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"""simple docstring"""
from __future__ import annotations
import math
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Tuple , lowerCAmelCase : str , lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if len(__SCREAMING_SNAKE_CASE ) == 0:
raise ValueError("Scores cannot be empty" )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 , node_index * 2 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , minimax(depth + 1 , node_index * 2 + 1 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , )
return min(
minimax(depth + 1 , node_index * 2 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , minimax(depth + 1 , node_index * 2 + 1 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , )
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : int = [90, 23, 6, 33, 21, 65, 123, 3_4423]
UpperCAmelCase__ : Union[str, Any] = math.log(len(__SCREAMING_SNAKE_CASE ) , 2 )
print("Optimal value : " , end="" )
print(minimax(0 , 0 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 704 |
"""simple docstring"""
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=30 , __UpperCamelCase=2 , __UpperCamelCase=3 , __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=3 , __UpperCamelCase=0.6 , __UpperCamelCase=None , )-> List[Any]:
UpperCAmelCase__ : str = parent
UpperCAmelCase__ : Optional[Any] = batch_size
UpperCAmelCase__ : Any = image_size
UpperCAmelCase__ : Dict = patch_size
UpperCAmelCase__ : Optional[Any] = num_channels
UpperCAmelCase__ : Union[str, Any] = is_training
UpperCAmelCase__ : Any = use_labels
UpperCAmelCase__ : List[Any] = hidden_size
UpperCAmelCase__ : Dict = num_hidden_layers
UpperCAmelCase__ : List[str] = num_attention_heads
UpperCAmelCase__ : List[Any] = intermediate_size
UpperCAmelCase__ : Dict = hidden_act
UpperCAmelCase__ : List[Any] = hidden_dropout_prob
UpperCAmelCase__ : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase__ : Optional[Any] = type_sequence_label_size
UpperCAmelCase__ : Union[str, Any] = initializer_range
UpperCAmelCase__ : int = mask_ratio
UpperCAmelCase__ : Tuple = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase__ : int = (image_size // patch_size) ** 2
UpperCAmelCase__ : str = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Optional[Any] = None
if self.use_labels:
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : Optional[int] = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self )-> int:
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = ViTMAEModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : List[str] = 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 )-> Optional[int]:
UpperCAmelCase__ : List[Any] = ViTMAEForPreTraining(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : Optional[int] = model(__UpperCamelCase )
UpperCAmelCase__ : List[str] = (self.image_size // self.patch_size) ** 2
UpperCAmelCase__ : List[str] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase__ : Dict = 1
UpperCAmelCase__ : str = ViTMAEForPreTraining(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase__ : List[str] = model(__UpperCamelCase )
UpperCAmelCase__ : List[str] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : Tuple = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : str = config_and_inputs
UpperCAmelCase__ : int = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_A = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
_A = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
_A = False
_A = False
_A = False
_A = False
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : Any = ViTMAEModelTester(self )
UpperCAmelCase__ : List[str] = ConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase , hidden_size=37 )
def lowerCAmelCase__ ( self )-> int:
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def lowerCAmelCase__ ( self )-> Dict:
pass
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : int = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase__ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ , UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class(__UpperCamelCase )
UpperCAmelCase__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Dict = [*signature.parameters.keys()]
UpperCAmelCase__ : Tuple = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Dict:
# make masks reproducible
np.random.seed(2 )
UpperCAmelCase__ : Tuple = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
UpperCAmelCase__ : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase__ : str = torch.from_numpy(__UpperCamelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase__ : Optional[Any] = pt_noise
super().check_pt_tf_models(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[Any] = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase__ : Optional[int] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
UpperCAmelCase__ : Optional[Any] = outputs[0].cpu().numpy()
UpperCAmelCase__ : Union[str, Any] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = model_class.from_pretrained(__UpperCamelCase )
model.to(__UpperCamelCase )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase__ : List[Any] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
# Make sure we don't have nans
UpperCAmelCase__ : Tuple = after_outputs[0].cpu().numpy()
UpperCAmelCase__ : int = 0
UpperCAmelCase__ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__UpperCamelCase , 1E-5 )
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> List[str]:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> Any:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> Optional[Any]:
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def lowerCAmelCase__ ( self )-> List[Any]:
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
pass
@slow
def lowerCAmelCase__ ( self )-> Union[str, Any]:
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : Tuple = ViTMAEModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase__ ( self )-> List[Any]:
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def lowerCAmelCase__ ( self )-> Optional[int]:
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCAmelCase__ : Any = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" ).to(__UpperCamelCase )
UpperCAmelCase__ : Tuple = self.default_image_processor
UpperCAmelCase__ : List[Any] = prepare_img()
UpperCAmelCase__ : Optional[Any] = image_processor(images=__UpperCamelCase , return_tensors="pt" ).to(__UpperCamelCase )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCAmelCase__ : List[Any] = ViTMAEConfig()
UpperCAmelCase__ : str = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase__ : Optional[int] = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : List[str] = model(**__UpperCamelCase , noise=torch.from_numpy(__UpperCamelCase ).to(device=__UpperCamelCase ) )
# verify the logits
UpperCAmelCase__ : str = torch.Size((1, 1_96, 7_68) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
UpperCAmelCase__ : List[Any] = torch.tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(__UpperCamelCase ) , atol=1E-4 ) )
| 660 | 0 |
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
A__ : List[Any] = {
"facebook/maskformer-swin-base-ade": (
"https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
A__ : str = logging.get_logger(__name__)
class _lowercase ( _UpperCAmelCase ):
'''simple docstring'''
_A = '''maskformer'''
_A = {'''hidden_size''': '''mask_feature_size'''}
_A = ['''resnet''', '''swin''']
_A = ['''detr''']
def __init__( self , __UpperCamelCase = 2_56 , __UpperCamelCase = 2_56 , __UpperCamelCase = 0.1 , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = 0.02 , __UpperCamelCase = 1.0 , __UpperCamelCase = 1.0 , __UpperCamelCase = 1.0 , __UpperCamelCase = 20.0 , __UpperCamelCase = None , **__UpperCamelCase , )-> List[str]:
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
UpperCAmelCase__ : int = SwinConfig(
image_size=3_84 , in_channels=3 , patch_size=4 , embed_dim=1_28 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(A_ , A_ ):
UpperCAmelCase__ : Optional[int] = backbone_config.pop("model_type" )
UpperCAmelCase__ : Tuple = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase__ : List[str] = config_class.from_dict(A_ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
F"Supported model types: {','.join(self.backbones_supported )}" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
UpperCAmelCase__ : Tuple = DetrConfig()
else:
# verify that the decoder is supported
UpperCAmelCase__ : int = (
decoder_config.pop("model_type" ) if isinstance(A_ , A_ ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
F"Transformer Decoder {decoder_type} not supported, please use one of"
F" {','.join(self.decoders_supported )}" )
if isinstance(A_ , A_ ):
UpperCAmelCase__ : List[Any] = CONFIG_MAPPING[decoder_type]
UpperCAmelCase__ : Union[str, Any] = config_class.from_dict(A_ )
UpperCAmelCase__ : Union[str, Any] = backbone_config
UpperCAmelCase__ : Union[str, Any] = decoder_config
# main feature dimension for the model
UpperCAmelCase__ : Optional[Any] = fpn_feature_size
UpperCAmelCase__ : Union[str, Any] = mask_feature_size
# initializer
UpperCAmelCase__ : List[Any] = init_std
UpperCAmelCase__ : List[str] = init_xavier_std
# Hungarian matcher && loss
UpperCAmelCase__ : Dict = cross_entropy_weight
UpperCAmelCase__ : Union[str, Any] = dice_weight
UpperCAmelCase__ : Union[str, Any] = mask_weight
UpperCAmelCase__ : str = use_auxiliary_loss
UpperCAmelCase__ : int = no_object_weight
UpperCAmelCase__ : Dict = output_auxiliary_logits
UpperCAmelCase__ : Optional[Any] = self.decoder_config.encoder_attention_heads
UpperCAmelCase__ : List[Any] = self.decoder_config.num_hidden_layers
super().__init__(**A_ )
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )-> Union[str, Any]:
return cls(
backbone_config=A_ , decoder_config=A_ , **A_ , )
def lowerCAmelCase__ ( self )-> Dict[str, any]:
UpperCAmelCase__ : Dict = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : Optional[int] = self.backbone_config.to_dict()
UpperCAmelCase__ : Dict = self.decoder_config.to_dict()
UpperCAmelCase__ : Tuple = self.__class__.model_type
return output
| 705 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class _lowercase :
'''simple docstring'''
_A = 42
# setable values
_A = 42
_A = 42
_A = None
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
return cls(common=__UpperCamelCase , init_noise_sigma=__UpperCamelCase , timesteps=__UpperCamelCase )
@dataclass
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
_A = [e.name for e in FlaxKarrasDiffusionSchedulers]
_A = 42
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
return True
@register_to_config
def __init__( self , __UpperCamelCase = 10_00 , __UpperCamelCase = 0.0001 , __UpperCamelCase = 0.02 , __UpperCamelCase = "linear" , __UpperCamelCase = None , __UpperCamelCase = "fixed_small" , __UpperCamelCase = True , __UpperCamelCase = "epsilon" , __UpperCamelCase = jnp.floataa , )-> List[str]:
UpperCAmelCase__ : int = dtype
def lowerCAmelCase__ ( self , __UpperCamelCase = None )-> DDPMSchedulerState:
if common is None:
UpperCAmelCase__ : int = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
UpperCAmelCase__ : Tuple = jnp.array(1.0 , dtype=self.dtype )
UpperCAmelCase__ : Tuple = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=__UpperCamelCase , init_noise_sigma=__UpperCamelCase , timesteps=__UpperCamelCase , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None )-> jnp.ndarray:
return sample
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = () )-> DDPMSchedulerState:
UpperCAmelCase__ : Dict = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
UpperCAmelCase__ : Optional[int] = (jnp.arange(0 , __UpperCamelCase ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=__UpperCamelCase , timesteps=__UpperCamelCase , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = state.common.alphas_cumprod[t]
UpperCAmelCase__ : int = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase__ : Any = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
UpperCAmelCase__ : Union[str, Any] = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
UpperCAmelCase__ : Dict = jnp.clip(__UpperCamelCase , a_min=1E-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
UpperCAmelCase__ : Tuple = jnp.log(jnp.clip(__UpperCamelCase , a_min=1E-20 ) )
elif variance_type == "fixed_large":
UpperCAmelCase__ : Union[str, Any] = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
UpperCAmelCase__ : Optional[int] = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
UpperCAmelCase__ : List[str] = variance
UpperCAmelCase__ : Union[str, Any] = state.common.betas[t]
UpperCAmelCase__ : Optional[int] = (predicted_variance + 1) / 2
UpperCAmelCase__ : Any = frac * max_log + (1 - frac) * min_log
return variance
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = True , )-> Union[FlaxDDPMSchedulerOutput, Tuple]:
UpperCAmelCase__ : List[str] = timestep
if key is None:
UpperCAmelCase__ : int = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = jnp.split(__UpperCamelCase , sample.shape[1] , axis=1 )
else:
UpperCAmelCase__ : Optional[Any] = None
# 1. compute alphas, betas
UpperCAmelCase__ : Union[str, Any] = state.common.alphas_cumprod[t]
UpperCAmelCase__ : Tuple = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
UpperCAmelCase__ : Union[str, Any] = 1 - alpha_prod_t
UpperCAmelCase__ : Tuple = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase__ : List[Any] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase__ : Any = model_output
elif self.config.prediction_type == "v_prediction":
UpperCAmelCase__ : Union[str, Any] = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
" for the FlaxDDPMScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase__ : List[Any] = jnp.clip(__UpperCamelCase , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ : List[str] = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
UpperCAmelCase__ : List[Any] = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ : Tuple = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
UpperCAmelCase__ : Any = jax.random.split(__UpperCamelCase , num=1 )
UpperCAmelCase__ : int = jax.random.normal(__UpperCamelCase , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(__UpperCamelCase , __UpperCamelCase , predicted_variance=__UpperCamelCase ) ** 0.5) * noise
UpperCAmelCase__ : Dict = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
UpperCAmelCase__ : Any = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase , state=__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> jnp.ndarray:
return add_noise_common(state.common , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> jnp.ndarray:
return get_velocity_common(state.common , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __len__( self )-> Tuple:
return self.config.num_train_timesteps
| 660 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : List[str] = FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small" )
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained("google/mt5-small" )
UpperCAmelCase__ : List[Any] = tokenizer("Hello there" , return_tensors="np" ).input_ids
UpperCAmelCase__ : str = tokenizer("Hi I am" , return_tensors="np" ).input_ids
UpperCAmelCase__ : Any = shift_tokens_right(__UpperCamelCase , model.config.pad_token_id , model.config.decoder_start_token_id )
UpperCAmelCase__ : List[Any] = model(__UpperCamelCase , decoder_input_ids=__UpperCamelCase ).logits
UpperCAmelCase__ : List[str] = optax.softmax_cross_entropy(__UpperCamelCase , onehot(__UpperCamelCase , logits.shape[-1] ) ).mean()
UpperCAmelCase__ : Optional[Any] = -(labels.shape[-1] * loss.item())
UpperCAmelCase__ : Optional[int] = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 706 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = ''
_A = 'hf-legacy' # "hf://"" is reserved for hffs
def __init__( self , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )-> str:
super().__init__(self , **__UpperCamelCase )
UpperCAmelCase__ : int = repo_info
UpperCAmelCase__ : Optional[int] = token
UpperCAmelCase__ : Optional[Any] = None
def lowerCAmelCase__ ( self )-> Optional[Any]:
if self.dir_cache is None:
UpperCAmelCase__ : str = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
UpperCAmelCase__ : str = {
"name": hf_file.rfilename,
"size": None,
"type": "file",
}
self.dir_cache.update(
{
str(__UpperCamelCase ): {"name": str(__UpperCamelCase ), "size": None, "type": "directory"}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = "rb" , **__UpperCamelCase , )-> List[Any]:
if not isinstance(self.repo_info , __UpperCamelCase ):
raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}" )
UpperCAmelCase__ : Union[str, Any] = hf_hub_url(self.repo_info.id , __UpperCamelCase , revision=self.repo_info.sha )
return fsspec.open(
__UpperCamelCase , mode=__UpperCamelCase , headers=get_authentication_headers_for_url(__UpperCamelCase , use_auth_token=self.token ) , client_kwargs={"trust_env": True} , ).open()
def lowerCAmelCase__ ( self , __UpperCamelCase , **__UpperCamelCase )-> List[str]:
self._get_dirs()
UpperCAmelCase__ : Union[str, Any] = self._strip_protocol(__UpperCamelCase )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase=False , **__UpperCamelCase )-> str:
self._get_dirs()
UpperCAmelCase__ : str = PurePosixPath(path.strip("/" ) )
UpperCAmelCase__ : Optional[Any] = {}
for p, f in self.dir_cache.items():
UpperCAmelCase__ : Optional[int] = PurePosixPath(p.strip("/" ) )
UpperCAmelCase__ : Dict = p.parent
if root == path:
UpperCAmelCase__ : Tuple = f
UpperCAmelCase__ : List[Any] = list(paths.values() )
if detail:
return out
else:
return sorted(f["name"] for f in out )
| 660 | 0 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
A__ : List[str] = {
"gwf-440k": {
"url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
"sample_rate": 48_000,
"sample_size": 65_536,
},
"jmann-small-190k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
"sample_rate": 48_000,
"sample_size": 65_536,
},
"jmann-large-580k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
"sample_rate": 48_000,
"sample_size": 131_072,
},
"maestro-uncond-150k": {
"url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
"sample_rate": 16_000,
"sample_size": 65_536,
},
"unlocked-uncond-250k": {
"url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
"sample_rate": 16_000,
"sample_size": 65_536,
},
"honk-140k": {
"url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
"sample_rate": 16_000,
"sample_size": 65_536,
},
}
def a__ ( lowerCAmelCase : Optional[Any] , lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
return torch.atana(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) / math.pi * 2
def a__ ( lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = torch.sin(t * math.pi / 2 ) ** 2
UpperCAmelCase__ : int = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
pass
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self , __UpperCamelCase )-> Optional[Any]:
super().__init__()
UpperCAmelCase__ : List[Any] = DiffusionAttnUnetaD(__UpperCamelCase , n_attn_layers=4 )
UpperCAmelCase__ : Any = deepcopy(self.diffusion )
UpperCAmelCase__ : Optional[int] = torch.quasirandom.SobolEngine(1 , scramble=__UpperCamelCase )
def a__ ( lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = MODELS_MAP[model_name]["url"]
os.system(F"wget {url} ./" )
return F"./{model_name}.ckpt"
A__ : Tuple = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
}
A__ : str = {
"8": "resnets.0",
"9": "attentions.0",
"10": "resnets.1",
"11": "attentions.1",
"12": "resnets.2",
"13": "attentions.2",
}
A__ : int = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
"8": "resnets.3",
"9": "attentions.3",
"10": "resnets.4",
"11": "attentions.4",
"12": "resnets.5",
"13": "attentions.5",
}
A__ : List[Any] = {
"0": "resnets.0",
"1": "resnets.1",
"2": "resnets.2",
"4": "resnets.0",
"5": "resnets.1",
"6": "resnets.2",
}
A__ : Any = {
"skip": "conv_skip",
"main.0": "conv_1",
"main.1": "group_norm_1",
"main.3": "conv_2",
"main.4": "group_norm_2",
}
A__ : List[str] = {
"norm": "group_norm",
"qkv_proj": ["query", "key", "value"],
"out_proj": ["proj_attn"],
}
def a__ ( lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
if name.startswith("skip" ):
return name.replace("skip" , RES_CONV_MAP["skip"] )
# name has to be of format main.{digit}
if not name.startswith("main." ):
raise ValueError(F"ResConvBlock error with {name}" )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def a__ ( lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
for key, value in ATTN_MAP.items():
if name.startswith(_SCREAMING_SNAKE_CASE ) and not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return name.replace(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif name.startswith(_SCREAMING_SNAKE_CASE ):
return [name.replace(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for v in value]
raise ValueError(F"Attn error with {name}" )
def a__ ( lowerCAmelCase : Optional[Any] , lowerCAmelCase : List[str]=13 ):
'''simple docstring'''
UpperCAmelCase__ : str = input_string
if string.split("." )[0] == "timestep_embed":
return string.replace("timestep_embed" , "time_proj" )
UpperCAmelCase__ : Optional[Any] = 0
if string.startswith("net.3." ):
depth += 1
UpperCAmelCase__ : Union[str, Any] = string[6:]
elif string.startswith("net." ):
UpperCAmelCase__ : List[Any] = string[4:]
while string.startswith("main.7." ):
depth += 1
UpperCAmelCase__ : Any = string[7:]
if string.startswith("main." ):
UpperCAmelCase__ : Optional[Any] = string[5:]
# mid block
if string[:2].isdigit():
UpperCAmelCase__ : Optional[int] = string[:2]
UpperCAmelCase__ : str = string[2:]
else:
UpperCAmelCase__ : int = string[0]
UpperCAmelCase__ : Dict = string[1:]
if depth == max_depth:
UpperCAmelCase__ : List[Any] = MID_NUM_TO_LAYER[layer_num]
UpperCAmelCase__ : List[str] = "mid_block"
elif depth > 0 and int(_SCREAMING_SNAKE_CASE ) < 7:
UpperCAmelCase__ : str = DOWN_NUM_TO_LAYER[layer_num]
UpperCAmelCase__ : List[str] = F"down_blocks.{depth}"
elif depth > 0 and int(_SCREAMING_SNAKE_CASE ) > 7:
UpperCAmelCase__ : Union[str, Any] = UP_NUM_TO_LAYER[layer_num]
UpperCAmelCase__ : Union[str, Any] = F"up_blocks.{max_depth - depth - 1}"
elif depth == 0:
UpperCAmelCase__ : Optional[Any] = DEPTH_0_TO_LAYER[layer_num]
UpperCAmelCase__ : int = F"up_blocks.{max_depth - 1}" if int(_SCREAMING_SNAKE_CASE ) > 3 else "down_blocks.0"
if not string_left.startswith("." ):
raise ValueError(F"Naming error with {input_string} and string_left: {string_left}." )
UpperCAmelCase__ : Union[str, Any] = string_left[1:]
if "resnets" in new_layer:
UpperCAmelCase__ : Optional[Any] = convert_resconv_naming(_SCREAMING_SNAKE_CASE )
elif "attentions" in new_layer:
UpperCAmelCase__ : Dict = convert_attn_naming(_SCREAMING_SNAKE_CASE )
UpperCAmelCase__ : Any = new_string_left
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCAmelCase__ : List[Any] = prefix + "." + new_layer + "." + string_left
else:
UpperCAmelCase__ : Optional[Any] = [prefix + "." + new_layer + "." + s for s in string_left]
return new_string
def a__ ( lowerCAmelCase : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = {}
for k, v in state_dict.items():
if k.endswith("kernel" ):
# up- and downsample layers, don't have trainable weights
continue
UpperCAmelCase__ : Any = rename(_SCREAMING_SNAKE_CASE )
# check if we need to transform from Conv => Linear for attention
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCAmelCase__ : Union[str, Any] = transform_conv_attns(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase__ : Dict = v
return new_state_dict
def a__ ( lowerCAmelCase : Optional[Any] , lowerCAmelCase : str , lowerCAmelCase : Tuple ):
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) == 1:
if len(v.shape ) == 3:
# weight
UpperCAmelCase__ : Any = v[:, :, 0]
else:
# bias
UpperCAmelCase__ : Any = v
else:
# qkv matrices
UpperCAmelCase__ : Any = v.shape[0]
UpperCAmelCase__ : Tuple = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
UpperCAmelCase__ : Optional[int] = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
UpperCAmelCase__ : str = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def a__ ( lowerCAmelCase : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
UpperCAmelCase__ : Optional[Any] = args.model_path.split("/" )[-1].split("." )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), F"Make sure to provide one of the official model names {MODELS_MAP.keys()}"
UpperCAmelCase__ : str = download(_SCREAMING_SNAKE_CASE )
UpperCAmelCase__ : List[Any] = MODELS_MAP[model_name]["sample_rate"]
UpperCAmelCase__ : Dict = MODELS_MAP[model_name]["sample_size"]
UpperCAmelCase__ : int = Object()
UpperCAmelCase__ : List[Any] = sample_size
UpperCAmelCase__ : List[str] = sample_rate
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Tuple = UNetaDModel(sample_size=_SCREAMING_SNAKE_CASE , sample_rate=_SCREAMING_SNAKE_CASE )
UpperCAmelCase__ : Tuple = diffusers_model.state_dict()
UpperCAmelCase__ : Dict = DiffusionUncond(_SCREAMING_SNAKE_CASE )
orig_model.load_state_dict(torch.load(args.model_path , map_location=_SCREAMING_SNAKE_CASE )["state_dict"] )
UpperCAmelCase__ : Tuple = orig_model.diffusion_ema.eval()
UpperCAmelCase__ : Optional[int] = orig_model.state_dict()
UpperCAmelCase__ : Optional[Any] = rename_orig_weights(_SCREAMING_SNAKE_CASE )
UpperCAmelCase__ : List[str] = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
UpperCAmelCase__ : Union[str, Any] = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(_SCREAMING_SNAKE_CASE ) == 0, F"Problem with {renamed_minus_diffusers}"
assert all(k.endswith("kernel" ) for k in list(_SCREAMING_SNAKE_CASE ) ), F"Problem with {diffusers_minus_renamed}"
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), F"Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"
if key == "time_proj.weight":
UpperCAmelCase__ : Optional[int] = value.squeeze()
UpperCAmelCase__ : str = value
diffusers_model.load_state_dict(_SCREAMING_SNAKE_CASE )
UpperCAmelCase__ : List[Any] = 100
UpperCAmelCase__ : List[str] = 33
UpperCAmelCase__ : Any = IPNDMScheduler(num_train_timesteps=_SCREAMING_SNAKE_CASE )
UpperCAmelCase__ : Dict = torch.manual_seed(_SCREAMING_SNAKE_CASE )
UpperCAmelCase__ : Optional[int] = torch.randn([1, 2, config.sample_size] , generator=_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
UpperCAmelCase__ : Optional[int] = torch.linspace(1 , 0 , steps + 1 , device=_SCREAMING_SNAKE_CASE )[:-1]
UpperCAmelCase__ : Dict = get_crash_schedule(_SCREAMING_SNAKE_CASE )
UpperCAmelCase__ : Optional[Any] = DanceDiffusionPipeline(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
UpperCAmelCase__ : str = torch.manual_seed(33 )
UpperCAmelCase__ : str = pipe(num_inference_steps=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).audios
UpperCAmelCase__ : Optional[int] = sampling.iplms_sample(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , {} )
UpperCAmelCase__ : Any = generated.clamp(-1 , 1 )
UpperCAmelCase__ : List[str] = (generated - audio).abs().sum()
UpperCAmelCase__ : Optional[int] = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print("Diff sum" , _SCREAMING_SNAKE_CASE )
print("Diff max" , _SCREAMING_SNAKE_CASE )
assert diff_max < 1E-3, F"Diff max: {diff_max} is too much :-/"
print(F"Conversion for {model_name} successful!" )
if __name__ == "__main__":
A__ : Dict = argparse.ArgumentParser()
parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
parser.add_argument(
"""--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
)
parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
A__ : str = parser.parse_args()
main(args)
| 707 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
A__ : Dict = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
if isinstance(lowerCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowerCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowerCAmelCase ):
return [[videos]]
raise ValueError(F"Could not make batched video from {videos}" )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = ['pixel_values']
def __init__( self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_55 , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )-> None:
super().__init__(**__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = size if size is not None else {"shortest_edge": 2_56}
UpperCAmelCase__ : List[Any] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : List[str] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
UpperCAmelCase__ : int = get_size_dict(__UpperCamelCase , param_name="crop_size" )
UpperCAmelCase__ : Dict = do_resize
UpperCAmelCase__ : Optional[int] = size
UpperCAmelCase__ : List[Any] = do_center_crop
UpperCAmelCase__ : str = crop_size
UpperCAmelCase__ : Optional[int] = resample
UpperCAmelCase__ : int = do_rescale
UpperCAmelCase__ : Union[str, Any] = rescale_factor
UpperCAmelCase__ : Union[str, Any] = offset
UpperCAmelCase__ : Dict = do_normalize
UpperCAmelCase__ : int = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase__ : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
if "shortest_edge" in size:
UpperCAmelCase__ : Union[str, Any] = get_resize_output_image_size(__UpperCamelCase , size["shortest_edge"] , default_to_square=__UpperCamelCase )
elif "height" in size and "width" in size:
UpperCAmelCase__ : Any = (size["height"], size["width"])
else:
raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[Any] = get_size_dict(__UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(__UpperCamelCase , size=(size["height"], size["width"]) , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = True , __UpperCamelCase = None , **__UpperCamelCase , )-> Tuple:
UpperCAmelCase__ : str = image.astype(np.floataa )
if offset:
UpperCAmelCase__ : Tuple = image - (scale / 2)
return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
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 = ChannelDimension.FIRST , )-> np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError("Size and resample 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." )
if offset and not do_rescale:
raise ValueError("For offset, do_rescale must also be set to True." )
# All transformations expect numpy arrays.
UpperCAmelCase__ : Optional[Any] = to_numpy_array(__UpperCamelCase )
if do_resize:
UpperCAmelCase__ : Union[str, Any] = self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase )
if do_center_crop:
UpperCAmelCase__ : int = self.center_crop(__UpperCamelCase , size=__UpperCamelCase )
if do_rescale:
UpperCAmelCase__ : List[str] = self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase , offset=__UpperCamelCase )
if do_normalize:
UpperCAmelCase__ : List[Any] = self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase )
UpperCAmelCase__ : Dict = to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase )
return image
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 , )-> PIL.Image.Image:
UpperCAmelCase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ : int = resample if resample is not None else self.resample
UpperCAmelCase__ : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase__ : int = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ : Optional[int] = offset if offset is not None else self.offset
UpperCAmelCase__ : Dict = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ : Dict = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ : Optional[int] = image_std if image_std is not None else self.image_std
UpperCAmelCase__ : List[str] = size if size is not None else self.size
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : Dict = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase__ : Tuple = get_size_dict(__UpperCamelCase , param_name="crop_size" )
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." )
UpperCAmelCase__ : List[str] = make_batched(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = [
[
self._preprocess_image(
image=__UpperCamelCase , do_resize=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , do_center_crop=__UpperCamelCase , crop_size=__UpperCamelCase , do_rescale=__UpperCamelCase , rescale_factor=__UpperCamelCase , offset=__UpperCamelCase , do_normalize=__UpperCamelCase , image_mean=__UpperCamelCase , image_std=__UpperCamelCase , data_format=__UpperCamelCase , )
for img in video
]
for video in videos
]
UpperCAmelCase__ : Dict = {"pixel_values": videos}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 660 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _lowercase ( A_ ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[Any]:
UpperCAmelCase__ : Tuple = dataset
UpperCAmelCase__ : Optional[int] = process
UpperCAmelCase__ : Optional[int] = params
def __len__( self )-> str:
return len(self.dataset )
def __getitem__( self , __UpperCamelCase )-> Tuple:
UpperCAmelCase__ : Dict = self.dataset[i]
UpperCAmelCase__ : Tuple = self.process(__UpperCamelCase , **self.params )
return processed
class _lowercase ( A_ ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None )-> List[str]:
UpperCAmelCase__ : List[str] = loader
UpperCAmelCase__ : Tuple = infer
UpperCAmelCase__ : str = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
UpperCAmelCase__ : Tuple = None
UpperCAmelCase__ : Tuple = loader_batch_size
# Internal bookkeeping
UpperCAmelCase__ : Union[str, Any] = None
UpperCAmelCase__ : Optional[Any] = None
def __len__( self )-> List[Any]:
return len(self.loader )
def __iter__( self )-> int:
UpperCAmelCase__ : Tuple = iter(self.loader )
return self
def lowerCAmelCase__ ( self )-> List[str]:
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
UpperCAmelCase__ : List[Any] = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
UpperCAmelCase__ : Any = {}
for k, element in self._loader_batch_data.items():
if isinstance(__UpperCamelCase , __UpperCamelCase ):
# Convert ModelOutput to tuple first
UpperCAmelCase__ : Any = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
UpperCAmelCase__ : Dict = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
UpperCAmelCase__ : Any = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(__UpperCamelCase , __UpperCamelCase ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
UpperCAmelCase__ : List[Any] = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
UpperCAmelCase__ : Optional[int] = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
UpperCAmelCase__ : List[Any] = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
UpperCAmelCase__ : List[str] = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
UpperCAmelCase__ : str = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
UpperCAmelCase__ : Optional[int] = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
UpperCAmelCase__ : Union[str, Any] = self._loader_batch_data.__class__(__UpperCamelCase )
self._loader_batch_index += 1
return result
def lowerCAmelCase__ ( self )-> str:
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
UpperCAmelCase__ : Any = next(self.iterator )
UpperCAmelCase__ : Optional[int] = self.infer(__UpperCamelCase , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(__UpperCamelCase , torch.Tensor ):
UpperCAmelCase__ : Optional[int] = processed
else:
UpperCAmelCase__ : Union[str, Any] = list(processed.keys() )[0]
UpperCAmelCase__ : List[Any] = processed[key]
if isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : Any = len(__UpperCamelCase )
else:
UpperCAmelCase__ : Dict = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
UpperCAmelCase__ : Dict = observed_batch_size
# Setting internal index to unwrap the batch
UpperCAmelCase__ : List[str] = processed
UpperCAmelCase__ : List[Any] = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class _lowercase ( A_ ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None )-> Optional[Any]:
super().__init__(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __iter__( self )-> Dict:
UpperCAmelCase__ : Optional[Any] = iter(self.loader )
UpperCAmelCase__ : Dict = None
return self
def lowerCAmelCase__ ( self )-> int:
if self.subiterator is None:
UpperCAmelCase__ : List[Any] = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
UpperCAmelCase__ : Optional[int] = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
UpperCAmelCase__ : List[Any] = self.infer(next(self.iterator ) , **self.params )
UpperCAmelCase__ : str = next(self.subiterator )
return processed
class _lowercase ( A_ ):
'''simple docstring'''
def __iter__( self )-> Any:
UpperCAmelCase__ : int = iter(self.loader )
return self
def lowerCAmelCase__ ( self )-> Union[str, Any]:
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
UpperCAmelCase__ : Optional[int] = False
UpperCAmelCase__ : Union[str, Any] = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
UpperCAmelCase__ : Dict = self.loader_batch_item()
UpperCAmelCase__ : Union[str, Any] = item.pop("is_last" )
accumulator.append(__UpperCamelCase )
if is_last:
return accumulator
while not is_last:
UpperCAmelCase__ : int = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(__UpperCamelCase , torch.Tensor ):
UpperCAmelCase__ : List[Any] = processed
else:
UpperCAmelCase__ : Tuple = list(processed.keys() )[0]
UpperCAmelCase__ : List[Any] = processed[key]
if isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : List[Any] = len(__UpperCamelCase )
else:
UpperCAmelCase__ : Dict = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
UpperCAmelCase__ : Union[str, Any] = observed_batch_size
UpperCAmelCase__ : List[Any] = processed
UpperCAmelCase__ : Tuple = 0
while self._loader_batch_index < self.loader_batch_size:
UpperCAmelCase__ : Tuple = self.loader_batch_item()
UpperCAmelCase__ : Dict = item.pop("is_last" )
accumulator.append(__UpperCamelCase )
if is_last:
return accumulator
else:
UpperCAmelCase__ : Any = processed
UpperCAmelCase__ : Optional[Any] = item.pop("is_last" )
accumulator.append(__UpperCamelCase )
return accumulator
class _lowercase ( A_ ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase )-> str:
UpperCAmelCase__ : Any = dataset
UpperCAmelCase__ : Optional[Any] = key
def __len__( self )-> Dict:
return len(self.dataset )
def __getitem__( self , __UpperCamelCase )-> Tuple:
return self.dataset[i][self.key]
class _lowercase ( A_ ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> int:
UpperCAmelCase__ : int = dataset
UpperCAmelCase__ : Dict = keya
UpperCAmelCase__ : List[str] = keya
def __len__( self )-> Union[str, Any]:
return len(self.dataset )
def __getitem__( self , __UpperCamelCase )-> Optional[Any]:
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 708 |
"""simple docstring"""
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowerCAmelCase , lowerCAmelCase ):
raise TypeError("Input value must be a 'int' type" )
return bin(lowerCAmelCase ).count("1" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 0 |
"""simple docstring"""
from collections.abc import Callable
def a__ ( lowerCAmelCase : Callable[[float], float] , lowerCAmelCase : float , lowerCAmelCase : float ):
'''simple docstring'''
UpperCAmelCase__ : float = a
UpperCAmelCase__ : float = b
if function(lowerCAmelCase__ ) == 0: # one of the a or b is a root for the function
return a
elif function(lowerCAmelCase__ ) == 0:
return b
elif (
function(lowerCAmelCase__ ) * function(lowerCAmelCase__ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("could not find root in given interval." )
else:
UpperCAmelCase__ : float = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(lowerCAmelCase__ ) == 0:
return mid
elif function(lowerCAmelCase__ ) * function(lowerCAmelCase__ ) < 0:
UpperCAmelCase__ : Optional[int] = mid
else:
UpperCAmelCase__ : Any = mid
UpperCAmelCase__ : Any = start + (end - start) / 2.0
return mid
def a__ ( lowerCAmelCase : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1_000))
import doctest
doctest.testmod()
| 709 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
A__ : Optional[Any] = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : bool , lowerCAmelCase : bool ):
'''simple docstring'''
def run_func(lowerCAmelCase : Dict ):
@wraps(lowerCAmelCase )
def run_in_eager_mode(*lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Dict ):
return func(*lowerCAmelCase , **lowerCAmelCase )
@wraps(lowerCAmelCase )
@tf.function(experimental_compile=lowerCAmelCase )
def run_in_graph_mode(*lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Optional[Any] ):
return func(*lowerCAmelCase , **lowerCAmelCase )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`." )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def a__ ( lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Dict = random.Random()
UpperCAmelCase__ : List[str] = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(lowerCAmelCase , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
_A = 42
_A = "TensorFlow"
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
return tf.__version__
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
# initialize GPU on separate process
UpperCAmelCase__ : Any = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Union[str, Any] = self._prepare_inference_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_speed(_inference )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
UpperCAmelCase__ : List[Any] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : List[Any] = self._prepare_train_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_speed(_train )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> [Memory, Optional[MemorySummary]]:
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __UpperCamelCase )
UpperCAmelCase__ : List[str] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Any = self._prepare_inference_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_memory(_inference )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> [Memory, Optional[MemorySummary]]:
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __UpperCamelCase )
UpperCAmelCase__ : Any = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Optional[Any] = self._prepare_train_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_memory(_train )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Callable[[], None]:
UpperCAmelCase__ : Union[str, Any] = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
UpperCAmelCase__ : Optional[int] = (
hasattr(__UpperCamelCase , "architectures" )
and isinstance(config.architectures , __UpperCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCAmelCase__ : str = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCAmelCase__ : Any = __import__("transformers" , fromlist=[model_class] )
UpperCAmelCase__ : List[Any] = getattr(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Dict = model_cls(__UpperCamelCase )
except ImportError:
raise ImportError(
F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
UpperCAmelCase__ : Any = TF_MODEL_MAPPING[config.__class__](__UpperCamelCase )
# encoder-decoder has vocab size saved differently
UpperCAmelCase__ : int = config.vocab_size if hasattr(__UpperCamelCase , "vocab_size" ) else config.encoder.vocab_size
UpperCAmelCase__ : Optional[Any] = random_input_ids(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(__UpperCamelCase , decoder_input_ids=__UpperCamelCase , training=__UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(__UpperCamelCase , training=__UpperCamelCase )
UpperCAmelCase__ : Dict = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Callable[[], None]:
UpperCAmelCase__ : List[Any] = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`." )
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
UpperCAmelCase__ : Any = (
hasattr(__UpperCamelCase , "architectures" )
and isinstance(config.architectures , __UpperCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCAmelCase__ : Any = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCAmelCase__ : int = __import__("transformers" , fromlist=[model_class] )
UpperCAmelCase__ : int = getattr(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = model_cls(__UpperCamelCase )
except ImportError:
raise ImportError(
F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
UpperCAmelCase__ : List[str] = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCamelCase )
# encoder-decoder has vocab size saved differently
UpperCAmelCase__ : Union[str, Any] = config.vocab_size if hasattr(__UpperCamelCase , "vocab_size" ) else config.encoder.vocab_size
UpperCAmelCase__ : Dict = random_input_ids(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
UpperCAmelCase__ : Union[str, Any] = model(__UpperCamelCase , decoder_input_ids=__UpperCamelCase , labels=__UpperCamelCase , training=__UpperCamelCase )[0]
UpperCAmelCase__ : Union[str, Any] = tf.gradients(__UpperCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
UpperCAmelCase__ : Union[str, Any] = model(__UpperCamelCase , labels=__UpperCamelCase , training=__UpperCamelCase )[0]
UpperCAmelCase__ : Any = tf.gradients(__UpperCamelCase , model.trainable_variables )
return gradients
UpperCAmelCase__ : str = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def lowerCAmelCase__ ( self , __UpperCamelCase )-> float:
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("Do inference on TPU. Running model 5 times to stabilize compilation" )
timeit.repeat(__UpperCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
UpperCAmelCase__ : Optional[Any] = timeit.repeat(
__UpperCamelCase , repeat=self.args.repeat , number=10 , )
return min(__UpperCamelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(F"Doesn't fit on GPU. {e}" )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> [Memory, MemorySummary]:
logger.info(
"Note that TensorFlow allocates more memory than "
"it might need to speed up computation. "
"The memory reported here corresponds to the memory "
"reported by `nvidia-smi`, which can vary depending "
"on total available memory on the GPU that is used." )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
" consumption line by line." )
UpperCAmelCase__ : List[str] = start_memory_tracing("transformers" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
" with `args.memory=False`" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"py3nvml not installed, we won't log GPU memory usage. "
"Install py3nvml (pip install py3nvml) to log information about GPU." )
UpperCAmelCase__ : Optional[int] = "N/A"
else:
logger.info(
"Measuring total GPU usage on GPU device. Make sure to not have additional processes"
" running on the same GPU." )
# init nvml
nvml.nvmlInit()
func()
UpperCAmelCase__ : Any = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
UpperCAmelCase__ : Optional[int] = nvml.nvmlDeviceGetMemoryInfo(__UpperCamelCase )
UpperCAmelCase__ : str = meminfo.used
UpperCAmelCase__ : int = Memory(__UpperCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
" TensorFlow." )
UpperCAmelCase__ : Any = None
else:
UpperCAmelCase__ : List[Any] = measure_peak_memory_cpu(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = Memory(__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
UpperCAmelCase__ : Optional[Any] = stop_memory_tracing(__UpperCamelCase )
if memory is None:
UpperCAmelCase__ : Tuple = summary.total
else:
UpperCAmelCase__ : int = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(F"Doesn't fit on GPU. {e}" )
return "N/A", None
| 660 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
A__ : Dict = """platform"""
import jax
import jax.numpy as jnp
from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
shift_tokens_right,
)
def a__ ( lowerCAmelCase : str , lowerCAmelCase : Tuple , lowerCAmelCase : Optional[Any]=None , lowerCAmelCase : Dict=None , lowerCAmelCase : Any=None , lowerCAmelCase : Dict=None , lowerCAmelCase : str=None , lowerCAmelCase : Union[str, Any]=None , ):
'''simple docstring'''
if attention_mask is None:
UpperCAmelCase__ : str = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
UpperCAmelCase__ : Union[str, Any] = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
UpperCAmelCase__ : Union[str, Any] = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase__ : int = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase__ : List[Any] = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=99 , __UpperCamelCase=16 , __UpperCamelCase=2 , __UpperCamelCase=4 , __UpperCamelCase=4 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=0 , __UpperCamelCase=0.02 , )-> Optional[Any]:
UpperCAmelCase__ : List[str] = parent
UpperCAmelCase__ : Optional[int] = batch_size
UpperCAmelCase__ : List[str] = seq_length
UpperCAmelCase__ : Optional[Any] = is_training
UpperCAmelCase__ : List[str] = use_labels
UpperCAmelCase__ : Tuple = vocab_size
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : Optional[Any] = num_hidden_layers
UpperCAmelCase__ : Optional[Any] = num_attention_heads
UpperCAmelCase__ : List[Any] = intermediate_size
UpperCAmelCase__ : Tuple = hidden_act
UpperCAmelCase__ : Optional[int] = hidden_dropout_prob
UpperCAmelCase__ : Optional[Any] = attention_probs_dropout_prob
UpperCAmelCase__ : List[str] = max_position_embeddings
UpperCAmelCase__ : str = eos_token_id
UpperCAmelCase__ : Any = pad_token_id
UpperCAmelCase__ : Any = bos_token_id
UpperCAmelCase__ : Optional[int] = initializer_range
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : Optional[int] = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
UpperCAmelCase__ : int = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
UpperCAmelCase__ : Optional[int] = shift_tokens_right(lowerCamelCase_ , 1 , 2 )
UpperCAmelCase__ : Any = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=lowerCamelCase_ , )
UpperCAmelCase__ : Optional[int] = prepare_blenderbot_inputs_dict(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
return config, inputs_dict
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[str]:
UpperCAmelCase__ : Optional[int] = 20
UpperCAmelCase__ : Optional[Any] = model_class_name(lowerCamelCase_ )
UpperCAmelCase__ : Any = model.encode(inputs_dict["input_ids"] )
UpperCAmelCase__ , UpperCAmelCase__ : str = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
UpperCAmelCase__ : Tuple = model.init_cache(decoder_input_ids.shape[0] , lowerCamelCase_ , lowerCamelCase_ )
UpperCAmelCase__ : Tuple = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="i4" )
UpperCAmelCase__ : Any = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCAmelCase__ : int = model.decode(
decoder_input_ids[:, :-1] , lowerCamelCase_ , decoder_attention_mask=lowerCamelCase_ , past_key_values=lowerCamelCase_ , decoder_position_ids=lowerCamelCase_ , )
UpperCAmelCase__ : Optional[int] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
UpperCAmelCase__ : Optional[int] = model.decode(
decoder_input_ids[:, -1:] , lowerCamelCase_ , decoder_attention_mask=lowerCamelCase_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCamelCase_ , )
UpperCAmelCase__ : Union[str, Any] = model.decode(lowerCamelCase_ , lowerCamelCase_ )
UpperCAmelCase__ : Optional[int] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F"Max diff is {diff}" )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[str]:
UpperCAmelCase__ : str = 20
UpperCAmelCase__ : List[str] = model_class_name(lowerCamelCase_ )
UpperCAmelCase__ : Union[str, Any] = model.encode(inputs_dict["input_ids"] )
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
UpperCAmelCase__ : Optional[Any] = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
UpperCAmelCase__ : Optional[int] = model.init_cache(decoder_input_ids.shape[0] , lowerCamelCase_ , lowerCamelCase_ )
UpperCAmelCase__ : Any = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCAmelCase__ : int = model.decode(
decoder_input_ids[:, :-1] , lowerCamelCase_ , decoder_attention_mask=lowerCamelCase_ , past_key_values=lowerCamelCase_ , decoder_position_ids=lowerCamelCase_ , )
UpperCAmelCase__ : List[Any] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
UpperCAmelCase__ : Optional[int] = model.decode(
decoder_input_ids[:, -1:] , lowerCamelCase_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCamelCase_ , decoder_position_ids=lowerCamelCase_ , )
UpperCAmelCase__ : str = model.decode(lowerCamelCase_ , lowerCamelCase_ , decoder_attention_mask=lowerCamelCase_ )
UpperCAmelCase__ : str = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F"Max diff is {diff}" )
@require_flax
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
_A = 99
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : Optional[int] = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
UpperCAmelCase__ : Dict = input_ids.shape[0]
UpperCAmelCase__ : int = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = self._get_config_and_data()
UpperCAmelCase__ : Optional[int] = FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase_ )
UpperCAmelCase__ : str = lm_model(input_ids=lowerCamelCase_ )
UpperCAmelCase__ : Union[str, Any] = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["logits"].shape , lowerCamelCase_ )
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : List[Any] = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
UpperCAmelCase__ : List[str] = FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase_ )
UpperCAmelCase__ : int = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
UpperCAmelCase__ : Any = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
UpperCAmelCase__ : Union[str, Any] = lm_model(input_ids=lowerCamelCase_ , decoder_input_ids=lowerCamelCase_ )
UpperCAmelCase__ : int = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["logits"].shape , lowerCamelCase_ )
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : Any = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
UpperCAmelCase__ : str = shift_tokens_right(lowerCamelCase_ , 1 , 2 )
UpperCAmelCase__ : Dict = np.equal(lowerCamelCase_ , 1 ).astype(np.floataa ).sum()
UpperCAmelCase__ : Tuple = np.equal(lowerCamelCase_ , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(lowerCamelCase_ , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class _lowercase ( a__ , unittest.TestCase , a__ ):
'''simple docstring'''
_A = True
_A = (
(
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallForConditionalGeneration,
)
if is_flax_available()
else ()
)
_A = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : int = FlaxBlenderbotSmallModelTester(self )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCAmelCase__ : Tuple = self._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ )
UpperCAmelCase__ : str = model_class(lowerCamelCase_ )
@jax.jit
def encode_jitted(__UpperCamelCase , __UpperCamelCase=None , **__UpperCamelCase ):
return model.encode(input_ids=lowerCamelCase_ , attention_mask=lowerCamelCase_ )
with self.subTest("JIT Enabled" ):
UpperCAmelCase__ : Tuple = encode_jitted(**lowerCamelCase_ ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
UpperCAmelCase__ : List[str] = encode_jitted(**lowerCamelCase_ ).to_tuple()
self.assertEqual(len(lowerCamelCase_ ) , len(lowerCamelCase_ ) )
for jitted_output, output in zip(lowerCamelCase_ , lowerCamelCase_ ):
self.assertEqual(jitted_output.shape , output.shape )
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCAmelCase__ : Union[str, Any] = model_class(lowerCamelCase_ )
UpperCAmelCase__ : int = model.encode(inputs_dict["input_ids"] , inputs_dict["attention_mask"] )
UpperCAmelCase__ : str = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
return model.decode(
decoder_input_ids=lowerCamelCase_ , decoder_attention_mask=lowerCamelCase_ , encoder_outputs=lowerCamelCase_ , )
with self.subTest("JIT Enabled" ):
UpperCAmelCase__ : Optional[Any] = decode_jitted(**lowerCamelCase_ ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
UpperCAmelCase__ : Optional[int] = decode_jitted(**lowerCamelCase_ ).to_tuple()
self.assertEqual(len(lowerCamelCase_ ) , len(lowerCamelCase_ ) )
for jitted_output, output in zip(lowerCamelCase_ , lowerCamelCase_ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def lowerCAmelCase__ ( self )-> Union[str, Any]:
for model_class_name in self.all_model_classes:
UpperCAmelCase__ : Optional[int] = model_class_name.from_pretrained("facebook/blenderbot_small-90M" )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
UpperCAmelCase__ : Any = np.ones((1, 1) ) * model.config.eos_token_id
UpperCAmelCase__ : List[Any] = model(lowerCamelCase_ )
self.assertIsNotNone(lowerCamelCase_ )
| 710 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
A__ : List[str] = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
A__ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 660 | 0 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def a__ ( lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : int ):
'''simple docstring'''
# prepare kernel
# the kernel size have to be odd
if (ksize % 2) == 0:
UpperCAmelCase__ : Tuple = ksize + 1
UpperCAmelCase__ : List[str] = np.zeros((ksize, ksize) , dtype=np.floataa )
# each value
for y in range(lowerCAmelCase ):
for x in range(lowerCAmelCase ):
# distance from center
UpperCAmelCase__ : Dict = x - ksize // 2
UpperCAmelCase__ : str = y - ksize // 2
# degree to radiant
UpperCAmelCase__ : Optional[Any] = theta / 180 * np.pi
UpperCAmelCase__ : str = np.cos(_theta )
UpperCAmelCase__ : List[str] = np.sin(_theta )
# get kernel x
UpperCAmelCase__ : Dict = cos_theta * px + sin_theta * py
# get kernel y
UpperCAmelCase__ : Any = -sin_theta * px + cos_theta * py
# fill kernel
UpperCAmelCase__ : List[Any] = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
A__ : Union[str, Any] = imread("""../image_data/lena.jpg""")
# turn image in gray scale value
A__ : str = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
A__ : str = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
A__ : Any = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
A__ : Any = out / out.max() * 255
A__ : int = out.astype(np.uinta)
imshow("""Original""", gray)
imshow("""Gabor filter with 20x20 mask and 6 directions""", out)
waitKey(0)
| 711 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
@register_to_config
def __init__( self , __UpperCamelCase = 7_68 , )-> Union[str, Any]:
super().__init__()
UpperCAmelCase__ : str = nn.Parameter(torch.zeros(1 , __UpperCamelCase ) )
UpperCAmelCase__ : Optional[int] = nn.Parameter(torch.ones(1 , __UpperCamelCase ) )
def lowerCAmelCase__ ( self , __UpperCamelCase = None , __UpperCamelCase = None , )-> Any:
UpperCAmelCase__ : Dict = nn.Parameter(self.mean.to(__UpperCamelCase ).to(__UpperCamelCase ) )
UpperCAmelCase__ : Any = nn.Parameter(self.std.to(__UpperCamelCase ).to(__UpperCamelCase ) )
return self
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Union[str, Any]:
UpperCAmelCase__ : Dict = (embeds - self.mean) * 1.0 / self.std
return embeds
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[Any]:
UpperCAmelCase__ : Any = (embeds * self.std) + self.mean
return embeds
| 660 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=30 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=10 , __UpperCamelCase=0.02 , __UpperCamelCase=3 , __UpperCamelCase=None , )-> Optional[int]:
UpperCAmelCase__ : int = parent
UpperCAmelCase__ : str = batch_size
UpperCAmelCase__ : Optional[Any] = image_size
UpperCAmelCase__ : Union[str, Any] = patch_size
UpperCAmelCase__ : Union[str, Any] = num_channels
UpperCAmelCase__ : Tuple = is_training
UpperCAmelCase__ : Union[str, Any] = use_labels
UpperCAmelCase__ : str = hidden_size
UpperCAmelCase__ : Tuple = num_hidden_layers
UpperCAmelCase__ : List[Any] = num_attention_heads
UpperCAmelCase__ : Tuple = intermediate_size
UpperCAmelCase__ : Dict = hidden_act
UpperCAmelCase__ : Union[str, Any] = hidden_dropout_prob
UpperCAmelCase__ : Tuple = attention_probs_dropout_prob
UpperCAmelCase__ : Optional[Any] = type_sequence_label_size
UpperCAmelCase__ : Tuple = initializer_range
UpperCAmelCase__ : Optional[int] = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase__ : int = (image_size // patch_size) ** 2
UpperCAmelCase__ : Optional[Any] = num_patches + 1
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : List[Any] = None
if self.use_labels:
UpperCAmelCase__ : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : List[str] = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self )-> Any:
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Any:
UpperCAmelCase__ : List[Any] = TFViTModel(config=lowerCAmelCase_ )
UpperCAmelCase__ : str = model(lowerCAmelCase_ , training=lowerCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
UpperCAmelCase__ : Optional[Any] = self.image_size // 2
UpperCAmelCase__ : Tuple = pixel_values[:, :, :image_size, :image_size]
UpperCAmelCase__ : Union[str, Any] = model(lowerCAmelCase_ , interpolate_pos_encoding=lowerCAmelCase_ , training=lowerCAmelCase_ )
UpperCAmelCase__ : Tuple = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Any:
UpperCAmelCase__ : Union[str, Any] = self.type_sequence_label_size
UpperCAmelCase__ : int = TFViTForImageClassification(lowerCAmelCase_ )
UpperCAmelCase__ : Any = model(lowerCAmelCase_ , labels=lowerCAmelCase_ , training=lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
UpperCAmelCase__ : Tuple = self.image_size // 2
UpperCAmelCase__ : Any = pixel_values[:, :, :image_size, :image_size]
UpperCAmelCase__ : str = model(lowerCAmelCase_ , interpolate_pos_encoding=lowerCAmelCase_ , training=lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCAmelCase__ : Optional[int] = 1
UpperCAmelCase__ : int = TFViTForImageClassification(lowerCAmelCase_ )
UpperCAmelCase__ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase__ : Optional[int] = model(lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ : int = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Dict = config_and_inputs
UpperCAmelCase__ : List[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class _lowercase ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
_A = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
_A = (
{'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
if is_tf_available()
else {}
)
_A = False
_A = False
_A = False
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : List[Any] = TFViTModelTester(self )
UpperCAmelCase__ : Tuple = ConfigTester(self , config_class=lowerCAmelCase_ , has_text_modality=lowerCAmelCase_ , hidden_size=37 )
def lowerCAmelCase__ ( self )-> int:
self.config_tester.run_common_tests()
@unittest.skip(reason="ViT does not use inputs_embeds" )
def lowerCAmelCase__ ( self )-> Dict:
pass
@unittest.skip(reason="ViT does not use inputs_embeds" )
def lowerCAmelCase__ ( self )-> str:
pass
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class(lowerCAmelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
UpperCAmelCase__ : Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCAmelCase_ , tf.keras.layers.Layer ) )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Dict = model_class(lowerCAmelCase_ )
UpperCAmelCase__ : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Tuple = [*signature.parameters.keys()]
UpperCAmelCase__ : str = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowerCAmelCase_ )
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_ )
def lowerCAmelCase__ ( self )-> int:
UpperCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_ )
@slow
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : Any = TFViTModel.from_pretrained("google/vit-base-patch16-224" )
self.assertIsNotNone(lowerCAmelCase_ )
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase__ ( self )-> str:
return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224" ) if is_vision_available() else None
@slow
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : Any = TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224" )
UpperCAmelCase__ : int = self.default_image_processor
UpperCAmelCase__ : str = prepare_img()
UpperCAmelCase__ : Optional[Any] = image_processor(images=lowerCAmelCase_ , return_tensors="tf" )
# forward pass
UpperCAmelCase__ : Tuple = model(**lowerCAmelCase_ )
# verify the logits
UpperCAmelCase__ : Optional[Any] = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase_ )
UpperCAmelCase__ : Any = tf.constant([-0.2744, 0.8215, -0.0836] )
tf.debugging.assert_near(outputs.logits[0, :3] , lowerCAmelCase_ , atol=1E-4 )
| 712 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : Any , lowerCAmelCase : List[Any] ):
'''simple docstring'''
# Construct model
if gpta_config_file == "":
UpperCAmelCase__ : Optional[int] = GPTaConfig()
else:
UpperCAmelCase__ : Dict = GPTaConfig.from_json_file(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = GPTaModel(lowerCAmelCase )
# Load weights from numpy
load_tf_weights_in_gpta(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Save pytorch-model
UpperCAmelCase__ : Optional[int] = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
UpperCAmelCase__ : Any = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(model.state_dict() , lowerCAmelCase )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(lowerCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
A__ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--gpt2_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained OpenAI model. \n"""
"""This specifies the model architecture."""
),
)
A__ : Optional[Any] = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 660 | 0 |
"""simple docstring"""
from __future__ import annotations
def a__ ( lowerCAmelCase : list , lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = []
UpperCAmelCase__ : int = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
UpperCAmelCase__ : int = result + left + right
return input_list
def a__ ( lowerCAmelCase : list ):
'''simple docstring'''
if len(a_ ) <= 1:
return input_list
UpperCAmelCase__ : List[str] = list(a_ )
# iteration for two-way merging
UpperCAmelCase__ : Optional[int] = 2
while p <= len(a_ ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(a_ ) , a_ ):
UpperCAmelCase__ : List[str] = i
UpperCAmelCase__ : int = i + p - 1
UpperCAmelCase__ : Dict = (low + high + 1) // 2
UpperCAmelCase__ : Optional[Any] = merge(a_ , a_ , a_ , a_ )
# final merge of last two parts
if p * 2 >= len(a_ ):
UpperCAmelCase__ : Dict = i
UpperCAmelCase__ : int = merge(a_ , 0 , a_ , len(a_ ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
A__ : Union[str, Any] = input("""Enter numbers separated by a comma:\n""").strip()
if user_input == "":
A__ : Any = []
else:
A__ : Dict = [int(item.strip()) for item in user_input.split(""",""")]
print(iter_merge_sort(unsorted))
| 713 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
A__ : Optional[int] = ["""small""", """medium""", """large"""]
A__ : Optional[int] = """lm_head.decoder.weight"""
A__ : Dict = """lm_head.weight"""
def a__ ( lowerCAmelCase : str , lowerCAmelCase : str ):
'''simple docstring'''
UpperCAmelCase__ : Dict = torch.load(lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = d.pop(lowerCAmelCase )
os.makedirs(lowerCAmelCase , exist_ok=lowerCAmelCase )
torch.save(lowerCAmelCase , os.path.join(lowerCAmelCase , lowerCAmelCase ) )
if __name__ == "__main__":
A__ : List[Any] = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
A__ : Tuple = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
A__ : Tuple = os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
A__ : str = f"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 660 | 0 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _lowercase ( TensorFormatter[Mapping, 'torch.Tensor', Mapping] ):
'''simple docstring'''
def __init__( self , __UpperCamelCase=None , **__UpperCamelCase )-> List[Any]:
super().__init__(features=_A )
UpperCAmelCase__ : Any = torch_tensor_kwargs
import torch # noqa import torch at initialization
def lowerCAmelCase__ ( self , __UpperCamelCase )-> int:
import torch
if isinstance(_A , _A ) and column:
if all(
isinstance(_A , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(_A )
return column
def lowerCAmelCase__ ( self , __UpperCamelCase )-> int:
import torch
if isinstance(_A , (str, bytes, type(_A )) ):
return value
elif isinstance(_A , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
UpperCAmelCase__ : Any = {}
if isinstance(_A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
UpperCAmelCase__ : str = {"dtype": torch.intaa}
elif isinstance(_A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
UpperCAmelCase__ : Dict = {"dtype": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(_A , PIL.Image.Image ):
UpperCAmelCase__ : Optional[int] = np.asarray(_A )
return torch.tensor(_A , **{**default_dtype, **self.torch_tensor_kwargs} )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Tuple:
import torch
# support for torch, tf, jax etc.
if hasattr(_A , "__array__" ) and not isinstance(_A , torch.Tensor ):
UpperCAmelCase__ : str = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(_A , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(_A ) for substruct in data_struct] )
elif isinstance(_A , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(_A ) for substruct in data_struct] )
return self._tensorize(_A )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Union[str, Any]:
return map_nested(self._recursive_tensorize , _A , map_list=_A )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[Any]:
UpperCAmelCase__ : List[Any] = self.numpy_arrow_extractor().extract_row(_A )
UpperCAmelCase__ : List[Any] = self.python_features_decoder.decode_row(_A )
return self.recursive_tensorize(_A )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[Any]:
UpperCAmelCase__ : List[str] = self.numpy_arrow_extractor().extract_column(_A )
UpperCAmelCase__ : Dict = self.python_features_decoder.decode_column(_A , pa_table.column_names[0] )
UpperCAmelCase__ : Any = self.recursive_tensorize(_A )
UpperCAmelCase__ : List[Any] = self._consolidate(_A )
return column
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Optional[Any]:
UpperCAmelCase__ : Optional[int] = self.numpy_arrow_extractor().extract_batch(_A )
UpperCAmelCase__ : Optional[Any] = self.python_features_decoder.decode_batch(_A )
UpperCAmelCase__ : Dict = self.recursive_tensorize(_A )
for column_name in batch:
UpperCAmelCase__ : Dict = self._consolidate(batch[column_name] )
return batch
| 714 |
"""simple docstring"""
from math import isqrt
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : List[Any] = False
return [i for i in range(2 , lowerCAmelCase ) if is_prime[i]]
def a__ ( lowerCAmelCase : int = 10**8 ):
'''simple docstring'''
UpperCAmelCase__ : Dict = calculate_prime_numbers(max_number // 2 )
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Tuple = len(lowerCAmelCase ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 660 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _lowercase :
'''simple docstring'''
@staticmethod
def lowerCAmelCase__ ( *__UpperCamelCase , **__UpperCamelCase )-> Any:
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
_A = MODEL_FOR_OBJECT_DETECTION_MAPPING
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> str:
UpperCAmelCase__ : Dict = ObjectDetectionPipeline(model=__UpperCamelCase , image_processor=__UpperCamelCase )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase )-> Any:
UpperCAmelCase__ : str = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" , threshold=0.0 )
self.assertGreater(len(__UpperCamelCase ) , 0 )
for detected_object in outputs:
self.assertEqual(
__UpperCamelCase , {
"score": ANY(__UpperCamelCase ),
"label": ANY(__UpperCamelCase ),
"box": {"xmin": ANY(__UpperCamelCase ), "ymin": ANY(__UpperCamelCase ), "xmax": ANY(__UpperCamelCase ), "ymax": ANY(__UpperCamelCase )},
} , )
import datasets
UpperCAmelCase__ : Union[str, Any] = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
UpperCAmelCase__ : List[Any] = [
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["file"],
# LA
dataset[1]["file"],
# L
dataset[2]["file"],
]
UpperCAmelCase__ : Optional[int] = object_detector(__UpperCamelCase , threshold=0.0 )
self.assertEqual(len(__UpperCamelCase ) , len(__UpperCamelCase ) )
for outputs in batch_outputs:
self.assertGreater(len(__UpperCamelCase ) , 0 )
for detected_object in outputs:
self.assertEqual(
__UpperCamelCase , {
"score": ANY(__UpperCamelCase ),
"label": ANY(__UpperCamelCase ),
"box": {"xmin": ANY(__UpperCamelCase ), "ymin": ANY(__UpperCamelCase ), "xmax": ANY(__UpperCamelCase ), "ymax": ANY(__UpperCamelCase )},
} , )
@require_tf
@unittest.skip("Object detection not implemented in TF" )
def lowerCAmelCase__ ( self )-> Tuple:
pass
@require_torch
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : int = "hf-internal-testing/tiny-detr-mobilenetsv3"
UpperCAmelCase__ : Any = AutoModelForObjectDetection.from_pretrained(__UpperCamelCase )
UpperCAmelCase__ : List[str] = AutoFeatureExtractor.from_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Any = ObjectDetectionPipeline(model=__UpperCamelCase , feature_extractor=__UpperCamelCase )
UpperCAmelCase__ : Any = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=0.0 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
] , )
UpperCAmelCase__ : int = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
],
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
],
] , )
@require_torch
@slow
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : Any = "facebook/detr-resnet-50"
UpperCAmelCase__ : Optional[Any] = AutoModelForObjectDetection.from_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Dict = AutoFeatureExtractor.from_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Dict = ObjectDetectionPipeline(model=__UpperCamelCase , feature_extractor=__UpperCamelCase )
UpperCAmelCase__ : Any = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
] , )
UpperCAmelCase__ : int = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
],
] , )
@require_torch
@slow
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : Optional[Any] = "facebook/detr-resnet-50"
UpperCAmelCase__ : Dict = pipeline("object-detection" , model=__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
] , )
UpperCAmelCase__ : Optional[int] = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
],
] , )
@require_torch
@slow
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : List[Any] = 0.9985
UpperCAmelCase__ : Dict = "facebook/detr-resnet-50"
UpperCAmelCase__ : Optional[Any] = pipeline("object-detection" , model=__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=__UpperCamelCase )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
] , )
@require_torch
@require_pytesseract
@slow
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : Union[str, Any] = "Narsil/layoutlmv3-finetuned-funsd"
UpperCAmelCase__ : Optional[Any] = 0.9993
UpperCAmelCase__ : Optional[Any] = pipeline("object-detection" , model=__UpperCamelCase , threshold=__UpperCamelCase )
UpperCAmelCase__ : List[str] = object_detector(
"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 2_94, "ymin": 2_54, "xmax": 3_43, "ymax": 2_64}},
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 2_94, "ymin": 2_54, "xmax": 3_43, "ymax": 2_64}},
] , )
| 715 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a__ ( lowerCAmelCase : str , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Dict , lowerCAmelCase : List[Any] ):
'''simple docstring'''
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Optional[int] = np.full((len(lowerCAmelCase ), sequence_length, 2) , lowerCAmelCase )
else:
UpperCAmelCase__ : Optional[Any] = np.full((len(lowerCAmelCase ), sequence_length) , lowerCAmelCase )
for i, tensor in enumerate(lowerCAmelCase ):
if padding_side == "right":
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Dict = tensor[:sequence_length]
else:
UpperCAmelCase__ : Tuple = tensor[:sequence_length]
else:
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Optional[Any] = tensor[:sequence_length]
else:
UpperCAmelCase__ : int = tensor[:sequence_length]
return out_tensor.tolist()
def a__ ( lowerCAmelCase : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = ord(lowerCAmelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
UpperCAmelCase__ : Optional[Any] = unicodedata.category(lowerCAmelCase )
if cat.startswith("P" ):
return True
return False
@dataclass
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
_A = True
_A = None
_A = None
_A = -100
_A = "pt"
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[str]:
import torch
UpperCAmelCase__ : Optional[Any] = "label" if "label" in features[0].keys() else "labels"
UpperCAmelCase__ : Dict = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
UpperCAmelCase__ : str = self.tokenizer.pad(
__UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" if labels is None else None , )
if labels is None:
return batch
UpperCAmelCase__ : Optional[Any] = torch.tensor(batch["entity_ids"] ).shape[1]
UpperCAmelCase__ : int = self.tokenizer.padding_side
if padding_side == "right":
UpperCAmelCase__ : int = [
list(__UpperCamelCase ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) for label in labels
]
else:
UpperCAmelCase__ : List[Any] = [
[self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) + list(__UpperCamelCase ) for label in labels
]
UpperCAmelCase__ : Optional[Any] = [feature["ner_tags"] for feature in features]
UpperCAmelCase__ : int = padding_tensor(__UpperCamelCase , -1 , __UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : List[Any] = [feature["original_entity_spans"] for feature in features]
UpperCAmelCase__ : int = padding_tensor(__UpperCamelCase , (-1, -1) , __UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[int] = {k: torch.tensor(__UpperCamelCase , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 660 | 0 |
"""simple docstring"""
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
A__ : Optional[int] = re.compile(R"""^(?P<major>\d+)""" R"""\.(?P<minor>\d+)""" R"""\.(?P<patch>\d+)$""")
@total_ordering
@dataclass
class _lowercase :
'''simple docstring'''
_A = 42
_A = None
_A = None
_A = None
_A = None
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Tuple = _str_to_version_tuple(self.version_str )
def __repr__( self )-> Dict:
return F"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"
@property
def lowerCAmelCase__ ( self )-> Dict:
return self.major, self.minor, self.patch
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Tuple:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
return Version(__UpperCamelCase )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
return other
raise TypeError(F"{other} (type {type(__UpperCamelCase )}) cannot be compared to version." )
def __eq__( self , __UpperCamelCase )-> Optional[int]:
try:
UpperCAmelCase__ : Any = self._validate_operand(__UpperCamelCase )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self , __UpperCamelCase )-> Union[str, Any]:
UpperCAmelCase__ : List[str] = self._validate_operand(__UpperCamelCase )
return self.tuple < other.tuple
def __hash__( self )-> Union[str, Any]:
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase )-> Optional[Any]:
UpperCAmelCase__ : Union[str, Any] = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
return self.version_str
def a__ ( lowerCAmelCase : Dict ):
'''simple docstring'''
UpperCAmelCase__ : str = _VERSION_REG.match(_UpperCamelCase )
if not res:
raise ValueError(F"Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits." )
return tuple(int(_UpperCamelCase ) for v in [res.group("major" ), res.group("minor" ), res.group("patch" )] )
def a__ ( lowerCAmelCase : Any ):
'''simple docstring'''
return ".".join(str(_UpperCamelCase ) for v in version_tuple )
| 716 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def a__ ( lowerCAmelCase : List[str] ):
'''simple docstring'''
def wrapper(*lowerCAmelCase : Any , **lowerCAmelCase : Tuple ):
UpperCAmelCase__ : Optional[int] = timeit.default_timer()
UpperCAmelCase__ : int = func(*lowerCAmelCase , **lowerCAmelCase )
UpperCAmelCase__ : List[Any] = timeit.default_timer() - starttime
return delta
UpperCAmelCase__ : int = func.__name__
return wrapper
def a__ ( lowerCAmelCase : dict , lowerCAmelCase : Optional[int]=100 , lowerCAmelCase : List[str]=None ):
'''simple docstring'''
UpperCAmelCase__ : str = []
UpperCAmelCase__ : Optional[Any] = seq_shapes or {}
for i in range(lowerCAmelCase ):
UpperCAmelCase__ : int = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(lowerCAmelCase , _ArrayXD ):
UpperCAmelCase__ : List[str] = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(lowerCAmelCase , datasets.Value ):
if v.dtype == "string":
UpperCAmelCase__ : Dict = "The small grey turtle was surprisingly fast when challenged."
else:
UpperCAmelCase__ : str = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(lowerCAmelCase , datasets.Sequence ):
while isinstance(lowerCAmelCase , datasets.Sequence ):
UpperCAmelCase__ : List[str] = v.feature
UpperCAmelCase__ : Optional[int] = seq_shapes[k]
UpperCAmelCase__ : Optional[int] = np.random.rand(*lowerCAmelCase ).astype(v.dtype )
UpperCAmelCase__ : Union[str, Any] = data
dummy_data.append((i, example) )
return dummy_data
def a__ ( lowerCAmelCase : List[str] , lowerCAmelCase : Tuple , lowerCAmelCase : List[str]=100 , lowerCAmelCase : Optional[int]=None ):
'''simple docstring'''
UpperCAmelCase__ : int = generate_examples(lowerCAmelCase , num_examples=lowerCAmelCase , seq_shapes=lowerCAmelCase )
with ArrowWriter(features=lowerCAmelCase , path=lowerCAmelCase ) as writer:
for key, record in dummy_data:
UpperCAmelCase__ : List[Any] = features.encode_example(lowerCAmelCase )
writer.write(lowerCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}." )
UpperCAmelCase__ : Optional[int] = datasets.Dataset.from_file(filename=lowerCAmelCase , info=datasets.DatasetInfo(features=lowerCAmelCase ) )
return dataset
| 660 | 0 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowercase ( __UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
_A = LongformerTokenizer
_A = True
_A = LongformerTokenizerFast
_A = True
def lowerCAmelCase__ ( self )-> str:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase__ : int = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
UpperCAmelCase__ : int = dict(zip(__UpperCamelCase , range(len(__UpperCamelCase ) ) ) )
UpperCAmelCase__ : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
UpperCAmelCase__ : List[str] = {"unk_token": "<unk>"}
UpperCAmelCase__ : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase__ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(__UpperCamelCase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(__UpperCamelCase ) )
def lowerCAmelCase__ ( self , **__UpperCamelCase )-> List[Any]:
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__UpperCamelCase )
def lowerCAmelCase__ ( self , **__UpperCamelCase )-> List[Any]:
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Tuple:
UpperCAmelCase__ : Optional[Any] = "lower newer"
UpperCAmelCase__ : str = "lower newer"
return input_text, output_text
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : Union[str, Any] = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
UpperCAmelCase__ : Optional[int] = "lower newer"
UpperCAmelCase__ : int = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
UpperCAmelCase__ : List[Any] = tokenizer.tokenize(__UpperCamelCase ) # , add_prefix_space=True)
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = tokens + [tokenizer.unk_token]
UpperCAmelCase__ : Dict = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase ) , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ : int = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" , add_special_tokens=__UpperCamelCase ) , [0, 3_14_14, 2_32, 3_28, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" , add_special_tokens=__UpperCamelCase ) , [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2] , )
@slow
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : List[Any] = self.tokenizer_class.from_pretrained("allenai/longformer-base-4096" )
UpperCAmelCase__ : Optional[Any] = tokenizer.encode("sequence builders" , add_special_tokens=__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = tokenizer.encode("multi-sequence build" , add_special_tokens=__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = tokenizer.encode(
"sequence builders" , add_special_tokens=__UpperCamelCase , add_prefix_space=__UpperCamelCase )
UpperCAmelCase__ : Dict = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=__UpperCamelCase , add_prefix_space=__UpperCamelCase )
UpperCAmelCase__ : Dict = tokenizer.build_inputs_with_special_tokens(__UpperCamelCase )
UpperCAmelCase__ : str = tokenizer.build_inputs_with_special_tokens(__UpperCamelCase , __UpperCamelCase )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : Optional[Any] = "Encode this sequence."
UpperCAmelCase__ : List[Any] = tokenizer.byte_encoder[" ".encode("utf-8" )[0]]
# Testing encoder arguments
UpperCAmelCase__ : Any = tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase , add_prefix_space=__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Tuple = tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase , add_prefix_space=__UpperCamelCase )
UpperCAmelCase__ : List[Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
UpperCAmelCase__ : Union[str, Any] = tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(__UpperCamelCase , __UpperCamelCase )
# Testing spaces after special tokens
UpperCAmelCase__ : Union[str, Any] = "<mask>"
tokenizer.add_special_tokens(
{"mask_token": AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase )} ) # mask token has a left space
UpperCAmelCase__ : Optional[Any] = tokenizer.convert_tokens_to_ids(__UpperCamelCase )
UpperCAmelCase__ : Dict = "Encode <mask> sequence"
UpperCAmelCase__ : Optional[Any] = "Encode <mask>sequence"
UpperCAmelCase__ : List[str] = tokenizer.encode(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = encoded.index(__UpperCamelCase )
UpperCAmelCase__ : Dict = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : List[str] = tokenizer.encode(__UpperCamelCase )
UpperCAmelCase__ : int = encoded.index(__UpperCamelCase )
UpperCAmelCase__ : Dict = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(__UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> int:
pass
def lowerCAmelCase__ ( self )-> Optional[Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
UpperCAmelCase__ : Dict = self.rust_tokenizer_class.from_pretrained(__UpperCamelCase , **__UpperCamelCase )
UpperCAmelCase__ : Dict = self.tokenizer_class.from_pretrained(__UpperCamelCase , **__UpperCamelCase )
UpperCAmelCase__ : List[str] = "A, <mask> AllenNLP sentence."
UpperCAmelCase__ : int = tokenizer_r.encode_plus(__UpperCamelCase , add_special_tokens=__UpperCamelCase , return_token_type_ids=__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = tokenizer_p.encode_plus(__UpperCamelCase , add_special_tokens=__UpperCamelCase , return_token_type_ids=__UpperCamelCase )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
UpperCAmelCase__ : List[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
UpperCAmelCase__ : int = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
__UpperCamelCase , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
__UpperCamelCase , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def lowerCAmelCase__ ( self )-> Dict:
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
UpperCAmelCase__ : str = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=__UpperCamelCase , add_prefix_space=__UpperCamelCase , trim_offsets=__UpperCamelCase )
UpperCAmelCase__ : Tuple = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
UpperCAmelCase__ : List[Any] = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] , __UpperCamelCase )
self.assertEqual(post_processor_state["add_prefix_space"] , __UpperCamelCase )
self.assertEqual(post_processor_state["trim_offsets"] , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> Any:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
UpperCAmelCase__ : List[str] = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
UpperCAmelCase__ : str = F"{text_of_1_token} {text_of_1_token}"
UpperCAmelCase__ : Dict = self.rust_tokenizer_class.from_pretrained(
__UpperCamelCase , use_fast=__UpperCamelCase , add_prefix_space=__UpperCamelCase , trim_offsets=__UpperCamelCase )
UpperCAmelCase__ : int = tokenizer_r(__UpperCamelCase , return_offsets_mapping=__UpperCamelCase , add_special_tokens=__UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__UpperCamelCase ) + 1, len(__UpperCamelCase ) + 1 + len(__UpperCamelCase )) , )
UpperCAmelCase__ : List[Any] = self.rust_tokenizer_class.from_pretrained(
__UpperCamelCase , use_fast=__UpperCamelCase , add_prefix_space=__UpperCamelCase , trim_offsets=__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = tokenizer_r(__UpperCamelCase , return_offsets_mapping=__UpperCamelCase , add_special_tokens=__UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__UpperCamelCase ) + 1, len(__UpperCamelCase ) + 1 + len(__UpperCamelCase )) , )
UpperCAmelCase__ : int = self.rust_tokenizer_class.from_pretrained(
__UpperCamelCase , use_fast=__UpperCamelCase , add_prefix_space=__UpperCamelCase , trim_offsets=__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = tokenizer_r(__UpperCamelCase , return_offsets_mapping=__UpperCamelCase , add_special_tokens=__UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__UpperCamelCase ), len(__UpperCamelCase ) + 1 + len(__UpperCamelCase )) , )
UpperCAmelCase__ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
__UpperCamelCase , use_fast=__UpperCamelCase , add_prefix_space=__UpperCamelCase , trim_offsets=__UpperCamelCase )
UpperCAmelCase__ : Tuple = tokenizer_r(__UpperCamelCase , return_offsets_mapping=__UpperCamelCase , add_special_tokens=__UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__UpperCamelCase ), len(__UpperCamelCase ) + 1 + len(__UpperCamelCase )) , )
UpperCAmelCase__ : Optional[int] = F" {text}"
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
UpperCAmelCase__ : str = self.rust_tokenizer_class.from_pretrained(
__UpperCamelCase , use_fast=__UpperCamelCase , add_prefix_space=__UpperCamelCase , trim_offsets=__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = tokenizer_r(__UpperCamelCase , return_offsets_mapping=__UpperCamelCase , add_special_tokens=__UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(__UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__UpperCamelCase ) + 1, 1 + len(__UpperCamelCase ) + 1 + len(__UpperCamelCase )) , )
UpperCAmelCase__ : Tuple = self.rust_tokenizer_class.from_pretrained(
__UpperCamelCase , use_fast=__UpperCamelCase , add_prefix_space=__UpperCamelCase , trim_offsets=__UpperCamelCase )
UpperCAmelCase__ : List[Any] = tokenizer_r(__UpperCamelCase , return_offsets_mapping=__UpperCamelCase , add_special_tokens=__UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(__UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__UpperCamelCase ), 1 + len(__UpperCamelCase ) + 1 + len(__UpperCamelCase )) , )
UpperCAmelCase__ : Tuple = self.rust_tokenizer_class.from_pretrained(
__UpperCamelCase , use_fast=__UpperCamelCase , add_prefix_space=__UpperCamelCase , trim_offsets=__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = tokenizer_r(__UpperCamelCase , return_offsets_mapping=__UpperCamelCase , add_special_tokens=__UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(__UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__UpperCamelCase ), 1 + len(__UpperCamelCase ) + 1 + len(__UpperCamelCase )) , )
| 717 |
"""simple docstring"""
from manim import *
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : str = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase__ : List[str] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
UpperCAmelCase__ : List[Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : Union[str, Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : List[Any] = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : int = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = VGroup(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = Text("CPU" , font_size=24 )
UpperCAmelCase__ : Any = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = [mem.copy() for i in range(4 )]
UpperCAmelCase__ : List[str] = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Union[str, Any] = Text("GPU" , font_size=24 )
UpperCAmelCase__ : Dict = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
gpu.move_to([-1, -1, 0] )
self.add(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : List[str] = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = Text("Model" , font_size=24 )
UpperCAmelCase__ : Dict = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
model.move_to([3, -1.0, 0] )
self.add(__UpperCamelCase )
UpperCAmelCase__ : List[str] = []
for i, rect in enumerate(__UpperCamelCase ):
rect.set_stroke(__UpperCamelCase )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
UpperCAmelCase__ : int = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__UpperCamelCase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__UpperCamelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=__UpperCamelCase , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=__UpperCamelCase , buff=0.0 )
self.add(__UpperCamelCase )
cpu_targs.append(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : Any = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = Text("Loaded Checkpoint" , font_size=24 )
UpperCAmelCase__ : Any = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , aligned_edge=__UpperCamelCase , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
UpperCAmelCase__ : Optional[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
UpperCAmelCase__ : Any = MarkupText(
F"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : str = MarkupText(
F"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(__UpperCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
UpperCAmelCase__ : Optional[Any] = MarkupText(
F"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__UpperCamelCase ) , Write(__UpperCamelCase ) )
self.play(Write(__UpperCamelCase , run_time=1 ) , Create(__UpperCamelCase , run_time=1 ) )
UpperCAmelCase__ : Union[str, Any] = []
UpperCAmelCase__ : List[str] = []
for i, rect in enumerate(__UpperCamelCase ):
UpperCAmelCase__ : Optional[Any] = fill.copy().set_fill(__UpperCamelCase , opacity=0.7 )
target.move_to(__UpperCamelCase )
first_animations.append(GrowFromCenter(__UpperCamelCase , run_time=1 ) )
UpperCAmelCase__ : List[str] = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(__UpperCamelCase , run_time=1.5 ) )
self.play(*__UpperCamelCase )
self.play(*__UpperCamelCase )
self.wait()
| 660 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> int:
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
UpperCAmelCase__ : List[str] = FlaxDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe" , safety_checker=lowerCamelCase__ , cache_dir=lowerCamelCase__ )
UpperCAmelCase__ : List[str] = [t[-1] for t in os.walk(os.path.join(lowerCamelCase__ , os.listdir(lowerCamelCase__ )[0] , "snapshots" ) )]
UpperCAmelCase__ : Optional[int] = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith(".bin" ) for f in files )
@slow
@require_flax
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Optional[int]:
UpperCAmelCase__ : Optional[Any] = FlaxStableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe" , safety_checker=lowerCamelCase__ )
UpperCAmelCase__ : List[Any] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
UpperCAmelCase__ : str = jax.random.PRNGKey(0 )
UpperCAmelCase__ : List[str] = 4
UpperCAmelCase__ : Tuple = jax.device_count()
UpperCAmelCase__ : Union[str, Any] = num_samples * [prompt]
UpperCAmelCase__ : Dict = pipeline.prepare_inputs(lowerCamelCase__ )
# shard inputs and rng
UpperCAmelCase__ : int = replicate(lowerCamelCase__ )
UpperCAmelCase__ : Optional[int] = jax.random.split(lowerCamelCase__ , lowerCamelCase__ )
UpperCAmelCase__ : Any = shard(lowerCamelCase__ )
UpperCAmelCase__ : Any = pipeline(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , jit=lowerCamelCase__ ).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.151_4745 ) < 1E-3
assert np.abs(np.abs(lowerCamelCase__ , dtype=np.floataa ).sum() - 4_99_47.8_75 ) < 5E-1
UpperCAmelCase__ : str = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(lowerCamelCase__ ) == num_samples
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : Optional[int] = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="flax" , safety_checker=lowerCamelCase__ )
UpperCAmelCase__ : str = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
UpperCAmelCase__ : str = jax.random.PRNGKey(0 )
UpperCAmelCase__ : Any = 50
UpperCAmelCase__ : Optional[int] = jax.device_count()
UpperCAmelCase__ : Any = num_samples * [prompt]
UpperCAmelCase__ : str = pipeline.prepare_inputs(lowerCamelCase__ )
# shard inputs and rng
UpperCAmelCase__ : int = replicate(lowerCamelCase__ )
UpperCAmelCase__ : List[Any] = jax.random.split(lowerCamelCase__ , lowerCamelCase__ )
UpperCAmelCase__ : Any = shard(lowerCamelCase__ )
UpperCAmelCase__ : List[str] = pipeline(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , jit=lowerCamelCase__ ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0565_2401) ) < 1E-3
assert np.abs((np.abs(lowerCamelCase__ , dtype=np.floataa ).sum() - 2_38_38_08.2) ) < 5E-1
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ : Optional[int] = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="bf16" , dtype=jnp.bfloataa , safety_checker=lowerCamelCase__ )
UpperCAmelCase__ : int = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
UpperCAmelCase__ : Dict = jax.random.PRNGKey(0 )
UpperCAmelCase__ : Union[str, Any] = 50
UpperCAmelCase__ : List[str] = jax.device_count()
UpperCAmelCase__ : List[str] = num_samples * [prompt]
UpperCAmelCase__ : List[Any] = pipeline.prepare_inputs(lowerCamelCase__ )
# shard inputs and rng
UpperCAmelCase__ : str = replicate(lowerCamelCase__ )
UpperCAmelCase__ : List[Any] = jax.random.split(lowerCamelCase__ , lowerCamelCase__ )
UpperCAmelCase__ : Optional[int] = shard(lowerCamelCase__ )
UpperCAmelCase__ : Any = pipeline(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , jit=lowerCamelCase__ ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0400_3906) ) < 1E-3
assert np.abs((np.abs(lowerCamelCase__ , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1
def lowerCAmelCase__ ( self )-> int:
UpperCAmelCase__ : List[str] = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="bf16" , dtype=jnp.bfloataa )
UpperCAmelCase__ : Tuple = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
UpperCAmelCase__ : List[Any] = jax.random.PRNGKey(0 )
UpperCAmelCase__ : List[str] = 50
UpperCAmelCase__ : Optional[Any] = jax.device_count()
UpperCAmelCase__ : Optional[int] = num_samples * [prompt]
UpperCAmelCase__ : Dict = pipeline.prepare_inputs(lowerCamelCase__ )
# shard inputs and rng
UpperCAmelCase__ : Optional[int] = replicate(lowerCamelCase__ )
UpperCAmelCase__ : str = jax.random.split(lowerCamelCase__ , lowerCamelCase__ )
UpperCAmelCase__ : Union[str, Any] = shard(lowerCamelCase__ )
UpperCAmelCase__ : Union[str, Any] = pipeline(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , jit=lowerCamelCase__ ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0400_3906) ) < 1E-3
assert np.abs((np.abs(lowerCamelCase__ , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : List[str] = FlaxDDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="scaled_linear" , set_alpha_to_one=lowerCamelCase__ , steps_offset=1 , )
UpperCAmelCase__ : Tuple = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="bf16" , dtype=jnp.bfloataa , scheduler=lowerCamelCase__ , safety_checker=lowerCamelCase__ , )
UpperCAmelCase__ : List[Any] = scheduler.create_state()
UpperCAmelCase__ : Union[str, Any] = scheduler_state
UpperCAmelCase__ : List[str] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
UpperCAmelCase__ : Tuple = jax.random.PRNGKey(0 )
UpperCAmelCase__ : Optional[int] = 50
UpperCAmelCase__ : int = jax.device_count()
UpperCAmelCase__ : Dict = num_samples * [prompt]
UpperCAmelCase__ : str = pipeline.prepare_inputs(lowerCamelCase__ )
# shard inputs and rng
UpperCAmelCase__ : List[Any] = replicate(lowerCamelCase__ )
UpperCAmelCase__ : Optional[Any] = jax.random.split(lowerCamelCase__ , lowerCamelCase__ )
UpperCAmelCase__ : Union[str, Any] = shard(lowerCamelCase__ )
UpperCAmelCase__ : Dict = pipeline(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , jit=lowerCamelCase__ ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4504_3945) ) < 1E-3
assert np.abs((np.abs(lowerCamelCase__ , dtype=np.floataa ).sum() - 2_34_76_93.5) ) < 5E-1
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ : List[Any] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
UpperCAmelCase__ : str = jax.device_count()
UpperCAmelCase__ : Union[str, Any] = num_samples * [prompt]
UpperCAmelCase__ : str = jax.random.split(jax.random.PRNGKey(0 ) , lowerCamelCase__ )
UpperCAmelCase__ : Dict = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="bf16" , dtype=jnp.bfloataa , safety_checker=lowerCamelCase__ , )
UpperCAmelCase__ : List[str] = replicate(lowerCamelCase__ )
UpperCAmelCase__ : Tuple = pipeline.prepare_inputs(lowerCamelCase__ )
UpperCAmelCase__ : int = shard(lowerCamelCase__ )
UpperCAmelCase__ : List[Any] = pipeline(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , jit=lowerCamelCase__ ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
UpperCAmelCase__ : Union[str, Any] = images[2, 0, 2_56, 10:17, 1]
# With memory efficient attention
UpperCAmelCase__ : Tuple = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="bf16" , dtype=jnp.bfloataa , safety_checker=lowerCamelCase__ , use_memory_efficient_attention=lowerCamelCase__ , )
UpperCAmelCase__ : Any = replicate(lowerCamelCase__ )
UpperCAmelCase__ : List[str] = pipeline.prepare_inputs(lowerCamelCase__ )
UpperCAmelCase__ : int = shard(lowerCamelCase__ )
UpperCAmelCase__ : Dict = pipeline(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , jit=lowerCamelCase__ ).images
assert images_eff.shape == (num_samples, 1, 5_12, 5_12, 3)
UpperCAmelCase__ : Union[str, Any] = images[2, 0, 2_56, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1E-2
| 718 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A__ : Tuple = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : nn.ModuleList , lowerCAmelCase : nn.ModuleList , lowerCAmelCase : List[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(lowerCAmelCase ) == len(lowerCAmelCase ), F"{len(lowerCAmelCase )} != {len(lowerCAmelCase )}"
dest_layers.load_state_dict(layers_to_copy.state_dict() )
A__ : List[Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
A__ : List[Any] = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def a__ ( lowerCAmelCase : Dict , lowerCAmelCase : Dict ):
'''simple docstring'''
try:
UpperCAmelCase__ : Tuple = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"
F" {n_student}" )
return list(range(lowerCAmelCase ) )
def a__ ( lowerCAmelCase : int , lowerCAmelCase : Tuple ):
'''simple docstring'''
if n_student > n_teacher:
raise ValueError(F"Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}" )
elif n_teacher == n_student:
return list(range(lowerCAmelCase ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def a__ ( lowerCAmelCase : Union[str, PreTrainedModel] , lowerCAmelCase : Union[str, Path] = "student" , lowerCAmelCase : Union[int, None] = None , lowerCAmelCase : Union[int, None] = None , lowerCAmelCase : List[str]=False , lowerCAmelCase : List[str]=None , lowerCAmelCase : List[str]=None , **lowerCAmelCase : List[str] , ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
assert (e is not None) or (d is not None), _msg
if isinstance(lowerCAmelCase , lowerCAmelCase ):
AutoTokenizer.from_pretrained(lowerCAmelCase ).save_pretrained(lowerCAmelCase ) # purely for convenience
UpperCAmelCase__ : List[str] = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).eval()
else:
assert isinstance(lowerCAmelCase , lowerCAmelCase ), F"teacher must be a model or string got type {type(lowerCAmelCase )}"
UpperCAmelCase__ : int = teacher.config.to_diff_dict()
try:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
UpperCAmelCase__ : Tuple = teacher_e
if d is None:
UpperCAmelCase__ : str = teacher_d
init_kwargs.update({"encoder_layers": e, "decoder_layers": d} )
except AttributeError: # T5
if hasattr(teacher.config , "num_encoder_layers" ):
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
UpperCAmelCase__ : Optional[Any] = teacher_e
if d is None:
UpperCAmelCase__ : Optional[Any] = teacher_d
if hasattr(teacher.config , "num_encoder_layers" ):
init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d} )
else:
init_kwargs.update({"num_layers": e, "num_decoder_layers": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(lowerCAmelCase )
# Copy weights
UpperCAmelCase__ : Tuple = teacher.config_class(**lowerCAmelCase )
UpperCAmelCase__ : List[str] = AutoModelForSeqaSeqLM.from_config(lowerCAmelCase )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
UpperCAmelCase__ : Optional[int] = student.load_state_dict(teacher.state_dict() , strict=lowerCAmelCase )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
UpperCAmelCase__ , UpperCAmelCase__ : int = list(range(lowerCAmelCase ) ), list(range(lowerCAmelCase ) )
logger.info(
F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"
F" {save_path}" )
student.save_pretrained(lowerCAmelCase )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
UpperCAmelCase__ : List[int] = pick_layers_to_copy(lowerCAmelCase , lowerCAmelCase )
if d_layers_to_copy is None:
UpperCAmelCase__ : List[int] = pick_layers_to_copy(lowerCAmelCase , lowerCAmelCase )
try:
if hasattr(
lowerCAmelCase , "prophetnet" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , lowerCAmelCase )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , lowerCAmelCase )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , lowerCAmelCase )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , lowerCAmelCase )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , lowerCAmelCase )
copy_layers(teacher.decoder.block , student.decoder.block , lowerCAmelCase )
logger.info(
F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}" )
UpperCAmelCase__ : int = {
"teacher_type": teacher.config.model_type,
"copied_encoder_layers": e_layers_to_copy,
"copied_decoder_layers": d_layers_to_copy,
}
student.save_pretrained(lowerCAmelCase )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 660 | 0 |
"""simple docstring"""
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def a__ ( lowerCAmelCase : Optional[int] ):
'''simple docstring'''
return EnvironmentCommand()
class _lowercase ( UpperCamelCase_ ):
'''simple docstring'''
@staticmethod
def lowerCAmelCase__ ( __UpperCamelCase )-> int:
UpperCAmelCase__ : Dict = parser.add_parser("env" )
download_parser.set_defaults(func=__UpperCamelCase )
def lowerCAmelCase__ ( self )-> int:
UpperCAmelCase__ : Union[str, Any] = huggingface_hub.__version__
UpperCAmelCase__ : str = '''not installed'''
UpperCAmelCase__ : str = '''NA'''
if is_torch_available():
import torch
UpperCAmelCase__ : Any = torch.__version__
UpperCAmelCase__ : Optional[Any] = torch.cuda.is_available()
UpperCAmelCase__ : Optional[int] = '''not installed'''
if is_transformers_available():
import transformers
UpperCAmelCase__ : Tuple = transformers.__version__
UpperCAmelCase__ : Optional[int] = '''not installed'''
if is_accelerate_available():
import accelerate
UpperCAmelCase__ : Optional[Any] = accelerate.__version__
UpperCAmelCase__ : int = '''not installed'''
if is_xformers_available():
import xformers
UpperCAmelCase__ : Any = xformers.__version__
UpperCAmelCase__ : Optional[Any] = {
'''`diffusers` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''PyTorch version (GPU?)''': F"{pt_version} ({pt_cuda_available})",
'''Huggingface_hub version''': hub_version,
'''Transformers version''': transformers_version,
'''Accelerate version''': accelerate_version,
'''xFormers version''': xformers_version,
'''Using GPU in script?''': '''<fill in>''',
'''Using distributed or parallel set-up in script?''': '''<fill in>''',
}
print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n" )
print(self.format_dict(__UpperCamelCase ) )
return info
@staticmethod
def lowerCAmelCase__ ( __UpperCamelCase )-> Tuple:
return "\n".join([F"- {prop}: {val}" for prop, val in d.items()] ) + "\n"
| 719 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _lowercase ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@property
def lowerCAmelCase__ ( self )-> int:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : Tuple = ort.SessionOptions()
UpperCAmelCase__ : List[str] = False
return options
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
UpperCAmelCase__ : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
UpperCAmelCase__ : str = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase__ : int = "A red cat sitting on a park bench"
UpperCAmelCase__ : Tuple = np.random.RandomState(0 )
UpperCAmelCase__ : Any = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , mask_image=__UpperCamelCase , guidance_scale=7.5 , num_inference_steps=10 , generator=__UpperCamelCase , output_type="np" , )
UpperCAmelCase__ : Tuple = output.images
UpperCAmelCase__ : Dict = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
UpperCAmelCase__ : Union[str, Any] = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
UpperCAmelCase__ : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
UpperCAmelCase__ : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
UpperCAmelCase__ : Optional[Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=__UpperCamelCase , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase__ : int = "A red cat sitting on a park bench"
UpperCAmelCase__ : List[str] = np.random.RandomState(0 )
UpperCAmelCase__ : str = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , mask_image=__UpperCamelCase , guidance_scale=7.5 , num_inference_steps=20 , generator=__UpperCamelCase , output_type="np" , )
UpperCAmelCase__ : List[str] = output.images
UpperCAmelCase__ : List[Any] = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
UpperCAmelCase__ : int = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 660 | 0 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
A__ : Optional[Any] = TypeVar("""T""")
A__ : List[str] = Union[List[T], Tuple[T, ...]]
A__ : int = Union[T, List[T], Dict[str, T]]
A__ : List[str] = Union[str, bytes, os.PathLike]
| 720 |
"""simple docstring"""
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 ..auto import CONFIG_MAPPING
A__ : Union[str, Any] = logging.get_logger(__name__)
A__ : Optional[int] = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'table-transformer'
_A = ['past_key_values']
_A = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=3 , __UpperCamelCase=1_00 , __UpperCamelCase=6 , __UpperCamelCase=20_48 , __UpperCamelCase=8 , __UpperCamelCase=6 , __UpperCamelCase=20_48 , __UpperCamelCase=8 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=True , __UpperCamelCase="relu" , __UpperCamelCase=2_56 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.02 , __UpperCamelCase=1.0 , __UpperCamelCase=False , __UpperCamelCase="sine" , __UpperCamelCase="resnet50" , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=1 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=1 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=0.1 , **__UpperCamelCase , )-> List[Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
UpperCAmelCase__ : Any = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : int = backbone_config.get("model_type" )
UpperCAmelCase__ : Optional[Any] = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase__ : int = config_class.from_dict(__UpperCamelCase )
# set timm attributes to None
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : List[str] = None, None, None
UpperCAmelCase__ : Optional[int] = use_timm_backbone
UpperCAmelCase__ : Dict = backbone_config
UpperCAmelCase__ : List[Any] = num_channels
UpperCAmelCase__ : Any = num_queries
UpperCAmelCase__ : int = d_model
UpperCAmelCase__ : Optional[int] = encoder_ffn_dim
UpperCAmelCase__ : str = encoder_layers
UpperCAmelCase__ : Dict = encoder_attention_heads
UpperCAmelCase__ : Optional[Any] = decoder_ffn_dim
UpperCAmelCase__ : Tuple = decoder_layers
UpperCAmelCase__ : Optional[Any] = decoder_attention_heads
UpperCAmelCase__ : List[str] = dropout
UpperCAmelCase__ : Tuple = attention_dropout
UpperCAmelCase__ : List[Any] = activation_dropout
UpperCAmelCase__ : Dict = activation_function
UpperCAmelCase__ : Optional[Any] = init_std
UpperCAmelCase__ : List[str] = init_xavier_std
UpperCAmelCase__ : int = encoder_layerdrop
UpperCAmelCase__ : Tuple = decoder_layerdrop
UpperCAmelCase__ : int = encoder_layers
UpperCAmelCase__ : Dict = auxiliary_loss
UpperCAmelCase__ : Union[str, Any] = position_embedding_type
UpperCAmelCase__ : List[str] = backbone
UpperCAmelCase__ : List[Any] = use_pretrained_backbone
UpperCAmelCase__ : List[str] = dilation
# Hungarian matcher
UpperCAmelCase__ : Dict = class_cost
UpperCAmelCase__ : Any = bbox_cost
UpperCAmelCase__ : Tuple = giou_cost
# Loss coefficients
UpperCAmelCase__ : Any = mask_loss_coefficient
UpperCAmelCase__ : Dict = dice_loss_coefficient
UpperCAmelCase__ : Any = bbox_loss_coefficient
UpperCAmelCase__ : Tuple = giou_loss_coefficient
UpperCAmelCase__ : List[Any] = eos_coefficient
super().__init__(is_encoder_decoder=__UpperCamelCase , **__UpperCamelCase )
@property
def lowerCAmelCase__ ( self )-> int:
return self.encoder_attention_heads
@property
def lowerCAmelCase__ ( self )-> int:
return self.d_model
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = version.parse('1.11' )
@property
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def lowerCAmelCase__ ( self )-> float:
return 1E-5
@property
def lowerCAmelCase__ ( self )-> int:
return 12
| 660 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
A__ : Tuple = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : Optional[int] , lowerCAmelCase : Any):
'''simple docstring'''
UpperCAmelCase__ : str = []
for i in range(encoder_config.num_hidden_layers):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F"encoder.deit.blocks.{i}.norm1.weight", F"encoder.encoder.layer.{i}.layernorm_before.weight"))
rename_keys.append((F"encoder.deit.blocks.{i}.norm1.bias", F"encoder.encoder.layer.{i}.layernorm_before.bias"))
rename_keys.append(
(F"encoder.deit.blocks.{i}.attn.proj.weight", F"encoder.encoder.layer.{i}.attention.output.dense.weight"))
rename_keys.append(
(F"encoder.deit.blocks.{i}.attn.proj.bias", F"encoder.encoder.layer.{i}.attention.output.dense.bias"))
rename_keys.append(
(F"encoder.deit.blocks.{i}.norm2.weight", F"encoder.encoder.layer.{i}.layernorm_after.weight"))
rename_keys.append((F"encoder.deit.blocks.{i}.norm2.bias", F"encoder.encoder.layer.{i}.layernorm_after.bias"))
rename_keys.append(
(F"encoder.deit.blocks.{i}.mlp.fc1.weight", F"encoder.encoder.layer.{i}.intermediate.dense.weight"))
rename_keys.append(
(F"encoder.deit.blocks.{i}.mlp.fc1.bias", F"encoder.encoder.layer.{i}.intermediate.dense.bias"))
rename_keys.append(
(F"encoder.deit.blocks.{i}.mlp.fc2.weight", F"encoder.encoder.layer.{i}.output.dense.weight"))
rename_keys.append((F"encoder.deit.blocks.{i}.mlp.fc2.bias", F"encoder.encoder.layer.{i}.output.dense.bias"))
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
("encoder.deit.cls_token", "encoder.embeddings.cls_token"),
("encoder.deit.pos_embed", "encoder.embeddings.position_embeddings"),
("encoder.deit.patch_embed.proj.weight", "encoder.embeddings.patch_embeddings.projection.weight"),
("encoder.deit.patch_embed.proj.bias", "encoder.embeddings.patch_embeddings.projection.bias"),
("encoder.deit.norm.weight", "encoder.layernorm.weight"),
("encoder.deit.norm.bias", "encoder.layernorm.bias"),
])
return rename_keys
def a__ ( lowerCAmelCase : Optional[int] , lowerCAmelCase : int):
'''simple docstring'''
for i in range(encoder_config.num_hidden_layers):
# queries, keys and values (only weights, no biases)
UpperCAmelCase__ : List[Any] = state_dict.pop(F"encoder.deit.blocks.{i}.attn.qkv.weight")
UpperCAmelCase__ : Tuple = in_proj_weight[
: encoder_config.hidden_size, :
]
UpperCAmelCase__ : Optional[Any] = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
UpperCAmelCase__ : str = in_proj_weight[
-encoder_config.hidden_size :, :
]
def a__ ( lowerCAmelCase : Tuple , lowerCAmelCase : Any , lowerCAmelCase : List[Any]):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = dct.pop(lowerCamelCase__)
UpperCAmelCase__ : int = val
def a__ ( lowerCAmelCase : Optional[Any]):
'''simple docstring'''
if "handwritten" in checkpoint_url:
UpperCAmelCase__ : Optional[Any] = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg" # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
UpperCAmelCase__ : Tuple = "https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"
UpperCAmelCase__ : Optional[Any] = Image.open(requests.get(lowerCamelCase__ , stream=lowerCamelCase__).raw).convert("RGB")
return im
@torch.no_grad()
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : int):
'''simple docstring'''
UpperCAmelCase__ : List[str] = ViTConfig(image_size=384 , qkv_bias=lowerCamelCase__)
UpperCAmelCase__ : Any = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
UpperCAmelCase__ : List[str] = 768
elif "large" in checkpoint_url:
# use ViT-large encoder
UpperCAmelCase__ : Union[str, Any] = 1024
UpperCAmelCase__ : Dict = 4096
UpperCAmelCase__ : Any = 24
UpperCAmelCase__ : Optional[Any] = 16
UpperCAmelCase__ : Optional[int] = 1024
else:
raise ValueError("Should either find \'base\' or \'large\' in checkpoint URL")
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : Tuple = "relu"
UpperCAmelCase__ : Dict = 1024
UpperCAmelCase__ : Dict = True
UpperCAmelCase__ : Optional[int] = False
UpperCAmelCase__ : List[str] = False
# load HuggingFace model
UpperCAmelCase__ : Dict = ViTModel(lowerCamelCase__ , add_pooling_layer=lowerCamelCase__)
UpperCAmelCase__ : Any = TrOCRForCausalLM(lowerCamelCase__)
UpperCAmelCase__ : Any = VisionEncoderDecoderModel(encoder=lowerCamelCase__ , decoder=lowerCamelCase__)
model.eval()
# load state_dict of original model, rename some keys
UpperCAmelCase__ : Dict = torch.hub.load_state_dict_from_url(lowerCamelCase__ , map_location="cpu" , check_hash=lowerCamelCase__)["model"]
UpperCAmelCase__ : int = create_rename_keys(lowerCamelCase__ , lowerCamelCase__)
for src, dest in rename_keys:
rename_key(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__)
read_in_q_k_v(lowerCamelCase__ , lowerCamelCase__)
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
UpperCAmelCase__ : List[Any] = state_dict.pop(lowerCamelCase__)
if key.startswith("decoder") and "output_projection" not in key:
UpperCAmelCase__ : List[str] = val
else:
UpperCAmelCase__ : int = val
# load state dict
model.load_state_dict(lowerCamelCase__)
# Check outputs on an image
UpperCAmelCase__ : Optional[Any] = ViTImageProcessor(size=encoder_config.image_size)
UpperCAmelCase__ : Optional[Any] = RobertaTokenizer.from_pretrained("roberta-large")
UpperCAmelCase__ : Any = TrOCRProcessor(lowerCamelCase__ , lowerCamelCase__)
UpperCAmelCase__ : int = processor(images=prepare_img(lowerCamelCase__) , return_tensors="pt").pixel_values
# verify logits
UpperCAmelCase__ : List[Any] = torch.tensor([[model.config.decoder.decoder_start_token_id]])
UpperCAmelCase__ : Dict = model(pixel_values=lowerCamelCase__ , decoder_input_ids=lowerCamelCase__)
UpperCAmelCase__ : Tuple = outputs.logits
UpperCAmelCase__ : Tuple = torch.Size([1, 1, 5_0265])
if "trocr-base-handwritten" in checkpoint_url:
UpperCAmelCase__ : Optional[Any] = torch.tensor(
[-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311])
elif "trocr-large-handwritten" in checkpoint_url:
UpperCAmelCase__ : List[str] = torch.tensor(
[-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170])
elif "trocr-base-printed" in checkpoint_url:
UpperCAmelCase__ : Tuple = torch.tensor(
[-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210])
elif "trocr-large-printed" in checkpoint_url:
UpperCAmelCase__ : Optional[int] = torch.tensor(
[-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535])
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :10] , lowerCamelCase__ , atol=1E-3), "First elements of logits not as expected"
Path(lowerCamelCase__).mkdir(exist_ok=lowerCamelCase__)
print(F"Saving model to {pytorch_dump_folder_path}")
model.save_pretrained(lowerCamelCase__)
print(F"Saving processor to {pytorch_dump_folder_path}")
processor.save_pretrained(lowerCamelCase__)
if __name__ == "__main__":
A__ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
type=str,
help="""URL to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
A__ : int = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 721 |
"""simple docstring"""
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
A__ : int = getLogger(__name__)
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : int = 8 , lowerCAmelCase : int = 1024 , lowerCAmelCase : List[Any]="val" , lowerCAmelCase : str=None , lowerCAmelCase : int=False , lowerCAmelCase : Dict="summarization" , lowerCAmelCase : int=None , lowerCAmelCase : List[str]=1 , lowerCAmelCase : Dict = None , lowerCAmelCase : List[str]="" , **lowerCAmelCase : int , ):
'''simple docstring'''
UpperCAmelCase__ : Dict = str(lowerCAmelCase )
assert local_rank is not None
torch.distributed.init_process_group(backend="nccl" , rank=lowerCAmelCase )
UpperCAmelCase__ : List[str] = Path(lowerCAmelCase )
UpperCAmelCase__ : str = save_dir.joinpath(F"rank_{local_rank}_output.json" )
torch.cuda.set_device(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).cuda()
if fpaa:
UpperCAmelCase__ : List[Any] = model.half()
# determine if we need to increase num_beams
use_task_specific_params(lowerCAmelCase , lowerCAmelCase ) # update config with task specific params
UpperCAmelCase__ : List[Any] = generate_kwargs.pop("num_beams" , model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
UpperCAmelCase__ : Any = num_return_sequences
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained(lowerCAmelCase )
logger.info(F"Inferred tokenizer type: {tokenizer.__class__}" ) # if this is wrong, check config.model_type.
if max_source_length is None:
UpperCAmelCase__ : int = tokenizer.model_max_length
if prefix is None:
UpperCAmelCase__ : Union[str, Any] = prefix or getattr(model.config , "prefix" , "" ) or ""
UpperCAmelCase__ : str = SeqaSeqDataset(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , max_target_length=1024 , type_path=lowerCAmelCase , n_obs=lowerCAmelCase , prefix=lowerCAmelCase , **lowerCAmelCase , )
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
UpperCAmelCase__ : Union[str, Any] = ds.make_sortish_sampler(lowerCAmelCase , distributed=lowerCAmelCase , add_extra_examples=lowerCAmelCase , shuffle=lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = DataLoader(lowerCAmelCase , sampler=lowerCAmelCase , batch_size=lowerCAmelCase , collate_fn=ds.collate_fn )
UpperCAmelCase__ : str = []
for batch in tqdm(lowerCAmelCase ):
UpperCAmelCase__ : Dict = model.generate(
input_ids=batch["input_ids"].to(model.device ) , attention_mask=batch["attention_mask"].to(model.device ) , num_return_sequences=lowerCAmelCase , num_beams=lowerCAmelCase , **lowerCAmelCase , )
UpperCAmelCase__ : int = tokenizer.batch_decode(lowerCAmelCase , skip_special_tokens=lowerCAmelCase , clean_up_tokenization_spaces=lowerCAmelCase )
UpperCAmelCase__ : int = batch["ids"]
if num_return_sequences > 1:
UpperCAmelCase__ : str = chunks(lowerCAmelCase , lowerCAmelCase ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(lowerCAmelCase ):
results.append({"pred": pred, "id": ids[i].item()} )
save_json(lowerCAmelCase , lowerCAmelCase )
return results, sampler.num_replicas
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : str = argparse.ArgumentParser(
epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate" )
parser.add_argument("--data_dir" , type=lowerCAmelCase , help="like cnn_dm/test.source" )
parser.add_argument(
"--model_name" , type=lowerCAmelCase , help="like facebook/bart-large-cnn,t5-base, etc." , default="sshleifer/distilbart-xsum-12-3" , )
parser.add_argument("--save_dir" , type=lowerCAmelCase , help="where to save" , default="tmp_gen" )
parser.add_argument("--max_source_length" , type=lowerCAmelCase , default=lowerCAmelCase )
parser.add_argument(
"--type_path" , type=lowerCAmelCase , default="test" , help="which subset to evaluate typically train/val/test" )
parser.add_argument("--task" , type=lowerCAmelCase , default="summarization" , help="used for task_specific_params + metrics" )
parser.add_argument("--bs" , type=lowerCAmelCase , default=8 , required=lowerCAmelCase , help="batch size" )
parser.add_argument(
"--local_rank" , type=lowerCAmelCase , default=-1 , required=lowerCAmelCase , help="should be passed by distributed.launch" )
parser.add_argument(
"--n_obs" , type=lowerCAmelCase , default=lowerCAmelCase , required=lowerCAmelCase , help="How many observations. Defaults to all." )
parser.add_argument(
"--num_return_sequences" , type=lowerCAmelCase , default=1 , required=lowerCAmelCase , help="How many sequences to return" )
parser.add_argument(
"--sync_timeout" , type=lowerCAmelCase , default=600 , required=lowerCAmelCase , help="How long should master process wait for other processes to finish." , )
parser.add_argument("--src_lang" , type=lowerCAmelCase , default=lowerCAmelCase , required=lowerCAmelCase )
parser.add_argument("--tgt_lang" , type=lowerCAmelCase , default=lowerCAmelCase , required=lowerCAmelCase )
parser.add_argument(
"--prefix" , type=lowerCAmelCase , required=lowerCAmelCase , default=lowerCAmelCase , help="will be added to the begininng of src examples" )
parser.add_argument("--fp16" , action="store_true" )
parser.add_argument("--debug" , action="store_true" )
UpperCAmelCase__ : Optional[int] = time.time()
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = parser.parse_known_args()
UpperCAmelCase__ : int = parse_numeric_n_bool_cl_kwargs(lowerCAmelCase )
if generate_kwargs and args.local_rank <= 0:
print(F"parsed the following generate kwargs: {generate_kwargs}" )
UpperCAmelCase__ : Dict = Path(args.save_dir + "_tmp" )
Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase ) # this handles locking.
UpperCAmelCase__ : List[str] = list(json_save_dir.glob("rank_*.json" ) )
if intermediate_files:
raise ValueError(F"Found files at {json_save_dir} please move or remove them." )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
UpperCAmelCase__ : List[str] = {}
if args.src_lang is not None:
UpperCAmelCase__ : str = args.src_lang
if args.tgt_lang is not None:
UpperCAmelCase__ : List[str] = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=lowerCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = eval_data_dir(
args.data_dir , lowerCAmelCase , args.model_name , type_path=args.type_path , bs=args.bs , fpaa=args.fpaa , task=args.task , local_rank=args.local_rank , n_obs=args.n_obs , max_source_length=args.max_source_length , num_return_sequences=args.num_return_sequences , prefix=args.prefix , dataset_kwargs=lowerCAmelCase , **lowerCAmelCase , )
if args.local_rank <= 0:
UpperCAmelCase__ : str = Path(args.save_dir )
save_dir.mkdir(exist_ok=lowerCAmelCase )
UpperCAmelCase__ : Tuple = gather_results_from_each_node(lowerCAmelCase , lowerCAmelCase , args.sync_timeout )
UpperCAmelCase__ : Union[str, Any] = combine_partial_results(lowerCAmelCase )
if args.num_return_sequences > 1:
UpperCAmelCase__ : int = save_dir.joinpath("pseudolabel_results.json" )
print(F"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/" )
save_json(lowerCAmelCase , lowerCAmelCase )
return
UpperCAmelCase__ : Optional[Any] = Path(args.data_dir ).joinpath(args.type_path + ".target" )
with open(lowerCAmelCase ) as f:
UpperCAmelCase__ : Optional[int] = [x.rstrip() for x in f.readlines()][: len(lowerCAmelCase )]
# Calculate metrics, save metrics, and save _generations.txt
UpperCAmelCase__ : List[Any] = "translation" in args.task
UpperCAmelCase__ : Optional[Any] = calculate_bleu if calc_bleu else calculate_rouge
UpperCAmelCase__ : Optional[Any] = "bleu" if calc_bleu else "rouge"
UpperCAmelCase__ : Dict = score_fn(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : List[Any] = len(lowerCAmelCase )
UpperCAmelCase__ : Union[str, Any] = time.time() - start_time
UpperCAmelCase__ : Optional[int] = round(runtime / metrics["n_obs"] , 4 )
UpperCAmelCase__ : Tuple = num_replicas
# TODO(@stas00): add whatever metadata to metrics
UpperCAmelCase__ : Any = save_dir.joinpath(F"{args.type_path}_{metric_name}.json" )
save_json(lowerCAmelCase , lowerCAmelCase , indent=lowerCAmelCase )
print(lowerCAmelCase )
write_txt_file(lowerCAmelCase , save_dir.joinpath(F"{args.type_path}_generations.txt" ) )
if args.debug:
write_txt_file(lowerCAmelCase , save_dir.joinpath(F"{args.type_path}.target" ) )
else:
shutil.rmtree(lowerCAmelCase )
def a__ ( lowerCAmelCase : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : str = []
for partial_result in partial_results:
records.extend(lowerCAmelCase )
UpperCAmelCase__ : Dict = sorted(lowerCAmelCase , key=lambda lowerCAmelCase : x["id"] )
UpperCAmelCase__ : List[str] = [x["pred"] for x in records]
return preds
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : int , lowerCAmelCase : Optional[int] ):
'''simple docstring'''
# WAIT FOR lots of .json files
UpperCAmelCase__ : int = time.time()
logger.info("waiting for all nodes to finish" )
UpperCAmelCase__ : Dict = None
while (time.time() - start_wait) < timeout:
UpperCAmelCase__ : str = list(save_dir.glob("rank_*.json" ) )
if len(lowerCAmelCase ) < num_replicas:
continue
try:
# make sure all json files are fully saved
UpperCAmelCase__ : Union[str, Any] = lmap(lowerCAmelCase , lowerCAmelCase )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError("Rank 0 gave up on waiting for other processes" )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 660 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase=7 , __UpperCamelCase=3 , __UpperCamelCase=30 , __UpperCamelCase=4_00 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=[0.5, 0.5, 0.5] , __UpperCamelCase=[0.5, 0.5, 0.5] , __UpperCamelCase=True , __UpperCamelCase=1 / 2_55 , __UpperCamelCase=True , )-> int:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
UpperCAmelCase__ : Any = size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
UpperCAmelCase__ : List[Any] = parent
UpperCAmelCase__ : str = batch_size
UpperCAmelCase__ : Any = num_channels
UpperCAmelCase__ : str = min_resolution
UpperCAmelCase__ : Tuple = max_resolution
UpperCAmelCase__ : List[Any] = do_resize
UpperCAmelCase__ : Union[str, Any] = size
UpperCAmelCase__ : Optional[int] = do_normalize
UpperCAmelCase__ : Any = image_mean
UpperCAmelCase__ : Any = image_std
UpperCAmelCase__ : List[Any] = do_rescale
UpperCAmelCase__ : int = rescale_factor
UpperCAmelCase__ : Optional[Any] = do_pad
def lowerCAmelCase__ ( self )-> Optional[int]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase=False )-> List[Any]:
if not batched:
UpperCAmelCase__ : List[Any] = image_inputs[0]
if isinstance(__UpperCamelCase , Image.Image ):
UpperCAmelCase__ : Dict = image.size
else:
UpperCAmelCase__ : List[str] = image.shape[1], image.shape[2]
if w < h:
UpperCAmelCase__ : Union[str, Any] = int(self.size["shortest_edge"] * h / w )
UpperCAmelCase__ : int = self.size["shortest_edge"]
elif w > h:
UpperCAmelCase__ : List[Any] = self.size["shortest_edge"]
UpperCAmelCase__ : Dict = int(self.size["shortest_edge"] * w / h )
else:
UpperCAmelCase__ : Union[str, Any] = self.size["shortest_edge"]
UpperCAmelCase__ : List[Any] = self.size["shortest_edge"]
else:
UpperCAmelCase__ : Any = []
for image in image_inputs:
UpperCAmelCase__ : Dict = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCAmelCase__ : Dict = max(__UpperCamelCase , key=lambda __UpperCamelCase : item[0] )[0]
UpperCAmelCase__ : Tuple = max(__UpperCamelCase , key=lambda __UpperCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _lowercase ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_A = ConditionalDetrImageProcessor if is_vision_available() else None
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : str = ConditionalDetrImageProcessingTester(self )
@property
def lowerCAmelCase__ ( self )-> List[Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__UpperCamelCase , "image_mean" ) )
self.assertTrue(hasattr(__UpperCamelCase , "image_std" ) )
self.assertTrue(hasattr(__UpperCamelCase , "do_normalize" ) )
self.assertTrue(hasattr(__UpperCamelCase , "do_resize" ) )
self.assertTrue(hasattr(__UpperCamelCase , "size" ) )
def lowerCAmelCase__ ( self )-> Optional[int]:
UpperCAmelCase__ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 13_33} )
self.assertEqual(image_processor.do_pad , __UpperCamelCase )
UpperCAmelCase__ : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__UpperCamelCase )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> str:
pass
def lowerCAmelCase__ ( self )-> List[Any]:
# Initialize image_processing
UpperCAmelCase__ : int = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , Image.Image )
# Test not batched input
UpperCAmelCase__ : Any = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
UpperCAmelCase__ : Any = self.image_processor_tester.get_expected_values(__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase__ : List[str] = self.image_processor_tester.get_expected_values(__UpperCamelCase , batched=__UpperCamelCase )
UpperCAmelCase__ : Optional[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,
expected_height,
expected_width,
) , )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
# Initialize image_processing
UpperCAmelCase__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase__ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase , numpify=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , np.ndarray )
# Test not batched input
UpperCAmelCase__ : int = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
UpperCAmelCase__ : List[str] = self.image_processor_tester.get_expected_values(__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase__ : Optional[int] = image_processing(__UpperCamelCase , return_tensors="pt" ).pixel_values
UpperCAmelCase__ : Tuple = self.image_processor_tester.get_expected_values(__UpperCamelCase , batched=__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase__ ( self )-> List[Any]:
# Initialize image_processing
UpperCAmelCase__ : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase__ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase , torchify=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , torch.Tensor )
# Test not batched input
UpperCAmelCase__ : Dict = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
UpperCAmelCase__ : int = self.image_processor_tester.get_expected_values(__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase__ : Union[str, Any] = image_processing(__UpperCamelCase , return_tensors="pt" ).pixel_values
UpperCAmelCase__ : int = self.image_processor_tester.get_expected_values(__UpperCamelCase , batched=__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def lowerCAmelCase__ ( self )-> str:
# prepare image and target
UpperCAmelCase__ : str = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
UpperCAmelCase__ : Optional[Any] = json.loads(f.read() )
UpperCAmelCase__ : Union[str, Any] = {"image_id": 3_97_69, "annotations": target}
# encode them
UpperCAmelCase__ : Any = ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50" )
UpperCAmelCase__ : List[Any] = image_processing(images=__UpperCamelCase , annotations=__UpperCamelCase , return_tensors="pt" )
# verify pixel values
UpperCAmelCase__ : List[str] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["pixel_values"].shape , __UpperCamelCase )
UpperCAmelCase__ : Tuple = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , __UpperCamelCase , atol=1E-4 ) )
# verify area
UpperCAmelCase__ : Optional[Any] = torch.tensor([5887.9600, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , __UpperCamelCase ) )
# verify boxes
UpperCAmelCase__ : List[Any] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , __UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , __UpperCamelCase , atol=1E-3 ) )
# verify image_id
UpperCAmelCase__ : Union[str, Any] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , __UpperCamelCase ) )
# verify is_crowd
UpperCAmelCase__ : Optional[int] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , __UpperCamelCase ) )
# verify class_labels
UpperCAmelCase__ : List[Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , __UpperCamelCase ) )
# verify orig_size
UpperCAmelCase__ : Any = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , __UpperCamelCase ) )
# verify size
UpperCAmelCase__ : Tuple = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , __UpperCamelCase ) )
@slow
def lowerCAmelCase__ ( self )-> Tuple:
# prepare image, target and masks_path
UpperCAmelCase__ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
UpperCAmelCase__ : Dict = json.loads(f.read() )
UpperCAmelCase__ : Tuple = {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}
UpperCAmelCase__ : Optional[Any] = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
UpperCAmelCase__ : str = ConditionalDetrImageProcessor(format="coco_panoptic" )
UpperCAmelCase__ : List[Any] = image_processing(images=__UpperCamelCase , annotations=__UpperCamelCase , masks_path=__UpperCamelCase , return_tensors="pt" )
# verify pixel values
UpperCAmelCase__ : Optional[int] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["pixel_values"].shape , __UpperCamelCase )
UpperCAmelCase__ : Any = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , __UpperCamelCase , atol=1E-4 ) )
# verify area
UpperCAmelCase__ : str = torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , __UpperCamelCase ) )
# verify boxes
UpperCAmelCase__ : List[Any] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , __UpperCamelCase )
UpperCAmelCase__ : List[str] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , __UpperCamelCase , atol=1E-3 ) )
# verify image_id
UpperCAmelCase__ : str = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , __UpperCamelCase ) )
# verify is_crowd
UpperCAmelCase__ : Tuple = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , __UpperCamelCase ) )
# verify class_labels
UpperCAmelCase__ : List[Any] = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , __UpperCamelCase ) )
# verify masks
UpperCAmelCase__ : Optional[int] = 82_28_73
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , __UpperCamelCase )
# verify orig_size
UpperCAmelCase__ : List[str] = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , __UpperCamelCase ) )
# verify size
UpperCAmelCase__ : str = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , __UpperCamelCase ) )
| 700 |
"""simple docstring"""
from timeit import timeit
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
UpperCAmelCase__ : Tuple = 0
while number:
number &= number - 1
result += 1
return result
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
UpperCAmelCase__ : Union[str, Any] = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def a__ ( ):
'''simple docstring'''
def do_benchmark(lowerCAmelCase : int ) -> None:
UpperCAmelCase__ : Dict = "import __main__ as z"
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(lowerCAmelCase ) = }" )
UpperCAmelCase__ : Tuple = timeit("z.get_set_bits_count_using_modulo_operator(25)" , setup=lowerCAmelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase ) = }" )
UpperCAmelCase__ : Any = timeit(
"z.get_set_bits_count_using_brian_kernighans_algorithm(25)" , setup=lowerCAmelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(lowerCAmelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 660 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase : List[str] ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
2: [4, 5],
3: [4],
4: [2, 3],
5: [2],
6: [0, 7],
7: [6],
8: [],
9: [1],
},
{
0: [4],
1: [6],
2: [],
3: [5, 6, 7],
4: [0, 6],
5: [3, 8, 9],
6: [1, 3, 4, 7],
7: [3, 6, 8, 9],
8: [5, 7],
9: [5, 7],
},
{
0: [1, 3],
1: [0, 2, 4],
2: [1, 3, 4],
3: [0, 2, 4],
4: [1, 2, 3],
},
][index]
def a__ ( lowerCAmelCase : dict[int, list[int]] ):
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Dict = len(lowerCAmelCase ) # No of vertices in graph
UpperCAmelCase__ : str = [0] * n
UpperCAmelCase__ : Optional[Any] = [False] * n
def dfs(lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : str , lowerCAmelCase : List[str] ):
UpperCAmelCase__ : int = True
UpperCAmelCase__ : List[Any] = id_
id_ += 1
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
dfs(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , id_ )
UpperCAmelCase__ : Any = min(low[at] , low[to] )
if id_ <= low[to]:
bridges.append((at, to) if at < to else (to, at) )
else:
# This edge is a back edge and cannot be a bridge
UpperCAmelCase__ : int = min(low[at] , low[to] )
UpperCAmelCase__ : list[tuple[int, int]] = []
for i in range(lowerCAmelCase ):
if not visited[i]:
dfs(lowerCAmelCase , -1 , lowerCAmelCase , id_ )
return bridges
if __name__ == "__main__":
import doctest
doctest.testmod()
| 701 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _lowercase ( unittest.TestCase , lowerCAmelCase_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : Optional[Any] = load_tool("text-classification" )
self.tool.setup()
UpperCAmelCase__ : List[str] = load_tool("text-classification" , remote=__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ : Dict = self.tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : List[Any] = self.remote_tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
def lowerCAmelCase__ ( self )-> Optional[int]:
UpperCAmelCase__ : Any = self.tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : str = self.remote_tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
| 660 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
A__ : Any = {
"""sample_size""": 32,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""num_class_embeds""": 1_000,
"""block_out_channels""": [32, 64],
"""attention_head_dim""": 8,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """scale_shift""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
A__ : List[str] = {
"""sample_size""": 64,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 3,
"""num_class_embeds""": 1_000,
"""block_out_channels""": [192, 192 * 2, 192 * 3, 192 * 4],
"""attention_head_dim""": 64,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """scale_shift""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
A__ : Optional[int] = {
"""sample_size""": 256,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""num_class_embeds""": None,
"""block_out_channels""": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
"""attention_head_dim""": 64,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""ResnetDownsampleBlock2D""",
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
"""ResnetUpsampleBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """default""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
A__ : Dict = {
"""num_train_timesteps""": 40,
"""sigma_min""": 0.002,
"""sigma_max""": 80.0,
}
A__ : Dict = {
"""num_train_timesteps""": 201,
"""sigma_min""": 0.002,
"""sigma_max""": 80.0,
}
A__ : Optional[int] = {
"""num_train_timesteps""": 151,
"""sigma_min""": 0.002,
"""sigma_max""": 80.0,
}
def a__ ( lowerCAmelCase : Dict ):
'''simple docstring'''
if isinstance(lowerCAmelCase , lowerCAmelCase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("boolean value expected" )
def a__ ( lowerCAmelCase : Union[str, Any] , lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : Optional[int] , lowerCAmelCase : Tuple=False ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = checkpoint[F"{old_prefix}.in_layers.0.weight"]
UpperCAmelCase__ : Optional[Any] = checkpoint[F"{old_prefix}.in_layers.0.bias"]
UpperCAmelCase__ : str = checkpoint[F"{old_prefix}.in_layers.2.weight"]
UpperCAmelCase__ : int = checkpoint[F"{old_prefix}.in_layers.2.bias"]
UpperCAmelCase__ : Optional[int] = checkpoint[F"{old_prefix}.emb_layers.1.weight"]
UpperCAmelCase__ : List[str] = checkpoint[F"{old_prefix}.emb_layers.1.bias"]
UpperCAmelCase__ : Dict = checkpoint[F"{old_prefix}.out_layers.0.weight"]
UpperCAmelCase__ : Tuple = checkpoint[F"{old_prefix}.out_layers.0.bias"]
UpperCAmelCase__ : str = checkpoint[F"{old_prefix}.out_layers.3.weight"]
UpperCAmelCase__ : Optional[Any] = checkpoint[F"{old_prefix}.out_layers.3.bias"]
if has_skip:
UpperCAmelCase__ : int = checkpoint[F"{old_prefix}.skip_connection.weight"]
UpperCAmelCase__ : str = checkpoint[F"{old_prefix}.skip_connection.bias"]
return new_checkpoint
def a__ ( lowerCAmelCase : int , lowerCAmelCase : str , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : str , lowerCAmelCase : Optional[int]=None ):
'''simple docstring'''
UpperCAmelCase__ : Dict = checkpoint[F"{old_prefix}.qkv.weight"].chunk(3 , dim=0 )
UpperCAmelCase__ : Optional[int] = checkpoint[F"{old_prefix}.qkv.bias"].chunk(3 , dim=0 )
UpperCAmelCase__ : int = checkpoint[F"{old_prefix}.norm.weight"]
UpperCAmelCase__ : Any = checkpoint[F"{old_prefix}.norm.bias"]
UpperCAmelCase__ : int = weight_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase__ : Union[str, Any] = bias_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase__ : Union[str, Any] = weight_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase__ : Optional[Any] = bias_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase__ : int = weight_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase__ : Union[str, Any] = bias_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase__ : int = (
checkpoint[F"{old_prefix}.proj_out.weight"].squeeze(-1 ).squeeze(-1 )
)
UpperCAmelCase__ : Optional[Any] = checkpoint[F"{old_prefix}.proj_out.bias"].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def a__ ( lowerCAmelCase : str , lowerCAmelCase : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = torch.load(lowerCAmelCase , map_location="cpu" )
UpperCAmelCase__ : Optional[Any] = {}
UpperCAmelCase__ : Optional[int] = checkpoint["time_embed.0.weight"]
UpperCAmelCase__ : str = checkpoint["time_embed.0.bias"]
UpperCAmelCase__ : Optional[Any] = checkpoint["time_embed.2.weight"]
UpperCAmelCase__ : int = checkpoint["time_embed.2.bias"]
if unet_config["num_class_embeds"] is not None:
UpperCAmelCase__ : str = checkpoint["label_emb.weight"]
UpperCAmelCase__ : List[str] = checkpoint["input_blocks.0.0.weight"]
UpperCAmelCase__ : int = checkpoint["input_blocks.0.0.bias"]
UpperCAmelCase__ : Any = unet_config["down_block_types"]
UpperCAmelCase__ : List[Any] = unet_config["layers_per_block"]
UpperCAmelCase__ : str = unet_config["attention_head_dim"]
UpperCAmelCase__ : List[str] = unet_config["block_out_channels"]
UpperCAmelCase__ : Optional[Any] = 1
UpperCAmelCase__ : List[Any] = channels_list[0]
for i, layer_type in enumerate(lowerCAmelCase ):
UpperCAmelCase__ : Optional[Any] = channels_list[i]
UpperCAmelCase__ : Tuple = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(lowerCAmelCase ):
UpperCAmelCase__ : str = F"down_blocks.{i}.resnets.{j}"
UpperCAmelCase__ : List[str] = F"input_blocks.{current_layer}.0"
UpperCAmelCase__ : Tuple = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase__ : int = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , has_skip=lowerCAmelCase )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(lowerCAmelCase ):
UpperCAmelCase__ : str = F"down_blocks.{i}.resnets.{j}"
UpperCAmelCase__ : List[str] = F"input_blocks.{current_layer}.0"
UpperCAmelCase__ : Optional[int] = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase__ : Any = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , has_skip=lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = F"down_blocks.{i}.attentions.{j}"
UpperCAmelCase__ : int = F"input_blocks.{current_layer}.1"
UpperCAmelCase__ : Any = convert_attention(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
current_layer += 1
if i != len(lowerCAmelCase ) - 1:
UpperCAmelCase__ : Dict = F"down_blocks.{i}.downsamplers.0"
UpperCAmelCase__ : Any = F"input_blocks.{current_layer}.0"
UpperCAmelCase__ : str = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
current_layer += 1
UpperCAmelCase__ : Union[str, Any] = current_channels
# hardcoded the mid-block for now
UpperCAmelCase__ : Dict = "mid_block.resnets.0"
UpperCAmelCase__ : Any = "middle_block.0"
UpperCAmelCase__ : str = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : List[str] = "mid_block.attentions.0"
UpperCAmelCase__ : List[str] = "middle_block.1"
UpperCAmelCase__ : Optional[int] = convert_attention(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = "mid_block.resnets.1"
UpperCAmelCase__ : Optional[int] = "middle_block.2"
UpperCAmelCase__ : Optional[int] = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : str = 0
UpperCAmelCase__ : Tuple = unet_config["up_block_types"]
for i, layer_type in enumerate(lowerCAmelCase ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase__ : int = F"up_blocks.{i}.resnets.{j}"
UpperCAmelCase__ : int = F"output_blocks.{current_layer}.0"
UpperCAmelCase__ : Optional[Any] = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , has_skip=lowerCAmelCase )
current_layer += 1
if i != len(lowerCAmelCase ) - 1:
UpperCAmelCase__ : str = F"up_blocks.{i}.upsamplers.0"
UpperCAmelCase__ : Any = F"output_blocks.{current_layer-1}.1"
UpperCAmelCase__ : Optional[Any] = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase__ : Union[str, Any] = F"up_blocks.{i}.resnets.{j}"
UpperCAmelCase__ : Tuple = F"output_blocks.{current_layer}.0"
UpperCAmelCase__ : Optional[Any] = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , has_skip=lowerCAmelCase )
UpperCAmelCase__ : List[str] = F"up_blocks.{i}.attentions.{j}"
UpperCAmelCase__ : Dict = F"output_blocks.{current_layer}.1"
UpperCAmelCase__ : int = convert_attention(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
current_layer += 1
if i != len(lowerCAmelCase ) - 1:
UpperCAmelCase__ : int = F"up_blocks.{i}.upsamplers.0"
UpperCAmelCase__ : Tuple = F"output_blocks.{current_layer-1}.2"
UpperCAmelCase__ : int = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : Union[str, Any] = checkpoint["out.0.weight"]
UpperCAmelCase__ : Optional[int] = checkpoint["out.0.bias"]
UpperCAmelCase__ : Any = checkpoint["out.2.weight"]
UpperCAmelCase__ : str = checkpoint["out.2.bias"]
return new_checkpoint
if __name__ == "__main__":
A__ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
parser.add_argument(
"""--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
)
parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")
A__ : List[str] = parser.parse_args()
A__ : Union[str, Any] = strabool(args.class_cond)
A__ : List[str] = os.path.basename(args.unet_path)
print(f"""Checkpoint: {ckpt_name}""")
# Get U-Net config
if "imagenet64" in ckpt_name:
A__ : Optional[Any] = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
A__ : Any = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
A__ : str = TEST_UNET_CONFIG
else:
raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")
if not args.class_cond:
A__ : Optional[int] = None
A__ : List[Any] = con_pt_to_diffuser(args.unet_path, unet_config)
A__ : str = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
A__ : Optional[Any] = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
A__ : List[Any] = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
A__ : Any = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")
A__ : List[Any] = CMStochasticIterativeScheduler(**scheduler_config)
A__ : Any = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 702 |
"""simple docstring"""
def a__ ( lowerCAmelCase : list , lowerCAmelCase : list ):
'''simple docstring'''
_validate_point(lowerCAmelCase )
_validate_point(lowerCAmelCase )
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(lowerCAmelCase , lowerCAmelCase ) ) )
def a__ ( lowerCAmelCase : list[float] ):
'''simple docstring'''
if point:
if isinstance(lowerCAmelCase , lowerCAmelCase ):
for item in point:
if not isinstance(lowerCAmelCase , (int, float) ):
UpperCAmelCase__ : Tuple = (
"Expected a list of numbers as input, found "
F"{type(lowerCAmelCase ).__name__}"
)
raise TypeError(lowerCAmelCase )
else:
UpperCAmelCase__ : Dict = F"Expected a list of numbers as input, found {type(lowerCAmelCase ).__name__}"
raise TypeError(lowerCAmelCase )
else:
raise ValueError("Missing an input" )
def a__ ( lowerCAmelCase : list , lowerCAmelCase : list ):
'''simple docstring'''
_validate_point(lowerCAmelCase )
_validate_point(lowerCAmelCase )
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(lowerCAmelCase , lowerCAmelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 0 |
import random
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = num - 1
UpperCAmelCase__ : Dict = 0
while s % 2 == 0:
UpperCAmelCase__ : Tuple = s // 2
t += 1
for _ in range(5 ):
UpperCAmelCase__ : Optional[int] = random.randrange(2 , num - 1 )
UpperCAmelCase__ : Optional[Any] = pow(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
if v != 1:
UpperCAmelCase__ : Union[str, Any] = 0
while v != (num - 1):
if i == t - 1:
return False
else:
UpperCAmelCase__ : Optional[int] = i + 1
UpperCAmelCase__ : Optional[int] = (v**2) % num
return True
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if num < 2:
return False
UpperCAmelCase__ : List[str] = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
101,
103,
107,
109,
113,
127,
131,
137,
139,
149,
151,
157,
163,
167,
173,
179,
181,
191,
193,
197,
199,
211,
223,
227,
229,
233,
239,
241,
251,
257,
263,
269,
271,
277,
281,
283,
293,
307,
311,
313,
317,
331,
337,
347,
349,
353,
359,
367,
373,
379,
383,
389,
397,
401,
409,
419,
421,
431,
433,
439,
443,
449,
457,
461,
463,
467,
479,
487,
491,
499,
503,
509,
521,
523,
541,
547,
557,
563,
569,
571,
577,
587,
593,
599,
601,
607,
613,
617,
619,
631,
641,
643,
647,
653,
659,
661,
673,
677,
683,
691,
701,
709,
719,
727,
733,
739,
743,
751,
757,
761,
769,
773,
787,
797,
809,
811,
821,
823,
827,
829,
839,
853,
857,
859,
863,
877,
881,
883,
887,
907,
911,
919,
929,
937,
941,
947,
953,
967,
971,
977,
983,
991,
997,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(lowerCAmelCase )
def a__ ( lowerCAmelCase : int = 1024 ):
'''simple docstring'''
while True:
UpperCAmelCase__ : Tuple = random.randrange(2 ** (keysize - 1) , 2 ** (keysize) )
if is_prime_low_num(lowerCAmelCase ):
return num
if __name__ == "__main__":
A__ : int = generate_large_prime()
print(("""Prime number:""", num))
print(("""is_prime_low_num:""", is_prime_low_num(num)))
| 703 |
"""simple docstring"""
import math
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a__ ( lowerCAmelCase : int = 1_0001 ):
'''simple docstring'''
try:
UpperCAmelCase__ : List[str] = int(lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError("Parameter nth must be int or castable to int." ) from None
if nth <= 0:
raise ValueError("Parameter nth must be greater than or equal to one." )
UpperCAmelCase__ : list[int] = []
UpperCAmelCase__ : str = 2
while len(lowerCAmelCase ) < nth:
if is_prime(lowerCAmelCase ):
primes.append(lowerCAmelCase )
num += 1
else:
num += 1
return primes[len(lowerCAmelCase ) - 1]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 660 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : List[Any] = XLMRobertaModel.from_pretrained("xlm-roberta-base" )
UpperCAmelCase__ : Optional[int] = torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
# The dog is cute and lives in the garden house
UpperCAmelCase__ : Optional[Any] = torch.Size((1, 12, 7_68) ) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase__ : Dict = torch.tensor(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCAmelCase__ : Optional[int] = model(__UpperCamelCase )["last_hidden_state"].detach()
self.assertEqual(output.shape , __UpperCamelCase )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , __UpperCamelCase , atol=1E-3 ) )
@slow
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : int = XLMRobertaModel.from_pretrained("xlm-roberta-large" )
UpperCAmelCase__ : Optional[Any] = torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
# The dog is cute and lives in the garden house
UpperCAmelCase__ : List[str] = torch.Size((1, 12, 10_24) ) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase__ : List[Any] = torch.tensor(
[[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCAmelCase__ : int = model(__UpperCamelCase )["last_hidden_state"].detach()
self.assertEqual(output.shape , __UpperCamelCase )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , __UpperCamelCase , atol=1E-3 ) )
| 704 |
"""simple docstring"""
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=30 , __UpperCamelCase=2 , __UpperCamelCase=3 , __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=3 , __UpperCamelCase=0.6 , __UpperCamelCase=None , )-> List[Any]:
UpperCAmelCase__ : str = parent
UpperCAmelCase__ : Optional[Any] = batch_size
UpperCAmelCase__ : Any = image_size
UpperCAmelCase__ : Dict = patch_size
UpperCAmelCase__ : Optional[Any] = num_channels
UpperCAmelCase__ : Union[str, Any] = is_training
UpperCAmelCase__ : Any = use_labels
UpperCAmelCase__ : List[Any] = hidden_size
UpperCAmelCase__ : Dict = num_hidden_layers
UpperCAmelCase__ : List[str] = num_attention_heads
UpperCAmelCase__ : List[Any] = intermediate_size
UpperCAmelCase__ : Dict = hidden_act
UpperCAmelCase__ : List[Any] = hidden_dropout_prob
UpperCAmelCase__ : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase__ : Optional[Any] = type_sequence_label_size
UpperCAmelCase__ : Union[str, Any] = initializer_range
UpperCAmelCase__ : int = mask_ratio
UpperCAmelCase__ : Tuple = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase__ : int = (image_size // patch_size) ** 2
UpperCAmelCase__ : str = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Optional[Any] = None
if self.use_labels:
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : Optional[int] = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self )-> int:
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = ViTMAEModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : List[str] = 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 )-> Optional[int]:
UpperCAmelCase__ : List[Any] = ViTMAEForPreTraining(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : Optional[int] = model(__UpperCamelCase )
UpperCAmelCase__ : List[str] = (self.image_size // self.patch_size) ** 2
UpperCAmelCase__ : List[str] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase__ : Dict = 1
UpperCAmelCase__ : str = ViTMAEForPreTraining(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase__ : List[str] = model(__UpperCamelCase )
UpperCAmelCase__ : List[str] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : Tuple = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : str = config_and_inputs
UpperCAmelCase__ : int = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_A = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
_A = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
_A = False
_A = False
_A = False
_A = False
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : Any = ViTMAEModelTester(self )
UpperCAmelCase__ : List[str] = ConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase , hidden_size=37 )
def lowerCAmelCase__ ( self )-> int:
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def lowerCAmelCase__ ( self )-> Dict:
pass
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : int = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase__ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ , UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class(__UpperCamelCase )
UpperCAmelCase__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Dict = [*signature.parameters.keys()]
UpperCAmelCase__ : Tuple = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Dict:
# make masks reproducible
np.random.seed(2 )
UpperCAmelCase__ : Tuple = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
UpperCAmelCase__ : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase__ : str = torch.from_numpy(__UpperCamelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase__ : Optional[Any] = pt_noise
super().check_pt_tf_models(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[Any] = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase__ : Optional[int] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
UpperCAmelCase__ : Optional[Any] = outputs[0].cpu().numpy()
UpperCAmelCase__ : Union[str, Any] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = model_class.from_pretrained(__UpperCamelCase )
model.to(__UpperCamelCase )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase__ : List[Any] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
# Make sure we don't have nans
UpperCAmelCase__ : Tuple = after_outputs[0].cpu().numpy()
UpperCAmelCase__ : int = 0
UpperCAmelCase__ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__UpperCamelCase , 1E-5 )
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> List[str]:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> Any:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> Optional[Any]:
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def lowerCAmelCase__ ( self )-> List[Any]:
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
pass
@slow
def lowerCAmelCase__ ( self )-> Union[str, Any]:
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : Tuple = ViTMAEModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase__ ( self )-> List[Any]:
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def lowerCAmelCase__ ( self )-> Optional[int]:
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCAmelCase__ : Any = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" ).to(__UpperCamelCase )
UpperCAmelCase__ : Tuple = self.default_image_processor
UpperCAmelCase__ : List[Any] = prepare_img()
UpperCAmelCase__ : Optional[Any] = image_processor(images=__UpperCamelCase , return_tensors="pt" ).to(__UpperCamelCase )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCAmelCase__ : List[Any] = ViTMAEConfig()
UpperCAmelCase__ : str = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase__ : Optional[int] = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : List[str] = model(**__UpperCamelCase , noise=torch.from_numpy(__UpperCamelCase ).to(device=__UpperCamelCase ) )
# verify the logits
UpperCAmelCase__ : str = torch.Size((1, 1_96, 7_68) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
UpperCAmelCase__ : List[Any] = torch.tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(__UpperCamelCase ) , atol=1E-4 ) )
| 660 | 0 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
A__ : Optional[int] = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""
A__ : Any = """
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
- `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
- `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
- `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
- `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
- `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
- `0`: If there is a zero division, the return value is `0`.
- `1`: If there is a zero division, the return value is `1`.
Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.
Examples:
Example 1-A simple example with some errors
>>> recall_metric = datasets.load_metric('recall')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
>>> print(results)
{'recall': 0.6666666666666666}
Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
>>> recall_metric = datasets.load_metric('recall')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
>>> print(results)
{'recall': 0.5}
Example 3-The same example as Example 1, but with `sample_weight` included.
>>> recall_metric = datasets.load_metric('recall')
>>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
>>> print(results)
{'recall': 0.55}
Example 4-A multiclass example, using different averages.
>>> recall_metric = datasets.load_metric('recall')
>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{'recall': array([1., 0., 0.])}
"""
A__ : str = """
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> List[str]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("int32" ) ),
"references": datasets.Sequence(datasets.Value("int32" ) ),
}
if self.config_name == "multilabel"
else {
"predictions": datasets.Value("int32" ),
"references": datasets.Value("int32" ),
} ) , reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"] , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=1 , __UpperCamelCase="binary" , __UpperCamelCase=None , __UpperCamelCase="warn" , )-> Union[str, Any]:
UpperCAmelCase__ : Tuple = recall_score(
__UpperCamelCase , __UpperCamelCase , labels=__UpperCamelCase , pos_label=__UpperCamelCase , average=__UpperCamelCase , sample_weight=__UpperCamelCase , zero_division=__UpperCamelCase , )
return {"recall": float(__UpperCamelCase ) if score.size == 1 else score}
| 705 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class _lowercase :
'''simple docstring'''
_A = 42
# setable values
_A = 42
_A = 42
_A = None
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
return cls(common=__UpperCamelCase , init_noise_sigma=__UpperCamelCase , timesteps=__UpperCamelCase )
@dataclass
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
_A = [e.name for e in FlaxKarrasDiffusionSchedulers]
_A = 42
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
return True
@register_to_config
def __init__( self , __UpperCamelCase = 10_00 , __UpperCamelCase = 0.0001 , __UpperCamelCase = 0.02 , __UpperCamelCase = "linear" , __UpperCamelCase = None , __UpperCamelCase = "fixed_small" , __UpperCamelCase = True , __UpperCamelCase = "epsilon" , __UpperCamelCase = jnp.floataa , )-> List[str]:
UpperCAmelCase__ : int = dtype
def lowerCAmelCase__ ( self , __UpperCamelCase = None )-> DDPMSchedulerState:
if common is None:
UpperCAmelCase__ : int = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
UpperCAmelCase__ : Tuple = jnp.array(1.0 , dtype=self.dtype )
UpperCAmelCase__ : Tuple = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=__UpperCamelCase , init_noise_sigma=__UpperCamelCase , timesteps=__UpperCamelCase , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None )-> jnp.ndarray:
return sample
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = () )-> DDPMSchedulerState:
UpperCAmelCase__ : Dict = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
UpperCAmelCase__ : Optional[int] = (jnp.arange(0 , __UpperCamelCase ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=__UpperCamelCase , timesteps=__UpperCamelCase , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = state.common.alphas_cumprod[t]
UpperCAmelCase__ : int = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase__ : Any = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
UpperCAmelCase__ : Union[str, Any] = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
UpperCAmelCase__ : Dict = jnp.clip(__UpperCamelCase , a_min=1E-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
UpperCAmelCase__ : Tuple = jnp.log(jnp.clip(__UpperCamelCase , a_min=1E-20 ) )
elif variance_type == "fixed_large":
UpperCAmelCase__ : Union[str, Any] = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
UpperCAmelCase__ : Optional[int] = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
UpperCAmelCase__ : List[str] = variance
UpperCAmelCase__ : Union[str, Any] = state.common.betas[t]
UpperCAmelCase__ : Optional[int] = (predicted_variance + 1) / 2
UpperCAmelCase__ : Any = frac * max_log + (1 - frac) * min_log
return variance
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = True , )-> Union[FlaxDDPMSchedulerOutput, Tuple]:
UpperCAmelCase__ : List[str] = timestep
if key is None:
UpperCAmelCase__ : int = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = jnp.split(__UpperCamelCase , sample.shape[1] , axis=1 )
else:
UpperCAmelCase__ : Optional[Any] = None
# 1. compute alphas, betas
UpperCAmelCase__ : Union[str, Any] = state.common.alphas_cumprod[t]
UpperCAmelCase__ : Tuple = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
UpperCAmelCase__ : Union[str, Any] = 1 - alpha_prod_t
UpperCAmelCase__ : Tuple = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase__ : List[Any] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase__ : Any = model_output
elif self.config.prediction_type == "v_prediction":
UpperCAmelCase__ : Union[str, Any] = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
" for the FlaxDDPMScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase__ : List[Any] = jnp.clip(__UpperCamelCase , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ : List[str] = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
UpperCAmelCase__ : List[Any] = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ : Tuple = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
UpperCAmelCase__ : Any = jax.random.split(__UpperCamelCase , num=1 )
UpperCAmelCase__ : int = jax.random.normal(__UpperCamelCase , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(__UpperCamelCase , __UpperCamelCase , predicted_variance=__UpperCamelCase ) ** 0.5) * noise
UpperCAmelCase__ : Dict = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
UpperCAmelCase__ : Any = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase , state=__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> jnp.ndarray:
return add_noise_common(state.common , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> jnp.ndarray:
return get_velocity_common(state.common , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __len__( self )-> Tuple:
return self.config.num_train_timesteps
| 660 | 0 |
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def a__ ( lowerCAmelCase : int = 100 ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = 1
UpperCAmelCase__ : int = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase__ : Optional[int] = pre_numerator
UpperCAmelCase__ : Optional[Any] = 2 * i // 3 if i % 3 == 0 else 1
UpperCAmelCase__ : List[Any] = cur_numerator
UpperCAmelCase__ : str = e_cont * pre_numerator + temp
return sum_digits(lowerCAmelCase )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 706 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = ''
_A = 'hf-legacy' # "hf://"" is reserved for hffs
def __init__( self , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )-> str:
super().__init__(self , **__UpperCamelCase )
UpperCAmelCase__ : int = repo_info
UpperCAmelCase__ : Optional[int] = token
UpperCAmelCase__ : Optional[Any] = None
def lowerCAmelCase__ ( self )-> Optional[Any]:
if self.dir_cache is None:
UpperCAmelCase__ : str = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
UpperCAmelCase__ : str = {
"name": hf_file.rfilename,
"size": None,
"type": "file",
}
self.dir_cache.update(
{
str(__UpperCamelCase ): {"name": str(__UpperCamelCase ), "size": None, "type": "directory"}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = "rb" , **__UpperCamelCase , )-> List[Any]:
if not isinstance(self.repo_info , __UpperCamelCase ):
raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}" )
UpperCAmelCase__ : Union[str, Any] = hf_hub_url(self.repo_info.id , __UpperCamelCase , revision=self.repo_info.sha )
return fsspec.open(
__UpperCamelCase , mode=__UpperCamelCase , headers=get_authentication_headers_for_url(__UpperCamelCase , use_auth_token=self.token ) , client_kwargs={"trust_env": True} , ).open()
def lowerCAmelCase__ ( self , __UpperCamelCase , **__UpperCamelCase )-> List[str]:
self._get_dirs()
UpperCAmelCase__ : Union[str, Any] = self._strip_protocol(__UpperCamelCase )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase=False , **__UpperCamelCase )-> str:
self._get_dirs()
UpperCAmelCase__ : str = PurePosixPath(path.strip("/" ) )
UpperCAmelCase__ : Optional[Any] = {}
for p, f in self.dir_cache.items():
UpperCAmelCase__ : Optional[int] = PurePosixPath(p.strip("/" ) )
UpperCAmelCase__ : Dict = p.parent
if root == path:
UpperCAmelCase__ : Tuple = f
UpperCAmelCase__ : List[Any] = list(paths.values() )
if detail:
return out
else:
return sorted(f["name"] for f in out )
| 660 | 0 |
A__ : Optional[int] = 8.314_4598
def a__ ( lowerCAmelCase : float , lowerCAmelCase : float ):
'''simple docstring'''
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol" )
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# example
A__ : List[Any] = 300
A__ : Dict = 28
A__ : Tuple = rms_speed_of_molecule(temperature, molar_mass)
print(f"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")
| 707 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
A__ : Dict = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
if isinstance(lowerCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowerCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowerCAmelCase ):
return [[videos]]
raise ValueError(F"Could not make batched video from {videos}" )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = ['pixel_values']
def __init__( self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_55 , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )-> None:
super().__init__(**__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = size if size is not None else {"shortest_edge": 2_56}
UpperCAmelCase__ : List[Any] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : List[str] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
UpperCAmelCase__ : int = get_size_dict(__UpperCamelCase , param_name="crop_size" )
UpperCAmelCase__ : Dict = do_resize
UpperCAmelCase__ : Optional[int] = size
UpperCAmelCase__ : List[Any] = do_center_crop
UpperCAmelCase__ : str = crop_size
UpperCAmelCase__ : Optional[int] = resample
UpperCAmelCase__ : int = do_rescale
UpperCAmelCase__ : Union[str, Any] = rescale_factor
UpperCAmelCase__ : Union[str, Any] = offset
UpperCAmelCase__ : Dict = do_normalize
UpperCAmelCase__ : int = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase__ : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
if "shortest_edge" in size:
UpperCAmelCase__ : Union[str, Any] = get_resize_output_image_size(__UpperCamelCase , size["shortest_edge"] , default_to_square=__UpperCamelCase )
elif "height" in size and "width" in size:
UpperCAmelCase__ : Any = (size["height"], size["width"])
else:
raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[Any] = get_size_dict(__UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(__UpperCamelCase , size=(size["height"], size["width"]) , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = True , __UpperCamelCase = None , **__UpperCamelCase , )-> Tuple:
UpperCAmelCase__ : str = image.astype(np.floataa )
if offset:
UpperCAmelCase__ : Tuple = image - (scale / 2)
return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
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 = ChannelDimension.FIRST , )-> np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError("Size and resample 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." )
if offset and not do_rescale:
raise ValueError("For offset, do_rescale must also be set to True." )
# All transformations expect numpy arrays.
UpperCAmelCase__ : Optional[Any] = to_numpy_array(__UpperCamelCase )
if do_resize:
UpperCAmelCase__ : Union[str, Any] = self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase )
if do_center_crop:
UpperCAmelCase__ : int = self.center_crop(__UpperCamelCase , size=__UpperCamelCase )
if do_rescale:
UpperCAmelCase__ : List[str] = self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase , offset=__UpperCamelCase )
if do_normalize:
UpperCAmelCase__ : List[Any] = self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase )
UpperCAmelCase__ : Dict = to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase )
return image
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 , )-> PIL.Image.Image:
UpperCAmelCase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ : int = resample if resample is not None else self.resample
UpperCAmelCase__ : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase__ : int = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ : Optional[int] = offset if offset is not None else self.offset
UpperCAmelCase__ : Dict = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ : Dict = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ : Optional[int] = image_std if image_std is not None else self.image_std
UpperCAmelCase__ : List[str] = size if size is not None else self.size
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : Dict = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase__ : Tuple = get_size_dict(__UpperCamelCase , param_name="crop_size" )
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." )
UpperCAmelCase__ : List[str] = make_batched(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = [
[
self._preprocess_image(
image=__UpperCamelCase , do_resize=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , do_center_crop=__UpperCamelCase , crop_size=__UpperCamelCase , do_rescale=__UpperCamelCase , rescale_factor=__UpperCamelCase , offset=__UpperCamelCase , do_normalize=__UpperCamelCase , image_mean=__UpperCamelCase , image_std=__UpperCamelCase , data_format=__UpperCamelCase , )
for img in video
]
for video in videos
]
UpperCAmelCase__ : Dict = {"pixel_values": videos}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 660 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Optional[Any] = logging.get_logger(__name__)
A__ : Dict = {
"""Salesforce/blip-vqa-base""": """https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json""",
"""Salesforce/blip-vqa-capfit-large""": (
"""https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"""
),
"""Salesforce/blip-image-captioning-base""": (
"""https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"""
),
"""Salesforce/blip-image-captioning-large""": (
"""https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"""
),
"""Salesforce/blip-itm-base-coco""": """https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json""",
"""Salesforce/blip-itm-large-coco""": """https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json""",
"""Salesforce/blip-itm-base-flikr""": """https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json""",
"""Salesforce/blip-itm-large-flikr""": (
"""https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"""
),
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'blip_text_model'
def __init__( self , __UpperCamelCase=3_05_24 , __UpperCamelCase=7_68 , __UpperCamelCase=7_68 , __UpperCamelCase=30_72 , __UpperCamelCase=7_68 , __UpperCamelCase=12 , __UpperCamelCase=8 , __UpperCamelCase=5_12 , __UpperCamelCase="gelu" , __UpperCamelCase=1E-12 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.02 , __UpperCamelCase=3_05_22 , __UpperCamelCase=2 , __UpperCamelCase=0 , __UpperCamelCase=1_02 , __UpperCamelCase=True , __UpperCamelCase=True , **__UpperCamelCase , )-> Union[str, Any]:
super().__init__(
pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , sep_token_id=__UpperCamelCase , **__UpperCamelCase , )
UpperCAmelCase__ : Tuple = vocab_size
UpperCAmelCase__ : Any = hidden_size
UpperCAmelCase__ : List[str] = encoder_hidden_size
UpperCAmelCase__ : List[Any] = intermediate_size
UpperCAmelCase__ : Tuple = projection_dim
UpperCAmelCase__ : List[Any] = hidden_dropout_prob
UpperCAmelCase__ : List[Any] = num_hidden_layers
UpperCAmelCase__ : List[Any] = num_attention_heads
UpperCAmelCase__ : Any = max_position_embeddings
UpperCAmelCase__ : Any = layer_norm_eps
UpperCAmelCase__ : List[Any] = hidden_act
UpperCAmelCase__ : Any = initializer_range
UpperCAmelCase__ : Tuple = attention_probs_dropout_prob
UpperCAmelCase__ : Tuple = is_decoder
UpperCAmelCase__ : str = use_cache
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , **__UpperCamelCase )-> "PretrainedConfig":
cls._set_token_in_kwargs(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = cls.get_config_dict(__UpperCamelCase , **__UpperCamelCase )
# get the text config dict if we are loading from BlipConfig
if config_dict.get("model_type" ) == "blip":
UpperCAmelCase__ : 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(__UpperCamelCase , **__UpperCamelCase )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'blip_vision_model'
def __init__( self , __UpperCamelCase=7_68 , __UpperCamelCase=30_72 , __UpperCamelCase=5_12 , __UpperCamelCase=12 , __UpperCamelCase=12 , __UpperCamelCase=3_84 , __UpperCamelCase=16 , __UpperCamelCase="gelu" , __UpperCamelCase=1E-5 , __UpperCamelCase=0.0 , __UpperCamelCase=1E-10 , **__UpperCamelCase , )-> List[str]:
super().__init__(**__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = hidden_size
UpperCAmelCase__ : Optional[Any] = intermediate_size
UpperCAmelCase__ : Optional[Any] = projection_dim
UpperCAmelCase__ : Any = num_hidden_layers
UpperCAmelCase__ : List[Any] = num_attention_heads
UpperCAmelCase__ : List[Any] = patch_size
UpperCAmelCase__ : List[Any] = image_size
UpperCAmelCase__ : int = initializer_range
UpperCAmelCase__ : Optional[int] = attention_dropout
UpperCAmelCase__ : Any = layer_norm_eps
UpperCAmelCase__ : List[str] = hidden_act
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , **__UpperCamelCase )-> "PretrainedConfig":
cls._set_token_in_kwargs(__UpperCamelCase )
UpperCAmelCase__ : Tuple = cls.get_config_dict(__UpperCamelCase , **__UpperCamelCase )
# get the vision config dict if we are loading from BlipConfig
if config_dict.get("model_type" ) == "blip":
UpperCAmelCase__ : Dict = 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(__UpperCamelCase , **__UpperCamelCase )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'blip'
_A = True
def __init__( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=5_12 , __UpperCamelCase=2.6592 , __UpperCamelCase=2_56 , **__UpperCamelCase , )-> Optional[Any]:
super().__init__(**__UpperCamelCase )
if text_config is None:
UpperCAmelCase__ : Dict = {}
logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values." )
if vision_config is None:
UpperCAmelCase__ : Tuple = {}
logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values." )
UpperCAmelCase__ : Dict = BlipTextConfig(**__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = BlipVisionConfig(**__UpperCamelCase )
UpperCAmelCase__ : Tuple = self.vision_config.hidden_size
UpperCAmelCase__ : Optional[int] = projection_dim
UpperCAmelCase__ : List[Any] = logit_scale_init_value
UpperCAmelCase__ : Tuple = 1.0
UpperCAmelCase__ : List[Any] = 0.02
UpperCAmelCase__ : List[Any] = image_text_hidden_size
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )-> Optional[int]:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ : str = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : Optional[Any] = self.text_config.to_dict()
UpperCAmelCase__ : Any = self.vision_config.to_dict()
UpperCAmelCase__ : Optional[int] = self.__class__.model_type
return output
| 708 |
"""simple docstring"""
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowerCAmelCase , lowerCAmelCase ):
raise TypeError("Input value must be a 'int' type" )
return bin(lowerCAmelCase ).count("1" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class _lowercase :
'''simple docstring'''
_A = 42
# setable values
_A = 42
_A = 42
_A = None
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
return cls(common=__UpperCamelCase , init_noise_sigma=__UpperCamelCase , timesteps=__UpperCamelCase )
@dataclass
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
_A = [e.name for e in FlaxKarrasDiffusionSchedulers]
_A = 42
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
return True
@register_to_config
def __init__( self , __UpperCamelCase = 10_00 , __UpperCamelCase = 0.0001 , __UpperCamelCase = 0.02 , __UpperCamelCase = "linear" , __UpperCamelCase = None , __UpperCamelCase = "fixed_small" , __UpperCamelCase = True , __UpperCamelCase = "epsilon" , __UpperCamelCase = jnp.floataa , )-> List[str]:
UpperCAmelCase__ : int = dtype
def lowerCAmelCase__ ( self , __UpperCamelCase = None )-> DDPMSchedulerState:
if common is None:
UpperCAmelCase__ : int = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
UpperCAmelCase__ : Tuple = jnp.array(1.0 , dtype=self.dtype )
UpperCAmelCase__ : Tuple = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=__UpperCamelCase , init_noise_sigma=__UpperCamelCase , timesteps=__UpperCamelCase , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None )-> jnp.ndarray:
return sample
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = () )-> DDPMSchedulerState:
UpperCAmelCase__ : Dict = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
UpperCAmelCase__ : Optional[int] = (jnp.arange(0 , __UpperCamelCase ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=__UpperCamelCase , timesteps=__UpperCamelCase , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = state.common.alphas_cumprod[t]
UpperCAmelCase__ : int = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase__ : Any = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
UpperCAmelCase__ : Union[str, Any] = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
UpperCAmelCase__ : Dict = jnp.clip(__UpperCamelCase , a_min=1E-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
UpperCAmelCase__ : Tuple = jnp.log(jnp.clip(__UpperCamelCase , a_min=1E-20 ) )
elif variance_type == "fixed_large":
UpperCAmelCase__ : Union[str, Any] = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
UpperCAmelCase__ : Optional[int] = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
UpperCAmelCase__ : List[str] = variance
UpperCAmelCase__ : Union[str, Any] = state.common.betas[t]
UpperCAmelCase__ : Optional[int] = (predicted_variance + 1) / 2
UpperCAmelCase__ : Any = frac * max_log + (1 - frac) * min_log
return variance
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = True , )-> Union[FlaxDDPMSchedulerOutput, Tuple]:
UpperCAmelCase__ : List[str] = timestep
if key is None:
UpperCAmelCase__ : int = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
UpperCAmelCase__ : List[str] = jnp.split(__UpperCamelCase , sample.shape[1] , axis=1 )
else:
UpperCAmelCase__ : Optional[Any] = None
# 1. compute alphas, betas
UpperCAmelCase__ : Union[str, Any] = state.common.alphas_cumprod[t]
UpperCAmelCase__ : Tuple = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
UpperCAmelCase__ : Union[str, Any] = 1 - alpha_prod_t
UpperCAmelCase__ : Tuple = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase__ : List[Any] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase__ : Any = model_output
elif self.config.prediction_type == "v_prediction":
UpperCAmelCase__ : Union[str, Any] = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
" for the FlaxDDPMScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase__ : List[Any] = jnp.clip(__UpperCamelCase , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ : List[str] = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
UpperCAmelCase__ : List[Any] = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ : Tuple = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
UpperCAmelCase__ : Any = jax.random.split(__UpperCamelCase , num=1 )
UpperCAmelCase__ : int = jax.random.normal(__UpperCamelCase , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(__UpperCamelCase , __UpperCamelCase , predicted_variance=__UpperCamelCase ) ** 0.5) * noise
UpperCAmelCase__ : Dict = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
UpperCAmelCase__ : Any = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase , state=__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> jnp.ndarray:
return add_noise_common(state.common , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> jnp.ndarray:
return get_velocity_common(state.common , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __len__( self )-> Tuple:
return self.config.num_train_timesteps
| 709 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
A__ : Optional[Any] = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : bool , lowerCAmelCase : bool ):
'''simple docstring'''
def run_func(lowerCAmelCase : Dict ):
@wraps(lowerCAmelCase )
def run_in_eager_mode(*lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Dict ):
return func(*lowerCAmelCase , **lowerCAmelCase )
@wraps(lowerCAmelCase )
@tf.function(experimental_compile=lowerCAmelCase )
def run_in_graph_mode(*lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Optional[Any] ):
return func(*lowerCAmelCase , **lowerCAmelCase )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`." )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def a__ ( lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Dict = random.Random()
UpperCAmelCase__ : List[str] = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(lowerCAmelCase , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
_A = 42
_A = "TensorFlow"
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
return tf.__version__
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
# initialize GPU on separate process
UpperCAmelCase__ : Any = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Union[str, Any] = self._prepare_inference_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_speed(_inference )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
UpperCAmelCase__ : List[Any] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : List[Any] = self._prepare_train_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_speed(_train )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> [Memory, Optional[MemorySummary]]:
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __UpperCamelCase )
UpperCAmelCase__ : List[str] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Any = self._prepare_inference_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_memory(_inference )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> [Memory, Optional[MemorySummary]]:
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __UpperCamelCase )
UpperCAmelCase__ : Any = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Optional[Any] = self._prepare_train_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_memory(_train )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Callable[[], None]:
UpperCAmelCase__ : Union[str, Any] = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
UpperCAmelCase__ : Optional[int] = (
hasattr(__UpperCamelCase , "architectures" )
and isinstance(config.architectures , __UpperCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCAmelCase__ : str = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCAmelCase__ : Any = __import__("transformers" , fromlist=[model_class] )
UpperCAmelCase__ : List[Any] = getattr(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Dict = model_cls(__UpperCamelCase )
except ImportError:
raise ImportError(
F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
UpperCAmelCase__ : Any = TF_MODEL_MAPPING[config.__class__](__UpperCamelCase )
# encoder-decoder has vocab size saved differently
UpperCAmelCase__ : int = config.vocab_size if hasattr(__UpperCamelCase , "vocab_size" ) else config.encoder.vocab_size
UpperCAmelCase__ : Optional[Any] = random_input_ids(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(__UpperCamelCase , decoder_input_ids=__UpperCamelCase , training=__UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(__UpperCamelCase , training=__UpperCamelCase )
UpperCAmelCase__ : Dict = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Callable[[], None]:
UpperCAmelCase__ : List[Any] = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`." )
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
UpperCAmelCase__ : Any = (
hasattr(__UpperCamelCase , "architectures" )
and isinstance(config.architectures , __UpperCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCAmelCase__ : Any = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCAmelCase__ : int = __import__("transformers" , fromlist=[model_class] )
UpperCAmelCase__ : int = getattr(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = model_cls(__UpperCamelCase )
except ImportError:
raise ImportError(
F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
UpperCAmelCase__ : List[str] = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCamelCase )
# encoder-decoder has vocab size saved differently
UpperCAmelCase__ : Union[str, Any] = config.vocab_size if hasattr(__UpperCamelCase , "vocab_size" ) else config.encoder.vocab_size
UpperCAmelCase__ : Dict = random_input_ids(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
UpperCAmelCase__ : Union[str, Any] = model(__UpperCamelCase , decoder_input_ids=__UpperCamelCase , labels=__UpperCamelCase , training=__UpperCamelCase )[0]
UpperCAmelCase__ : Union[str, Any] = tf.gradients(__UpperCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
UpperCAmelCase__ : Union[str, Any] = model(__UpperCamelCase , labels=__UpperCamelCase , training=__UpperCamelCase )[0]
UpperCAmelCase__ : Any = tf.gradients(__UpperCamelCase , model.trainable_variables )
return gradients
UpperCAmelCase__ : str = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def lowerCAmelCase__ ( self , __UpperCamelCase )-> float:
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("Do inference on TPU. Running model 5 times to stabilize compilation" )
timeit.repeat(__UpperCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
UpperCAmelCase__ : Optional[Any] = timeit.repeat(
__UpperCamelCase , repeat=self.args.repeat , number=10 , )
return min(__UpperCamelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(F"Doesn't fit on GPU. {e}" )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> [Memory, MemorySummary]:
logger.info(
"Note that TensorFlow allocates more memory than "
"it might need to speed up computation. "
"The memory reported here corresponds to the memory "
"reported by `nvidia-smi`, which can vary depending "
"on total available memory on the GPU that is used." )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
" consumption line by line." )
UpperCAmelCase__ : List[str] = start_memory_tracing("transformers" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
" with `args.memory=False`" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"py3nvml not installed, we won't log GPU memory usage. "
"Install py3nvml (pip install py3nvml) to log information about GPU." )
UpperCAmelCase__ : Optional[int] = "N/A"
else:
logger.info(
"Measuring total GPU usage on GPU device. Make sure to not have additional processes"
" running on the same GPU." )
# init nvml
nvml.nvmlInit()
func()
UpperCAmelCase__ : Any = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
UpperCAmelCase__ : Optional[int] = nvml.nvmlDeviceGetMemoryInfo(__UpperCamelCase )
UpperCAmelCase__ : str = meminfo.used
UpperCAmelCase__ : int = Memory(__UpperCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
" TensorFlow." )
UpperCAmelCase__ : Any = None
else:
UpperCAmelCase__ : List[Any] = measure_peak_memory_cpu(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = Memory(__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
UpperCAmelCase__ : Optional[Any] = stop_memory_tracing(__UpperCamelCase )
if memory is None:
UpperCAmelCase__ : Tuple = summary.total
else:
UpperCAmelCase__ : int = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(F"Doesn't fit on GPU. {e}" )
return "N/A", None
| 660 | 0 |
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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 transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : List[str] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__UpperCamelCase , "embed_dim" ) )
self.parent.assertTrue(hasattr(__UpperCamelCase , "num_heads" ) )
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=64 , __UpperCamelCase=3 , __UpperCamelCase=[16, 48, 96] , __UpperCamelCase=[1, 3, 6] , __UpperCamelCase=[1, 2, 10] , __UpperCamelCase=[7, 3, 3] , __UpperCamelCase=[4, 2, 2] , __UpperCamelCase=[2, 1, 1] , __UpperCamelCase=[2, 2, 2] , __UpperCamelCase=[False, False, True] , __UpperCamelCase=[0.0, 0.0, 0.0] , __UpperCamelCase=0.02 , __UpperCamelCase=1E-12 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=2 , )-> List[Any]:
UpperCAmelCase__ : Tuple = parent
UpperCAmelCase__ : Any = batch_size
UpperCAmelCase__ : Optional[Any] = image_size
UpperCAmelCase__ : Tuple = patch_sizes
UpperCAmelCase__ : List[Any] = patch_stride
UpperCAmelCase__ : Union[str, Any] = patch_padding
UpperCAmelCase__ : int = is_training
UpperCAmelCase__ : Any = use_labels
UpperCAmelCase__ : Union[str, Any] = num_labels
UpperCAmelCase__ : List[str] = num_channels
UpperCAmelCase__ : Dict = embed_dim
UpperCAmelCase__ : Union[str, Any] = num_heads
UpperCAmelCase__ : Union[str, Any] = stride_kv
UpperCAmelCase__ : Tuple = depth
UpperCAmelCase__ : Any = cls_token
UpperCAmelCase__ : Optional[int] = attention_drop_rate
UpperCAmelCase__ : int = initializer_range
UpperCAmelCase__ : List[Any] = layer_norm_eps
def lowerCAmelCase__ ( self )-> int:
UpperCAmelCase__ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : int = None
if self.use_labels:
UpperCAmelCase__ : str = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase__ : Optional[int] = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self )-> Any:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Optional[Any]:
UpperCAmelCase__ : Any = CvtModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : Optional[int] = model(__UpperCamelCase )
UpperCAmelCase__ : Any = (self.image_size, self.image_size)
UpperCAmelCase__ : str = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
UpperCAmelCase__ : Dict = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
UpperCAmelCase__ : Optional[Any] = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> int:
UpperCAmelCase__ : str = self.num_labels
UpperCAmelCase__ : List[Any] = CvtForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : Tuple = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase__ ( self )-> int:
UpperCAmelCase__ : int = self.prepare_config_and_inputs()
UpperCAmelCase__ : Tuple = config_and_inputs
UpperCAmelCase__ : Optional[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_A = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
_A = (
{'feature-extraction': CvtModel, 'image-classification': CvtForImageClassification}
if is_torch_available()
else {}
)
_A = False
_A = False
_A = False
_A = False
_A = False
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : Dict = CvtModelTester(self )
UpperCAmelCase__ : str = ConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase , hidden_size=37 )
def lowerCAmelCase__ ( self )-> List[Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCAmelCase__ ( self )-> Union[str, Any]:
return
@unittest.skip(reason="Cvt does not output attentions" )
def lowerCAmelCase__ ( self )-> Optional[int]:
pass
@unittest.skip(reason="Cvt does not use inputs_embeds" )
def lowerCAmelCase__ ( self )-> Optional[Any]:
pass
@unittest.skip(reason="Cvt does not support input and output embeddings" )
def lowerCAmelCase__ ( self )-> Optional[int]:
pass
def lowerCAmelCase__ ( self )-> Optional[int]:
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Any = model_class(__UpperCamelCase )
UpperCAmelCase__ : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Dict = [*signature.parameters.keys()]
UpperCAmelCase__ : List[str] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def lowerCAmelCase__ ( self )-> str:
def check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : Tuple = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
UpperCAmelCase__ : str = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
UpperCAmelCase__ : int = outputs.hidden_states
UpperCAmelCase__ : Any = len(self.model_tester.depth )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Dict = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase__ : str = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowerCAmelCase__ ( self )-> int:
pass
@slow
def lowerCAmelCase__ ( self )-> Optional[int]:
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : str = CvtModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase__ ( self )-> List[Any]:
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : Tuple = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(__UpperCamelCase )
UpperCAmelCase__ : Dict = self.default_image_processor
UpperCAmelCase__ : Any = prepare_img()
UpperCAmelCase__ : Optional[int] = image_processor(images=__UpperCamelCase , return_tensors="pt" ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Tuple = model(**__UpperCamelCase )
# verify the logits
UpperCAmelCase__ : Optional[Any] = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
UpperCAmelCase__ : Dict = torch.tensor([0.9285, 0.9015, -0.3150] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
| 710 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
A__ : List[str] = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
A__ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 660 | 0 |
"""simple docstring"""
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__ : List[Any] = """bert-base-cased"""
A__ : Any = """google/pegasus-xsum"""
A__ : str = [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
A__ : str = ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
A__ : str = """patrickvonplaten/t5-tiny-random"""
A__ : List[str] = """sshleifer/bart-tiny-random"""
A__ : Optional[int] = """sshleifer/tiny-mbart"""
A__ : str = """sshleifer/tiny-marian-en-de"""
def a__ ( lowerCAmelCase : Path , lowerCAmelCase : list ):
'''simple docstring'''
UpperCAmelCase__ : str = "\n".join(lowerCAmelCase )
Path(lowerCAmelCase ).open("w" ).writelines(lowerCAmelCase )
def a__ ( lowerCAmelCase : str ):
'''simple docstring'''
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(lowerCAmelCase , F"{split}.source" ) , lowerCAmelCase )
_dump_articles(os.path.join(lowerCAmelCase , F"{split}.target" ) , lowerCAmelCase )
return tmp_dir
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Dict:
'''simple docstring'''
UpperCAmelCase__ : Any = AutoTokenizer.from_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Tuple = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
UpperCAmelCase__ : Optional[Any] = max(len(tokenizer.encode(__UpperCamelCase ) ) for a in ARTICLES )
UpperCAmelCase__ : Tuple = max(len(tokenizer.encode(__UpperCamelCase ) ) for a in SUMMARIES )
UpperCAmelCase__ : Dict = 4
UpperCAmelCase__ : Tuple = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
UpperCAmelCase__ : List[str] = "ro_RO", "de_DE" # ignored for all but mbart, but never causes error.
UpperCAmelCase__ : Dict = SeqaSeqDataset(
__UpperCamelCase , data_dir=__UpperCamelCase , type_path="train" , max_source_length=__UpperCamelCase , max_target_length=__UpperCamelCase , src_lang=__UpperCamelCase , tgt_lang=__UpperCamelCase , )
UpperCAmelCase__ : str = 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
UpperCAmelCase__ : List[Any] = 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 )-> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : Tuple = AutoTokenizer.from_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
UpperCAmelCase__ : Union[str, Any] = max(len(tokenizer.encode(__UpperCamelCase ) ) for a in ARTICLES )
UpperCAmelCase__ : Any = max(len(tokenizer.encode(__UpperCamelCase ) ) for a in SUMMARIES )
UpperCAmelCase__ : List[Any] = 4
UpperCAmelCase__ : Tuple = LegacySeqaSeqDataset(
__UpperCamelCase , data_dir=__UpperCamelCase , type_path="train" , max_source_length=20 , max_target_length=__UpperCamelCase , )
UpperCAmelCase__ : str = 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 )-> Dict:
'''simple docstring'''
UpperCAmelCase__ : Tuple = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25" )
UpperCAmelCase__ : str = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
UpperCAmelCase__ : Dict = tmp_dir.joinpath("train.source" ).open().readlines()
UpperCAmelCase__ : Tuple = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(__UpperCamelCase , __UpperCamelCase , 1_28 , __UpperCamelCase )
UpperCAmelCase__ : Dict = {x.name for x in tmp_dir.iterdir()}
UpperCAmelCase__ : Any = {x.name for x in save_dir.iterdir()}
UpperCAmelCase__ : Dict = 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 )-> Optional[int]:
'''simple docstring'''
if not FAIRSEQ_AVAILABLE:
return
UpperCAmelCase__ : Tuple = self._get_dataset(max_len=64 )
UpperCAmelCase__ : List[str] = 64
UpperCAmelCase__ : Optional[int] = ds.make_dynamic_sampler(__UpperCamelCase , required_batch_size_multiple=__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = [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
UpperCAmelCase__ : str = DataLoader(__UpperCamelCase , batch_sampler=__UpperCamelCase , collate_fn=ds.collate_fn , num_workers=2 )
UpperCAmelCase__ : Tuple = []
UpperCAmelCase__ : str = []
for batch in data_loader:
UpperCAmelCase__ : Optional[int] = batch["input_ids"].shape
UpperCAmelCase__ : List[str] = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
UpperCAmelCase__ : List[Any] = 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 )-> Tuple:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self._get_dataset(max_len=5_12 )
UpperCAmelCase__ : Tuple = 2
UpperCAmelCase__ : Optional[Any] = ds.make_sortish_sampler(__UpperCamelCase , shuffle=__UpperCamelCase )
UpperCAmelCase__ : int = DataLoader(__UpperCamelCase , batch_size=__UpperCamelCase , collate_fn=ds.collate_fn , num_workers=2 )
UpperCAmelCase__ : int = DataLoader(__UpperCamelCase , batch_size=__UpperCamelCase , collate_fn=ds.collate_fn , num_workers=2 , sampler=__UpperCamelCase )
UpperCAmelCase__ : str = 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=10_00 , __UpperCamelCase=1_28 )-> Any:
'''simple docstring'''
if os.getenv("USE_REAL_DATA" , __UpperCamelCase ):
UpperCAmelCase__ : Optional[int] = "examples/seq2seq/wmt_en_ro"
UpperCAmelCase__ : Optional[int] = max_len * 2 * 64
if not Path(__UpperCamelCase ).joinpath("train.len" ).exists():
save_len_file(__UpperCamelCase , __UpperCamelCase )
else:
UpperCAmelCase__ : List[Any] = "examples/seq2seq/test_data/wmt_en_ro"
UpperCAmelCase__ : Any = max_len * 4
save_len_file(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained(__UpperCamelCase )
UpperCAmelCase__ : List[str] = 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 )-> Dict:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self._get_dataset()
UpperCAmelCase__ : Any = set(DistributedSortishSampler(__UpperCamelCase , 2_56 , num_replicas=2 , rank=0 , add_extra_examples=__UpperCamelCase ) )
UpperCAmelCase__ : int = set(DistributedSortishSampler(__UpperCamelCase , 2_56 , 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 )-> str:
'''simple docstring'''
UpperCAmelCase__ : Tuple = AutoTokenizer.from_pretrained(__UpperCamelCase , use_fast=__UpperCamelCase )
if tok_name == MBART_TINY:
UpperCAmelCase__ : str = 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" , )
UpperCAmelCase__ : Dict = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
UpperCAmelCase__ : Optional[int] = 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 , )
UpperCAmelCase__ : Optional[Any] = 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
| 711 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
@register_to_config
def __init__( self , __UpperCamelCase = 7_68 , )-> Union[str, Any]:
super().__init__()
UpperCAmelCase__ : str = nn.Parameter(torch.zeros(1 , __UpperCamelCase ) )
UpperCAmelCase__ : Optional[int] = nn.Parameter(torch.ones(1 , __UpperCamelCase ) )
def lowerCAmelCase__ ( self , __UpperCamelCase = None , __UpperCamelCase = None , )-> Any:
UpperCAmelCase__ : Dict = nn.Parameter(self.mean.to(__UpperCamelCase ).to(__UpperCamelCase ) )
UpperCAmelCase__ : Any = nn.Parameter(self.std.to(__UpperCamelCase ).to(__UpperCamelCase ) )
return self
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Union[str, Any]:
UpperCAmelCase__ : Dict = (embeds - self.mean) * 1.0 / self.std
return embeds
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[Any]:
UpperCAmelCase__ : Any = (embeds * self.std) + self.mean
return embeds
| 660 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_A = StableDiffusionDiffEditPipeline
_A = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
_A = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
_A = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_A = frozenset([] )
def lowerCAmelCase__ ( self )-> Any:
torch.manual_seed(0 )
UpperCAmelCase__ : Union[str, Any] = 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 , attention_head_dim=(2, 4) , use_linear_projection=__UpperCamelCase , )
UpperCAmelCase__ : int = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=__UpperCamelCase , set_alpha_to_one=__UpperCamelCase , )
UpperCAmelCase__ : Optional[Any] = DDIMInverseScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=__UpperCamelCase , set_alpha_to_zero=__UpperCamelCase , )
torch.manual_seed(0 )
UpperCAmelCase__ : Dict = 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 , sample_size=1_28 , )
torch.manual_seed(0 )
UpperCAmelCase__ : Tuple = 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=10_00 , hidden_act="gelu" , projection_dim=5_12 , )
UpperCAmelCase__ : Optional[int] = CLIPTextModel(__UpperCamelCase )
UpperCAmelCase__ : int = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
UpperCAmelCase__ : str = {
"unet": unet,
"scheduler": scheduler,
"inverse_scheduler": inverse_scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase=0 )-> List[Any]:
UpperCAmelCase__ : Optional[Any] = floats_tensor((1, 16, 16) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
if str(__UpperCamelCase ).startswith("mps" ):
UpperCAmelCase__ : List[Any] = torch.manual_seed(__UpperCamelCase )
else:
UpperCAmelCase__ : str = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = {
"prompt": "a dog and a newt",
"mask_image": mask,
"image_latents": latents,
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase=0 )-> str:
UpperCAmelCase__ : str = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase__ : Union[str, Any] = Image.fromarray(np.uinta(__UpperCamelCase ) ).convert("RGB" )
if str(__UpperCamelCase ).startswith("mps" ):
UpperCAmelCase__ : str = torch.manual_seed(__UpperCamelCase )
else:
UpperCAmelCase__ : Optional[Any] = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
UpperCAmelCase__ : List[str] = {
"image": image,
"source_prompt": "a cat and a frog",
"target_prompt": "a dog and a newt",
"generator": generator,
"num_inference_steps": 2,
"num_maps_per_mask": 2,
"mask_encode_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase=0 )-> str:
UpperCAmelCase__ : List[str] = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
UpperCAmelCase__ : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase__ : Tuple = Image.fromarray(np.uinta(__UpperCamelCase ) ).convert("RGB" )
if str(__UpperCamelCase ).startswith("mps" ):
UpperCAmelCase__ : Optional[int] = torch.manual_seed(__UpperCamelCase )
else:
UpperCAmelCase__ : Optional[Any] = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = {
"image": image,
"prompt": "a cat and a frog",
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"decode_latents": True,
"output_type": "numpy",
}
return inputs
def lowerCAmelCase__ ( self )-> Union[str, Any]:
if not hasattr(self.pipeline_class , "_optional_components" ):
return
UpperCAmelCase__ : List[Any] = self.get_dummy_components()
UpperCAmelCase__ : List[str] = self.pipeline_class(**__UpperCamelCase )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
UpperCAmelCase__ : Optional[Any] = self.get_dummy_inputs(__UpperCamelCase )
UpperCAmelCase__ : Tuple = pipe(**__UpperCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = self.pipeline_class.from_pretrained(__UpperCamelCase )
pipe_loaded.to(__UpperCamelCase )
pipe_loaded.set_progress_bar_config(disable=__UpperCamelCase )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(__UpperCamelCase , __UpperCamelCase ) is None , F"`{optional_component}` did not stay set to None after loading." , )
UpperCAmelCase__ : Dict = self.get_dummy_inputs(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = pipe_loaded(**__UpperCamelCase )[0]
UpperCAmelCase__ : str = np.abs(output - output_loaded ).max()
self.assertLess(__UpperCamelCase , 1E-4 )
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : List[Any] = "cpu"
UpperCAmelCase__ : List[Any] = self.get_dummy_components()
UpperCAmelCase__ : Union[str, Any] = self.pipeline_class(**__UpperCamelCase )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = self.get_dummy_mask_inputs(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = pipe.generate_mask(**__UpperCamelCase )
UpperCAmelCase__ : Dict = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
UpperCAmelCase__ : Any = np.array([0] * 9 )
UpperCAmelCase__ : List[Any] = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__UpperCamelCase , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : List[str] = "cpu"
UpperCAmelCase__ : str = self.get_dummy_components()
UpperCAmelCase__ : Dict = self.pipeline_class(**__UpperCamelCase )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase__ : int = self.get_dummy_inversion_inputs(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = pipe.invert(**__UpperCamelCase ).images
UpperCAmelCase__ : int = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
UpperCAmelCase__ : str = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] , )
UpperCAmelCase__ : str = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__UpperCamelCase , 1E-3 )
def lowerCAmelCase__ ( self )-> Dict:
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : Union[str, Any] = "cpu"
UpperCAmelCase__ : Dict = self.get_dummy_components()
UpperCAmelCase__ : List[str] = {"beta_start": 0.0_0085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
UpperCAmelCase__ : Any = DPMSolverMultistepScheduler(**__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = DPMSolverMultistepInverseScheduler(**__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = self.pipeline_class(**__UpperCamelCase )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = self.get_dummy_inversion_inputs(__UpperCamelCase )
UpperCAmelCase__ : List[str] = pipe.invert(**__UpperCamelCase ).images
UpperCAmelCase__ : Any = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
UpperCAmelCase__ : Any = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] , )
UpperCAmelCase__ : Union[str, Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__UpperCamelCase , 1E-3 )
@require_torch_gpu
@slow
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Union[str, Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def lowerCAmelCase__ ( cls )-> Any:
UpperCAmelCase__ : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png" )
UpperCAmelCase__ : Tuple = raw_image.convert("RGB" ).resize((7_68, 7_68) )
UpperCAmelCase__ : int = raw_image
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : Any = torch.manual_seed(0 )
UpperCAmelCase__ : Dict = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=__UpperCamelCase , torch_dtype=torch.floataa )
UpperCAmelCase__ : Optional[Any] = DDIMScheduler.from_config(pipe.scheduler.config )
UpperCAmelCase__ : Optional[int] = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = "a bowl of fruit"
UpperCAmelCase__ : Optional[Any] = "a bowl of pears"
UpperCAmelCase__ : Optional[Any] = pipe.generate_mask(
image=self.raw_image , source_prompt=__UpperCamelCase , target_prompt=__UpperCamelCase , generator=__UpperCamelCase , )
UpperCAmelCase__ : Union[str, Any] = pipe.invert(
prompt=__UpperCamelCase , image=self.raw_image , inpaint_strength=0.7 , generator=__UpperCamelCase ).latents
UpperCAmelCase__ : Any = pipe(
prompt=__UpperCamelCase , mask_image=__UpperCamelCase , image_latents=__UpperCamelCase , generator=__UpperCamelCase , negative_prompt=__UpperCamelCase , inpaint_strength=0.7 , output_type="numpy" , ).images[0]
UpperCAmelCase__ : Tuple = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((7_68, 7_68) ) )
/ 2_55
)
assert np.abs((expected_image - image).max() ) < 5E-1
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : Dict = torch.manual_seed(0 )
UpperCAmelCase__ : Optional[Any] = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=__UpperCamelCase , torch_dtype=torch.floataa )
UpperCAmelCase__ : str = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
UpperCAmelCase__ : Optional[Any] = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase__ : Tuple = "a bowl of fruit"
UpperCAmelCase__ : List[Any] = "a bowl of pears"
UpperCAmelCase__ : Optional[Any] = pipe.generate_mask(
image=self.raw_image , source_prompt=__UpperCamelCase , target_prompt=__UpperCamelCase , generator=__UpperCamelCase , )
UpperCAmelCase__ : Any = pipe.invert(
prompt=__UpperCamelCase , image=self.raw_image , inpaint_strength=0.7 , generator=__UpperCamelCase , num_inference_steps=25 , ).latents
UpperCAmelCase__ : Any = pipe(
prompt=__UpperCamelCase , mask_image=__UpperCamelCase , image_latents=__UpperCamelCase , generator=__UpperCamelCase , negative_prompt=__UpperCamelCase , inpaint_strength=0.7 , num_inference_steps=25 , output_type="numpy" , ).images[0]
UpperCAmelCase__ : Optional[Any] = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((7_68, 7_68) ) )
/ 2_55
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 712 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : Any , lowerCAmelCase : List[Any] ):
'''simple docstring'''
# Construct model
if gpta_config_file == "":
UpperCAmelCase__ : Optional[int] = GPTaConfig()
else:
UpperCAmelCase__ : Dict = GPTaConfig.from_json_file(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = GPTaModel(lowerCAmelCase )
# Load weights from numpy
load_tf_weights_in_gpta(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Save pytorch-model
UpperCAmelCase__ : Optional[int] = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
UpperCAmelCase__ : Any = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(model.state_dict() , lowerCAmelCase )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(lowerCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
A__ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--gpt2_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained OpenAI model. \n"""
"""This specifies the model architecture."""
),
)
A__ : Optional[Any] = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 660 | 0 |
"""simple docstring"""
import warnings
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
A__ : str = logging.get_logger(__name__)
A__ : str = {
"""nvidia/segformer-b0-finetuned-ade-512-512""": (
"""https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'segformer'
def __init__( self , __UpperCamelCase=3 , __UpperCamelCase=4 , __UpperCamelCase=[2, 2, 2, 2] , __UpperCamelCase=[8, 4, 2, 1] , __UpperCamelCase=[32, 64, 1_60, 2_56] , __UpperCamelCase=[7, 3, 3, 3] , __UpperCamelCase=[4, 2, 2, 2] , __UpperCamelCase=[1, 2, 5, 8] , __UpperCamelCase=[4, 4, 4, 4] , __UpperCamelCase="gelu" , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase=0.02 , __UpperCamelCase=0.1 , __UpperCamelCase=1E-6 , __UpperCamelCase=2_56 , __UpperCamelCase=2_55 , **__UpperCamelCase , )-> Optional[int]:
super().__init__(**__UpperCamelCase )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
"Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
" removed, as the behaviour will default to that of reshape_last_stage = True." , __UpperCamelCase , )
UpperCAmelCase__ : Optional[int] = num_channels
UpperCAmelCase__ : Union[str, Any] = num_encoder_blocks
UpperCAmelCase__ : Tuple = depths
UpperCAmelCase__ : List[str] = sr_ratios
UpperCAmelCase__ : Union[str, Any] = hidden_sizes
UpperCAmelCase__ : Any = patch_sizes
UpperCAmelCase__ : List[Any] = strides
UpperCAmelCase__ : Optional[int] = mlp_ratios
UpperCAmelCase__ : Tuple = num_attention_heads
UpperCAmelCase__ : Any = hidden_act
UpperCAmelCase__ : Optional[int] = hidden_dropout_prob
UpperCAmelCase__ : Union[str, Any] = attention_probs_dropout_prob
UpperCAmelCase__ : Any = classifier_dropout_prob
UpperCAmelCase__ : Dict = initializer_range
UpperCAmelCase__ : Tuple = drop_path_rate
UpperCAmelCase__ : Optional[int] = layer_norm_eps
UpperCAmelCase__ : List[Any] = decoder_hidden_size
UpperCAmelCase__ : int = kwargs.get("reshape_last_stage" , __UpperCamelCase )
UpperCAmelCase__ : List[Any] = semantic_loss_ignore_index
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = version.parse('1.11' )
@property
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def lowerCAmelCase__ ( self )-> float:
return 1E-4
@property
def lowerCAmelCase__ ( self )-> int:
return 12
| 713 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
A__ : Optional[int] = ["""small""", """medium""", """large"""]
A__ : Optional[int] = """lm_head.decoder.weight"""
A__ : Dict = """lm_head.weight"""
def a__ ( lowerCAmelCase : str , lowerCAmelCase : str ):
'''simple docstring'''
UpperCAmelCase__ : Dict = torch.load(lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = d.pop(lowerCAmelCase )
os.makedirs(lowerCAmelCase , exist_ok=lowerCAmelCase )
torch.save(lowerCAmelCase , os.path.join(lowerCAmelCase , lowerCAmelCase ) )
if __name__ == "__main__":
A__ : List[Any] = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
A__ : Tuple = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
A__ : Tuple = os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
A__ : str = f"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 660 | 0 |
"""simple docstring"""
from __future__ import annotations
def a__ ( lowerCAmelCase : str , lowerCAmelCase : str ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = get_failure_array(lowerCAmelCase )
# 2) Step through text searching for pattern
UpperCAmelCase__ : Any = 0, 0 # index into text, pattern
while i < len(lowerCAmelCase ):
if pattern[j] == text[i]:
if j == (len(lowerCAmelCase ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
UpperCAmelCase__ : Optional[Any] = failure[j - 1]
continue
i += 1
return False
def a__ ( lowerCAmelCase : str ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = [0]
UpperCAmelCase__ : int = 0
UpperCAmelCase__ : List[Any] = 1
while j < len(lowerCAmelCase ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
UpperCAmelCase__ : Union[str, Any] = failure[i - 1]
continue
j += 1
failure.append(lowerCAmelCase )
return failure
if __name__ == "__main__":
# Test 1)
A__ : Tuple = """abc1abc12"""
A__ : Tuple = """alskfjaldsabc1abc1abc12k23adsfabcabc"""
A__ : List[Any] = """alskfjaldsk23adsfabcabc"""
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
A__ : str = """ABABX"""
A__ : List[str] = """ABABZABABYABABX"""
assert kmp(pattern, text)
# Test 3)
A__ : Optional[int] = """AAAB"""
A__ : Optional[int] = """ABAAAAAB"""
assert kmp(pattern, text)
# Test 4)
A__ : Union[str, Any] = """abcdabcy"""
A__ : Tuple = """abcxabcdabxabcdabcdabcy"""
assert kmp(pattern, text)
# Test 5)
A__ : Tuple = """aabaabaaa"""
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 714 |
"""simple docstring"""
from math import isqrt
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : List[Any] = False
return [i for i in range(2 , lowerCAmelCase ) if is_prime[i]]
def a__ ( lowerCAmelCase : int = 10**8 ):
'''simple docstring'''
UpperCAmelCase__ : Dict = calculate_prime_numbers(max_number // 2 )
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Tuple = len(lowerCAmelCase ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 660 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
A__ : List[str] = TypeVar("""T""")
class _lowercase ( Generic[T] ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase )-> None:
UpperCAmelCase__ : Any | T = None
UpperCAmelCase__ : int = len(__UpperCamelCase )
UpperCAmelCase__ : list[T] = [any_type for _ in range(self.N )] + arr
UpperCAmelCase__ : Tuple = fnc
self.build()
def lowerCAmelCase__ ( self )-> None:
for p in range(self.N - 1 , 0 , -1 ):
UpperCAmelCase__ : Dict = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase )-> None:
p += self.N
UpperCAmelCase__ : Any = v
while p > 1:
UpperCAmelCase__ : str = p // 2
UpperCAmelCase__ : Tuple = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase )-> T | None: # noqa: E741
UpperCAmelCase__ : Optional[int] = l + self.N, r + self.N
UpperCAmelCase__ : T | None = None
while l <= r:
if l % 2 == 1:
UpperCAmelCase__ : Union[str, Any] = self.st[l] if res is None else self.fn(__UpperCamelCase , self.st[l] )
if r % 2 == 0:
UpperCAmelCase__ : Any = self.st[r] if res is None else self.fn(__UpperCamelCase , self.st[r] )
UpperCAmelCase__ : Optional[int] = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
A__ : Optional[Any] = [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]
A__ : List[str] = {
0: 7,
1: 2,
2: 6,
3: -14,
4: 5,
5: 4,
6: 7,
7: -10,
8: 9,
9: 10,
10: 12,
11: 1,
}
A__ : str = SegmentTree(test_array, min)
A__ : Tuple = SegmentTree(test_array, max)
A__ : List[str] = SegmentTree(test_array, lambda a, b: a + b)
def a__ ( ):
'''simple docstring'''
for i in range(len(lowerCAmelCase ) ):
for j in range(lowerCAmelCase , len(lowerCAmelCase ) ):
UpperCAmelCase__ : int = reduce(lowerCAmelCase , test_array[i : j + 1] )
UpperCAmelCase__ : Tuple = reduce(lowerCAmelCase , test_array[i : j + 1] )
UpperCAmelCase__ : List[Any] = reduce(lambda lowerCAmelCase , lowerCAmelCase : a + b , test_array[i : j + 1] )
assert min_range == min_segment_tree.query(lowerCAmelCase , lowerCAmelCase )
assert max_range == max_segment_tree.query(lowerCAmelCase , lowerCAmelCase )
assert sum_range == sum_segment_tree.query(lowerCAmelCase , lowerCAmelCase )
test_all_segments()
for index, value in test_updates.items():
A__ : int = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 715 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a__ ( lowerCAmelCase : str , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Dict , lowerCAmelCase : List[Any] ):
'''simple docstring'''
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Optional[int] = np.full((len(lowerCAmelCase ), sequence_length, 2) , lowerCAmelCase )
else:
UpperCAmelCase__ : Optional[Any] = np.full((len(lowerCAmelCase ), sequence_length) , lowerCAmelCase )
for i, tensor in enumerate(lowerCAmelCase ):
if padding_side == "right":
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Dict = tensor[:sequence_length]
else:
UpperCAmelCase__ : Tuple = tensor[:sequence_length]
else:
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Optional[Any] = tensor[:sequence_length]
else:
UpperCAmelCase__ : int = tensor[:sequence_length]
return out_tensor.tolist()
def a__ ( lowerCAmelCase : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = ord(lowerCAmelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
UpperCAmelCase__ : Optional[Any] = unicodedata.category(lowerCAmelCase )
if cat.startswith("P" ):
return True
return False
@dataclass
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
_A = True
_A = None
_A = None
_A = -100
_A = "pt"
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[str]:
import torch
UpperCAmelCase__ : Optional[Any] = "label" if "label" in features[0].keys() else "labels"
UpperCAmelCase__ : Dict = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
UpperCAmelCase__ : str = self.tokenizer.pad(
__UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" if labels is None else None , )
if labels is None:
return batch
UpperCAmelCase__ : Optional[Any] = torch.tensor(batch["entity_ids"] ).shape[1]
UpperCAmelCase__ : int = self.tokenizer.padding_side
if padding_side == "right":
UpperCAmelCase__ : int = [
list(__UpperCamelCase ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) for label in labels
]
else:
UpperCAmelCase__ : List[Any] = [
[self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) + list(__UpperCamelCase ) for label in labels
]
UpperCAmelCase__ : Optional[Any] = [feature["ner_tags"] for feature in features]
UpperCAmelCase__ : int = padding_tensor(__UpperCamelCase , -1 , __UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : List[Any] = [feature["original_entity_spans"] for feature in features]
UpperCAmelCase__ : int = padding_tensor(__UpperCamelCase , (-1, -1) , __UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[int] = {k: torch.tensor(__UpperCamelCase , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 660 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, 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 LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=99 , __UpperCamelCase=32 , __UpperCamelCase=5 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=5_12 , __UpperCamelCase=16 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=3 , __UpperCamelCase=4 , __UpperCamelCase=None , )-> Dict:
UpperCAmelCase__ : str = parent
UpperCAmelCase__ : Union[str, Any] = batch_size
UpperCAmelCase__ : str = seq_length
UpperCAmelCase__ : int = is_training
UpperCAmelCase__ : Union[str, Any] = use_input_mask
UpperCAmelCase__ : int = use_token_type_ids
UpperCAmelCase__ : List[Any] = use_labels
UpperCAmelCase__ : Optional[Any] = vocab_size
UpperCAmelCase__ : Dict = hidden_size
UpperCAmelCase__ : Dict = num_hidden_layers
UpperCAmelCase__ : Union[str, Any] = num_attention_heads
UpperCAmelCase__ : List[Any] = intermediate_size
UpperCAmelCase__ : str = hidden_act
UpperCAmelCase__ : Optional[int] = hidden_dropout_prob
UpperCAmelCase__ : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase__ : List[Any] = max_position_embeddings
UpperCAmelCase__ : Optional[Any] = type_vocab_size
UpperCAmelCase__ : str = type_sequence_label_size
UpperCAmelCase__ : Optional[Any] = initializer_range
UpperCAmelCase__ : List[str] = num_labels
UpperCAmelCase__ : Union[str, Any] = num_choices
UpperCAmelCase__ : Union[str, Any] = scope
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase__ : List[str] = None
if self.use_input_mask:
UpperCAmelCase__ : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ : Any = None
if self.use_token_type_ids:
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase__ : List[str] = None
UpperCAmelCase__ : List[str] = None
UpperCAmelCase__ : Optional[int] = None
if self.use_labels:
UpperCAmelCase__ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase__ : str = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase__ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase__ ( self )-> List[Any]:
return LlamaConfig(
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 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[str]:
UpperCAmelCase__ : Union[str, Any] = LlamaModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : List[Any] = model(__UpperCamelCase , attention_mask=__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = 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 , )-> Tuple:
UpperCAmelCase__ : Optional[int] = True
UpperCAmelCase__ : Dict = LlamaModel(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : int = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=__UpperCamelCase , )
UpperCAmelCase__ : Any = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , )
UpperCAmelCase__ : Any = model(__UpperCamelCase , attention_mask=__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 , )-> Optional[Any]:
UpperCAmelCase__ : List[str] = LlamaForCausalLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : Dict = model(__UpperCamelCase , attention_mask=__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 , __UpperCamelCase , __UpperCamelCase , )-> int:
UpperCAmelCase__ : Optional[int] = True
UpperCAmelCase__ : Optional[Any] = True
UpperCAmelCase__ : List[Any] = LlamaForCausalLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
# first forward pass
UpperCAmelCase__ : List[Any] = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=__UpperCamelCase , use_cache=__UpperCamelCase , )
UpperCAmelCase__ : Dict = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCAmelCase__ : Optional[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase__ : Optional[Any] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCAmelCase__ : List[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCAmelCase__ : str = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCAmelCase__ : Tuple = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=__UpperCamelCase , output_hidden_states=__UpperCamelCase , )["hidden_states"][0]
UpperCAmelCase__ : int = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=__UpperCamelCase , past_key_values=__UpperCamelCase , output_hidden_states=__UpperCamelCase , )["hidden_states"][0]
# select random slice
UpperCAmelCase__ : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCAmelCase__ : Any = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCAmelCase__ : str = 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(__UpperCamelCase , __UpperCamelCase , atol=1E-3 ) )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : Dict = self.prepare_config_and_inputs()
(
UpperCAmelCase__
) : Any = config_and_inputs
UpperCAmelCase__ : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_A = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
_A = (LlamaForCausalLM,) if is_torch_available() else ()
_A = (
{
'feature-extraction': LlamaModel,
'text-classification': LlamaForSequenceClassification,
'text-generation': LlamaForCausalLM,
'zero-shot': LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
_A = False
_A = False
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : Optional[int] = LlamaModelTester(self )
UpperCAmelCase__ : Any = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=37 )
def lowerCAmelCase__ ( self )-> int:
self.config_tester.run_common_tests()
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Optional[int]:
UpperCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCAmelCase__ : int = type
self.model_tester.create_and_check_model(*__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : Optional[Any] = 3
UpperCAmelCase__ : int = input_dict["input_ids"]
UpperCAmelCase__ : Any = input_ids.ne(1 ).to(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCAmelCase__ : List[Any] = LlamaForSequenceClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : Any = model(__UpperCamelCase , attention_mask=__UpperCamelCase , labels=__UpperCamelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : List[Any] = 3
UpperCAmelCase__ : List[Any] = "single_label_classification"
UpperCAmelCase__ : List[Any] = input_dict["input_ids"]
UpperCAmelCase__ : str = input_ids.ne(1 ).to(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCAmelCase__ : Optional[int] = LlamaForSequenceClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : Tuple = model(__UpperCamelCase , attention_mask=__UpperCamelCase , labels=__UpperCamelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowerCAmelCase__ ( self )-> int:
UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : Any = 3
UpperCAmelCase__ : Union[str, Any] = "multi_label_classification"
UpperCAmelCase__ : Optional[Any] = input_dict["input_ids"]
UpperCAmelCase__ : str = input_ids.ne(1 ).to(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
UpperCAmelCase__ : int = LlamaForSequenceClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : Union[str, Any] = model(__UpperCamelCase , attention_mask=__UpperCamelCase , labels=__UpperCamelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip("LLaMA buffers include complex numbers, which breaks this test" )
def lowerCAmelCase__ ( self )-> int:
pass
@parameterized.expand([("linear",), ("dynamic",)] )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[str]:
UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : Optional[int] = ids_tensor([1, 10] , config.vocab_size )
UpperCAmelCase__ : 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
UpperCAmelCase__ : Optional[Any] = LlamaModel(__UpperCamelCase )
original_model.to(__UpperCamelCase )
original_model.eval()
UpperCAmelCase__ : Optional[Any] = original_model(__UpperCamelCase ).last_hidden_state
UpperCAmelCase__ : Dict = original_model(__UpperCamelCase ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
UpperCAmelCase__ : Union[str, Any] = {"type": scaling_type, "factor": 10.0}
UpperCAmelCase__ : Union[str, Any] = LlamaModel(__UpperCamelCase )
scaled_model.to(__UpperCamelCase )
scaled_model.eval()
UpperCAmelCase__ : Tuple = scaled_model(__UpperCamelCase ).last_hidden_state
UpperCAmelCase__ : Any = scaled_model(__UpperCamelCase ).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(__UpperCamelCase , __UpperCamelCase , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-5 ) )
@require_torch
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!" )
@slow
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ : Dict = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
UpperCAmelCase__ : List[str] = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf" , device_map="auto" )
UpperCAmelCase__ : List[Any] = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
UpperCAmelCase__ : int = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]] )
torch.testing.assert_close(out.mean(-1 ) , __UpperCamelCase , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
UpperCAmelCase__ : List[Any] = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , __UpperCamelCase , atol=1E-5 , rtol=1E-5 )
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!" )
@slow
def lowerCAmelCase__ ( self )-> int:
UpperCAmelCase__ : str = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
UpperCAmelCase__ : Optional[Any] = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf" , device_map="auto" )
UpperCAmelCase__ : List[str] = model(torch.tensor(__UpperCamelCase ) )
# Expected mean on dim = -1
UpperCAmelCase__ : str = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]] )
torch.testing.assert_close(out.mean(-1 ) , __UpperCamelCase , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
UpperCAmelCase__ : str = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , __UpperCamelCase , atol=1E-5 , rtol=1E-5 )
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!" )
@slow
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : int = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
UpperCAmelCase__ : int = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf" , device_map="auto" )
UpperCAmelCase__ : Union[str, Any] = model(torch.tensor(__UpperCamelCase ) )
# Expected mean on dim = -1
UpperCAmelCase__ : Optional[Any] = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]] )
torch.testing.assert_close(out.mean(-1 ) , __UpperCamelCase , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
UpperCAmelCase__ : Tuple = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , __UpperCamelCase , atol=1E-2 , rtol=1E-2 )
@unittest.skip(
"Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test" )
@slow
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ : Optional[int] = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
UpperCAmelCase__ : Union[str, Any] = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf" , device_map="auto" )
UpperCAmelCase__ : Optional[int] = model(torch.tensor(__UpperCamelCase ) )
UpperCAmelCase__ : List[str] = torch.tensor(
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , __UpperCamelCase , atol=1E-2 , rtol=1E-2 )
# fmt: off
UpperCAmelCase__ : List[str] = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , __UpperCamelCase , atol=1E-5 , rtol=1E-5 )
@unittest.skip("Model is curently gated" )
@slow
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : Dict = "Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"
UpperCAmelCase__ : int = "Simply put, the theory of relativity states that "
UpperCAmelCase__ : List[str] = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf" )
UpperCAmelCase__ : Any = tokenizer.encode(__UpperCamelCase , return_tensors="pt" )
UpperCAmelCase__ : Tuple = LlamaForCausalLM.from_pretrained(
"meta-llama/Llama-2-13b-chat-hf" , device_map="sequential" , use_safetensors=__UpperCamelCase )
# greedy generation outputs
UpperCAmelCase__ : List[str] = model.generate(__UpperCamelCase , max_new_tokens=64 , top_p=__UpperCamelCase , temperature=1 , do_sample=__UpperCamelCase )
UpperCAmelCase__ : List[str] = tokenizer.decode(generated_ids[0] , skip_special_tokens=__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
| 716 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def a__ ( lowerCAmelCase : List[str] ):
'''simple docstring'''
def wrapper(*lowerCAmelCase : Any , **lowerCAmelCase : Tuple ):
UpperCAmelCase__ : Optional[int] = timeit.default_timer()
UpperCAmelCase__ : int = func(*lowerCAmelCase , **lowerCAmelCase )
UpperCAmelCase__ : List[Any] = timeit.default_timer() - starttime
return delta
UpperCAmelCase__ : int = func.__name__
return wrapper
def a__ ( lowerCAmelCase : dict , lowerCAmelCase : Optional[int]=100 , lowerCAmelCase : List[str]=None ):
'''simple docstring'''
UpperCAmelCase__ : str = []
UpperCAmelCase__ : Optional[Any] = seq_shapes or {}
for i in range(lowerCAmelCase ):
UpperCAmelCase__ : int = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(lowerCAmelCase , _ArrayXD ):
UpperCAmelCase__ : List[str] = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(lowerCAmelCase , datasets.Value ):
if v.dtype == "string":
UpperCAmelCase__ : Dict = "The small grey turtle was surprisingly fast when challenged."
else:
UpperCAmelCase__ : str = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(lowerCAmelCase , datasets.Sequence ):
while isinstance(lowerCAmelCase , datasets.Sequence ):
UpperCAmelCase__ : List[str] = v.feature
UpperCAmelCase__ : Optional[int] = seq_shapes[k]
UpperCAmelCase__ : Optional[int] = np.random.rand(*lowerCAmelCase ).astype(v.dtype )
UpperCAmelCase__ : Union[str, Any] = data
dummy_data.append((i, example) )
return dummy_data
def a__ ( lowerCAmelCase : List[str] , lowerCAmelCase : Tuple , lowerCAmelCase : List[str]=100 , lowerCAmelCase : Optional[int]=None ):
'''simple docstring'''
UpperCAmelCase__ : int = generate_examples(lowerCAmelCase , num_examples=lowerCAmelCase , seq_shapes=lowerCAmelCase )
with ArrowWriter(features=lowerCAmelCase , path=lowerCAmelCase ) as writer:
for key, record in dummy_data:
UpperCAmelCase__ : List[Any] = features.encode_example(lowerCAmelCase )
writer.write(lowerCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}." )
UpperCAmelCase__ : Optional[int] = datasets.Dataset.from_file(filename=lowerCAmelCase , info=datasets.DatasetInfo(features=lowerCAmelCase ) )
return dataset
| 660 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : str = logging.get_logger(__name__)
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'timm_backbone'
def __init__( self , __UpperCamelCase=None , __UpperCamelCase=3 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=None , **__UpperCamelCase , )-> Union[str, Any]:
super().__init__(**__UpperCamelCase )
UpperCAmelCase__ : int = backbone
UpperCAmelCase__ : Tuple = num_channels
UpperCAmelCase__ : List[Any] = features_only
UpperCAmelCase__ : str = use_pretrained_backbone
UpperCAmelCase__ : Any = True
UpperCAmelCase__ : List[str] = out_indices if out_indices is not None else (-1,)
| 717 |
"""simple docstring"""
from manim import *
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : str = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase__ : List[str] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
UpperCAmelCase__ : List[Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : Union[str, Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : List[Any] = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : int = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = VGroup(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = Text("CPU" , font_size=24 )
UpperCAmelCase__ : Any = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = [mem.copy() for i in range(4 )]
UpperCAmelCase__ : List[str] = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Union[str, Any] = Text("GPU" , font_size=24 )
UpperCAmelCase__ : Dict = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
gpu.move_to([-1, -1, 0] )
self.add(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : List[str] = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = Text("Model" , font_size=24 )
UpperCAmelCase__ : Dict = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
model.move_to([3, -1.0, 0] )
self.add(__UpperCamelCase )
UpperCAmelCase__ : List[str] = []
for i, rect in enumerate(__UpperCamelCase ):
rect.set_stroke(__UpperCamelCase )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
UpperCAmelCase__ : int = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__UpperCamelCase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__UpperCamelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=__UpperCamelCase , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=__UpperCamelCase , buff=0.0 )
self.add(__UpperCamelCase )
cpu_targs.append(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : Any = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = Text("Loaded Checkpoint" , font_size=24 )
UpperCAmelCase__ : Any = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , aligned_edge=__UpperCamelCase , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
UpperCAmelCase__ : Optional[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
UpperCAmelCase__ : Any = MarkupText(
F"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : str = MarkupText(
F"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(__UpperCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
UpperCAmelCase__ : Optional[Any] = MarkupText(
F"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__UpperCamelCase ) , Write(__UpperCamelCase ) )
self.play(Write(__UpperCamelCase , run_time=1 ) , Create(__UpperCamelCase , run_time=1 ) )
UpperCAmelCase__ : Union[str, Any] = []
UpperCAmelCase__ : List[str] = []
for i, rect in enumerate(__UpperCamelCase ):
UpperCAmelCase__ : Optional[Any] = fill.copy().set_fill(__UpperCamelCase , opacity=0.7 )
target.move_to(__UpperCamelCase )
first_animations.append(GrowFromCenter(__UpperCamelCase , run_time=1 ) )
UpperCAmelCase__ : List[str] = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(__UpperCamelCase , run_time=1.5 ) )
self.play(*__UpperCamelCase )
self.play(*__UpperCamelCase )
self.wait()
| 660 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase : dict ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = set()
# edges = list of graph's edges
UpperCAmelCase__ : Tuple = get_edges(lowerCAmelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
UpperCAmelCase__ : int = edges.pop()
chosen_vertices.add(lowerCAmelCase )
chosen_vertices.add(lowerCAmelCase )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(lowerCAmelCase )
return chosen_vertices
def a__ ( lowerCAmelCase : dict ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 718 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A__ : Tuple = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : nn.ModuleList , lowerCAmelCase : nn.ModuleList , lowerCAmelCase : List[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(lowerCAmelCase ) == len(lowerCAmelCase ), F"{len(lowerCAmelCase )} != {len(lowerCAmelCase )}"
dest_layers.load_state_dict(layers_to_copy.state_dict() )
A__ : List[Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
A__ : List[Any] = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def a__ ( lowerCAmelCase : Dict , lowerCAmelCase : Dict ):
'''simple docstring'''
try:
UpperCAmelCase__ : Tuple = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"
F" {n_student}" )
return list(range(lowerCAmelCase ) )
def a__ ( lowerCAmelCase : int , lowerCAmelCase : Tuple ):
'''simple docstring'''
if n_student > n_teacher:
raise ValueError(F"Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}" )
elif n_teacher == n_student:
return list(range(lowerCAmelCase ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def a__ ( lowerCAmelCase : Union[str, PreTrainedModel] , lowerCAmelCase : Union[str, Path] = "student" , lowerCAmelCase : Union[int, None] = None , lowerCAmelCase : Union[int, None] = None , lowerCAmelCase : List[str]=False , lowerCAmelCase : List[str]=None , lowerCAmelCase : List[str]=None , **lowerCAmelCase : List[str] , ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
assert (e is not None) or (d is not None), _msg
if isinstance(lowerCAmelCase , lowerCAmelCase ):
AutoTokenizer.from_pretrained(lowerCAmelCase ).save_pretrained(lowerCAmelCase ) # purely for convenience
UpperCAmelCase__ : List[str] = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).eval()
else:
assert isinstance(lowerCAmelCase , lowerCAmelCase ), F"teacher must be a model or string got type {type(lowerCAmelCase )}"
UpperCAmelCase__ : int = teacher.config.to_diff_dict()
try:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
UpperCAmelCase__ : Tuple = teacher_e
if d is None:
UpperCAmelCase__ : str = teacher_d
init_kwargs.update({"encoder_layers": e, "decoder_layers": d} )
except AttributeError: # T5
if hasattr(teacher.config , "num_encoder_layers" ):
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
UpperCAmelCase__ : Optional[Any] = teacher_e
if d is None:
UpperCAmelCase__ : Optional[Any] = teacher_d
if hasattr(teacher.config , "num_encoder_layers" ):
init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d} )
else:
init_kwargs.update({"num_layers": e, "num_decoder_layers": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(lowerCAmelCase )
# Copy weights
UpperCAmelCase__ : Tuple = teacher.config_class(**lowerCAmelCase )
UpperCAmelCase__ : List[str] = AutoModelForSeqaSeqLM.from_config(lowerCAmelCase )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
UpperCAmelCase__ : Optional[int] = student.load_state_dict(teacher.state_dict() , strict=lowerCAmelCase )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
UpperCAmelCase__ , UpperCAmelCase__ : int = list(range(lowerCAmelCase ) ), list(range(lowerCAmelCase ) )
logger.info(
F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"
F" {save_path}" )
student.save_pretrained(lowerCAmelCase )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
UpperCAmelCase__ : List[int] = pick_layers_to_copy(lowerCAmelCase , lowerCAmelCase )
if d_layers_to_copy is None:
UpperCAmelCase__ : List[int] = pick_layers_to_copy(lowerCAmelCase , lowerCAmelCase )
try:
if hasattr(
lowerCAmelCase , "prophetnet" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , lowerCAmelCase )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , lowerCAmelCase )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , lowerCAmelCase )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , lowerCAmelCase )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , lowerCAmelCase )
copy_layers(teacher.decoder.block , student.decoder.block , lowerCAmelCase )
logger.info(
F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}" )
UpperCAmelCase__ : int = {
"teacher_type": teacher.config.model_type,
"copied_encoder_layers": e_layers_to_copy,
"copied_decoder_layers": d_layers_to_copy,
}
student.save_pretrained(lowerCAmelCase )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 660 | 0 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging
A__ : Optional[int] = logging.get_logger(__name__)
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = ['pixel_values']
def __init__( self , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_55 , __UpperCamelCase = True , __UpperCamelCase = 8 , **__UpperCamelCase , )-> None:
super().__init__(**__UpperCamelCase )
UpperCAmelCase__ : List[str] = do_rescale
UpperCAmelCase__ : List[Any] = rescale_factor
UpperCAmelCase__ : Union[str, Any] = do_pad
UpperCAmelCase__ : Tuple = pad_size
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase )-> np.ndarray:
return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None )-> Optional[int]:
UpperCAmelCase__ : List[Any] = get_image_size(__UpperCamelCase )
UpperCAmelCase__ : Dict = (old_height // size + 1) * size - old_height
UpperCAmelCase__ : Optional[int] = (old_width // size + 1) * size - old_width
return pad(__UpperCamelCase , ((0, pad_height), (0, pad_width)) , mode="symmetric" , data_format=__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = ChannelDimension.FIRST , **__UpperCamelCase , )-> Tuple:
UpperCAmelCase__ : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ : Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ : Union[str, Any] = do_pad if do_pad is not None else self.do_pad
UpperCAmelCase__ : List[Any] = pad_size if pad_size is not None else self.pad_size
UpperCAmelCase__ : str = 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_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
# All transformations expect numpy arrays.
UpperCAmelCase__ : Union[str, Any] = [to_numpy_array(__UpperCamelCase ) for image in images]
if do_rescale:
UpperCAmelCase__ : List[Any] = [self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase ) for image in images]
if do_pad:
UpperCAmelCase__ : Optional[int] = [self.pad(__UpperCamelCase , size=__UpperCamelCase ) for image in images]
UpperCAmelCase__ : Dict = [to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase ) for image in images]
UpperCAmelCase__ : List[str] = {"pixel_values": images}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 719 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _lowercase ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@property
def lowerCAmelCase__ ( self )-> int:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : Tuple = ort.SessionOptions()
UpperCAmelCase__ : List[str] = False
return options
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
UpperCAmelCase__ : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
UpperCAmelCase__ : str = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase__ : int = "A red cat sitting on a park bench"
UpperCAmelCase__ : Tuple = np.random.RandomState(0 )
UpperCAmelCase__ : Any = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , mask_image=__UpperCamelCase , guidance_scale=7.5 , num_inference_steps=10 , generator=__UpperCamelCase , output_type="np" , )
UpperCAmelCase__ : Tuple = output.images
UpperCAmelCase__ : Dict = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
UpperCAmelCase__ : Union[str, Any] = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
UpperCAmelCase__ : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
UpperCAmelCase__ : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
UpperCAmelCase__ : Optional[Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=__UpperCamelCase , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase__ : int = "A red cat sitting on a park bench"
UpperCAmelCase__ : List[str] = np.random.RandomState(0 )
UpperCAmelCase__ : str = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , mask_image=__UpperCamelCase , guidance_scale=7.5 , num_inference_steps=20 , generator=__UpperCamelCase , output_type="np" , )
UpperCAmelCase__ : List[str] = output.images
UpperCAmelCase__ : List[Any] = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
UpperCAmelCase__ : int = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 660 | 0 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def a__ ( lowerCAmelCase : List[str] ):
'''simple docstring'''
def wrapper(*lowerCAmelCase : Any , **lowerCAmelCase : Tuple ):
UpperCAmelCase__ : Optional[int] = timeit.default_timer()
UpperCAmelCase__ : int = func(*lowerCAmelCase , **lowerCAmelCase )
UpperCAmelCase__ : List[Any] = timeit.default_timer() - starttime
return delta
UpperCAmelCase__ : int = func.__name__
return wrapper
def a__ ( lowerCAmelCase : dict , lowerCAmelCase : Optional[int]=100 , lowerCAmelCase : List[str]=None ):
'''simple docstring'''
UpperCAmelCase__ : str = []
UpperCAmelCase__ : Optional[Any] = seq_shapes or {}
for i in range(lowerCAmelCase ):
UpperCAmelCase__ : int = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(lowerCAmelCase , _ArrayXD ):
UpperCAmelCase__ : List[str] = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(lowerCAmelCase , datasets.Value ):
if v.dtype == "string":
UpperCAmelCase__ : Dict = "The small grey turtle was surprisingly fast when challenged."
else:
UpperCAmelCase__ : str = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(lowerCAmelCase , datasets.Sequence ):
while isinstance(lowerCAmelCase , datasets.Sequence ):
UpperCAmelCase__ : List[str] = v.feature
UpperCAmelCase__ : Optional[int] = seq_shapes[k]
UpperCAmelCase__ : Optional[int] = np.random.rand(*lowerCAmelCase ).astype(v.dtype )
UpperCAmelCase__ : Union[str, Any] = data
dummy_data.append((i, example) )
return dummy_data
def a__ ( lowerCAmelCase : List[str] , lowerCAmelCase : Tuple , lowerCAmelCase : List[str]=100 , lowerCAmelCase : Optional[int]=None ):
'''simple docstring'''
UpperCAmelCase__ : int = generate_examples(lowerCAmelCase , num_examples=lowerCAmelCase , seq_shapes=lowerCAmelCase )
with ArrowWriter(features=lowerCAmelCase , path=lowerCAmelCase ) as writer:
for key, record in dummy_data:
UpperCAmelCase__ : List[Any] = features.encode_example(lowerCAmelCase )
writer.write(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}." )
UpperCAmelCase__ : Optional[int] = datasets.Dataset.from_file(filename=lowerCAmelCase , info=datasets.DatasetInfo(features=lowerCAmelCase ) )
return dataset
| 720 |
"""simple docstring"""
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 ..auto import CONFIG_MAPPING
A__ : Union[str, Any] = logging.get_logger(__name__)
A__ : Optional[int] = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'table-transformer'
_A = ['past_key_values']
_A = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=3 , __UpperCamelCase=1_00 , __UpperCamelCase=6 , __UpperCamelCase=20_48 , __UpperCamelCase=8 , __UpperCamelCase=6 , __UpperCamelCase=20_48 , __UpperCamelCase=8 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=True , __UpperCamelCase="relu" , __UpperCamelCase=2_56 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.02 , __UpperCamelCase=1.0 , __UpperCamelCase=False , __UpperCamelCase="sine" , __UpperCamelCase="resnet50" , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=1 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=1 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=0.1 , **__UpperCamelCase , )-> List[Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
UpperCAmelCase__ : Any = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : int = backbone_config.get("model_type" )
UpperCAmelCase__ : Optional[Any] = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase__ : int = config_class.from_dict(__UpperCamelCase )
# set timm attributes to None
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : List[str] = None, None, None
UpperCAmelCase__ : Optional[int] = use_timm_backbone
UpperCAmelCase__ : Dict = backbone_config
UpperCAmelCase__ : List[Any] = num_channels
UpperCAmelCase__ : Any = num_queries
UpperCAmelCase__ : int = d_model
UpperCAmelCase__ : Optional[int] = encoder_ffn_dim
UpperCAmelCase__ : str = encoder_layers
UpperCAmelCase__ : Dict = encoder_attention_heads
UpperCAmelCase__ : Optional[Any] = decoder_ffn_dim
UpperCAmelCase__ : Tuple = decoder_layers
UpperCAmelCase__ : Optional[Any] = decoder_attention_heads
UpperCAmelCase__ : List[str] = dropout
UpperCAmelCase__ : Tuple = attention_dropout
UpperCAmelCase__ : List[Any] = activation_dropout
UpperCAmelCase__ : Dict = activation_function
UpperCAmelCase__ : Optional[Any] = init_std
UpperCAmelCase__ : List[str] = init_xavier_std
UpperCAmelCase__ : int = encoder_layerdrop
UpperCAmelCase__ : Tuple = decoder_layerdrop
UpperCAmelCase__ : int = encoder_layers
UpperCAmelCase__ : Dict = auxiliary_loss
UpperCAmelCase__ : Union[str, Any] = position_embedding_type
UpperCAmelCase__ : List[str] = backbone
UpperCAmelCase__ : List[Any] = use_pretrained_backbone
UpperCAmelCase__ : List[str] = dilation
# Hungarian matcher
UpperCAmelCase__ : Dict = class_cost
UpperCAmelCase__ : Any = bbox_cost
UpperCAmelCase__ : Tuple = giou_cost
# Loss coefficients
UpperCAmelCase__ : Any = mask_loss_coefficient
UpperCAmelCase__ : Dict = dice_loss_coefficient
UpperCAmelCase__ : Any = bbox_loss_coefficient
UpperCAmelCase__ : Tuple = giou_loss_coefficient
UpperCAmelCase__ : List[Any] = eos_coefficient
super().__init__(is_encoder_decoder=__UpperCamelCase , **__UpperCamelCase )
@property
def lowerCAmelCase__ ( self )-> int:
return self.encoder_attention_heads
@property
def lowerCAmelCase__ ( self )-> int:
return self.d_model
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = version.parse('1.11' )
@property
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def lowerCAmelCase__ ( self )-> float:
return 1E-5
@property
def lowerCAmelCase__ ( self )-> int:
return 12
| 660 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase : List[str]):
'''simple docstring'''
UpperCAmelCase__ : Dict = [0] * len(lowerCAmelCase)
UpperCAmelCase__ : Any = []
UpperCAmelCase__ : str = [1] * len(lowerCAmelCase)
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(lowerCAmelCase)):
if indegree[i] == 0:
queue.append(lowerCAmelCase)
while queue:
UpperCAmelCase__ : Union[str, Any] = queue.pop(0)
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
UpperCAmelCase__ : Dict = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(lowerCAmelCase)
print(max(lowerCAmelCase))
# Adjacency list of Graph
A__ : Optional[Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 721 |
"""simple docstring"""
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
A__ : int = getLogger(__name__)
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : int = 8 , lowerCAmelCase : int = 1024 , lowerCAmelCase : List[Any]="val" , lowerCAmelCase : str=None , lowerCAmelCase : int=False , lowerCAmelCase : Dict="summarization" , lowerCAmelCase : int=None , lowerCAmelCase : List[str]=1 , lowerCAmelCase : Dict = None , lowerCAmelCase : List[str]="" , **lowerCAmelCase : int , ):
'''simple docstring'''
UpperCAmelCase__ : Dict = str(lowerCAmelCase )
assert local_rank is not None
torch.distributed.init_process_group(backend="nccl" , rank=lowerCAmelCase )
UpperCAmelCase__ : List[str] = Path(lowerCAmelCase )
UpperCAmelCase__ : str = save_dir.joinpath(F"rank_{local_rank}_output.json" )
torch.cuda.set_device(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).cuda()
if fpaa:
UpperCAmelCase__ : List[Any] = model.half()
# determine if we need to increase num_beams
use_task_specific_params(lowerCAmelCase , lowerCAmelCase ) # update config with task specific params
UpperCAmelCase__ : List[Any] = generate_kwargs.pop("num_beams" , model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
UpperCAmelCase__ : Any = num_return_sequences
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained(lowerCAmelCase )
logger.info(F"Inferred tokenizer type: {tokenizer.__class__}" ) # if this is wrong, check config.model_type.
if max_source_length is None:
UpperCAmelCase__ : int = tokenizer.model_max_length
if prefix is None:
UpperCAmelCase__ : Union[str, Any] = prefix or getattr(model.config , "prefix" , "" ) or ""
UpperCAmelCase__ : str = SeqaSeqDataset(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , max_target_length=1024 , type_path=lowerCAmelCase , n_obs=lowerCAmelCase , prefix=lowerCAmelCase , **lowerCAmelCase , )
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
UpperCAmelCase__ : Union[str, Any] = ds.make_sortish_sampler(lowerCAmelCase , distributed=lowerCAmelCase , add_extra_examples=lowerCAmelCase , shuffle=lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = DataLoader(lowerCAmelCase , sampler=lowerCAmelCase , batch_size=lowerCAmelCase , collate_fn=ds.collate_fn )
UpperCAmelCase__ : str = []
for batch in tqdm(lowerCAmelCase ):
UpperCAmelCase__ : Dict = model.generate(
input_ids=batch["input_ids"].to(model.device ) , attention_mask=batch["attention_mask"].to(model.device ) , num_return_sequences=lowerCAmelCase , num_beams=lowerCAmelCase , **lowerCAmelCase , )
UpperCAmelCase__ : int = tokenizer.batch_decode(lowerCAmelCase , skip_special_tokens=lowerCAmelCase , clean_up_tokenization_spaces=lowerCAmelCase )
UpperCAmelCase__ : int = batch["ids"]
if num_return_sequences > 1:
UpperCAmelCase__ : str = chunks(lowerCAmelCase , lowerCAmelCase ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(lowerCAmelCase ):
results.append({"pred": pred, "id": ids[i].item()} )
save_json(lowerCAmelCase , lowerCAmelCase )
return results, sampler.num_replicas
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : str = argparse.ArgumentParser(
epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate" )
parser.add_argument("--data_dir" , type=lowerCAmelCase , help="like cnn_dm/test.source" )
parser.add_argument(
"--model_name" , type=lowerCAmelCase , help="like facebook/bart-large-cnn,t5-base, etc." , default="sshleifer/distilbart-xsum-12-3" , )
parser.add_argument("--save_dir" , type=lowerCAmelCase , help="where to save" , default="tmp_gen" )
parser.add_argument("--max_source_length" , type=lowerCAmelCase , default=lowerCAmelCase )
parser.add_argument(
"--type_path" , type=lowerCAmelCase , default="test" , help="which subset to evaluate typically train/val/test" )
parser.add_argument("--task" , type=lowerCAmelCase , default="summarization" , help="used for task_specific_params + metrics" )
parser.add_argument("--bs" , type=lowerCAmelCase , default=8 , required=lowerCAmelCase , help="batch size" )
parser.add_argument(
"--local_rank" , type=lowerCAmelCase , default=-1 , required=lowerCAmelCase , help="should be passed by distributed.launch" )
parser.add_argument(
"--n_obs" , type=lowerCAmelCase , default=lowerCAmelCase , required=lowerCAmelCase , help="How many observations. Defaults to all." )
parser.add_argument(
"--num_return_sequences" , type=lowerCAmelCase , default=1 , required=lowerCAmelCase , help="How many sequences to return" )
parser.add_argument(
"--sync_timeout" , type=lowerCAmelCase , default=600 , required=lowerCAmelCase , help="How long should master process wait for other processes to finish." , )
parser.add_argument("--src_lang" , type=lowerCAmelCase , default=lowerCAmelCase , required=lowerCAmelCase )
parser.add_argument("--tgt_lang" , type=lowerCAmelCase , default=lowerCAmelCase , required=lowerCAmelCase )
parser.add_argument(
"--prefix" , type=lowerCAmelCase , required=lowerCAmelCase , default=lowerCAmelCase , help="will be added to the begininng of src examples" )
parser.add_argument("--fp16" , action="store_true" )
parser.add_argument("--debug" , action="store_true" )
UpperCAmelCase__ : Optional[int] = time.time()
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = parser.parse_known_args()
UpperCAmelCase__ : int = parse_numeric_n_bool_cl_kwargs(lowerCAmelCase )
if generate_kwargs and args.local_rank <= 0:
print(F"parsed the following generate kwargs: {generate_kwargs}" )
UpperCAmelCase__ : Dict = Path(args.save_dir + "_tmp" )
Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase ) # this handles locking.
UpperCAmelCase__ : List[str] = list(json_save_dir.glob("rank_*.json" ) )
if intermediate_files:
raise ValueError(F"Found files at {json_save_dir} please move or remove them." )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
UpperCAmelCase__ : List[str] = {}
if args.src_lang is not None:
UpperCAmelCase__ : str = args.src_lang
if args.tgt_lang is not None:
UpperCAmelCase__ : List[str] = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=lowerCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = eval_data_dir(
args.data_dir , lowerCAmelCase , args.model_name , type_path=args.type_path , bs=args.bs , fpaa=args.fpaa , task=args.task , local_rank=args.local_rank , n_obs=args.n_obs , max_source_length=args.max_source_length , num_return_sequences=args.num_return_sequences , prefix=args.prefix , dataset_kwargs=lowerCAmelCase , **lowerCAmelCase , )
if args.local_rank <= 0:
UpperCAmelCase__ : str = Path(args.save_dir )
save_dir.mkdir(exist_ok=lowerCAmelCase )
UpperCAmelCase__ : Tuple = gather_results_from_each_node(lowerCAmelCase , lowerCAmelCase , args.sync_timeout )
UpperCAmelCase__ : Union[str, Any] = combine_partial_results(lowerCAmelCase )
if args.num_return_sequences > 1:
UpperCAmelCase__ : int = save_dir.joinpath("pseudolabel_results.json" )
print(F"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/" )
save_json(lowerCAmelCase , lowerCAmelCase )
return
UpperCAmelCase__ : Optional[Any] = Path(args.data_dir ).joinpath(args.type_path + ".target" )
with open(lowerCAmelCase ) as f:
UpperCAmelCase__ : Optional[int] = [x.rstrip() for x in f.readlines()][: len(lowerCAmelCase )]
# Calculate metrics, save metrics, and save _generations.txt
UpperCAmelCase__ : List[Any] = "translation" in args.task
UpperCAmelCase__ : Optional[Any] = calculate_bleu if calc_bleu else calculate_rouge
UpperCAmelCase__ : Optional[Any] = "bleu" if calc_bleu else "rouge"
UpperCAmelCase__ : Dict = score_fn(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : List[Any] = len(lowerCAmelCase )
UpperCAmelCase__ : Union[str, Any] = time.time() - start_time
UpperCAmelCase__ : Optional[int] = round(runtime / metrics["n_obs"] , 4 )
UpperCAmelCase__ : Tuple = num_replicas
# TODO(@stas00): add whatever metadata to metrics
UpperCAmelCase__ : Any = save_dir.joinpath(F"{args.type_path}_{metric_name}.json" )
save_json(lowerCAmelCase , lowerCAmelCase , indent=lowerCAmelCase )
print(lowerCAmelCase )
write_txt_file(lowerCAmelCase , save_dir.joinpath(F"{args.type_path}_generations.txt" ) )
if args.debug:
write_txt_file(lowerCAmelCase , save_dir.joinpath(F"{args.type_path}.target" ) )
else:
shutil.rmtree(lowerCAmelCase )
def a__ ( lowerCAmelCase : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : str = []
for partial_result in partial_results:
records.extend(lowerCAmelCase )
UpperCAmelCase__ : Dict = sorted(lowerCAmelCase , key=lambda lowerCAmelCase : x["id"] )
UpperCAmelCase__ : List[str] = [x["pred"] for x in records]
return preds
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : int , lowerCAmelCase : Optional[int] ):
'''simple docstring'''
# WAIT FOR lots of .json files
UpperCAmelCase__ : int = time.time()
logger.info("waiting for all nodes to finish" )
UpperCAmelCase__ : Dict = None
while (time.time() - start_wait) < timeout:
UpperCAmelCase__ : str = list(save_dir.glob("rank_*.json" ) )
if len(lowerCAmelCase ) < num_replicas:
continue
try:
# make sure all json files are fully saved
UpperCAmelCase__ : Union[str, Any] = lmap(lowerCAmelCase , lowerCAmelCase )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError("Rank 0 gave up on waiting for other processes" )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 660 | 0 |
"""simple docstring"""
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
A__ : str = logging.get_logger(__name__)
A__ : List[Any] = {
"""microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
_A = 'resnet'
_A = ['basic', 'bottleneck']
def __init__( self , __UpperCamelCase=3 , __UpperCamelCase=64 , __UpperCamelCase=[2_56, 5_12, 10_24, 20_48] , __UpperCamelCase=[3, 4, 6, 3] , __UpperCamelCase="bottleneck" , __UpperCamelCase="relu" , __UpperCamelCase=False , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase , )-> Union[str, Any]:
super().__init__(**__UpperCamelCase )
if layer_type not in self.layer_types:
raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types )}" )
UpperCAmelCase__ : List[str] = num_channels
UpperCAmelCase__ : str = embedding_size
UpperCAmelCase__ : Optional[int] = hidden_sizes
UpperCAmelCase__ : str = depths
UpperCAmelCase__ : Optional[int] = layer_type
UpperCAmelCase__ : str = hidden_act
UpperCAmelCase__ : Tuple = downsample_in_first_stage
UpperCAmelCase__ : str = ["stem"] + [F"stage{idx}" for idx in range(1 , len(__UpperCamelCase ) + 1 )]
UpperCAmelCase__ : Any = get_aligned_output_features_output_indices(
out_features=__UpperCamelCase , out_indices=__UpperCamelCase , stage_names=self.stage_names )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = version.parse('1.11' )
@property
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def lowerCAmelCase__ ( self )-> float:
return 1E-3
| 700 |
"""simple docstring"""
from timeit import timeit
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
UpperCAmelCase__ : Tuple = 0
while number:
number &= number - 1
result += 1
return result
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
UpperCAmelCase__ : Union[str, Any] = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def a__ ( ):
'''simple docstring'''
def do_benchmark(lowerCAmelCase : int ) -> None:
UpperCAmelCase__ : Dict = "import __main__ as z"
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(lowerCAmelCase ) = }" )
UpperCAmelCase__ : Tuple = timeit("z.get_set_bits_count_using_modulo_operator(25)" , setup=lowerCAmelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase ) = }" )
UpperCAmelCase__ : Any = timeit(
"z.get_set_bits_count_using_brian_kernighans_algorithm(25)" , setup=lowerCAmelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(lowerCAmelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 660 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ : Dict = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""tokenization_biogpt""": ["""BioGptTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
"""BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BioGptForCausalLM""",
"""BioGptForTokenClassification""",
"""BioGptForSequenceClassification""",
"""BioGptModel""",
"""BioGptPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
A__ : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 701 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _lowercase ( unittest.TestCase , lowerCAmelCase_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : Optional[Any] = load_tool("text-classification" )
self.tool.setup()
UpperCAmelCase__ : List[str] = load_tool("text-classification" , remote=__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ : Dict = self.tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : List[Any] = self.remote_tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
def lowerCAmelCase__ ( self )-> Optional[int]:
UpperCAmelCase__ : Any = self.tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : str = self.remote_tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
| 660 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
A__ : Dict = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""",
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'albert'
def __init__( self , __UpperCamelCase=3_00_00 , __UpperCamelCase=1_28 , __UpperCamelCase=40_96 , __UpperCamelCase=12 , __UpperCamelCase=1 , __UpperCamelCase=64 , __UpperCamelCase=1_63_84 , __UpperCamelCase=1 , __UpperCamelCase="gelu_new" , __UpperCamelCase=0 , __UpperCamelCase=0 , __UpperCamelCase=5_12 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=1E-12 , __UpperCamelCase=0.1 , __UpperCamelCase="absolute" , __UpperCamelCase=0 , __UpperCamelCase=2 , __UpperCamelCase=3 , **__UpperCamelCase , )-> int:
super().__init__(pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
UpperCAmelCase__ : List[Any] = vocab_size
UpperCAmelCase__ : Tuple = embedding_size
UpperCAmelCase__ : Optional[Any] = hidden_size
UpperCAmelCase__ : int = num_hidden_layers
UpperCAmelCase__ : Any = num_hidden_groups
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : str = inner_group_num
UpperCAmelCase__ : List[Any] = hidden_act
UpperCAmelCase__ : Optional[Any] = intermediate_size
UpperCAmelCase__ : Tuple = hidden_dropout_prob
UpperCAmelCase__ : Dict = attention_probs_dropout_prob
UpperCAmelCase__ : Optional[int] = max_position_embeddings
UpperCAmelCase__ : Dict = type_vocab_size
UpperCAmelCase__ : str = initializer_range
UpperCAmelCase__ : Dict = layer_norm_eps
UpperCAmelCase__ : Union[str, Any] = classifier_dropout_prob
UpperCAmelCase__ : int = position_embedding_type
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
@property
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
UpperCAmelCase__ : int = {0: "batch", 1: "choice", 2: "sequence"}
else:
UpperCAmelCase__ : List[str] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 702 |
"""simple docstring"""
def a__ ( lowerCAmelCase : list , lowerCAmelCase : list ):
'''simple docstring'''
_validate_point(lowerCAmelCase )
_validate_point(lowerCAmelCase )
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(lowerCAmelCase , lowerCAmelCase ) ) )
def a__ ( lowerCAmelCase : list[float] ):
'''simple docstring'''
if point:
if isinstance(lowerCAmelCase , lowerCAmelCase ):
for item in point:
if not isinstance(lowerCAmelCase , (int, float) ):
UpperCAmelCase__ : Tuple = (
"Expected a list of numbers as input, found "
F"{type(lowerCAmelCase ).__name__}"
)
raise TypeError(lowerCAmelCase )
else:
UpperCAmelCase__ : Dict = F"Expected a list of numbers as input, found {type(lowerCAmelCase ).__name__}"
raise TypeError(lowerCAmelCase )
else:
raise ValueError("Missing an input" )
def a__ ( lowerCAmelCase : list , lowerCAmelCase : list ):
'''simple docstring'''
_validate_point(lowerCAmelCase )
_validate_point(lowerCAmelCase )
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(lowerCAmelCase , lowerCAmelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 0 |
A__ : Union[str, Any] = {"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
A__ : Union[str, Any] = ["""a""", """b""", """c""", """d""", """e"""]
def a__ ( lowerCAmelCase : Any , lowerCAmelCase : List[str] , lowerCAmelCase : Dict ):
'''simple docstring'''
UpperCAmelCase__ : str = start
# add current to visited
visited.append(lowerCAmelCase )
UpperCAmelCase__ : List[str] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
UpperCAmelCase__ : List[Any] = topological_sort(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# if all neighbors visited add current to sort
sort.append(lowerCAmelCase )
# if all vertices haven't been visited select a new one to visit
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
for vertice in vertices:
if vertice not in visited:
UpperCAmelCase__ : Optional[int] = topological_sort(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# return sort
return sort
if __name__ == "__main__":
A__ : List[str] = topological_sort("""a""", [], [])
print(sort)
| 703 |
"""simple docstring"""
import math
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a__ ( lowerCAmelCase : int = 1_0001 ):
'''simple docstring'''
try:
UpperCAmelCase__ : List[str] = int(lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError("Parameter nth must be int or castable to int." ) from None
if nth <= 0:
raise ValueError("Parameter nth must be greater than or equal to one." )
UpperCAmelCase__ : list[int] = []
UpperCAmelCase__ : str = 2
while len(lowerCAmelCase ) < nth:
if is_prime(lowerCAmelCase ):
primes.append(lowerCAmelCase )
num += 1
else:
num += 1
return primes[len(lowerCAmelCase ) - 1]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 660 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
A__ : Union[str, Any] = logging.get_logger(__name__)
A__ : int = """Hello, World!"""
A__ : Any = """en_XX"""
def a__ ( lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : bool ):
'''simple docstring'''
UpperCAmelCase__ : Any = Path("data_bin" )
UpperCAmelCase__ : Optional[Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(lowerCAmelCase ).parent ) , checkpoint_file=Path(lowerCAmelCase ).name , _name="xmod_base" , arch="xmod_base" , task="multilingual_masked_lm" , data_name_or_path=str(lowerCAmelCase ) , bpe="sentencepiece" , sentencepiece_model=str(Path(lowerCAmelCase ).parent / "sentencepiece.bpe.model" ) , src_dict=str(data_dir / "dict.txt" ) , )
xmod.eval() # disable dropout
print(lowerCAmelCase )
UpperCAmelCase__ : Dict = xmod.model.encoder.sentence_encoder
UpperCAmelCase__ : int = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1E-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , "bottleneck" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
UpperCAmelCase__ : str = xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our X-MOD config:" , lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = XmodForSequenceClassification(lowerCAmelCase ) if classification_head else XmodForMaskedLM(lowerCAmelCase )
model.eval()
# Now let's copy all the weights.
# Embeddings
UpperCAmelCase__ : List[Any] = xmod_sent_encoder.embed_tokens.weight
UpperCAmelCase__ : Optional[int] = xmod_sent_encoder.embed_positions.weight
UpperCAmelCase__ : Union[str, Any] = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
UpperCAmelCase__ : Any = xmod_sent_encoder.layernorm_embedding.weight
UpperCAmelCase__ : Tuple = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
UpperCAmelCase__ : int = model.roberta.encoder.layer[i]
UpperCAmelCase__ : Tuple = xmod_sent_encoder.layers[i]
# self attention
UpperCAmelCase__ : Union[str, Any] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("Dimensions of self-attention weights do not match." )
UpperCAmelCase__ : Any = xmod_layer.self_attn.q_proj.weight
UpperCAmelCase__ : Tuple = xmod_layer.self_attn.q_proj.bias
UpperCAmelCase__ : List[Any] = xmod_layer.self_attn.k_proj.weight
UpperCAmelCase__ : Any = xmod_layer.self_attn.k_proj.bias
UpperCAmelCase__ : List[Any] = xmod_layer.self_attn.v_proj.weight
UpperCAmelCase__ : List[Any] = xmod_layer.self_attn.v_proj.bias
# self-attention output
UpperCAmelCase__ : Optional[Any] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("Dimensions of self-attention output weights do not match." )
UpperCAmelCase__ : List[Any] = xmod_layer.self_attn.out_proj.weight
UpperCAmelCase__ : Any = xmod_layer.self_attn.out_proj.bias
UpperCAmelCase__ : Union[str, Any] = xmod_layer.self_attn_layer_norm.weight
UpperCAmelCase__ : Optional[Any] = xmod_layer.self_attn_layer_norm.bias
# intermediate
UpperCAmelCase__ : List[str] = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of intermediate weights do not match." )
UpperCAmelCase__ : Optional[int] = xmod_layer.fca.weight
UpperCAmelCase__ : str = xmod_layer.fca.bias
# output
UpperCAmelCase__ : Union[str, Any] = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of feed-forward weights do not match." )
UpperCAmelCase__ : str = xmod_layer.fca.weight
UpperCAmelCase__ : List[Any] = xmod_layer.fca.bias
UpperCAmelCase__ : Any = xmod_layer.final_layer_norm.weight
UpperCAmelCase__ : Tuple = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
UpperCAmelCase__ : List[str] = xmod_layer.adapter_layer_norm.weight
UpperCAmelCase__ : Any = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("Lists of language adapters do not match." )
for lang_code, adapter in xmod_layer.adapter_modules.items():
UpperCAmelCase__ : Any = bert_output.adapter_modules[lang_code]
UpperCAmelCase__ : Dict = xmod_layer.adapter_modules[lang_code]
UpperCAmelCase__ : Optional[Any] = from_adapter.fca.weight
UpperCAmelCase__ : Dict = from_adapter.fca.bias
UpperCAmelCase__ : Optional[int] = from_adapter.fca.weight
UpperCAmelCase__ : str = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
UpperCAmelCase__ : str = xmod_sent_encoder.layer_norm.weight
UpperCAmelCase__ : List[Any] = xmod_sent_encoder.layer_norm.bias
if classification_head:
UpperCAmelCase__ : Dict = xmod.model.classification_heads["mnli"].dense.weight
UpperCAmelCase__ : Optional[int] = xmod.model.classification_heads["mnli"].dense.bias
UpperCAmelCase__ : Optional[Any] = xmod.model.classification_heads["mnli"].out_proj.weight
UpperCAmelCase__ : Optional[int] = xmod.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
UpperCAmelCase__ : List[Any] = xmod.model.encoder.lm_head.dense.weight
UpperCAmelCase__ : Dict = xmod.model.encoder.lm_head.dense.bias
UpperCAmelCase__ : Union[str, Any] = xmod.model.encoder.lm_head.layer_norm.weight
UpperCAmelCase__ : Dict = xmod.model.encoder.lm_head.layer_norm.bias
UpperCAmelCase__ : Dict = xmod.model.encoder.lm_head.weight
UpperCAmelCase__ : Any = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
UpperCAmelCase__ : Dict = xmod.encode(lowerCAmelCase ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(lowerCAmelCase )
UpperCAmelCase__ : Dict = model(lowerCAmelCase )[0]
if classification_head:
UpperCAmelCase__ : Any = xmod.model.classification_heads["mnli"](xmod.extract_features(lowerCAmelCase ) )
else:
UpperCAmelCase__ : Dict = xmod.model(lowerCAmelCase , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
UpperCAmelCase__ : str = torch.max(torch.abs(our_output - their_output ) ).item()
print(F"max_absolute_diff = {max_absolute_diff}" ) # ~ 1e-7
UpperCAmelCase__ : Optional[int] = torch.allclose(lowerCAmelCase , lowerCAmelCase , atol=1E-3 )
print("Do both models output the same tensors?" , "🔥" if success else "💩" )
if not success:
raise Exception("Something went wRoNg" )
Path(lowerCAmelCase ).mkdir(parents=lowerCAmelCase , exist_ok=lowerCAmelCase )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(lowerCAmelCase )
if __name__ == "__main__":
A__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
A__ : int = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 704 |
"""simple docstring"""
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=30 , __UpperCamelCase=2 , __UpperCamelCase=3 , __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=3 , __UpperCamelCase=0.6 , __UpperCamelCase=None , )-> List[Any]:
UpperCAmelCase__ : str = parent
UpperCAmelCase__ : Optional[Any] = batch_size
UpperCAmelCase__ : Any = image_size
UpperCAmelCase__ : Dict = patch_size
UpperCAmelCase__ : Optional[Any] = num_channels
UpperCAmelCase__ : Union[str, Any] = is_training
UpperCAmelCase__ : Any = use_labels
UpperCAmelCase__ : List[Any] = hidden_size
UpperCAmelCase__ : Dict = num_hidden_layers
UpperCAmelCase__ : List[str] = num_attention_heads
UpperCAmelCase__ : List[Any] = intermediate_size
UpperCAmelCase__ : Dict = hidden_act
UpperCAmelCase__ : List[Any] = hidden_dropout_prob
UpperCAmelCase__ : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase__ : Optional[Any] = type_sequence_label_size
UpperCAmelCase__ : Union[str, Any] = initializer_range
UpperCAmelCase__ : int = mask_ratio
UpperCAmelCase__ : Tuple = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase__ : int = (image_size // patch_size) ** 2
UpperCAmelCase__ : str = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Optional[Any] = None
if self.use_labels:
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : Optional[int] = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self )-> int:
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = ViTMAEModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : List[str] = 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 )-> Optional[int]:
UpperCAmelCase__ : List[Any] = ViTMAEForPreTraining(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : Optional[int] = model(__UpperCamelCase )
UpperCAmelCase__ : List[str] = (self.image_size // self.patch_size) ** 2
UpperCAmelCase__ : List[str] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase__ : Dict = 1
UpperCAmelCase__ : str = ViTMAEForPreTraining(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase__ : List[str] = model(__UpperCamelCase )
UpperCAmelCase__ : List[str] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : Tuple = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : str = config_and_inputs
UpperCAmelCase__ : int = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_A = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
_A = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
_A = False
_A = False
_A = False
_A = False
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : Any = ViTMAEModelTester(self )
UpperCAmelCase__ : List[str] = ConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase , hidden_size=37 )
def lowerCAmelCase__ ( self )-> int:
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def lowerCAmelCase__ ( self )-> Dict:
pass
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : int = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase__ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ , UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class(__UpperCamelCase )
UpperCAmelCase__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Dict = [*signature.parameters.keys()]
UpperCAmelCase__ : Tuple = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Dict:
# make masks reproducible
np.random.seed(2 )
UpperCAmelCase__ : Tuple = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
UpperCAmelCase__ : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase__ : str = torch.from_numpy(__UpperCamelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase__ : Optional[Any] = pt_noise
super().check_pt_tf_models(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[Any] = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase__ : Optional[int] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
UpperCAmelCase__ : Optional[Any] = outputs[0].cpu().numpy()
UpperCAmelCase__ : Union[str, Any] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = model_class.from_pretrained(__UpperCamelCase )
model.to(__UpperCamelCase )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase__ : List[Any] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
# Make sure we don't have nans
UpperCAmelCase__ : Tuple = after_outputs[0].cpu().numpy()
UpperCAmelCase__ : int = 0
UpperCAmelCase__ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__UpperCamelCase , 1E-5 )
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> List[str]:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> Any:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> Optional[Any]:
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def lowerCAmelCase__ ( self )-> List[Any]:
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
pass
@slow
def lowerCAmelCase__ ( self )-> Union[str, Any]:
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : Tuple = ViTMAEModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase__ ( self )-> List[Any]:
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def lowerCAmelCase__ ( self )-> Optional[int]:
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCAmelCase__ : Any = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" ).to(__UpperCamelCase )
UpperCAmelCase__ : Tuple = self.default_image_processor
UpperCAmelCase__ : List[Any] = prepare_img()
UpperCAmelCase__ : Optional[Any] = image_processor(images=__UpperCamelCase , return_tensors="pt" ).to(__UpperCamelCase )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCAmelCase__ : List[Any] = ViTMAEConfig()
UpperCAmelCase__ : str = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase__ : Optional[int] = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : List[str] = model(**__UpperCamelCase , noise=torch.from_numpy(__UpperCamelCase ).to(device=__UpperCamelCase ) )
# verify the logits
UpperCAmelCase__ : str = torch.Size((1, 1_96, 7_68) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
UpperCAmelCase__ : List[Any] = torch.tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(__UpperCamelCase ) , atol=1E-4 ) )
| 660 | 0 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
A__ : Optional[int] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
A__ : Dict = typing.Union[np.floataa, int, float] # noqa: UP007
def a__ ( lowerCAmelCase : Vector , lowerCAmelCase : Vector ):
'''simple docstring'''
return np.sqrt(np.sum((np.asarray(lowerCAmelCase ) - np.asarray(lowerCAmelCase )) ** 2 ) )
def a__ ( lowerCAmelCase : Vector , lowerCAmelCase : Vector ):
'''simple docstring'''
return sum((va - va) ** 2 for va, va in zip(lowerCAmelCase , lowerCAmelCase ) ) ** (1 / 2)
if __name__ == "__main__":
def a__ ( ):
'''simple docstring'''
from timeit import timeit
print("Without Numpy" )
print(
timeit(
"euclidean_distance_no_np([1, 2, 3], [4, 5, 6])" , number=1_0000 , globals=globals() , ) )
print("With Numpy" )
print(
timeit(
"euclidean_distance([1, 2, 3], [4, 5, 6])" , number=1_0000 , globals=globals() , ) )
benchmark()
| 705 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class _lowercase :
'''simple docstring'''
_A = 42
# setable values
_A = 42
_A = 42
_A = None
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
return cls(common=__UpperCamelCase , init_noise_sigma=__UpperCamelCase , timesteps=__UpperCamelCase )
@dataclass
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
_A = [e.name for e in FlaxKarrasDiffusionSchedulers]
_A = 42
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
return True
@register_to_config
def __init__( self , __UpperCamelCase = 10_00 , __UpperCamelCase = 0.0001 , __UpperCamelCase = 0.02 , __UpperCamelCase = "linear" , __UpperCamelCase = None , __UpperCamelCase = "fixed_small" , __UpperCamelCase = True , __UpperCamelCase = "epsilon" , __UpperCamelCase = jnp.floataa , )-> List[str]:
UpperCAmelCase__ : int = dtype
def lowerCAmelCase__ ( self , __UpperCamelCase = None )-> DDPMSchedulerState:
if common is None:
UpperCAmelCase__ : int = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
UpperCAmelCase__ : Tuple = jnp.array(1.0 , dtype=self.dtype )
UpperCAmelCase__ : Tuple = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=__UpperCamelCase , init_noise_sigma=__UpperCamelCase , timesteps=__UpperCamelCase , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None )-> jnp.ndarray:
return sample
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = () )-> DDPMSchedulerState:
UpperCAmelCase__ : Dict = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
UpperCAmelCase__ : Optional[int] = (jnp.arange(0 , __UpperCamelCase ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=__UpperCamelCase , timesteps=__UpperCamelCase , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = state.common.alphas_cumprod[t]
UpperCAmelCase__ : int = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase__ : Any = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
UpperCAmelCase__ : Union[str, Any] = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
UpperCAmelCase__ : Dict = jnp.clip(__UpperCamelCase , a_min=1E-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
UpperCAmelCase__ : Tuple = jnp.log(jnp.clip(__UpperCamelCase , a_min=1E-20 ) )
elif variance_type == "fixed_large":
UpperCAmelCase__ : Union[str, Any] = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
UpperCAmelCase__ : Optional[int] = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
UpperCAmelCase__ : List[str] = variance
UpperCAmelCase__ : Union[str, Any] = state.common.betas[t]
UpperCAmelCase__ : Optional[int] = (predicted_variance + 1) / 2
UpperCAmelCase__ : Any = frac * max_log + (1 - frac) * min_log
return variance
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = True , )-> Union[FlaxDDPMSchedulerOutput, Tuple]:
UpperCAmelCase__ : List[str] = timestep
if key is None:
UpperCAmelCase__ : int = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = jnp.split(__UpperCamelCase , sample.shape[1] , axis=1 )
else:
UpperCAmelCase__ : Optional[Any] = None
# 1. compute alphas, betas
UpperCAmelCase__ : Union[str, Any] = state.common.alphas_cumprod[t]
UpperCAmelCase__ : Tuple = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
UpperCAmelCase__ : Union[str, Any] = 1 - alpha_prod_t
UpperCAmelCase__ : Tuple = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase__ : List[Any] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase__ : Any = model_output
elif self.config.prediction_type == "v_prediction":
UpperCAmelCase__ : Union[str, Any] = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
" for the FlaxDDPMScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase__ : List[Any] = jnp.clip(__UpperCamelCase , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ : List[str] = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
UpperCAmelCase__ : List[Any] = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ : Tuple = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
UpperCAmelCase__ : Any = jax.random.split(__UpperCamelCase , num=1 )
UpperCAmelCase__ : int = jax.random.normal(__UpperCamelCase , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(__UpperCamelCase , __UpperCamelCase , predicted_variance=__UpperCamelCase ) ** 0.5) * noise
UpperCAmelCase__ : Dict = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
UpperCAmelCase__ : Any = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase , state=__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> jnp.ndarray:
return add_noise_common(state.common , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> jnp.ndarray:
return get_velocity_common(state.common , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __len__( self )-> Tuple:
return self.config.num_train_timesteps
| 660 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
A__ : Any = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
A__ : Optional[int] = 250_004
A__ : Tuple = 250_020
@require_sentencepiece
@require_tokenizers
class _lowercase ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_A = MBartTokenizer
_A = MBartTokenizerFast
_A = True
_A = True
def lowerCAmelCase__ ( self )-> Any:
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase__ : Dict = MBartTokenizer(__UpperCamelCase , keep_accents=__UpperCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : Any = MBartTokenizer(__UpperCamelCase , keep_accents=__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = tokenizer.tokenize("This is a test" )
self.assertListEqual(__UpperCamelCase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCamelCase ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
UpperCAmelCase__ : List[Any] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
__UpperCamelCase , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
UpperCAmelCase__ : int = tokenizer.convert_tokens_to_ids(__UpperCamelCase )
self.assertListEqual(
__UpperCamelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
UpperCAmelCase__ : str = tokenizer.convert_ids_to_tokens(__UpperCamelCase )
self.assertListEqual(
__UpperCamelCase , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def lowerCAmelCase__ ( self )-> Optional[Any]:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
UpperCAmelCase__ : str = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
UpperCAmelCase__ : Any = self.rust_tokenizer_class.from_pretrained(__UpperCamelCase , **__UpperCamelCase )
UpperCAmelCase__ : int = self.tokenizer_class.from_pretrained(__UpperCamelCase , **__UpperCamelCase )
UpperCAmelCase__ : int = tempfile.mkdtemp()
UpperCAmelCase__ : str = tokenizer_r.save_pretrained(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = tokenizer_p.save_pretrained(__UpperCamelCase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
UpperCAmelCase__ : int = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(__UpperCamelCase , __UpperCamelCase )
# Checks everything loads correctly in the same way
UpperCAmelCase__ : int = tokenizer_r.from_pretrained(__UpperCamelCase )
UpperCAmelCase__ : str = tokenizer_p.from_pretrained(__UpperCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__UpperCamelCase , __UpperCamelCase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__UpperCamelCase )
# Save tokenizer rust, legacy_format=True
UpperCAmelCase__ : Optional[Any] = tempfile.mkdtemp()
UpperCAmelCase__ : Any = tokenizer_r.save_pretrained(__UpperCamelCase , legacy_format=__UpperCamelCase )
UpperCAmelCase__ : Tuple = tokenizer_p.save_pretrained(__UpperCamelCase )
# Checks it save with the same files
self.assertSequenceEqual(__UpperCamelCase , __UpperCamelCase )
# Checks everything loads correctly in the same way
UpperCAmelCase__ : Optional[Any] = tokenizer_r.from_pretrained(__UpperCamelCase )
UpperCAmelCase__ : int = tokenizer_p.from_pretrained(__UpperCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__UpperCamelCase , __UpperCamelCase ) )
shutil.rmtree(__UpperCamelCase )
# Save tokenizer rust, legacy_format=False
UpperCAmelCase__ : Any = tempfile.mkdtemp()
UpperCAmelCase__ : Union[str, Any] = tokenizer_r.save_pretrained(__UpperCamelCase , legacy_format=__UpperCamelCase )
UpperCAmelCase__ : List[str] = tokenizer_p.save_pretrained(__UpperCamelCase )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
UpperCAmelCase__ : List[str] = tokenizer_r.from_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = tokenizer_p.from_pretrained(__UpperCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__UpperCamelCase , __UpperCamelCase ) )
shutil.rmtree(__UpperCamelCase )
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
_A = 'facebook/mbart-large-en-ro'
_A = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
_A = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
_A = [8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]
@classmethod
def lowerCAmelCase__ ( cls )-> List[Any]:
UpperCAmelCase__ : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en_XX" , tgt_lang="ro_RO" )
UpperCAmelCase__ : Any = 1
return cls
def lowerCAmelCase__ ( self )-> Dict:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] , 25_00_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] , 25_00_04 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] , 25_00_20 )
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : Tuple = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> Tuple:
self.assertIn(__UpperCamelCase , self.tokenizer.all_special_ids )
UpperCAmelCase__ : Dict = [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
UpperCAmelCase__ : List[str] = self.tokenizer.decode(__UpperCamelCase , skip_special_tokens=__UpperCamelCase )
UpperCAmelCase__ : List[str] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
self.assertNotIn(self.tokenizer.eos_token , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> int:
UpperCAmelCase__ : Optional[Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , __UpperCamelCase )
UpperCAmelCase__ : Optional[int] = 10
UpperCAmelCase__ : Optional[Any] = self.tokenizer(__UpperCamelCase , max_length=__UpperCamelCase , truncation=__UpperCamelCase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , __UpperCamelCase )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> str:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [25_00_26, 25_00_01] )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : List[Any] = tempfile.mkdtemp()
UpperCAmelCase__ : Union[str, Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Tuple = MBartTokenizer.from_pretrained(__UpperCamelCase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __UpperCamelCase )
@require_torch
def lowerCAmelCase__ ( self )-> int:
UpperCAmelCase__ : Optional[int] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__UpperCamelCase , return_tensors="pt" )
UpperCAmelCase__ : Tuple = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : List[str] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
UpperCAmelCase__ : Any = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
UpperCAmelCase__ : str = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __UpperCamelCase )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ : Dict = self.tokenizer(self.src_text , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=3 , return_tensors="pt" )
UpperCAmelCase__ : Dict = self.tokenizer(
text_target=self.tgt_text , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=10 , return_tensors="pt" )
UpperCAmelCase__ : Dict = targets["input_ids"]
UpperCAmelCase__ : List[Any] = shift_tokens_right(__UpperCamelCase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : List[Any] = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="ar_AR" )
self.assertEqual(
nested_simplify(__UpperCamelCase ) , {
# A, test, EOS, en_XX
"input_ids": [[62, 30_34, 2, 25_00_04]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 25_00_01,
} , )
| 706 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = ''
_A = 'hf-legacy' # "hf://"" is reserved for hffs
def __init__( self , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )-> str:
super().__init__(self , **__UpperCamelCase )
UpperCAmelCase__ : int = repo_info
UpperCAmelCase__ : Optional[int] = token
UpperCAmelCase__ : Optional[Any] = None
def lowerCAmelCase__ ( self )-> Optional[Any]:
if self.dir_cache is None:
UpperCAmelCase__ : str = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
UpperCAmelCase__ : str = {
"name": hf_file.rfilename,
"size": None,
"type": "file",
}
self.dir_cache.update(
{
str(__UpperCamelCase ): {"name": str(__UpperCamelCase ), "size": None, "type": "directory"}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = "rb" , **__UpperCamelCase , )-> List[Any]:
if not isinstance(self.repo_info , __UpperCamelCase ):
raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}" )
UpperCAmelCase__ : Union[str, Any] = hf_hub_url(self.repo_info.id , __UpperCamelCase , revision=self.repo_info.sha )
return fsspec.open(
__UpperCamelCase , mode=__UpperCamelCase , headers=get_authentication_headers_for_url(__UpperCamelCase , use_auth_token=self.token ) , client_kwargs={"trust_env": True} , ).open()
def lowerCAmelCase__ ( self , __UpperCamelCase , **__UpperCamelCase )-> List[str]:
self._get_dirs()
UpperCAmelCase__ : Union[str, Any] = self._strip_protocol(__UpperCamelCase )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase=False , **__UpperCamelCase )-> str:
self._get_dirs()
UpperCAmelCase__ : str = PurePosixPath(path.strip("/" ) )
UpperCAmelCase__ : Optional[Any] = {}
for p, f in self.dir_cache.items():
UpperCAmelCase__ : Optional[int] = PurePosixPath(p.strip("/" ) )
UpperCAmelCase__ : Dict = p.parent
if root == path:
UpperCAmelCase__ : Tuple = f
UpperCAmelCase__ : List[Any] = list(paths.values() )
if detail:
return out
else:
return sorted(f["name"] for f in out )
| 660 | 0 |
def a__ ( lowerCAmelCase : int = 10**9 ):
'''simple docstring'''
UpperCAmelCase__ : Any = 1
UpperCAmelCase__ : Tuple = 2
UpperCAmelCase__ : List[Any] = 0
UpperCAmelCase__ : List[Any] = 0
UpperCAmelCase__ : List[str] = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
UpperCAmelCase__ : Any = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f"""{solution() = }""")
| 707 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
A__ : Dict = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
if isinstance(lowerCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowerCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowerCAmelCase ):
return [[videos]]
raise ValueError(F"Could not make batched video from {videos}" )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = ['pixel_values']
def __init__( self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_55 , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )-> None:
super().__init__(**__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = size if size is not None else {"shortest_edge": 2_56}
UpperCAmelCase__ : List[Any] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : List[str] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
UpperCAmelCase__ : int = get_size_dict(__UpperCamelCase , param_name="crop_size" )
UpperCAmelCase__ : Dict = do_resize
UpperCAmelCase__ : Optional[int] = size
UpperCAmelCase__ : List[Any] = do_center_crop
UpperCAmelCase__ : str = crop_size
UpperCAmelCase__ : Optional[int] = resample
UpperCAmelCase__ : int = do_rescale
UpperCAmelCase__ : Union[str, Any] = rescale_factor
UpperCAmelCase__ : Union[str, Any] = offset
UpperCAmelCase__ : Dict = do_normalize
UpperCAmelCase__ : int = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase__ : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
if "shortest_edge" in size:
UpperCAmelCase__ : Union[str, Any] = get_resize_output_image_size(__UpperCamelCase , size["shortest_edge"] , default_to_square=__UpperCamelCase )
elif "height" in size and "width" in size:
UpperCAmelCase__ : Any = (size["height"], size["width"])
else:
raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[Any] = get_size_dict(__UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(__UpperCamelCase , size=(size["height"], size["width"]) , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = True , __UpperCamelCase = None , **__UpperCamelCase , )-> Tuple:
UpperCAmelCase__ : str = image.astype(np.floataa )
if offset:
UpperCAmelCase__ : Tuple = image - (scale / 2)
return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
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 = ChannelDimension.FIRST , )-> np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError("Size and resample 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." )
if offset and not do_rescale:
raise ValueError("For offset, do_rescale must also be set to True." )
# All transformations expect numpy arrays.
UpperCAmelCase__ : Optional[Any] = to_numpy_array(__UpperCamelCase )
if do_resize:
UpperCAmelCase__ : Union[str, Any] = self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase )
if do_center_crop:
UpperCAmelCase__ : int = self.center_crop(__UpperCamelCase , size=__UpperCamelCase )
if do_rescale:
UpperCAmelCase__ : List[str] = self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase , offset=__UpperCamelCase )
if do_normalize:
UpperCAmelCase__ : List[Any] = self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase )
UpperCAmelCase__ : Dict = to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase )
return image
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 , )-> PIL.Image.Image:
UpperCAmelCase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ : int = resample if resample is not None else self.resample
UpperCAmelCase__ : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase__ : int = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ : Optional[int] = offset if offset is not None else self.offset
UpperCAmelCase__ : Dict = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ : Dict = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ : Optional[int] = image_std if image_std is not None else self.image_std
UpperCAmelCase__ : List[str] = size if size is not None else self.size
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : Dict = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase__ : Tuple = get_size_dict(__UpperCamelCase , param_name="crop_size" )
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." )
UpperCAmelCase__ : List[str] = make_batched(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = [
[
self._preprocess_image(
image=__UpperCamelCase , do_resize=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , do_center_crop=__UpperCamelCase , crop_size=__UpperCamelCase , do_rescale=__UpperCamelCase , rescale_factor=__UpperCamelCase , offset=__UpperCamelCase , do_normalize=__UpperCamelCase , image_mean=__UpperCamelCase , image_std=__UpperCamelCase , data_format=__UpperCamelCase , )
for img in video
]
for video in videos
]
UpperCAmelCase__ : Dict = {"pixel_values": videos}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 660 | 0 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
A__ : Optional[Any] = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : bool , lowerCAmelCase : bool ):
'''simple docstring'''
def run_func(lowerCAmelCase : Dict ):
@wraps(lowerCAmelCase )
def run_in_eager_mode(*lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Dict ):
return func(*lowerCAmelCase , **lowerCAmelCase )
@wraps(lowerCAmelCase )
@tf.function(experimental_compile=lowerCAmelCase )
def run_in_graph_mode(*lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Optional[Any] ):
return func(*lowerCAmelCase , **lowerCAmelCase )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`." )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def a__ ( lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Dict = random.Random()
UpperCAmelCase__ : List[str] = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(lowerCAmelCase , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
_A = 42
_A = 'TensorFlow'
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
return tf.__version__
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
# initialize GPU on separate process
UpperCAmelCase__ : Any = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Union[str, Any] = self._prepare_inference_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_speed(_inference )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
UpperCAmelCase__ : List[Any] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : List[Any] = self._prepare_train_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_speed(_train )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> [Memory, Optional[MemorySummary]]:
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __UpperCamelCase )
UpperCAmelCase__ : List[str] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Any = self._prepare_inference_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_memory(_inference )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> [Memory, Optional[MemorySummary]]:
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __UpperCamelCase )
UpperCAmelCase__ : Any = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Optional[Any] = self._prepare_train_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_memory(_train )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Callable[[], None]:
UpperCAmelCase__ : Union[str, Any] = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
UpperCAmelCase__ : Optional[int] = (
hasattr(__UpperCamelCase , "architectures" )
and isinstance(config.architectures , __UpperCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCAmelCase__ : str = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCAmelCase__ : Any = __import__("transformers" , fromlist=[model_class] )
UpperCAmelCase__ : List[Any] = getattr(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Dict = model_cls(__UpperCamelCase )
except ImportError:
raise ImportError(
F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
UpperCAmelCase__ : Any = TF_MODEL_MAPPING[config.__class__](__UpperCamelCase )
# encoder-decoder has vocab size saved differently
UpperCAmelCase__ : int = config.vocab_size if hasattr(__UpperCamelCase , "vocab_size" ) else config.encoder.vocab_size
UpperCAmelCase__ : Optional[Any] = random_input_ids(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(__UpperCamelCase , decoder_input_ids=__UpperCamelCase , training=__UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(__UpperCamelCase , training=__UpperCamelCase )
UpperCAmelCase__ : Dict = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Callable[[], None]:
UpperCAmelCase__ : List[Any] = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`." )
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
UpperCAmelCase__ : Any = (
hasattr(__UpperCamelCase , "architectures" )
and isinstance(config.architectures , __UpperCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCAmelCase__ : Any = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCAmelCase__ : int = __import__("transformers" , fromlist=[model_class] )
UpperCAmelCase__ : int = getattr(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = model_cls(__UpperCamelCase )
except ImportError:
raise ImportError(
F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
UpperCAmelCase__ : List[str] = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCamelCase )
# encoder-decoder has vocab size saved differently
UpperCAmelCase__ : Union[str, Any] = config.vocab_size if hasattr(__UpperCamelCase , "vocab_size" ) else config.encoder.vocab_size
UpperCAmelCase__ : Dict = random_input_ids(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
UpperCAmelCase__ : Union[str, Any] = model(__UpperCamelCase , decoder_input_ids=__UpperCamelCase , labels=__UpperCamelCase , training=__UpperCamelCase )[0]
UpperCAmelCase__ : Union[str, Any] = tf.gradients(__UpperCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
UpperCAmelCase__ : Union[str, Any] = model(__UpperCamelCase , labels=__UpperCamelCase , training=__UpperCamelCase )[0]
UpperCAmelCase__ : Any = tf.gradients(__UpperCamelCase , model.trainable_variables )
return gradients
UpperCAmelCase__ : str = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def lowerCAmelCase__ ( self , __UpperCamelCase )-> float:
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("Do inference on TPU. Running model 5 times to stabilize compilation" )
timeit.repeat(__UpperCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
UpperCAmelCase__ : Optional[Any] = timeit.repeat(
__UpperCamelCase , repeat=self.args.repeat , number=10 , )
return min(__UpperCamelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(F"Doesn't fit on GPU. {e}" )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> [Memory, MemorySummary]:
logger.info(
"Note that TensorFlow allocates more memory than "
"it might need to speed up computation. "
"The memory reported here corresponds to the memory "
"reported by `nvidia-smi`, which can vary depending "
"on total available memory on the GPU that is used." )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
" consumption line by line." )
UpperCAmelCase__ : List[str] = start_memory_tracing("transformers" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
" with `args.memory=False`" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"py3nvml not installed, we won't log GPU memory usage. "
"Install py3nvml (pip install py3nvml) to log information about GPU." )
UpperCAmelCase__ : Optional[int] = "N/A"
else:
logger.info(
"Measuring total GPU usage on GPU device. Make sure to not have additional processes"
" running on the same GPU." )
# init nvml
nvml.nvmlInit()
func()
UpperCAmelCase__ : Any = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
UpperCAmelCase__ : Optional[int] = nvml.nvmlDeviceGetMemoryInfo(__UpperCamelCase )
UpperCAmelCase__ : str = meminfo.used
UpperCAmelCase__ : int = Memory(__UpperCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
" TensorFlow." )
UpperCAmelCase__ : Any = None
else:
UpperCAmelCase__ : List[Any] = measure_peak_memory_cpu(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = Memory(__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
UpperCAmelCase__ : Optional[Any] = stop_memory_tracing(__UpperCamelCase )
if memory is None:
UpperCAmelCase__ : Tuple = summary.total
else:
UpperCAmelCase__ : int = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(F"Doesn't fit on GPU. {e}" )
return "N/A", None
| 708 |
"""simple docstring"""
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowerCAmelCase , lowerCAmelCase ):
raise TypeError("Input value must be a 'int' type" )
return bin(lowerCAmelCase ).count("1" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase : int = 100_0000 ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = set(range(3 , lowerCAmelCase , 2 ) )
primes.add(2 )
for p in range(3 , lowerCAmelCase , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , lowerCAmelCase , lowerCAmelCase ) ) )
UpperCAmelCase__ : int = [float(lowerCAmelCase ) for n in range(limit + 1 )]
for p in primes:
for n in range(lowerCAmelCase , limit + 1 , lowerCAmelCase ):
phi[n] *= 1 - 1 / p
return int(sum(phi[2:] ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 709 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
A__ : Optional[Any] = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : bool , lowerCAmelCase : bool ):
'''simple docstring'''
def run_func(lowerCAmelCase : Dict ):
@wraps(lowerCAmelCase )
def run_in_eager_mode(*lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Dict ):
return func(*lowerCAmelCase , **lowerCAmelCase )
@wraps(lowerCAmelCase )
@tf.function(experimental_compile=lowerCAmelCase )
def run_in_graph_mode(*lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Optional[Any] ):
return func(*lowerCAmelCase , **lowerCAmelCase )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`." )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def a__ ( lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Dict = random.Random()
UpperCAmelCase__ : List[str] = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(lowerCAmelCase , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
_A = 42
_A = "TensorFlow"
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
return tf.__version__
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
# initialize GPU on separate process
UpperCAmelCase__ : Any = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Union[str, Any] = self._prepare_inference_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_speed(_inference )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
UpperCAmelCase__ : List[Any] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : List[Any] = self._prepare_train_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_speed(_train )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> [Memory, Optional[MemorySummary]]:
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __UpperCamelCase )
UpperCAmelCase__ : List[str] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Any = self._prepare_inference_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_memory(_inference )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> [Memory, Optional[MemorySummary]]:
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __UpperCamelCase )
UpperCAmelCase__ : Any = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Optional[Any] = self._prepare_train_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_memory(_train )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Callable[[], None]:
UpperCAmelCase__ : Union[str, Any] = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
UpperCAmelCase__ : Optional[int] = (
hasattr(__UpperCamelCase , "architectures" )
and isinstance(config.architectures , __UpperCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCAmelCase__ : str = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCAmelCase__ : Any = __import__("transformers" , fromlist=[model_class] )
UpperCAmelCase__ : List[Any] = getattr(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Dict = model_cls(__UpperCamelCase )
except ImportError:
raise ImportError(
F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
UpperCAmelCase__ : Any = TF_MODEL_MAPPING[config.__class__](__UpperCamelCase )
# encoder-decoder has vocab size saved differently
UpperCAmelCase__ : int = config.vocab_size if hasattr(__UpperCamelCase , "vocab_size" ) else config.encoder.vocab_size
UpperCAmelCase__ : Optional[Any] = random_input_ids(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(__UpperCamelCase , decoder_input_ids=__UpperCamelCase , training=__UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(__UpperCamelCase , training=__UpperCamelCase )
UpperCAmelCase__ : Dict = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Callable[[], None]:
UpperCAmelCase__ : List[Any] = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`." )
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
UpperCAmelCase__ : Any = (
hasattr(__UpperCamelCase , "architectures" )
and isinstance(config.architectures , __UpperCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCAmelCase__ : Any = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCAmelCase__ : int = __import__("transformers" , fromlist=[model_class] )
UpperCAmelCase__ : int = getattr(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = model_cls(__UpperCamelCase )
except ImportError:
raise ImportError(
F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
UpperCAmelCase__ : List[str] = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCamelCase )
# encoder-decoder has vocab size saved differently
UpperCAmelCase__ : Union[str, Any] = config.vocab_size if hasattr(__UpperCamelCase , "vocab_size" ) else config.encoder.vocab_size
UpperCAmelCase__ : Dict = random_input_ids(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
UpperCAmelCase__ : Union[str, Any] = model(__UpperCamelCase , decoder_input_ids=__UpperCamelCase , labels=__UpperCamelCase , training=__UpperCamelCase )[0]
UpperCAmelCase__ : Union[str, Any] = tf.gradients(__UpperCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
UpperCAmelCase__ : Union[str, Any] = model(__UpperCamelCase , labels=__UpperCamelCase , training=__UpperCamelCase )[0]
UpperCAmelCase__ : Any = tf.gradients(__UpperCamelCase , model.trainable_variables )
return gradients
UpperCAmelCase__ : str = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def lowerCAmelCase__ ( self , __UpperCamelCase )-> float:
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("Do inference on TPU. Running model 5 times to stabilize compilation" )
timeit.repeat(__UpperCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
UpperCAmelCase__ : Optional[Any] = timeit.repeat(
__UpperCamelCase , repeat=self.args.repeat , number=10 , )
return min(__UpperCamelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(F"Doesn't fit on GPU. {e}" )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> [Memory, MemorySummary]:
logger.info(
"Note that TensorFlow allocates more memory than "
"it might need to speed up computation. "
"The memory reported here corresponds to the memory "
"reported by `nvidia-smi`, which can vary depending "
"on total available memory on the GPU that is used." )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
" consumption line by line." )
UpperCAmelCase__ : List[str] = start_memory_tracing("transformers" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
" with `args.memory=False`" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"py3nvml not installed, we won't log GPU memory usage. "
"Install py3nvml (pip install py3nvml) to log information about GPU." )
UpperCAmelCase__ : Optional[int] = "N/A"
else:
logger.info(
"Measuring total GPU usage on GPU device. Make sure to not have additional processes"
" running on the same GPU." )
# init nvml
nvml.nvmlInit()
func()
UpperCAmelCase__ : Any = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
UpperCAmelCase__ : Optional[int] = nvml.nvmlDeviceGetMemoryInfo(__UpperCamelCase )
UpperCAmelCase__ : str = meminfo.used
UpperCAmelCase__ : int = Memory(__UpperCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
" TensorFlow." )
UpperCAmelCase__ : Any = None
else:
UpperCAmelCase__ : List[Any] = measure_peak_memory_cpu(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = Memory(__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
UpperCAmelCase__ : Optional[Any] = stop_memory_tracing(__UpperCamelCase )
if memory is None:
UpperCAmelCase__ : Tuple = summary.total
else:
UpperCAmelCase__ : int = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(F"Doesn't fit on GPU. {e}" )
return "N/A", None
| 660 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
A__ : Dict = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
if isinstance(lowerCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowerCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowerCAmelCase ):
return [[videos]]
raise ValueError(F"Could not make batched video from {videos}" )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = ['pixel_values']
def __init__( self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_55 , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )-> None:
super().__init__(**__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = size if size is not None else {"shortest_edge": 2_56}
UpperCAmelCase__ : List[Any] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : List[str] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
UpperCAmelCase__ : int = get_size_dict(__UpperCamelCase , param_name="crop_size" )
UpperCAmelCase__ : Dict = do_resize
UpperCAmelCase__ : Optional[int] = size
UpperCAmelCase__ : List[Any] = do_center_crop
UpperCAmelCase__ : str = crop_size
UpperCAmelCase__ : Optional[int] = resample
UpperCAmelCase__ : int = do_rescale
UpperCAmelCase__ : Union[str, Any] = rescale_factor
UpperCAmelCase__ : Union[str, Any] = offset
UpperCAmelCase__ : Dict = do_normalize
UpperCAmelCase__ : int = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase__ : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
if "shortest_edge" in size:
UpperCAmelCase__ : Union[str, Any] = get_resize_output_image_size(__UpperCamelCase , size["shortest_edge"] , default_to_square=__UpperCamelCase )
elif "height" in size and "width" in size:
UpperCAmelCase__ : Any = (size["height"], size["width"])
else:
raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[Any] = get_size_dict(__UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(__UpperCamelCase , size=(size["height"], size["width"]) , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = True , __UpperCamelCase = None , **__UpperCamelCase , )-> Tuple:
UpperCAmelCase__ : str = image.astype(np.floataa )
if offset:
UpperCAmelCase__ : Tuple = image - (scale / 2)
return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
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 = ChannelDimension.FIRST , )-> np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError("Size and resample 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." )
if offset and not do_rescale:
raise ValueError("For offset, do_rescale must also be set to True." )
# All transformations expect numpy arrays.
UpperCAmelCase__ : Optional[Any] = to_numpy_array(__UpperCamelCase )
if do_resize:
UpperCAmelCase__ : Union[str, Any] = self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase )
if do_center_crop:
UpperCAmelCase__ : int = self.center_crop(__UpperCamelCase , size=__UpperCamelCase )
if do_rescale:
UpperCAmelCase__ : List[str] = self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase , offset=__UpperCamelCase )
if do_normalize:
UpperCAmelCase__ : List[Any] = self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase )
UpperCAmelCase__ : Dict = to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase )
return image
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 , )-> PIL.Image.Image:
UpperCAmelCase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ : int = resample if resample is not None else self.resample
UpperCAmelCase__ : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase__ : int = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ : Optional[int] = offset if offset is not None else self.offset
UpperCAmelCase__ : Dict = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ : Dict = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ : Optional[int] = image_std if image_std is not None else self.image_std
UpperCAmelCase__ : List[str] = size if size is not None else self.size
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : Dict = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase__ : Tuple = get_size_dict(__UpperCamelCase , param_name="crop_size" )
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." )
UpperCAmelCase__ : List[str] = make_batched(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = [
[
self._preprocess_image(
image=__UpperCamelCase , do_resize=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , do_center_crop=__UpperCamelCase , crop_size=__UpperCamelCase , do_rescale=__UpperCamelCase , rescale_factor=__UpperCamelCase , offset=__UpperCamelCase , do_normalize=__UpperCamelCase , image_mean=__UpperCamelCase , image_std=__UpperCamelCase , data_format=__UpperCamelCase , )
for img in video
]
for video in videos
]
UpperCAmelCase__ : Dict = {"pixel_values": videos}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 710 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
A__ : List[str] = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
A__ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 660 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
A__ : str = logging.get_logger("""transformers.models.speecht5""")
A__ : List[str] = {
"""speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
"""speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
"""speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
"""speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
A__ : Union[str, Any] = {
"""text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
"""text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
A__ : str = {
"""speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
"""speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
"""speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
"""speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
"""speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
A__ : Optional[int] = {
"""speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
"""speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
"""speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
"""speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
"""speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
"""speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
"""speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
"""speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
"""speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
"""speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
"""speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
"""speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
A__ : Tuple = {
"""text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
A__ : int = {
"""text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
A__ : int = {
"""encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
"""encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
"""encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
"""encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
"""encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
"""encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
"""encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
"""encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
"""encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
A__ : List[Any] = {
"""decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
"""decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
"""decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
"""decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
"""decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
"""decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
"""decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
"""decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
"""decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
"""decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
"""decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
"""decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
"""decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
A__ : List[Any] = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
A__ : Tuple = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
A__ : int = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
A__ : List[Any] = []
A__ : int = [
"""encoder.version""",
"""encoder.layers.*.norm_k.weight""",
"""encoder.layers.*.norm_k.bias""",
"""decoder.version""",
"""decoder.layers.*.norm_k.weight""",
"""decoder.layers.*.norm_k.bias""",
"""decoder.pos_emb.pe_k""",
"""speech_encoder_prenet.embed_positions._float_tensor""",
"""text_decoder_prenet.embed_positions._float_tensor""",
]
A__ : Any = IGNORE_KEYS + [
"""encoder.proj""",
"""text_encoder_prenet.*""",
"""speech_decoder_prenet.*""",
"""speech_decoder_postnet.*""",
]
A__ : Tuple = IGNORE_KEYS + [
"""encoder.proj""",
"""speech_encoder_prenet.*""",
"""text_decoder_prenet.*""",
"""text_decoder_postnet.*""",
]
A__ : Union[str, Any] = IGNORE_KEYS + [
"""encoder.proj""",
"""text_encoder_prenet.*""",
"""text_decoder_prenet.*""",
"""text_decoder_postnet.*""",
]
def a__ ( lowerCAmelCase : Dict , lowerCAmelCase : List[str] , lowerCAmelCase : int , lowerCAmelCase : Optional[int] , lowerCAmelCase : Tuple ):
'''simple docstring'''
for attribute in key.split("." ):
UpperCAmelCase__ : Union[str, Any] = getattr(lowerCAmelCase , lowerCAmelCase )
if weight_type is not None:
UpperCAmelCase__ : List[str] = getattr(lowerCAmelCase , lowerCAmelCase ).shape
else:
UpperCAmelCase__ : Tuple = 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":
UpperCAmelCase__ : Union[str, Any] = value
elif weight_type == "weight_g":
UpperCAmelCase__ : Optional[int] = value
elif weight_type == "weight_v":
UpperCAmelCase__ : str = value
elif weight_type == "bias":
UpperCAmelCase__ : str = value
elif weight_type == "running_mean":
UpperCAmelCase__ : Tuple = value
elif weight_type == "running_var":
UpperCAmelCase__ : Dict = value
elif weight_type == "num_batches_tracked":
UpperCAmelCase__ : int = value
else:
UpperCAmelCase__ : Any = value
logger.info(F"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}." )
def a__ ( lowerCAmelCase : Tuple , lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
for key in ignore_keys:
if key.endswith(".*" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
UpperCAmelCase__ : Dict = key.split(".*." )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def a__ ( lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = []
if task == "s2t":
UpperCAmelCase__ : int = hf_model.speechta.encoder.prenet.feature_encoder
UpperCAmelCase__ : Union[str, Any] = MAPPING_S2T
UpperCAmelCase__ : str = IGNORE_KEYS_S2T
elif task == "t2s":
UpperCAmelCase__ : Optional[int] = None
UpperCAmelCase__ : List[Any] = MAPPING_T2S
UpperCAmelCase__ : Any = IGNORE_KEYS_T2S
elif task == "s2s":
UpperCAmelCase__ : str = hf_model.speechta.encoder.prenet.feature_encoder
UpperCAmelCase__ : int = MAPPING_S2S
UpperCAmelCase__ : Tuple = IGNORE_KEYS_S2S
else:
raise ValueError(F"Unsupported task: {task}" )
for name, value in fairseq_dict.items():
if should_ignore(lowerCAmelCase , lowerCAmelCase ):
logger.info(F"{name} was ignored" )
continue
UpperCAmelCase__ : str = False
if "conv_layers" in name:
load_conv_layer(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , hf_model.config.feat_extract_norm == "group" , )
UpperCAmelCase__ : Any = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
UpperCAmelCase__ : List[Any] = key.split(".*." )
if prefix in name and suffix in name:
UpperCAmelCase__ : List[str] = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
UpperCAmelCase__ : str = True
if "*" in mapped_key:
UpperCAmelCase__ : List[Any] = name.split(lowerCAmelCase )[0].split("." )[-2]
UpperCAmelCase__ : Union[str, Any] = mapped_key.replace("*" , lowerCAmelCase )
if "weight_g" in name:
UpperCAmelCase__ : Optional[int] = "weight_g"
elif "weight_v" in name:
UpperCAmelCase__ : str = "weight_v"
elif "bias" in name:
UpperCAmelCase__ : Union[str, Any] = "bias"
elif "weight" in name:
UpperCAmelCase__ : List[Any] = "weight"
elif "running_mean" in name:
UpperCAmelCase__ : int = "running_mean"
elif "running_var" in name:
UpperCAmelCase__ : str = "running_var"
elif "num_batches_tracked" in name:
UpperCAmelCase__ : Optional[Any] = "num_batches_tracked"
else:
UpperCAmelCase__ : Dict = None
set_recursively(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
continue
if not is_used:
unused_weights.append(lowerCAmelCase )
logger.warning(F"Unused weights: {unused_weights}" )
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : Dict , lowerCAmelCase : List[Any] , lowerCAmelCase : Tuple , lowerCAmelCase : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = full_name.split("conv_layers." )[-1]
UpperCAmelCase__ : List[Any] = name.split("." )
UpperCAmelCase__ : List[str] = int(items[0] )
UpperCAmelCase__ : Union[str, Any] = 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." )
UpperCAmelCase__ : Any = 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." )
UpperCAmelCase__ : Tuple = 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.conv_layers[layer_id].layer_norm.bias.data.shape} was found." )
UpperCAmelCase__ : Any = 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.conv_layers[layer_id].layer_norm.weight.data.shape} was found." )
UpperCAmelCase__ : Optional[Any] = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(lowerCAmelCase )
@torch.no_grad()
def a__ ( lowerCAmelCase : Optional[int] , lowerCAmelCase : str , lowerCAmelCase : int , lowerCAmelCase : Union[str, Any]=None , lowerCAmelCase : Union[str, Any]=None , lowerCAmelCase : Any=None , ):
'''simple docstring'''
if config_path is not None:
UpperCAmelCase__ : Any = SpeechTaConfig.from_pretrained(lowerCAmelCase )
else:
UpperCAmelCase__ : Any = SpeechTaConfig()
if task == "s2t":
UpperCAmelCase__ : Optional[int] = config.max_text_positions
UpperCAmelCase__ : str = SpeechTaForSpeechToText(lowerCAmelCase )
elif task == "t2s":
UpperCAmelCase__ : Dict = 1876
UpperCAmelCase__ : int = 600
UpperCAmelCase__ : Tuple = config.max_speech_positions
UpperCAmelCase__ : str = SpeechTaForTextToSpeech(lowerCAmelCase )
elif task == "s2s":
UpperCAmelCase__ : List[str] = 1876
UpperCAmelCase__ : int = config.max_speech_positions
UpperCAmelCase__ : Union[str, Any] = SpeechTaForSpeechToSpeech(lowerCAmelCase )
else:
raise ValueError(F"Unknown task name: {task}" )
if vocab_path:
UpperCAmelCase__ : List[Any] = SpeechTaTokenizer(lowerCAmelCase , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
UpperCAmelCase__ : List[Any] = AddedToken("<mask>" , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase )
UpperCAmelCase__ : Tuple = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token} )
tokenizer.add_tokens(["<ctc_blank>"] )
UpperCAmelCase__ : Optional[int] = SpeechTaFeatureExtractor()
UpperCAmelCase__ : Union[str, Any] = SpeechTaProcessor(tokenizer=lowerCAmelCase , feature_extractor=lowerCAmelCase )
processor.save_pretrained(lowerCAmelCase )
UpperCAmelCase__ : int = torch.load(lowerCAmelCase )
recursively_load_weights(fairseq_checkpoint["model"] , lowerCAmelCase , lowerCAmelCase )
model.save_pretrained(lowerCAmelCase )
if repo_id:
print("Pushing to the hub..." )
processor.push_to_hub(lowerCAmelCase )
model.push_to_hub(lowerCAmelCase )
if __name__ == "__main__":
A__ : Dict = argparse.ArgumentParser()
parser.add_argument(
"""--task""",
default="""s2t""",
type=str,
help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
)
parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
A__ : Any = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 711 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
@register_to_config
def __init__( self , __UpperCamelCase = 7_68 , )-> Union[str, Any]:
super().__init__()
UpperCAmelCase__ : str = nn.Parameter(torch.zeros(1 , __UpperCamelCase ) )
UpperCAmelCase__ : Optional[int] = nn.Parameter(torch.ones(1 , __UpperCamelCase ) )
def lowerCAmelCase__ ( self , __UpperCamelCase = None , __UpperCamelCase = None , )-> Any:
UpperCAmelCase__ : Dict = nn.Parameter(self.mean.to(__UpperCamelCase ).to(__UpperCamelCase ) )
UpperCAmelCase__ : Any = nn.Parameter(self.std.to(__UpperCamelCase ).to(__UpperCamelCase ) )
return self
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Union[str, Any]:
UpperCAmelCase__ : Dict = (embeds - self.mean) * 1.0 / self.std
return embeds
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[Any]:
UpperCAmelCase__ : Any = (embeds * self.std) + self.mean
return embeds
| 660 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : Any , lowerCAmelCase : List[Any] ):
'''simple docstring'''
# Construct model
if gpta_config_file == "":
UpperCAmelCase__ : Optional[int] = GPTaConfig()
else:
UpperCAmelCase__ : Dict = GPTaConfig.from_json_file(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = GPTaModel(lowerCAmelCase )
# Load weights from numpy
load_tf_weights_in_gpta(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Save pytorch-model
UpperCAmelCase__ : Optional[int] = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
UpperCAmelCase__ : Any = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(model.state_dict() , lowerCAmelCase )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(lowerCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
A__ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--gpt2_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained OpenAI model. \n"""
"""This specifies the model architecture."""
),
)
A__ : Optional[Any] = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 712 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : Any , lowerCAmelCase : List[Any] ):
'''simple docstring'''
# Construct model
if gpta_config_file == "":
UpperCAmelCase__ : Optional[int] = GPTaConfig()
else:
UpperCAmelCase__ : Dict = GPTaConfig.from_json_file(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = GPTaModel(lowerCAmelCase )
# Load weights from numpy
load_tf_weights_in_gpta(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Save pytorch-model
UpperCAmelCase__ : Optional[int] = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
UpperCAmelCase__ : Any = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(model.state_dict() , lowerCAmelCase )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(lowerCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
A__ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--gpt2_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained OpenAI model. \n"""
"""This specifies the model architecture."""
),
)
A__ : Optional[Any] = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 660 | 0 |
"""simple docstring"""
A__ : str = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
A__ : str = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
A__ : Any = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 713 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
A__ : Optional[int] = ["""small""", """medium""", """large"""]
A__ : Optional[int] = """lm_head.decoder.weight"""
A__ : Dict = """lm_head.weight"""
def a__ ( lowerCAmelCase : str , lowerCAmelCase : str ):
'''simple docstring'''
UpperCAmelCase__ : Dict = torch.load(lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = d.pop(lowerCAmelCase )
os.makedirs(lowerCAmelCase , exist_ok=lowerCAmelCase )
torch.save(lowerCAmelCase , os.path.join(lowerCAmelCase , lowerCAmelCase ) )
if __name__ == "__main__":
A__ : List[Any] = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
A__ : Tuple = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
A__ : Tuple = os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
A__ : str = f"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 660 | 0 |
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
A__ : Optional[int] = _symbol_database.Default()
A__ : int = _descriptor_pool.Default().AddSerializedFile(
b"""\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"""
)
A__ : str = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
A__ : Dict = None
A__ : List[str] = b"""H\003"""
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
A__ : Tuple = 45
A__ : List[Any] = 1_581
A__ : int = 1_517
A__ : str = 1_570
A__ : Union[str, Any] = 1_584
A__ : List[str] = 1_793
A__ : List[str] = 1_795
A__ : List[Any] = 1_916
A__ : Optional[Any] = 1_864
A__ : str = 1_905
A__ : Optional[Any] = 1_919
A__ : Optional[int] = 2_429
A__ : int = 2_208
A__ : int = 2_418
A__ : Union[str, Any] = 2_323
A__ : Dict = 2_407
# @@protoc_insertion_point(module_scope)
| 714 |
"""simple docstring"""
from math import isqrt
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : List[Any] = False
return [i for i in range(2 , lowerCAmelCase ) if is_prime[i]]
def a__ ( lowerCAmelCase : int = 10**8 ):
'''simple docstring'''
UpperCAmelCase__ : Dict = calculate_prime_numbers(max_number // 2 )
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Tuple = len(lowerCAmelCase ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 660 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ : Optional[Any] = {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : List[Any] = ["""XLNetTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple = ["""XLNetTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[Any] = [
"""XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XLNetForMultipleChoice""",
"""XLNetForQuestionAnswering""",
"""XLNetForQuestionAnsweringSimple""",
"""XLNetForSequenceClassification""",
"""XLNetForTokenClassification""",
"""XLNetLMHeadModel""",
"""XLNetModel""",
"""XLNetPreTrainedModel""",
"""load_tf_weights_in_xlnet""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
"""TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFXLNetForMultipleChoice""",
"""TFXLNetForQuestionAnsweringSimple""",
"""TFXLNetForSequenceClassification""",
"""TFXLNetForTokenClassification""",
"""TFXLNetLMHeadModel""",
"""TFXLNetMainLayer""",
"""TFXLNetModel""",
"""TFXLNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
A__ : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 715 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a__ ( lowerCAmelCase : str , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Dict , lowerCAmelCase : List[Any] ):
'''simple docstring'''
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Optional[int] = np.full((len(lowerCAmelCase ), sequence_length, 2) , lowerCAmelCase )
else:
UpperCAmelCase__ : Optional[Any] = np.full((len(lowerCAmelCase ), sequence_length) , lowerCAmelCase )
for i, tensor in enumerate(lowerCAmelCase ):
if padding_side == "right":
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Dict = tensor[:sequence_length]
else:
UpperCAmelCase__ : Tuple = tensor[:sequence_length]
else:
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Optional[Any] = tensor[:sequence_length]
else:
UpperCAmelCase__ : int = tensor[:sequence_length]
return out_tensor.tolist()
def a__ ( lowerCAmelCase : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = ord(lowerCAmelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
UpperCAmelCase__ : Optional[Any] = unicodedata.category(lowerCAmelCase )
if cat.startswith("P" ):
return True
return False
@dataclass
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
_A = True
_A = None
_A = None
_A = -100
_A = "pt"
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[str]:
import torch
UpperCAmelCase__ : Optional[Any] = "label" if "label" in features[0].keys() else "labels"
UpperCAmelCase__ : Dict = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
UpperCAmelCase__ : str = self.tokenizer.pad(
__UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" if labels is None else None , )
if labels is None:
return batch
UpperCAmelCase__ : Optional[Any] = torch.tensor(batch["entity_ids"] ).shape[1]
UpperCAmelCase__ : int = self.tokenizer.padding_side
if padding_side == "right":
UpperCAmelCase__ : int = [
list(__UpperCamelCase ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) for label in labels
]
else:
UpperCAmelCase__ : List[Any] = [
[self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) + list(__UpperCamelCase ) for label in labels
]
UpperCAmelCase__ : Optional[Any] = [feature["ner_tags"] for feature in features]
UpperCAmelCase__ : int = padding_tensor(__UpperCamelCase , -1 , __UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : List[Any] = [feature["original_entity_spans"] for feature in features]
UpperCAmelCase__ : int = padding_tensor(__UpperCamelCase , (-1, -1) , __UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[int] = {k: torch.tensor(__UpperCamelCase , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 660 | 0 |
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A__ : str = logging.get_logger(__name__)
A__ : Any = {"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}
A__ : int = {
"""vocab_file""": {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
},
"""emoji_file""": {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
},
}
A__ : Dict = {
"""abeja/gpt-neox-japanese-2.7b""": 2_048,
}
def a__ ( lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
with open(lowerCAmelCase , "r" , encoding="utf-8" ) as f:
UpperCAmelCase__ : Optional[Any] = json.loads(f.read() )
UpperCAmelCase__ : Optional[int] = collections.OrderedDict()
UpperCAmelCase__ : List[str] = collections.OrderedDict()
UpperCAmelCase__ : int = collections.OrderedDict()
with open(lowerCAmelCase , "r" , encoding="utf-8" ) as f:
UpperCAmelCase__ : Dict = f.readlines()
UpperCAmelCase__ : List[str] = [[t.rstrip("\n" )] if (t == "," or "," not in t) else t.rstrip("\n" ).split("," ) for t in token]
for idx, b in enumerate(lowerCAmelCase ):
UpperCAmelCase__ : List[Any] = b
UpperCAmelCase__ : str = idx
for wd in b:
UpperCAmelCase__ : str = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = VOCAB_FILES_NAMES
_A = PRETRAINED_VOCAB_FILES_MAP
_A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A = ['input_ids', 'attention_mask']
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase="<|endoftext|>" , __UpperCamelCase="<|endoftext|>" , __UpperCamelCase="<|startoftext|>" , __UpperCamelCase="<|endoftext|>" , __UpperCamelCase=False , **__UpperCamelCase , )-> Dict:
super().__init__(
unk_token=__UpperCamelCase , pad_token=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , do_clean_text=__UpperCamelCase , **__UpperCamelCase , )
if not os.path.isfile(__UpperCamelCase ):
raise ValueError(
F"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
" model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
if not os.path.isfile(__UpperCamelCase ):
raise ValueError(
F"Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"
" pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
UpperCAmelCase__ : List[Any] = do_clean_text
UpperCAmelCase__ : int = load_vocab_and_emoji(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Tuple = SubWordJapaneseTokenizer(
vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji )
@property
def lowerCAmelCase__ ( self )-> Union[str, Any]:
# self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
return len(self.raw_vocab )
def lowerCAmelCase__ ( self )-> Optional[Any]:
return dict(self.raw_vocab , **self.added_tokens_encoder )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> int:
return self.subword_tokenizer.tokenize(__UpperCamelCase , clean=self.do_clean_text )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Optional[int]:
return self.vocab.get(__UpperCamelCase , self.vocab.get(self.unk_token ) )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Union[str, Any]:
return self.subword_tokenizer.convert_id_to_token(__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Optional[Any]:
UpperCAmelCase__ : str = "".join(__UpperCamelCase ).strip()
return out_string
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[int]:
UpperCAmelCase__ : Dict = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase ) + [self.eos_token_id] )
if len(__UpperCamelCase ) > self.model_max_length:
UpperCAmelCase__ : List[str] = input_ids[-self.model_max_length :]
return input_ids
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None )-> Tuple[str]:
UpperCAmelCase__ : Any = 0
if os.path.isdir(__UpperCamelCase ):
UpperCAmelCase__ : Optional[int] = os.path.join(
__UpperCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase__ : Any = os.path.join(
__UpperCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"] )
else:
UpperCAmelCase__ : Tuple = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
)
UpperCAmelCase__ : List[str] = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
)
with open(__UpperCamelCase , "w" , encoding="utf-8" ) as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!" )
UpperCAmelCase__ : str = token_index
writer.write(",".join(__UpperCamelCase ) + "\n" )
index += 1
with open(__UpperCamelCase , "w" , encoding="utf-8" ) as writer:
json.dump(self.emoji , __UpperCamelCase )
return vocab_file, emoji_file
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Optional[int]:
UpperCAmelCase__ : Dict = vocab # same as swe
UpperCAmelCase__ : Optional[int] = ids_to_tokens # same as bpe
UpperCAmelCase__ : str = emoji
UpperCAmelCase__ : List[Any] = np.max([len(__UpperCamelCase ) for w in self.vocab.keys()] )
UpperCAmelCase__ : Tuple = re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)" )
UpperCAmelCase__ : Optional[int] = re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*" )
UpperCAmelCase__ : List[str] = re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}" )
UpperCAmelCase__ : Any = re.compile(
r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase__ : List[str] = re.compile(
r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase__ : int = re.compile(
r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*" )
UpperCAmelCase__ : List[str] = "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
UpperCAmelCase__ : Tuple = "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
UpperCAmelCase__ : Optional[int] = str.maketrans({k: "<BLOCK>" for k in keisen + blocks} )
def __len__( self )-> List[Any]:
return len(self.ids_to_tokens )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Dict:
UpperCAmelCase__ : Optional[int] = self.content_repattera.sub("<URL>" , __UpperCamelCase )
UpperCAmelCase__ : Any = self.content_repattera.sub("<EMAIL>" , __UpperCamelCase )
UpperCAmelCase__ : str = self.content_repattera.sub("<TEL>" , __UpperCamelCase )
UpperCAmelCase__ : Optional[int] = self.content_repattera.sub("<DATE>" , __UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = self.content_repattera.sub("<DATE>" , __UpperCamelCase )
UpperCAmelCase__ : Optional[int] = self.content_repattera.sub("<PRICE>" , __UpperCamelCase )
UpperCAmelCase__ : List[str] = content.translate(self.content_transa )
while "<BLOCK><BLOCK>" in content:
UpperCAmelCase__ : Union[str, Any] = content.replace("<BLOCK><BLOCK>" , "<BLOCK>" )
return content
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase=False )-> List[Any]:
UpperCAmelCase__ : Optional[int] = text.replace(" " , "<SP>" )
UpperCAmelCase__ : Any = text.replace(" " , "<SP>" )
UpperCAmelCase__ : Union[str, Any] = text.replace("\r\n" , "<BR>" )
UpperCAmelCase__ : List[str] = text.replace("\n" , "<BR>" )
UpperCAmelCase__ : Any = text.replace("\r" , "<BR>" )
UpperCAmelCase__ : int = text.replace("\t" , "<TAB>" )
UpperCAmelCase__ : str = text.replace("—" , "ー" )
UpperCAmelCase__ : Optional[Any] = text.replace("−" , "ー" )
for k, v in self.emoji["emoji"].items():
if k in text:
UpperCAmelCase__ : Any = text.replace(__UpperCamelCase , __UpperCamelCase )
if clean:
UpperCAmelCase__ : Any = self.clean_text(__UpperCamelCase )
def check_simbol(__UpperCamelCase ):
UpperCAmelCase__ : Dict = x.encode()
if len(__UpperCamelCase ) == 1 and len(__UpperCamelCase ) == 2:
UpperCAmelCase__ : Optional[Any] = (int(e[0] ) << 8) + int(e[1] )
if (
(c >= 0Xc2a1 and c <= 0Xc2bf)
or (c >= 0Xc780 and c <= 0Xc783)
or (c >= 0Xcab9 and c <= 0Xcbbf)
or (c >= 0Xcc80 and c <= 0Xcda2)
):
return True
return False
def checkuae(__UpperCamelCase ):
UpperCAmelCase__ : Any = x.encode()
if len(__UpperCamelCase ) == 1 and len(__UpperCamelCase ) == 3:
UpperCAmelCase__ : Optional[int] = (int(e[0] ) << 16) + (int(e[1] ) << 8) + int(e[2] )
if c >= 0Xe2_8080 and c <= 0Xe2_b07f:
return True
return False
UpperCAmelCase__ : Any = 0
UpperCAmelCase__ : str = []
while pos < len(__UpperCamelCase ):
UpperCAmelCase__ : Any = min(len(__UpperCamelCase ) , pos + self.maxlen + 1 ) if text[pos] == "<" else pos + 3
UpperCAmelCase__ : Any = [] # (token_id, token, pos)
for e in range(__UpperCamelCase , __UpperCamelCase , -1 ):
UpperCAmelCase__ : str = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(__UpperCamelCase ) > 2:
UpperCAmelCase__ : Optional[int] = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e) )
if len(__UpperCamelCase ) > 0:
# the smallest token_id is adopted
UpperCAmelCase__ : List[str] = sorted(__UpperCamelCase , key=lambda __UpperCamelCase : x[0] )[0]
result.append(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = e
else:
UpperCAmelCase__ : Union[str, Any] = pos + 1
UpperCAmelCase__ : Optional[int] = text[pos:end]
if check_simbol(__UpperCamelCase ):
result.append("<KIGOU>" )
elif checkuae(__UpperCamelCase ):
result.append("<U2000U2BFF>" )
else:
for i in wd.encode("utf-8" ):
result.append("<|byte%d|>" % i )
UpperCAmelCase__ : List[Any] = end
return result
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase="\n" )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = []
UpperCAmelCase__ : str = []
UpperCAmelCase__ : List[Any] = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2] ) )
else:
if len(__UpperCamelCase ) > 0:
words.append(bytearray(__UpperCamelCase ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase__ : Dict = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji["emoji_inv"][word] )
elif word == "<SP>":
words.append(" " )
elif word == "<BR>":
words.append(__UpperCamelCase )
elif word == "<TAB>":
words.append("\t" )
elif word == "<BLOCK>":
words.append("▀" )
elif word == "<KIGOU>":
words.append("ǀ" )
elif word == "<U2000U2BFF>":
words.append("‖" )
else:
words.append(__UpperCamelCase )
if len(__UpperCamelCase ) > 0:
words.append(bytearray(__UpperCamelCase ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase__ : str = "".join(__UpperCamelCase )
return text
| 716 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def a__ ( lowerCAmelCase : List[str] ):
'''simple docstring'''
def wrapper(*lowerCAmelCase : Any , **lowerCAmelCase : Tuple ):
UpperCAmelCase__ : Optional[int] = timeit.default_timer()
UpperCAmelCase__ : int = func(*lowerCAmelCase , **lowerCAmelCase )
UpperCAmelCase__ : List[Any] = timeit.default_timer() - starttime
return delta
UpperCAmelCase__ : int = func.__name__
return wrapper
def a__ ( lowerCAmelCase : dict , lowerCAmelCase : Optional[int]=100 , lowerCAmelCase : List[str]=None ):
'''simple docstring'''
UpperCAmelCase__ : str = []
UpperCAmelCase__ : Optional[Any] = seq_shapes or {}
for i in range(lowerCAmelCase ):
UpperCAmelCase__ : int = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(lowerCAmelCase , _ArrayXD ):
UpperCAmelCase__ : List[str] = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(lowerCAmelCase , datasets.Value ):
if v.dtype == "string":
UpperCAmelCase__ : Dict = "The small grey turtle was surprisingly fast when challenged."
else:
UpperCAmelCase__ : str = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(lowerCAmelCase , datasets.Sequence ):
while isinstance(lowerCAmelCase , datasets.Sequence ):
UpperCAmelCase__ : List[str] = v.feature
UpperCAmelCase__ : Optional[int] = seq_shapes[k]
UpperCAmelCase__ : Optional[int] = np.random.rand(*lowerCAmelCase ).astype(v.dtype )
UpperCAmelCase__ : Union[str, Any] = data
dummy_data.append((i, example) )
return dummy_data
def a__ ( lowerCAmelCase : List[str] , lowerCAmelCase : Tuple , lowerCAmelCase : List[str]=100 , lowerCAmelCase : Optional[int]=None ):
'''simple docstring'''
UpperCAmelCase__ : int = generate_examples(lowerCAmelCase , num_examples=lowerCAmelCase , seq_shapes=lowerCAmelCase )
with ArrowWriter(features=lowerCAmelCase , path=lowerCAmelCase ) as writer:
for key, record in dummy_data:
UpperCAmelCase__ : List[Any] = features.encode_example(lowerCAmelCase )
writer.write(lowerCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}." )
UpperCAmelCase__ : Optional[int] = datasets.Dataset.from_file(filename=lowerCAmelCase , info=datasets.DatasetInfo(features=lowerCAmelCase ) )
return dataset
| 660 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
A__ : List[Any] = logging.get_logger(__name__)
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )-> Union[str, Any]:
UpperCAmelCase__ : str = feature_size
UpperCAmelCase__ : List[str] = sampling_rate
UpperCAmelCase__ : List[str] = padding_value
UpperCAmelCase__ : Dict = kwargs.pop("padding_side" , "right" )
UpperCAmelCase__ : Optional[int] = kwargs.pop("return_attention_mask" , __UpperCamelCase )
super().__init__(**__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , )-> BatchFeature:
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(__UpperCamelCase , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
UpperCAmelCase__ : Any = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
F" to this method that includes {self.model_input_names[0]}, but you provided"
F" {list(processed_features.keys() )}" )
UpperCAmelCase__ : Optional[Any] = processed_features[self.model_input_names[0]]
UpperCAmelCase__ : List[str] = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(__UpperCamelCase ) == 0:
if return_attention_mask:
UpperCAmelCase__ : Optional[int] = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
UpperCAmelCase__ : Union[str, Any] = required_input[0]
if isinstance(__UpperCamelCase , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
UpperCAmelCase__ : Tuple = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(__UpperCamelCase ):
UpperCAmelCase__ : Optional[Any] = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(__UpperCamelCase ):
UpperCAmelCase__ : int = "tf"
elif is_torch_tensor(__UpperCamelCase ):
UpperCAmelCase__ : Optional[Any] = "pt"
elif isinstance(__UpperCamelCase , (int, float, list, tuple, np.ndarray) ):
UpperCAmelCase__ : Optional[int] = "np"
else:
raise ValueError(
F"type of {first_element} unknown: {type(__UpperCamelCase )}. "
"Should be one of a python, numpy, pytorch or tensorflow object." )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
UpperCAmelCase__ : List[Any] = to_numpy(__UpperCamelCase )
else:
UpperCAmelCase__ : Tuple = [to_numpy(__UpperCamelCase ) for v in value]
# Convert padding_strategy in PaddingStrategy
UpperCAmelCase__ : str = self._get_padding_strategies(padding=__UpperCamelCase , max_length=__UpperCamelCase )
UpperCAmelCase__ : Tuple = processed_features[self.model_input_names[0]]
UpperCAmelCase__ : Optional[int] = len(__UpperCamelCase )
if not all(len(__UpperCamelCase ) == batch_size for v in processed_features.values() ):
raise ValueError("Some items in the output dictionary have a different batch size than others." )
UpperCAmelCase__ : str = []
for i in range(__UpperCamelCase ):
UpperCAmelCase__ : List[str] = {k: v[i] for k, v in processed_features.items()}
# truncation
UpperCAmelCase__ : Tuple = self._truncate(
__UpperCamelCase , max_length=__UpperCamelCase , pad_to_multiple_of=__UpperCamelCase , truncation=__UpperCamelCase , )
truncated_inputs.append(__UpperCamelCase )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
UpperCAmelCase__ : List[Any] = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
UpperCAmelCase__ : Tuple = PaddingStrategy.MAX_LENGTH
UpperCAmelCase__ : List[str] = {}
for i in range(__UpperCamelCase ):
# padding
UpperCAmelCase__ : int = self._pad(
truncated_inputs[i] , max_length=__UpperCamelCase , padding_strategy=__UpperCamelCase , pad_to_multiple_of=__UpperCamelCase , return_attention_mask=__UpperCamelCase , )
for key, value in outputs.items():
if key not in batch_outputs:
UpperCAmelCase__ : List[str] = []
if value.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : List[str] = value.astype(np.floataa )
batch_outputs[key].append(__UpperCamelCase )
return BatchFeature(__UpperCamelCase , tensor_type=__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = PaddingStrategy.DO_NOT_PAD , __UpperCamelCase = None , __UpperCamelCase = None , )-> dict:
UpperCAmelCase__ : str = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
UpperCAmelCase__ : Dict = len(__UpperCamelCase )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
UpperCAmelCase__ : Tuple = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
UpperCAmelCase__ : List[str] = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__UpperCamelCase ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
UpperCAmelCase__ : List[str] = np.ones(len(__UpperCamelCase ) , dtype=np.intaa )
if needs_to_be_padded:
UpperCAmelCase__ : List[Any] = max_length - len(__UpperCamelCase )
if self.padding_side == "right":
if return_attention_mask:
UpperCAmelCase__ : Optional[Any] = np.pad(
processed_features["attention_mask"] , (0, difference) )
UpperCAmelCase__ : Any = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
UpperCAmelCase__ : List[Any] = np.pad(
__UpperCamelCase , __UpperCamelCase , "constant" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
UpperCAmelCase__ : List[str] = np.pad(
processed_features["attention_mask"] , (difference, 0) )
UpperCAmelCase__ : Dict = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
UpperCAmelCase__ : List[Any] = np.pad(
__UpperCamelCase , __UpperCamelCase , "constant" , constant_values=self.padding_value )
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return processed_features
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , )-> Optional[Any]:
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined." )
UpperCAmelCase__ : Optional[int] = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
UpperCAmelCase__ : Tuple = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
UpperCAmelCase__ : Optional[Any] = len(__UpperCamelCase ) > max_length
if needs_to_be_truncated:
UpperCAmelCase__ : Dict = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
UpperCAmelCase__ : int = processed_features["attention_mask"][:max_length]
return processed_features
def lowerCAmelCase__ ( self , __UpperCamelCase=False , __UpperCamelCase=None )-> Union[str, Any]:
# Get padding strategy
if padding is not False:
if padding is True:
UpperCAmelCase__ : Dict = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : Union[str, Any] = PaddingStrategy(__UpperCamelCase )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : Tuple = padding
else:
UpperCAmelCase__ : Union[str, Any] = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`." )
return padding_strategy
| 717 |
"""simple docstring"""
from manim import *
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : str = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase__ : List[str] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
UpperCAmelCase__ : List[Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : Union[str, Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : List[Any] = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : int = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = VGroup(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = Text("CPU" , font_size=24 )
UpperCAmelCase__ : Any = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = [mem.copy() for i in range(4 )]
UpperCAmelCase__ : List[str] = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Union[str, Any] = Text("GPU" , font_size=24 )
UpperCAmelCase__ : Dict = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
gpu.move_to([-1, -1, 0] )
self.add(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : List[str] = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = Text("Model" , font_size=24 )
UpperCAmelCase__ : Dict = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
model.move_to([3, -1.0, 0] )
self.add(__UpperCamelCase )
UpperCAmelCase__ : List[str] = []
for i, rect in enumerate(__UpperCamelCase ):
rect.set_stroke(__UpperCamelCase )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
UpperCAmelCase__ : int = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__UpperCamelCase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__UpperCamelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=__UpperCamelCase , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=__UpperCamelCase , buff=0.0 )
self.add(__UpperCamelCase )
cpu_targs.append(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : Any = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = Text("Loaded Checkpoint" , font_size=24 )
UpperCAmelCase__ : Any = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , aligned_edge=__UpperCamelCase , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
UpperCAmelCase__ : Optional[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
UpperCAmelCase__ : Any = MarkupText(
F"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : str = MarkupText(
F"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(__UpperCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
UpperCAmelCase__ : Optional[Any] = MarkupText(
F"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__UpperCamelCase ) , Write(__UpperCamelCase ) )
self.play(Write(__UpperCamelCase , run_time=1 ) , Create(__UpperCamelCase , run_time=1 ) )
UpperCAmelCase__ : Union[str, Any] = []
UpperCAmelCase__ : List[str] = []
for i, rect in enumerate(__UpperCamelCase ):
UpperCAmelCase__ : Optional[Any] = fill.copy().set_fill(__UpperCamelCase , opacity=0.7 )
target.move_to(__UpperCamelCase )
first_animations.append(GrowFromCenter(__UpperCamelCase , run_time=1 ) )
UpperCAmelCase__ : List[str] = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(__UpperCamelCase , run_time=1.5 ) )
self.play(*__UpperCamelCase )
self.play(*__UpperCamelCase )
self.wait()
| 660 | 0 |
"""simple docstring"""
import argparse
import os
import re
A__ : int = """src/diffusers"""
# Pattern that looks at the indentation in a line.
A__ : Optional[int] = re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
A__ : Tuple = re.compile(R"""^\s*\"([^\"]+)\":""")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
A__ : Union[str, Any] = re.compile(R"""^\s*_import_structure\[\"([^\"]+)\"\]""")
# Pattern that matches `"key",` and puts `key` in group 0.
A__ : List[Any] = re.compile(R"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
A__ : Any = re.compile(R"""\[([^\]]+)\]""")
def a__ ( lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = _re_indent.search(lowerCAmelCase )
return "" if search is None else search.groups()[0]
def a__ ( lowerCAmelCase : Any , lowerCAmelCase : List[str]="" , lowerCAmelCase : str=None , lowerCAmelCase : int=None ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = 0
UpperCAmelCase__ : Optional[int] = code.split("\n" )
if start_prompt is not None:
while not lines[index].startswith(lowerCAmelCase ):
index += 1
UpperCAmelCase__ : str = ["\n".join(lines[:index] )]
else:
UpperCAmelCase__ : List[Any] = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCAmelCase__ : str = [lines[index]]
index += 1
while index < len(lowerCAmelCase ) and (end_prompt is None or not lines[index].startswith(lowerCAmelCase )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(lowerCAmelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + " " ):
current_block.append(lines[index] )
blocks.append("\n".join(lowerCAmelCase ) )
if index < len(lowerCAmelCase ) - 1:
UpperCAmelCase__ : Union[str, Any] = [lines[index + 1]]
index += 1
else:
UpperCAmelCase__ : Optional[Any] = []
else:
blocks.append("\n".join(lowerCAmelCase ) )
UpperCAmelCase__ : Union[str, Any] = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(lowerCAmelCase ) > 0:
blocks.append("\n".join(lowerCAmelCase ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(lowerCAmelCase ):
blocks.append("\n".join(lines[index:] ) )
return blocks
def a__ ( lowerCAmelCase : str ):
'''simple docstring'''
def _inner(lowerCAmelCase : Dict ):
return key(lowerCAmelCase ).lower().replace("_" , "" )
return _inner
def a__ ( lowerCAmelCase : Tuple , lowerCAmelCase : Tuple=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(lowerCAmelCase : Dict ):
return x
if key is None:
UpperCAmelCase__ : Dict = noop
# Constants are all uppercase, they go first.
UpperCAmelCase__ : Dict = [obj for obj in objects if key(lowerCAmelCase ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCAmelCase__ : Tuple = [obj for obj in objects if key(lowerCAmelCase )[0].isupper() and not key(lowerCAmelCase ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCAmelCase__ : Tuple = [obj for obj in objects if not key(lowerCAmelCase )[0].isupper()]
UpperCAmelCase__ : Union[str, Any] = ignore_underscore(lowerCAmelCase )
return sorted(lowerCAmelCase , key=lowerCAmelCase ) + sorted(lowerCAmelCase , key=lowerCAmelCase ) + sorted(lowerCAmelCase , key=lowerCAmelCase )
def a__ ( lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(lowerCAmelCase : List[Any] ):
UpperCAmelCase__ : int = match.groups()[0]
if "," not in imports:
return F"[{imports}]"
UpperCAmelCase__ : str = [part.strip().replace("\"" , "" ) for part in imports.split("," )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCAmelCase__ : Dict = keys[:-1]
return "[" + ", ".join([F"\"{k}\"" for k in sort_objects(lowerCAmelCase )] ) + "]"
UpperCAmelCase__ : List[Any] = import_statement.split("\n" )
if len(lowerCAmelCase ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCAmelCase__ : str = 2 if lines[1].strip() == "[" else 1
UpperCAmelCase__ : int = [(i, _re_strip_line.search(lowerCAmelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCAmelCase__ : int = sort_objects(lowerCAmelCase , key=lambda lowerCAmelCase : x[1] )
UpperCAmelCase__ : str = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(lowerCAmelCase ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCAmelCase__ : Union[str, Any] = _re_bracket_content.sub(_replace , lines[1] )
else:
UpperCAmelCase__ : Optional[Any] = [part.strip().replace("\"" , "" ) for part in lines[1].split("," )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCAmelCase__ : Tuple = keys[:-1]
UpperCAmelCase__ : Union[str, Any] = get_indent(lines[1] ) + ", ".join([F"\"{k}\"" for k in sort_objects(lowerCAmelCase )] )
return "\n".join(lowerCAmelCase )
else:
# Finally we have to deal with imports fitting on one line
UpperCAmelCase__ : Any = _re_bracket_content.sub(_replace , lowerCAmelCase )
return import_statement
def a__ ( lowerCAmelCase : int , lowerCAmelCase : Union[str, Any]=True ):
'''simple docstring'''
with open(lowerCAmelCase , "r" ) as f:
UpperCAmelCase__ : List[str] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCAmelCase__ : Tuple = split_code_in_indented_blocks(
lowerCAmelCase , start_prompt="_import_structure = {" , end_prompt="if TYPE_CHECKING:" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(lowerCAmelCase ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCAmelCase__ : Optional[int] = main_blocks[block_idx]
UpperCAmelCase__ : Any = block.split("\n" )
# Get to the start of the imports.
UpperCAmelCase__ : Tuple = 0
while line_idx < len(lowerCAmelCase ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCAmelCase__ : Tuple = len(lowerCAmelCase )
else:
line_idx += 1
if line_idx >= len(lowerCAmelCase ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCAmelCase__ : Dict = "\n".join(block_lines[line_idx:-1] )
UpperCAmelCase__ : Union[str, Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCAmelCase__ : Union[str, Any] = split_code_in_indented_blocks(lowerCAmelCase , indent_level=lowerCAmelCase )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCAmelCase__ : str = _re_direct_key if "_import_structure" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCAmelCase__ : Optional[int] = [(pattern.search(lowerCAmelCase ).groups()[0] if pattern.search(lowerCAmelCase ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCAmelCase__ : Optional[int] = [(i, key) for i, key in enumerate(lowerCAmelCase ) if key is not None]
UpperCAmelCase__ : str = [x[0] for x in sorted(lowerCAmelCase , key=lambda lowerCAmelCase : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCAmelCase__ : str = 0
UpperCAmelCase__ : Union[str, Any] = []
for i in range(len(lowerCAmelCase ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
UpperCAmelCase__ : int = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(lowerCAmelCase )
count += 1
# And we put our main block back together with its first and last line.
UpperCAmelCase__ : Dict = "\n".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(lowerCAmelCase ):
if check_only:
return True
else:
print(F"Overwriting {file}." )
with open(lowerCAmelCase , "w" ) as f:
f.write("\n".join(lowerCAmelCase ) )
def a__ ( lowerCAmelCase : Optional[Any]=True ):
'''simple docstring'''
UpperCAmelCase__ : int = []
for root, _, files in os.walk(lowerCAmelCase ):
if "__init__.py" in files:
UpperCAmelCase__ : Tuple = sort_imports(os.path.join(lowerCAmelCase , "__init__.py" ) , check_only=lowerCAmelCase )
if result:
UpperCAmelCase__ : List[Any] = [os.path.join(lowerCAmelCase , "__init__.py" )]
if len(lowerCAmelCase ) > 0:
raise ValueError(F"Would overwrite {len(lowerCAmelCase )} files, run `make style`." )
if __name__ == "__main__":
A__ : str = argparse.ArgumentParser()
parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
A__ : List[Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 718 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A__ : Tuple = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : nn.ModuleList , lowerCAmelCase : nn.ModuleList , lowerCAmelCase : List[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(lowerCAmelCase ) == len(lowerCAmelCase ), F"{len(lowerCAmelCase )} != {len(lowerCAmelCase )}"
dest_layers.load_state_dict(layers_to_copy.state_dict() )
A__ : List[Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
A__ : List[Any] = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def a__ ( lowerCAmelCase : Dict , lowerCAmelCase : Dict ):
'''simple docstring'''
try:
UpperCAmelCase__ : Tuple = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"
F" {n_student}" )
return list(range(lowerCAmelCase ) )
def a__ ( lowerCAmelCase : int , lowerCAmelCase : Tuple ):
'''simple docstring'''
if n_student > n_teacher:
raise ValueError(F"Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}" )
elif n_teacher == n_student:
return list(range(lowerCAmelCase ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def a__ ( lowerCAmelCase : Union[str, PreTrainedModel] , lowerCAmelCase : Union[str, Path] = "student" , lowerCAmelCase : Union[int, None] = None , lowerCAmelCase : Union[int, None] = None , lowerCAmelCase : List[str]=False , lowerCAmelCase : List[str]=None , lowerCAmelCase : List[str]=None , **lowerCAmelCase : List[str] , ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
assert (e is not None) or (d is not None), _msg
if isinstance(lowerCAmelCase , lowerCAmelCase ):
AutoTokenizer.from_pretrained(lowerCAmelCase ).save_pretrained(lowerCAmelCase ) # purely for convenience
UpperCAmelCase__ : List[str] = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).eval()
else:
assert isinstance(lowerCAmelCase , lowerCAmelCase ), F"teacher must be a model or string got type {type(lowerCAmelCase )}"
UpperCAmelCase__ : int = teacher.config.to_diff_dict()
try:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
UpperCAmelCase__ : Tuple = teacher_e
if d is None:
UpperCAmelCase__ : str = teacher_d
init_kwargs.update({"encoder_layers": e, "decoder_layers": d} )
except AttributeError: # T5
if hasattr(teacher.config , "num_encoder_layers" ):
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
UpperCAmelCase__ : Optional[Any] = teacher_e
if d is None:
UpperCAmelCase__ : Optional[Any] = teacher_d
if hasattr(teacher.config , "num_encoder_layers" ):
init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d} )
else:
init_kwargs.update({"num_layers": e, "num_decoder_layers": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(lowerCAmelCase )
# Copy weights
UpperCAmelCase__ : Tuple = teacher.config_class(**lowerCAmelCase )
UpperCAmelCase__ : List[str] = AutoModelForSeqaSeqLM.from_config(lowerCAmelCase )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
UpperCAmelCase__ : Optional[int] = student.load_state_dict(teacher.state_dict() , strict=lowerCAmelCase )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
UpperCAmelCase__ , UpperCAmelCase__ : int = list(range(lowerCAmelCase ) ), list(range(lowerCAmelCase ) )
logger.info(
F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"
F" {save_path}" )
student.save_pretrained(lowerCAmelCase )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
UpperCAmelCase__ : List[int] = pick_layers_to_copy(lowerCAmelCase , lowerCAmelCase )
if d_layers_to_copy is None:
UpperCAmelCase__ : List[int] = pick_layers_to_copy(lowerCAmelCase , lowerCAmelCase )
try:
if hasattr(
lowerCAmelCase , "prophetnet" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , lowerCAmelCase )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , lowerCAmelCase )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , lowerCAmelCase )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , lowerCAmelCase )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , lowerCAmelCase )
copy_layers(teacher.decoder.block , student.decoder.block , lowerCAmelCase )
logger.info(
F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}" )
UpperCAmelCase__ : int = {
"teacher_type": teacher.config.model_type,
"copied_encoder_layers": e_layers_to_copy,
"copied_decoder_layers": d_layers_to_copy,
}
student.save_pretrained(lowerCAmelCase )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 660 | 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
A__ : str = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def a__ ( lowerCAmelCase : Optional[int] ):
'''simple docstring'''
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 a__ ( lowerCAmelCase : Tuple , lowerCAmelCase : List[Any] , lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
return max(metric_fn(lowerCAmelCase , lowerCAmelCase ) for gt in ground_truths )
def a__ ( lowerCAmelCase : Union[str, Any] , lowerCAmelCase : str , lowerCAmelCase : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = [line.strip() for line in open(lowerCAmelCase , "r" ).readlines()]
UpperCAmelCase__ : Tuple = []
if args.gold_data_mode == "qa":
UpperCAmelCase__ : int = pd.read_csv(lowerCAmelCase , sep="\t" , header=lowerCAmelCase )
for answer_list in data[1]:
UpperCAmelCase__ : List[str] = ast.literal_eval(lowerCAmelCase )
answers.append(lowerCAmelCase )
else:
UpperCAmelCase__ : Tuple = [line.strip() for line in open(lowerCAmelCase , "r" ).readlines()]
UpperCAmelCase__ : Dict = [[reference] for reference in references]
UpperCAmelCase__ : Optional[int] = 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 )
UpperCAmelCase__ : Optional[int] = 100.0 * em / total
UpperCAmelCase__ : List[Any] = 100.0 * fa / total
logger.info(F"F1: {fa:.2f}" )
logger.info(F"EM: {em:.2f}" )
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : Dict , lowerCAmelCase : str ):
'''simple docstring'''
UpperCAmelCase__ : Dict = args.k
UpperCAmelCase__ : Optional[int] = [line.strip() for line in open(lowerCAmelCase , "r" ).readlines()]
UpperCAmelCase__ : List[Any] = [line.strip() for line in open(lowerCAmelCase , "r" ).readlines()]
UpperCAmelCase__ : Optional[int] = 0
for hypo, reference in zip(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Union[str, Any] = set(hypo.split("\t" )[:k] )
UpperCAmelCase__ : Optional[Any] = set(reference.split("\t" ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
UpperCAmelCase__ : int = 100.0 * em / total
logger.info(F"Precision@{k}: {em: .2f}" )
def a__ ( lowerCAmelCase : str , lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[int] ):
'''simple docstring'''
def strip_title(lowerCAmelCase : List[str] ):
if title.startswith("\"" ):
UpperCAmelCase__ : Any = title[1:]
if title.endswith("\"" ):
UpperCAmelCase__ : Union[str, Any] = title[:-1]
return title
UpperCAmelCase__ : Dict = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
lowerCAmelCase , return_tensors="pt" , padding=lowerCAmelCase , truncation=lowerCAmelCase , )["input_ids"].to(args.device )
UpperCAmelCase__ : Any = rag_model.rag.question_encoder(lowerCAmelCase )
UpperCAmelCase__ : Union[str, Any] = question_enc_outputs[0]
UpperCAmelCase__ : List[str] = 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" , )
UpperCAmelCase__ : str = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
UpperCAmelCase__ : List[Any] = []
for docs in all_docs:
UpperCAmelCase__ : Union[str, Any] = [strip_title(lowerCAmelCase ) for title in docs["title"]]
provenance_strings.append("\t".join(lowerCAmelCase ) )
return provenance_strings
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
with torch.no_grad():
UpperCAmelCase__ : Optional[Any] = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
lowerCAmelCase , return_tensors="pt" , padding=lowerCAmelCase , truncation=lowerCAmelCase )
UpperCAmelCase__ : Union[str, Any] = inputs_dict.input_ids.to(args.device )
UpperCAmelCase__ : Any = inputs_dict.attention_mask.to(args.device )
UpperCAmelCase__ : Tuple = 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]] , )
UpperCAmelCase__ : Any = 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 a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = 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." , )
UpperCAmelCase__ : Optional[Any] = parser.parse_args()
UpperCAmelCase__ : List[str] = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
return args
def a__ ( lowerCAmelCase : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = {}
if args.model_type is None:
UpperCAmelCase__ : Optional[int] = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith("rag" ):
UpperCAmelCase__ : List[str] = RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
UpperCAmelCase__ : Any = args.n_docs
if args.index_name is not None:
UpperCAmelCase__ : Union[str, Any] = args.index_name
if args.index_path is not None:
UpperCAmelCase__ : Optional[Any] = args.index_path
else:
UpperCAmelCase__ : Optional[int] = BartForConditionalGeneration
UpperCAmelCase__ : Union[str, Any] = (
[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 )
UpperCAmelCase__ : Optional[int] = get_scores if args.eval_mode == "e2e" else get_precision_at_k
UpperCAmelCase__ : Any = 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" ):
UpperCAmelCase__ : Tuple = RagRetriever.from_pretrained(lowerCAmelCase , **lowerCAmelCase )
UpperCAmelCase__ : int = model_class.from_pretrained(lowerCAmelCase , retriever=lowerCAmelCase , **lowerCAmelCase )
model.retriever.init_retrieval()
else:
UpperCAmelCase__ : int = 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:
UpperCAmelCase__ : str = []
for line in tqdm(lowerCAmelCase ):
questions.append(line.strip() )
if len(lowerCAmelCase ) == args.eval_batch_size:
UpperCAmelCase__ : str = evaluate_batch_fn(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
preds_file.write("\n".join(lowerCAmelCase ) + "\n" )
preds_file.flush()
UpperCAmelCase__ : Optional[int] = []
if len(lowerCAmelCase ) > 0:
UpperCAmelCase__ : Optional[int] = 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__":
A__ : List[Any] = get_args()
main(args)
| 719 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _lowercase ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@property
def lowerCAmelCase__ ( self )-> int:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : Tuple = ort.SessionOptions()
UpperCAmelCase__ : List[str] = False
return options
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
UpperCAmelCase__ : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
UpperCAmelCase__ : str = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase__ : int = "A red cat sitting on a park bench"
UpperCAmelCase__ : Tuple = np.random.RandomState(0 )
UpperCAmelCase__ : Any = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , mask_image=__UpperCamelCase , guidance_scale=7.5 , num_inference_steps=10 , generator=__UpperCamelCase , output_type="np" , )
UpperCAmelCase__ : Tuple = output.images
UpperCAmelCase__ : Dict = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
UpperCAmelCase__ : Union[str, Any] = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
UpperCAmelCase__ : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
UpperCAmelCase__ : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
UpperCAmelCase__ : Optional[Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=__UpperCamelCase , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase__ : int = "A red cat sitting on a park bench"
UpperCAmelCase__ : List[str] = np.random.RandomState(0 )
UpperCAmelCase__ : str = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , mask_image=__UpperCamelCase , guidance_scale=7.5 , num_inference_steps=20 , generator=__UpperCamelCase , output_type="np" , )
UpperCAmelCase__ : List[str] = output.images
UpperCAmelCase__ : List[Any] = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
UpperCAmelCase__ : int = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 660 | 0 |
"""simple docstring"""
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 ..auto import CONFIG_MAPPING
A__ : Union[str, Any] = logging.get_logger(__name__)
A__ : Optional[int] = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class _lowercase ( lowerCAmelCase_ ):
_A = 'table-transformer'
_A = ['past_key_values']
_A = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=3 , __UpperCamelCase=1_00 , __UpperCamelCase=6 , __UpperCamelCase=20_48 , __UpperCamelCase=8 , __UpperCamelCase=6 , __UpperCamelCase=20_48 , __UpperCamelCase=8 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=True , __UpperCamelCase="relu" , __UpperCamelCase=2_56 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.02 , __UpperCamelCase=1.0 , __UpperCamelCase=False , __UpperCamelCase="sine" , __UpperCamelCase="resnet50" , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=1 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=1 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=0.1 , **__UpperCamelCase , )-> List[Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
UpperCAmelCase__ : Any = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : int = backbone_config.get("model_type" )
UpperCAmelCase__ : Optional[Any] = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase__ : int = config_class.from_dict(__UpperCamelCase )
# set timm attributes to None
UpperCAmelCase__ : List[str] = None, None, None
UpperCAmelCase__ : Optional[int] = use_timm_backbone
UpperCAmelCase__ : Dict = backbone_config
UpperCAmelCase__ : List[Any] = num_channels
UpperCAmelCase__ : Any = num_queries
UpperCAmelCase__ : int = d_model
UpperCAmelCase__ : Optional[int] = encoder_ffn_dim
UpperCAmelCase__ : str = encoder_layers
UpperCAmelCase__ : Dict = encoder_attention_heads
UpperCAmelCase__ : Optional[Any] = decoder_ffn_dim
UpperCAmelCase__ : Tuple = decoder_layers
UpperCAmelCase__ : Optional[Any] = decoder_attention_heads
UpperCAmelCase__ : List[str] = dropout
UpperCAmelCase__ : Tuple = attention_dropout
UpperCAmelCase__ : List[Any] = activation_dropout
UpperCAmelCase__ : Dict = activation_function
UpperCAmelCase__ : Optional[Any] = init_std
UpperCAmelCase__ : List[str] = init_xavier_std
UpperCAmelCase__ : int = encoder_layerdrop
UpperCAmelCase__ : Tuple = decoder_layerdrop
UpperCAmelCase__ : int = encoder_layers
UpperCAmelCase__ : Dict = auxiliary_loss
UpperCAmelCase__ : Union[str, Any] = position_embedding_type
UpperCAmelCase__ : List[str] = backbone
UpperCAmelCase__ : List[Any] = use_pretrained_backbone
UpperCAmelCase__ : List[str] = dilation
# Hungarian matcher
UpperCAmelCase__ : Dict = class_cost
UpperCAmelCase__ : Any = bbox_cost
UpperCAmelCase__ : Tuple = giou_cost
# Loss coefficients
UpperCAmelCase__ : Any = mask_loss_coefficient
UpperCAmelCase__ : Dict = dice_loss_coefficient
UpperCAmelCase__ : Any = bbox_loss_coefficient
UpperCAmelCase__ : Tuple = giou_loss_coefficient
UpperCAmelCase__ : List[Any] = eos_coefficient
super().__init__(is_encoder_decoder=__UpperCamelCase , **__UpperCamelCase )
@property
def lowerCAmelCase__ ( self )-> int:
return self.encoder_attention_heads
@property
def lowerCAmelCase__ ( self )-> int:
return self.d_model
class _lowercase ( lowerCAmelCase_ ):
_A = version.parse('1.11' )
@property
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def lowerCAmelCase__ ( self )-> float:
return 1E-5
@property
def lowerCAmelCase__ ( self )-> int:
return 12
| 720 |
"""simple docstring"""
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 ..auto import CONFIG_MAPPING
A__ : Union[str, Any] = logging.get_logger(__name__)
A__ : Optional[int] = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'table-transformer'
_A = ['past_key_values']
_A = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=3 , __UpperCamelCase=1_00 , __UpperCamelCase=6 , __UpperCamelCase=20_48 , __UpperCamelCase=8 , __UpperCamelCase=6 , __UpperCamelCase=20_48 , __UpperCamelCase=8 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=True , __UpperCamelCase="relu" , __UpperCamelCase=2_56 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.02 , __UpperCamelCase=1.0 , __UpperCamelCase=False , __UpperCamelCase="sine" , __UpperCamelCase="resnet50" , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=1 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=1 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=0.1 , **__UpperCamelCase , )-> List[Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
UpperCAmelCase__ : Any = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : int = backbone_config.get("model_type" )
UpperCAmelCase__ : Optional[Any] = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase__ : int = config_class.from_dict(__UpperCamelCase )
# set timm attributes to None
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : List[str] = None, None, None
UpperCAmelCase__ : Optional[int] = use_timm_backbone
UpperCAmelCase__ : Dict = backbone_config
UpperCAmelCase__ : List[Any] = num_channels
UpperCAmelCase__ : Any = num_queries
UpperCAmelCase__ : int = d_model
UpperCAmelCase__ : Optional[int] = encoder_ffn_dim
UpperCAmelCase__ : str = encoder_layers
UpperCAmelCase__ : Dict = encoder_attention_heads
UpperCAmelCase__ : Optional[Any] = decoder_ffn_dim
UpperCAmelCase__ : Tuple = decoder_layers
UpperCAmelCase__ : Optional[Any] = decoder_attention_heads
UpperCAmelCase__ : List[str] = dropout
UpperCAmelCase__ : Tuple = attention_dropout
UpperCAmelCase__ : List[Any] = activation_dropout
UpperCAmelCase__ : Dict = activation_function
UpperCAmelCase__ : Optional[Any] = init_std
UpperCAmelCase__ : List[str] = init_xavier_std
UpperCAmelCase__ : int = encoder_layerdrop
UpperCAmelCase__ : Tuple = decoder_layerdrop
UpperCAmelCase__ : int = encoder_layers
UpperCAmelCase__ : Dict = auxiliary_loss
UpperCAmelCase__ : Union[str, Any] = position_embedding_type
UpperCAmelCase__ : List[str] = backbone
UpperCAmelCase__ : List[Any] = use_pretrained_backbone
UpperCAmelCase__ : List[str] = dilation
# Hungarian matcher
UpperCAmelCase__ : Dict = class_cost
UpperCAmelCase__ : Any = bbox_cost
UpperCAmelCase__ : Tuple = giou_cost
# Loss coefficients
UpperCAmelCase__ : Any = mask_loss_coefficient
UpperCAmelCase__ : Dict = dice_loss_coefficient
UpperCAmelCase__ : Any = bbox_loss_coefficient
UpperCAmelCase__ : Tuple = giou_loss_coefficient
UpperCAmelCase__ : List[Any] = eos_coefficient
super().__init__(is_encoder_decoder=__UpperCamelCase , **__UpperCamelCase )
@property
def lowerCAmelCase__ ( self )-> int:
return self.encoder_attention_heads
@property
def lowerCAmelCase__ ( self )-> int:
return self.d_model
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = version.parse('1.11' )
@property
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def lowerCAmelCase__ ( self )-> float:
return 1E-5
@property
def lowerCAmelCase__ ( self )-> int:
return 12
| 660 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A__ : Union[str, Any] = logging.get_logger(__name__)
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self , *__UpperCamelCase , **__UpperCamelCase )-> None:
warnings.warn(
"The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use PoolFormerImageProcessor instead." , __UpperCamelCase , )
super().__init__(*__UpperCamelCase , **__UpperCamelCase )
| 721 |
"""simple docstring"""
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
A__ : int = getLogger(__name__)
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : int = 8 , lowerCAmelCase : int = 1024 , lowerCAmelCase : List[Any]="val" , lowerCAmelCase : str=None , lowerCAmelCase : int=False , lowerCAmelCase : Dict="summarization" , lowerCAmelCase : int=None , lowerCAmelCase : List[str]=1 , lowerCAmelCase : Dict = None , lowerCAmelCase : List[str]="" , **lowerCAmelCase : int , ):
'''simple docstring'''
UpperCAmelCase__ : Dict = str(lowerCAmelCase )
assert local_rank is not None
torch.distributed.init_process_group(backend="nccl" , rank=lowerCAmelCase )
UpperCAmelCase__ : List[str] = Path(lowerCAmelCase )
UpperCAmelCase__ : str = save_dir.joinpath(F"rank_{local_rank}_output.json" )
torch.cuda.set_device(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).cuda()
if fpaa:
UpperCAmelCase__ : List[Any] = model.half()
# determine if we need to increase num_beams
use_task_specific_params(lowerCAmelCase , lowerCAmelCase ) # update config with task specific params
UpperCAmelCase__ : List[Any] = generate_kwargs.pop("num_beams" , model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
UpperCAmelCase__ : Any = num_return_sequences
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained(lowerCAmelCase )
logger.info(F"Inferred tokenizer type: {tokenizer.__class__}" ) # if this is wrong, check config.model_type.
if max_source_length is None:
UpperCAmelCase__ : int = tokenizer.model_max_length
if prefix is None:
UpperCAmelCase__ : Union[str, Any] = prefix or getattr(model.config , "prefix" , "" ) or ""
UpperCAmelCase__ : str = SeqaSeqDataset(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , max_target_length=1024 , type_path=lowerCAmelCase , n_obs=lowerCAmelCase , prefix=lowerCAmelCase , **lowerCAmelCase , )
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
UpperCAmelCase__ : Union[str, Any] = ds.make_sortish_sampler(lowerCAmelCase , distributed=lowerCAmelCase , add_extra_examples=lowerCAmelCase , shuffle=lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = DataLoader(lowerCAmelCase , sampler=lowerCAmelCase , batch_size=lowerCAmelCase , collate_fn=ds.collate_fn )
UpperCAmelCase__ : str = []
for batch in tqdm(lowerCAmelCase ):
UpperCAmelCase__ : Dict = model.generate(
input_ids=batch["input_ids"].to(model.device ) , attention_mask=batch["attention_mask"].to(model.device ) , num_return_sequences=lowerCAmelCase , num_beams=lowerCAmelCase , **lowerCAmelCase , )
UpperCAmelCase__ : int = tokenizer.batch_decode(lowerCAmelCase , skip_special_tokens=lowerCAmelCase , clean_up_tokenization_spaces=lowerCAmelCase )
UpperCAmelCase__ : int = batch["ids"]
if num_return_sequences > 1:
UpperCAmelCase__ : str = chunks(lowerCAmelCase , lowerCAmelCase ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(lowerCAmelCase ):
results.append({"pred": pred, "id": ids[i].item()} )
save_json(lowerCAmelCase , lowerCAmelCase )
return results, sampler.num_replicas
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : str = argparse.ArgumentParser(
epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate" )
parser.add_argument("--data_dir" , type=lowerCAmelCase , help="like cnn_dm/test.source" )
parser.add_argument(
"--model_name" , type=lowerCAmelCase , help="like facebook/bart-large-cnn,t5-base, etc." , default="sshleifer/distilbart-xsum-12-3" , )
parser.add_argument("--save_dir" , type=lowerCAmelCase , help="where to save" , default="tmp_gen" )
parser.add_argument("--max_source_length" , type=lowerCAmelCase , default=lowerCAmelCase )
parser.add_argument(
"--type_path" , type=lowerCAmelCase , default="test" , help="which subset to evaluate typically train/val/test" )
parser.add_argument("--task" , type=lowerCAmelCase , default="summarization" , help="used for task_specific_params + metrics" )
parser.add_argument("--bs" , type=lowerCAmelCase , default=8 , required=lowerCAmelCase , help="batch size" )
parser.add_argument(
"--local_rank" , type=lowerCAmelCase , default=-1 , required=lowerCAmelCase , help="should be passed by distributed.launch" )
parser.add_argument(
"--n_obs" , type=lowerCAmelCase , default=lowerCAmelCase , required=lowerCAmelCase , help="How many observations. Defaults to all." )
parser.add_argument(
"--num_return_sequences" , type=lowerCAmelCase , default=1 , required=lowerCAmelCase , help="How many sequences to return" )
parser.add_argument(
"--sync_timeout" , type=lowerCAmelCase , default=600 , required=lowerCAmelCase , help="How long should master process wait for other processes to finish." , )
parser.add_argument("--src_lang" , type=lowerCAmelCase , default=lowerCAmelCase , required=lowerCAmelCase )
parser.add_argument("--tgt_lang" , type=lowerCAmelCase , default=lowerCAmelCase , required=lowerCAmelCase )
parser.add_argument(
"--prefix" , type=lowerCAmelCase , required=lowerCAmelCase , default=lowerCAmelCase , help="will be added to the begininng of src examples" )
parser.add_argument("--fp16" , action="store_true" )
parser.add_argument("--debug" , action="store_true" )
UpperCAmelCase__ : Optional[int] = time.time()
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = parser.parse_known_args()
UpperCAmelCase__ : int = parse_numeric_n_bool_cl_kwargs(lowerCAmelCase )
if generate_kwargs and args.local_rank <= 0:
print(F"parsed the following generate kwargs: {generate_kwargs}" )
UpperCAmelCase__ : Dict = Path(args.save_dir + "_tmp" )
Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase ) # this handles locking.
UpperCAmelCase__ : List[str] = list(json_save_dir.glob("rank_*.json" ) )
if intermediate_files:
raise ValueError(F"Found files at {json_save_dir} please move or remove them." )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
UpperCAmelCase__ : List[str] = {}
if args.src_lang is not None:
UpperCAmelCase__ : str = args.src_lang
if args.tgt_lang is not None:
UpperCAmelCase__ : List[str] = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=lowerCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = eval_data_dir(
args.data_dir , lowerCAmelCase , args.model_name , type_path=args.type_path , bs=args.bs , fpaa=args.fpaa , task=args.task , local_rank=args.local_rank , n_obs=args.n_obs , max_source_length=args.max_source_length , num_return_sequences=args.num_return_sequences , prefix=args.prefix , dataset_kwargs=lowerCAmelCase , **lowerCAmelCase , )
if args.local_rank <= 0:
UpperCAmelCase__ : str = Path(args.save_dir )
save_dir.mkdir(exist_ok=lowerCAmelCase )
UpperCAmelCase__ : Tuple = gather_results_from_each_node(lowerCAmelCase , lowerCAmelCase , args.sync_timeout )
UpperCAmelCase__ : Union[str, Any] = combine_partial_results(lowerCAmelCase )
if args.num_return_sequences > 1:
UpperCAmelCase__ : int = save_dir.joinpath("pseudolabel_results.json" )
print(F"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/" )
save_json(lowerCAmelCase , lowerCAmelCase )
return
UpperCAmelCase__ : Optional[Any] = Path(args.data_dir ).joinpath(args.type_path + ".target" )
with open(lowerCAmelCase ) as f:
UpperCAmelCase__ : Optional[int] = [x.rstrip() for x in f.readlines()][: len(lowerCAmelCase )]
# Calculate metrics, save metrics, and save _generations.txt
UpperCAmelCase__ : List[Any] = "translation" in args.task
UpperCAmelCase__ : Optional[Any] = calculate_bleu if calc_bleu else calculate_rouge
UpperCAmelCase__ : Optional[Any] = "bleu" if calc_bleu else "rouge"
UpperCAmelCase__ : Dict = score_fn(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : List[Any] = len(lowerCAmelCase )
UpperCAmelCase__ : Union[str, Any] = time.time() - start_time
UpperCAmelCase__ : Optional[int] = round(runtime / metrics["n_obs"] , 4 )
UpperCAmelCase__ : Tuple = num_replicas
# TODO(@stas00): add whatever metadata to metrics
UpperCAmelCase__ : Any = save_dir.joinpath(F"{args.type_path}_{metric_name}.json" )
save_json(lowerCAmelCase , lowerCAmelCase , indent=lowerCAmelCase )
print(lowerCAmelCase )
write_txt_file(lowerCAmelCase , save_dir.joinpath(F"{args.type_path}_generations.txt" ) )
if args.debug:
write_txt_file(lowerCAmelCase , save_dir.joinpath(F"{args.type_path}.target" ) )
else:
shutil.rmtree(lowerCAmelCase )
def a__ ( lowerCAmelCase : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : str = []
for partial_result in partial_results:
records.extend(lowerCAmelCase )
UpperCAmelCase__ : Dict = sorted(lowerCAmelCase , key=lambda lowerCAmelCase : x["id"] )
UpperCAmelCase__ : List[str] = [x["pred"] for x in records]
return preds
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : int , lowerCAmelCase : Optional[int] ):
'''simple docstring'''
# WAIT FOR lots of .json files
UpperCAmelCase__ : int = time.time()
logger.info("waiting for all nodes to finish" )
UpperCAmelCase__ : Dict = None
while (time.time() - start_wait) < timeout:
UpperCAmelCase__ : str = list(save_dir.glob("rank_*.json" ) )
if len(lowerCAmelCase ) < num_replicas:
continue
try:
# make sure all json files are fully saved
UpperCAmelCase__ : Union[str, Any] = lmap(lowerCAmelCase , lowerCAmelCase )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError("Rank 0 gave up on waiting for other processes" )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 660 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if num < 0:
return False
UpperCAmelCase__ : int = num
UpperCAmelCase__ : int = 0
while num > 0:
UpperCAmelCase__ : Any = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod()
| 700 |
"""simple docstring"""
from timeit import timeit
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
UpperCAmelCase__ : Tuple = 0
while number:
number &= number - 1
result += 1
return result
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
UpperCAmelCase__ : Union[str, Any] = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def a__ ( ):
'''simple docstring'''
def do_benchmark(lowerCAmelCase : int ) -> None:
UpperCAmelCase__ : Dict = "import __main__ as z"
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(lowerCAmelCase ) = }" )
UpperCAmelCase__ : Tuple = timeit("z.get_set_bits_count_using_modulo_operator(25)" , setup=lowerCAmelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase ) = }" )
UpperCAmelCase__ : Any = timeit(
"z.get_set_bits_count_using_brian_kernighans_algorithm(25)" , setup=lowerCAmelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(lowerCAmelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 660 | 0 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A__ : List[str] = logging.get_logger(__name__)
A__ : Tuple = {
"""google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'umt5'
_A = ['past_key_values']
def __init__( self , __UpperCamelCase=25_01_12 , __UpperCamelCase=5_12 , __UpperCamelCase=64 , __UpperCamelCase=10_24 , __UpperCamelCase=8 , __UpperCamelCase=None , __UpperCamelCase=6 , __UpperCamelCase=32 , __UpperCamelCase=1_28 , __UpperCamelCase=0.1 , __UpperCamelCase=1E-6 , __UpperCamelCase=1.0 , __UpperCamelCase="gated-gelu" , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase="T5Tokenizer" , __UpperCamelCase=True , __UpperCamelCase=0 , __UpperCamelCase=1 , __UpperCamelCase=0 , **__UpperCamelCase , )-> str:
super().__init__(
is_encoder_decoder=__UpperCamelCase , tokenizer_class=__UpperCamelCase , tie_word_embeddings=__UpperCamelCase , pad_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , decoder_start_token_id=__UpperCamelCase , **__UpperCamelCase , )
UpperCAmelCase__ : Any = vocab_size
UpperCAmelCase__ : str = d_model
UpperCAmelCase__ : Dict = d_kv
UpperCAmelCase__ : List[str] = d_ff
UpperCAmelCase__ : Tuple = num_layers
UpperCAmelCase__ : Tuple = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
UpperCAmelCase__ : Optional[int] = num_heads
UpperCAmelCase__ : Dict = relative_attention_num_buckets
UpperCAmelCase__ : Any = relative_attention_max_distance
UpperCAmelCase__ : int = dropout_rate
UpperCAmelCase__ : Optional[Any] = layer_norm_epsilon
UpperCAmelCase__ : Tuple = initializer_factor
UpperCAmelCase__ : Optional[Any] = feed_forward_proj
UpperCAmelCase__ : Optional[int] = use_cache
UpperCAmelCase__ : str = self.feed_forward_proj.split("-" )
UpperCAmelCase__ : List[str] = act_info[-1]
UpperCAmelCase__ : Optional[Any] = act_info[0] == "gated"
if len(__UpperCamelCase ) > 1 and act_info[0] != "gated" or len(__UpperCamelCase ) > 2:
raise ValueError(
F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
"Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
"'gated-gelu' or 'relu'" )
if feed_forward_proj == "gated-gelu":
UpperCAmelCase__ : Union[str, Any] = "gelu_new"
@property
def lowerCAmelCase__ ( self )-> Optional[Any]:
return self.d_model
@property
def lowerCAmelCase__ ( self )-> Dict:
return self.num_heads
@property
def lowerCAmelCase__ ( self )-> int:
return self.num_layers
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
UpperCAmelCase__ : Any = {
"input_ids": {0: "batch", 1: "encoder_sequence"},
"attention_mask": {0: "batch", 1: "encoder_sequence"},
}
if self.use_past:
UpperCAmelCase__ : Optional[int] = "past_encoder_sequence + sequence"
UpperCAmelCase__ : List[str] = {0: "batch"}
UpperCAmelCase__ : Dict = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
UpperCAmelCase__ : Optional[Any] = {0: "batch", 1: "decoder_sequence"}
UpperCAmelCase__ : Dict = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(__UpperCamelCase , direction="inputs" )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def lowerCAmelCase__ ( self )-> int:
return 13
@property
def lowerCAmelCase__ ( self )-> float:
return 5E-4
| 701 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _lowercase ( unittest.TestCase , lowerCAmelCase_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : Optional[Any] = load_tool("text-classification" )
self.tool.setup()
UpperCAmelCase__ : List[str] = load_tool("text-classification" , remote=__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ : Dict = self.tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : List[Any] = self.remote_tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
def lowerCAmelCase__ ( self )-> Optional[int]:
UpperCAmelCase__ : Any = self.tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : str = self.remote_tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
| 660 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase )-> str:
UpperCAmelCase__ : list[dict] = []
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "output": []} )
for keyword in keywords:
self.add_keyword(__UpperCamelCase )
self.set_fail_transitions()
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase )-> int | None:
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def lowerCAmelCase__ ( self , __UpperCamelCase )-> None:
UpperCAmelCase__ : Optional[Any] = 0
for character in keyword:
UpperCAmelCase__ : Tuple = self.find_next_state(__UpperCamelCase , __UpperCamelCase )
if next_state is None:
self.adlist.append(
{
"value": character,
"next_states": [],
"fail_state": 0,
"output": [],
} )
self.adlist[current_state]["next_states"].append(len(self.adlist ) - 1 )
UpperCAmelCase__ : List[Any] = len(self.adlist ) - 1
else:
UpperCAmelCase__ : List[Any] = next_state
self.adlist[current_state]["output"].append(__UpperCamelCase )
def lowerCAmelCase__ ( self )-> None:
UpperCAmelCase__ : deque = deque()
for node in self.adlist[0]["next_states"]:
q.append(__UpperCamelCase )
UpperCAmelCase__ : Dict = 0
while q:
UpperCAmelCase__ : Union[str, Any] = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = self.adlist[r]["fail_state"]
while (
self.find_next_state(__UpperCamelCase , self.adlist[child]["value"] ) is None
and state != 0
):
UpperCAmelCase__ : Tuple = self.adlist[state]["fail_state"]
UpperCAmelCase__ : Optional[Any] = self.find_next_state(
__UpperCamelCase , self.adlist[child]["value"] )
if self.adlist[child]["fail_state"] is None:
UpperCAmelCase__ : Any = 0
UpperCAmelCase__ : Any = (
self.adlist[child]["output"]
+ self.adlist[self.adlist[child]["fail_state"]]["output"]
)
def lowerCAmelCase__ ( self , __UpperCamelCase )-> dict[str, list[int]]:
UpperCAmelCase__ : dict = {} # returns a dict with keywords and list of its occurrences
UpperCAmelCase__ : int = 0
for i in range(len(__UpperCamelCase ) ):
while (
self.find_next_state(__UpperCamelCase , string[i] ) is None
and current_state != 0
):
UpperCAmelCase__ : Dict = self.adlist[current_state]["fail_state"]
UpperCAmelCase__ : Optional[Any] = self.find_next_state(__UpperCamelCase , string[i] )
if next_state is None:
UpperCAmelCase__ : List[Any] = 0
else:
UpperCAmelCase__ : Optional[Any] = next_state
for key in self.adlist[current_state]["output"]:
if key not in result:
UpperCAmelCase__ : Any = []
result[key].append(i - len(__UpperCamelCase ) + 1 )
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 702 |
"""simple docstring"""
def a__ ( lowerCAmelCase : list , lowerCAmelCase : list ):
'''simple docstring'''
_validate_point(lowerCAmelCase )
_validate_point(lowerCAmelCase )
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(lowerCAmelCase , lowerCAmelCase ) ) )
def a__ ( lowerCAmelCase : list[float] ):
'''simple docstring'''
if point:
if isinstance(lowerCAmelCase , lowerCAmelCase ):
for item in point:
if not isinstance(lowerCAmelCase , (int, float) ):
UpperCAmelCase__ : Tuple = (
"Expected a list of numbers as input, found "
F"{type(lowerCAmelCase ).__name__}"
)
raise TypeError(lowerCAmelCase )
else:
UpperCAmelCase__ : Dict = F"Expected a list of numbers as input, found {type(lowerCAmelCase ).__name__}"
raise TypeError(lowerCAmelCase )
else:
raise ValueError("Missing an input" )
def a__ ( lowerCAmelCase : list , lowerCAmelCase : list ):
'''simple docstring'''
_validate_point(lowerCAmelCase )
_validate_point(lowerCAmelCase )
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(lowerCAmelCase , lowerCAmelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 0 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( lowerCAmelCase : str ):
'''simple docstring'''
return x + 2
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : Dict = "x = 3"
UpperCAmelCase__ : Optional[Any] = {}
UpperCAmelCase__ : int = evaluate(__UpperCamelCase , {} , state=__UpperCamelCase )
assert result == 3
self.assertDictEqual(__UpperCamelCase , {"x": 3} )
UpperCAmelCase__ : Optional[Any] = "x = y"
UpperCAmelCase__ : Optional[int] = {"y": 5}
UpperCAmelCase__ : Any = evaluate(__UpperCamelCase , {} , state=__UpperCamelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__UpperCamelCase , {"x": 5, "y": 5} )
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ : Optional[Any] = "y = add_two(x)"
UpperCAmelCase__ : List[str] = {"x": 3}
UpperCAmelCase__ : Tuple = evaluate(__UpperCamelCase , {"add_two": add_two} , state=__UpperCamelCase )
assert result == 5
self.assertDictEqual(__UpperCamelCase , {"x": 3, "y": 5} )
# Won't work without the tool
with CaptureStdout() as out:
UpperCAmelCase__ : Any = evaluate(__UpperCamelCase , {} , state=__UpperCamelCase )
assert result is None
assert "tried to execute add_two" in out.out
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = "x = 3"
UpperCAmelCase__ : List[str] = {}
UpperCAmelCase__ : Dict = evaluate(__UpperCamelCase , {} , state=__UpperCamelCase )
assert result == 3
self.assertDictEqual(__UpperCamelCase , {"x": 3} )
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : List[str] = "test_dict = {'x': x, 'y': add_two(x)}"
UpperCAmelCase__ : Optional[Any] = {"x": 3}
UpperCAmelCase__ : int = evaluate(__UpperCamelCase , {"add_two": add_two} , state=__UpperCamelCase )
self.assertDictEqual(__UpperCamelCase , {"x": 3, "y": 5} )
self.assertDictEqual(__UpperCamelCase , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : Optional[int] = "x = 3\ny = 5"
UpperCAmelCase__ : Optional[int] = {}
UpperCAmelCase__ : List[str] = evaluate(__UpperCamelCase , {} , state=__UpperCamelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__UpperCamelCase , {"x": 3, "y": 5} )
def lowerCAmelCase__ ( self )-> Optional[int]:
UpperCAmelCase__ : Union[str, Any] = "text = f'This is x: {x}.'"
UpperCAmelCase__ : Any = {"x": 3}
UpperCAmelCase__ : Union[str, Any] = evaluate(__UpperCamelCase , {} , state=__UpperCamelCase )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(__UpperCamelCase , {"x": 3, "text": "This is x: 3."} )
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ : Union[str, Any] = "if x <= 3:\n y = 2\nelse:\n y = 5"
UpperCAmelCase__ : str = {"x": 3}
UpperCAmelCase__ : Union[str, Any] = evaluate(__UpperCamelCase , {} , state=__UpperCamelCase )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(__UpperCamelCase , {"x": 3, "y": 2} )
UpperCAmelCase__ : Tuple = {"x": 8}
UpperCAmelCase__ : str = evaluate(__UpperCamelCase , {} , state=__UpperCamelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__UpperCamelCase , {"x": 8, "y": 5} )
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : Optional[int] = "test_list = [x, add_two(x)]"
UpperCAmelCase__ : str = {"x": 3}
UpperCAmelCase__ : Tuple = evaluate(__UpperCamelCase , {"add_two": add_two} , state=__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , [3, 5] )
self.assertDictEqual(__UpperCamelCase , {"x": 3, "test_list": [3, 5]} )
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ : int = "y = x"
UpperCAmelCase__ : Optional[int] = {"x": 3}
UpperCAmelCase__ : Optional[int] = evaluate(__UpperCamelCase , {} , state=__UpperCamelCase )
assert result == 3
self.assertDictEqual(__UpperCamelCase , {"x": 3, "y": 3} )
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : Any = "test_list = [x, add_two(x)]\ntest_list[1]"
UpperCAmelCase__ : Tuple = {"x": 3}
UpperCAmelCase__ : str = evaluate(__UpperCamelCase , {"add_two": add_two} , state=__UpperCamelCase )
assert result == 5
self.assertDictEqual(__UpperCamelCase , {"x": 3, "test_list": [3, 5]} )
UpperCAmelCase__ : List[Any] = "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
UpperCAmelCase__ : Union[str, Any] = {"x": 3}
UpperCAmelCase__ : Optional[int] = evaluate(__UpperCamelCase , {"add_two": add_two} , state=__UpperCamelCase )
assert result == 5
self.assertDictEqual(__UpperCamelCase , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : Any = "x = 0\nfor i in range(3):\n x = i"
UpperCAmelCase__ : Optional[Any] = {}
UpperCAmelCase__ : str = evaluate(__UpperCamelCase , {"range": range} , state=__UpperCamelCase )
assert result == 2
self.assertDictEqual(__UpperCamelCase , {"x": 2, "i": 2} )
| 703 |
"""simple docstring"""
import math
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a__ ( lowerCAmelCase : int = 1_0001 ):
'''simple docstring'''
try:
UpperCAmelCase__ : List[str] = int(lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError("Parameter nth must be int or castable to int." ) from None
if nth <= 0:
raise ValueError("Parameter nth must be greater than or equal to one." )
UpperCAmelCase__ : list[int] = []
UpperCAmelCase__ : str = 2
while len(lowerCAmelCase ) < nth:
if is_prime(lowerCAmelCase ):
primes.append(lowerCAmelCase )
num += 1
else:
num += 1
return primes[len(lowerCAmelCase ) - 1]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 660 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase : list ):
'''simple docstring'''
if len(lowerCAmelCase ) <= 1:
return [tuple(lowerCAmelCase )]
UpperCAmelCase__ : str = []
def generate(lowerCAmelCase : int , lowerCAmelCase : list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , lowerCAmelCase )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
UpperCAmelCase__ : List[Any] = arr[k - 1], arr[i]
else: # k is odd
UpperCAmelCase__ : Any = arr[k - 1], arr[0]
generate(k - 1 , lowerCAmelCase )
generate(len(lowerCAmelCase ) , lowerCAmelCase )
return res
if __name__ == "__main__":
A__ : Optional[Any] = input("""Enter numbers separated by a comma:\n""").strip()
A__ : Optional[int] = [int(item) for item in user_input.split(""",""")]
print(heaps(arr))
| 704 |
"""simple docstring"""
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=30 , __UpperCamelCase=2 , __UpperCamelCase=3 , __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=3 , __UpperCamelCase=0.6 , __UpperCamelCase=None , )-> List[Any]:
UpperCAmelCase__ : str = parent
UpperCAmelCase__ : Optional[Any] = batch_size
UpperCAmelCase__ : Any = image_size
UpperCAmelCase__ : Dict = patch_size
UpperCAmelCase__ : Optional[Any] = num_channels
UpperCAmelCase__ : Union[str, Any] = is_training
UpperCAmelCase__ : Any = use_labels
UpperCAmelCase__ : List[Any] = hidden_size
UpperCAmelCase__ : Dict = num_hidden_layers
UpperCAmelCase__ : List[str] = num_attention_heads
UpperCAmelCase__ : List[Any] = intermediate_size
UpperCAmelCase__ : Dict = hidden_act
UpperCAmelCase__ : List[Any] = hidden_dropout_prob
UpperCAmelCase__ : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase__ : Optional[Any] = type_sequence_label_size
UpperCAmelCase__ : Union[str, Any] = initializer_range
UpperCAmelCase__ : int = mask_ratio
UpperCAmelCase__ : Tuple = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase__ : int = (image_size // patch_size) ** 2
UpperCAmelCase__ : str = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Optional[Any] = None
if self.use_labels:
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : Optional[int] = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self )-> int:
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = ViTMAEModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : List[str] = 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 )-> Optional[int]:
UpperCAmelCase__ : List[Any] = ViTMAEForPreTraining(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : Optional[int] = model(__UpperCamelCase )
UpperCAmelCase__ : List[str] = (self.image_size // self.patch_size) ** 2
UpperCAmelCase__ : List[str] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase__ : Dict = 1
UpperCAmelCase__ : str = ViTMAEForPreTraining(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase__ : List[str] = model(__UpperCamelCase )
UpperCAmelCase__ : List[str] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : Tuple = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : str = config_and_inputs
UpperCAmelCase__ : int = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_A = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
_A = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
_A = False
_A = False
_A = False
_A = False
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : Any = ViTMAEModelTester(self )
UpperCAmelCase__ : List[str] = ConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase , hidden_size=37 )
def lowerCAmelCase__ ( self )-> int:
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def lowerCAmelCase__ ( self )-> Dict:
pass
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : int = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase__ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ , UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class(__UpperCamelCase )
UpperCAmelCase__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Dict = [*signature.parameters.keys()]
UpperCAmelCase__ : Tuple = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Dict:
# make masks reproducible
np.random.seed(2 )
UpperCAmelCase__ : Tuple = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
UpperCAmelCase__ : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase__ : str = torch.from_numpy(__UpperCamelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase__ : Optional[Any] = pt_noise
super().check_pt_tf_models(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[Any] = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase__ : Optional[int] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
UpperCAmelCase__ : Optional[Any] = outputs[0].cpu().numpy()
UpperCAmelCase__ : Union[str, Any] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = model_class.from_pretrained(__UpperCamelCase )
model.to(__UpperCamelCase )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase__ : List[Any] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
# Make sure we don't have nans
UpperCAmelCase__ : Tuple = after_outputs[0].cpu().numpy()
UpperCAmelCase__ : int = 0
UpperCAmelCase__ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__UpperCamelCase , 1E-5 )
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> List[str]:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> Any:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> Optional[Any]:
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def lowerCAmelCase__ ( self )-> List[Any]:
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
pass
@slow
def lowerCAmelCase__ ( self )-> Union[str, Any]:
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : Tuple = ViTMAEModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase__ ( self )-> List[Any]:
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def lowerCAmelCase__ ( self )-> Optional[int]:
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCAmelCase__ : Any = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" ).to(__UpperCamelCase )
UpperCAmelCase__ : Tuple = self.default_image_processor
UpperCAmelCase__ : List[Any] = prepare_img()
UpperCAmelCase__ : Optional[Any] = image_processor(images=__UpperCamelCase , return_tensors="pt" ).to(__UpperCamelCase )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCAmelCase__ : List[Any] = ViTMAEConfig()
UpperCAmelCase__ : str = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase__ : Optional[int] = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : List[str] = model(**__UpperCamelCase , noise=torch.from_numpy(__UpperCamelCase ).to(device=__UpperCamelCase ) )
# verify the logits
UpperCAmelCase__ : str = torch.Size((1, 1_96, 7_68) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
UpperCAmelCase__ : List[Any] = torch.tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(__UpperCamelCase ) , atol=1E-4 ) )
| 660 | 0 |
"""simple docstring"""
from sklearn.metrics import mean_squared_error
import datasets
A__ : Union[str, Any] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
A__ : List[Any] = """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""
A__ : Tuple = """
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
\"raw_values\" : Returns a full set of errors in case of multioutput input.
\"uniform_average\" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric(\"mse\")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{'mse': 0.6123724356957945}
If you're using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{'mse': array([0.41666667, 1. ])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> str:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
] , )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("float" ) ),
"references": datasets.Sequence(datasets.Value("float" ) ),
}
else:
return {
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
}
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase="uniform_average" , __UpperCamelCase=True )-> int:
UpperCAmelCase__ : List[str] = mean_squared_error(
__UpperCamelCase , __UpperCamelCase , sample_weight=__UpperCamelCase , multioutput=__UpperCamelCase , squared=__UpperCamelCase )
return {"mse": mse}
| 705 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class _lowercase :
'''simple docstring'''
_A = 42
# setable values
_A = 42
_A = 42
_A = None
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
return cls(common=__UpperCamelCase , init_noise_sigma=__UpperCamelCase , timesteps=__UpperCamelCase )
@dataclass
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
_A = [e.name for e in FlaxKarrasDiffusionSchedulers]
_A = 42
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
return True
@register_to_config
def __init__( self , __UpperCamelCase = 10_00 , __UpperCamelCase = 0.0001 , __UpperCamelCase = 0.02 , __UpperCamelCase = "linear" , __UpperCamelCase = None , __UpperCamelCase = "fixed_small" , __UpperCamelCase = True , __UpperCamelCase = "epsilon" , __UpperCamelCase = jnp.floataa , )-> List[str]:
UpperCAmelCase__ : int = dtype
def lowerCAmelCase__ ( self , __UpperCamelCase = None )-> DDPMSchedulerState:
if common is None:
UpperCAmelCase__ : int = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
UpperCAmelCase__ : Tuple = jnp.array(1.0 , dtype=self.dtype )
UpperCAmelCase__ : Tuple = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=__UpperCamelCase , init_noise_sigma=__UpperCamelCase , timesteps=__UpperCamelCase , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None )-> jnp.ndarray:
return sample
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = () )-> DDPMSchedulerState:
UpperCAmelCase__ : Dict = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
UpperCAmelCase__ : Optional[int] = (jnp.arange(0 , __UpperCamelCase ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=__UpperCamelCase , timesteps=__UpperCamelCase , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = state.common.alphas_cumprod[t]
UpperCAmelCase__ : int = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase__ : Any = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
UpperCAmelCase__ : Union[str, Any] = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
UpperCAmelCase__ : Dict = jnp.clip(__UpperCamelCase , a_min=1E-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
UpperCAmelCase__ : Tuple = jnp.log(jnp.clip(__UpperCamelCase , a_min=1E-20 ) )
elif variance_type == "fixed_large":
UpperCAmelCase__ : Union[str, Any] = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
UpperCAmelCase__ : Optional[int] = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
UpperCAmelCase__ : List[str] = variance
UpperCAmelCase__ : Union[str, Any] = state.common.betas[t]
UpperCAmelCase__ : Optional[int] = (predicted_variance + 1) / 2
UpperCAmelCase__ : Any = frac * max_log + (1 - frac) * min_log
return variance
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = True , )-> Union[FlaxDDPMSchedulerOutput, Tuple]:
UpperCAmelCase__ : List[str] = timestep
if key is None:
UpperCAmelCase__ : int = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = jnp.split(__UpperCamelCase , sample.shape[1] , axis=1 )
else:
UpperCAmelCase__ : Optional[Any] = None
# 1. compute alphas, betas
UpperCAmelCase__ : Union[str, Any] = state.common.alphas_cumprod[t]
UpperCAmelCase__ : Tuple = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
UpperCAmelCase__ : Union[str, Any] = 1 - alpha_prod_t
UpperCAmelCase__ : Tuple = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase__ : List[Any] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase__ : Any = model_output
elif self.config.prediction_type == "v_prediction":
UpperCAmelCase__ : Union[str, Any] = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
" for the FlaxDDPMScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase__ : List[Any] = jnp.clip(__UpperCamelCase , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ : List[str] = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
UpperCAmelCase__ : List[Any] = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ : Tuple = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
UpperCAmelCase__ : Any = jax.random.split(__UpperCamelCase , num=1 )
UpperCAmelCase__ : int = jax.random.normal(__UpperCamelCase , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(__UpperCamelCase , __UpperCamelCase , predicted_variance=__UpperCamelCase ) ** 0.5) * noise
UpperCAmelCase__ : Dict = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
UpperCAmelCase__ : Any = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase , state=__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> jnp.ndarray:
return add_noise_common(state.common , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> jnp.ndarray:
return get_velocity_common(state.common , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __len__( self )-> Tuple:
return self.config.num_train_timesteps
| 660 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Dict = logging.get_logger(__name__)
A__ : str = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'git_vision_model'
def __init__( self , __UpperCamelCase=7_68 , __UpperCamelCase=30_72 , __UpperCamelCase=12 , __UpperCamelCase=12 , __UpperCamelCase=3 , __UpperCamelCase=2_24 , __UpperCamelCase=16 , __UpperCamelCase="quick_gelu" , __UpperCamelCase=1E-5 , __UpperCamelCase=0.0 , __UpperCamelCase=0.02 , **__UpperCamelCase , )-> Tuple:
super().__init__(**__UpperCamelCase )
UpperCAmelCase__ : int = hidden_size
UpperCAmelCase__ : Tuple = intermediate_size
UpperCAmelCase__ : str = num_hidden_layers
UpperCAmelCase__ : List[str] = num_attention_heads
UpperCAmelCase__ : Dict = num_channels
UpperCAmelCase__ : Optional[int] = patch_size
UpperCAmelCase__ : Any = image_size
UpperCAmelCase__ : Tuple = initializer_range
UpperCAmelCase__ : int = attention_dropout
UpperCAmelCase__ : Dict = layer_norm_eps
UpperCAmelCase__ : str = hidden_act
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , **__UpperCamelCase )-> "PretrainedConfig":
cls._set_token_in_kwargs(__UpperCamelCase )
UpperCAmelCase__ : Tuple = cls.get_config_dict(__UpperCamelCase , **__UpperCamelCase )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("model_type" ) == "git":
UpperCAmelCase__ : List[str] = 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(__UpperCamelCase , **__UpperCamelCase )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'git'
def __init__( self , __UpperCamelCase=None , __UpperCamelCase=3_05_22 , __UpperCamelCase=7_68 , __UpperCamelCase=6 , __UpperCamelCase=12 , __UpperCamelCase=30_72 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=10_24 , __UpperCamelCase=0.02 , __UpperCamelCase=1E-12 , __UpperCamelCase=0 , __UpperCamelCase="absolute" , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=1_01 , __UpperCamelCase=1_02 , __UpperCamelCase=None , **__UpperCamelCase , )-> List[str]:
super().__init__(bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , pad_token_id=__UpperCamelCase , **__UpperCamelCase )
if vision_config is None:
UpperCAmelCase__ : List[str] = {}
logger.info("vision_config is None. initializing the GitVisionConfig with default values." )
UpperCAmelCase__ : Dict = GitVisionConfig(**__UpperCamelCase )
UpperCAmelCase__ : Tuple = vocab_size
UpperCAmelCase__ : List[Any] = hidden_size
UpperCAmelCase__ : Any = num_hidden_layers
UpperCAmelCase__ : Optional[Any] = num_attention_heads
UpperCAmelCase__ : List[Any] = hidden_act
UpperCAmelCase__ : List[str] = intermediate_size
UpperCAmelCase__ : List[Any] = hidden_dropout_prob
UpperCAmelCase__ : Union[str, Any] = attention_probs_dropout_prob
UpperCAmelCase__ : List[str] = max_position_embeddings
UpperCAmelCase__ : List[Any] = initializer_range
UpperCAmelCase__ : Any = layer_norm_eps
UpperCAmelCase__ : Union[str, Any] = position_embedding_type
UpperCAmelCase__ : List[Any] = use_cache
UpperCAmelCase__ : str = tie_word_embeddings
UpperCAmelCase__ : str = num_image_with_embedding
UpperCAmelCase__ : str = bos_token_id
UpperCAmelCase__ : Any = eos_token_id
def lowerCAmelCase__ ( self )-> Optional[int]:
UpperCAmelCase__ : int = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : int = self.vision_config.to_dict()
UpperCAmelCase__ : Dict = self.__class__.model_type
return output
| 706 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = ''
_A = 'hf-legacy' # "hf://"" is reserved for hffs
def __init__( self , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )-> str:
super().__init__(self , **__UpperCamelCase )
UpperCAmelCase__ : int = repo_info
UpperCAmelCase__ : Optional[int] = token
UpperCAmelCase__ : Optional[Any] = None
def lowerCAmelCase__ ( self )-> Optional[Any]:
if self.dir_cache is None:
UpperCAmelCase__ : str = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
UpperCAmelCase__ : str = {
"name": hf_file.rfilename,
"size": None,
"type": "file",
}
self.dir_cache.update(
{
str(__UpperCamelCase ): {"name": str(__UpperCamelCase ), "size": None, "type": "directory"}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = "rb" , **__UpperCamelCase , )-> List[Any]:
if not isinstance(self.repo_info , __UpperCamelCase ):
raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}" )
UpperCAmelCase__ : Union[str, Any] = hf_hub_url(self.repo_info.id , __UpperCamelCase , revision=self.repo_info.sha )
return fsspec.open(
__UpperCamelCase , mode=__UpperCamelCase , headers=get_authentication_headers_for_url(__UpperCamelCase , use_auth_token=self.token ) , client_kwargs={"trust_env": True} , ).open()
def lowerCAmelCase__ ( self , __UpperCamelCase , **__UpperCamelCase )-> List[str]:
self._get_dirs()
UpperCAmelCase__ : Union[str, Any] = self._strip_protocol(__UpperCamelCase )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase=False , **__UpperCamelCase )-> str:
self._get_dirs()
UpperCAmelCase__ : str = PurePosixPath(path.strip("/" ) )
UpperCAmelCase__ : Optional[Any] = {}
for p, f in self.dir_cache.items():
UpperCAmelCase__ : Optional[int] = PurePosixPath(p.strip("/" ) )
UpperCAmelCase__ : Dict = p.parent
if root == path:
UpperCAmelCase__ : Tuple = f
UpperCAmelCase__ : List[Any] = list(paths.values() )
if detail:
return out
else:
return sorted(f["name"] for f in out )
| 660 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
A__ : List[str] = None
A__ : Union[str, Any] = logging.get_logger(__name__)
A__ : Optional[Any] = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
A__ : Optional[Any] = {
"""vocab_file""": {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
},
"""tokenizer_file""": {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
},
}
A__ : List[str] = {
"""camembert-base""": 512,
}
A__ : str = """▁"""
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = VOCAB_FILES_NAMES
_A = PRETRAINED_VOCAB_FILES_MAP
_A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A = ['input_ids', 'attention_mask']
_A = CamembertTokenizer
def __init__( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase="<s>" , __UpperCamelCase="</s>" , __UpperCamelCase="</s>" , __UpperCamelCase="<s>" , __UpperCamelCase="<unk>" , __UpperCamelCase="<pad>" , __UpperCamelCase="<mask>" , __UpperCamelCase=["<s>NOTUSED", "</s>NOTUSED"] , **__UpperCamelCase , )-> Dict:
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase__ : str = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else mask_token
super().__init__(
__UpperCamelCase , tokenizer_file=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , sep_token=__UpperCamelCase , cls_token=__UpperCamelCase , unk_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , additional_special_tokens=__UpperCamelCase , **__UpperCamelCase , )
UpperCAmelCase__ : str = vocab_file
UpperCAmelCase__ : Union[str, Any] = False if not self.vocab_file else True
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None )-> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase__ : Union[str, Any] = [self.cls_token_id]
UpperCAmelCase__ : Optional[int] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None )-> List[int]:
UpperCAmelCase__ : int = [self.sep_token_id]
UpperCAmelCase__ : List[str] = [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 lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None )-> Tuple[str]:
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(__UpperCamelCase ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
UpperCAmelCase__ : int = os.path.join(
__UpperCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ):
copyfile(self.vocab_file , __UpperCamelCase )
return (out_vocab_file,)
| 707 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
A__ : Dict = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
if isinstance(lowerCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowerCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowerCAmelCase ):
return [[videos]]
raise ValueError(F"Could not make batched video from {videos}" )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = ['pixel_values']
def __init__( self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_55 , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )-> None:
super().__init__(**__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = size if size is not None else {"shortest_edge": 2_56}
UpperCAmelCase__ : List[Any] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : List[str] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
UpperCAmelCase__ : int = get_size_dict(__UpperCamelCase , param_name="crop_size" )
UpperCAmelCase__ : Dict = do_resize
UpperCAmelCase__ : Optional[int] = size
UpperCAmelCase__ : List[Any] = do_center_crop
UpperCAmelCase__ : str = crop_size
UpperCAmelCase__ : Optional[int] = resample
UpperCAmelCase__ : int = do_rescale
UpperCAmelCase__ : Union[str, Any] = rescale_factor
UpperCAmelCase__ : Union[str, Any] = offset
UpperCAmelCase__ : Dict = do_normalize
UpperCAmelCase__ : int = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase__ : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
if "shortest_edge" in size:
UpperCAmelCase__ : Union[str, Any] = get_resize_output_image_size(__UpperCamelCase , size["shortest_edge"] , default_to_square=__UpperCamelCase )
elif "height" in size and "width" in size:
UpperCAmelCase__ : Any = (size["height"], size["width"])
else:
raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[Any] = get_size_dict(__UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(__UpperCamelCase , size=(size["height"], size["width"]) , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = True , __UpperCamelCase = None , **__UpperCamelCase , )-> Tuple:
UpperCAmelCase__ : str = image.astype(np.floataa )
if offset:
UpperCAmelCase__ : Tuple = image - (scale / 2)
return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
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 = ChannelDimension.FIRST , )-> np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError("Size and resample 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." )
if offset and not do_rescale:
raise ValueError("For offset, do_rescale must also be set to True." )
# All transformations expect numpy arrays.
UpperCAmelCase__ : Optional[Any] = to_numpy_array(__UpperCamelCase )
if do_resize:
UpperCAmelCase__ : Union[str, Any] = self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase )
if do_center_crop:
UpperCAmelCase__ : int = self.center_crop(__UpperCamelCase , size=__UpperCamelCase )
if do_rescale:
UpperCAmelCase__ : List[str] = self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase , offset=__UpperCamelCase )
if do_normalize:
UpperCAmelCase__ : List[Any] = self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase )
UpperCAmelCase__ : Dict = to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase )
return image
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 , )-> PIL.Image.Image:
UpperCAmelCase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ : int = resample if resample is not None else self.resample
UpperCAmelCase__ : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase__ : int = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ : Optional[int] = offset if offset is not None else self.offset
UpperCAmelCase__ : Dict = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ : Dict = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ : Optional[int] = image_std if image_std is not None else self.image_std
UpperCAmelCase__ : List[str] = size if size is not None else self.size
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : Dict = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase__ : Tuple = get_size_dict(__UpperCamelCase , param_name="crop_size" )
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." )
UpperCAmelCase__ : List[str] = make_batched(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = [
[
self._preprocess_image(
image=__UpperCamelCase , do_resize=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , do_center_crop=__UpperCamelCase , crop_size=__UpperCamelCase , do_rescale=__UpperCamelCase , rescale_factor=__UpperCamelCase , offset=__UpperCamelCase , do_normalize=__UpperCamelCase , image_mean=__UpperCamelCase , image_std=__UpperCamelCase , data_format=__UpperCamelCase , )
for img in video
]
for video in videos
]
UpperCAmelCase__ : Dict = {"pixel_values": videos}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 660 | 0 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a__ ( lowerCAmelCase : str , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Dict , lowerCAmelCase : List[Any] ):
'''simple docstring'''
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Optional[int] = np.full((len(lowerCAmelCase ), sequence_length, 2) , lowerCAmelCase )
else:
UpperCAmelCase__ : Optional[Any] = np.full((len(lowerCAmelCase ), sequence_length) , lowerCAmelCase )
for i, tensor in enumerate(lowerCAmelCase ):
if padding_side == "right":
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Dict = tensor[:sequence_length]
else:
UpperCAmelCase__ : Tuple = tensor[:sequence_length]
else:
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Optional[Any] = tensor[:sequence_length]
else:
UpperCAmelCase__ : int = tensor[:sequence_length]
return out_tensor.tolist()
def a__ ( lowerCAmelCase : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = ord(lowerCAmelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
UpperCAmelCase__ : Optional[Any] = unicodedata.category(lowerCAmelCase )
if cat.startswith("P" ):
return True
return False
@dataclass
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
_A = True
_A = None
_A = None
_A = -100
_A = 'pt'
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[str]:
import torch
UpperCAmelCase__ : Optional[Any] = "label" if "label" in features[0].keys() else "labels"
UpperCAmelCase__ : Dict = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
UpperCAmelCase__ : str = self.tokenizer.pad(
__UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" if labels is None else None , )
if labels is None:
return batch
UpperCAmelCase__ : Optional[Any] = torch.tensor(batch["entity_ids"] ).shape[1]
UpperCAmelCase__ : int = self.tokenizer.padding_side
if padding_side == "right":
UpperCAmelCase__ : int = [
list(__UpperCamelCase ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) for label in labels
]
else:
UpperCAmelCase__ : List[Any] = [
[self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) + list(__UpperCamelCase ) for label in labels
]
UpperCAmelCase__ : Optional[Any] = [feature["ner_tags"] for feature in features]
UpperCAmelCase__ : int = padding_tensor(__UpperCamelCase , -1 , __UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : List[Any] = [feature["original_entity_spans"] for feature in features]
UpperCAmelCase__ : int = padding_tensor(__UpperCamelCase , (-1, -1) , __UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[int] = {k: torch.tensor(__UpperCamelCase , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 708 |
"""simple docstring"""
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowerCAmelCase , lowerCAmelCase ):
raise TypeError("Input value must be a 'int' type" )
return bin(lowerCAmelCase ).count("1" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def a__ ( lowerCAmelCase : Callable[[int | float], int | float] , lowerCAmelCase : int | float , lowerCAmelCase : int | float , lowerCAmelCase : int = 100 , ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = x_start
UpperCAmelCase__ : Optional[Any] = fnc(lowerCAmelCase )
UpperCAmelCase__ : Tuple = 0.0
for _ in range(lowerCAmelCase ):
# Approximates curve as a sequence of linear lines and sums their length
UpperCAmelCase__ : str = (x_end - x_start) / steps + xa
UpperCAmelCase__ : str = fnc(lowerCAmelCase )
length += math.hypot(xa - xa , fxa - fxa )
# Increment step
UpperCAmelCase__ : Tuple = xa
UpperCAmelCase__ : int = fxa
return length
if __name__ == "__main__":
def a__ ( lowerCAmelCase : Any ):
'''simple docstring'''
return math.sin(10 * x )
print("""f(x) = sin(10 * x)""")
print("""The length of the curve from x = -10 to x = 10 is:""")
A__ : str = 10
while i <= 100_000:
print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
i *= 10
| 709 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
A__ : Optional[Any] = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : bool , lowerCAmelCase : bool ):
'''simple docstring'''
def run_func(lowerCAmelCase : Dict ):
@wraps(lowerCAmelCase )
def run_in_eager_mode(*lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Dict ):
return func(*lowerCAmelCase , **lowerCAmelCase )
@wraps(lowerCAmelCase )
@tf.function(experimental_compile=lowerCAmelCase )
def run_in_graph_mode(*lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Optional[Any] ):
return func(*lowerCAmelCase , **lowerCAmelCase )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`." )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def a__ ( lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Dict = random.Random()
UpperCAmelCase__ : List[str] = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(lowerCAmelCase , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
_A = 42
_A = "TensorFlow"
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
return tf.__version__
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
# initialize GPU on separate process
UpperCAmelCase__ : Any = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Union[str, Any] = self._prepare_inference_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_speed(_inference )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
UpperCAmelCase__ : List[Any] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : List[Any] = self._prepare_train_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_speed(_train )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> [Memory, Optional[MemorySummary]]:
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __UpperCamelCase )
UpperCAmelCase__ : List[str] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Any = self._prepare_inference_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_memory(_inference )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> [Memory, Optional[MemorySummary]]:
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __UpperCamelCase )
UpperCAmelCase__ : Any = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Optional[Any] = self._prepare_train_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_memory(_train )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Callable[[], None]:
UpperCAmelCase__ : Union[str, Any] = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
UpperCAmelCase__ : Optional[int] = (
hasattr(__UpperCamelCase , "architectures" )
and isinstance(config.architectures , __UpperCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCAmelCase__ : str = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCAmelCase__ : Any = __import__("transformers" , fromlist=[model_class] )
UpperCAmelCase__ : List[Any] = getattr(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Dict = model_cls(__UpperCamelCase )
except ImportError:
raise ImportError(
F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
UpperCAmelCase__ : Any = TF_MODEL_MAPPING[config.__class__](__UpperCamelCase )
# encoder-decoder has vocab size saved differently
UpperCAmelCase__ : int = config.vocab_size if hasattr(__UpperCamelCase , "vocab_size" ) else config.encoder.vocab_size
UpperCAmelCase__ : Optional[Any] = random_input_ids(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(__UpperCamelCase , decoder_input_ids=__UpperCamelCase , training=__UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(__UpperCamelCase , training=__UpperCamelCase )
UpperCAmelCase__ : Dict = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Callable[[], None]:
UpperCAmelCase__ : List[Any] = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`." )
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
UpperCAmelCase__ : Any = (
hasattr(__UpperCamelCase , "architectures" )
and isinstance(config.architectures , __UpperCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCAmelCase__ : Any = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCAmelCase__ : int = __import__("transformers" , fromlist=[model_class] )
UpperCAmelCase__ : int = getattr(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = model_cls(__UpperCamelCase )
except ImportError:
raise ImportError(
F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
UpperCAmelCase__ : List[str] = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCamelCase )
# encoder-decoder has vocab size saved differently
UpperCAmelCase__ : Union[str, Any] = config.vocab_size if hasattr(__UpperCamelCase , "vocab_size" ) else config.encoder.vocab_size
UpperCAmelCase__ : Dict = random_input_ids(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
UpperCAmelCase__ : Union[str, Any] = model(__UpperCamelCase , decoder_input_ids=__UpperCamelCase , labels=__UpperCamelCase , training=__UpperCamelCase )[0]
UpperCAmelCase__ : Union[str, Any] = tf.gradients(__UpperCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
UpperCAmelCase__ : Union[str, Any] = model(__UpperCamelCase , labels=__UpperCamelCase , training=__UpperCamelCase )[0]
UpperCAmelCase__ : Any = tf.gradients(__UpperCamelCase , model.trainable_variables )
return gradients
UpperCAmelCase__ : str = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def lowerCAmelCase__ ( self , __UpperCamelCase )-> float:
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("Do inference on TPU. Running model 5 times to stabilize compilation" )
timeit.repeat(__UpperCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
UpperCAmelCase__ : Optional[Any] = timeit.repeat(
__UpperCamelCase , repeat=self.args.repeat , number=10 , )
return min(__UpperCamelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(F"Doesn't fit on GPU. {e}" )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> [Memory, MemorySummary]:
logger.info(
"Note that TensorFlow allocates more memory than "
"it might need to speed up computation. "
"The memory reported here corresponds to the memory "
"reported by `nvidia-smi`, which can vary depending "
"on total available memory on the GPU that is used." )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
" consumption line by line." )
UpperCAmelCase__ : List[str] = start_memory_tracing("transformers" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
" with `args.memory=False`" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"py3nvml not installed, we won't log GPU memory usage. "
"Install py3nvml (pip install py3nvml) to log information about GPU." )
UpperCAmelCase__ : Optional[int] = "N/A"
else:
logger.info(
"Measuring total GPU usage on GPU device. Make sure to not have additional processes"
" running on the same GPU." )
# init nvml
nvml.nvmlInit()
func()
UpperCAmelCase__ : Any = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
UpperCAmelCase__ : Optional[int] = nvml.nvmlDeviceGetMemoryInfo(__UpperCamelCase )
UpperCAmelCase__ : str = meminfo.used
UpperCAmelCase__ : int = Memory(__UpperCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
" TensorFlow." )
UpperCAmelCase__ : Any = None
else:
UpperCAmelCase__ : List[Any] = measure_peak_memory_cpu(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = Memory(__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
UpperCAmelCase__ : Optional[Any] = stop_memory_tracing(__UpperCamelCase )
if memory is None:
UpperCAmelCase__ : Tuple = summary.total
else:
UpperCAmelCase__ : int = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(F"Doesn't fit on GPU. {e}" )
return "N/A", None
| 660 | 0 |
"""simple docstring"""
import qiskit
def a__ ( lowerCAmelCase : int , lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
UpperCAmelCase__ : List[str] = qiskit.QuantumCircuit(lowerCAmelCase , lowerCAmelCase )
# Map the quantum measurement to the classical bits
circuit.measure([0] , [0] )
# Execute the circuit on the simulator
UpperCAmelCase__ : Optional[int] = qiskit.execute(lowerCAmelCase , lowerCAmelCase , shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(lowerCAmelCase )
if __name__ == "__main__":
print(f"""Total count for various states are: {single_qubit_measure(1, 1)}""")
| 710 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
A__ : List[str] = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
A__ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 660 | 0 |
"""simple docstring"""
from __future__ import annotations
def a__ ( lowerCAmelCase : list[int] ):
'''simple docstring'''
if len(lowerCAmelCase ) == 0:
return array
UpperCAmelCase__ : Union[str, Any] = min(lowerCAmelCase ), max(lowerCAmelCase )
# Compute the variables
UpperCAmelCase__ : Tuple = _max - _min + 1
UpperCAmelCase__ : Dict = [0] * holes_range, [0] * holes_range
# Make the sorting.
for i in array:
UpperCAmelCase__ : List[str] = i - _min
UpperCAmelCase__ : Union[str, Any] = i
holes_repeat[index] += 1
# Makes the array back by replacing the numbers.
UpperCAmelCase__ : Union[str, Any] = 0
for i in range(lowerCAmelCase ):
while holes_repeat[i] > 0:
UpperCAmelCase__ : Optional[int] = holes[i]
index += 1
holes_repeat[i] -= 1
# Returns the sorted array.
return array
if __name__ == "__main__":
import doctest
doctest.testmod()
A__ : Any = input("""Enter numbers separated by comma:\n""")
A__ : str = [int(x) for x in user_input.split(""",""")]
print(pigeon_sort(unsorted))
| 711 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
@register_to_config
def __init__( self , __UpperCamelCase = 7_68 , )-> Union[str, Any]:
super().__init__()
UpperCAmelCase__ : str = nn.Parameter(torch.zeros(1 , __UpperCamelCase ) )
UpperCAmelCase__ : Optional[int] = nn.Parameter(torch.ones(1 , __UpperCamelCase ) )
def lowerCAmelCase__ ( self , __UpperCamelCase = None , __UpperCamelCase = None , )-> Any:
UpperCAmelCase__ : Dict = nn.Parameter(self.mean.to(__UpperCamelCase ).to(__UpperCamelCase ) )
UpperCAmelCase__ : Any = nn.Parameter(self.std.to(__UpperCamelCase ).to(__UpperCamelCase ) )
return self
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Union[str, Any]:
UpperCAmelCase__ : Dict = (embeds - self.mean) * 1.0 / self.std
return embeds
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[Any]:
UpperCAmelCase__ : Any = (embeds * self.std) + self.mean
return embeds
| 660 | 0 |
"""simple docstring"""
from __future__ import annotations
A__ : Optional[Any] = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a__ ( lowerCAmelCase : list[list[int]] , lowerCAmelCase : list[int] , lowerCAmelCase : list[int] , lowerCAmelCase : int , lowerCAmelCase : list[list[int]] , ):
'''simple docstring'''
UpperCAmelCase__ : Any = [
[0 for col in range(len(grid[0] ) )] for row in range(len(lowerCAmelCase ) )
] # the reference grid
UpperCAmelCase__ : Tuple = 1
UpperCAmelCase__ : List[str] = [
[0 for col in range(len(grid[0] ) )] for row in range(len(lowerCAmelCase ) )
] # the action grid
UpperCAmelCase__ : Optional[int] = init[0]
UpperCAmelCase__ : Optional[int] = init[1]
UpperCAmelCase__ : Optional[Any] = 0
UpperCAmelCase__ : int = g + heuristic[x][y] # cost from starting cell to destination cell
UpperCAmelCase__ : Any = [[f, g, x, y]]
UpperCAmelCase__ : Any = False # flag that is set when search is complete
UpperCAmelCase__ : Tuple = False # flag set if we can't find expand
while not found and not resign:
if len(lowerCAmelCase ) == 0:
raise ValueError("Algorithm is unable to find solution" )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
UpperCAmelCase__ : List[Any] = cell.pop()
UpperCAmelCase__ : Dict = next_cell[2]
UpperCAmelCase__ : Tuple = next_cell[3]
UpperCAmelCase__ : Dict = next_cell[1]
if x == goal[0] and y == goal[1]:
UpperCAmelCase__ : Dict = True
else:
for i in range(len(lowerCAmelCase ) ): # to try out different valid actions
UpperCAmelCase__ : Optional[int] = x + DIRECTIONS[i][0]
UpperCAmelCase__ : List[str] = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(lowerCAmelCase ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
UpperCAmelCase__ : List[Any] = g + cost
UpperCAmelCase__ : List[Any] = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
UpperCAmelCase__ : Any = 1
UpperCAmelCase__ : List[Any] = i
UpperCAmelCase__ : str = []
UpperCAmelCase__ : Optional[int] = goal[0]
UpperCAmelCase__ : str = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
UpperCAmelCase__ : int = x - DIRECTIONS[action[x][y]][0]
UpperCAmelCase__ : Optional[int] = y - DIRECTIONS[action[x][y]][1]
UpperCAmelCase__ : Dict = xa
UpperCAmelCase__ : Tuple = ya
invpath.append([x, y] )
UpperCAmelCase__ : Tuple = []
for i in range(len(lowerCAmelCase ) ):
path.append(invpath[len(lowerCAmelCase ) - 1 - i] )
return path, action
if __name__ == "__main__":
A__ : int = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
A__ : Tuple = [0, 0]
# all coordinates are given in format [y,x]
A__ : List[Any] = [len(grid) - 1, len(grid[0]) - 1]
A__ : List[Any] = 1
# the cost map which pushes the path closer to the goal
A__ : int = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
A__ : List[Any] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
A__ : int = 99
A__ : Optional[int] = search(grid, init, goal, cost, heuristic)
print("""ACTION MAP""")
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 712 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : Any , lowerCAmelCase : List[Any] ):
'''simple docstring'''
# Construct model
if gpta_config_file == "":
UpperCAmelCase__ : Optional[int] = GPTaConfig()
else:
UpperCAmelCase__ : Dict = GPTaConfig.from_json_file(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = GPTaModel(lowerCAmelCase )
# Load weights from numpy
load_tf_weights_in_gpta(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Save pytorch-model
UpperCAmelCase__ : Optional[int] = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
UpperCAmelCase__ : Any = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(model.state_dict() , lowerCAmelCase )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(lowerCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
A__ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--gpt2_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained OpenAI model. \n"""
"""This specifies the model architecture."""
),
)
A__ : Optional[Any] = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 660 | 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_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase=7 , __UpperCamelCase=3 , __UpperCamelCase=18 , __UpperCamelCase=30 , __UpperCamelCase=4_00 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=[0.5, 0.5, 0.5] , __UpperCamelCase=[0.5, 0.5, 0.5] , )-> int:
UpperCAmelCase__ : Optional[int] = size if size is not None else {"shortest_edge": 18}
UpperCAmelCase__ : Dict = crop_size if crop_size is not None else {"height": 18, "width": 18}
UpperCAmelCase__ : List[Any] = parent
UpperCAmelCase__ : Tuple = batch_size
UpperCAmelCase__ : List[Any] = num_channels
UpperCAmelCase__ : Optional[Any] = image_size
UpperCAmelCase__ : List[Any] = min_resolution
UpperCAmelCase__ : Any = max_resolution
UpperCAmelCase__ : Dict = do_resize
UpperCAmelCase__ : List[Any] = size
UpperCAmelCase__ : Any = do_center_crop
UpperCAmelCase__ : Optional[int] = crop_size
UpperCAmelCase__ : Union[str, Any] = do_normalize
UpperCAmelCase__ : str = image_mean
UpperCAmelCase__ : Dict = image_std
def lowerCAmelCase__ ( self )-> Optional[Any]:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class _lowercase ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_A = LevitImageProcessor if is_vision_available() else None
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : str = LevitImageProcessingTester(self )
@property
def lowerCAmelCase__ ( self )-> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__UpperCamelCase , "image_mean" ) )
self.assertTrue(hasattr(__UpperCamelCase , "image_std" ) )
self.assertTrue(hasattr(__UpperCamelCase , "do_normalize" ) )
self.assertTrue(hasattr(__UpperCamelCase , "do_resize" ) )
self.assertTrue(hasattr(__UpperCamelCase , "do_center_crop" ) )
self.assertTrue(hasattr(__UpperCamelCase , "size" ) )
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : Optional[Any] = 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} )
UpperCAmelCase__ : Union[str, Any] = 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 lowerCAmelCase__ ( self )-> int:
pass
def lowerCAmelCase__ ( self )-> List[str]:
# Initialize image_processing
UpperCAmelCase__ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase__ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , Image.Image )
# Test not batched input
UpperCAmelCase__ : Optional[int] = 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
UpperCAmelCase__ : 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"],
) , )
def lowerCAmelCase__ ( self )-> Optional[Any]:
# Initialize image_processing
UpperCAmelCase__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase , numpify=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , np.ndarray )
# Test not batched input
UpperCAmelCase__ : Tuple = 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
UpperCAmelCase__ : List[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 lowerCAmelCase__ ( self )-> Union[str, Any]:
# Initialize image_processing
UpperCAmelCase__ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase__ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase , torchify=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , torch.Tensor )
# Test not batched input
UpperCAmelCase__ : 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
UpperCAmelCase__ : 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"],
) , )
| 713 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
A__ : Optional[int] = ["""small""", """medium""", """large"""]
A__ : Optional[int] = """lm_head.decoder.weight"""
A__ : Dict = """lm_head.weight"""
def a__ ( lowerCAmelCase : str , lowerCAmelCase : str ):
'''simple docstring'''
UpperCAmelCase__ : Dict = torch.load(lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = d.pop(lowerCAmelCase )
os.makedirs(lowerCAmelCase , exist_ok=lowerCAmelCase )
torch.save(lowerCAmelCase , os.path.join(lowerCAmelCase , lowerCAmelCase ) )
if __name__ == "__main__":
A__ : List[Any] = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
A__ : Tuple = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
A__ : Tuple = os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
A__ : str = f"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 660 | 0 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
A__ : Optional[int] = ["""\nclass""", """\ndef""", """\n#""", """\n@""", """\nprint""", """\nif"""]
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=1 )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = tokenizer
UpperCAmelCase__ : Any = dataset
UpperCAmelCase__ : int = len(__UpperCamelCase ) if n_tasks is None else n_tasks
UpperCAmelCase__ : Dict = n_copies
def __iter__( self )-> int:
UpperCAmelCase__ : int = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]["prompt"].strip() )
UpperCAmelCase__ : Union[str, Any] = self.tokenizer(__UpperCamelCase , padding=__UpperCamelCase , return_tensors="pt" )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Optional[Any]:
UpperCAmelCase__ : Optional[int] = start_length
UpperCAmelCase__ : Union[str, Any] = eof_strings
UpperCAmelCase__ : int = tokenizer
def __call__( self , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )-> str:
UpperCAmelCase__ : Optional[Any] = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
UpperCAmelCase__ : Optional[Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(__UpperCamelCase )
def a__ ( lowerCAmelCase : Dict ):
'''simple docstring'''
UpperCAmelCase__ : int = re.split("(%s)" % "|".join(lowerCAmelCase ) , lowerCAmelCase )
# last string should be ""
return "".join(string_list[:-2] )
def a__ ( lowerCAmelCase : Tuple , lowerCAmelCase : Any , lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : Dict , lowerCAmelCase : List[str]=20 , **lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = defaultdict(lowerCAmelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(lowerCAmelCase ) ):
with torch.no_grad():
UpperCAmelCase__ : List[str] = batch["ids"].shape[-1]
UpperCAmelCase__ : Dict = accelerator.unwrap_model(lowerCAmelCase ).generate(
input_ids=batch["ids"][:, : batch["input_len"]] , num_return_sequences=lowerCAmelCase , **lowerCAmelCase )
# each task is generated batch_size times
UpperCAmelCase__ : Any = batch["task_id"].repeat(lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = accelerator.pad_across_processes(
lowerCAmelCase , dim=1 , pad_index=tokenizer.pad_token_id )
UpperCAmelCase__ : str = accelerator.gather((generated_tokens, generated_tasks) )
UpperCAmelCase__ : Optional[Any] = generated_tokens.cpu().numpy()
UpperCAmelCase__ : Any = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(lowerCAmelCase , lowerCAmelCase ):
gen_token_dict[task].append(lowerCAmelCase )
UpperCAmelCase__ : Tuple = [[] for _ in range(lowerCAmelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
UpperCAmelCase__ : Optional[int] = tokenizer.decode(lowerCAmelCase , skip_special_tokens=lowerCAmelCase , clean_up_tokenization_spaces=lowerCAmelCase )
code_gens[task].append(remove_last_block(lowerCAmelCase ) )
return code_gens
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = HfArgumentParser(lowerCAmelCase )
UpperCAmelCase__ : Dict = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
UpperCAmelCase__ : Dict = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
UpperCAmelCase__ : Optional[int] = "false"
if args.num_workers is None:
UpperCAmelCase__ : Dict = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
UpperCAmelCase__ : List[Any] = Accelerator()
set_seed(args.seed , device_specific=lowerCAmelCase )
# Load model and tokenizer
UpperCAmelCase__ : Tuple = AutoTokenizer.from_pretrained(args.model_ckpt )
UpperCAmelCase__ : int = tokenizer.eos_token
UpperCAmelCase__ : Tuple = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
UpperCAmelCase__ : Any = {
"do_sample": args.do_sample,
"temperature": args.temperature,
"max_new_tokens": args.max_new_tokens,
"top_p": args.top_p,
"top_k": args.top_k,
"stopping_criteria": StoppingCriteriaList([EndOfFunctionCriteria(0 , lowerCAmelCase , lowerCAmelCase )] ),
}
# Load evaluation dataset and metric
UpperCAmelCase__ : Optional[Any] = load_dataset("openai_humaneval" )
UpperCAmelCase__ : int = load_metric("code_eval" )
UpperCAmelCase__ : Union[str, Any] = args.num_tasks if args.num_tasks is not None else len(human_eval["test"] )
UpperCAmelCase__ : Union[str, Any] = args.n_samples // args.batch_size
UpperCAmelCase__ : Union[str, Any] = TokenizedDataset(lowerCAmelCase , human_eval["test"] , n_copies=lowerCAmelCase , n_tasks=lowerCAmelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
UpperCAmelCase__ : List[Any] = DataLoader(lowerCAmelCase , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
UpperCAmelCase__ : List[Any] = code_eval_metric.compute(references=[""] , predictions=[[""]] )
except ValueError as exception:
print(
"Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL=\"1\"`"
" flag to enable code evaluation." )
raise exception
UpperCAmelCase__ : Optional[Any] = accelerator.prepare(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : Union[str, Any] = complete_code(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , n_tasks=lowerCAmelCase , batch_size=args.batch_size , **lowerCAmelCase , )
if accelerator.is_main_process:
UpperCAmelCase__ : Optional[int] = []
for task in tqdm(range(lowerCAmelCase ) ):
UpperCAmelCase__ : List[Any] = human_eval["test"][task]["test"]
UpperCAmelCase__ : Optional[int] = F"check({human_eval['test'][task]['entry_point']})"
references.append("\n" + test_func + "\n" + entry_point )
# Evaluate completions with "code_eval" metric
UpperCAmelCase__ : Tuple = code_eval_metric.compute(
references=lowerCAmelCase , predictions=lowerCAmelCase , num_workers=args.num_workers )
print(F"Results: {pass_at_k}" )
# Save results to json file
with open(args.output_file , "w" ) as fp:
json.dump(lowerCAmelCase , lowerCAmelCase )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 714 |
"""simple docstring"""
from math import isqrt
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : List[Any] = False
return [i for i in range(2 , lowerCAmelCase ) if is_prime[i]]
def a__ ( lowerCAmelCase : int = 10**8 ):
'''simple docstring'''
UpperCAmelCase__ : Dict = calculate_prime_numbers(max_number // 2 )
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Tuple = len(lowerCAmelCase ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 660 | 0 |
"""simple docstring"""
from __future__ import annotations
def a__ ( lowerCAmelCase : list[list[int]] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = len(lowerCAmelCase )
# We need to create solution object to save path.
UpperCAmelCase__ : Optional[Any] = [[0 for _ in range(lowerCAmelCase )] for _ in range(lowerCAmelCase )]
UpperCAmelCase__ : Any = run_maze(lowerCAmelCase , 0 , 0 , lowerCAmelCase )
if solved:
print("\n".join(str(lowerCAmelCase ) for row in solutions ) )
else:
print("No solution exists!" )
return solved
def a__ ( lowerCAmelCase : list[list[int]] , lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : list[list[int]] ):
'''simple docstring'''
UpperCAmelCase__ : Any = len(lowerCAmelCase )
# Final check point.
if i == j == (size - 1):
UpperCAmelCase__ : int = 1
return True
UpperCAmelCase__ : Tuple = (not i < 0) and (not j < 0) # Check lower bounds
UpperCAmelCase__ : Dict = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
UpperCAmelCase__ : Optional[Any] = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
UpperCAmelCase__ : str = 1
# check for directions
if (
run_maze(lowerCAmelCase , i + 1 , lowerCAmelCase , lowerCAmelCase )
or run_maze(lowerCAmelCase , lowerCAmelCase , j + 1 , lowerCAmelCase )
or run_maze(lowerCAmelCase , i - 1 , lowerCAmelCase , lowerCAmelCase )
or run_maze(lowerCAmelCase , lowerCAmelCase , j - 1 , lowerCAmelCase )
):
return True
UpperCAmelCase__ : Dict = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 715 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a__ ( lowerCAmelCase : str , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Dict , lowerCAmelCase : List[Any] ):
'''simple docstring'''
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Optional[int] = np.full((len(lowerCAmelCase ), sequence_length, 2) , lowerCAmelCase )
else:
UpperCAmelCase__ : Optional[Any] = np.full((len(lowerCAmelCase ), sequence_length) , lowerCAmelCase )
for i, tensor in enumerate(lowerCAmelCase ):
if padding_side == "right":
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Dict = tensor[:sequence_length]
else:
UpperCAmelCase__ : Tuple = tensor[:sequence_length]
else:
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Optional[Any] = tensor[:sequence_length]
else:
UpperCAmelCase__ : int = tensor[:sequence_length]
return out_tensor.tolist()
def a__ ( lowerCAmelCase : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = ord(lowerCAmelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
UpperCAmelCase__ : Optional[Any] = unicodedata.category(lowerCAmelCase )
if cat.startswith("P" ):
return True
return False
@dataclass
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
_A = True
_A = None
_A = None
_A = -100
_A = "pt"
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[str]:
import torch
UpperCAmelCase__ : Optional[Any] = "label" if "label" in features[0].keys() else "labels"
UpperCAmelCase__ : Dict = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
UpperCAmelCase__ : str = self.tokenizer.pad(
__UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" if labels is None else None , )
if labels is None:
return batch
UpperCAmelCase__ : Optional[Any] = torch.tensor(batch["entity_ids"] ).shape[1]
UpperCAmelCase__ : int = self.tokenizer.padding_side
if padding_side == "right":
UpperCAmelCase__ : int = [
list(__UpperCamelCase ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) for label in labels
]
else:
UpperCAmelCase__ : List[Any] = [
[self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) + list(__UpperCamelCase ) for label in labels
]
UpperCAmelCase__ : Optional[Any] = [feature["ner_tags"] for feature in features]
UpperCAmelCase__ : int = padding_tensor(__UpperCamelCase , -1 , __UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : List[Any] = [feature["original_entity_spans"] for feature in features]
UpperCAmelCase__ : int = padding_tensor(__UpperCamelCase , (-1, -1) , __UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[int] = {k: torch.tensor(__UpperCamelCase , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 660 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A__ : Optional[Any] = logging.get_logger(__name__)
A__ : List[Any] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
A__ : Optional[Any] = {
"""vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
"""merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
"""tokenizer_config_file""": {
"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
},
}
A__ : int = {"""facebook/blenderbot-3B""": 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
UpperCAmelCase__ : List[str] = bs[:]
UpperCAmelCase__ : Union[str, Any] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowerCAmelCase )
cs.append(2**8 + n )
n += 1
UpperCAmelCase__ : Optional[Any] = [chr(lowerCAmelCase ) for n in cs]
return dict(zip(lowerCAmelCase , lowerCAmelCase ) )
def a__ ( lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = set()
UpperCAmelCase__ : Dict = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
UpperCAmelCase__ : List[Any] = char
return pairs
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = VOCAB_FILES_NAMES
_A = PRETRAINED_VOCAB_FILES_MAP
_A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A = ['input_ids', 'attention_mask']
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase="replace" , __UpperCamelCase="<s>" , __UpperCamelCase="</s>" , __UpperCamelCase="</s>" , __UpperCamelCase="<s>" , __UpperCamelCase="<unk>" , __UpperCamelCase="<pad>" , __UpperCamelCase="<mask>" , __UpperCamelCase=False , **__UpperCamelCase , )-> Optional[Any]:
UpperCAmelCase__ : Optional[int] = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else bos_token
UpperCAmelCase__ : Optional[Any] = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else eos_token
UpperCAmelCase__ : int = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else sep_token
UpperCAmelCase__ : Optional[int] = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else cls_token
UpperCAmelCase__ : Tuple = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else unk_token
UpperCAmelCase__ : str = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase__ : Union[str, Any] = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else mask_token
super().__init__(
errors=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , unk_token=__UpperCamelCase , sep_token=__UpperCamelCase , cls_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , add_prefix_space=__UpperCamelCase , **__UpperCamelCase , )
with open(__UpperCamelCase , encoding="utf-8" ) as vocab_handle:
UpperCAmelCase__ : Dict = json.load(__UpperCamelCase )
UpperCAmelCase__ : Any = {v: k for k, v in self.encoder.items()}
UpperCAmelCase__ : Optional[int] = errors # how to handle errors in decoding
UpperCAmelCase__ : Dict = bytes_to_unicode()
UpperCAmelCase__ : Union[str, Any] = {v: k for k, v in self.byte_encoder.items()}
with open(__UpperCamelCase , encoding="utf-8" ) as merges_handle:
UpperCAmelCase__ : str = merges_handle.read().split("\n" )[1:-1]
UpperCAmelCase__ : Optional[int] = [tuple(merge.split() ) for merge in bpe_merges]
UpperCAmelCase__ : str = dict(zip(__UpperCamelCase , range(len(__UpperCamelCase ) ) ) )
UpperCAmelCase__ : int = {}
UpperCAmelCase__ : str = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
UpperCAmelCase__ : List[Any] = re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def lowerCAmelCase__ ( self )-> Optional[Any]:
return len(self.encoder )
def lowerCAmelCase__ ( self )-> Optional[int]:
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Tuple:
if token in self.cache:
return self.cache[token]
UpperCAmelCase__ : Optional[Any] = tuple(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = get_pairs(__UpperCamelCase )
if not pairs:
return token
while True:
UpperCAmelCase__ : str = min(__UpperCamelCase , key=lambda __UpperCamelCase : self.bpe_ranks.get(__UpperCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
UpperCAmelCase__ : List[str] = bigram
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : List[str] = 0
while i < len(__UpperCamelCase ):
try:
UpperCAmelCase__ : Dict = word.index(__UpperCamelCase , __UpperCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
UpperCAmelCase__ : Tuple = j
if word[i] == first and i < len(__UpperCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
UpperCAmelCase__ : List[Any] = tuple(__UpperCamelCase )
UpperCAmelCase__ : int = new_word
if len(__UpperCamelCase ) == 1:
break
else:
UpperCAmelCase__ : Any = get_pairs(__UpperCamelCase )
UpperCAmelCase__ : Dict = " ".join(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = word
return word
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Any:
UpperCAmelCase__ : Any = []
for token in re.findall(self.pat , __UpperCamelCase ):
UpperCAmelCase__ : Dict = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCamelCase ).split(" " ) )
return bpe_tokens
def lowerCAmelCase__ ( self , __UpperCamelCase )-> int:
return self.encoder.get(__UpperCamelCase , self.encoder.get(self.unk_token ) )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Tuple:
return self.decoder.get(__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Optional[Any]:
UpperCAmelCase__ : List[str] = "".join(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None )-> Tuple[str]:
if not os.path.isdir(__UpperCamelCase ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
UpperCAmelCase__ : Tuple = os.path.join(
__UpperCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase__ : List[str] = os.path.join(
__UpperCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(__UpperCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__UpperCamelCase , ensure_ascii=__UpperCamelCase ) + "\n" )
UpperCAmelCase__ : Dict = 0
with open(__UpperCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __UpperCamelCase : kv[1] ):
if index != token_index:
logger.warning(
F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!" )
UpperCAmelCase__ : str = token_index
writer.write(" ".join(__UpperCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = False )-> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCamelCase , token_ids_a=__UpperCamelCase , already_has_special_tokens=__UpperCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(__UpperCamelCase )) + [1]
return [1] + ([0] * len(__UpperCamelCase )) + [1, 1] + ([0] * len(__UpperCamelCase )) + [1]
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None )-> List[int]:
UpperCAmelCase__ : Optional[int] = [self.sep_token_id]
UpperCAmelCase__ : Dict = [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 lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase=False , **__UpperCamelCase )-> Optional[int]:
UpperCAmelCase__ : str = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__UpperCamelCase ) > 0 and not text[0].isspace()):
UpperCAmelCase__ : Optional[int] = " " + text
return (text, kwargs)
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None )-> List[Any]:
return token_ids_a + [self.eos_token_id]
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[int]:
UpperCAmelCase__ : Union[str, Any] = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(" " + text )
else:
# Generated responses should contain them already.
inputs.append(__UpperCamelCase )
UpperCAmelCase__ : Any = " ".join(__UpperCamelCase )
UpperCAmelCase__ : int = self.encode(__UpperCamelCase )
if len(__UpperCamelCase ) > self.model_max_length:
UpperCAmelCase__ : List[str] = input_ids[-self.model_max_length :]
logger.warning(F"Trimmed input from conversation as it was longer than {self.model_max_length} tokens." )
return input_ids
| 716 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def a__ ( lowerCAmelCase : List[str] ):
'''simple docstring'''
def wrapper(*lowerCAmelCase : Any , **lowerCAmelCase : Tuple ):
UpperCAmelCase__ : Optional[int] = timeit.default_timer()
UpperCAmelCase__ : int = func(*lowerCAmelCase , **lowerCAmelCase )
UpperCAmelCase__ : List[Any] = timeit.default_timer() - starttime
return delta
UpperCAmelCase__ : int = func.__name__
return wrapper
def a__ ( lowerCAmelCase : dict , lowerCAmelCase : Optional[int]=100 , lowerCAmelCase : List[str]=None ):
'''simple docstring'''
UpperCAmelCase__ : str = []
UpperCAmelCase__ : Optional[Any] = seq_shapes or {}
for i in range(lowerCAmelCase ):
UpperCAmelCase__ : int = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(lowerCAmelCase , _ArrayXD ):
UpperCAmelCase__ : List[str] = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(lowerCAmelCase , datasets.Value ):
if v.dtype == "string":
UpperCAmelCase__ : Dict = "The small grey turtle was surprisingly fast when challenged."
else:
UpperCAmelCase__ : str = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(lowerCAmelCase , datasets.Sequence ):
while isinstance(lowerCAmelCase , datasets.Sequence ):
UpperCAmelCase__ : List[str] = v.feature
UpperCAmelCase__ : Optional[int] = seq_shapes[k]
UpperCAmelCase__ : Optional[int] = np.random.rand(*lowerCAmelCase ).astype(v.dtype )
UpperCAmelCase__ : Union[str, Any] = data
dummy_data.append((i, example) )
return dummy_data
def a__ ( lowerCAmelCase : List[str] , lowerCAmelCase : Tuple , lowerCAmelCase : List[str]=100 , lowerCAmelCase : Optional[int]=None ):
'''simple docstring'''
UpperCAmelCase__ : int = generate_examples(lowerCAmelCase , num_examples=lowerCAmelCase , seq_shapes=lowerCAmelCase )
with ArrowWriter(features=lowerCAmelCase , path=lowerCAmelCase ) as writer:
for key, record in dummy_data:
UpperCAmelCase__ : List[Any] = features.encode_example(lowerCAmelCase )
writer.write(lowerCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}." )
UpperCAmelCase__ : Optional[int] = datasets.Dataset.from_file(filename=lowerCAmelCase , info=datasets.DatasetInfo(features=lowerCAmelCase ) )
return dataset
| 660 | 0 |
"""simple docstring"""
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def a__ ( lowerCAmelCase : Any ):
'''simple docstring'''
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def a__ ( lowerCAmelCase : Optional[int] ):
'''simple docstring'''
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase )-> Optional[Any]:
UpperCAmelCase__ : Optional[Any] = metric_id
class _lowercase :
'''simple docstring'''
_A = [MetricMock(lowerCAmelCase_ ) for metric_id in ['accuracy', 'mse', 'precision', 'codeparrot/apps_metric']]
def lowerCAmelCase__ ( self )-> Optional[Any]:
return self._metrics
monkeypatch.setattr("datasets.inspect.huggingface_hub" , HfhMock() )
@pytest.mark.parametrize(
"func, args" , [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def a__ ( lowerCAmelCase : List[str] , lowerCAmelCase : List[Any] , lowerCAmelCase : int , lowerCAmelCase : List[str] , lowerCAmelCase : List[Any] ):
'''simple docstring'''
if "tmp_path" in args:
UpperCAmelCase__ : Tuple = tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
with pytest.warns(lowerCAmelCase , match="https://huggingface.co/docs/evaluate" ):
func(*lowerCAmelCase )
| 717 |
"""simple docstring"""
from manim import *
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : str = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase__ : List[str] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
UpperCAmelCase__ : List[Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : Union[str, Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : List[Any] = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : int = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = VGroup(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = Text("CPU" , font_size=24 )
UpperCAmelCase__ : Any = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = [mem.copy() for i in range(4 )]
UpperCAmelCase__ : List[str] = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Union[str, Any] = Text("GPU" , font_size=24 )
UpperCAmelCase__ : Dict = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
gpu.move_to([-1, -1, 0] )
self.add(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : List[str] = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = Text("Model" , font_size=24 )
UpperCAmelCase__ : Dict = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
model.move_to([3, -1.0, 0] )
self.add(__UpperCamelCase )
UpperCAmelCase__ : List[str] = []
for i, rect in enumerate(__UpperCamelCase ):
rect.set_stroke(__UpperCamelCase )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
UpperCAmelCase__ : int = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__UpperCamelCase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__UpperCamelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=__UpperCamelCase , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=__UpperCamelCase , buff=0.0 )
self.add(__UpperCamelCase )
cpu_targs.append(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase__ : Any = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
UpperCAmelCase__ : Tuple = Text("Loaded Checkpoint" , font_size=24 )
UpperCAmelCase__ : Any = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , aligned_edge=__UpperCamelCase , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
UpperCAmelCase__ : Optional[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
UpperCAmelCase__ : Any = MarkupText(
F"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : str = MarkupText(
F"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(__UpperCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
UpperCAmelCase__ : Optional[Any] = MarkupText(
F"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__UpperCamelCase ) , Write(__UpperCamelCase ) )
self.play(Write(__UpperCamelCase , run_time=1 ) , Create(__UpperCamelCase , run_time=1 ) )
UpperCAmelCase__ : Union[str, Any] = []
UpperCAmelCase__ : List[str] = []
for i, rect in enumerate(__UpperCamelCase ):
UpperCAmelCase__ : Optional[Any] = fill.copy().set_fill(__UpperCamelCase , opacity=0.7 )
target.move_to(__UpperCamelCase )
first_animations.append(GrowFromCenter(__UpperCamelCase , run_time=1 ) )
UpperCAmelCase__ : List[str] = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(__UpperCamelCase , run_time=1.5 ) )
self.play(*__UpperCamelCase )
self.play(*__UpperCamelCase )
self.wait()
| 660 | 0 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
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 MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def a__ ( lowerCAmelCase : Any , lowerCAmelCase : Any , lowerCAmelCase : List[Any] , lowerCAmelCase : List[str]=None , lowerCAmelCase : int=None , lowerCAmelCase : List[Any]=None , lowerCAmelCase : List[str]=None , lowerCAmelCase : str=None , ):
'''simple docstring'''
if attention_mask is None:
UpperCAmelCase__ : Optional[Any] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
UpperCAmelCase__ : Any = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
UpperCAmelCase__ : Optional[int] = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=lowerCAmelCase )
if decoder_head_mask is None:
UpperCAmelCase__ : Optional[Any] = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=lowerCAmelCase )
if cross_attn_head_mask is None:
UpperCAmelCase__ : Optional[int] = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=lowerCAmelCase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=99 , __UpperCamelCase=16 , __UpperCamelCase=2 , __UpperCamelCase=4 , __UpperCamelCase=4 , __UpperCamelCase="relu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=20 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=0 , )-> Dict:
UpperCAmelCase__ : Any = parent
UpperCAmelCase__ : Dict = batch_size
UpperCAmelCase__ : Tuple = seq_length
UpperCAmelCase__ : Dict = is_training
UpperCAmelCase__ : List[Any] = use_labels
UpperCAmelCase__ : Dict = vocab_size
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : Optional[int] = num_hidden_layers
UpperCAmelCase__ : List[str] = num_attention_heads
UpperCAmelCase__ : str = intermediate_size
UpperCAmelCase__ : Optional[int] = hidden_act
UpperCAmelCase__ : Optional[int] = hidden_dropout_prob
UpperCAmelCase__ : Optional[Any] = attention_probs_dropout_prob
UpperCAmelCase__ : Optional[Any] = encoder_layerdrop
UpperCAmelCase__ : List[str] = decoder_layerdrop
UpperCAmelCase__ : Optional[int] = max_position_embeddings
UpperCAmelCase__ : List[str] = eos_token_id
UpperCAmelCase__ : Union[str, Any] = pad_token_id
UpperCAmelCase__ : int = bos_token_id
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase__ : Optional[Any] = self.eos_token_id # Eos Token
UpperCAmelCase__ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
UpperCAmelCase__ : Tuple = input_ids.clamp(self.pad_token_id + 1 )
UpperCAmelCase__ : List[str] = decoder_input_ids.clamp(self.pad_token_id + 1 )
UpperCAmelCase__ : Dict = self.get_config()
UpperCAmelCase__ : Optional[int] = prepare_mam_aaa_inputs_dict(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return config, inputs_dict
def lowerCAmelCase__ ( self )-> Optional[Any]:
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : Dict = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase )-> Dict:
UpperCAmelCase__ : Optional[Any] = MaMaaaModel(config=__UpperCamelCase ).get_decoder().to(__UpperCamelCase ).eval()
UpperCAmelCase__ : Dict = inputs_dict["input_ids"]
UpperCAmelCase__ : str = inputs_dict["attention_mask"]
UpperCAmelCase__ : List[str] = inputs_dict["head_mask"]
# first forward pass
UpperCAmelCase__ : Dict = model(__UpperCamelCase , attention_mask=__UpperCamelCase , head_mask=__UpperCamelCase , use_cache=__UpperCamelCase )
UpperCAmelCase__ : str = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
UpperCAmelCase__ : str = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase__ : int = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
UpperCAmelCase__ : Union[str, Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCAmelCase__ : List[str] = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
UpperCAmelCase__ : Optional[Any] = model(__UpperCamelCase , attention_mask=__UpperCamelCase )["last_hidden_state"]
UpperCAmelCase__ : Optional[Any] = model(__UpperCamelCase , attention_mask=__UpperCamelCase , past_key_values=__UpperCamelCase )[
"last_hidden_state"
]
# select random slice
UpperCAmelCase__ : Tuple = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCAmelCase__ : str = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCAmelCase__ : 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(__UpperCamelCase , __UpperCamelCase , atol=1E-2 ) )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase )-> Any:
UpperCAmelCase__ : Tuple = MaMaaaModel(config=__UpperCamelCase ).to(__UpperCamelCase ).eval()
UpperCAmelCase__ : List[str] = model(**__UpperCamelCase )
UpperCAmelCase__ : str = outputs.encoder_last_hidden_state
UpperCAmelCase__ : str = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase__ : List[str] = model.get_encoder()
encoder.save_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Dict = MaMaaaEncoder.from_pretrained(__UpperCamelCase ).to(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = encoder(inputs_dict["input_ids"] , attention_mask=inputs_dict["attention_mask"] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase__ : Optional[Any] = model.get_decoder()
decoder.save_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Any = MaMaaaDecoder.from_pretrained(__UpperCamelCase ).to(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = decoder(
input_ids=inputs_dict["decoder_input_ids"] , attention_mask=inputs_dict["decoder_attention_mask"] , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=inputs_dict["attention_mask"] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_A = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
_A = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
_A = (
{
'conversational': MaMaaaForConditionalGeneration,
'feature-extraction': MaMaaaModel,
'summarization': MaMaaaForConditionalGeneration,
'text2text-generation': MaMaaaForConditionalGeneration,
'translation': MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
_A = True
_A = True
_A = False
_A = False
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Optional[Any]:
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : Optional[int] = MaMaaaModelTester(self )
UpperCAmelCase__ : Optional[Any] = ConfigTester(self , config_class=__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Optional[int]:
self.config_tester.run_common_tests()
def lowerCAmelCase__ ( self )-> Optional[int]:
UpperCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Optional[Any] = model_class(__UpperCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = model_class.from_pretrained(__UpperCamelCase , output_loading_info=__UpperCamelCase )
self.assertEqual(info["missing_keys"] , [] )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
UpperCAmelCase__ : List[str] = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : List[Any] = copy.deepcopy(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
if not self.is_encoder_decoder:
UpperCAmelCase__ : Tuple = inputs["input_ids"]
del inputs["input_ids"]
else:
UpperCAmelCase__ : Tuple = inputs["input_ids"]
UpperCAmelCase__ : Any = inputs.get("decoder_input_ids" , __UpperCamelCase )
del inputs["input_ids"]
inputs.pop("decoder_input_ids" , __UpperCamelCase )
UpperCAmelCase__ : int = model.get_input_embeddings()
if not self.is_encoder_decoder:
UpperCAmelCase__ : int = wte(__UpperCamelCase )
else:
UpperCAmelCase__ : Optional[int] = wte(__UpperCamelCase )
UpperCAmelCase__ : int = wte(__UpperCamelCase )
with torch.no_grad():
model(**__UpperCamelCase )[0]
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
UpperCAmelCase__ : Any = input_dict["input_ids"]
UpperCAmelCase__ : Union[str, Any] = input_ids.ne(1 ).to(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = MaMaaaForConditionalGeneration(__UpperCamelCase ).eval().to(__UpperCamelCase )
if torch_device == "cuda":
model.half()
model.generate(__UpperCamelCase , attention_mask=__UpperCamelCase )
model.generate(num_beams=4 , do_sample=__UpperCamelCase , early_stopping=__UpperCamelCase , num_return_sequences=3 )
def a__ ( lowerCAmelCase : Optional[int] ):
'''simple docstring'''
return torch.tensor(lowerCAmelCase , dtype=torch.long , device=lowerCAmelCase )
A__ : Dict = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase__ ( self )-> Tuple:
return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" )
def lowerCAmelCase__ ( self )-> int:
UpperCAmelCase__ : Union[str, Any] = MaMaaaModel.from_pretrained("facebook/m2m100_418M" ).to(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]] )
UpperCAmelCase__ : Optional[int] = _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]] )
UpperCAmelCase__ : Optional[Any] = prepare_mam_aaa_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
with torch.no_grad():
UpperCAmelCase__ : Dict = model(**__UpperCamelCase )[0]
UpperCAmelCase__ : Any = torch.Size((1, 11, 10_24) )
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
UpperCAmelCase__ : Dict = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] , device=__UpperCamelCase )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=__UpperCamelCase ) )
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : Optional[Any] = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(__UpperCamelCase )
# change to intended input
UpperCAmelCase__ : Union[str, Any] = _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]] )
UpperCAmelCase__ : List[str] = _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]] )
UpperCAmelCase__ : List[Any] = prepare_mam_aaa_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
with torch.no_grad():
UpperCAmelCase__ : Optional[int] = model(**__UpperCamelCase )[0]
UpperCAmelCase__ : str = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
UpperCAmelCase__ : Tuple = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] , device=__UpperCamelCase )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=__UpperCamelCase ) )
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : Union[str, Any] = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" , src_lang="fr" , tgt_lang="en" )
UpperCAmelCase__ : Tuple = [
"L'affaire NSA souligne l'absence totale de débat sur le renseignement",
"Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
"Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
" Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
" l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
UpperCAmelCase__ : str = tokenizer(__UpperCamelCase , padding=__UpperCamelCase , return_tensors="pt" )
UpperCAmelCase__ : Dict = model.generate(
input_ids=dct["input_ids"].to(__UpperCamelCase ) , attention_mask=dct["attention_mask"].to(__UpperCamelCase ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id("en" ) , )
UpperCAmelCase__ : int = [
"The NSA case highlights the total absence of intelligence debate",
"I think there are two levels of response from the French government.",
"When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
" Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
" communications in France.",
]
UpperCAmelCase__ : Dict = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=__UpperCamelCase , skip_special_tokens=__UpperCamelCase )
assert generated == expected_en
| 718 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A__ : Tuple = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : nn.ModuleList , lowerCAmelCase : nn.ModuleList , lowerCAmelCase : List[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(lowerCAmelCase ) == len(lowerCAmelCase ), F"{len(lowerCAmelCase )} != {len(lowerCAmelCase )}"
dest_layers.load_state_dict(layers_to_copy.state_dict() )
A__ : List[Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
A__ : List[Any] = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def a__ ( lowerCAmelCase : Dict , lowerCAmelCase : Dict ):
'''simple docstring'''
try:
UpperCAmelCase__ : Tuple = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"
F" {n_student}" )
return list(range(lowerCAmelCase ) )
def a__ ( lowerCAmelCase : int , lowerCAmelCase : Tuple ):
'''simple docstring'''
if n_student > n_teacher:
raise ValueError(F"Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}" )
elif n_teacher == n_student:
return list(range(lowerCAmelCase ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def a__ ( lowerCAmelCase : Union[str, PreTrainedModel] , lowerCAmelCase : Union[str, Path] = "student" , lowerCAmelCase : Union[int, None] = None , lowerCAmelCase : Union[int, None] = None , lowerCAmelCase : List[str]=False , lowerCAmelCase : List[str]=None , lowerCAmelCase : List[str]=None , **lowerCAmelCase : List[str] , ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
assert (e is not None) or (d is not None), _msg
if isinstance(lowerCAmelCase , lowerCAmelCase ):
AutoTokenizer.from_pretrained(lowerCAmelCase ).save_pretrained(lowerCAmelCase ) # purely for convenience
UpperCAmelCase__ : List[str] = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).eval()
else:
assert isinstance(lowerCAmelCase , lowerCAmelCase ), F"teacher must be a model or string got type {type(lowerCAmelCase )}"
UpperCAmelCase__ : int = teacher.config.to_diff_dict()
try:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
UpperCAmelCase__ : Tuple = teacher_e
if d is None:
UpperCAmelCase__ : str = teacher_d
init_kwargs.update({"encoder_layers": e, "decoder_layers": d} )
except AttributeError: # T5
if hasattr(teacher.config , "num_encoder_layers" ):
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
UpperCAmelCase__ : Optional[Any] = teacher_e
if d is None:
UpperCAmelCase__ : Optional[Any] = teacher_d
if hasattr(teacher.config , "num_encoder_layers" ):
init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d} )
else:
init_kwargs.update({"num_layers": e, "num_decoder_layers": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(lowerCAmelCase )
# Copy weights
UpperCAmelCase__ : Tuple = teacher.config_class(**lowerCAmelCase )
UpperCAmelCase__ : List[str] = AutoModelForSeqaSeqLM.from_config(lowerCAmelCase )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
UpperCAmelCase__ : Optional[int] = student.load_state_dict(teacher.state_dict() , strict=lowerCAmelCase )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
UpperCAmelCase__ , UpperCAmelCase__ : int = list(range(lowerCAmelCase ) ), list(range(lowerCAmelCase ) )
logger.info(
F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"
F" {save_path}" )
student.save_pretrained(lowerCAmelCase )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
UpperCAmelCase__ : List[int] = pick_layers_to_copy(lowerCAmelCase , lowerCAmelCase )
if d_layers_to_copy is None:
UpperCAmelCase__ : List[int] = pick_layers_to_copy(lowerCAmelCase , lowerCAmelCase )
try:
if hasattr(
lowerCAmelCase , "prophetnet" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , lowerCAmelCase )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , lowerCAmelCase )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , lowerCAmelCase )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , lowerCAmelCase )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , lowerCAmelCase )
copy_layers(teacher.decoder.block , student.decoder.block , lowerCAmelCase )
logger.info(
F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}" )
UpperCAmelCase__ : int = {
"teacher_type": teacher.config.model_type,
"copied_encoder_layers": e_layers_to_copy,
"copied_decoder_layers": d_layers_to_copy,
}
student.save_pretrained(lowerCAmelCase )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 660 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase : list , lowerCAmelCase : list ):
'''simple docstring'''
_validate_point(lowerCAmelCase )
_validate_point(lowerCAmelCase )
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(lowerCAmelCase , lowerCAmelCase ) ) )
def a__ ( lowerCAmelCase : list[float] ):
'''simple docstring'''
if point:
if isinstance(lowerCAmelCase , lowerCAmelCase ):
for item in point:
if not isinstance(lowerCAmelCase , (int, float) ):
UpperCAmelCase__ : Tuple = (
"Expected a list of numbers as input, found "
F"{type(lowerCAmelCase ).__name__}"
)
raise TypeError(lowerCAmelCase )
else:
UpperCAmelCase__ : Dict = F"Expected a list of numbers as input, found {type(lowerCAmelCase ).__name__}"
raise TypeError(lowerCAmelCase )
else:
raise ValueError("Missing an input" )
def a__ ( lowerCAmelCase : list , lowerCAmelCase : list ):
'''simple docstring'''
_validate_point(lowerCAmelCase )
_validate_point(lowerCAmelCase )
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(lowerCAmelCase , lowerCAmelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 719 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _lowercase ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@property
def lowerCAmelCase__ ( self )-> int:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : Tuple = ort.SessionOptions()
UpperCAmelCase__ : List[str] = False
return options
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
UpperCAmelCase__ : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
UpperCAmelCase__ : str = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase__ : int = "A red cat sitting on a park bench"
UpperCAmelCase__ : Tuple = np.random.RandomState(0 )
UpperCAmelCase__ : Any = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , mask_image=__UpperCamelCase , guidance_scale=7.5 , num_inference_steps=10 , generator=__UpperCamelCase , output_type="np" , )
UpperCAmelCase__ : Tuple = output.images
UpperCAmelCase__ : Dict = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
UpperCAmelCase__ : Union[str, Any] = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
UpperCAmelCase__ : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
UpperCAmelCase__ : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
UpperCAmelCase__ : Optional[Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=__UpperCamelCase , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase__ : int = "A red cat sitting on a park bench"
UpperCAmelCase__ : List[str] = np.random.RandomState(0 )
UpperCAmelCase__ : str = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , mask_image=__UpperCamelCase , guidance_scale=7.5 , num_inference_steps=20 , generator=__UpperCamelCase , output_type="np" , )
UpperCAmelCase__ : List[str] = output.images
UpperCAmelCase__ : List[Any] = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
UpperCAmelCase__ : int = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 660 | 0 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _lowercase ( unittest.TestCase ):
def lowerCAmelCase__ ( self )-> List[Any]:
debug_launcher(test_script.main )
def lowerCAmelCase__ ( self )-> List[Any]:
debug_launcher(test_ops.main )
| 720 |
"""simple docstring"""
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 ..auto import CONFIG_MAPPING
A__ : Union[str, Any] = logging.get_logger(__name__)
A__ : Optional[int] = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'table-transformer'
_A = ['past_key_values']
_A = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=3 , __UpperCamelCase=1_00 , __UpperCamelCase=6 , __UpperCamelCase=20_48 , __UpperCamelCase=8 , __UpperCamelCase=6 , __UpperCamelCase=20_48 , __UpperCamelCase=8 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=True , __UpperCamelCase="relu" , __UpperCamelCase=2_56 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.02 , __UpperCamelCase=1.0 , __UpperCamelCase=False , __UpperCamelCase="sine" , __UpperCamelCase="resnet50" , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=1 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=1 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=0.1 , **__UpperCamelCase , )-> List[Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
UpperCAmelCase__ : Any = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : int = backbone_config.get("model_type" )
UpperCAmelCase__ : Optional[Any] = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase__ : int = config_class.from_dict(__UpperCamelCase )
# set timm attributes to None
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : List[str] = None, None, None
UpperCAmelCase__ : Optional[int] = use_timm_backbone
UpperCAmelCase__ : Dict = backbone_config
UpperCAmelCase__ : List[Any] = num_channels
UpperCAmelCase__ : Any = num_queries
UpperCAmelCase__ : int = d_model
UpperCAmelCase__ : Optional[int] = encoder_ffn_dim
UpperCAmelCase__ : str = encoder_layers
UpperCAmelCase__ : Dict = encoder_attention_heads
UpperCAmelCase__ : Optional[Any] = decoder_ffn_dim
UpperCAmelCase__ : Tuple = decoder_layers
UpperCAmelCase__ : Optional[Any] = decoder_attention_heads
UpperCAmelCase__ : List[str] = dropout
UpperCAmelCase__ : Tuple = attention_dropout
UpperCAmelCase__ : List[Any] = activation_dropout
UpperCAmelCase__ : Dict = activation_function
UpperCAmelCase__ : Optional[Any] = init_std
UpperCAmelCase__ : List[str] = init_xavier_std
UpperCAmelCase__ : int = encoder_layerdrop
UpperCAmelCase__ : Tuple = decoder_layerdrop
UpperCAmelCase__ : int = encoder_layers
UpperCAmelCase__ : Dict = auxiliary_loss
UpperCAmelCase__ : Union[str, Any] = position_embedding_type
UpperCAmelCase__ : List[str] = backbone
UpperCAmelCase__ : List[Any] = use_pretrained_backbone
UpperCAmelCase__ : List[str] = dilation
# Hungarian matcher
UpperCAmelCase__ : Dict = class_cost
UpperCAmelCase__ : Any = bbox_cost
UpperCAmelCase__ : Tuple = giou_cost
# Loss coefficients
UpperCAmelCase__ : Any = mask_loss_coefficient
UpperCAmelCase__ : Dict = dice_loss_coefficient
UpperCAmelCase__ : Any = bbox_loss_coefficient
UpperCAmelCase__ : Tuple = giou_loss_coefficient
UpperCAmelCase__ : List[Any] = eos_coefficient
super().__init__(is_encoder_decoder=__UpperCamelCase , **__UpperCamelCase )
@property
def lowerCAmelCase__ ( self )-> int:
return self.encoder_attention_heads
@property
def lowerCAmelCase__ ( self )-> int:
return self.d_model
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = version.parse('1.11' )
@property
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def lowerCAmelCase__ ( self )-> float:
return 1E-5
@property
def lowerCAmelCase__ ( self )-> int:
return 12
| 660 | 0 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = (UnCLIPScheduler,)
def lowerCAmelCase__ ( self , **__UpperCamelCase )-> int:
UpperCAmelCase__ : Any = {
"num_train_timesteps": 10_00,
"variance_type": "fixed_small_log",
"clip_sample": True,
"clip_sample_range": 1.0,
"prediction_type": "epsilon",
}
config.update(**__UpperCamelCase )
return config
def lowerCAmelCase__ ( self )-> int:
for timesteps in [1, 5, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Optional[Any]:
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=__UpperCamelCase )
def lowerCAmelCase__ ( self )-> str:
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Any:
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Dict:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=__UpperCamelCase )
def lowerCAmelCase__ ( self )-> List[Any]:
for time_step in [0, 5_00, 9_99]:
for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=__UpperCamelCase , prev_timestep=__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : int = self.scheduler_classes[0]
UpperCAmelCase__ : Optional[Any] = self.get_scheduler_config(variance_type="fixed_small_log" )
UpperCAmelCase__ : Tuple = scheduler_class(**__UpperCamelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.00_00E-10 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.054_9625 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.999_4987 ) ) < 1E-5
def lowerCAmelCase__ ( self )-> Optional[int]:
UpperCAmelCase__ : List[str] = self.scheduler_classes[0]
UpperCAmelCase__ : Optional[int] = self.get_scheduler_config(variance_type="learned_range" )
UpperCAmelCase__ : str = scheduler_class(**__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = 0.5
assert scheduler._get_variance(1 , predicted_variance=__UpperCamelCase ) - -10.171_2790 < 1E-5
assert scheduler._get_variance(4_87 , predicted_variance=__UpperCamelCase ) - -5.799_8052 < 1E-5
assert scheduler._get_variance(9_99 , predicted_variance=__UpperCamelCase ) - -0.001_0011 < 1E-5
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : Dict = self.scheduler_classes[0]
UpperCAmelCase__ : Any = self.get_scheduler_config()
UpperCAmelCase__ : Any = scheduler_class(**__UpperCamelCase )
UpperCAmelCase__ : List[Any] = scheduler.timesteps
UpperCAmelCase__ : Tuple = self.dummy_model()
UpperCAmelCase__ : int = self.dummy_sample_deter
UpperCAmelCase__ : Optional[int] = torch.manual_seed(0 )
for i, t in enumerate(__UpperCamelCase ):
# 1. predict noise residual
UpperCAmelCase__ : List[str] = model(__UpperCamelCase , __UpperCamelCase )
# 2. predict previous mean of sample x_t-1
UpperCAmelCase__ : Dict = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , generator=__UpperCamelCase ).prev_sample
UpperCAmelCase__ : List[Any] = pred_prev_sample
UpperCAmelCase__ : Tuple = torch.sum(torch.abs(__UpperCamelCase ) )
UpperCAmelCase__ : List[str] = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_sum.item() - 252.268_2495 ) < 1E-2
assert abs(result_mean.item() - 0.328_4743 ) < 1E-3
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : List[Any] = self.scheduler_classes[0]
UpperCAmelCase__ : Dict = self.get_scheduler_config()
UpperCAmelCase__ : str = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(25 )
UpperCAmelCase__ : List[str] = scheduler.timesteps
UpperCAmelCase__ : List[str] = self.dummy_model()
UpperCAmelCase__ : List[str] = self.dummy_sample_deter
UpperCAmelCase__ : Union[str, Any] = torch.manual_seed(0 )
for i, t in enumerate(__UpperCamelCase ):
# 1. predict noise residual
UpperCAmelCase__ : List[Any] = model(__UpperCamelCase , __UpperCamelCase )
if i + 1 == timesteps.shape[0]:
UpperCAmelCase__ : Any = None
else:
UpperCAmelCase__ : Optional[Any] = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
UpperCAmelCase__ : Any = scheduler.step(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , prev_timestep=__UpperCamelCase , generator=__UpperCamelCase ).prev_sample
UpperCAmelCase__ : Tuple = pred_prev_sample
UpperCAmelCase__ : Optional[int] = torch.sum(torch.abs(__UpperCamelCase ) )
UpperCAmelCase__ : Optional[Any] = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_sum.item() - 258.204_4983 ) < 1E-2
assert abs(result_mean.item() - 0.336_2038 ) < 1E-3
def lowerCAmelCase__ ( self )-> Tuple:
pass
def lowerCAmelCase__ ( self )-> Union[str, Any]:
pass
| 721 |
"""simple docstring"""
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
A__ : int = getLogger(__name__)
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : int = 8 , lowerCAmelCase : int = 1024 , lowerCAmelCase : List[Any]="val" , lowerCAmelCase : str=None , lowerCAmelCase : int=False , lowerCAmelCase : Dict="summarization" , lowerCAmelCase : int=None , lowerCAmelCase : List[str]=1 , lowerCAmelCase : Dict = None , lowerCAmelCase : List[str]="" , **lowerCAmelCase : int , ):
'''simple docstring'''
UpperCAmelCase__ : Dict = str(lowerCAmelCase )
assert local_rank is not None
torch.distributed.init_process_group(backend="nccl" , rank=lowerCAmelCase )
UpperCAmelCase__ : List[str] = Path(lowerCAmelCase )
UpperCAmelCase__ : str = save_dir.joinpath(F"rank_{local_rank}_output.json" )
torch.cuda.set_device(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).cuda()
if fpaa:
UpperCAmelCase__ : List[Any] = model.half()
# determine if we need to increase num_beams
use_task_specific_params(lowerCAmelCase , lowerCAmelCase ) # update config with task specific params
UpperCAmelCase__ : List[Any] = generate_kwargs.pop("num_beams" , model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
UpperCAmelCase__ : Any = num_return_sequences
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained(lowerCAmelCase )
logger.info(F"Inferred tokenizer type: {tokenizer.__class__}" ) # if this is wrong, check config.model_type.
if max_source_length is None:
UpperCAmelCase__ : int = tokenizer.model_max_length
if prefix is None:
UpperCAmelCase__ : Union[str, Any] = prefix or getattr(model.config , "prefix" , "" ) or ""
UpperCAmelCase__ : str = SeqaSeqDataset(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , max_target_length=1024 , type_path=lowerCAmelCase , n_obs=lowerCAmelCase , prefix=lowerCAmelCase , **lowerCAmelCase , )
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
UpperCAmelCase__ : Union[str, Any] = ds.make_sortish_sampler(lowerCAmelCase , distributed=lowerCAmelCase , add_extra_examples=lowerCAmelCase , shuffle=lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = DataLoader(lowerCAmelCase , sampler=lowerCAmelCase , batch_size=lowerCAmelCase , collate_fn=ds.collate_fn )
UpperCAmelCase__ : str = []
for batch in tqdm(lowerCAmelCase ):
UpperCAmelCase__ : Dict = model.generate(
input_ids=batch["input_ids"].to(model.device ) , attention_mask=batch["attention_mask"].to(model.device ) , num_return_sequences=lowerCAmelCase , num_beams=lowerCAmelCase , **lowerCAmelCase , )
UpperCAmelCase__ : int = tokenizer.batch_decode(lowerCAmelCase , skip_special_tokens=lowerCAmelCase , clean_up_tokenization_spaces=lowerCAmelCase )
UpperCAmelCase__ : int = batch["ids"]
if num_return_sequences > 1:
UpperCAmelCase__ : str = chunks(lowerCAmelCase , lowerCAmelCase ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(lowerCAmelCase ):
results.append({"pred": pred, "id": ids[i].item()} )
save_json(lowerCAmelCase , lowerCAmelCase )
return results, sampler.num_replicas
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : str = argparse.ArgumentParser(
epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate" )
parser.add_argument("--data_dir" , type=lowerCAmelCase , help="like cnn_dm/test.source" )
parser.add_argument(
"--model_name" , type=lowerCAmelCase , help="like facebook/bart-large-cnn,t5-base, etc." , default="sshleifer/distilbart-xsum-12-3" , )
parser.add_argument("--save_dir" , type=lowerCAmelCase , help="where to save" , default="tmp_gen" )
parser.add_argument("--max_source_length" , type=lowerCAmelCase , default=lowerCAmelCase )
parser.add_argument(
"--type_path" , type=lowerCAmelCase , default="test" , help="which subset to evaluate typically train/val/test" )
parser.add_argument("--task" , type=lowerCAmelCase , default="summarization" , help="used for task_specific_params + metrics" )
parser.add_argument("--bs" , type=lowerCAmelCase , default=8 , required=lowerCAmelCase , help="batch size" )
parser.add_argument(
"--local_rank" , type=lowerCAmelCase , default=-1 , required=lowerCAmelCase , help="should be passed by distributed.launch" )
parser.add_argument(
"--n_obs" , type=lowerCAmelCase , default=lowerCAmelCase , required=lowerCAmelCase , help="How many observations. Defaults to all." )
parser.add_argument(
"--num_return_sequences" , type=lowerCAmelCase , default=1 , required=lowerCAmelCase , help="How many sequences to return" )
parser.add_argument(
"--sync_timeout" , type=lowerCAmelCase , default=600 , required=lowerCAmelCase , help="How long should master process wait for other processes to finish." , )
parser.add_argument("--src_lang" , type=lowerCAmelCase , default=lowerCAmelCase , required=lowerCAmelCase )
parser.add_argument("--tgt_lang" , type=lowerCAmelCase , default=lowerCAmelCase , required=lowerCAmelCase )
parser.add_argument(
"--prefix" , type=lowerCAmelCase , required=lowerCAmelCase , default=lowerCAmelCase , help="will be added to the begininng of src examples" )
parser.add_argument("--fp16" , action="store_true" )
parser.add_argument("--debug" , action="store_true" )
UpperCAmelCase__ : Optional[int] = time.time()
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = parser.parse_known_args()
UpperCAmelCase__ : int = parse_numeric_n_bool_cl_kwargs(lowerCAmelCase )
if generate_kwargs and args.local_rank <= 0:
print(F"parsed the following generate kwargs: {generate_kwargs}" )
UpperCAmelCase__ : Dict = Path(args.save_dir + "_tmp" )
Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase ) # this handles locking.
UpperCAmelCase__ : List[str] = list(json_save_dir.glob("rank_*.json" ) )
if intermediate_files:
raise ValueError(F"Found files at {json_save_dir} please move or remove them." )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
UpperCAmelCase__ : List[str] = {}
if args.src_lang is not None:
UpperCAmelCase__ : str = args.src_lang
if args.tgt_lang is not None:
UpperCAmelCase__ : List[str] = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=lowerCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = eval_data_dir(
args.data_dir , lowerCAmelCase , args.model_name , type_path=args.type_path , bs=args.bs , fpaa=args.fpaa , task=args.task , local_rank=args.local_rank , n_obs=args.n_obs , max_source_length=args.max_source_length , num_return_sequences=args.num_return_sequences , prefix=args.prefix , dataset_kwargs=lowerCAmelCase , **lowerCAmelCase , )
if args.local_rank <= 0:
UpperCAmelCase__ : str = Path(args.save_dir )
save_dir.mkdir(exist_ok=lowerCAmelCase )
UpperCAmelCase__ : Tuple = gather_results_from_each_node(lowerCAmelCase , lowerCAmelCase , args.sync_timeout )
UpperCAmelCase__ : Union[str, Any] = combine_partial_results(lowerCAmelCase )
if args.num_return_sequences > 1:
UpperCAmelCase__ : int = save_dir.joinpath("pseudolabel_results.json" )
print(F"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/" )
save_json(lowerCAmelCase , lowerCAmelCase )
return
UpperCAmelCase__ : Optional[Any] = Path(args.data_dir ).joinpath(args.type_path + ".target" )
with open(lowerCAmelCase ) as f:
UpperCAmelCase__ : Optional[int] = [x.rstrip() for x in f.readlines()][: len(lowerCAmelCase )]
# Calculate metrics, save metrics, and save _generations.txt
UpperCAmelCase__ : List[Any] = "translation" in args.task
UpperCAmelCase__ : Optional[Any] = calculate_bleu if calc_bleu else calculate_rouge
UpperCAmelCase__ : Optional[Any] = "bleu" if calc_bleu else "rouge"
UpperCAmelCase__ : Dict = score_fn(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : List[Any] = len(lowerCAmelCase )
UpperCAmelCase__ : Union[str, Any] = time.time() - start_time
UpperCAmelCase__ : Optional[int] = round(runtime / metrics["n_obs"] , 4 )
UpperCAmelCase__ : Tuple = num_replicas
# TODO(@stas00): add whatever metadata to metrics
UpperCAmelCase__ : Any = save_dir.joinpath(F"{args.type_path}_{metric_name}.json" )
save_json(lowerCAmelCase , lowerCAmelCase , indent=lowerCAmelCase )
print(lowerCAmelCase )
write_txt_file(lowerCAmelCase , save_dir.joinpath(F"{args.type_path}_generations.txt" ) )
if args.debug:
write_txt_file(lowerCAmelCase , save_dir.joinpath(F"{args.type_path}.target" ) )
else:
shutil.rmtree(lowerCAmelCase )
def a__ ( lowerCAmelCase : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : str = []
for partial_result in partial_results:
records.extend(lowerCAmelCase )
UpperCAmelCase__ : Dict = sorted(lowerCAmelCase , key=lambda lowerCAmelCase : x["id"] )
UpperCAmelCase__ : List[str] = [x["pred"] for x in records]
return preds
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : int , lowerCAmelCase : Optional[int] ):
'''simple docstring'''
# WAIT FOR lots of .json files
UpperCAmelCase__ : int = time.time()
logger.info("waiting for all nodes to finish" )
UpperCAmelCase__ : Dict = None
while (time.time() - start_wait) < timeout:
UpperCAmelCase__ : str = list(save_dir.glob("rank_*.json" ) )
if len(lowerCAmelCase ) < num_replicas:
continue
try:
# make sure all json files are fully saved
UpperCAmelCase__ : Union[str, Any] = lmap(lowerCAmelCase , lowerCAmelCase )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError("Rank 0 gave up on waiting for other processes" )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 660 | 0 |
"""simple docstring"""
import requests
A__ : Optional[int] = """YOUR API KEY"""
def a__ ( lowerCAmelCase : str , lowerCAmelCase : str = giphy_api_key ):
'''simple docstring'''
UpperCAmelCase__ : str = "+".join(query.split() )
UpperCAmelCase__ : Dict = F"https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"
UpperCAmelCase__ : List[Any] = requests.get(lowerCAmelCase ).json()["data"]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("""\n""".join(get_gifs("""space ship""")))
| 700 |
"""simple docstring"""
from timeit import timeit
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
UpperCAmelCase__ : Tuple = 0
while number:
number &= number - 1
result += 1
return result
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
UpperCAmelCase__ : Union[str, Any] = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def a__ ( ):
'''simple docstring'''
def do_benchmark(lowerCAmelCase : int ) -> None:
UpperCAmelCase__ : Dict = "import __main__ as z"
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(lowerCAmelCase ) = }" )
UpperCAmelCase__ : Tuple = timeit("z.get_set_bits_count_using_modulo_operator(25)" , setup=lowerCAmelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase ) = }" )
UpperCAmelCase__ : Any = timeit(
"z.get_set_bits_count_using_brian_kernighans_algorithm(25)" , setup=lowerCAmelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(lowerCAmelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 660 | 0 |
"""simple docstring"""
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
| 701 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _lowercase ( unittest.TestCase , lowerCAmelCase_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : Optional[Any] = load_tool("text-classification" )
self.tool.setup()
UpperCAmelCase__ : List[str] = load_tool("text-classification" , remote=__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
UpperCAmelCase__ : Dict = self.tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : List[Any] = self.remote_tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
def lowerCAmelCase__ ( self )-> Optional[int]:
UpperCAmelCase__ : Any = self.tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
def lowerCAmelCase__ ( self )-> str:
UpperCAmelCase__ : str = self.remote_tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__UpperCamelCase , "positive" )
| 660 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( lowerCAmelCase : str , lowerCAmelCase : Tuple , lowerCAmelCase : Dict ):
'''simple docstring'''
UpperCAmelCase__ : int = BertConfig.from_json_file(lowerCAmelCase )
print(F"Building PyTorch model from configuration: {config}" )
UpperCAmelCase__ : int = BertForPreTraining(lowerCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , lowerCAmelCase )
if __name__ == "__main__":
A__ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--bert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained BERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
A__ : Any = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 702 |
"""simple docstring"""
def a__ ( lowerCAmelCase : list , lowerCAmelCase : list ):
'''simple docstring'''
_validate_point(lowerCAmelCase )
_validate_point(lowerCAmelCase )
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(lowerCAmelCase , lowerCAmelCase ) ) )
def a__ ( lowerCAmelCase : list[float] ):
'''simple docstring'''
if point:
if isinstance(lowerCAmelCase , lowerCAmelCase ):
for item in point:
if not isinstance(lowerCAmelCase , (int, float) ):
UpperCAmelCase__ : Tuple = (
"Expected a list of numbers as input, found "
F"{type(lowerCAmelCase ).__name__}"
)
raise TypeError(lowerCAmelCase )
else:
UpperCAmelCase__ : Dict = F"Expected a list of numbers as input, found {type(lowerCAmelCase ).__name__}"
raise TypeError(lowerCAmelCase )
else:
raise ValueError("Missing an input" )
def a__ ( lowerCAmelCase : list , lowerCAmelCase : list ):
'''simple docstring'''
_validate_point(lowerCAmelCase )
_validate_point(lowerCAmelCase )
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(lowerCAmelCase , lowerCAmelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 0 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : List[Any] = FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
UpperCAmelCase__ : Optional[Any] = AutoTokenizer.from_pretrained("xlm-roberta-base" )
UpperCAmelCase__ : str = "The dog is cute and lives in the garden house"
UpperCAmelCase__ : str = jnp.array([tokenizer.encode(__UpperCamelCase )] )
UpperCAmelCase__ : Optional[Any] = (1, 12, 7_68) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase__ : Union[str, Any] = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
UpperCAmelCase__ : Tuple = model(__UpperCamelCase )["last_hidden_state"]
self.assertEqual(output.shape , __UpperCamelCase )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , __UpperCamelCase , atol=1E-3 ) )
| 703 |
"""simple docstring"""
import math
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a__ ( lowerCAmelCase : int = 1_0001 ):
'''simple docstring'''
try:
UpperCAmelCase__ : List[str] = int(lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError("Parameter nth must be int or castable to int." ) from None
if nth <= 0:
raise ValueError("Parameter nth must be greater than or equal to one." )
UpperCAmelCase__ : list[int] = []
UpperCAmelCase__ : str = 2
while len(lowerCAmelCase ) < nth:
if is_prime(lowerCAmelCase ):
primes.append(lowerCAmelCase )
num += 1
else:
num += 1
return primes[len(lowerCAmelCase ) - 1]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 660 | 0 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
A__ : Any = logging.getLogger(__name__)
def a__ ( lowerCAmelCase : Optional[Any] , lowerCAmelCase : int ):
'''simple docstring'''
# save results
if os.path.exists(lowerCAmelCase ):
if os.path.exists(os.path.join(lowerCAmelCase , "config.json" ) ) and os.path.isfile(
os.path.join(lowerCAmelCase , "config.json" ) ):
os.remove(os.path.join(lowerCAmelCase , "config.json" ) )
if os.path.exists(os.path.join(lowerCAmelCase , "pytorch_model.bin" ) ) and os.path.isfile(
os.path.join(lowerCAmelCase , "pytorch_model.bin" ) ):
os.remove(os.path.join(lowerCAmelCase , "pytorch_model.bin" ) )
else:
os.makedirs(lowerCAmelCase )
model.save_pretrained(lowerCAmelCase )
def a__ ( lowerCAmelCase : Any , lowerCAmelCase : Dict=False ):
'''simple docstring'''
UpperCAmelCase__ : Any = 2
if unlogit:
UpperCAmelCase__ : Union[str, Any] = torch.pow(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : List[str] = p * torch.log(lowerCAmelCase )
UpperCAmelCase__ : Any = 0
return -plogp.sum(dim=-1 )
def a__ ( lowerCAmelCase : str ):
'''simple docstring'''
logger.info("lv, h >\t" + "\t".join(F"{x + 1}" for x in range(len(lowerCAmelCase ) ) ) )
for row in range(len(lowerCAmelCase ) ):
if tensor.dtype != torch.long:
logger.info(F"layer {row + 1}:\t" + "\t".join(F"{x:.5f}" for x in tensor[row].cpu().data ) )
else:
logger.info(F"layer {row + 1}:\t" + "\t".join(F"{x:d}" for x in tensor[row].cpu().data ) )
def a__ ( lowerCAmelCase : Any , lowerCAmelCase : Any , lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[int]=True , lowerCAmelCase : List[str]=True , lowerCAmelCase : List[str]=None , lowerCAmelCase : str=False ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = model.config.num_hidden_layers, model.config.num_attention_heads
UpperCAmelCase__ : List[str] = torch.zeros(lowerCAmelCase , lowerCAmelCase ).to(args.device )
UpperCAmelCase__ : Optional[Any] = torch.zeros(lowerCAmelCase , lowerCAmelCase ).to(args.device )
if head_mask is None:
UpperCAmelCase__ : Optional[Any] = torch.ones(lowerCAmelCase , lowerCAmelCase ).to(args.device )
head_mask.requires_grad_(requires_grad=lowerCAmelCase )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
UpperCAmelCase__ : Dict = None
UpperCAmelCase__ : List[str] = 0.0
UpperCAmelCase__ : int = 0.0
for step, inputs in enumerate(tqdm(lowerCAmelCase , desc="Iteration" , disable=args.local_rank not in [-1, 0] ) ):
UpperCAmelCase__ : Tuple = tuple(t.to(args.device ) for t in inputs )
(UpperCAmelCase__ ) : Optional[Any] = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
UpperCAmelCase__ : Union[str, Any] = model(lowerCAmelCase , labels=lowerCAmelCase , head_mask=lowerCAmelCase )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
UpperCAmelCase__ : Optional[int] = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(lowerCAmelCase ):
UpperCAmelCase__ : Union[str, Any] = entropy(attn.detach() , lowerCAmelCase )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(lowerCAmelCase ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
UpperCAmelCase__ : int = 2
UpperCAmelCase__ : Dict = torch.pow(torch.pow(lowerCAmelCase , lowerCAmelCase ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1E-20
if not args.dont_normalize_global_importance:
UpperCAmelCase__ : Tuple = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info("Attention entropies" )
print_ad_tensor(lowerCAmelCase )
if compute_importance:
logger.info("Head importance scores" )
print_ad_tensor(lowerCAmelCase )
logger.info("Head ranked by importance scores" )
UpperCAmelCase__ : Dict = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
UpperCAmelCase__ : Optional[int] = torch.arange(
head_importance.numel() , device=args.device )
UpperCAmelCase__ : Any = head_ranks.view_as(lowerCAmelCase )
print_ad_tensor(lowerCAmelCase )
return attn_entropy, head_importance, total_loss
def a__ ( lowerCAmelCase : Optional[Any] , lowerCAmelCase : Tuple , lowerCAmelCase : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Any = compute_heads_importance(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , compute_entropy=lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = 1 / loss # instead of downsteam score use the LM loss
logger.info("Pruning: original score: %f, threshold: %f" , lowerCAmelCase , original_score * args.masking_threshold )
UpperCAmelCase__ : str = torch.ones_like(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
UpperCAmelCase__ : List[Any] = original_score
while current_score >= original_score * args.masking_threshold:
UpperCAmelCase__ : List[Any] = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
UpperCAmelCase__ : Dict = float("Inf" )
UpperCAmelCase__ : List[Any] = head_importance.view(-1 ).sort()[1]
if len(lowerCAmelCase ) <= num_to_mask:
print("BREAK BY num_to_mask" )
break
# mask heads
UpperCAmelCase__ : Any = current_heads_to_mask[:num_to_mask]
logger.info("Heads to mask: %s" , str(current_heads_to_mask.tolist() ) )
UpperCAmelCase__ : Tuple = new_head_mask.view(-1 )
UpperCAmelCase__ : Optional[int] = 0.0
UpperCAmelCase__ : Any = new_head_mask.view_as(lowerCAmelCase )
UpperCAmelCase__ : List[Any] = new_head_mask.clone().detach()
print_ad_tensor(lowerCAmelCase )
# Compute metric and head importance again
UpperCAmelCase__ : List[str] = compute_heads_importance(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , compute_entropy=lowerCAmelCase , head_mask=lowerCAmelCase )
UpperCAmelCase__ : int = 1 / loss
logger.info(
"Masking: current score: %f, remaining heads %d (%.1f percents)" , lowerCAmelCase , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info("Final head mask" )
print_ad_tensor(lowerCAmelCase )
np.save(os.path.join(args.output_dir , "head_mask.npy" ) , head_mask.detach().cpu().numpy() )
return head_mask
def a__ ( lowerCAmelCase : Any , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : List[Any] , lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = datetime.now()
UpperCAmelCase__ : Any = compute_heads_importance(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , compute_entropy=lowerCAmelCase , compute_importance=lowerCAmelCase , head_mask=lowerCAmelCase )
UpperCAmelCase__ : Union[str, Any] = 1 / loss
UpperCAmelCase__ : int = datetime.now() - before_time
UpperCAmelCase__ : Dict = sum(p.numel() for p in model.parameters() )
UpperCAmelCase__ : Optional[int] = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowerCAmelCase ) )
}
for k, v in heads_to_prune.items():
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : List[Any] = [
v,
]
assert sum(len(lowerCAmelCase ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(lowerCAmelCase )
UpperCAmelCase__ : Tuple = sum(p.numel() for p in model.parameters() )
UpperCAmelCase__ : Optional[int] = datetime.now()
UpperCAmelCase__ : int = compute_heads_importance(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , compute_entropy=lowerCAmelCase , compute_importance=lowerCAmelCase , head_mask=lowerCAmelCase , actually_pruned=lowerCAmelCase , )
UpperCAmelCase__ : List[str] = 1 / loss
UpperCAmelCase__ : Optional[int] = datetime.now() - before_time
logger.info(
"Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)" , lowerCAmelCase , lowerCAmelCase , pruned_num_params / original_num_params * 100 , )
logger.info("Pruning: score with masking: %f score with pruning: %f" , lowerCAmelCase , lowerCAmelCase )
logger.info("Pruning: speed ratio (original timing / new timing): %f percents" , original_time / new_time * 100 )
save_model(lowerCAmelCase , args.output_dir )
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir" , default=lowerCAmelCase , type=lowerCAmelCase , required=lowerCAmelCase , help="The input data dir. Should contain the .tsv files (or other data files) for the task." , )
parser.add_argument(
"--model_name_or_path" , default=lowerCAmelCase , type=lowerCAmelCase , required=lowerCAmelCase , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--output_dir" , default=lowerCAmelCase , type=lowerCAmelCase , required=lowerCAmelCase , help="The output directory where the model predictions and checkpoints will be written." , )
# Other parameters
parser.add_argument(
"--config_name" , default="" , type=lowerCAmelCase , help="Pretrained config name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--tokenizer_name" , default="" , type=lowerCAmelCase , help="Pretrained tokenizer name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--cache_dir" , default=lowerCAmelCase , type=lowerCAmelCase , help="Where do you want to store the pre-trained models downloaded from s3" , )
parser.add_argument(
"--data_subset" , type=lowerCAmelCase , default=-1 , help="If > 0: limit the data to a subset of data_subset instances." )
parser.add_argument(
"--overwrite_output_dir" , action="store_true" , help="Whether to overwrite data in output directory" )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" )
parser.add_argument(
"--dont_normalize_importance_by_layer" , action="store_true" , help="Don't normalize importance score by layers" )
parser.add_argument(
"--dont_normalize_global_importance" , action="store_true" , help="Don't normalize all importance scores between 0 and 1" , )
parser.add_argument(
"--try_masking" , action="store_true" , help="Whether to try to mask head until a threshold of accuracy." )
parser.add_argument(
"--masking_threshold" , default=0.9 , type=lowerCAmelCase , help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)." , )
parser.add_argument(
"--masking_amount" , default=0.1 , type=lowerCAmelCase , help="Amount to heads to masking at each masking step." )
parser.add_argument("--metric_name" , default="acc" , type=lowerCAmelCase , help="Metric to use for head masking." )
parser.add_argument(
"--max_seq_length" , default=128 , type=lowerCAmelCase , help=(
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, sequences shorter padded."
) , )
parser.add_argument("--batch_size" , default=1 , type=lowerCAmelCase , help="Batch size." )
parser.add_argument("--seed" , type=lowerCAmelCase , default=42 )
parser.add_argument("--local_rank" , type=lowerCAmelCase , default=-1 , help="local_rank for distributed training on gpus" )
parser.add_argument("--no_cuda" , action="store_true" , help="Whether not to use CUDA when available" )
parser.add_argument("--server_ip" , type=lowerCAmelCase , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=lowerCAmelCase , default="" , help="Can be used for distant debugging." )
UpperCAmelCase__ : Any = parser.parse_args()
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=lowerCAmelCase )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
UpperCAmelCase__ : List[str] = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
UpperCAmelCase__ : List[str] = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
UpperCAmelCase__ : Any = torch.device("cuda" , args.local_rank )
UpperCAmelCase__ : int = 1
torch.distributed.init_process_group(backend="nccl" ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
UpperCAmelCase__ : str = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
UpperCAmelCase__ : List[str] = nn.parallel.DistributedDataParallel(
lowerCAmelCase , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=lowerCAmelCase )
elif args.n_gpu > 1:
UpperCAmelCase__ : Union[str, Any] = nn.DataParallel(lowerCAmelCase )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=lowerCAmelCase )
torch.save(lowerCAmelCase , os.path.join(args.output_dir , "run_args.bin" ) )
logger.info("Training/evaluation parameters %s" , lowerCAmelCase )
# Prepare dataset
UpperCAmelCase__ : Dict = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
UpperCAmelCase__ : str = (torch.from_numpy(lowerCAmelCase ),)
UpperCAmelCase__ : int = TensorDataset(*lowerCAmelCase )
UpperCAmelCase__ : List[Any] = RandomSampler(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = DataLoader(lowerCAmelCase , sampler=lowerCAmelCase , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
UpperCAmelCase__ : Optional[int] = mask_heads(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
prune_heads(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
if __name__ == "__main__":
main()
| 704 |
"""simple docstring"""
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=30 , __UpperCamelCase=2 , __UpperCamelCase=3 , __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=3 , __UpperCamelCase=0.6 , __UpperCamelCase=None , )-> List[Any]:
UpperCAmelCase__ : str = parent
UpperCAmelCase__ : Optional[Any] = batch_size
UpperCAmelCase__ : Any = image_size
UpperCAmelCase__ : Dict = patch_size
UpperCAmelCase__ : Optional[Any] = num_channels
UpperCAmelCase__ : Union[str, Any] = is_training
UpperCAmelCase__ : Any = use_labels
UpperCAmelCase__ : List[Any] = hidden_size
UpperCAmelCase__ : Dict = num_hidden_layers
UpperCAmelCase__ : List[str] = num_attention_heads
UpperCAmelCase__ : List[Any] = intermediate_size
UpperCAmelCase__ : Dict = hidden_act
UpperCAmelCase__ : List[Any] = hidden_dropout_prob
UpperCAmelCase__ : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase__ : Optional[Any] = type_sequence_label_size
UpperCAmelCase__ : Union[str, Any] = initializer_range
UpperCAmelCase__ : int = mask_ratio
UpperCAmelCase__ : Tuple = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase__ : int = (image_size // patch_size) ** 2
UpperCAmelCase__ : str = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Optional[Any] = None
if self.use_labels:
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : Optional[int] = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self )-> int:
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = ViTMAEModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : List[str] = 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 )-> Optional[int]:
UpperCAmelCase__ : List[Any] = ViTMAEForPreTraining(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : Optional[int] = model(__UpperCamelCase )
UpperCAmelCase__ : List[str] = (self.image_size // self.patch_size) ** 2
UpperCAmelCase__ : List[str] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase__ : Dict = 1
UpperCAmelCase__ : str = ViTMAEForPreTraining(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
UpperCAmelCase__ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase__ : List[str] = model(__UpperCamelCase )
UpperCAmelCase__ : List[str] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : Tuple = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : str = config_and_inputs
UpperCAmelCase__ : int = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_A = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
_A = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
_A = False
_A = False
_A = False
_A = False
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ : Any = ViTMAEModelTester(self )
UpperCAmelCase__ : List[str] = ConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase , hidden_size=37 )
def lowerCAmelCase__ ( self )-> int:
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def lowerCAmelCase__ ( self )-> Dict:
pass
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : int = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase__ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def lowerCAmelCase__ ( self )-> Optional[Any]:
UpperCAmelCase__ , UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class(__UpperCamelCase )
UpperCAmelCase__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Dict = [*signature.parameters.keys()]
UpperCAmelCase__ : Tuple = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> Any:
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Dict:
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Dict:
# make masks reproducible
np.random.seed(2 )
UpperCAmelCase__ : Tuple = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
UpperCAmelCase__ : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase__ : str = torch.from_numpy(__UpperCamelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase__ : Optional[Any] = pt_noise
super().check_pt_tf_models(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase__ ( self )-> List[Any]:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[Any] = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase__ : Optional[int] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
UpperCAmelCase__ : Optional[Any] = outputs[0].cpu().numpy()
UpperCAmelCase__ : Union[str, Any] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = model_class.from_pretrained(__UpperCamelCase )
model.to(__UpperCamelCase )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase__ : List[Any] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
# Make sure we don't have nans
UpperCAmelCase__ : Tuple = after_outputs[0].cpu().numpy()
UpperCAmelCase__ : int = 0
UpperCAmelCase__ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__UpperCamelCase , 1E-5 )
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> List[str]:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> Any:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCAmelCase__ ( self )-> Optional[Any]:
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def lowerCAmelCase__ ( self )-> List[Any]:
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowerCAmelCase__ ( self )-> Union[str, Any]:
pass
@slow
def lowerCAmelCase__ ( self )-> Union[str, Any]:
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : Tuple = ViTMAEModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase__ ( self )-> List[Any]:
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def lowerCAmelCase__ ( self )-> Optional[int]:
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCAmelCase__ : Any = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" ).to(__UpperCamelCase )
UpperCAmelCase__ : Tuple = self.default_image_processor
UpperCAmelCase__ : List[Any] = prepare_img()
UpperCAmelCase__ : Optional[Any] = image_processor(images=__UpperCamelCase , return_tensors="pt" ).to(__UpperCamelCase )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCAmelCase__ : List[Any] = ViTMAEConfig()
UpperCAmelCase__ : str = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase__ : Optional[int] = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : List[str] = model(**__UpperCamelCase , noise=torch.from_numpy(__UpperCamelCase ).to(device=__UpperCamelCase ) )
# verify the logits
UpperCAmelCase__ : str = torch.Size((1, 1_96, 7_68) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
UpperCAmelCase__ : List[Any] = torch.tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(__UpperCamelCase ) , atol=1E-4 ) )
| 660 | 0 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def a__ ( lowerCAmelCase : Dict , lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Any = XCLIPTextConfig()
# derive patch size from model name
UpperCAmelCase__ : List[Any] = model_name.find("patch" )
UpperCAmelCase__ : Optional[int] = int(model_name[start_idx + len("patch" ) : start_idx + len("patch" ) + 2] )
UpperCAmelCase__ : Optional[Any] = XCLIPVisionConfig(patch_size=lowerCAmelCase , num_frames=lowerCAmelCase )
if "large" in model_name:
UpperCAmelCase__ : Any = 768
UpperCAmelCase__ : int = 3072
UpperCAmelCase__ : Dict = 12
UpperCAmelCase__ : Optional[int] = 1024
UpperCAmelCase__ : Optional[Any] = 4096
UpperCAmelCase__ : Optional[Any] = 16
UpperCAmelCase__ : Tuple = 24
UpperCAmelCase__ : List[Any] = 768
UpperCAmelCase__ : int = 3072
if model_name == "xclip-large-patch14-16-frames":
UpperCAmelCase__ : str = 336
UpperCAmelCase__ : int = XCLIPConfig.from_text_vision_configs(lowerCAmelCase , lowerCAmelCase )
if "large" in model_name:
UpperCAmelCase__ : Optional[Any] = 768
return config
def a__ ( lowerCAmelCase : List[Any] ):
'''simple docstring'''
# text encoder
if name == "token_embedding.weight":
UpperCAmelCase__ : Optional[Any] = name.replace("token_embedding.weight" , "text_model.embeddings.token_embedding.weight" )
if name == "positional_embedding":
UpperCAmelCase__ : Tuple = name.replace("positional_embedding" , "text_model.embeddings.position_embedding.weight" )
if "ln_1" in name:
UpperCAmelCase__ : Any = name.replace("ln_1" , "layer_norm1" )
if "ln_2" in name:
UpperCAmelCase__ : Dict = name.replace("ln_2" , "layer_norm2" )
if "c_fc" in name:
UpperCAmelCase__ : int = name.replace("c_fc" , "fc1" )
if "c_proj" in name:
UpperCAmelCase__ : int = name.replace("c_proj" , "fc2" )
if name.startswith("transformer.resblocks" ):
UpperCAmelCase__ : List[str] = name.replace("transformer.resblocks" , "text_model.encoder.layers" )
if "attn.out_proj" in name and "message" not in name:
UpperCAmelCase__ : str = name.replace("attn.out_proj" , "self_attn.out_proj" )
if "ln_final" in name:
UpperCAmelCase__ : Tuple = name.replace("ln_final" , "text_model.final_layer_norm" )
# visual encoder
if name == "visual.class_embedding":
UpperCAmelCase__ : Optional[Any] = name.replace("visual.class_embedding" , "vision_model.embeddings.class_embedding" )
if name == "visual.positional_embedding":
UpperCAmelCase__ : Any = name.replace("visual.positional_embedding" , "vision_model.embeddings.position_embedding.weight" )
if name.startswith("visual.transformer.resblocks" ):
UpperCAmelCase__ : Optional[Any] = name.replace("visual.transformer.resblocks" , "vision_model.encoder.layers" )
if "visual.conv1" in name:
UpperCAmelCase__ : Union[str, Any] = name.replace("visual.conv1" , "vision_model.embeddings.patch_embedding" )
if "visual.ln_pre" in name:
UpperCAmelCase__ : Optional[Any] = name.replace("visual.ln_pre" , "vision_model.pre_layernorm" )
if "visual.ln_post" in name:
UpperCAmelCase__ : Union[str, Any] = name.replace("visual.ln_post" , "vision_model.post_layernorm" )
if "visual.proj" in name:
UpperCAmelCase__ : List[Any] = name.replace("visual.proj" , "visual_projection.weight" )
if "text_projection" in name:
UpperCAmelCase__ : int = name.replace("text_projection" , "text_projection.weight" )
# things on top
if "prompts_visual_proj" in name:
UpperCAmelCase__ : Tuple = name.replace("prompts_visual_proj" , "prompts_visual_projection" )
if "prompts_visual_ln" in name:
UpperCAmelCase__ : List[str] = name.replace("prompts_visual_ln" , "prompts_visual_layernorm" )
# mit
if name == "mit.positional_embedding":
UpperCAmelCase__ : Optional[int] = name.replace("positional" , "position" )
if name.startswith("mit.resblocks" ):
UpperCAmelCase__ : List[str] = name.replace("mit.resblocks" , "mit.encoder.layers" )
# prompts generator
if name.startswith("prompts_generator.norm" ):
UpperCAmelCase__ : int = name.replace("prompts_generator.norm" , "prompts_generator.layernorm" )
return name
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : Tuple ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
UpperCAmelCase__ : Union[str, Any] = orig_state_dict.pop(lowerCAmelCase )
if "attn.in_proj" in key:
UpperCAmelCase__ : Optional[Any] = key.split("." )
if key.startswith("visual" ):
UpperCAmelCase__ : Union[str, Any] = key_split[3]
UpperCAmelCase__ : int = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
UpperCAmelCase__ : Dict = val[
:dim, :
]
UpperCAmelCase__ : Optional[int] = val[
dim : dim * 2, :
]
UpperCAmelCase__ : Any = val[
-dim:, :
]
else:
UpperCAmelCase__ : Optional[int] = val[
:dim
]
UpperCAmelCase__ : List[Any] = val[
dim : dim * 2
]
UpperCAmelCase__ : int = val[
-dim:
]
else:
if "weight" in key:
UpperCAmelCase__ : int = val[
:dim, :
]
UpperCAmelCase__ : Optional[Any] = val[
dim : dim * 2, :
]
UpperCAmelCase__ : Union[str, Any] = val[
-dim:, :
]
else:
UpperCAmelCase__ : List[Any] = val[:dim]
UpperCAmelCase__ : List[Any] = val[
dim : dim * 2
]
UpperCAmelCase__ : Tuple = val[-dim:]
elif key.startswith("mit" ):
UpperCAmelCase__ : Optional[Any] = key_split[2]
UpperCAmelCase__ : str = config.vision_config.mit_hidden_size
if "weight" in key:
UpperCAmelCase__ : Optional[Any] = val[:dim, :]
UpperCAmelCase__ : List[Any] = val[dim : dim * 2, :]
UpperCAmelCase__ : Tuple = val[-dim:, :]
else:
UpperCAmelCase__ : Optional[int] = val[:dim]
UpperCAmelCase__ : Any = val[dim : dim * 2]
UpperCAmelCase__ : Tuple = val[-dim:]
else:
UpperCAmelCase__ : Any = key_split[2]
UpperCAmelCase__ : List[str] = config.text_config.hidden_size
if "weight" in key:
UpperCAmelCase__ : List[Any] = val[:dim, :]
UpperCAmelCase__ : List[str] = val[
dim : dim * 2, :
]
UpperCAmelCase__ : List[Any] = val[-dim:, :]
else:
UpperCAmelCase__ : List[str] = val[:dim]
UpperCAmelCase__ : List[str] = val[
dim : dim * 2
]
UpperCAmelCase__ : List[Any] = val[-dim:]
else:
UpperCAmelCase__ : List[str] = rename_key(lowerCAmelCase )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
UpperCAmelCase__ : Optional[int] = val.T
UpperCAmelCase__ : List[str] = val
return orig_state_dict
def a__ ( lowerCAmelCase : List[str] ):
'''simple docstring'''
if num_frames == 8:
UpperCAmelCase__ : int = "eating_spaghetti_8_frames.npy"
elif num_frames == 16:
UpperCAmelCase__ : Optional[int] = "eating_spaghetti.npy"
elif num_frames == 32:
UpperCAmelCase__ : Optional[int] = "eating_spaghetti_32_frames.npy"
UpperCAmelCase__ : Any = hf_hub_download(
repo_id="hf-internal-testing/spaghetti-video" , filename=lowerCAmelCase , repo_type="dataset" , )
UpperCAmelCase__ : Union[str, Any] = np.load(lowerCAmelCase )
return list(lowerCAmelCase )
def a__ ( lowerCAmelCase : str , lowerCAmelCase : str=None , lowerCAmelCase : Any=False ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = {
# fully supervised kinetics-400 checkpoints
"xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
"xclip-base-patch32-16-frames": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
),
"xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
"xclip-base-patch16-16-frames": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
),
"xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
"xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f",
# fully supervised kinetics-600 checkpoints
"xclip-base-patch16-kinetics-600": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
),
"xclip-base-patch16-kinetics-600-16-frames": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
),
"xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
# few shot
"xclip-base-patch16-hmdb-2-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
),
"xclip-base-patch16-hmdb-4-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
),
"xclip-base-patch16-hmdb-8-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
),
"xclip-base-patch16-hmdb-16-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
),
"xclip-base-patch16-ucf-2-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
),
"xclip-base-patch16-ucf-4-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
),
"xclip-base-patch16-ucf-8-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
),
"xclip-base-patch16-ucf-16-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
),
# zero shot
"xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
}
UpperCAmelCase__ : Dict = model_to_url[model_name]
UpperCAmelCase__ : int = 8
if "16-frames" in model_name:
UpperCAmelCase__ : Optional[Any] = 16
elif "shot" in model_name:
UpperCAmelCase__ : Union[str, Any] = 32
UpperCAmelCase__ : str = get_xclip_config(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : List[str] = XCLIPModel(lowerCAmelCase )
model.eval()
if "drive" in checkpoint_url:
UpperCAmelCase__ : int = "pytorch_model.bin"
gdown.cached_download(lowerCAmelCase , lowerCAmelCase , quiet=lowerCAmelCase )
UpperCAmelCase__ : Any = torch.load(lowerCAmelCase , map_location="cpu" )["model"]
else:
UpperCAmelCase__ : Tuple = torch.hub.load_state_dict_from_url(lowerCAmelCase )["model"]
UpperCAmelCase__ : Any = convert_state_dict(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : Tuple = XCLIPModel(lowerCAmelCase )
UpperCAmelCase__ : Union[str, Any] = model.load_state_dict(lowerCAmelCase , strict=lowerCAmelCase )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
UpperCAmelCase__ : Dict = 336 if model_name == "xclip-large-patch14-16-frames" else 224
UpperCAmelCase__ : str = VideoMAEImageProcessor(size=lowerCAmelCase )
UpperCAmelCase__ : int = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32" )
UpperCAmelCase__ : int = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32" )
UpperCAmelCase__ : Dict = XCLIPProcessor(image_processor=lowerCAmelCase , tokenizer=lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = prepare_video(lowerCAmelCase )
UpperCAmelCase__ : Dict = processor(
text=["playing sports", "eating spaghetti", "go shopping"] , videos=lowerCAmelCase , return_tensors="pt" , padding=lowerCAmelCase )
print("Shape of pixel values:" , inputs.pixel_values.shape )
with torch.no_grad():
UpperCAmelCase__ : Union[str, Any] = model(**lowerCAmelCase )
# Verify outputs
UpperCAmelCase__ : Union[str, Any] = outputs.logits_per_video
UpperCAmelCase__ : List[str] = logits_per_video.softmax(dim=1 )
print("Probs:" , lowerCAmelCase )
# kinetics-400
if model_name == "xclip-base-patch32":
UpperCAmelCase__ : Dict = torch.tensor([[0.0019, 0.9951, 0.0030]] )
elif model_name == "xclip-base-patch32-16-frames":
UpperCAmelCase__ : Dict = torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]] )
elif model_name == "xclip-base-patch16":
UpperCAmelCase__ : int = torch.tensor([[0.0083, 0.9681, 0.0236]] )
elif model_name == "xclip-base-patch16-16-frames":
UpperCAmelCase__ : Any = torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]] )
elif model_name == "xclip-large-patch14":
UpperCAmelCase__ : Optional[int] = torch.tensor([[0.0062, 0.9864, 0.0075]] )
elif model_name == "xclip-large-patch14-16-frames":
UpperCAmelCase__ : Tuple = torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
UpperCAmelCase__ : Optional[int] = torch.tensor([[0.0555, 0.8914, 0.0531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
UpperCAmelCase__ : str = torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
UpperCAmelCase__ : List[Any] = torch.tensor([[0.0036, 0.9920, 0.0045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
UpperCAmelCase__ : int = torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
UpperCAmelCase__ : List[str] = torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
UpperCAmelCase__ : Dict = torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
UpperCAmelCase__ : Optional[int] = torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
UpperCAmelCase__ : Tuple = torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
UpperCAmelCase__ : Tuple = torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
UpperCAmelCase__ : Any = torch.tensor([[0.0027, 0.9904, 0.0070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
UpperCAmelCase__ : Optional[int] = torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
UpperCAmelCase__ : Dict = torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-03]] )
else:
raise ValueError(F"Model name {model_name} not supported" )
assert torch.allclose(lowerCAmelCase , lowerCAmelCase , atol=1E-3 )
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(lowerCAmelCase )
if push_to_hub:
print("Pushing model, processor and slow tokenizer files to the hub..." )
model.push_to_hub(lowerCAmelCase , organization="nielsr" )
processor.push_to_hub(lowerCAmelCase , organization="nielsr" )
slow_tokenizer.push_to_hub(lowerCAmelCase , organization="nielsr" )
if __name__ == "__main__":
A__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""xclip-base-patch32""",
type=str,
help="""Name of the model.""",
)
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__ : Optional[int] = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 705 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class _lowercase :
'''simple docstring'''
_A = 42
# setable values
_A = 42
_A = 42
_A = None
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
return cls(common=__UpperCamelCase , init_noise_sigma=__UpperCamelCase , timesteps=__UpperCamelCase )
@dataclass
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
_A = [e.name for e in FlaxKarrasDiffusionSchedulers]
_A = 42
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
return True
@register_to_config
def __init__( self , __UpperCamelCase = 10_00 , __UpperCamelCase = 0.0001 , __UpperCamelCase = 0.02 , __UpperCamelCase = "linear" , __UpperCamelCase = None , __UpperCamelCase = "fixed_small" , __UpperCamelCase = True , __UpperCamelCase = "epsilon" , __UpperCamelCase = jnp.floataa , )-> List[str]:
UpperCAmelCase__ : int = dtype
def lowerCAmelCase__ ( self , __UpperCamelCase = None )-> DDPMSchedulerState:
if common is None:
UpperCAmelCase__ : int = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
UpperCAmelCase__ : Tuple = jnp.array(1.0 , dtype=self.dtype )
UpperCAmelCase__ : Tuple = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=__UpperCamelCase , init_noise_sigma=__UpperCamelCase , timesteps=__UpperCamelCase , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None )-> jnp.ndarray:
return sample
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = () )-> DDPMSchedulerState:
UpperCAmelCase__ : Dict = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
UpperCAmelCase__ : Optional[int] = (jnp.arange(0 , __UpperCamelCase ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=__UpperCamelCase , timesteps=__UpperCamelCase , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = state.common.alphas_cumprod[t]
UpperCAmelCase__ : int = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase__ : Any = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
UpperCAmelCase__ : Union[str, Any] = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
UpperCAmelCase__ : Dict = jnp.clip(__UpperCamelCase , a_min=1E-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
UpperCAmelCase__ : Tuple = jnp.log(jnp.clip(__UpperCamelCase , a_min=1E-20 ) )
elif variance_type == "fixed_large":
UpperCAmelCase__ : Union[str, Any] = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
UpperCAmelCase__ : Optional[int] = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
UpperCAmelCase__ : List[str] = variance
UpperCAmelCase__ : Union[str, Any] = state.common.betas[t]
UpperCAmelCase__ : Optional[int] = (predicted_variance + 1) / 2
UpperCAmelCase__ : Any = frac * max_log + (1 - frac) * min_log
return variance
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = True , )-> Union[FlaxDDPMSchedulerOutput, Tuple]:
UpperCAmelCase__ : List[str] = timestep
if key is None:
UpperCAmelCase__ : int = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = jnp.split(__UpperCamelCase , sample.shape[1] , axis=1 )
else:
UpperCAmelCase__ : Optional[Any] = None
# 1. compute alphas, betas
UpperCAmelCase__ : Union[str, Any] = state.common.alphas_cumprod[t]
UpperCAmelCase__ : Tuple = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
UpperCAmelCase__ : Union[str, Any] = 1 - alpha_prod_t
UpperCAmelCase__ : Tuple = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase__ : List[Any] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase__ : Any = model_output
elif self.config.prediction_type == "v_prediction":
UpperCAmelCase__ : Union[str, Any] = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
" for the FlaxDDPMScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase__ : List[Any] = jnp.clip(__UpperCamelCase , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ : List[str] = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
UpperCAmelCase__ : List[Any] = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ : Tuple = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
UpperCAmelCase__ : Any = jax.random.split(__UpperCamelCase , num=1 )
UpperCAmelCase__ : int = jax.random.normal(__UpperCamelCase , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(__UpperCamelCase , __UpperCamelCase , predicted_variance=__UpperCamelCase ) ** 0.5) * noise
UpperCAmelCase__ : Dict = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
UpperCAmelCase__ : Any = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase , state=__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> jnp.ndarray:
return add_noise_common(state.common , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> jnp.ndarray:
return get_velocity_common(state.common , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __len__( self )-> Tuple:
return self.config.num_train_timesteps
| 660 | 0 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : List[str] = logging.get_logger(__name__)
A__ : int = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
"""facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'encodec'
def __init__( self , __UpperCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0] , __UpperCamelCase=2_40_00 , __UpperCamelCase=1 , __UpperCamelCase=False , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=1_28 , __UpperCamelCase=32 , __UpperCamelCase=1 , __UpperCamelCase=[8, 5, 4, 2] , __UpperCamelCase="weight_norm" , __UpperCamelCase=7 , __UpperCamelCase=7 , __UpperCamelCase=3 , __UpperCamelCase=2 , __UpperCamelCase=True , __UpperCamelCase="reflect" , __UpperCamelCase=2 , __UpperCamelCase=2 , __UpperCamelCase=1.0 , __UpperCamelCase=10_24 , __UpperCamelCase=None , __UpperCamelCase=True , **__UpperCamelCase , )-> str:
UpperCAmelCase__ : Any = target_bandwidths
UpperCAmelCase__ : Tuple = sampling_rate
UpperCAmelCase__ : Union[str, Any] = audio_channels
UpperCAmelCase__ : List[Any] = normalize
UpperCAmelCase__ : Optional[Any] = chunk_length_s
UpperCAmelCase__ : int = overlap
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : Dict = num_filters
UpperCAmelCase__ : Any = num_residual_layers
UpperCAmelCase__ : Tuple = upsampling_ratios
UpperCAmelCase__ : Dict = norm_type
UpperCAmelCase__ : Optional[int] = kernel_size
UpperCAmelCase__ : Optional[int] = last_kernel_size
UpperCAmelCase__ : str = residual_kernel_size
UpperCAmelCase__ : Union[str, Any] = dilation_growth_rate
UpperCAmelCase__ : str = use_causal_conv
UpperCAmelCase__ : Optional[Any] = pad_mode
UpperCAmelCase__ : Any = compress
UpperCAmelCase__ : Any = num_lstm_layers
UpperCAmelCase__ : Any = trim_right_ratio
UpperCAmelCase__ : List[str] = codebook_size
UpperCAmelCase__ : Optional[int] = codebook_dim if codebook_dim is not None else hidden_size
UpperCAmelCase__ : Optional[Any] = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
F"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}" )
super().__init__(**__UpperCamelCase )
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def lowerCAmelCase__ ( self )-> int:
UpperCAmelCase__ : Union[str, Any] = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def lowerCAmelCase__ ( self )-> int:
return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 706 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = ''
_A = 'hf-legacy' # "hf://"" is reserved for hffs
def __init__( self , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )-> str:
super().__init__(self , **__UpperCamelCase )
UpperCAmelCase__ : int = repo_info
UpperCAmelCase__ : Optional[int] = token
UpperCAmelCase__ : Optional[Any] = None
def lowerCAmelCase__ ( self )-> Optional[Any]:
if self.dir_cache is None:
UpperCAmelCase__ : str = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
UpperCAmelCase__ : str = {
"name": hf_file.rfilename,
"size": None,
"type": "file",
}
self.dir_cache.update(
{
str(__UpperCamelCase ): {"name": str(__UpperCamelCase ), "size": None, "type": "directory"}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = "rb" , **__UpperCamelCase , )-> List[Any]:
if not isinstance(self.repo_info , __UpperCamelCase ):
raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}" )
UpperCAmelCase__ : Union[str, Any] = hf_hub_url(self.repo_info.id , __UpperCamelCase , revision=self.repo_info.sha )
return fsspec.open(
__UpperCamelCase , mode=__UpperCamelCase , headers=get_authentication_headers_for_url(__UpperCamelCase , use_auth_token=self.token ) , client_kwargs={"trust_env": True} , ).open()
def lowerCAmelCase__ ( self , __UpperCamelCase , **__UpperCamelCase )-> List[str]:
self._get_dirs()
UpperCAmelCase__ : Union[str, Any] = self._strip_protocol(__UpperCamelCase )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase=False , **__UpperCamelCase )-> str:
self._get_dirs()
UpperCAmelCase__ : str = PurePosixPath(path.strip("/" ) )
UpperCAmelCase__ : Optional[Any] = {}
for p, f in self.dir_cache.items():
UpperCAmelCase__ : Optional[int] = PurePosixPath(p.strip("/" ) )
UpperCAmelCase__ : Dict = p.parent
if root == path:
UpperCAmelCase__ : Tuple = f
UpperCAmelCase__ : List[Any] = list(paths.values() )
if detail:
return out
else:
return sorted(f["name"] for f in out )
| 660 | 0 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
A__ : Tuple = {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.Image.Resampling.BILINEAR,
"""bicubic""": PIL.Image.Resampling.BICUBIC,
"""lanczos""": PIL.Image.Resampling.LANCZOS,
"""nearest""": PIL.Image.Resampling.NEAREST,
}
else:
A__ : Union[str, Any] = {
"""linear""": PIL.Image.LINEAR,
"""bilinear""": PIL.Image.BILINEAR,
"""bicubic""": PIL.Image.BICUBIC,
"""lanczos""": PIL.Image.LANCZOS,
"""nearest""": PIL.Image.NEAREST,
}
def a__ ( lowerCAmelCase : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Any = (images / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase__ : str = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
UpperCAmelCase__ : Optional[Any] = numpy_to_pil(lowerCAmelCase )
return images
def a__ ( lowerCAmelCase : List[Any] ):
'''simple docstring'''
if images.ndim == 3:
UpperCAmelCase__ : str = images[None, ...]
UpperCAmelCase__ : str = (images * 255).round().astype("uint8" )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
UpperCAmelCase__ : Any = [Image.fromarray(image.squeeze() , mode="L" ) for image in images]
else:
UpperCAmelCase__ : List[str] = [Image.fromarray(lowerCAmelCase ) for image in images]
return pil_images
| 707 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
A__ : Dict = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
if isinstance(lowerCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowerCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowerCAmelCase ):
return [[videos]]
raise ValueError(F"Could not make batched video from {videos}" )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = ['pixel_values']
def __init__( self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_55 , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )-> None:
super().__init__(**__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = size if size is not None else {"shortest_edge": 2_56}
UpperCAmelCase__ : List[Any] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : List[str] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
UpperCAmelCase__ : int = get_size_dict(__UpperCamelCase , param_name="crop_size" )
UpperCAmelCase__ : Dict = do_resize
UpperCAmelCase__ : Optional[int] = size
UpperCAmelCase__ : List[Any] = do_center_crop
UpperCAmelCase__ : str = crop_size
UpperCAmelCase__ : Optional[int] = resample
UpperCAmelCase__ : int = do_rescale
UpperCAmelCase__ : Union[str, Any] = rescale_factor
UpperCAmelCase__ : Union[str, Any] = offset
UpperCAmelCase__ : Dict = do_normalize
UpperCAmelCase__ : int = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase__ : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
if "shortest_edge" in size:
UpperCAmelCase__ : Union[str, Any] = get_resize_output_image_size(__UpperCamelCase , size["shortest_edge"] , default_to_square=__UpperCamelCase )
elif "height" in size and "width" in size:
UpperCAmelCase__ : Any = (size["height"], size["width"])
else:
raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[Any] = get_size_dict(__UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(__UpperCamelCase , size=(size["height"], size["width"]) , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = True , __UpperCamelCase = None , **__UpperCamelCase , )-> Tuple:
UpperCAmelCase__ : str = image.astype(np.floataa )
if offset:
UpperCAmelCase__ : Tuple = image - (scale / 2)
return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
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 = ChannelDimension.FIRST , )-> np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError("Size and resample 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." )
if offset and not do_rescale:
raise ValueError("For offset, do_rescale must also be set to True." )
# All transformations expect numpy arrays.
UpperCAmelCase__ : Optional[Any] = to_numpy_array(__UpperCamelCase )
if do_resize:
UpperCAmelCase__ : Union[str, Any] = self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase )
if do_center_crop:
UpperCAmelCase__ : int = self.center_crop(__UpperCamelCase , size=__UpperCamelCase )
if do_rescale:
UpperCAmelCase__ : List[str] = self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase , offset=__UpperCamelCase )
if do_normalize:
UpperCAmelCase__ : List[Any] = self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase )
UpperCAmelCase__ : Dict = to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase )
return image
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 , )-> PIL.Image.Image:
UpperCAmelCase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ : int = resample if resample is not None else self.resample
UpperCAmelCase__ : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase__ : int = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ : Optional[int] = offset if offset is not None else self.offset
UpperCAmelCase__ : Dict = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ : Dict = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ : Optional[int] = image_std if image_std is not None else self.image_std
UpperCAmelCase__ : List[str] = size if size is not None else self.size
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : Dict = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase__ : Tuple = get_size_dict(__UpperCamelCase , param_name="crop_size" )
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." )
UpperCAmelCase__ : List[str] = make_batched(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = [
[
self._preprocess_image(
image=__UpperCamelCase , do_resize=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , do_center_crop=__UpperCamelCase , crop_size=__UpperCamelCase , do_rescale=__UpperCamelCase , rescale_factor=__UpperCamelCase , offset=__UpperCamelCase , do_normalize=__UpperCamelCase , image_mean=__UpperCamelCase , image_std=__UpperCamelCase , data_format=__UpperCamelCase , )
for img in video
]
for video in videos
]
UpperCAmelCase__ : Dict = {"pixel_values": videos}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 660 | 0 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
A__ : List[Any] = logging.getLogger(__name__)
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : Dict = argparse.ArgumentParser(
description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." )
parser.add_argument("--file_path" , type=lowerCAmelCase , default="data/dump.txt" , help="The path to the data." )
parser.add_argument("--tokenizer_type" , type=lowerCAmelCase , default="bert" , choices=["bert", "roberta", "gpt2"] )
parser.add_argument("--tokenizer_name" , type=lowerCAmelCase , default="bert-base-uncased" , help="The tokenizer to use." )
parser.add_argument("--dump_file" , type=lowerCAmelCase , default="data/dump" , help="The dump file prefix." )
UpperCAmelCase__ : Tuple = parser.parse_args()
logger.info(F"Loading Tokenizer ({args.tokenizer_name})" )
if args.tokenizer_type == "bert":
UpperCAmelCase__ : Optional[Any] = BertTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase__ : Tuple = tokenizer.special_tokens_map["cls_token"] # `[CLS]`
UpperCAmelCase__ : Optional[int] = tokenizer.special_tokens_map["sep_token"] # `[SEP]`
elif args.tokenizer_type == "roberta":
UpperCAmelCase__ : Optional[int] = RobertaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase__ : Any = tokenizer.special_tokens_map["cls_token"] # `<s>`
UpperCAmelCase__ : Tuple = tokenizer.special_tokens_map["sep_token"] # `</s>`
elif args.tokenizer_type == "gpt2":
UpperCAmelCase__ : Tuple = GPTaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase__ : Dict = tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
UpperCAmelCase__ : Any = tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
logger.info(F"Loading text from {args.file_path}" )
with open(args.file_path , "r" , encoding="utf8" ) as fp:
UpperCAmelCase__ : int = fp.readlines()
logger.info("Start encoding" )
logger.info(F"{len(lowerCAmelCase )} examples to process." )
UpperCAmelCase__ : List[Any] = []
UpperCAmelCase__ : Dict = 0
UpperCAmelCase__ : Union[str, Any] = 1_0000
UpperCAmelCase__ : List[Any] = time.time()
for text in data:
UpperCAmelCase__ : List[str] = F"{bos} {text.strip()} {sep}"
UpperCAmelCase__ : Dict = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
rslt.append(lowerCAmelCase )
iter += 1
if iter % interval == 0:
UpperCAmelCase__ : int = time.time()
logger.info(F"{iter} examples processed. - {(end-start):.2f}s/{interval}expl" )
UpperCAmelCase__ : List[Any] = time.time()
logger.info("Finished binarization" )
logger.info(F"{len(lowerCAmelCase )} examples processed." )
UpperCAmelCase__ : Optional[int] = F"{args.dump_file}.{args.tokenizer_name}.pickle"
UpperCAmelCase__ : Union[str, Any] = tokenizer.vocab_size
if vocab_size < (1 << 16):
UpperCAmelCase__ : int = [np.uintaa(lowerCAmelCase ) for d in rslt]
else:
UpperCAmelCase__ : Union[str, Any] = [np.intaa(lowerCAmelCase ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F"Dump to {dp_file}" )
with open(lowerCAmelCase , "wb" ) as handle:
pickle.dump(rslt_ , lowerCAmelCase , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 708 |
"""simple docstring"""
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowerCAmelCase , lowerCAmelCase ):
raise TypeError("Input value must be a 'int' type" )
return bin(lowerCAmelCase ).count("1" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A__ : Union[str, Any] = {
"""configuration_wav2vec2""": ["""WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Wav2Vec2Config"""],
"""feature_extraction_wav2vec2""": ["""Wav2Vec2FeatureExtractor"""],
"""processing_wav2vec2""": ["""Wav2Vec2Processor"""],
"""tokenization_wav2vec2""": ["""Wav2Vec2CTCTokenizer""", """Wav2Vec2Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
"""WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Wav2Vec2ForAudioFrameClassification""",
"""Wav2Vec2ForCTC""",
"""Wav2Vec2ForMaskedLM""",
"""Wav2Vec2ForPreTraining""",
"""Wav2Vec2ForSequenceClassification""",
"""Wav2Vec2ForXVector""",
"""Wav2Vec2Model""",
"""Wav2Vec2PreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[Any] = [
"""TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFWav2Vec2ForCTC""",
"""TFWav2Vec2Model""",
"""TFWav2Vec2PreTrainedModel""",
"""TFWav2Vec2ForSequenceClassification""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
"""FlaxWav2Vec2ForCTC""",
"""FlaxWav2Vec2ForPreTraining""",
"""FlaxWav2Vec2Model""",
"""FlaxWav2Vec2PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
A__ : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 709 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
A__ : Optional[Any] = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : bool , lowerCAmelCase : bool ):
'''simple docstring'''
def run_func(lowerCAmelCase : Dict ):
@wraps(lowerCAmelCase )
def run_in_eager_mode(*lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Dict ):
return func(*lowerCAmelCase , **lowerCAmelCase )
@wraps(lowerCAmelCase )
@tf.function(experimental_compile=lowerCAmelCase )
def run_in_graph_mode(*lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Optional[Any] ):
return func(*lowerCAmelCase , **lowerCAmelCase )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`." )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def a__ ( lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Dict = random.Random()
UpperCAmelCase__ : List[str] = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(lowerCAmelCase , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
_A = 42
_A = "TensorFlow"
@property
def lowerCAmelCase__ ( self )-> Optional[int]:
return tf.__version__
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
# initialize GPU on separate process
UpperCAmelCase__ : Any = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Union[str, Any] = self._prepare_inference_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_speed(_inference )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
UpperCAmelCase__ : List[Any] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : List[Any] = self._prepare_train_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_speed(_train )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> [Memory, Optional[MemorySummary]]:
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __UpperCamelCase )
UpperCAmelCase__ : List[str] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Any = self._prepare_inference_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_memory(_inference )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> [Memory, Optional[MemorySummary]]:
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __UpperCamelCase )
UpperCAmelCase__ : Any = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
UpperCAmelCase__ : Optional[Any] = self._prepare_train_func(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self._measure_memory(_train )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Callable[[], None]:
UpperCAmelCase__ : Union[str, Any] = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
UpperCAmelCase__ : Optional[int] = (
hasattr(__UpperCamelCase , "architectures" )
and isinstance(config.architectures , __UpperCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCAmelCase__ : str = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCAmelCase__ : Any = __import__("transformers" , fromlist=[model_class] )
UpperCAmelCase__ : List[Any] = getattr(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Dict = model_cls(__UpperCamelCase )
except ImportError:
raise ImportError(
F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
UpperCAmelCase__ : Any = TF_MODEL_MAPPING[config.__class__](__UpperCamelCase )
# encoder-decoder has vocab size saved differently
UpperCAmelCase__ : int = config.vocab_size if hasattr(__UpperCamelCase , "vocab_size" ) else config.encoder.vocab_size
UpperCAmelCase__ : Optional[Any] = random_input_ids(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(__UpperCamelCase , decoder_input_ids=__UpperCamelCase , training=__UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(__UpperCamelCase , training=__UpperCamelCase )
UpperCAmelCase__ : Dict = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Callable[[], None]:
UpperCAmelCase__ : List[Any] = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`." )
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
UpperCAmelCase__ : Any = (
hasattr(__UpperCamelCase , "architectures" )
and isinstance(config.architectures , __UpperCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
UpperCAmelCase__ : Any = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
UpperCAmelCase__ : int = __import__("transformers" , fromlist=[model_class] )
UpperCAmelCase__ : int = getattr(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = model_cls(__UpperCamelCase )
except ImportError:
raise ImportError(
F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
UpperCAmelCase__ : List[str] = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCamelCase )
# encoder-decoder has vocab size saved differently
UpperCAmelCase__ : Union[str, Any] = config.vocab_size if hasattr(__UpperCamelCase , "vocab_size" ) else config.encoder.vocab_size
UpperCAmelCase__ : Dict = random_input_ids(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
UpperCAmelCase__ : Union[str, Any] = model(__UpperCamelCase , decoder_input_ids=__UpperCamelCase , labels=__UpperCamelCase , training=__UpperCamelCase )[0]
UpperCAmelCase__ : Union[str, Any] = tf.gradients(__UpperCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
UpperCAmelCase__ : Union[str, Any] = model(__UpperCamelCase , labels=__UpperCamelCase , training=__UpperCamelCase )[0]
UpperCAmelCase__ : Any = tf.gradients(__UpperCamelCase , model.trainable_variables )
return gradients
UpperCAmelCase__ : str = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def lowerCAmelCase__ ( self , __UpperCamelCase )-> float:
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("Do inference on TPU. Running model 5 times to stabilize compilation" )
timeit.repeat(__UpperCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
UpperCAmelCase__ : Optional[Any] = timeit.repeat(
__UpperCamelCase , repeat=self.args.repeat , number=10 , )
return min(__UpperCamelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(F"Doesn't fit on GPU. {e}" )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> [Memory, MemorySummary]:
logger.info(
"Note that TensorFlow allocates more memory than "
"it might need to speed up computation. "
"The memory reported here corresponds to the memory "
"reported by `nvidia-smi`, which can vary depending "
"on total available memory on the GPU that is used." )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
" consumption line by line." )
UpperCAmelCase__ : List[str] = start_memory_tracing("transformers" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
" with `args.memory=False`" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"py3nvml not installed, we won't log GPU memory usage. "
"Install py3nvml (pip install py3nvml) to log information about GPU." )
UpperCAmelCase__ : Optional[int] = "N/A"
else:
logger.info(
"Measuring total GPU usage on GPU device. Make sure to not have additional processes"
" running on the same GPU." )
# init nvml
nvml.nvmlInit()
func()
UpperCAmelCase__ : Any = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
UpperCAmelCase__ : Optional[int] = nvml.nvmlDeviceGetMemoryInfo(__UpperCamelCase )
UpperCAmelCase__ : str = meminfo.used
UpperCAmelCase__ : int = Memory(__UpperCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
" TensorFlow." )
UpperCAmelCase__ : Any = None
else:
UpperCAmelCase__ : List[Any] = measure_peak_memory_cpu(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = Memory(__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
UpperCAmelCase__ : Optional[Any] = stop_memory_tracing(__UpperCamelCase )
if memory is None:
UpperCAmelCase__ : Tuple = summary.total
else:
UpperCAmelCase__ : int = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(F"Doesn't fit on GPU. {e}" )
return "N/A", None
| 660 | 0 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ : Optional[int] = logging.get_logger(__name__)
A__ : Dict = {
"""google/owlvit-base-patch32""": """https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json""",
"""google/owlvit-base-patch16""": """https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json""",
"""google/owlvit-large-patch14""": """https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json""",
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'owlvit_text_model'
def __init__( self , __UpperCamelCase=4_94_08 , __UpperCamelCase=5_12 , __UpperCamelCase=20_48 , __UpperCamelCase=12 , __UpperCamelCase=8 , __UpperCamelCase=16 , __UpperCamelCase="quick_gelu" , __UpperCamelCase=1E-5 , __UpperCamelCase=0.0 , __UpperCamelCase=0.02 , __UpperCamelCase=1.0 , __UpperCamelCase=0 , __UpperCamelCase=4_94_06 , __UpperCamelCase=4_94_07 , **__UpperCamelCase , )-> Any:
super().__init__(pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
UpperCAmelCase__ : Dict = vocab_size
UpperCAmelCase__ : int = hidden_size
UpperCAmelCase__ : List[Any] = intermediate_size
UpperCAmelCase__ : Any = num_hidden_layers
UpperCAmelCase__ : str = num_attention_heads
UpperCAmelCase__ : Union[str, Any] = max_position_embeddings
UpperCAmelCase__ : List[str] = hidden_act
UpperCAmelCase__ : Tuple = layer_norm_eps
UpperCAmelCase__ : Tuple = attention_dropout
UpperCAmelCase__ : str = initializer_range
UpperCAmelCase__ : str = initializer_factor
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , **__UpperCamelCase )-> "PretrainedConfig":
cls._set_token_in_kwargs(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = cls.get_config_dict(__UpperCamelCase , **__UpperCamelCase )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("model_type" ) == "owlvit":
UpperCAmelCase__ : List[str] = 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(__UpperCamelCase , **__UpperCamelCase )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'owlvit_vision_model'
def __init__( self , __UpperCamelCase=7_68 , __UpperCamelCase=30_72 , __UpperCamelCase=12 , __UpperCamelCase=12 , __UpperCamelCase=3 , __UpperCamelCase=7_68 , __UpperCamelCase=32 , __UpperCamelCase="quick_gelu" , __UpperCamelCase=1E-5 , __UpperCamelCase=0.0 , __UpperCamelCase=0.02 , __UpperCamelCase=1.0 , **__UpperCamelCase , )-> List[Any]:
super().__init__(**__UpperCamelCase )
UpperCAmelCase__ : Tuple = hidden_size
UpperCAmelCase__ : Dict = intermediate_size
UpperCAmelCase__ : str = num_hidden_layers
UpperCAmelCase__ : List[Any] = num_attention_heads
UpperCAmelCase__ : str = num_channels
UpperCAmelCase__ : Union[str, Any] = image_size
UpperCAmelCase__ : Optional[Any] = patch_size
UpperCAmelCase__ : Tuple = hidden_act
UpperCAmelCase__ : Any = layer_norm_eps
UpperCAmelCase__ : Any = attention_dropout
UpperCAmelCase__ : Any = initializer_range
UpperCAmelCase__ : List[Any] = initializer_factor
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , **__UpperCamelCase )-> "PretrainedConfig":
cls._set_token_in_kwargs(__UpperCamelCase )
UpperCAmelCase__ : Tuple = cls.get_config_dict(__UpperCamelCase , **__UpperCamelCase )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("model_type" ) == "owlvit":
UpperCAmelCase__ : List[str] = 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(__UpperCamelCase , **__UpperCamelCase )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'owlvit'
_A = True
def __init__( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=5_12 , __UpperCamelCase=2.6592 , __UpperCamelCase=True , **__UpperCamelCase , )-> str:
super().__init__(**__UpperCamelCase )
if text_config is None:
UpperCAmelCase__ : List[str] = {}
logger.info("text_config is None. Initializing the OwlViTTextConfig with default values." )
if vision_config is None:
UpperCAmelCase__ : Any = {}
logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values." )
UpperCAmelCase__ : Optional[int] = OwlViTTextConfig(**__UpperCamelCase )
UpperCAmelCase__ : Tuple = OwlViTVisionConfig(**__UpperCamelCase )
UpperCAmelCase__ : Any = projection_dim
UpperCAmelCase__ : int = logit_scale_init_value
UpperCAmelCase__ : Union[str, Any] = return_dict
UpperCAmelCase__ : Dict = 1.0
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , **__UpperCamelCase )-> "PretrainedConfig":
cls._set_token_in_kwargs(__UpperCamelCase )
UpperCAmelCase__ : Tuple = cls.get_config_dict(__UpperCamelCase , **__UpperCamelCase )
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(__UpperCamelCase , **__UpperCamelCase )
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )-> Tuple:
UpperCAmelCase__ : Optional[Any] = {}
UpperCAmelCase__ : Union[str, Any] = text_config
UpperCAmelCase__ : Optional[Any] = vision_config
return cls.from_dict(__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self )-> List[str]:
UpperCAmelCase__ : Optional[Any] = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : Any = self.text_config.to_dict()
UpperCAmelCase__ : Optional[int] = self.vision_config.to_dict()
UpperCAmelCase__ : int = self.__class__.model_type
return output
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
@property
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("attention_mask", {0: "batch", 1: "sequence"}),
] )
@property
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("logits_per_image", {0: "batch"}),
("logits_per_text", {0: "batch"}),
("text_embeds", {0: "batch"}),
("image_embeds", {0: "batch"}),
] )
@property
def lowerCAmelCase__ ( self )-> float:
return 1E-4
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = -1 , __UpperCamelCase = -1 , __UpperCamelCase = None , )-> Mapping[str, Any]:
UpperCAmelCase__ : List[Any] = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__UpperCamelCase , seq_length=__UpperCamelCase , framework=__UpperCamelCase )
UpperCAmelCase__ : str = super().generate_dummy_inputs(
processor.image_processor , batch_size=__UpperCamelCase , framework=__UpperCamelCase )
return {**text_input_dict, **image_input_dict}
@property
def lowerCAmelCase__ ( self )-> int:
return 14
| 710 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
A__ : List[str] = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
A__ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 660 | 0 |
"""simple docstring"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=0.2 , __UpperCamelCase=0.2 )-> str:
'''simple docstring'''
UpperCAmelCase__ : List[str] = bp_numa
UpperCAmelCase__ : str = bp_numa
UpperCAmelCase__ : List[Any] = bp_numa
UpperCAmelCase__ : Union[str, Any] = conva_get[:2]
UpperCAmelCase__ : Dict = conva_get[2]
UpperCAmelCase__ : List[Any] = size_pa
UpperCAmelCase__ : Optional[int] = rate_w
UpperCAmelCase__ : Any = rate_t
UpperCAmelCase__ : List[str] = [
np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0] ) + 0.5 )
for i in range(self.conva[1] )
]
UpperCAmelCase__ : int = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
UpperCAmelCase__ : Optional[Any] = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
UpperCAmelCase__ : Dict = -2 * np.random.rand(self.conva[1] ) + 1
UpperCAmelCase__ : Union[str, Any] = -2 * np.random.rand(self.num_bpa ) + 1
UpperCAmelCase__ : Optional[Any] = -2 * np.random.rand(self.num_bpa ) + 1
def lowerCAmelCase__ ( self , __UpperCamelCase )-> int:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = {
"num_bp1": self.num_bpa,
"num_bp2": self.num_bpa,
"num_bp3": self.num_bpa,
"conv1": self.conva,
"step_conv1": self.step_conva,
"size_pooling1": self.size_poolinga,
"rate_weight": self.rate_weight,
"rate_thre": self.rate_thre,
"w_conv1": self.w_conva,
"wkj": self.wkj,
"vji": self.vji,
"thre_conv1": self.thre_conva,
"thre_bp2": self.thre_bpa,
"thre_bp3": self.thre_bpa,
}
with open(__UpperCamelCase , "wb" ) as f:
pickle.dump(__UpperCamelCase , __UpperCamelCase )
print(F"Model saved： {save_path}" )
@classmethod
def lowerCAmelCase__ ( cls , __UpperCamelCase )-> Dict:
'''simple docstring'''
with open(__UpperCamelCase , "rb" ) as f:
UpperCAmelCase__ : Union[str, Any] = pickle.load(__UpperCamelCase ) # noqa: S301
UpperCAmelCase__ : Optional[Any] = model_dic.get("conv1" )
conv_get.append(model_dic.get("step_conv1" ) )
UpperCAmelCase__ : str = model_dic.get("size_pooling1" )
UpperCAmelCase__ : List[str] = model_dic.get("num_bp1" )
UpperCAmelCase__ : Union[str, Any] = model_dic.get("num_bp2" )
UpperCAmelCase__ : Dict = model_dic.get("num_bp3" )
UpperCAmelCase__ : int = model_dic.get("rate_weight" )
UpperCAmelCase__ : str = model_dic.get("rate_thre" )
# create model instance
UpperCAmelCase__ : int = CNN(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# modify model parameter
UpperCAmelCase__ : List[Any] = model_dic.get("w_conv1" )
UpperCAmelCase__ : int = model_dic.get("wkj" )
UpperCAmelCase__ : Any = model_dic.get("vji" )
UpperCAmelCase__ : int = model_dic.get("thre_conv1" )
UpperCAmelCase__ : Optional[int] = model_dic.get("thre_bp2" )
UpperCAmelCase__ : Dict = model_dic.get("thre_bp3" )
return conv_ins
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Dict:
'''simple docstring'''
return 1 / (1 + np.exp(-1 * x ))
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Tuple:
'''simple docstring'''
return round(__UpperCamelCase , 3 )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Tuple:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = convs[0]
UpperCAmelCase__ : Union[str, Any] = convs[1]
UpperCAmelCase__ : Tuple = np.shape(__UpperCamelCase )[0]
# get the data slice of original image data, data_focus
UpperCAmelCase__ : Optional[int] = []
for i_focus in range(0 , size_data - size_conv + 1 , __UpperCamelCase ):
for j_focus in range(0 , size_data - size_conv + 1 , __UpperCamelCase ):
UpperCAmelCase__ : int = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(__UpperCamelCase )
# calculate the feature map of every single kernel, and saved as list of matrix
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : Union[str, Any] = int((size_data - size_conv) / conv_step + 1 )
for i_map in range(__UpperCamelCase ):
UpperCAmelCase__ : str = []
for i_focus in range(len(__UpperCamelCase ) ):
UpperCAmelCase__ : Dict = (
np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map] ) )
- thre_convs[i_map]
)
featuremap.append(self.sig(__UpperCamelCase ) )
UpperCAmelCase__ : int = np.asmatrix(__UpperCamelCase ).reshape(
__UpperCamelCase , __UpperCamelCase )
data_featuremap.append(__UpperCamelCase )
# expanding the data slice to One dimenssion
UpperCAmelCase__ : Any = []
for each_focus in data_focus:
focusa_list.extend(self.Expand_Mat(__UpperCamelCase ) )
UpperCAmelCase__ : List[str] = np.asarray(__UpperCamelCase )
return focus_list, data_featuremap
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase="average_pool" )-> Tuple:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = len(featuremaps[0] )
UpperCAmelCase__ : Dict = int(size_map / size_pooling )
UpperCAmelCase__ : Dict = []
for i_map in range(len(__UpperCamelCase ) ):
UpperCAmelCase__ : Optional[Any] = featuremaps[i_map]
UpperCAmelCase__ : List[str] = []
for i_focus in range(0 , __UpperCamelCase , __UpperCamelCase ):
for j_focus in range(0 , __UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : List[str] = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(__UpperCamelCase ) )
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(__UpperCamelCase ) )
UpperCAmelCase__ : Dict = np.asmatrix(__UpperCamelCase ).reshape(__UpperCamelCase , __UpperCamelCase )
featuremap_pooled.append(__UpperCamelCase )
return featuremap_pooled
def lowerCAmelCase__ ( self , __UpperCamelCase )-> int:
'''simple docstring'''
UpperCAmelCase__ : List[str] = []
for i in range(len(__UpperCamelCase ) ):
UpperCAmelCase__ : Optional[Any] = np.shape(data[i] )
UpperCAmelCase__ : Tuple = data[i].reshape(1 , shapes[0] * shapes[1] )
UpperCAmelCase__ : Optional[Any] = data_listed.getA().tolist()[0]
data_expanded.extend(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = np.asarray(__UpperCamelCase )
return data_expanded
def lowerCAmelCase__ ( self , __UpperCamelCase )-> int:
'''simple docstring'''
UpperCAmelCase__ : Tuple = np.asarray(__UpperCamelCase )
UpperCAmelCase__ : Any = np.shape(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = data_mat.reshape(1 , shapes[0] * shapes[1] )
return data_expanded
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ : List[str] = []
UpperCAmelCase__ : Tuple = 0
for i_map in range(__UpperCamelCase ):
UpperCAmelCase__ : str = np.ones((size_map, size_map) )
for i in range(0 , __UpperCamelCase , __UpperCamelCase ):
for j in range(0 , __UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : List[Any] = pd_pool[
i_pool
]
UpperCAmelCase__ : str = i_pool + 1
UpperCAmelCase__ : Optional[Any] = np.multiply(
__UpperCamelCase , np.multiply(out_map[i_map] , (1 - out_map[i_map]) ) )
pd_all.append(__UpperCamelCase )
return pd_all
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=bool )-> Optional[int]:
'''simple docstring'''
print("----------------------Start Training-------------------------" )
print((" - - Shape: Train_Data ", np.shape(__UpperCamelCase )) )
print((" - - Shape: Teach_Data ", np.shape(__UpperCamelCase )) )
UpperCAmelCase__ : Union[str, Any] = 0
UpperCAmelCase__ : Union[str, Any] = []
UpperCAmelCase__ : Any = 1_00_00
while rp < n_repeat and mse >= error_accuracy:
UpperCAmelCase__ : str = 0
print(F"-------------Learning Time {rp}--------------" )
for p in range(len(__UpperCamelCase ) ):
# print('------------Learning Image: %d--------------'%p)
UpperCAmelCase__ : Union[str, Any] = np.asmatrix(datas_train[p] )
UpperCAmelCase__ : int = np.asarray(datas_teach[p] )
UpperCAmelCase__ : Any = self.convolute(
__UpperCamelCase , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
UpperCAmelCase__ : Dict = self.pooling(__UpperCamelCase , self.size_poolinga )
UpperCAmelCase__ : List[str] = np.shape(__UpperCamelCase )
UpperCAmelCase__ : str = self._expand(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = data_bp_input
UpperCAmelCase__ : str = np.dot(__UpperCamelCase , self.vji.T ) - self.thre_bpa
UpperCAmelCase__ : Dict = self.sig(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = np.dot(__UpperCamelCase , self.wkj.T ) - self.thre_bpa
UpperCAmelCase__ : Optional[int] = self.sig(__UpperCamelCase )
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
UpperCAmelCase__ : List[str] = np.multiply(
(data_teach - bp_outa) , np.multiply(__UpperCamelCase , (1 - bp_outa) ) )
UpperCAmelCase__ : Tuple = np.multiply(
np.dot(__UpperCamelCase , self.wkj ) , np.multiply(__UpperCamelCase , (1 - bp_outa) ) )
UpperCAmelCase__ : int = np.dot(__UpperCamelCase , self.vji )
UpperCAmelCase__ : str = pd_i_all / (self.size_poolinga * self.size_poolinga)
UpperCAmelCase__ : List[Any] = pd_conva_pooled.T.getA().tolist()
UpperCAmelCase__ : str = self._calculate_gradient_from_pool(
__UpperCamelCase , __UpperCamelCase , shape_featuremapa[0] , shape_featuremapa[1] , self.size_poolinga , )
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conva[1] ):
UpperCAmelCase__ : Any = self._expand_mat(pd_conva_all[k_conv] )
UpperCAmelCase__ : Optional[int] = self.rate_weight * np.dot(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Tuple = self.w_conva[k_conv] + delta_w.reshape(
(self.conva[0], self.conva[0]) )
UpperCAmelCase__ : int = (
self.thre_conva[k_conv]
- np.sum(pd_conva_all[k_conv] ) * self.rate_thre
)
# all connected layer
UpperCAmelCase__ : str = self.wkj + pd_k_all.T * bp_outa * self.rate_weight
UpperCAmelCase__ : Tuple = self.vji + pd_j_all.T * bp_outa * self.rate_weight
UpperCAmelCase__ : Optional[int] = self.thre_bpa - pd_k_all * self.rate_thre
UpperCAmelCase__ : Any = self.thre_bpa - pd_j_all * self.rate_thre
# calculate the sum error of all single image
UpperCAmelCase__ : Optional[Any] = np.sum(abs(data_teach - bp_outa ) )
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
UpperCAmelCase__ : int = rp + 1
UpperCAmelCase__ : Union[str, Any] = error_count / patterns
all_mse.append(__UpperCamelCase )
def draw_error():
UpperCAmelCase__ : Optional[int] = [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
plt.plot(__UpperCamelCase , "+-" )
plt.plot(__UpperCamelCase , "r--" )
plt.xlabel("Learning Times" )
plt.ylabel("All_mse" )
plt.grid(__UpperCamelCase , alpha=0.5 )
plt.show()
print("------------------Training Complished---------------------" )
print((" - - Training epoch: ", rp, F" - - Mse: {mse:.6f}") )
if draw_e:
draw_error()
return mse
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Dict:
'''simple docstring'''
UpperCAmelCase__ : List[str] = []
print("-------------------Start Testing-------------------------" )
print((" - - Shape: Test_Data ", np.shape(__UpperCamelCase )) )
for p in range(len(__UpperCamelCase ) ):
UpperCAmelCase__ : Tuple = np.asmatrix(datas_test[p] )
UpperCAmelCase__ : Optional[Any] = self.convolute(
__UpperCamelCase , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
UpperCAmelCase__ : Any = self.pooling(__UpperCamelCase , self.size_poolinga )
UpperCAmelCase__ : Tuple = self._expand(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = data_bp_input
UpperCAmelCase__ : Optional[int] = bp_outa * self.vji.T - self.thre_bpa
UpperCAmelCase__ : Any = self.sig(__UpperCamelCase )
UpperCAmelCase__ : List[str] = bp_outa * self.wkj.T - self.thre_bpa
UpperCAmelCase__ : Any = self.sig(__UpperCamelCase )
produce_out.extend(bp_outa.getA().tolist() )
UpperCAmelCase__ : Tuple = [list(map(self.do_round , __UpperCamelCase ) ) for each in produce_out]
return np.asarray(__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> int:
'''simple docstring'''
UpperCAmelCase__ : Any = np.asmatrix(__UpperCamelCase )
UpperCAmelCase__ : str = self.convolute(
__UpperCamelCase , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
UpperCAmelCase__ : Dict = self.pooling(__UpperCamelCase , self.size_poolinga )
return data_conveda, data_pooleda
if __name__ == "__main__":
pass
| 711 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
@register_to_config
def __init__( self , __UpperCamelCase = 7_68 , )-> Union[str, Any]:
super().__init__()
UpperCAmelCase__ : str = nn.Parameter(torch.zeros(1 , __UpperCamelCase ) )
UpperCAmelCase__ : Optional[int] = nn.Parameter(torch.ones(1 , __UpperCamelCase ) )
def lowerCAmelCase__ ( self , __UpperCamelCase = None , __UpperCamelCase = None , )-> Any:
UpperCAmelCase__ : Dict = nn.Parameter(self.mean.to(__UpperCamelCase ).to(__UpperCamelCase ) )
UpperCAmelCase__ : Any = nn.Parameter(self.std.to(__UpperCamelCase ).to(__UpperCamelCase ) )
return self
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Union[str, Any]:
UpperCAmelCase__ : Dict = (embeds - self.mean) * 1.0 / self.std
return embeds
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[Any]:
UpperCAmelCase__ : Any = (embeds * self.std) + self.mean
return embeds
| 660 | 0 |
"""simple docstring"""
A__ : int = {
"""Pillow""": """Pillow<10.0.0""",
"""accelerate""": """accelerate>=0.20.3""",
"""av""": """av==9.2.0""",
"""beautifulsoup4""": """beautifulsoup4""",
"""black""": """black~=23.1""",
"""codecarbon""": """codecarbon==1.2.0""",
"""cookiecutter""": """cookiecutter==1.7.3""",
"""dataclasses""": """dataclasses""",
"""datasets""": """datasets!=2.5.0""",
"""decord""": """decord==0.6.0""",
"""deepspeed""": """deepspeed>=0.9.3""",
"""diffusers""": """diffusers""",
"""dill""": """dill<0.3.5""",
"""evaluate""": """evaluate>=0.2.0""",
"""fairscale""": """fairscale>0.3""",
"""faiss-cpu""": """faiss-cpu""",
"""fastapi""": """fastapi""",
"""filelock""": """filelock""",
"""flax""": """flax>=0.4.1,<=0.7.0""",
"""ftfy""": """ftfy""",
"""fugashi""": """fugashi>=1.0""",
"""GitPython""": """GitPython<3.1.19""",
"""hf-doc-builder""": """hf-doc-builder>=0.3.0""",
"""huggingface-hub""": """huggingface-hub>=0.14.1,<1.0""",
"""importlib_metadata""": """importlib_metadata""",
"""ipadic""": """ipadic>=1.0.0,<2.0""",
"""isort""": """isort>=5.5.4""",
"""jax""": """jax>=0.2.8,!=0.3.2,<=0.4.13""",
"""jaxlib""": """jaxlib>=0.1.65,<=0.4.13""",
"""jieba""": """jieba""",
"""kenlm""": """kenlm""",
"""keras-nlp""": """keras-nlp>=0.3.1""",
"""librosa""": """librosa""",
"""nltk""": """nltk""",
"""natten""": """natten>=0.14.6""",
"""numpy""": """numpy>=1.17""",
"""onnxconverter-common""": """onnxconverter-common""",
"""onnxruntime-tools""": """onnxruntime-tools>=1.4.2""",
"""onnxruntime""": """onnxruntime>=1.4.0""",
"""opencv-python""": """opencv-python""",
"""optuna""": """optuna""",
"""optax""": """optax>=0.0.8,<=0.1.4""",
"""packaging""": """packaging>=20.0""",
"""parameterized""": """parameterized""",
"""phonemizer""": """phonemizer""",
"""protobuf""": """protobuf""",
"""psutil""": """psutil""",
"""pyyaml""": """pyyaml>=5.1""",
"""pydantic""": """pydantic<2""",
"""pytest""": """pytest>=7.2.0""",
"""pytest-timeout""": """pytest-timeout""",
"""pytest-xdist""": """pytest-xdist""",
"""python""": """python>=3.8.0""",
"""ray[tune]""": """ray[tune]""",
"""regex""": """regex!=2019.12.17""",
"""requests""": """requests""",
"""rhoknp""": """rhoknp>=1.1.0,<1.3.1""",
"""rjieba""": """rjieba""",
"""rouge-score""": """rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1""",
"""ruff""": """ruff>=0.0.241,<=0.0.259""",
"""sacrebleu""": """sacrebleu>=1.4.12,<2.0.0""",
"""sacremoses""": """sacremoses""",
"""safetensors""": """safetensors>=0.3.1""",
"""sagemaker""": """sagemaker>=2.31.0""",
"""scikit-learn""": """scikit-learn""",
"""sentencepiece""": """sentencepiece>=0.1.91,!=0.1.92""",
"""sigopt""": """sigopt""",
"""starlette""": """starlette""",
"""sudachipy""": """sudachipy>=0.6.6""",
"""sudachidict_core""": """sudachidict_core>=20220729""",
"""tensorflow-cpu""": """tensorflow-cpu>=2.6,<2.14""",
"""tensorflow""": """tensorflow>=2.6,<2.14""",
"""tensorflow-text""": """tensorflow-text<2.14""",
"""tf2onnx""": """tf2onnx""",
"""timeout-decorator""": """timeout-decorator""",
"""timm""": """timm""",
"""tokenizers""": """tokenizers>=0.11.1,!=0.11.3,<0.14""",
"""torch""": """torch>=1.9,!=1.12.0""",
"""torchaudio""": """torchaudio""",
"""torchvision""": """torchvision""",
"""pyctcdecode""": """pyctcdecode>=0.4.0""",
"""tqdm""": """tqdm>=4.27""",
"""unidic""": """unidic>=1.0.2""",
"""unidic_lite""": """unidic_lite>=1.0.7""",
"""urllib3""": """urllib3<2.0.0""",
"""uvicorn""": """uvicorn""",
}
| 712 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : Any , lowerCAmelCase : List[Any] ):
'''simple docstring'''
# Construct model
if gpta_config_file == "":
UpperCAmelCase__ : Optional[int] = GPTaConfig()
else:
UpperCAmelCase__ : Dict = GPTaConfig.from_json_file(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = GPTaModel(lowerCAmelCase )
# Load weights from numpy
load_tf_weights_in_gpta(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Save pytorch-model
UpperCAmelCase__ : Optional[int] = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
UpperCAmelCase__ : Any = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(model.state_dict() , lowerCAmelCase )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(lowerCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
A__ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--gpt2_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained OpenAI model. \n"""
"""This specifies the model architecture."""
),
)
A__ : Optional[Any] = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 660 | 0 |
"""simple docstring"""
import numpy as np
def a__ ( lowerCAmelCase : np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 713 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
A__ : Optional[int] = ["""small""", """medium""", """large"""]
A__ : Optional[int] = """lm_head.decoder.weight"""
A__ : Dict = """lm_head.weight"""
def a__ ( lowerCAmelCase : str , lowerCAmelCase : str ):
'''simple docstring'''
UpperCAmelCase__ : Dict = torch.load(lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = d.pop(lowerCAmelCase )
os.makedirs(lowerCAmelCase , exist_ok=lowerCAmelCase )
torch.save(lowerCAmelCase , os.path.join(lowerCAmelCase , lowerCAmelCase ) )
if __name__ == "__main__":
A__ : List[Any] = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
A__ : Tuple = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
A__ : Tuple = os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
A__ : str = f"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 660 | 0 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
A__ : Union[str, Any] = logging.get_logger(__name__)
# General docstring
A__ : Tuple = """RegNetConfig"""
# Base docstring
A__ : List[Any] = """facebook/regnet-y-040"""
A__ : Union[str, Any] = [1, 1_088, 7, 7]
# Image classification docstring
A__ : List[Any] = """facebook/regnet-y-040"""
A__ : List[str] = """tabby, tabby cat"""
A__ : List[Any] = [
"""facebook/regnet-y-040""",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 3 , __UpperCamelCase = 1 , __UpperCamelCase = 1 , __UpperCamelCase = "relu" , )-> Any:
super().__init__()
UpperCAmelCase__ : Union[str, Any] = nn.Convad(
__UpperCamelCase , __UpperCamelCase , kernel_size=__UpperCamelCase , stride=__UpperCamelCase , padding=kernel_size // 2 , groups=__UpperCamelCase , bias=__UpperCamelCase , )
UpperCAmelCase__ : Optional[Any] = nn.BatchNormad(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = ACTaFN[activation] if activation is not None else nn.Identity()
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Any:
UpperCAmelCase__ : List[str] = self.convolution(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = self.normalization(__UpperCamelCase )
UpperCAmelCase__ : Any = self.activation(__UpperCamelCase )
return hidden_state
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self , __UpperCamelCase )-> str:
super().__init__()
UpperCAmelCase__ : Dict = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
UpperCAmelCase__ : Optional[int] = config.num_channels
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Any:
UpperCAmelCase__ : Dict = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
UpperCAmelCase__ : Optional[Any] = self.embedder(__UpperCamelCase )
return hidden_state
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 2 )-> Union[str, Any]:
super().__init__()
UpperCAmelCase__ : Optional[Any] = nn.Convad(__UpperCamelCase , __UpperCamelCase , kernel_size=1 , stride=__UpperCamelCase , bias=__UpperCamelCase )
UpperCAmelCase__ : int = nn.BatchNormad(__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Tensor:
UpperCAmelCase__ : List[str] = self.convolution(__UpperCamelCase )
UpperCAmelCase__ : str = self.normalization(__UpperCamelCase )
return hidden_state
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase )-> Optional[int]:
super().__init__()
UpperCAmelCase__ : Union[str, Any] = nn.AdaptiveAvgPoolad((1, 1) )
UpperCAmelCase__ : Union[str, Any] = nn.Sequential(
nn.Convad(__UpperCamelCase , __UpperCamelCase , kernel_size=1 ) , nn.ReLU() , nn.Convad(__UpperCamelCase , __UpperCamelCase , kernel_size=1 ) , nn.Sigmoid() , )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> int:
# b c h w -> b c 1 1
UpperCAmelCase__ : Union[str, Any] = self.pooler(__UpperCamelCase )
UpperCAmelCase__ : Any = self.attention(__UpperCamelCase )
UpperCAmelCase__ : List[str] = hidden_state * attention
return hidden_state
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 1 )-> List[str]:
super().__init__()
UpperCAmelCase__ : Tuple = in_channels != out_channels or stride != 1
UpperCAmelCase__ : List[str] = max(1 , out_channels // config.groups_width )
UpperCAmelCase__ : str = (
RegNetShortCut(__UpperCamelCase , __UpperCamelCase , stride=__UpperCamelCase ) if should_apply_shortcut else nn.Identity()
)
UpperCAmelCase__ : List[Any] = nn.Sequential(
RegNetConvLayer(__UpperCamelCase , __UpperCamelCase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(__UpperCamelCase , __UpperCamelCase , stride=__UpperCamelCase , groups=__UpperCamelCase , activation=config.hidden_act ) , RegNetConvLayer(__UpperCamelCase , __UpperCamelCase , kernel_size=1 , activation=__UpperCamelCase ) , )
UpperCAmelCase__ : str = ACTaFN[config.hidden_act]
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Dict:
UpperCAmelCase__ : Optional[Any] = hidden_state
UpperCAmelCase__ : Dict = self.layer(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = self.shortcut(__UpperCamelCase )
hidden_state += residual
UpperCAmelCase__ : Tuple = self.activation(__UpperCamelCase )
return hidden_state
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 1 )-> str:
super().__init__()
UpperCAmelCase__ : Any = in_channels != out_channels or stride != 1
UpperCAmelCase__ : str = max(1 , out_channels // config.groups_width )
UpperCAmelCase__ : Union[str, Any] = (
RegNetShortCut(__UpperCamelCase , __UpperCamelCase , stride=__UpperCamelCase ) if should_apply_shortcut else nn.Identity()
)
UpperCAmelCase__ : List[str] = nn.Sequential(
RegNetConvLayer(__UpperCamelCase , __UpperCamelCase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(__UpperCamelCase , __UpperCamelCase , stride=__UpperCamelCase , groups=__UpperCamelCase , activation=config.hidden_act ) , RegNetSELayer(__UpperCamelCase , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(__UpperCamelCase , __UpperCamelCase , kernel_size=1 , activation=__UpperCamelCase ) , )
UpperCAmelCase__ : List[str] = ACTaFN[config.hidden_act]
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Tuple:
UpperCAmelCase__ : Any = hidden_state
UpperCAmelCase__ : Optional[Any] = self.layer(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = self.shortcut(__UpperCamelCase )
hidden_state += residual
UpperCAmelCase__ : str = self.activation(__UpperCamelCase )
return hidden_state
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 2 , __UpperCamelCase = 2 , )-> str:
super().__init__()
UpperCAmelCase__ : Optional[int] = RegNetXLayer if config.layer_type == "x" else RegNetYLayer
UpperCAmelCase__ : Any = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , stride=__UpperCamelCase , ) , *[layer(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) for _ in range(depth - 1 )] , )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Optional[int]:
UpperCAmelCase__ : List[Any] = self.layers(__UpperCamelCase )
return hidden_state
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self , __UpperCamelCase )-> Tuple:
super().__init__()
UpperCAmelCase__ : int = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
__UpperCamelCase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
UpperCAmelCase__ : int = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(__UpperCamelCase , config.depths[1:] ):
self.stages.append(RegNetStage(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , depth=__UpperCamelCase ) )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = False , __UpperCamelCase = True )-> BaseModelOutputWithNoAttention:
UpperCAmelCase__ : Dict = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
UpperCAmelCase__ : Tuple = hidden_states + (hidden_state,)
UpperCAmelCase__ : Optional[Any] = stage_module(__UpperCamelCase )
if output_hidden_states:
UpperCAmelCase__ : Tuple = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=__UpperCamelCase , hidden_states=__UpperCamelCase )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = RegNetConfig
_A = 'regnet'
_A = 'pixel_values'
_A = True
def lowerCAmelCase__ ( self , __UpperCamelCase )-> str:
if isinstance(__UpperCamelCase , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="fan_out" , nonlinearity="relu" )
elif isinstance(__UpperCamelCase , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase=False )-> str:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : str = value
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 ([`RegNetConfig`]): 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
[`ConvNextImageProcessor.__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 [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'The bare RegNet model outputting raw features without any specific head on top.' , lowerCAmelCase_ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self , __UpperCamelCase )-> Tuple:
super().__init__(__UpperCamelCase )
UpperCAmelCase__ : List[str] = config
UpperCAmelCase__ : Dict = RegNetEmbeddings(__UpperCamelCase )
UpperCAmelCase__ : List[str] = RegNetEncoder(__UpperCamelCase )
UpperCAmelCase__ : List[str] = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__UpperCamelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__UpperCamelCase , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None )-> BaseModelOutputWithPoolingAndNoAttention:
UpperCAmelCase__ : Dict = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase__ : Any = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase__ : Optional[Any] = self.embedder(__UpperCamelCase )
UpperCAmelCase__ : Any = self.encoder(
__UpperCamelCase , output_hidden_states=__UpperCamelCase , return_dict=__UpperCamelCase )
UpperCAmelCase__ : Dict = encoder_outputs[0]
UpperCAmelCase__ : Tuple = self.pooler(__UpperCamelCase )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__UpperCamelCase , pooler_output=__UpperCamelCase , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , lowerCAmelCase_ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self , __UpperCamelCase )-> List[Any]:
super().__init__(__UpperCamelCase )
UpperCAmelCase__ : str = config.num_labels
UpperCAmelCase__ : List[Any] = RegNetModel(__UpperCamelCase )
# classification head
UpperCAmelCase__ : Any = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , 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(__UpperCamelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__UpperCamelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowerCAmelCase__ ( self , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , )-> ImageClassifierOutputWithNoAttention:
UpperCAmelCase__ : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase__ : Any = self.regnet(__UpperCamelCase , output_hidden_states=__UpperCamelCase , return_dict=__UpperCamelCase )
UpperCAmelCase__ : List[str] = outputs.pooler_output if return_dict else outputs[1]
UpperCAmelCase__ : Optional[Any] = self.classifier(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
UpperCAmelCase__ : Optional[int] = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
UpperCAmelCase__ : str = "single_label_classification"
else:
UpperCAmelCase__ : Tuple = "multi_label_classification"
if self.config.problem_type == "regression":
UpperCAmelCase__ : Tuple = MSELoss()
if self.num_labels == 1:
UpperCAmelCase__ : Dict = loss_fct(logits.squeeze() , labels.squeeze() )
else:
UpperCAmelCase__ : int = loss_fct(__UpperCamelCase , __UpperCamelCase )
elif self.config.problem_type == "single_label_classification":
UpperCAmelCase__ : Optional[int] = CrossEntropyLoss()
UpperCAmelCase__ : List[str] = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
UpperCAmelCase__ : Optional[Any] = BCEWithLogitsLoss()
UpperCAmelCase__ : Dict = loss_fct(__UpperCamelCase , __UpperCamelCase )
if not return_dict:
UpperCAmelCase__ : Any = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=__UpperCamelCase , logits=__UpperCamelCase , hidden_states=outputs.hidden_states )
| 714 |
"""simple docstring"""
from math import isqrt
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : List[Any] = False
return [i for i in range(2 , lowerCAmelCase ) if is_prime[i]]
def a__ ( lowerCAmelCase : int = 10**8 ):
'''simple docstring'''
UpperCAmelCase__ : Dict = calculate_prime_numbers(max_number // 2 )
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Tuple = len(lowerCAmelCase ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 660 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 0 )-> None:
UpperCAmelCase__ : Dict = row, column
UpperCAmelCase__ : Union[str, Any] = [[default_value for c in range(__UpperCamelCase )] for r in range(__UpperCamelCase )]
def __str__( self )-> str:
UpperCAmelCase__ : List[Any] = F"Matrix consist of {self.row} rows and {self.column} columns\n"
# Make string identifier
UpperCAmelCase__ : Union[str, Any] = 0
for row_vector in self.array:
for obj in row_vector:
UpperCAmelCase__ : int = max(__UpperCamelCase , len(str(__UpperCamelCase ) ) )
UpperCAmelCase__ : Optional[Any] = F"%{max_element_length}s"
# Make string and return
def single_line(__UpperCamelCase ) -> str:
nonlocal string_format_identifier
UpperCAmelCase__ : Union[str, Any] = "["
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
line += "]"
return line
s += "\n".join(single_line(__UpperCamelCase ) for row_vector in self.array )
return s
def __repr__( self )-> str:
return str(self )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> bool:
if not (isinstance(__UpperCamelCase , (list, tuple) ) and len(__UpperCamelCase ) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__( self , __UpperCamelCase )-> Any:
assert self.validate_indicies(__UpperCamelCase )
return self.array[loc[0]][loc[1]]
def __setitem__( self , __UpperCamelCase , __UpperCamelCase )-> None:
assert self.validate_indicies(__UpperCamelCase )
UpperCAmelCase__ : Dict = value
def __add__( self , __UpperCamelCase )-> Matrix:
assert isinstance(__UpperCamelCase , __UpperCamelCase )
assert self.row == another.row and self.column == another.column
# Add
UpperCAmelCase__ : Union[str, Any] = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
UpperCAmelCase__ : Union[str, Any] = self[r, c] + another[r, c]
return result
def __neg__( self )-> Matrix:
UpperCAmelCase__ : Union[str, Any] = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
UpperCAmelCase__ : List[Any] = -self[r, c]
return result
def __sub__( self , __UpperCamelCase )-> Matrix:
return self + (-another)
def __mul__( self , __UpperCamelCase )-> Matrix:
if isinstance(__UpperCamelCase , (int, float) ): # Scalar multiplication
UpperCAmelCase__ : Any = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
UpperCAmelCase__ : Optional[int] = self[r, c] * another
return result
elif isinstance(__UpperCamelCase , __UpperCamelCase ): # Matrix multiplication
assert self.column == another.row
UpperCAmelCase__ : int = Matrix(self.row , another.column )
for r in range(self.row ):
for c in range(another.column ):
for i in range(self.column ):
result[r, c] += self[r, i] * another[i, c]
return result
else:
UpperCAmelCase__ : List[str] = F"Unsupported type given for another ({type(__UpperCamelCase )})"
raise TypeError(__UpperCamelCase )
def lowerCAmelCase__ ( self )-> Matrix:
UpperCAmelCase__ : Dict = Matrix(self.column , self.row )
for r in range(self.row ):
for c in range(self.column ):
UpperCAmelCase__ : List[str] = self[r, c]
return result
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase )-> Any:
assert isinstance(__UpperCamelCase , __UpperCamelCase ) and isinstance(__UpperCamelCase , __UpperCamelCase )
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
UpperCAmelCase__ : List[str] = v.transpose()
UpperCAmelCase__ : Dict = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def a__ ( ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = Matrix(3 , 3 , 0 )
for i in range(3 ):
UpperCAmelCase__ : List[str] = 1
print(F"a^(-1) is {ainv}" )
# u, v
UpperCAmelCase__ : str = Matrix(3 , 1 , 0 )
UpperCAmelCase__ : Union[str, Any] = 1, 2, -3
UpperCAmelCase__ : int = Matrix(3 , 1 , 0 )
UpperCAmelCase__ : List[Any] = 4, -2, 5
print(F"u is {u}" )
print(F"v is {v}" )
print(F"uv^T is {u * v.transpose()}" )
# Sherman Morrison
print(F"(a + uv^T)^(-1) is {ainv.sherman_morrison(lowerCAmelCase , lowerCAmelCase )}" )
def a__ ( ):
'''simple docstring'''
import doctest
doctest.testmod()
testa()
| 715 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a__ ( lowerCAmelCase : str , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Dict , lowerCAmelCase : List[Any] ):
'''simple docstring'''
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Optional[int] = np.full((len(lowerCAmelCase ), sequence_length, 2) , lowerCAmelCase )
else:
UpperCAmelCase__ : Optional[Any] = np.full((len(lowerCAmelCase ), sequence_length) , lowerCAmelCase )
for i, tensor in enumerate(lowerCAmelCase ):
if padding_side == "right":
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Dict = tensor[:sequence_length]
else:
UpperCAmelCase__ : Tuple = tensor[:sequence_length]
else:
if isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Optional[Any] = tensor[:sequence_length]
else:
UpperCAmelCase__ : int = tensor[:sequence_length]
return out_tensor.tolist()
def a__ ( lowerCAmelCase : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = ord(lowerCAmelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
UpperCAmelCase__ : Optional[Any] = unicodedata.category(lowerCAmelCase )
if cat.startswith("P" ):
return True
return False
@dataclass
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 42
_A = True
_A = None
_A = None
_A = -100
_A = "pt"
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[str]:
import torch
UpperCAmelCase__ : Optional[Any] = "label" if "label" in features[0].keys() else "labels"
UpperCAmelCase__ : Dict = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
UpperCAmelCase__ : str = self.tokenizer.pad(
__UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" if labels is None else None , )
if labels is None:
return batch
UpperCAmelCase__ : Optional[Any] = torch.tensor(batch["entity_ids"] ).shape[1]
UpperCAmelCase__ : int = self.tokenizer.padding_side
if padding_side == "right":
UpperCAmelCase__ : int = [
list(__UpperCamelCase ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) for label in labels
]
else:
UpperCAmelCase__ : List[Any] = [
[self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) + list(__UpperCamelCase ) for label in labels
]
UpperCAmelCase__ : Optional[Any] = [feature["ner_tags"] for feature in features]
UpperCAmelCase__ : int = padding_tensor(__UpperCamelCase , -1 , __UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : List[Any] = [feature["original_entity_spans"] for feature in features]
UpperCAmelCase__ : int = padding_tensor(__UpperCamelCase , (-1, -1) , __UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Optional[int] = {k: torch.tensor(__UpperCamelCase , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 660 | 0 |
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