = input_str.replace(''' ''' , '''''' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(_snake_case ) == 26
def _snake_case ( _snake_case : str = "The quick brown fox jumps over the lazy dog" , ):
lowerCAmelCase : Tuple = [False] * 26
for char in input_str:
if char.islower():
lowerCAmelCase : int = True
elif char.isupper():
lowerCAmelCase : Optional[Any] = True
return all(_snake_case )
def _snake_case ( _snake_case : str = "The quick brown fox jumps over the lazy dog" , ):
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def _snake_case ( ):
from timeit import timeit
lowerCAmelCase : Optional[Any] = '''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
print(timeit('''is_pangram()''' , setup=_snake_case ) )
print(timeit('''is_pangram_faster()''' , setup=_snake_case ) )
print(timeit('''is_pangram_fastest()''' , setup=_snake_case ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 60 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
return F"""gaussian_noise_s={seed}_shape={"_".join([str(__UpperCAmelCase ) for s in shape] )}.npy"""
def lowerCamelCase ( self ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowerCamelCase ( self , __UpperCAmelCase=0 , __UpperCAmelCase=(4, 4, 64, 64) , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = jnp.bfloataa if fpaa else jnp.floataa
__lowerCamelCase = jnp.array(load_hf_numpy(self.get_file_format(__UpperCAmelCase , __UpperCAmelCase ) ) , dtype=__UpperCAmelCase )
return image
def lowerCamelCase ( self , __UpperCAmelCase=False , __UpperCAmelCase="CompVis/stable-diffusion-v1-4" ):
'''simple docstring'''
__lowerCamelCase = jnp.bfloataa if fpaa else jnp.floataa
__lowerCamelCase = '''bf16''' if fpaa else None
__lowerCamelCase ,__lowerCamelCase = FlaxUNetaDConditionModel.from_pretrained(
__UpperCAmelCase , subfolder='''unet''' , dtype=__UpperCAmelCase , revision=__UpperCAmelCase )
return model, params
def lowerCamelCase ( self , __UpperCAmelCase=0 , __UpperCAmelCase=(4, 77, 768) , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = jnp.bfloataa if fpaa else jnp.floataa
__lowerCamelCase = jnp.array(load_hf_numpy(self.get_file_format(__UpperCAmelCase , __UpperCAmelCase ) ) , dtype=__UpperCAmelCase )
return hidden_states
@parameterized.expand(
[
# fmt: off
[83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
[17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
[8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
[3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
# fmt: on
] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4''' , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_latents(__UpperCAmelCase , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_encoder_hidden_states(__UpperCAmelCase , fpaa=__UpperCAmelCase )
__lowerCamelCase = model.apply(
{'''params''': params} , __UpperCAmelCase , jnp.array(__UpperCAmelCase , dtype=jnp.intaa ) , encoder_hidden_states=__UpperCAmelCase , ).sample
assert sample.shape == latents.shape
__lowerCamelCase = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
__lowerCamelCase = jnp.array(__UpperCAmelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
assert jnp.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
[17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
[8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
[3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
# fmt: on
] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.get_unet_model(model_id='''stabilityai/stable-diffusion-2''' , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_latents(__UpperCAmelCase , shape=(4, 4, 96, 96) , fpaa=__UpperCAmelCase )
__lowerCamelCase = self.get_encoder_hidden_states(__UpperCAmelCase , shape=(4, 77, 1024) , fpaa=__UpperCAmelCase )
__lowerCamelCase = model.apply(
{'''params''': params} , __UpperCAmelCase , jnp.array(__UpperCAmelCase , dtype=jnp.intaa ) , encoder_hidden_states=__UpperCAmelCase , ).sample
assert sample.shape == latents.shape
__lowerCamelCase = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
__lowerCamelCase = jnp.array(__UpperCAmelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
assert jnp.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-2 )
| 330 | 0 |
"""simple docstring"""
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Tuple = len(__lowerCamelCase )
for _ in range(__lowerCamelCase ):
for i in range(_ % 2, arr_size - 1, 2 ):
if arr[i + 1] < arr[i]:
UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
_a = list(range(10, 0, -1))
print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")
| 61 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""MMBTForClassification""", """MMBTModel""", """ModalEmbeddings"""]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 0 |
from heapq import heappop, heappush
import numpy as np
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : tuple[int, int] , SCREAMING_SNAKE_CASE__ : tuple[int, int] , SCREAMING_SNAKE_CASE__ : bool , ):
__UpperCamelCase , __UpperCamelCase =grid.shape
__UpperCamelCase =[-1, 1, 0, 0]
__UpperCamelCase =[0, 0, -1, 1]
if allow_diagonal:
dx += [-1, -1, 1, 1]
dy += [-1, 1, -1, 1]
__UpperCamelCase , __UpperCamelCase =[(0, source)], set()
__UpperCamelCase =np.full((rows, cols) , np.inf )
__UpperCamelCase =0
__UpperCamelCase =np.empty((rows, cols) , dtype=SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =None
while queue:
((__UpperCamelCase) , (__UpperCamelCase)) =heappop(SCREAMING_SNAKE_CASE__ )
if (x, y) in visited:
continue
visited.add((x, y) )
if (x, y) == destination:
__UpperCamelCase =[]
while (x, y) != source:
path.append((x, y) )
__UpperCamelCase , __UpperCamelCase =predecessors[x, y]
path.append(SCREAMING_SNAKE_CASE__ ) # add the source manually
path.reverse()
return matrix[destination], path
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
__UpperCamelCase , __UpperCamelCase =x + dx[i], y + dy[i]
if 0 <= nx < rows and 0 <= ny < cols:
__UpperCamelCase =grid[nx][ny]
if next_node == 1 and matrix[nx, ny] > dist + 1:
heappush(SCREAMING_SNAKE_CASE__ , (dist + 1, (nx, ny)) )
__UpperCamelCase =dist + 1
__UpperCamelCase =(x, y)
return np.inf, []
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def a__ ( _UpperCamelCase : Optional[int] ):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class __lowerCAmelCase ( nn.Module ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
super().__init__()
__lowerCamelCase = module
__lowerCamelCase = nn.Sequential(
nn.Linear(module.in_features , __UpperCAmelCase , bias=__UpperCAmelCase ) , nn.Linear(__UpperCAmelCase , module.out_features , bias=__UpperCAmelCase ) , )
__lowerCamelCase = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=__UpperCAmelCase )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def lowerCamelCase ( self , __UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return self.module(__UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ) + self.adapter(__UpperCAmelCase )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __lowerCAmelCase ( unittest.TestCase ):
# We keep the constants inside the init function and model loading inside setUp function
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
# Therefore here we use only bloom-1b3 to test our module
lowerCAmelCase__ = """bigscience/bloom-1b7"""
# Constant values
lowerCAmelCase__ = 2.1_09_65_95_52_69_25_74
lowerCAmelCase__ = """Hello my name is"""
lowerCAmelCase__ = set()
EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I""" )
EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n""" )
EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University""" )
lowerCAmelCase__ = 1_0
def lowerCamelCase ( self ):
'''simple docstring'''
# Models and tokenizer
__lowerCamelCase = AutoTokenizer.from_pretrained(self.model_name )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# Models and tokenizer
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='''auto''' )
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
def lowerCamelCase ( self ):
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_abit.config
self.assertTrue(hasattr(__UpperCAmelCase , '''quantization_config''' ) )
__lowerCamelCase = config.to_dict()
__lowerCamelCase = config.to_diff_dict()
__lowerCamelCase = config.to_json_string()
def lowerCamelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
__lowerCamelCase = self.model_fpaa.get_memory_footprint()
__lowerCamelCase = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
__lowerCamelCase = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def lowerCamelCase ( self ):
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(__UpperCAmelCase , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = self.model_abit.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BitsAndBytesConfig()
__lowerCamelCase = True
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = model_abit_from_config.generate(
input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def lowerCamelCase ( self ):
'''simple docstring'''
with self.assertRaises(__UpperCAmelCase ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BitsAndBytesConfig()
with self.assertRaises(__UpperCAmelCase ):
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__UpperCAmelCase , load_in_abit=__UpperCAmelCase , device_map='''auto''' , bnb_abit_quant_type='''nf4''' , )
def lowerCamelCase ( self ):
'''simple docstring'''
with self.assertRaises(__UpperCAmelCase ):
# Tries with `str`
self.model_abit.to('''cpu''' )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.to(torch.device('''cuda:0''' ) )
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(__UpperCAmelCase ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
__lowerCamelCase = self.model_fpaa.to(torch.floataa )
__lowerCamelCase = self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.to('''cpu''' )
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.half()
# Check this does not throw an error
__lowerCamelCase = self.model_fpaa.float()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''t5-small''' , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __lowerCAmelCase ( unittest.TestCase ):
@classmethod
def lowerCamelCase ( cls ):
'''simple docstring'''
__lowerCamelCase = '''t5-small'''
__lowerCamelCase = '''google/flan-t5-small''' # flan-t5 uses dense-act instead of dense-relu-dense
__lowerCamelCase = AutoTokenizer.from_pretrained(cls.model_name )
__lowerCamelCase = '''Translate in German: Hello, my dog is cute'''
def lowerCamelCase ( self ):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
from transformers import TaForConditionalGeneration
__lowerCamelCase = TaForConditionalGeneration._keep_in_fpaa_modules
__lowerCamelCase = None
# test with `t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
# test with `flan-t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
__lowerCamelCase = modules
def lowerCamelCase ( self ):
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
# test with `flan-t5-small`
__lowerCamelCase = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
__lowerCamelCase = model.generate(**__UpperCAmelCase )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
# model_name
__lowerCamelCase = '''bigscience/bloom-560m'''
__lowerCamelCase = '''t5-small'''
# Different types of model
__lowerCamelCase = AutoModel.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# Sequence classification model
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# CausalLM model
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
# Seq2seq model
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=__UpperCAmelCase , device_map='''auto''' )
def lowerCamelCase ( self ):
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = pipeline(
'''text-generation''' , model=self.model_name , model_kwargs={'''device_map''': '''auto''', '''load_in_4bit''': True, '''torch_dtype''': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
__lowerCamelCase = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]['''generated_text'''] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=__UpperCAmelCase , device_map='''balanced''' )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
__lowerCamelCase = self.tokenizer(self.input_text , return_tensors='''pt''' )
# Second real batch
__lowerCamelCase = model_parallel.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=__UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''facebook/opt-350m'''
super().setUp()
def lowerCamelCase ( self ):
'''simple docstring'''
if version.parse(importlib.metadata.version('''bitsandbytes''' ) ) < version.parse('''0.37.0''' ):
return
# Step 1: freeze all parameters
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__UpperCAmelCase )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
__lowerCamelCase = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
__lowerCamelCase = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(__UpperCAmelCase ) ):
__lowerCamelCase = LoRALayer(module.q_proj , rank=16 )
__lowerCamelCase = LoRALayer(module.k_proj , rank=16 )
__lowerCamelCase = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
__lowerCamelCase = self.tokenizer('''Test batch ''' , return_tensors='''pt''' ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
__lowerCamelCase = model.forward(**__UpperCAmelCase )
out.logits.norm().backward()
for module in model.modules():
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(__UpperCAmelCase , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = """gpt2-xl"""
lowerCAmelCase__ = 3.31_91_85_48_54_15_21_87
| 330 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase_ : str = logging.get_logger(__name__)
def _lowerCamelCase ( lowercase : int , lowercase : Union[str, Any]=False ) -> Optional[Any]:
_a = []
# fmt: off
# stem:
rename_keys.append(("cls_token", "vit.embeddings.cls_token") )
rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings") )
rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias") )
# backbone
rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias") )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias') )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias') )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
("pre_logits.fc.weight", "pooler.dense.weight"),
("pre_logits.fc.bias", "pooler.dense.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
_a = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
# fmt: on
return rename_keys
def _lowerCamelCase ( lowercase : Optional[Any] , lowercase : Tuple , lowercase : List[str]=False ) -> List[Any]:
for i in range(config.num_hidden_layers ):
if base_model:
_a = ""
else:
_a = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_a = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
_a = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_a = in_proj_weight[
: config.hidden_size, :
]
_a = in_proj_bias[: config.hidden_size]
_a = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_a = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_a = in_proj_weight[
-config.hidden_size :, :
]
_a = in_proj_bias[-config.hidden_size :]
def _lowerCamelCase ( lowercase : str ) -> List[str]:
_a = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(lowercase , lowercase )
def _lowerCamelCase ( lowercase : Optional[int] , lowercase : List[str] , lowercase : Union[str, Any] ) -> int:
_a = dct.pop(lowercase )
_a = val
def _lowerCamelCase ( ) -> Dict:
_a = "http://images.cocodataset.org/val2017/000000039769.jpg"
_a = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
@torch.no_grad()
def _lowerCamelCase ( lowercase : Optional[Any] , lowercase : List[str] , lowercase : List[Any]=False ) -> List[str]:
_a = BitConfig(
global_padding="same" , layer_type="bottleneck" , depths=(3, 4, 9) , out_features=["stage3"] , embedding_dynamic_padding=lowercase , )
_a = ViTHybridConfig(backbone_config=lowercase , image_size=384 , num_labels=1000 )
_a = False
# load original model from timm
_a = timm.create_model(lowercase , pretrained=lowercase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
_a = timm_model.state_dict()
if base_model:
remove_classification_head_(lowercase )
_a = create_rename_keys(lowercase , lowercase )
for src, dest in rename_keys:
rename_key(lowercase , lowercase , lowercase )
read_in_q_k_v(lowercase , lowercase , lowercase )
_a = "huggingface/label-files"
_a = "imagenet-1k-id2label.json"
_a = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="dataset" ) , "r" ) )
_a = {int(lowercase ): v for k, v in idalabel.items()}
_a = idalabel
_a = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
_a = ViTHybridModel(lowercase ).eval()
else:
_a = ViTHybridForImageClassification(lowercase ).eval()
model.load_state_dict(lowercase )
# create image processor
_a = create_transform(**resolve_data_config({} , model=lowercase ) )
_a = transform.transforms
_a = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
_a = ViTHybridImageProcessor(
do_resize=lowercase , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=lowercase , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=lowercase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
_a = prepare_img()
_a = transform(lowercase ).unsqueeze(0 )
_a = processor(lowercase , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(lowercase , lowercase )
# verify logits
with torch.no_grad():
_a = model(lowercase )
_a = outputs.logits
print("Predicted class:" , logits.argmax(-1 ).item() )
if base_model:
_a = timm_model.forward_features(lowercase )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(lowercase , outputs.pooler_output , atol=1E-3 )
else:
_a = timm_model(lowercase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(lowercase , outputs.logits , atol=1E-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(lowercase ).mkdir(exist_ok=lowercase )
print(F'Saving model {vit_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(lowercase )
print(F'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(lowercase )
if push_to_hub:
print(F'Pushing model and processor to the hub {vit_name}' )
model.push_to_hub(F'ybelkada/{vit_name}' )
processor.push_to_hub(F'ybelkada/{vit_name}' )
if __name__ == "__main__":
lowerCAmelCase_ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_r50_s16_384',
type=str,
help='Name of the hybrid ViT timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
lowerCAmelCase_ : Union[str, Any] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 63 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = 42
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
lowerCAmelCase__ = True
@register_to_config
def __init__( self , __UpperCAmelCase = 3 , __UpperCAmelCase = 3 , __UpperCAmelCase = ("DownEncoderBlock2D",) , __UpperCAmelCase = ("UpDecoderBlock2D",) , __UpperCAmelCase = (64,) , __UpperCAmelCase = 1 , __UpperCAmelCase = "silu" , __UpperCAmelCase = 4 , __UpperCAmelCase = 32 , __UpperCAmelCase = 32 , __UpperCAmelCase = 0.18_215 , ):
'''simple docstring'''
super().__init__()
# pass init params to Encoder
__lowerCamelCase = Encoder(
in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , down_block_types=__UpperCAmelCase , block_out_channels=__UpperCAmelCase , layers_per_block=__UpperCAmelCase , act_fn=__UpperCAmelCase , norm_num_groups=__UpperCAmelCase , double_z=__UpperCAmelCase , )
# pass init params to Decoder
__lowerCamelCase = Decoder(
in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , up_block_types=__UpperCAmelCase , block_out_channels=__UpperCAmelCase , layers_per_block=__UpperCAmelCase , norm_num_groups=__UpperCAmelCase , act_fn=__UpperCAmelCase , )
__lowerCamelCase = nn.Convad(2 * latent_channels , 2 * latent_channels , 1 )
__lowerCamelCase = nn.Convad(__UpperCAmelCase , __UpperCAmelCase , 1 )
__lowerCamelCase = False
__lowerCamelCase = False
# only relevant if vae tiling is enabled
__lowerCamelCase = self.config.sample_size
__lowerCamelCase = (
self.config.sample_size[0]
if isinstance(self.config.sample_size , (list, tuple) )
else self.config.sample_size
)
__lowerCamelCase = int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
__lowerCamelCase = 0.25
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
if isinstance(__UpperCAmelCase , (Encoder, Decoder) ):
__lowerCamelCase = value
def lowerCamelCase ( self , __UpperCAmelCase = True ):
'''simple docstring'''
__lowerCamelCase = use_tiling
def lowerCamelCase ( self ):
'''simple docstring'''
self.enable_tiling(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = True
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = {}
def fn_recursive_add_processors(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
if hasattr(__UpperCAmelCase , '''set_processor''' ):
__lowerCamelCase = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(F"""{name}.{sub_name}""" , __UpperCAmelCase , __UpperCAmelCase )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
return processors
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = len(self.attn_processors.keys() )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(__UpperCAmelCase ) != count:
raise ValueError(
F"""A dict of processors was passed, but the number of processors {len(__UpperCAmelCase )} does not match the"""
F""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" )
def fn_recursive_attn_processor(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
if hasattr(__UpperCAmelCase , '''set_processor''' ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
module.set_processor(__UpperCAmelCase )
else:
module.set_processor(processor.pop(F"""{name}.processor""" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(F"""{name}.{sub_name}""" , __UpperCAmelCase , __UpperCAmelCase )
for name, module in self.named_children():
fn_recursive_attn_processor(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(__UpperCAmelCase , return_dict=__UpperCAmelCase )
if self.use_slicing and x.shape[0] > 1:
__lowerCamelCase = [self.encoder(__UpperCAmelCase ) for x_slice in x.split(1 )]
__lowerCamelCase = torch.cat(__UpperCAmelCase )
else:
__lowerCamelCase = self.encoder(__UpperCAmelCase )
__lowerCamelCase = self.quant_conv(__UpperCAmelCase )
__lowerCamelCase = DiagonalGaussianDistribution(__UpperCAmelCase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.tiled_decode(__UpperCAmelCase , return_dict=__UpperCAmelCase )
__lowerCamelCase = self.post_quant_conv(__UpperCAmelCase )
__lowerCamelCase = self.decoder(__UpperCAmelCase )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__UpperCAmelCase )
@apply_forward_hook
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
if self.use_slicing and z.shape[0] > 1:
__lowerCamelCase = [self._decode(__UpperCAmelCase ).sample for z_slice in z.split(1 )]
__lowerCamelCase = torch.cat(__UpperCAmelCase )
else:
__lowerCamelCase = self._decode(__UpperCAmelCase ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = min(a.shape[2] , b.shape[2] , __UpperCAmelCase )
for y in range(__UpperCAmelCase ):
__lowerCamelCase = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = min(a.shape[3] , b.shape[3] , __UpperCAmelCase )
for x in range(__UpperCAmelCase ):
__lowerCamelCase = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
__lowerCamelCase = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
__lowerCamelCase = int(self.tile_latent_min_size * self.tile_overlap_factor )
__lowerCamelCase = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
__lowerCamelCase = []
for i in range(0 , x.shape[2] , __UpperCAmelCase ):
__lowerCamelCase = []
for j in range(0 , x.shape[3] , __UpperCAmelCase ):
__lowerCamelCase = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
__lowerCamelCase = self.encoder(__UpperCAmelCase )
__lowerCamelCase = self.quant_conv(__UpperCAmelCase )
row.append(__UpperCAmelCase )
rows.append(__UpperCAmelCase )
__lowerCamelCase = []
for i, row in enumerate(__UpperCAmelCase ):
__lowerCamelCase = []
for j, tile in enumerate(__UpperCAmelCase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
__lowerCamelCase = self.blend_v(rows[i - 1][j] , __UpperCAmelCase , __UpperCAmelCase )
if j > 0:
__lowerCamelCase = self.blend_h(row[j - 1] , __UpperCAmelCase , __UpperCAmelCase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__UpperCAmelCase , dim=3 ) )
__lowerCamelCase = torch.cat(__UpperCAmelCase , dim=2 )
__lowerCamelCase = DiagonalGaussianDistribution(__UpperCAmelCase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True ):
'''simple docstring'''
__lowerCamelCase = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
__lowerCamelCase = int(self.tile_sample_min_size * self.tile_overlap_factor )
__lowerCamelCase = self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
__lowerCamelCase = []
for i in range(0 , z.shape[2] , __UpperCAmelCase ):
__lowerCamelCase = []
for j in range(0 , z.shape[3] , __UpperCAmelCase ):
__lowerCamelCase = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
__lowerCamelCase = self.post_quant_conv(__UpperCAmelCase )
__lowerCamelCase = self.decoder(__UpperCAmelCase )
row.append(__UpperCAmelCase )
rows.append(__UpperCAmelCase )
__lowerCamelCase = []
for i, row in enumerate(__UpperCAmelCase ):
__lowerCamelCase = []
for j, tile in enumerate(__UpperCAmelCase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
__lowerCamelCase = self.blend_v(rows[i - 1][j] , __UpperCAmelCase , __UpperCAmelCase )
if j > 0:
__lowerCamelCase = self.blend_h(row[j - 1] , __UpperCAmelCase , __UpperCAmelCase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__UpperCAmelCase , dim=3 ) )
__lowerCamelCase = torch.cat(__UpperCAmelCase , dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False , __UpperCAmelCase = True , __UpperCAmelCase = None , ):
'''simple docstring'''
__lowerCamelCase = sample
__lowerCamelCase = self.encode(__UpperCAmelCase ).latent_dist
if sample_posterior:
__lowerCamelCase = posterior.sample(generator=__UpperCAmelCase )
else:
__lowerCamelCase = posterior.mode()
__lowerCamelCase = self.decode(__UpperCAmelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=__UpperCAmelCase )
| 330 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase( __a ):
'''simple docstring'''
@staticmethod
@abstractmethod
def UpperCamelCase_ ( a_: ArgumentParser ):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def UpperCamelCase_ ( self: List[Any] ):
'''simple docstring'''
raise NotImplementedError()
| 64 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
a_ = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""kernel""", """weight"""),
("""beta""", """bias"""),
("""gamma""", """weight"""),
("""pegasus""", """model"""),
]
a_ = [
(""".output.dense""", """.fc2"""),
("""intermediate.LayerNorm""", """final_layer_norm"""),
("""intermediate.dense""", """fc1"""),
]
a_ = (
INIT_COMMON
+ [
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.out_proj"""),
("""attention.self""", """self_attn"""),
("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
("""attention.encdec""", """encoder_attn"""),
("""key""", """k_proj"""),
("""value""", """v_proj"""),
("""query""", """q_proj"""),
("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
]
+ END_COMMON
)
a_ = (
INIT_COMMON
+ [
("""embeddings.word_embeddings""", """shared.weight"""),
("""embeddings.position_embeddings""", """embed_positions.weight"""),
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.output"""),
("""attention.self""", """self_attn.self"""),
("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
]
+ END_COMMON
)
a_ = [
"""encdec/key/bias""",
"""encdec/query/bias""",
"""encdec/value/bias""",
"""self/key/bias""",
"""self/query/bias""",
"""self/value/bias""",
"""encdec_output/dense/bias""",
"""attention/output/dense/bias""",
]
def a__ ( _UpperCamelCase : Optional[int] ,_UpperCamelCase : Optional[Any] ):
for tf_name, hf_name in patterns:
__lowerCamelCase = k.replace(_UpperCamelCase ,_UpperCamelCase )
return k
def a__ ( _UpperCamelCase : dict ,_UpperCamelCase : dict ):
__lowerCamelCase = BigBirdPegasusConfig(**_UpperCamelCase )
__lowerCamelCase = BigBirdPegasusForConditionalGeneration(_UpperCamelCase )
__lowerCamelCase = torch_model.state_dict()
__lowerCamelCase = {}
# separating decoder weights
__lowerCamelCase = {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
__lowerCamelCase = {k: tf_weights[k] for k in tf_weights if not k.startswith('''pegasus/decoder''' )}
for k, v in tqdm(decoder_weights.items() ,'''tf -> hf conversion''' ):
__lowerCamelCase = [k.endswith(_UpperCamelCase ) for ending in KEYS_TO_IGNORE]
if any(_UpperCamelCase ):
continue
__lowerCamelCase = DECODER_PATTERNS
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase ,_UpperCamelCase )
if new_k not in state_dict:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
__lowerCamelCase = v.T
__lowerCamelCase = torch.from_numpy(_UpperCamelCase )
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items() ,'''tf -> hf conversion''' ):
__lowerCamelCase = [k.endswith(_UpperCamelCase ) for ending in KEYS_TO_IGNORE]
if any(_UpperCamelCase ):
continue
__lowerCamelCase = REMAINING_PATTERNS
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase ,_UpperCamelCase )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
__lowerCamelCase = v.T
__lowerCamelCase = torch.from_numpy(_UpperCamelCase )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
__lowerCamelCase = mapping['''model.embed_positions.weight''']
__lowerCamelCase = mapping.pop('''model.embed_positions.weight''' )
__lowerCamelCase ,__lowerCamelCase = torch_model.load_state_dict(_UpperCamelCase ,strict=_UpperCamelCase )
__lowerCamelCase = [
k
for k in missing
if k
not in [
'''final_logits_bias''',
'''model.encoder.embed_tokens.weight''',
'''model.decoder.embed_tokens.weight''',
'''lm_head.weight''',
]
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = tf.train.list_variables(_UpperCamelCase )
__lowerCamelCase = {}
__lowerCamelCase = ['''global_step''']
for name, shape in tqdm(_UpperCamelCase ,desc='''converting tf checkpoint to dict''' ):
__lowerCamelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
__lowerCamelCase = tf.train.load_variable(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = array
return tf_weights
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : dict ):
__lowerCamelCase = get_tf_weights_as_numpy(_UpperCamelCase )
__lowerCamelCase = convert_bigbird_pegasus(_UpperCamelCase ,_UpperCamelCase )
torch_model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
a_ = parser.parse_args()
a_ = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 330 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class A :
def __init__(self : Any , __UpperCAmelCase : Any , __UpperCAmelCase : Tuple=1_3 , __UpperCAmelCase : int=7 , __UpperCAmelCase : List[Any]=True , __UpperCAmelCase : Any=True , __UpperCAmelCase : Dict=True , __UpperCAmelCase : str=True , __UpperCAmelCase : Union[str, Any]=9_9 , __UpperCAmelCase : Tuple=3_2 , __UpperCAmelCase : List[Any]=2 , __UpperCAmelCase : List[Any]=4 , __UpperCAmelCase : str=3_7 , __UpperCAmelCase : Union[str, Any]="gelu" , __UpperCAmelCase : Tuple=0.1 , __UpperCAmelCase : List[Any]=0.1 , __UpperCAmelCase : Optional[Any]=5_1_2 , __UpperCAmelCase : Tuple=1_6 , __UpperCAmelCase : Any=2 , __UpperCAmelCase : Union[str, Any]=0.02 , __UpperCAmelCase : Optional[int]=3 , __UpperCAmelCase : str=4 , __UpperCAmelCase : str=None , ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = parent
UpperCAmelCase__ = 1_3
UpperCAmelCase__ = 7
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = 9_9
UpperCAmelCase__ = 3_8_4
UpperCAmelCase__ = 2
UpperCAmelCase__ = 4
UpperCAmelCase__ = 3_7
UpperCAmelCase__ = "gelu"
UpperCAmelCase__ = 0.1
UpperCAmelCase__ = 0.1
UpperCAmelCase__ = 5_1_2
UpperCAmelCase__ = 1_6
UpperCAmelCase__ = 2
UpperCAmelCase__ = 0.02
UpperCAmelCase__ = 3
UpperCAmelCase__ = 4
UpperCAmelCase__ = 1_2_8
UpperCAmelCase__ = 2
UpperCAmelCase__ = 9
UpperCAmelCase__ = 1
UpperCAmelCase__ = None
def lowercase_ (self : Any ) -> str:
"""simple docstring"""
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase__ = None
if self.use_input_mask:
UpperCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ = None
if self.use_token_type_ids:
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = None
if self.use_labels:
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase__ = ConvBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=__UpperCAmelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase_ (self : Optional[int] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Dict , __UpperCAmelCase : List[Any] , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : Tuple ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = TFConvBertModel(config=__UpperCAmelCase )
UpperCAmelCase__ = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
UpperCAmelCase__ = [input_ids, input_mask]
UpperCAmelCase__ = model(__UpperCAmelCase )
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase_ (self : Optional[int] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : List[str] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict , __UpperCAmelCase : int ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = TFConvBertForMaskedLM(config=__UpperCAmelCase )
UpperCAmelCase__ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase_ (self : List[Any] , __UpperCAmelCase : int , __UpperCAmelCase : str , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : List[Any] ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = self.num_labels
UpperCAmelCase__ = TFConvBertForSequenceClassification(config=__UpperCAmelCase )
UpperCAmelCase__ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase_ (self : Optional[int] , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : str , __UpperCAmelCase : List[str] ) -> str:
"""simple docstring"""
UpperCAmelCase__ = self.num_choices
UpperCAmelCase__ = TFConvBertForMultipleChoice(config=__UpperCAmelCase )
UpperCAmelCase__ = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase__ = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase__ = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase__ = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowercase_ (self : Any , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : str , __UpperCAmelCase : int ) -> int:
"""simple docstring"""
UpperCAmelCase__ = self.num_labels
UpperCAmelCase__ = TFConvBertForTokenClassification(config=__UpperCAmelCase )
UpperCAmelCase__ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase_ (self : str , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Any , __UpperCAmelCase : int , __UpperCAmelCase : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase__ = TFConvBertForQuestionAnswering(config=__UpperCAmelCase )
UpperCAmelCase__ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowercase_ (self : Tuple ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) = config_and_inputs
UpperCAmelCase__ = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class A ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
__UpperCAmelCase : Tuple = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
__UpperCAmelCase : Optional[Any] = (
{
'feature-extraction': TFConvBertModel,
'fill-mask': TFConvBertForMaskedLM,
'question-answering': TFConvBertForQuestionAnswering,
'text-classification': TFConvBertForSequenceClassification,
'token-classification': TFConvBertForTokenClassification,
'zero-shot': TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__UpperCAmelCase : List[Any] = False
__UpperCAmelCase : Optional[Any] = False
__UpperCAmelCase : str = False
def lowercase_ (self : Tuple ) -> str:
"""simple docstring"""
UpperCAmelCase__ = TFConvBertModelTester(self )
UpperCAmelCase__ = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=3_7 )
def lowercase_ (self : str ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowercase_ (self : Optional[int] ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowercase_ (self : str ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowercase_ (self : int ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
def lowercase_ (self : Tuple ) -> str:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
def lowercase_ (self : Optional[Any] ) -> int:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowercase_ (self : Optional[int] ) -> str:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
@slow
def lowercase_ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ = True
UpperCAmelCase__ = True
if hasattr(__UpperCAmelCase , "use_cache" ):
UpperCAmelCase__ = True
UpperCAmelCase__ = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
UpperCAmelCase__ = getattr(self.model_tester , "key_length" , __UpperCAmelCase )
for model_class in self.all_model_classes:
UpperCAmelCase__ = self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase__ = model_class(__UpperCAmelCase )
UpperCAmelCase__ = len(model(__UpperCAmelCase ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__UpperCAmelCase , saved_model=__UpperCAmelCase )
UpperCAmelCase__ = os.path.join(__UpperCAmelCase , "saved_model" , "1" )
UpperCAmelCase__ = tf.keras.models.load_model(__UpperCAmelCase )
UpperCAmelCase__ = model(__UpperCAmelCase )
if self.is_encoder_decoder:
UpperCAmelCase__ = outputs["encoder_hidden_states"]
UpperCAmelCase__ = outputs["encoder_attentions"]
else:
UpperCAmelCase__ = outputs["hidden_states"]
UpperCAmelCase__ = outputs["attentions"]
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
UpperCAmelCase__ = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def lowercase_ (self : Any ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
self.assertIsNotNone(__UpperCAmelCase )
def lowercase_ (self : int ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ = True
UpperCAmelCase__ = getattr(self.model_tester , "decoder_seq_length" , self.model_tester.seq_length )
UpperCAmelCase__ = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
UpperCAmelCase__ = getattr(self.model_tester , "key_length" , __UpperCAmelCase )
UpperCAmelCase__ = getattr(self.model_tester , "key_length" , __UpperCAmelCase )
def check_decoder_attentions_output(__UpperCAmelCase : List[str] ):
UpperCAmelCase__ = len(__UpperCAmelCase )
self.assertEqual(out_len % 2 , 0 )
UpperCAmelCase__ = outputs.decoder_attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(__UpperCAmelCase : List[str] ):
UpperCAmelCase__ = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
UpperCAmelCase__ = True
UpperCAmelCase__ = False
UpperCAmelCase__ = model_class(__UpperCAmelCase )
UpperCAmelCase__ = model(self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
UpperCAmelCase__ = len(__UpperCAmelCase )
self.assertEqual(config.output_hidden_states , __UpperCAmelCase )
check_encoder_attentions_output(__UpperCAmelCase )
if self.is_encoder_decoder:
UpperCAmelCase__ = model_class(__UpperCAmelCase )
UpperCAmelCase__ = model(self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCAmelCase )
check_decoder_attentions_output(__UpperCAmelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
UpperCAmelCase__ = True
UpperCAmelCase__ = model_class(__UpperCAmelCase )
UpperCAmelCase__ = model(self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCAmelCase )
check_encoder_attentions_output(__UpperCAmelCase )
# Check attention is always last and order is fine
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = model_class(__UpperCAmelCase )
UpperCAmelCase__ = model(self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(__UpperCAmelCase ) )
self.assertEqual(model.config.output_hidden_states , __UpperCAmelCase )
check_encoder_attentions_output(__UpperCAmelCase )
@require_tf
class A ( unittest.TestCase ):
@slow
def lowercase_ (self : Dict ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
UpperCAmelCase__ = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase__ = model(__UpperCAmelCase )[0]
UpperCAmelCase__ = [1, 6, 7_6_8]
self.assertEqual(output.shape , __UpperCAmelCase )
UpperCAmelCase__ = tf.constant(
[
[
[-0.03475493, -0.4686034, -0.30638832],
[0.22637248, -0.26988646, -0.7423424],
[0.10324868, -0.45013508, -0.58280784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 )
| 65 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
a_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase=None , __UpperCAmelCase=None ):
'''simple docstring'''
if not conversation_id:
__lowerCamelCase = uuid.uuida()
if past_user_inputs is None:
__lowerCamelCase = []
if generated_responses is None:
__lowerCamelCase = []
__lowerCamelCase = conversation_id
__lowerCamelCase = past_user_inputs
__lowerCamelCase = generated_responses
__lowerCamelCase = text
def __eq__( self , __UpperCAmelCase ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False ):
'''simple docstring'''
if self.new_user_input:
if overwrite:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
F"""with: \"{text}\".""" )
__lowerCamelCase = text
else:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input""" )
else:
__lowerCamelCase = text
def lowerCamelCase ( self ):
'''simple docstring'''
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
__lowerCamelCase = None
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
self.generated_responses.append(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
'''simple docstring'''
__lowerCamelCase = F"""Conversation id: {self.uuid} \n"""
for is_user, text in self.iter_texts():
__lowerCamelCase = '''user''' if is_user else '''bot'''
output += F"""{name} >> {text} \n"""
return output
@add_end_docstrings(
lowerCAmelCase__ , r"""
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
""" , )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
if self.tokenizer.pad_token_id is None:
__lowerCamelCase = self.tokenizer.eos_token
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = {}
__lowerCamelCase = {}
__lowerCamelCase = {}
if min_length_for_response is not None:
__lowerCamelCase = min_length_for_response
if minimum_tokens is not None:
__lowerCamelCase = minimum_tokens
if "max_length" in generate_kwargs:
__lowerCamelCase = generate_kwargs['''max_length''']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
__lowerCamelCase = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(__UpperCAmelCase )
return preprocess_params, forward_params, postprocess_params
def __call__( self , __UpperCAmelCase , __UpperCAmelCase=0 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = super().__call__(__UpperCAmelCase , num_workers=__UpperCAmelCase , **__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(__UpperCAmelCase ) == 1:
return outputs[0]
return outputs
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=32 ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise ValueError('''ConversationalPipeline, expects Conversation as inputs''' )
if conversation.new_user_input is None:
raise ValueError(
F"""Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. """
'''Add user inputs with the conversation\'s `add_user_input` method''' )
if hasattr(self.tokenizer , '''_build_conversation_input_ids''' ):
__lowerCamelCase = self.tokenizer._build_conversation_input_ids(__UpperCAmelCase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
__lowerCamelCase = self._legacy_parse_and_tokenize(__UpperCAmelCase )
if self.framework == "pt":
__lowerCamelCase = torch.LongTensor([input_ids] )
elif self.framework == "tf":
__lowerCamelCase = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=10 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = generate_kwargs.get('''max_length''' , self.model.config.max_length )
__lowerCamelCase = model_inputs['''input_ids'''].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})""" )
__lowerCamelCase = max_length - minimum_tokens
__lowerCamelCase = model_inputs['''input_ids'''][:, -trim:]
if "attention_mask" in model_inputs:
__lowerCamelCase = model_inputs['''attention_mask'''][:, -trim:]
__lowerCamelCase = model_inputs.pop('''conversation''' )
__lowerCamelCase = max_length
__lowerCamelCase = self.model.generate(**__UpperCAmelCase , **__UpperCAmelCase )
if self.model.config.is_encoder_decoder:
__lowerCamelCase = 1
else:
__lowerCamelCase = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=True ):
'''simple docstring'''
__lowerCamelCase = model_outputs['''output_ids''']
__lowerCamelCase = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase , )
__lowerCamelCase = model_outputs['''conversation''']
conversation.mark_processed()
conversation.append_response(__UpperCAmelCase )
return conversation
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.tokenizer.eos_token_id
__lowerCamelCase = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) )
if len(__UpperCAmelCase ) > self.tokenizer.model_max_length:
__lowerCamelCase = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 330 | 0 |
"""simple docstring"""
from math import isqrt, loga
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Tuple = [True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2, _lowercase, _lowercase ):
snake_case_ :Dict = False
return [i for i in range(2, _lowercase ) if is_prime[i]]
def A_ ( _lowercase = 800800, _lowercase = 800800 ):
'''simple docstring'''
snake_case_ :Union[str, Any] = degree * loga(_lowercase )
snake_case_ :Tuple = int(_lowercase )
snake_case_ :List[str] = calculate_prime_numbers(_lowercase )
snake_case_ :Union[str, Any] = 0
snake_case_ :List[str] = 0
snake_case_ :Optional[Any] = len(_lowercase ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(F"""{solution() = }""")
| 66 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
a_ = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["""memory_attention""", """encoder_attn"""],
["""attention""", """attn"""],
["""/""", """."""],
[""".LayerNorm.gamma""", """_layer_norm.weight"""],
[""".LayerNorm.beta""", """_layer_norm.bias"""],
["""r.layer_""", """r.layers."""],
["""output_proj""", """out_proj"""],
["""ffn.dense_1.""", """fc2."""],
["""ffn.dense.""", """fc1."""],
["""ffn_layer_norm""", """final_layer_norm"""],
["""kernel""", """weight"""],
["""encoder_layer_norm.""", """encoder.layer_norm."""],
["""decoder_layer_norm.""", """decoder.layer_norm."""],
["""embeddings.weights""", """shared.weight"""],
]
def a__ ( _UpperCamelCase : int ):
for pegasus_name, hf_name in PATTERNS:
__lowerCamelCase = k.replace(_UpperCamelCase ,_UpperCamelCase )
return k
def a__ ( _UpperCamelCase : dict ,_UpperCamelCase : dict ):
__lowerCamelCase = DEFAULTS.copy()
cfg_kwargs.update(_UpperCamelCase )
__lowerCamelCase = PegasusConfig(**_UpperCamelCase )
__lowerCamelCase = PegasusForConditionalGeneration(_UpperCamelCase )
__lowerCamelCase = torch_model.model.state_dict()
__lowerCamelCase = {}
for k, v in tf_weights.items():
__lowerCamelCase = rename_state_dict_key(_UpperCamelCase )
if new_k not in sd:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if "dense" in k or "proj" in new_k:
__lowerCamelCase = v.T
__lowerCamelCase = torch.tensor(_UpperCamelCase ,dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
# make sure embedding.padding_idx is respected
__lowerCamelCase = torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1] )
__lowerCamelCase = mapping['''shared.weight''']
__lowerCamelCase = mapping['''shared.weight''']
__lowerCamelCase = {k: torch.zeros_like(_UpperCamelCase ) for k, v in sd.items() if k.endswith('''bias''' ) and k not in mapping}
mapping.update(**_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = torch_model.model.load_state_dict(_UpperCamelCase ,strict=_UpperCamelCase )
__lowerCamelCase = [
k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def a__ ( _UpperCamelCase : str="./ckpt/aeslc/model.ckpt-32000" ):
__lowerCamelCase = tf.train.list_variables(_UpperCamelCase )
__lowerCamelCase = {}
__lowerCamelCase = ['''Adafactor''', '''global_step''']
for name, shape in tqdm(_UpperCamelCase ,desc='''converting tf checkpoint to dict''' ):
__lowerCamelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
__lowerCamelCase = tf.train.load_variable(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = array
return tf_weights
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ):
# save tokenizer first
__lowerCamelCase = Path(_UpperCamelCase ).parent.name
__lowerCamelCase = task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
__lowerCamelCase = PegasusTokenizer.from_pretrained('''sshleifer/pegasus''' ,model_max_length=_UpperCamelCase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(_UpperCamelCase )
# convert model
__lowerCamelCase = get_tf_weights_as_numpy(_UpperCamelCase )
__lowerCamelCase = task_specific_params[F"""summarization_{dataset}"""]
if dataset == "large":
__lowerCamelCase = task_specific_params
__lowerCamelCase = convert_pegasus(_UpperCamelCase ,_UpperCamelCase )
torch_model.save_pretrained(_UpperCamelCase )
__lowerCamelCase = torch_model.state_dict()
sd.pop('''model.decoder.embed_positions.weight''' )
sd.pop('''model.encoder.embed_positions.weight''' )
torch.save(_UpperCamelCase ,Path(_UpperCamelCase ) / '''pytorch_model.bin''' )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
a_ = parser.parse_args()
if args.save_dir is None:
a_ = Path(args.tf_ckpt_path).parent.name
a_ = os.path.join("""pegasus""", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 330 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class a__ ( unittest.TestCase ):
lowerCamelCase : Optional[int] =MODEL_FOR_CAUSAL_LM_MAPPING
lowerCamelCase : int =TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
__lowerCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
__lowerCamelCase = text_generator('''This is a test''' , do_sample=a )
self.assertEqual(
a , [
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
] , )
__lowerCamelCase = text_generator(['''This is a test''', '''This is a second test'''] )
self.assertEqual(
a , [
[
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
''' oscope. oscope. FiliFili@@'''
)
}
],
] , )
__lowerCamelCase = text_generator('''This is a test''' , do_sample=a , num_return_sequences=2 , return_tensors=a )
self.assertEqual(
a , [
{'''generated_token_ids''': ANY(a )},
{'''generated_token_ids''': ANY(a )},
] , )
__lowerCamelCase = text_generator.model.config.eos_token_id
__lowerCamelCase = '''<pad>'''
__lowerCamelCase = text_generator(
['''This is a test''', '''This is a second test'''] , do_sample=a , num_return_sequences=2 , batch_size=2 , return_tensors=a , )
self.assertEqual(
a , [
[
{'''generated_token_ids''': ANY(a )},
{'''generated_token_ids''': ANY(a )},
],
[
{'''generated_token_ids''': ANY(a )},
{'''generated_token_ids''': ANY(a )},
],
] , )
@require_tf
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
__lowerCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
__lowerCamelCase = text_generator('''This is a test''' , do_sample=a )
self.assertEqual(
a , [
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
] , )
__lowerCamelCase = text_generator(['''This is a test''', '''This is a second test'''] , do_sample=a )
self.assertEqual(
a , [
[
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
)
}
],
] , )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , a : Dict , a : int , a : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase = TextGenerationPipeline(model=a , tokenizer=a )
return text_generator, ["This is a test", "Another test"]
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
__lowerCamelCase = '''Hello I believe in'''
__lowerCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
__lowerCamelCase = text_generator(a )
self.assertEqual(
a , [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] , )
__lowerCamelCase = text_generator(a , stop_sequence=''' fe''' )
self.assertEqual(a , [{'''generated_text''': '''Hello I believe in fe'''}] )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , a : List[str] , a : Optional[int] ):
"""simple docstring"""
__lowerCamelCase = text_generator.model
__lowerCamelCase = text_generator.tokenizer
__lowerCamelCase = text_generator('''This is a test''' )
self.assertEqual(a , [{'''generated_text''': ANY(a )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
__lowerCamelCase = text_generator('''This is a test''' , return_full_text=a )
self.assertEqual(a , [{'''generated_text''': ANY(a )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
__lowerCamelCase = pipeline(task='''text-generation''' , model=a , tokenizer=a , return_full_text=a )
__lowerCamelCase = text_generator('''This is a test''' )
self.assertEqual(a , [{'''generated_text''': ANY(a )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
__lowerCamelCase = text_generator('''This is a test''' , return_full_text=a )
self.assertEqual(a , [{'''generated_text''': ANY(a )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
__lowerCamelCase = text_generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=a )
self.assertEqual(
a , [
[{'''generated_text''': ANY(a )}, {'''generated_text''': ANY(a )}],
[{'''generated_text''': ANY(a )}, {'''generated_text''': ANY(a )}],
] , )
if text_generator.tokenizer.pad_token is not None:
__lowerCamelCase = text_generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=a )
self.assertEqual(
a , [
[{'''generated_text''': ANY(a )}, {'''generated_text''': ANY(a )}],
[{'''generated_text''': ANY(a )}, {'''generated_text''': ANY(a )}],
] , )
with self.assertRaises(a ):
__lowerCamelCase = text_generator('''test''' , return_full_text=a , return_text=a )
with self.assertRaises(a ):
__lowerCamelCase = text_generator('''test''' , return_full_text=a , return_tensors=a )
with self.assertRaises(a ):
__lowerCamelCase = text_generator('''test''' , return_text=a , return_tensors=a )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
__lowerCamelCase = text_generator('''''' )
self.assertEqual(a , [{'''generated_text''': ANY(a )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
__lowerCamelCase = text_generator('''''' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
__lowerCamelCase = ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
if (
tokenizer.model_max_length < 1_00_00
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('''This is a test''' * 5_00 , max_new_tokens=20 )
__lowerCamelCase = text_generator('''This is a test''' * 5_00 , handle_long_generation='''hole''' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(a ):
text_generator(
'''This is a test''' * 5_00 , handle_long_generation='''hole''' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
import torch
# Classic `model_kwargs`
__lowerCamelCase = pipeline(
model='''hf-internal-testing/tiny-random-bloom''' , model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
__lowerCamelCase = pipe('''This is a test''' )
self.assertEqual(
a , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
__lowerCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
__lowerCamelCase = pipe('''This is a test''' )
self.assertEqual(
a , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
__lowerCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
__lowerCamelCase = pipe('''This is a test''' )
self.assertEqual(
a , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
@require_torch
@require_torch_gpu
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
import torch
__lowerCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device=0 , torch_dtype=torch.floataa )
pipe('''This is a test''' )
@require_torch
@require_accelerate
@require_torch_gpu
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
import torch
__lowerCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.floataa )
pipe('''This is a test''' , do_sample=a , top_p=0.5 )
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
__lowerCamelCase = '''Hello world'''
__lowerCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
__lowerCamelCase = logging.get_logger('''transformers.generation.tf_utils''' )
else:
__lowerCamelCase = logging.get_logger('''transformers.generation.utils''' )
__lowerCamelCase = '''Both `max_new_tokens`''' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(a ) as cl:
__lowerCamelCase = text_generator(a , max_length=10 , max_new_tokens=1 )
self.assertIn(a , cl.out )
# The user only sets one -> no warning
with CaptureLogger(a ) as cl:
__lowerCamelCase = text_generator(a , max_new_tokens=1 )
self.assertNotIn(a , cl.out )
with CaptureLogger(a ) as cl:
__lowerCamelCase = text_generator(a , max_length=10 )
self.assertNotIn(a , cl.out )
| 67 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
a_ = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : Tuple ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Dict ):
for attribute in key.split('''.''' ):
__lowerCamelCase = getattr(_UpperCamelCase ,_UpperCamelCase )
if weight_type is not None:
__lowerCamelCase = getattr(_UpperCamelCase ,_UpperCamelCase ).shape
else:
__lowerCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__lowerCamelCase = value
elif weight_type == "weight_g":
__lowerCamelCase = value
elif weight_type == "weight_v":
__lowerCamelCase = value
elif weight_type == "bias":
__lowerCamelCase = value
else:
__lowerCamelCase = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def a__ ( _UpperCamelCase : Any ,_UpperCamelCase : Any ):
__lowerCamelCase = []
__lowerCamelCase = fairseq_model.state_dict()
__lowerCamelCase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowerCamelCase = False
if "conv_layers" in name:
load_conv_layer(
_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,hf_model.config.feat_extract_norm == '''group''' ,)
__lowerCamelCase = True
else:
for key, mapped_key in MAPPING.items():
__lowerCamelCase = '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
# special case since naming is very similar
continue
__lowerCamelCase = True
if "*" in mapped_key:
__lowerCamelCase = name.split(_UpperCamelCase )[0].split('''.''' )[-2]
__lowerCamelCase = mapped_key.replace('''*''' ,_UpperCamelCase )
if "weight_g" in name:
__lowerCamelCase = '''weight_g'''
elif "weight_v" in name:
__lowerCamelCase = '''weight_v'''
elif "bias" in name:
__lowerCamelCase = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCamelCase = '''weight'''
else:
__lowerCamelCase = None
set_recursively(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
continue
if not is_used:
unused_weights.append(_UpperCamelCase )
logger.warning(F"""Unused weights: {unused_weights}""" )
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Dict ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = full_name.split('''conv_layers.''' )[-1]
__lowerCamelCase = name.split('''.''' )
__lowerCamelCase = int(items[0] )
__lowerCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_UpperCamelCase )
@torch.no_grad()
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : List[str] ,_UpperCamelCase : Tuple=None ,_UpperCamelCase : Tuple=None ,_UpperCamelCase : List[Any]=True ):
if config_path is not None:
__lowerCamelCase = UniSpeechSatConfig.from_pretrained(_UpperCamelCase )
else:
__lowerCamelCase = UniSpeechSatConfig()
__lowerCamelCase = ''''''
if is_finetuned:
__lowerCamelCase = UniSpeechSatForCTC(_UpperCamelCase )
else:
__lowerCamelCase = UniSpeechSatForPreTraining(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
__lowerCamelCase = model[0].eval()
recursively_load_weights(_UpperCamelCase ,_UpperCamelCase )
hf_wavavec.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
a_ = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 330 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""microsoft/swin-tiny-patch4-window7-224""": (
"""https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class a__ ( snake_case , snake_case ):
"""simple docstring"""
__lowerCamelCase = 'swin'
__lowerCamelCase = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , lowercase=224 , lowercase=4 , lowercase=3 , lowercase=96 , lowercase=[2, 2, 6, 2] , lowercase=[3, 6, 12, 24] , lowercase=7 , lowercase=4.0 , lowercase=True , lowercase=0.0 , lowercase=0.0 , lowercase=0.1 , lowercase="gelu" , lowercase=False , lowercase=0.02 , lowercase=1e-5 , lowercase=32 , lowercase=None , lowercase=None , **lowercase , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**lowercase )
A__ = image_size
A__ = patch_size
A__ = num_channels
A__ = embed_dim
A__ = depths
A__ = len(lowercase )
A__ = num_heads
A__ = window_size
A__ = mlp_ratio
A__ = qkv_bias
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = drop_path_rate
A__ = hidden_act
A__ = use_absolute_embeddings
A__ = layer_norm_eps
A__ = initializer_range
A__ = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
A__ = int(embed_dim * 2 ** (len(lowercase ) - 1) )
A__ = ["stem"] + [F'stage{idx}' for idx in range(1 , len(lowercase ) + 1 )]
A__ , A__ = get_aligned_output_features_output_indices(
out_features=lowercase , out_indices=lowercase , stage_names=self.stage_names )
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = version.parse('1.11' )
@property
def UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def UpperCamelCase ( self ) -> float:
'''simple docstring'''
return 1e-4
| 68 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
a_ = logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase__ )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
requires_backends(self , '''vision''' )
self.check_model_type(__UpperCAmelCase )
def __call__( self , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return super().__call__(__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , **__UpperCAmelCase ):
'''simple docstring'''
return {}, {}, {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = load_image(__UpperCAmelCase )
__lowerCamelCase = image.size
__lowerCamelCase = self.image_processor(images=__UpperCAmelCase , return_tensors=self.framework )
return model_inputs
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.model(**__UpperCAmelCase )
return model_outputs
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = model_outputs.predicted_depth
__lowerCamelCase = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode='''bicubic''' , align_corners=__UpperCAmelCase )
__lowerCamelCase = prediction.squeeze().cpu().numpy()
__lowerCamelCase = (output * 255 / np.max(__UpperCAmelCase )).astype('''uint8''' )
__lowerCamelCase = Image.fromarray(__UpperCAmelCase )
__lowerCamelCase = {}
__lowerCamelCase = predicted_depth
__lowerCamelCase = depth
return output_dict
| 330 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class UpperCamelCase ( unittest.TestCase ):
def a_ ( self) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self) -> Optional[int]:
snake_case_ = StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4')
snake_case_ = sd_pipe.to(lowerCAmelCase__)
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)
sd_pipe.set_scheduler('sample_euler')
snake_case_ = 'A painting of a squirrel eating a burger'
snake_case_ = torch.manual_seed(0)
snake_case_ = sd_pipe([prompt], generator=lowerCAmelCase__, guidance_scale=9.0, num_inference_steps=20, output_type='np')
snake_case_ = output.images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
snake_case_ = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def a_ ( self) -> Any:
snake_case_ = StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base')
snake_case_ = sd_pipe.to(lowerCAmelCase__)
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)
sd_pipe.set_scheduler('sample_euler')
snake_case_ = 'A painting of a squirrel eating a burger'
snake_case_ = torch.manual_seed(0)
snake_case_ = sd_pipe([prompt], generator=lowerCAmelCase__, guidance_scale=9.0, num_inference_steps=20, output_type='np')
snake_case_ = output.images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
snake_case_ = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-1
def a_ ( self) -> Any:
snake_case_ = StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base')
snake_case_ = sd_pipe.to(lowerCAmelCase__)
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)
sd_pipe.set_scheduler('sample_dpmpp_2m')
snake_case_ = 'A painting of a squirrel eating a burger'
snake_case_ = torch.manual_seed(0)
snake_case_ = sd_pipe(
[prompt], generator=lowerCAmelCase__, guidance_scale=7.5, num_inference_steps=15, output_type='np', use_karras_sigmas=lowerCAmelCase__, )
snake_case_ = output.images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
snake_case_ = np.array(
[0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 69 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
a_ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = ["""pixel_values"""]
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = True , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
__lowerCamelCase = size if size is not None else {'''shortest_edge''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
__lowerCamelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase , param_name='''crop_size''' )
__lowerCamelCase = do_resize
__lowerCamelCase = size
__lowerCamelCase = resample
__lowerCamelCase = do_center_crop
__lowerCamelCase = crop_size
__lowerCamelCase = do_rescale
__lowerCamelCase = rescale_factor
__lowerCamelCase = do_normalize
__lowerCamelCase = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__lowerCamelCase = image_std if image_std is not None else OPENAI_CLIP_STD
__lowerCamelCase = do_convert_rgb
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
__lowerCamelCase = get_resize_output_image_size(__UpperCAmelCase , size=size['''shortest_edge'''] , default_to_square=__UpperCAmelCase )
return resize(__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = get_size_dict(__UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(__UpperCAmelCase , size=(size['''height'''], size['''width''']) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = do_resize if do_resize is not None else self.do_resize
__lowerCamelCase = size if size is not None else self.size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = resample if resample is not None else self.resample
__lowerCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowerCamelCase = crop_size if crop_size is not None else self.crop_size
__lowerCamelCase = get_size_dict(__UpperCAmelCase , param_name='''crop_size''' , default_to_square=__UpperCAmelCase )
__lowerCamelCase = do_rescale if do_rescale is not None else self.do_rescale
__lowerCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCamelCase = do_normalize if do_normalize is not None else self.do_normalize
__lowerCamelCase = image_mean if image_mean is not None else self.image_mean
__lowerCamelCase = image_std if image_std is not None else self.image_std
__lowerCamelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__lowerCamelCase = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__lowerCamelCase = [convert_to_rgb(__UpperCAmelCase ) for image in images]
# All transformations expect numpy arrays.
__lowerCamelCase = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
__lowerCamelCase = [self.resize(image=__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase ) for image in images]
if do_center_crop:
__lowerCamelCase = [self.center_crop(image=__UpperCAmelCase , size=__UpperCAmelCase ) for image in images]
if do_rescale:
__lowerCamelCase = [self.rescale(image=__UpperCAmelCase , scale=__UpperCAmelCase ) for image in images]
if do_normalize:
__lowerCamelCase = [self.normalize(image=__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase ) for image in images]
__lowerCamelCase = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
__lowerCamelCase = {'''pixel_values''': images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase )
| 330 | 0 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
A__ : Optional[Any] ='''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
A__ : Union[str, Any] =BASE_URL + '''/user'''
# https://github.com/settings/tokens
A__ : Optional[int] =os.environ.get('''USER_TOKEN''', '''''')
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = {
"""Authorization""": f"token {auth_token}",
"""Accept""": """application/vnd.github.v3+json""",
}
return requests.get(lowerCAmelCase , headers=lowerCAmelCase ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(F"""{key}: {value}""")
else:
raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')
| 70 |
from __future__ import annotations
from typing import Generic, TypeVar
a_ = TypeVar("""T""")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = data
__lowerCamelCase = self
__lowerCamelCase = 0
class __lowerCAmelCase ( Generic[T] ):
def __init__( self ):
'''simple docstring'''
# map from node name to the node object
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# create a new set with x as its member
__lowerCamelCase = DisjointSetTreeNode(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# find the set x belongs to (with path-compression)
__lowerCamelCase = self.map[data]
if elem_ref != elem_ref.parent:
__lowerCamelCase = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# helper function for union operation
if nodea.rank > nodea.rank:
__lowerCamelCase = nodea
else:
__lowerCamelCase = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# merge 2 disjoint sets
self.link(self.find_set(__UpperCAmelCase ) , self.find_set(__UpperCAmelCase ) )
class __lowerCAmelCase ( Generic[T] ):
def __init__( self ):
'''simple docstring'''
# connections: map from the node to the neighbouring nodes (with weights)
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
# add a node ONLY if its not present in the graph
if node not in self.connections:
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
# add an edge with the given weight
self.add_node(__UpperCAmelCase )
self.add_node(__UpperCAmelCase )
__lowerCamelCase = weight
__lowerCamelCase = weight
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda __UpperCAmelCase : x[2] )
# creating the disjoint set
__lowerCamelCase = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(__UpperCAmelCase )
# MST generation
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = edges[index]
index += 1
__lowerCamelCase = disjoint_set.find_set(__UpperCAmelCase )
__lowerCamelCase = disjoint_set.find_set(__UpperCAmelCase )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
disjoint_set.union(__UpperCAmelCase , __UpperCAmelCase )
return graph
| 330 | 0 |
import os
from distutils.util import strtobool
def A ( a_ ,a_ ) -> Dict:
for e in env_keys:
__UpperCamelCase : Union[str, Any] =int(os.environ.get(a_ ,-1 ) )
if val >= 0:
return val
return default
def A ( a_ ,a_=False ) -> Tuple:
__UpperCamelCase : Tuple =os.environ.get(a_ ,str(a_ ) )
return strtobool(a_ ) == 1 # As its name indicates `strtobool` actually returns an int...
def A ( a_ ,a_="no" ) -> int:
__UpperCamelCase : Any =os.environ.get(a_ ,str(a_ ) )
return value
| 71 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=2 , __UpperCAmelCase=8 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=16 , __UpperCAmelCase=5 , __UpperCAmelCase=2 , __UpperCAmelCase=36 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=512 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_input_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase ( self ):
'''simple docstring'''
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.get_config()
__lowerCamelCase = 300
return config
def lowerCamelCase ( self ):
'''simple docstring'''
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = self.prepare_config_and_inputs()
__lowerCamelCase = True
__lowerCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = True
__lowerCamelCase = MraModel(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , )
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , )
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MraForQuestionAnswering(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MraForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = MraForTokenClassification(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_choices
__lowerCamelCase = MraForMultipleChoice(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,(
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = ()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCamelCase = type
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = MraModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@unittest.skip(reason='''MRA does not output attentions''' )
def lowerCamelCase ( self ):
'''simple docstring'''
return
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowerCamelCase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowerCamelCase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = 50265
__lowerCamelCase = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' )
__lowerCamelCase = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = 50265
__lowerCamelCase = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor(
[[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 330 | 0 |
"""simple docstring"""
def snake_case_ ( A_ : str, A_ : str ):
'''simple docstring'''
if not (isinstance(A_, A_ ) and isinstance(A_, A_ )):
raise ValueError('''longest_common_substring() takes two strings for inputs''' )
_lowerCamelCase : List[Any] = len(A_ )
_lowerCamelCase : Optional[Any] = len(A_ )
_lowerCamelCase : Tuple = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
_lowerCamelCase : Union[str, Any] = 0
_lowerCamelCase : Tuple = 0
for i in range(1, texta_length + 1 ):
for j in range(1, texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
_lowerCamelCase : Any = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
_lowerCamelCase : Optional[int] = i
_lowerCamelCase : Optional[Any] = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""EncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""TFEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""FlaxEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 0 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
a =open # noqa: we just need to have a builtin inside this module to test it properly
| 73 |
from string import ascii_lowercase, ascii_uppercase
def a__ ( _UpperCamelCase : str ):
if not sentence:
return ""
__lowerCamelCase = dict(zip(_UpperCamelCase ,_UpperCamelCase ) )
return lower_to_upper.get(sentence[0] ,sentence[0] ) + sentence[1:]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 330 | 0 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
_lowerCamelCase: str = (PNDMScheduler,)
_lowerCamelCase: Tuple = (('''num_inference_steps''', 50),)
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ,**A_ : List[Any] ) -> Union[str, Any]:
A = {
'num_train_timesteps': 1000,
'beta_start': 0.00_01,
'beta_end': 0.02,
'beta_schedule': 'linear',
}
config.update(**A_ )
return config
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,A_ : Optional[int]=0 ,**A_ : Optional[int] ) -> Optional[Any]:
A = dict(self.forward_default_kwargs )
A = kwargs.pop('num_inference_steps' ,A_ )
A = self.dummy_sample
A = 0.1 * sample
A = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
A = self.get_scheduler_config(**A_ )
A = scheduler_class(**A_ )
scheduler.set_timesteps(A_ )
# copy over dummy past residuals
A = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A_ )
A = scheduler_class.from_pretrained(A_ )
new_scheduler.set_timesteps(A_ )
# copy over dummy past residuals
A = dummy_past_residuals[:]
A = scheduler.step_prk(A_ ,A_ ,A_ ,**A_ ).prev_sample
A = new_scheduler.step_prk(A_ ,A_ ,A_ ,**A_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
A = scheduler.step_plms(A_ ,A_ ,A_ ,**A_ ).prev_sample
A = new_scheduler.step_plms(A_ ,A_ ,A_ ,**A_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
pass
def _SCREAMING_SNAKE_CASE ( self : Any ,A_ : List[Any]=0 ,**A_ : List[str] ) -> Tuple:
A = dict(self.forward_default_kwargs )
A = kwargs.pop('num_inference_steps' ,A_ )
A = self.dummy_sample
A = 0.1 * sample
A = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
A = self.get_scheduler_config()
A = scheduler_class(**A_ )
scheduler.set_timesteps(A_ )
# copy over dummy past residuals (must be after setting timesteps)
A = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A_ )
A = scheduler_class.from_pretrained(A_ )
# copy over dummy past residuals
new_scheduler.set_timesteps(A_ )
# copy over dummy past residual (must be after setting timesteps)
A = dummy_past_residuals[:]
A = scheduler.step_prk(A_ ,A_ ,A_ ,**A_ ).prev_sample
A = new_scheduler.step_prk(A_ ,A_ ,A_ ,**A_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
A = scheduler.step_plms(A_ ,A_ ,A_ ,**A_ ).prev_sample
A = new_scheduler.step_plms(A_ ,A_ ,A_ ,**A_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ,**A_ : List[Any] ) -> Any:
A = self.scheduler_classes[0]
A = self.get_scheduler_config(**A_ )
A = scheduler_class(**A_ )
A = 10
A = self.dummy_model()
A = self.dummy_sample_deter
scheduler.set_timesteps(A_ )
for i, t in enumerate(scheduler.prk_timesteps ):
A = model(A_ ,A_ )
A = scheduler.step_prk(A_ ,A_ ,A_ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
A = model(A_ ,A_ )
A = scheduler.step_plms(A_ ,A_ ,A_ ).prev_sample
return sample
def _SCREAMING_SNAKE_CASE ( self : int ) -> List[str]:
A = dict(self.forward_default_kwargs )
A = kwargs.pop('num_inference_steps' ,A_ )
for scheduler_class in self.scheduler_classes:
A = self.get_scheduler_config()
A = scheduler_class(**A_ )
A = self.dummy_sample
A = 0.1 * sample
if num_inference_steps is not None and hasattr(A_ ,'set_timesteps' ):
scheduler.set_timesteps(A_ )
elif num_inference_steps is not None and not hasattr(A_ ,'set_timesteps' ):
A = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
A = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
A = dummy_past_residuals[:]
A = scheduler.step_prk(A_ ,0 ,A_ ,**A_ ).prev_sample
A = scheduler.step_prk(A_ ,1 ,A_ ,**A_ ).prev_sample
self.assertEqual(output_a.shape ,sample.shape )
self.assertEqual(output_a.shape ,output_a.shape )
A = scheduler.step_plms(A_ ,0 ,A_ ,**A_ ).prev_sample
A = scheduler.step_plms(A_ ,1 ,A_ ,**A_ ).prev_sample
self.assertEqual(output_a.shape ,sample.shape )
self.assertEqual(output_a.shape ,output_a.shape )
def _SCREAMING_SNAKE_CASE ( self : str ) -> str:
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=A_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[Any]:
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=A_ )
A = self.scheduler_classes[0]
A = self.get_scheduler_config(steps_offset=1 )
A = scheduler_class(**A_ )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps ,torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) ,)
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[int]:
for beta_start, beta_end in zip([0.00_01, 0.0_01] ,[0.0_02, 0.02] ):
self.check_over_configs(beta_start=A_ ,beta_end=A_ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Any:
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=A_ )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> str:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A_ )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Union[str, Any]:
for t in [1, 5, 10]:
self.check_over_forward(time_step=A_ )
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Union[str, Any]:
for t, num_inference_steps in zip([1, 5, 10] ,[10, 50, 100] ):
self.check_over_forward(num_inference_steps=A_ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[Any]:
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
A = 27
for scheduler_class in self.scheduler_classes:
A = self.dummy_sample
A = 0.1 * sample
A = self.get_scheduler_config()
A = scheduler_class(**A_ )
scheduler.set_timesteps(A_ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
A = scheduler.step_prk(A_ ,A_ ,A_ ).prev_sample
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Any:
with self.assertRaises(A_ ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**A_ )
scheduler.step_plms(self.dummy_sample ,1 ,self.dummy_sample ).prev_sample
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Union[str, Any]:
A = self.full_loop()
A = torch.sum(torch.abs(A_ ) )
A = torch.mean(torch.abs(A_ ) )
assert abs(result_sum.item() - 1_98.13_18 ) < 1e-2
assert abs(result_mean.item() - 0.25_80 ) < 1e-3
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> str:
A = self.full_loop(prediction_type='v_prediction' )
A = torch.sum(torch.abs(A_ ) )
A = torch.mean(torch.abs(A_ ) )
assert abs(result_sum.item() - 67.39_86 ) < 1e-2
assert abs(result_mean.item() - 0.08_78 ) < 1e-3
def _SCREAMING_SNAKE_CASE ( self : str ) -> Optional[int]:
# We specify different beta, so that the first alpha is 0.99
A = self.full_loop(set_alpha_to_one=A_ ,beta_start=0.01 )
A = torch.sum(torch.abs(A_ ) )
A = torch.mean(torch.abs(A_ ) )
assert abs(result_sum.item() - 2_30.03_99 ) < 1e-2
assert abs(result_mean.item() - 0.29_95 ) < 1e-3
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[Any]:
# We specify different beta, so that the first alpha is 0.99
A = self.full_loop(set_alpha_to_one=A_ ,beta_start=0.01 )
A = torch.sum(torch.abs(A_ ) )
A = torch.mean(torch.abs(A_ ) )
assert abs(result_sum.item() - 1_86.94_82 ) < 1e-2
assert abs(result_mean.item() - 0.24_34 ) < 1e-3 | 74 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowerCAmelCase ( lowerCAmelCase__ ):
@slow
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' , '''prajjwal1/bert-tiny''' )
__lowerCamelCase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
__lowerCamelCase = bertabert.config.encoder.vocab_size
__lowerCamelCase = tokenizer.sep_token_id
__lowerCamelCase = tokenizer.cls_token_id
__lowerCamelCase = 128
__lowerCamelCase = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''train[:1%]''' )
__lowerCamelCase = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''validation[:1%]''' )
__lowerCamelCase = train_dataset.select(range(32 ) )
__lowerCamelCase = val_dataset.select(range(16 ) )
__lowerCamelCase = 4
def _map_to_encoder_decoder_inputs(__UpperCAmelCase ):
# Tokenizer will automatically set [BOS] <text> [EOS]
__lowerCamelCase = tokenizer(batch['''article'''] , padding='''max_length''' , truncation=__UpperCAmelCase , max_length=512 )
__lowerCamelCase = tokenizer(batch['''highlights'''] , padding='''max_length''' , truncation=__UpperCAmelCase , max_length=128 )
__lowerCamelCase = inputs.input_ids
__lowerCamelCase = inputs.attention_mask
__lowerCamelCase = outputs.input_ids
__lowerCamelCase = outputs.input_ids.copy()
__lowerCamelCase = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
]
__lowerCamelCase = outputs.attention_mask
assert all(len(__UpperCAmelCase ) == 512 for x in inputs.input_ids )
assert all(len(__UpperCAmelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(__UpperCAmelCase ):
__lowerCamelCase = pred.label_ids
__lowerCamelCase = pred.predictions
# all unnecessary tokens are removed
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
__lowerCamelCase = sum([int(pred_str[i] == label_str[i] ) for i in range(len(__UpperCAmelCase ) )] ) / len(__UpperCAmelCase )
return {"accuracy": accuracy}
# map train dataset
__lowerCamelCase = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=__UpperCAmelCase , batch_size=__UpperCAmelCase , remove_columns=['''article''', '''highlights'''] , )
train_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
# same for validation dataset
__lowerCamelCase = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=__UpperCAmelCase , batch_size=__UpperCAmelCase , remove_columns=['''article''', '''highlights'''] , )
val_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
__lowerCamelCase = self.get_auto_remove_tmp_dir()
__lowerCamelCase = SeqaSeqTrainingArguments(
output_dir=__UpperCAmelCase , per_device_train_batch_size=__UpperCAmelCase , per_device_eval_batch_size=__UpperCAmelCase , predict_with_generate=__UpperCAmelCase , evaluation_strategy='''steps''' , do_train=__UpperCAmelCase , do_eval=__UpperCAmelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
__lowerCamelCase = SeqaSeqTrainer(
model=__UpperCAmelCase , args=__UpperCAmelCase , compute_metrics=_compute_metrics , train_dataset=__UpperCAmelCase , eval_dataset=__UpperCAmelCase , tokenizer=__UpperCAmelCase , )
# start training
trainer.train()
| 330 | 0 |
'''simple docstring'''
a_ : Any = """Input must be a string of 8 numbers plus letter"""
a_ : Union[str, Any] = """TRWAGMYFPDXBNJZSQVHLCKE"""
def a_ ( __snake_case : str ) -> bool:
"""simple docstring"""
if not isinstance(__snake_case , __snake_case ):
lowerCamelCase_ =F'''Expected string as input, found {type(__snake_case ).__name__}'''
raise TypeError(__snake_case )
lowerCamelCase_ =spanish_id.replace('''-''' , '''''' ).upper()
if len(__snake_case ) != 9:
raise ValueError(__snake_case )
try:
lowerCamelCase_ =int(spanish_id_clean[0:8] )
lowerCamelCase_ =spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__snake_case ) from ex
if letter.isdigit():
raise ValueError(__snake_case )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_timm_backbone""": ["""TimmBackboneConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""TimmBackbone"""]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 330 | 0 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Dict = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE : int = ya
SCREAMING_SNAKE_CASE : int = xa
for k in range(_a):
SCREAMING_SNAKE_CASE : Any = y[k] + step_size * ode_func(_a , y[k])
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod() | 76 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase=0.01 , __UpperCAmelCase=1000 ):
'''simple docstring'''
__lowerCamelCase = p_stop
__lowerCamelCase = max_length
def __iter__( self ):
'''simple docstring'''
__lowerCamelCase = 0
__lowerCamelCase = False
while not stop and count < self.max_length:
yield count
count += 1
__lowerCamelCase = random.random() < self.p_stop
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=True ):
'''simple docstring'''
__lowerCamelCase = [
BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
for i in range(2 )
]
__lowerCamelCase = [list(__UpperCAmelCase ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(__UpperCAmelCase ) for shard in batch_sampler_shards] , [len(__UpperCAmelCase ) for e in expected] )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of total batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of total batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
# Check the shards when the dataset is a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__lowerCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
__lowerCamelCase = [BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , even_batches=__UpperCAmelCase ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=2 , __UpperCAmelCase=False ):
'''simple docstring'''
random.seed(__UpperCAmelCase )
__lowerCamelCase = list(__UpperCAmelCase )
__lowerCamelCase = [
IterableDatasetShard(
__UpperCAmelCase , batch_size=__UpperCAmelCase , drop_last=__UpperCAmelCase , num_processes=__UpperCAmelCase , process_index=__UpperCAmelCase , split_batches=__UpperCAmelCase , )
for i in range(__UpperCAmelCase )
]
__lowerCamelCase = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(__UpperCAmelCase )
iterable_dataset_lists.append(list(__UpperCAmelCase ) )
__lowerCamelCase = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
__lowerCamelCase = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
self.assertTrue(len(__UpperCAmelCase ) % shard_batch_size == 0 )
__lowerCamelCase = []
for idx in range(0 , len(__UpperCAmelCase ) , __UpperCAmelCase ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(__UpperCAmelCase ) < len(__UpperCAmelCase ):
reference += reference
self.assertListEqual(__UpperCAmelCase , reference[: len(__UpperCAmelCase )] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = 42
__lowerCamelCase = RandomIterableDataset()
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
# Edge case with a very small dataset
__lowerCamelCase = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = BatchSampler(range(16 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__lowerCamelCase = SkipBatchSampler(__UpperCAmelCase , 2 )
self.assertListEqual(list(__UpperCAmelCase ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DataLoader(list(range(16 ) ) , batch_size=4 )
__lowerCamelCase = skip_first_batches(__UpperCAmelCase , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def lowerCamelCase ( self ):
'''simple docstring'''
Accelerator()
__lowerCamelCase = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 330 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase : Optional[int] = logging.get_logger(__name__)
_UpperCamelCase : Dict = "▁"
_UpperCamelCase : Optional[Any] = {"vocab_file": "sentencepiece.bpe.model"}
_UpperCamelCase : Dict = {
"vocab_file": {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
}
}
_UpperCamelCase : Tuple = {
"facebook/xglm-564M": 20_48,
}
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : Optional[int] = VOCAB_FILES_NAMES
lowerCamelCase__ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ : List[str] = ["input_ids", "attention_mask"]
def __init__( self , a , a="<s>" , a="</s>" , a="</s>" , a="<s>" , a="<unk>" , a="<pad>" , a = None , **a , ) -> None:
lowercase__ : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
lowercase__ : int = 7
lowercase__ : int = [f"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
lowercase__ : Optional[int] = kwargs.get('additional_special_tokens' , [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=a , eos_token=a , unk_token=a , sep_token=a , cls_token=a , pad_token=a , sp_model_kwargs=self.sp_model_kwargs , **a , )
lowercase__ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(a ) )
lowercase__ : Union[str, Any] = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
lowercase__ : List[str] = 1
# Mimic fairseq token-to-id alignment for the first 4 token
lowercase__ : Union[str, Any] = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
lowercase__ : Tuple = len(self.sp_model )
lowercase__ : List[Any] = {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(a )
lowercase__ : str = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ) -> List[str]:
lowercase__ : List[Any] = self.__dict__.copy()
lowercase__ : Tuple = None
lowercase__ : Dict = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , a ) -> int:
lowercase__ : Optional[Any] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
lowercase__ : Tuple = {}
lowercase__ : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def _UpperCAmelCase ( self , a , a = None ) -> List[int]:
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
lowercase__ : Optional[Any] = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def _UpperCAmelCase ( self , a , a = None , a = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=a , token_ids_a=a , already_has_special_tokens=a )
if token_ids_a is None:
return [1] + ([0] * len(a ))
return [1] + ([0] * len(a )) + [1, 1] + ([0] * len(a ))
def _UpperCAmelCase ( self , a , a = None ) -> List[int]:
lowercase__ : Union[str, Any] = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def _UpperCAmelCase ( self ) -> Optional[int]:
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def _UpperCAmelCase ( self ) -> Dict:
lowercase__ : Optional[Any] = {self.convert_ids_to_tokens(a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _UpperCAmelCase ( self , a ) -> List[str]:
return self.sp_model.encode(a , out_type=a )
def _UpperCAmelCase ( self , a ) -> Any:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowercase__ : Any = self.sp_model.PieceToId(a )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _UpperCAmelCase ( self , a ) -> List[Any]:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _UpperCAmelCase ( self , a ) -> Dict:
lowercase__ : List[Any] = ''.join(a ).replace(a , ' ' ).strip()
return out_string
def _UpperCAmelCase ( self , a , a = None ) -> Tuple[str]:
if not os.path.isdir(a ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase__ : Any = os.path.join(
a , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , a )
elif not os.path.isfile(self.vocab_file ):
with open(a , 'wb' ) as fi:
lowercase__ : Any = self.sp_model.serialized_model_proto()
fi.write(a )
return (out_vocab_file,)
| 77 |
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 330 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case_ = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
"""SqueezeBertOnnxConfig""",
],
"""tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ["""SqueezeBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SqueezeBertForMaskedLM""",
"""SqueezeBertForMultipleChoice""",
"""SqueezeBertForQuestionAnswering""",
"""SqueezeBertForSequenceClassification""",
"""SqueezeBertForTokenClassification""",
"""SqueezeBertModel""",
"""SqueezeBertModule""",
"""SqueezeBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_squeezebert import (
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
SqueezeBertConfig,
SqueezeBertOnnxConfig,
)
from .tokenization_squeezebert import SqueezeBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_squeezebert import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
SqueezeBertModule,
SqueezeBertPreTrainedModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 78 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
a_ = 16
a_ = 32
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : int = 16 ):
__lowerCamelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' )
def tokenize_function(_UpperCamelCase : Optional[Any] ):
# max_length=None => use the model max length (it's actually the default)
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__lowerCamelCase = datasets.map(
_UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowerCamelCase = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__lowerCamelCase = 16
elif accelerator.mixed_precision != "no":
__lowerCamelCase = 8
else:
__lowerCamelCase = None
return tokenizer.pad(
_UpperCamelCase ,padding='''longest''' ,max_length=_UpperCamelCase ,pad_to_multiple_of=_UpperCamelCase ,return_tensors='''pt''' ,)
# Instantiate dataloaders.
__lowerCamelCase = DataLoader(
tokenized_datasets['''train'''] ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=_UpperCamelCase )
__lowerCamelCase = DataLoader(
tokenized_datasets['''validation'''] ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=_UpperCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
a_ = mocked_dataloaders # noqa: F811
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ):
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,_UpperCamelCase ) == "1":
__lowerCamelCase = 2
# Initialize accelerator
__lowerCamelCase = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCamelCase = config['''lr''']
__lowerCamelCase = int(config['''num_epochs'''] )
__lowerCamelCase = int(config['''seed'''] )
__lowerCamelCase = int(config['''batch_size'''] )
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=_UpperCamelCase )
def inner_training_loop(_UpperCamelCase : Union[str, Any] ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(_UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,return_dict=_UpperCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowerCamelCase = model.to(accelerator.device )
# Instantiate optimizer
__lowerCamelCase = AdamW(params=model.parameters() ,lr=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = get_dataloaders(_UpperCamelCase ,_UpperCamelCase )
# Instantiate scheduler
__lowerCamelCase = get_linear_schedule_with_warmup(
optimizer=_UpperCamelCase ,num_warmup_steps=1_00 ,num_training_steps=(len(_UpperCamelCase ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(
_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
# Now we train the model
for epoch in range(_UpperCamelCase ):
model.train()
for step, batch in enumerate(_UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.loss
accelerator.backward(_UpperCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_UpperCamelCase ,references=_UpperCamelCase ,)
__lowerCamelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" ,_UpperCamelCase )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def a__ ( ):
__lowerCamelCase = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' ,type=_UpperCamelCase ,default=_UpperCamelCase ,choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' ,)
parser.add_argument('''--cpu''' ,action='''store_true''' ,help='''If passed, will train on the CPU.''' )
__lowerCamelCase = parser.parse_args()
__lowerCamelCase = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_UpperCamelCase ,_UpperCamelCase )
if __name__ == "__main__":
main()
| 330 | 0 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
lowerCamelCase_ = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
lowerCamelCase_ = {
'''facebook/blenderbot_small-90M''': 5_12,
}
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
snake_case = VOCAB_FILES_NAMES
snake_case = PRETRAINED_VOCAB_FILES_MAP
snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case = BlenderbotSmallTokenizer
def __init__( self : Optional[int] , __UpperCAmelCase : str=None , __UpperCAmelCase : int=None , __UpperCAmelCase : Any="<|endoftext|>" , __UpperCAmelCase : Optional[int]="<|endoftext|>" , __UpperCAmelCase : Any="<|endoftext|>" , __UpperCAmelCase : List[Any]=False , __UpperCAmelCase : Dict=True , **__UpperCAmelCase : str , ):
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=__UpperCAmelCase , merges=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , trim_offsets=__UpperCAmelCase , ) , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , **__UpperCAmelCase , )
_A = add_prefix_space
def lowerCAmelCase ( self : Tuple , __UpperCAmelCase : int , __UpperCAmelCase : int=None ):
'''simple docstring'''
_A = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ):
'''simple docstring'''
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 79 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
a_ = None
try:
import msvcrt
except ImportError:
a_ = None
try:
import fcntl
except ImportError:
a_ = None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
a_ = OSError
# Data
# ------------------------------------------------
a_ = [
"""Timeout""",
"""BaseFileLock""",
"""WindowsFileLock""",
"""UnixFileLock""",
"""SoftFileLock""",
"""FileLock""",
]
a_ = """3.0.12"""
a_ = None
def a__ ( ):
global _logger
__lowerCamelCase = _logger or logging.getLogger(__name__ )
return _logger
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = lock_file
return None
def __str__( self ):
'''simple docstring'''
__lowerCamelCase = F"""The file lock '{self.lock_file}' could not be acquired."""
return temp
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = lock
return None
def __enter__( self ):
'''simple docstring'''
return self.lock
def __exit__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.lock.release()
return None
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = max_filename_length if max_filename_length is not None else 255
# Hash the filename if it's too long
__lowerCamelCase = self.hash_filename_if_too_long(__UpperCAmelCase , __UpperCAmelCase )
# The path to the lock file.
__lowerCamelCase = lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
__lowerCamelCase = None
# The default timeout value.
__lowerCamelCase = timeout
# We use this lock primarily for the lock counter.
__lowerCamelCase = threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
__lowerCamelCase = 0
return None
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._lock_file
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._timeout
@timeout.setter
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = float(__UpperCAmelCase )
return None
def lowerCamelCase ( self ):
'''simple docstring'''
raise NotImplementedError()
def lowerCamelCase ( self ):
'''simple docstring'''
raise NotImplementedError()
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return self._lock_file_fd is not None
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=0.05 ):
'''simple docstring'''
# Use the default timeout, if no timeout is provided.
if timeout is None:
__lowerCamelCase = self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
__lowerCamelCase = id(self )
__lowerCamelCase = self._lock_file
__lowerCamelCase = time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(F"""Attempting to acquire lock {lock_id} on {lock_filename}""" )
self._acquire()
if self.is_locked:
logger().debug(F"""Lock {lock_id} acquired on {lock_filename}""" )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(F"""Timeout on acquiring lock {lock_id} on {lock_filename}""" )
raise Timeout(self._lock_file )
else:
logger().debug(
F"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...""" )
time.sleep(__UpperCAmelCase )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
__lowerCamelCase = max(0 , self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def lowerCamelCase ( self , __UpperCAmelCase=False ):
'''simple docstring'''
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
__lowerCamelCase = id(self )
__lowerCamelCase = self._lock_file
logger().debug(F"""Attempting to release lock {lock_id} on {lock_filename}""" )
self._release()
__lowerCamelCase = 0
logger().debug(F"""Lock {lock_id} released on {lock_filename}""" )
return None
def __enter__( self ):
'''simple docstring'''
self.acquire()
return self
def __exit__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.release()
return None
def __del__( self ):
'''simple docstring'''
self.release(force=__UpperCAmelCase )
return None
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = os.path.basename(__UpperCAmelCase )
if len(__UpperCAmelCase ) > max_length and max_length > 0:
__lowerCamelCase = os.path.dirname(__UpperCAmelCase )
__lowerCamelCase = str(hash(__UpperCAmelCase ) )
__lowerCamelCase = filename[: max_length - len(__UpperCAmelCase ) - 8] + '''...''' + hashed_filename + '''.lock'''
return os.path.join(__UpperCAmelCase , __UpperCAmelCase )
else:
return path
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
from .file_utils import relative_to_absolute_path
super().__init__(__UpperCAmelCase , timeout=__UpperCAmelCase , max_filename_length=__UpperCAmelCase )
__lowerCamelCase = '''\\\\?\\''' + relative_to_absolute_path(self.lock_file )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
except OSError:
pass
else:
try:
msvcrt.locking(__UpperCAmelCase , msvcrt.LK_NBLCK , 1 )
except OSError:
os.close(__UpperCAmelCase )
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self._lock_file_fd
__lowerCamelCase = None
msvcrt.locking(__UpperCAmelCase , msvcrt.LK_UNLCK , 1 )
os.close(__UpperCAmelCase )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=-1 , __UpperCAmelCase=None ):
'''simple docstring'''
__lowerCamelCase = os.statvfs(os.path.dirname(__UpperCAmelCase ) ).f_namemax
super().__init__(__UpperCAmelCase , timeout=__UpperCAmelCase , max_filename_length=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_RDWR | os.O_CREAT | os.O_TRUNC
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
try:
fcntl.flock(__UpperCAmelCase , fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(__UpperCAmelCase )
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
# Do not remove the lockfile:
#
# https://github.com/benediktschmitt/py-filelock/issues/31
# https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
__lowerCamelCase = self._lock_file_fd
__lowerCamelCase = None
fcntl.flock(__UpperCAmelCase , fcntl.LOCK_UN )
os.close(__UpperCAmelCase )
return None
class __lowerCAmelCase ( lowerCAmelCase__ ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
__lowerCamelCase = os.open(self._lock_file , __UpperCAmelCase )
except OSError:
pass
else:
__lowerCamelCase = fd
return None
def lowerCamelCase ( self ):
'''simple docstring'''
os.close(self._lock_file_fd )
__lowerCamelCase = None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
a_ = None
if msvcrt:
a_ = WindowsFileLock
elif fcntl:
a_ = UnixFileLock
else:
a_ = SoftFileLock
if warnings is not None:
warnings.warn("""only soft file lock is available""")
| 330 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class lowercase_ ( unittest.TestCase ):
def __a ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def __a ( self ):
torch.manual_seed(0 )
UpperCamelCase__ = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(1_28, 1_28) , down_block_types=("AttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "AttnUpBlock2D") , )
return model
@property
def __a ( self ):
torch.manual_seed(0 )
UpperCamelCase__ = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(1_28, 1_28) , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , cross_attention_dim=10 , )
return model
@property
def __a ( self ):
torch.manual_seed(0 )
UpperCamelCase__ = AutoencoderKL(
sample_size=(1_28, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(1_28, 1_28) , down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D") , up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D") , )
UpperCamelCase__ = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(1_28, 1_28) , down_block_types=("AttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "AttnUpBlock2D") , )
return vqvae, unet
@slow
def __a ( self ):
UpperCamelCase__ = "cpu" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase__ = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
UpperCamelCase__ = DDPMScheduler()
UpperCamelCase__ = AudioDiffusionPipeline(vqvae=a , unet=self.dummy_unet , mel=a , scheduler=a )
UpperCamelCase__ = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
UpperCamelCase__ = torch.Generator(device=a ).manual_seed(42 )
UpperCamelCase__ = pipe(generator=a , steps=4 )
UpperCamelCase__ = output.audios[0]
UpperCamelCase__ = output.images[0]
UpperCamelCase__ = torch.Generator(device=a ).manual_seed(42 )
UpperCamelCase__ = pipe(generator=a , steps=4 , return_dict=a )
UpperCamelCase__ = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
UpperCamelCase__ = np.frombuffer(image.tobytes() , dtype="uint8" )[:10]
UpperCamelCase__ = np.frombuffer(image_from_tuple.tobytes() , dtype="uint8" )[:10]
UpperCamelCase__ = np.array([69, 2_55, 2_55, 2_55, 0, 0, 77, 1_81, 12, 1_27] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
UpperCamelCase__ = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] , )
UpperCamelCase__ = DDIMScheduler()
UpperCamelCase__ = self.dummy_vqvae_and_unet
UpperCamelCase__ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=a , scheduler=a )
UpperCamelCase__ = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
np.random.seed(0 )
UpperCamelCase__ = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
UpperCamelCase__ = torch.Generator(device=a ).manual_seed(42 )
UpperCamelCase__ = pipe(raw_audio=a , generator=a , start_step=5 , steps=10 )
UpperCamelCase__ = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
UpperCamelCase__ = np.frombuffer(image.tobytes() , dtype="uint8" )[:10]
UpperCamelCase__ = np.array([1_20, 1_17, 1_10, 1_09, 1_38, 1_67, 1_38, 1_48, 1_32, 1_21] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
UpperCamelCase__ = self.dummy_unet_condition
UpperCamelCase__ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=a , mel=a , scheduler=a )
UpperCamelCase__ = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
np.random.seed(0 )
UpperCamelCase__ = torch.rand((1, 1, 10) )
UpperCamelCase__ = pipe(generator=a , encoding=a )
UpperCamelCase__ = output.images[0]
UpperCamelCase__ = np.frombuffer(image.tobytes() , dtype="uint8" )[:10]
UpperCamelCase__ = np.array([1_07, 1_03, 1_20, 1_27, 1_42, 1_22, 1_13, 1_22, 97, 1_11] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class lowercase_ ( unittest.TestCase ):
def __a ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self ):
UpperCamelCase__ = torch_device
UpperCamelCase__ = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256" )
UpperCamelCase__ = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
UpperCamelCase__ = torch.Generator(device=a ).manual_seed(42 )
UpperCamelCase__ = pipe(generator=a )
UpperCamelCase__ = output.audios[0]
UpperCamelCase__ = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
UpperCamelCase__ = np.frombuffer(image.tobytes() , dtype="uint8" )[:10]
UpperCamelCase__ = np.array([1_51, 1_67, 1_54, 1_44, 1_22, 1_34, 1_21, 1_05, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 80 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=10 , __UpperCAmelCase=3 , __UpperCAmelCase=2 , __UpperCAmelCase=2 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=10 , __UpperCAmelCase=0.02 , __UpperCAmelCase="divided_space_time" , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = image_size
__lowerCamelCase = num_channels
__lowerCamelCase = patch_size
__lowerCamelCase = num_frames
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = attention_type
__lowerCamelCase = initializer_range
__lowerCamelCase = scope
__lowerCamelCase = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
__lowerCamelCase = (image_size // patch_size) ** 2
__lowerCamelCase = (num_frames) * self.num_patches_per_frame + 1
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
__lowerCamelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , attention_type=self.attention_type , )
__lowerCamelCase = self.num_labels
return config
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TimesformerModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase )
# verify the logits shape
__lowerCamelCase = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = config_and_inputs
__lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
lowerCAmelCase__ = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerModelTester(self )
__lowerCamelCase = ConfigTester(
self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(__UpperCAmelCase )
if return_labels:
if model_class in get_values(__UpperCAmelCase ):
__lowerCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__UpperCAmelCase )
return inputs_dict
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(__UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCAmelCase , nn.Linear ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(__UpperCAmelCase )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = TimesformerModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
if not self.has_attentions:
pass
else:
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = True
for model_class in self.all_model_classes:
__lowerCamelCase = self.model_tester.seq_length
__lowerCamelCase = self.model_tester.num_frames
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
__lowerCamelCase = len(__UpperCAmelCase )
# Check attention is always last and order is fine
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
self.assertEqual(out_len + 1 , len(__UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def lowerCamelCase ( self ):
'''simple docstring'''
def check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.hidden_states
__lowerCamelCase = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
__lowerCamelCase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def a__ ( ):
__lowerCamelCase = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' ,filename='''eating_spaghetti.npy''' ,repo_type='''dataset''' )
__lowerCamelCase = np.load(_UpperCamelCase )
return list(_UpperCamelCase )
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
__UpperCAmelCase )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_video()
__lowerCamelCase = image_processor(video[:8] , return_tensors='''pt''' ).to(__UpperCAmelCase )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(**__UpperCAmelCase )
# verify the logits
__lowerCamelCase = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor([-0.3_016, -0.7_713, -0.4_205] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 330 | 0 |
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