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from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
'LiltForQuestionAnswering',
'LiltForSequenceClassification',
'LiltForTokenClassification',
'LiltModel',
'LiltPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
def __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE : Union[str, Any] = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Any = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" , return_dict=a )[0]
SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "google/ncsnpp-celebahq-256"
SCREAMING_SNAKE_CASE : List[Any] = UNetaDModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Optional[Any] = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = pipe(num_inference_steps=20 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a_ = abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def lowerCamelCase__ ( _a):
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_a)
def lowerCamelCase__ ( _a):
from diffusers.utils.testing_utils import pytest_terminal_summary_main
SCREAMING_SNAKE_CASE : Optional[Any] = terminalreporter.config.getoption("--make-reports")
if make_reports:
pytest_terminal_summary_main(_a , id=_a) | 25 |
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
SCREAMING_SNAKE_CASE : Optional[Any] = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res | 25 | 1 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[str] ) -> str:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Optional[Any] = False
return options
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : str = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : List[str] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy" )
# using the PNDM scheduler by default
SCREAMING_SNAKE_CASE : Dict = OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Tuple = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Optional[Any] = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = pipe(
prompt=a , image=a , mask_image=a , strength=0.75 , guidance_scale=7.5 , num_inference_steps=15 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : Optional[int] = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 1e-2 | 25 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='roformer'
def __init__( self : Dict , a : Any=5_0000 , a : List[Any]=None , a : str=768 , a : str=12 , a : Tuple=12 , a : Optional[Any]=3072 , a : List[str]="gelu" , a : List[Any]=0.1 , a : Union[str, Any]=0.1 , a : Tuple=1536 , a : List[str]=2 , a : Tuple=0.02 , a : Any=1e-12 , a : Optional[int]=0 , a : Union[str, Any]=False , a : int=True , **a : str , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=a , **a )
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size if embedding_size is None else embedding_size
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = type_vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE : List[str] = rotary_value
SCREAMING_SNAKE_CASE : int = use_cache
class _UpperCamelCase ( __A ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE : str = {0: "batch", 1: "sequence"}
SCREAMING_SNAKE_CASE : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] ) | 25 | 1 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, 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_ = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.plbart.modeling_plbart import shift_tokens_right
a_ = 5_0003
a_ = 5_0002
@require_sentencepiece
@require_tokenizers
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =PLBartTokenizer
lowerCamelCase__ =None
lowerCamelCase__ =False
def __UpperCamelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE : int = PLBartTokenizer(a , language_codes="base" , keep_accents=a )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = PLBartTokenizer(a , language_codes="base" , keep_accents=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.tokenize("This is a test" )
self.assertListEqual(a , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(a ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
SCREAMING_SNAKE_CASE : int = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
a , [
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",
".",
] , )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.convert_tokens_to_ids(a )
self.assertListEqual(
a , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
SCREAMING_SNAKE_CASE : int = tokenizer.convert_ids_to_tokens(a )
self.assertListEqual(
a , [
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>",
".",
] , )
SCREAMING_SNAKE_CASE : List[str] = tokenizer.vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = [tokenizer.convert_ids_to_tokens(a ) for x in range(end - 4 , a )]
self.assertListEqual(a , ["__java__", "__python__", "__en_XX__", "<mask>"] )
SCREAMING_SNAKE_CASE : Tuple = "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
SCREAMING_SNAKE_CASE : Any = tokenizer(a ).input_ids
self.assertEqual(
tokenizer.decode(a , skip_special_tokens=a , clean_up_tokenization_spaces=a ) , a , )
def __UpperCamelCase ( self : List[str] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = PLBartTokenizer(a , language_codes="multi" , keep_accents=a )
SCREAMING_SNAKE_CASE : Tuple = tokenizer.tokenize("This is a test" )
self.assertListEqual(a , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(a ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
SCREAMING_SNAKE_CASE : Tuple = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
a , [
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",
".",
] , )
SCREAMING_SNAKE_CASE : int = tokenizer.convert_tokens_to_ids(a )
self.assertListEqual(
a , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.convert_ids_to_tokens(a )
self.assertListEqual(
a , [
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>",
".",
] , )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.vocab_size
SCREAMING_SNAKE_CASE : Dict = [tokenizer.convert_ids_to_tokens(a ) for x in range(end - 7 , a )]
self.assertListEqual(
a , ["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"] )
SCREAMING_SNAKE_CASE : List[Any] = "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer(a ).input_ids
self.assertEqual(
tokenizer.decode(a , skip_special_tokens=a , clean_up_tokenization_spaces=a ) , a , )
@require_torch
@require_sentencepiece
@require_tokenizers
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ ='uclanlp/plbart-python-en_XX'
lowerCamelCase__ =[
'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
]
lowerCamelCase__ =[
'Returns the maximum value of a b c.',
'Sums the values of a b c.',
]
lowerCamelCase__ =[
134,
5452,
33460,
33441,
33463,
33465,
33463,
33449,
988,
20,
33456,
19,
33456,
771,
39,
4258,
889,
3318,
33441,
33463,
33465,
33463,
33449,
2471,
2,
PYTHON_CODE,
]
@classmethod
def __UpperCamelCase ( cls : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : PLBartTokenizer = PLBartTokenizer.from_pretrained(
cls.checkpoint_name , language_codes="base" , src_lang="python" , tgt_lang="en_XX" )
SCREAMING_SNAKE_CASE : Optional[int] = 1
return cls
def __UpperCamelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"] , 5_0001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"] , 5_0002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"] , 5_0003 )
def __UpperCamelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , a )
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
self.assertIn(a , self.tokenizer.all_special_ids )
SCREAMING_SNAKE_CASE : Dict = [EN_CODE, 9037, 3_3442, 57, 752, 153, 14, 56, 18, 9, 2]
SCREAMING_SNAKE_CASE : str = self.tokenizer.decode(a , skip_special_tokens=a )
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=a )
self.assertEqual(a , a )
self.assertNotIn(self.tokenizer.eos_token , a )
def __UpperCamelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
self.assertIsInstance(src_text[0] , a )
SCREAMING_SNAKE_CASE : Optional[int] = 10
SCREAMING_SNAKE_CASE : Dict = self.tokenizer(a , max_length=a , truncation=a ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , a )
self.assertEqual(len(a ) , a )
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"] ) , [5_0004, 5_0001] )
def __UpperCamelCase ( self : List[str] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : Any = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(a )
SCREAMING_SNAKE_CASE : Dict = PLBartTokenizer.from_pretrained(a )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , a )
@require_torch
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=a , return_tensors="pt" )
SCREAMING_SNAKE_CASE : Optional[Any] = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
self.assertEqual(batch.input_ids[1][-2:].tolist() , [2, PYTHON_CODE] )
self.assertEqual(batch.decoder_input_ids[1][0] , a )
self.assertEqual(batch.decoder_input_ids[1][-1] , 2 )
self.assertEqual(batch.labels[1][-2:].tolist() , [2, EN_CODE] )
@require_torch
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=a , truncation=a , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
SCREAMING_SNAKE_CASE : Tuple = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
self.assertIsInstance(a , a )
self.assertEqual((2, 26) , batch.input_ids.shape )
self.assertEqual((2, 26) , batch.attention_mask.shape )
SCREAMING_SNAKE_CASE : Union[str, Any] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , a )
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, PYTHON_CODE] )
def __UpperCamelCase ( self : Dict ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.tokenizer(self.src_text , padding=a , truncation=a , max_length=3 , return_tensors="pt" )
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer(
text_target=self.tgt_text , padding=a , truncation=a , max_length=10 , return_tensors="pt" )
SCREAMING_SNAKE_CASE : Union[str, Any] = targets["input_ids"]
SCREAMING_SNAKE_CASE : Dict = shift_tokens_right(a , 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 __UpperCamelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="java" )
self.assertEqual(
nested_simplify(a ) , {
# A, test, EOS, en_XX
"input_ids": [[150, 242, 2, 5_0003]],
"attention_mask": [[1, 1, 1, 1]],
# java
"forced_bos_token_id": 5_0001,
} , ) | 25 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__name__)
a_ = 'Hello world! cécé herlolip'
a_ = namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = BertAbsConfig(
temp_dir="." , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder="bert" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : Dict = torch.load(_a , lambda _a , _a: storage)
SCREAMING_SNAKE_CASE : str = AbsSummarizer(_a , torch.device("cpu") , _a)
original.eval()
SCREAMING_SNAKE_CASE : List[str] = BertAbsSummarizer(_a , torch.device("cpu"))
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model")
new_model.bert.load_state_dict(original.bert.state_dict())
new_model.decoder.load_state_dict(original.decoder.state_dict())
new_model.generator.load_state_dict(original.generator.state_dict())
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical")
SCREAMING_SNAKE_CASE : List[str] = BertTokenizer.from_pretrained("bert-base-uncased")
# prepare the model inputs
SCREAMING_SNAKE_CASE : List[str] = tokenizer.encode("This is sample éàalj'-.")
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode("This is sample 3 éàalj'-.")
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0
# forward pass
SCREAMING_SNAKE_CASE : List[Any] = encoder_input_ids
SCREAMING_SNAKE_CASE : List[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Dict = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : Optional[int] = original(_a , _a , _a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Dict = original.generator(_a)
SCREAMING_SNAKE_CASE : Any = new_model(
_a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Tuple = new_model.generator(_a)
SCREAMING_SNAKE_CASE : List[Any] = torch.max(torch.abs(output_converted_model - output_original_model)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : int = torch.allclose(_a , _a , atol=1E-3)
if are_identical:
logging.info("all weights are equal up to 1e-3")
else:
raise ValueError("the weights are different. The new model is likely different from the original one.")
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary")
torch.save(
new_model.state_dict() , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin")
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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.',
)
a_ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
) | 25 | 1 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , a : Optional[Any] , a : Dict=3 , a : Optional[int]=32 , a : Any=3 , a : Optional[Any]=10 , a : List[str]=[10, 20, 30, 40] , a : Dict=[1, 1, 2, 1] , a : Dict=True , a : int=True , a : List[Any]="relu" , a : List[str]=3 , a : List[Any]=None , ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = parent
SCREAMING_SNAKE_CASE : int = batch_size
SCREAMING_SNAKE_CASE : Optional[Any] = image_size
SCREAMING_SNAKE_CASE : Optional[int] = num_channels
SCREAMING_SNAKE_CASE : str = embeddings_size
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_sizes
SCREAMING_SNAKE_CASE : Optional[Any] = depths
SCREAMING_SNAKE_CASE : int = is_training
SCREAMING_SNAKE_CASE : Dict = use_labels
SCREAMING_SNAKE_CASE : Any = hidden_act
SCREAMING_SNAKE_CASE : str = num_labels
SCREAMING_SNAKE_CASE : int = scope
SCREAMING_SNAKE_CASE : str = len(a )
def __UpperCamelCase ( self : int ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE : Dict = None
if self.use_labels:
SCREAMING_SNAKE_CASE : int = ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE : str = self.get_config()
return config, pixel_values, labels
def __UpperCamelCase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def __UpperCamelCase ( self : Optional[int] , a : Optional[int] , a : Optional[int] , a : Tuple ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = TFRegNetModel(config=a )
SCREAMING_SNAKE_CASE : int = model(a , training=a )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def __UpperCamelCase ( self : Dict , a : Any , a : List[str] , a : Optional[Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.num_labels
SCREAMING_SNAKE_CASE : Tuple = TFRegNetForImageClassification(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = model(a , labels=a , training=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE : Dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class _UpperCamelCase ( __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
lowerCamelCase__ =(
{'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
if is_tf_available()
else {}
)
lowerCamelCase__ =False
lowerCamelCase__ =False
lowerCamelCase__ =False
lowerCamelCase__ =False
lowerCamelCase__ =False
def __UpperCamelCase ( self : int ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = TFRegNetModelTester(self )
SCREAMING_SNAKE_CASE : List[str] = ConfigTester(self , config_class=a , has_text_modality=a )
def __UpperCamelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
return
@unittest.skip(reason="RegNet does not use inputs_embeds" )
def __UpperCamelCase ( self : str ) -> int:
"""simple docstring"""
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU" ) ) == 0 , reason="TF does not support backprop for grouped convolutions on CPU." , )
@slow
def __UpperCamelCase ( self : Dict ) -> List[str]:
"""simple docstring"""
super().test_keras_fit()
@unittest.skip(reason="RegNet does not support input and output embeddings" )
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Optional[Any] = model_class(a )
SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE : Any = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE : Union[str, Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , a )
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def __UpperCamelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
def check_hidden_states_output(a : Dict , a : Optional[int] , a : Any ):
SCREAMING_SNAKE_CASE : str = model_class(a )
SCREAMING_SNAKE_CASE : str = model(**self._prepare_for_class(a , a ) , training=a )
SCREAMING_SNAKE_CASE : Optional[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
SCREAMING_SNAKE_CASE : List[str] = self.model_tester.num_stages
self.assertEqual(len(a ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE : int = ["basic", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
SCREAMING_SNAKE_CASE : int = layer_type
SCREAMING_SNAKE_CASE : Optional[Any] = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE : List[Any] = True
check_hidden_states_output(a , a , a )
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(a : int , a : Union[str, Any] , a : int , a : Dict={} ):
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , return_dict=a , **a )
SCREAMING_SNAKE_CASE : Any = model(a , return_dict=a , **a ).to_tuple()
def recursive_check(a : Optional[Any] , a : Tuple ):
if isinstance(a , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(a , a ):
recursive_check(a , a )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(a , a ) ) , msg=(
"Tuple and dict output are not equal. Difference:"
F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"
) , )
recursive_check(a , a )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : int = model_class(a )
SCREAMING_SNAKE_CASE : List[str] = self._prepare_for_class(a , a )
SCREAMING_SNAKE_CASE : int = self._prepare_for_class(a , a )
check_equivalence(a , a , a )
SCREAMING_SNAKE_CASE : Dict = self._prepare_for_class(a , a , return_labels=a )
SCREAMING_SNAKE_CASE : List[Any] = self._prepare_for_class(a , a , return_labels=a )
check_equivalence(a , a , a )
SCREAMING_SNAKE_CASE : str = self._prepare_for_class(a , a )
SCREAMING_SNAKE_CASE : List[str] = self._prepare_for_class(a , a )
check_equivalence(a , a , a , {"output_hidden_states": True} )
SCREAMING_SNAKE_CASE : int = self._prepare_for_class(a , a , return_labels=a )
SCREAMING_SNAKE_CASE : Dict = self._prepare_for_class(a , a , return_labels=a )
check_equivalence(a , a , a , {"output_hidden_states": True} )
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a )
@slow
def __UpperCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : str = TFRegNetModel.from_pretrained(a )
self.assertIsNotNone(a )
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_tf
@require_vision
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __UpperCamelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __UpperCamelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
SCREAMING_SNAKE_CASE : List[str] = self.default_image_processor
SCREAMING_SNAKE_CASE : List[str] = prepare_img()
SCREAMING_SNAKE_CASE : Any = image_processor(images=a , return_tensors="tf" )
# forward pass
SCREAMING_SNAKE_CASE : Tuple = model(**a , training=a )
# verify the logits
SCREAMING_SNAKE_CASE : int = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , a )
SCREAMING_SNAKE_CASE : Optional[Any] = tf.constant([-0.4180, -1.5051, -3.4836] )
tf.debugging.assert_near(outputs.logits[0, :3] , a , atol=1e-4 ) | 25 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
a_ = parser.parse_args()
a_ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
a_ = CLIPImageProcessor()
a_ = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
a_ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path) | 25 | 1 |
def lowerCamelCase__ ( _a):
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 lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = len(_a) # No of vertices in graph
SCREAMING_SNAKE_CASE : str = [0] * n
SCREAMING_SNAKE_CASE : str = [False] * n
def dfs(_a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : str = id_
id_ += 1
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
dfs(_a , _a , _a , id_)
SCREAMING_SNAKE_CASE : List[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
SCREAMING_SNAKE_CASE : Tuple = min(low[at] , low[to])
SCREAMING_SNAKE_CASE : list[tuple[int, int]] = []
for i in range(_a):
if not visited[i]:
dfs(_a , -1 , _a , id_)
return bridges
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Swinv2ForImageClassification',
'Swinv2ForMaskedImageModeling',
'Swinv2Model',
'Swinv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = tf.convert_to_tensor(
[
[
8.222_0991, # 3rd highest value; idx. 0
-0.562_0044,
5.2322_9752,
4.038_6393,
-6.879_8378,
-0.5478_5802,
-3.201_2153,
2.9277_7176,
1.8817_1953,
7.3534_1276, # 5th highest value; idx. 9
8.4320_7833, # 2nd highest value; idx. 10
-9.8571_1836,
-5.9620_9236,
-1.1303_9161,
-7.111_5294,
-0.836_9633,
-5.318_6408,
7.0642_7407,
0.8136_9344,
-0.8202_3817,
-5.917_9796,
0.5881_3443,
-6.9977_8438,
4.7155_1189,
-0.1877_1637,
7.4402_0759, # 4th highest value; idx. 25
9.3845_0987, # 1st highest value; idx. 26
2.1266_2941,
-9.3256_2038,
2.3565_2522,
], # cummulative prob of 5 highest values <= 0.6
[
0.5842_5518,
4.5313_9238,
-5.5751_0464,
-6.2803_0699,
-7.1952_9503,
-4.0212_2551,
1.3933_7037,
-6.0670_7057,
1.5948_0517,
-9.64_3119,
0.0390_7799,
0.6723_1762,
-8.8820_6726,
6.2711_5922, # 4th highest value; idx. 13
2.2852_0723,
4.8276_7506,
4.3042_1368,
8.827_5313, # 2nd highest value; idx. 17
5.4402_9958, # 5th highest value; idx. 18
-4.473_5794,
7.3857_9536, # 3rd highest value; idx. 20
-2.9105_1663,
2.6194_6077,
-2.567_4762,
-9.4895_9302,
-4.0292_2645,
-1.3541_6918,
9.6770_2323, # 1st highest value; idx. 27
-5.8947_8553,
1.8537_0467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
SCREAMING_SNAKE_CASE : Optional[int] = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
SCREAMING_SNAKE_CASE : List[Any] = tf.convert_to_tensor(
[8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023] , dtype=tf.floataa , ) # expected non filtered values as noted above
SCREAMING_SNAKE_CASE : Union[str, Any] = tf_top_k_top_p_filtering(a , top_k=10 , top_p=0.6 , min_tokens_to_keep=4 )
SCREAMING_SNAKE_CASE : List[Any] = output[output != -float("inf" )]
SCREAMING_SNAKE_CASE : Tuple = tf.cast(
tf.where(tf.not_equal(a , tf.constant(-float("inf" ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(a , a , rtol=1e-12 )
tf.debugging.assert_equal(a , a )
@require_tf
class _UpperCamelCase ( unittest.TestCase , __A ):
'''simple docstring'''
if is_tf_available():
lowerCamelCase__ ={
'AutoModelForCausalLM': TFAutoModelForCausalLM,
'AutoModelForSpeechSeq2Seq': TFAutoModelForSpeechSeqaSeq,
'AutoModelForSeq2SeqLM': TFAutoModelForSeqaSeqLM,
'AutoModelForVision2Seq': TFAutoModelForVisionaSeq,
'LogitsProcessorList': TFLogitsProcessorList,
'MinLengthLogitsProcessor': TFMinLengthLogitsProcessor,
'create_tensor_fn': tf.convert_to_tensor,
'floats_tensor': floats_tensor,
'return_tensors': 'tf',
}
@slow
def __UpperCamelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
SCREAMING_SNAKE_CASE : str = 2
SCREAMING_SNAKE_CASE : List[str] = 2
class _UpperCamelCase ( tf.Module ):
'''simple docstring'''
def __init__( self : List[str] , a : Optional[int] ) -> str:
"""simple docstring"""
super(a , self ).__init__()
SCREAMING_SNAKE_CASE : Dict = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name="input_ids" ),
tf.TensorSpec((None, input_length) , tf.intaa , name="attention_mask" ),
) , jit_compile=a , )
def __UpperCamelCase ( self : Union[str, Any] , a : List[str] , a : Any ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model.generate(
input_ids=a , attention_mask=a , max_new_tokens=a , return_dict_in_generate=a , )
return {"sequences": outputs["sequences"]}
SCREAMING_SNAKE_CASE : Dict = [[2, 0], [102, 103]]
SCREAMING_SNAKE_CASE : Optional[int] = [[1, 0], [1, 1]]
SCREAMING_SNAKE_CASE : Any = DummyModel(model=a )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(a , a , signatures={"serving_default": dummy_model.serving} )
SCREAMING_SNAKE_CASE : Tuple = tf.saved_model.load(a ).signatures["serving_default"]
for batch_size in range(1 , len(a ) + 1 ):
SCREAMING_SNAKE_CASE : Optional[int] = {
"input_ids": tf.constant(dummy_input_ids[:batch_size] ),
"attention_mask": tf.constant(dummy_attention_masks[:batch_size] ),
}
SCREAMING_SNAKE_CASE : Dict = serving_func(**a )["sequences"]
SCREAMING_SNAKE_CASE : Union[str, Any] = test_model.generate(**a , max_new_tokens=a )
tf.debugging.assert_equal(a , a )
@slow
def __UpperCamelCase ( self : Any ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
SCREAMING_SNAKE_CASE : int = 1
SCREAMING_SNAKE_CASE : Union[str, Any] = 2
class _UpperCamelCase ( tf.Module ):
'''simple docstring'''
def __init__( self : List[str] , a : List[Any] ) -> Optional[int]:
"""simple docstring"""
super(a , self ).__init__()
SCREAMING_SNAKE_CASE : List[Any] = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name="input_ids" ),
tf.TensorSpec((batch_size, None) , tf.intaa , name="attention_mask" ),
) , jit_compile=a , )
def __UpperCamelCase ( self : str , a : List[str] , a : str ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.model.generate(
input_ids=a , attention_mask=a , max_new_tokens=a , return_dict_in_generate=a , )
return {"sequences": outputs["sequences"]}
SCREAMING_SNAKE_CASE : str = [[2], [102, 103]]
SCREAMING_SNAKE_CASE : str = [[1], [1, 1]]
SCREAMING_SNAKE_CASE : Optional[Any] = DummyModel(model=a )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(a , a , signatures={"serving_default": dummy_model.serving} )
SCREAMING_SNAKE_CASE : List[Any] = tf.saved_model.load(a ).signatures["serving_default"]
for input_row in range(len(a ) ):
SCREAMING_SNAKE_CASE : List[str] = {
"input_ids": tf.constant([dummy_input_ids[input_row]] ),
"attention_mask": tf.constant([dummy_attention_masks[input_row]] ),
}
SCREAMING_SNAKE_CASE : Union[str, Any] = serving_func(**a )["sequences"]
SCREAMING_SNAKE_CASE : str = test_model.generate(**a , max_new_tokens=a )
tf.debugging.assert_equal(a , a )
@slow
@require_tensorflow_text
def __UpperCamelCase ( self : Tuple ) -> str:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id="google/flan-t5-small" , filename="spiece.model" , local_dir=a )
class _UpperCamelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE : List[str] = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(a , "spiece.model" ) , "rb" ).read() )
SCREAMING_SNAKE_CASE : str = TFAutoModelForSeqaSeqLM.from_pretrained("hf-internal-testing/tiny-random-t5" )
def __UpperCamelCase ( self : Union[str, Any] , a : Optional[Any] , *a : Union[str, Any] , **a : int ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer.tokenize(a )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[int] = text.pad_model_inputs(
a , max_seq_length=64 , pad_value=self.model.config.pad_token_id )
SCREAMING_SNAKE_CASE : str = self.model.generate(input_ids=a , attention_mask=a )
return self.tokenizer.detokenize(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = CompleteSentenceTransformer()
SCREAMING_SNAKE_CASE : Optional[Any] = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name="inputs" )
SCREAMING_SNAKE_CASE : Dict = complete_model(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = tf.keras.Model(a , a )
keras_model.save(a )
def __UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = {
"do_sample": True,
"num_beams": 1,
"top_p": 0.7,
"top_k": 10,
"temperature": 0.7,
}
SCREAMING_SNAKE_CASE : Dict = 14
SCREAMING_SNAKE_CASE : int = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
SCREAMING_SNAKE_CASE : int = "Hello, my dog is cute and"
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(a , return_tensors="tf" )
SCREAMING_SNAKE_CASE : str = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
SCREAMING_SNAKE_CASE : Dict = 638
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(":/CPU:0" ):
tf.random.set_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = model.generate(**a , eos_token_id=a , **a )
self.assertTrue(expectation == len(generated_tokens[0] ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = [638, 198]
with tf.device(":/CPU:0" ):
tf.random.set_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = model.generate(**a , eos_token_id=a , **a )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def __UpperCamelCase ( self : Tuple ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart" )
SCREAMING_SNAKE_CASE : Optional[int] = "Hugging Face is a technology company based in New York and Paris."
SCREAMING_SNAKE_CASE : List[Any] = bart_tokenizer(a , return_tensors="tf" ).input_ids
SCREAMING_SNAKE_CASE : Dict = TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart" )
SCREAMING_SNAKE_CASE : Dict = bart_model.generate(a ).numpy()
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __UpperCamelCase ( self : Optional[int] , a : int , a : List[str]=None , **a : List[str] ) -> Optional[int]:
"""simple docstring"""
return super().call(a , **a )
SCREAMING_SNAKE_CASE : Optional[int] = FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart" )
SCREAMING_SNAKE_CASE : Union[str, Any] = bart_model.generate(a , foo="bar" ).numpy()
self.assertTrue(np.array_equal(a , a ) )
class _UpperCamelCase ( bart_model.model.encoder.__class__ ):
'''simple docstring'''
def __UpperCamelCase ( self : Tuple , a : Any , **a : List[Any] ) -> Optional[Any]:
"""simple docstring"""
return super().call(a , **a )
SCREAMING_SNAKE_CASE : int = FakeEncoder(bart_model.config , bart_model.model.shared )
SCREAMING_SNAKE_CASE : List[str] = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
SCREAMING_SNAKE_CASE : Union[str, Any] = bart_model.generate(a ).numpy()
with self.assertRaises(a ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(a , foo="bar" ) | 25 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values")
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values")
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase__ ( _a , _a , _a):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values")
SCREAMING_SNAKE_CASE : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def lowerCamelCase__ ( _a , _a):
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values")
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori")
return 4 * pow(_a , 2) * torus_radius * tube_radius
def lowerCamelCase__ ( _a , _a):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def lowerCamelCase__ ( _a , _a , _a):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values")
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("Given three sides do not form a triangle")
SCREAMING_SNAKE_CASE : List[str] = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE : Optional[int] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea))
return area
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def lowerCamelCase__ ( _a , _a , _a):
if basea < 0 or basea < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values")
return 1 / 2 * (basea + basea) * height
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def lowerCamelCase__ ( _a , _a):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("area_rhombus() only accepts non-negative values")
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase__ ( _a , _a):
if not isinstance(_a , _a) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides")
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side")
return (sides * length**2) / (4 * tan(pi / sides))
return (sides * length**2) / (4 * tan(pi / sides))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('[DEMO] Areas of various geometric shapes: \n')
print(F'''Rectangle: {area_rectangle(10, 20) = }''')
print(F'''Square: {area_square(10) = }''')
print(F'''Triangle: {area_triangle(10, 10) = }''')
print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(F'''Rhombus: {area_rhombus(10, 20) = }''')
print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(F'''Circle: {area_circle(20) = }''')
print(F'''Ellipse: {area_ellipse(10, 20) = }''')
print('\nSurface Areas of various geometric shapes: \n')
print(F'''Cube: {surface_area_cube(20) = }''')
print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(F'''Sphere: {surface_area_sphere(20) = }''')
print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(F'''Cone: {surface_area_cone(10, 20) = }''')
print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(F'''Torus: {surface_area_torus(20, 10) = }''')
print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(F'''Square: {area_reg_polygon(4, 10) = }''')
print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''') | 25 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : int , a : Collection[float] | None = None ) -> None:
"""simple docstring"""
if components is None:
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Tuple = list(a )
def __len__( self : Optional[int] ) -> int:
"""simple docstring"""
return len(self.__components )
def __str__( self : str ) -> str:
"""simple docstring"""
return "(" + ",".join(map(a , self.__components ) ) + ")"
def __add__( self : Union[str, Any] , a : Vector ) -> Vector:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = len(self )
if size == len(a ):
SCREAMING_SNAKE_CASE : int = [self.__components[i] + other.component(a ) for i in range(a )]
return Vector(a )
else:
raise Exception("must have the same size" )
def __sub__( self : List[str] , a : Vector ) -> Vector:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = len(self )
if size == len(a ):
SCREAMING_SNAKE_CASE : str = [self.__components[i] - other.component(a ) for i in range(a )]
return Vector(a )
else: # error case
raise Exception("must have the same size" )
@overload
def __mul__( self : List[str] , a : float ) -> Vector:
"""simple docstring"""
...
@overload
def __mul__( self : List[Any] , a : Vector ) -> float:
"""simple docstring"""
...
def __mul__( self : Optional[int] , a : float | Vector ) -> float | Vector:
"""simple docstring"""
if isinstance(a , (float, int) ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [c * other for c in self.__components]
return Vector(a )
elif isinstance(a , a ) and len(self ) == len(a ):
SCREAMING_SNAKE_CASE : List[Any] = len(self )
SCREAMING_SNAKE_CASE : str = [self.__components[i] * other.component(a ) for i in range(a )]
return sum(a )
else: # error case
raise Exception("invalid operand!" )
def __UpperCamelCase ( self : Tuple ) -> Vector:
"""simple docstring"""
return Vector(self.__components )
def __UpperCamelCase ( self : Union[str, Any] , a : int ) -> float:
"""simple docstring"""
if isinstance(a , a ) and -len(self.__components ) <= i < len(self.__components ):
return self.__components[i]
else:
raise Exception("index out of range" )
def __UpperCamelCase ( self : str , a : int , a : float ) -> None:
"""simple docstring"""
assert -len(self.__components ) <= pos < len(self.__components )
SCREAMING_SNAKE_CASE : Union[str, Any] = value
def __UpperCamelCase ( self : str ) -> float:
"""simple docstring"""
if len(self.__components ) == 0:
raise Exception("Vector is empty" )
SCREAMING_SNAKE_CASE : Optional[int] = [c**2 for c in self.__components]
return math.sqrt(sum(a ) )
def __UpperCamelCase ( self : int , a : Vector , a : bool = False ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self * other
SCREAMING_SNAKE_CASE : Tuple = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den ) )
else:
return math.acos(num / den )
def lowerCamelCase__ ( _a):
assert isinstance(_a , _a)
return Vector([0] * dimension)
def lowerCamelCase__ ( _a , _a):
assert isinstance(_a , _a) and (isinstance(_a , _a))
SCREAMING_SNAKE_CASE : Union[str, Any] = [0] * dimension
SCREAMING_SNAKE_CASE : Any = 1
return Vector(_a)
def lowerCamelCase__ ( _a , _a , _a):
assert (
isinstance(_a , _a)
and isinstance(_a , _a)
and (isinstance(_a , (int, float)))
)
return x * scalar + y
def lowerCamelCase__ ( _a , _a , _a):
random.seed(_a)
SCREAMING_SNAKE_CASE : Union[str, Any] = [random.randint(_a , _a) for _ in range(_a)]
return Vector(_a)
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : int , a : list[list[float]] , a : int , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = matrix
SCREAMING_SNAKE_CASE : Optional[int] = w
SCREAMING_SNAKE_CASE : Optional[Any] = h
def __str__( self : Any ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ""
for i in range(self.__height ):
ans += "|"
for j in range(self.__width ):
if j < self.__width - 1:
ans += str(self.__matrix[i][j] ) + ","
else:
ans += str(self.__matrix[i][j] ) + "|\n"
return ans
def __add__( self : List[Any] , a : Matrix ) -> Matrix:
"""simple docstring"""
if self.__width == other.width() and self.__height == other.height():
SCREAMING_SNAKE_CASE : Dict = []
for i in range(self.__height ):
SCREAMING_SNAKE_CASE : Any = [
self.__matrix[i][j] + other.component(a , a )
for j in range(self.__width )
]
matrix.append(a )
return Matrix(a , self.__width , self.__height )
else:
raise Exception("matrix must have the same dimension!" )
def __sub__( self : Optional[Any] , a : Matrix ) -> Matrix:
"""simple docstring"""
if self.__width == other.width() and self.__height == other.height():
SCREAMING_SNAKE_CASE : Union[str, Any] = []
for i in range(self.__height ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [
self.__matrix[i][j] - other.component(a , a )
for j in range(self.__width )
]
matrix.append(a )
return Matrix(a , self.__width , self.__height )
else:
raise Exception("matrices must have the same dimension!" )
@overload
def __mul__( self : List[str] , a : float ) -> Matrix:
"""simple docstring"""
...
@overload
def __mul__( self : Any , a : Vector ) -> Vector:
"""simple docstring"""
...
def __mul__( self : str , a : float | Vector ) -> Vector | Matrix:
"""simple docstring"""
if isinstance(a , a ): # matrix-vector
if len(a ) == self.__width:
SCREAMING_SNAKE_CASE : Union[str, Any] = zero_vector(self.__height )
for i in range(self.__height ):
SCREAMING_SNAKE_CASE : Optional[int] = [
self.__matrix[i][j] * other.component(a )
for j in range(self.__width )
]
ans.change_component(a , sum(a ) )
return ans
else:
raise Exception(
"vector must have the same size as the "
"number of columns of the matrix!" )
elif isinstance(a , (int, float) ): # matrix-scalar
SCREAMING_SNAKE_CASE : str = [
[self.__matrix[i][j] * other for j in range(self.__width )]
for i in range(self.__height )
]
return Matrix(a , self.__width , self.__height )
return None
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
return self.__height
def __UpperCamelCase ( self : Tuple ) -> int:
"""simple docstring"""
return self.__width
def __UpperCamelCase ( self : Any , a : int , a : int ) -> float:
"""simple docstring"""
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception("change_component: indices out of bounds" )
def __UpperCamelCase ( self : Tuple , a : int , a : int , a : float ) -> None:
"""simple docstring"""
if 0 <= x < self.__height and 0 <= y < self.__width:
SCREAMING_SNAKE_CASE : str = value
else:
raise Exception("change_component: indices out of bounds" )
def __UpperCamelCase ( self : int , a : int , a : int ) -> float:
"""simple docstring"""
if self.__height != self.__width:
raise Exception("Matrix is not square" )
SCREAMING_SNAKE_CASE : List[str] = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(a ) ):
SCREAMING_SNAKE_CASE : Optional[int] = minor[i][:y] + minor[i][y + 1 :]
return Matrix(a , self.__width - 1 , self.__height - 1 ).determinant()
def __UpperCamelCase ( self : int , a : int , a : int ) -> float:
"""simple docstring"""
if self.__height != self.__width:
raise Exception("Matrix is not square" )
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(a , a )
else:
raise Exception("Indices out of bounds" )
def __UpperCamelCase ( self : Dict ) -> float:
"""simple docstring"""
if self.__height != self.__width:
raise Exception("Matrix is not square" )
if self.__height < 1:
raise Exception("Matrix has no element" )
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
SCREAMING_SNAKE_CASE : List[Any] = [
self.__matrix[0][y] * self.cofactor(0 , a ) for y in range(self.__width )
]
return sum(a )
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : list[list[float]] = [[0] * n for _ in range(_a)]
return Matrix(_a , _a , _a)
def lowerCamelCase__ ( _a , _a , _a , _a):
random.seed(_a)
SCREAMING_SNAKE_CASE : list[list[float]] = [
[random.randint(_a , _a) for _ in range(_a)] for _ in range(_a)
]
return Matrix(_a , _a , _a) | 25 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 1 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='align_text_model'
def __init__( self : Tuple , a : int=3_0522 , a : int=768 , a : Optional[int]=12 , a : Union[str, Any]=12 , a : List[str]=3072 , a : str="gelu" , a : int=0.1 , a : Tuple=0.1 , a : str=512 , a : List[str]=2 , a : Dict=0.02 , a : Optional[int]=1e-12 , a : List[Any]=0 , a : str="absolute" , a : Optional[int]=True , **a : Optional[int] , ) -> Any:
"""simple docstring"""
super().__init__(**a )
SCREAMING_SNAKE_CASE : Union[str, Any] = vocab_size
SCREAMING_SNAKE_CASE : str = hidden_size
SCREAMING_SNAKE_CASE : Tuple = num_hidden_layers
SCREAMING_SNAKE_CASE : List[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : str = hidden_act
SCREAMING_SNAKE_CASE : Dict = intermediate_size
SCREAMING_SNAKE_CASE : str = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE : Tuple = type_vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = initializer_range
SCREAMING_SNAKE_CASE : str = layer_norm_eps
SCREAMING_SNAKE_CASE : Optional[int] = position_embedding_type
SCREAMING_SNAKE_CASE : Optional[int] = use_cache
SCREAMING_SNAKE_CASE : Any = pad_token_id
@classmethod
def __UpperCamelCase ( cls : int , a : Union[str, os.PathLike] , **a : List[str] ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(a )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : int = cls.get_config_dict(a , **a )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE : Dict = 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(a , **a )
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='align_vision_model'
def __init__( self : int , a : int = 3 , a : int = 600 , a : float = 2.0 , a : float = 3.1 , a : int = 8 , a : List[int] = [3, 3, 5, 3, 5, 5, 3] , a : List[int] = [32, 16, 24, 40, 80, 112, 192] , a : List[int] = [16, 24, 40, 80, 112, 192, 320] , a : List[int] = [] , a : List[int] = [1, 2, 2, 2, 1, 2, 1] , a : List[int] = [1, 2, 2, 3, 3, 4, 1] , a : List[int] = [1, 6, 6, 6, 6, 6, 6] , a : float = 0.25 , a : str = "swish" , a : int = 2560 , a : str = "mean" , a : float = 0.02 , a : float = 0.001 , a : float = 0.99 , a : float = 0.2 , **a : List[Any] , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**a )
SCREAMING_SNAKE_CASE : Dict = num_channels
SCREAMING_SNAKE_CASE : List[str] = image_size
SCREAMING_SNAKE_CASE : Dict = width_coefficient
SCREAMING_SNAKE_CASE : List[Any] = depth_coefficient
SCREAMING_SNAKE_CASE : Union[str, Any] = depth_divisor
SCREAMING_SNAKE_CASE : Tuple = kernel_sizes
SCREAMING_SNAKE_CASE : List[str] = in_channels
SCREAMING_SNAKE_CASE : Any = out_channels
SCREAMING_SNAKE_CASE : str = depthwise_padding
SCREAMING_SNAKE_CASE : Optional[int] = strides
SCREAMING_SNAKE_CASE : str = num_block_repeats
SCREAMING_SNAKE_CASE : int = expand_ratios
SCREAMING_SNAKE_CASE : Optional[int] = squeeze_expansion_ratio
SCREAMING_SNAKE_CASE : int = hidden_act
SCREAMING_SNAKE_CASE : Tuple = hidden_dim
SCREAMING_SNAKE_CASE : Union[str, Any] = pooling_type
SCREAMING_SNAKE_CASE : Dict = initializer_range
SCREAMING_SNAKE_CASE : List[str] = batch_norm_eps
SCREAMING_SNAKE_CASE : Tuple = batch_norm_momentum
SCREAMING_SNAKE_CASE : Tuple = drop_connect_rate
SCREAMING_SNAKE_CASE : List[Any] = sum(a ) * 4
@classmethod
def __UpperCamelCase ( cls : int , a : Union[str, os.PathLike] , **a : Dict ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(a )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str = cls.get_config_dict(a , **a )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE : List[Any] = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
F"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(a , **a )
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='align'
lowerCamelCase__ =True
def __init__( self : Dict , a : Optional[Any]=None , a : List[str]=None , a : List[str]=640 , a : Optional[Any]=1.0 , a : int=0.02 , **a : Optional[Any] , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**a )
if text_config is None:
SCREAMING_SNAKE_CASE : int = {}
logger.info("text_config is None. Initializing the AlignTextConfig with default values." )
if vision_config is None:
SCREAMING_SNAKE_CASE : List[str] = {}
logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )
SCREAMING_SNAKE_CASE : List[Any] = AlignTextConfig(**a )
SCREAMING_SNAKE_CASE : Optional[int] = AlignVisionConfig(**a )
SCREAMING_SNAKE_CASE : int = projection_dim
SCREAMING_SNAKE_CASE : Dict = temperature_init_value
SCREAMING_SNAKE_CASE : str = initializer_range
@classmethod
def __UpperCamelCase ( cls : Union[str, Any] , a : AlignTextConfig , a : AlignVisionConfig , **a : Tuple ) -> Any:
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **a )
def __UpperCamelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE : Any = self.text_config.to_dict()
SCREAMING_SNAKE_CASE : Optional[Any] = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE : str = self.__class__.model_type
return output | 25 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_a)
if n > 1:
factors.append(_a)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 1 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ = {
'facebook/mask2former-swin-small-coco-instance': (
'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
a_ = logging.get_logger(__name__)
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='mask2former'
lowerCamelCase__ =['swin']
lowerCamelCase__ ={'hidden_size': 'hidden_dim'}
def __init__( self : Dict , a : Optional[Dict] = None , a : int = 256 , a : int = 256 , a : int = 256 , a : int = 1024 , a : str = "relu" , a : int = 6 , a : int = 10 , a : int = 8 , a : float = 0.0 , a : int = 2048 , a : bool = False , a : bool = False , a : int = 4 , a : int = 255 , a : int = 100 , a : float = 0.1 , a : float = 2.0 , a : float = 5.0 , a : float = 5.0 , a : int = 1_2544 , a : float = 3.0 , a : float = 0.75 , a : float = 0.02 , a : float = 1.0 , a : bool = True , a : List[int] = [4, 8, 16, 32] , a : bool = None , **a : Optional[int] , ) -> List[Any]:
"""simple docstring"""
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone." )
SCREAMING_SNAKE_CASE : Tuple = CONFIG_MAPPING["swin"](
image_size=224 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=a , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(a , a ):
SCREAMING_SNAKE_CASE : int = backbone_config.pop("model_type" )
SCREAMING_SNAKE_CASE : List[Any] = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE : Tuple = 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 Mask2Former. "
F"Supported model types: {','.join(self.backbones_supported )}" )
SCREAMING_SNAKE_CASE : Optional[Any] = backbone_config
SCREAMING_SNAKE_CASE : int = feature_size
SCREAMING_SNAKE_CASE : Dict = mask_feature_size
SCREAMING_SNAKE_CASE : Any = hidden_dim
SCREAMING_SNAKE_CASE : Any = encoder_feedforward_dim
SCREAMING_SNAKE_CASE : Tuple = activation_function
SCREAMING_SNAKE_CASE : Tuple = encoder_layers
SCREAMING_SNAKE_CASE : Dict = decoder_layers
SCREAMING_SNAKE_CASE : List[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : List[Any] = dropout
SCREAMING_SNAKE_CASE : int = dim_feedforward
SCREAMING_SNAKE_CASE : int = pre_norm
SCREAMING_SNAKE_CASE : Union[str, Any] = enforce_input_projection
SCREAMING_SNAKE_CASE : int = common_stride
SCREAMING_SNAKE_CASE : Optional[Any] = ignore_value
SCREAMING_SNAKE_CASE : Tuple = num_queries
SCREAMING_SNAKE_CASE : List[str] = no_object_weight
SCREAMING_SNAKE_CASE : Optional[int] = class_weight
SCREAMING_SNAKE_CASE : int = mask_weight
SCREAMING_SNAKE_CASE : Tuple = dice_weight
SCREAMING_SNAKE_CASE : Union[str, Any] = train_num_points
SCREAMING_SNAKE_CASE : Tuple = oversample_ratio
SCREAMING_SNAKE_CASE : int = importance_sample_ratio
SCREAMING_SNAKE_CASE : Any = init_std
SCREAMING_SNAKE_CASE : Union[str, Any] = init_xavier_std
SCREAMING_SNAKE_CASE : List[Any] = use_auxiliary_loss
SCREAMING_SNAKE_CASE : Optional[int] = feature_strides
SCREAMING_SNAKE_CASE : Tuple = output_auxiliary_logits
SCREAMING_SNAKE_CASE : str = decoder_layers
super().__init__(**a )
@classmethod
def __UpperCamelCase ( cls : Any , a : PretrainedConfig , **a : str ) -> Optional[int]:
"""simple docstring"""
return cls(
backbone_config=a , **a , )
def __UpperCamelCase ( self : Optional[Any] ) -> Dict[str, any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE : int = self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE : str = self.__class__.model_type
return output | 25 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : int = float(_a)
SCREAMING_SNAKE_CASE : Dict = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_cos() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_cos() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : str = float(_a)
SCREAMING_SNAKE_CASE : Any = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15)) | 25 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='big_bird'
def __init__( self : Optional[Any] , a : List[Any]=5_0358 , a : Tuple=768 , a : List[str]=12 , a : Any=12 , a : Dict=3072 , a : int="gelu_new" , a : List[Any]=0.1 , a : int=0.1 , a : Union[str, Any]=4096 , a : Tuple=2 , a : List[Any]=0.02 , a : Any=1e-12 , a : Tuple=True , a : Tuple=0 , a : Optional[int]=1 , a : int=2 , a : Dict=66 , a : str="block_sparse" , a : List[str]=True , a : Dict=False , a : List[str]=64 , a : int=3 , a : List[Any]=None , **a : int , ) -> Tuple:
"""simple docstring"""
super().__init__(
pad_token_id=a , bos_token_id=a , eos_token_id=a , sep_token_id=a , **a , )
SCREAMING_SNAKE_CASE : List[str] = vocab_size
SCREAMING_SNAKE_CASE : int = max_position_embeddings
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE : List[str] = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Dict = intermediate_size
SCREAMING_SNAKE_CASE : Any = hidden_act
SCREAMING_SNAKE_CASE : Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
SCREAMING_SNAKE_CASE : Dict = layer_norm_eps
SCREAMING_SNAKE_CASE : Optional[int] = use_cache
SCREAMING_SNAKE_CASE : Tuple = rescale_embeddings
SCREAMING_SNAKE_CASE : Optional[int] = attention_type
SCREAMING_SNAKE_CASE : Optional[Any] = use_bias
SCREAMING_SNAKE_CASE : Any = block_size
SCREAMING_SNAKE_CASE : Optional[Any] = num_random_blocks
SCREAMING_SNAKE_CASE : str = classifier_dropout
class _UpperCamelCase ( __A ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[Any] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : str = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE : Optional[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] ) | 25 |
from __future__ import annotations
import math
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = size
# approximate the overall size of segment tree with given value
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
SCREAMING_SNAKE_CASE : Union[str, Any] = [0 for i in range(0 , 4 * size )]
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )] # flag for lazy update
def __UpperCamelCase ( self : Tuple , a : int ) -> int:
"""simple docstring"""
return idx * 2
def __UpperCamelCase ( self : str , a : int ) -> int:
"""simple docstring"""
return idx * 2 + 1
def __UpperCamelCase ( self : int , a : int , a : int , a : int , a : list[int] ) -> None:
"""simple docstring"""
if left_element == right_element:
SCREAMING_SNAKE_CASE : int = a[left_element - 1]
else:
SCREAMING_SNAKE_CASE : Optional[int] = (left_element + right_element) // 2
self.build(self.left(a ) , a , a , a )
self.build(self.right(a ) , mid + 1 , a , a )
SCREAMING_SNAKE_CASE : List[Any] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : int , a : int , a : int , a : int , a : int ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : Any = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[str] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : List[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
SCREAMING_SNAKE_CASE : Optional[Any] = val
if left_element != right_element:
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Optional[Any] = True
return True
SCREAMING_SNAKE_CASE : int = (left_element + right_element) // 2
self.update(self.left(a ) , a , a , a , a , a )
self.update(self.right(a ) , mid + 1 , a , a , a , a )
SCREAMING_SNAKE_CASE : Optional[int] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
return True
def __UpperCamelCase ( self : Dict , a : int , a : int , a : int , a : int , a : int ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[Any] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
SCREAMING_SNAKE_CASE : Dict = (left_element + right_element) // 2
SCREAMING_SNAKE_CASE : Tuple = self.query(self.left(a ) , a , a , a , a )
SCREAMING_SNAKE_CASE : Tuple = self.query(self.right(a ) , mid + 1 , a , a , a )
return max(a , a )
def __str__( self : str ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , a , a ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
a_ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
a_ = 15
a_ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 25 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =DDIMPipeline
lowerCamelCase__ =UNCONDITIONAL_IMAGE_GENERATION_PARAMS
lowerCamelCase__ =PipelineTesterMixin.required_optional_params - {
'num_images_per_prompt',
'latents',
'callback',
'callback_steps',
}
lowerCamelCase__ =UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
lowerCamelCase__ =False
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
SCREAMING_SNAKE_CASE : Optional[int] = DDIMScheduler()
SCREAMING_SNAKE_CASE : Union[str, Any] = {"unet": unet, "scheduler": scheduler}
return components
def __UpperCamelCase ( self : Tuple , a : Tuple , a : List[Any]=0 ) -> Union[str, Any]:
"""simple docstring"""
if str(a ).startswith("mps" ):
SCREAMING_SNAKE_CASE : Optional[int] = torch.manual_seed(a )
else:
SCREAMING_SNAKE_CASE : Optional[int] = torch.Generator(device=a ).manual_seed(a )
SCREAMING_SNAKE_CASE : List[str] = {
"batch_size": 1,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def __UpperCamelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = "cpu"
SCREAMING_SNAKE_CASE : Any = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Dict = self.pipeline_class(**a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_inputs(a )
SCREAMING_SNAKE_CASE : Any = pipe(**a ).images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
SCREAMING_SNAKE_CASE : Tuple = np.array(
[1.0_00e00, 5.7_17e-01, 4.7_17e-01, 1.0_00e00, 0.0_00e00, 1.0_00e00, 3.0_00e-04, 0.0_00e00, 9.0_00e-04] )
SCREAMING_SNAKE_CASE : List[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(a , 1e-3 )
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
super().test_save_load_local(expected_max_difference=3e-3 )
def __UpperCamelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = "google/ddpm-cifar10-32"
SCREAMING_SNAKE_CASE : Union[str, Any] = UNetaDModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : Optional[int] = DDIMScheduler()
SCREAMING_SNAKE_CASE : Dict = DDIMPipeline(unet=a , scheduler=a )
ddim.to(a )
ddim.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[int] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = ddim(generator=a , eta=0.0 , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : Dict ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = "google/ddpm-ema-bedroom-256"
SCREAMING_SNAKE_CASE : Any = UNetaDModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : List[Any] = DDIMScheduler.from_pretrained(a )
SCREAMING_SNAKE_CASE : int = DDIMPipeline(unet=a , scheduler=a )
ddpm.to(a )
ddpm.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = ddpm(generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE : Any = np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
SCREAMING_SNAKE_CASE : str = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : Optional[int] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : Tuple = output.images
SCREAMING_SNAKE_CASE : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Tuple = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : List[str] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_dpmpp_2m" )
SCREAMING_SNAKE_CASE : str = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = sd_pipe(
[prompt] , generator=a , guidance_scale=7.5 , num_inference_steps=15 , output_type="np" , use_karras_sigmas=a , )
SCREAMING_SNAKE_CASE : str = output.images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 1 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : List[str] = np.inf
def set_batch_size(_a) -> None:
nonlocal batch_size
if isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Union[str, Any] = min(_a , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS)
elif isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Dict = min(_a , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS)
elif isinstance(_a , _a) and feature.dtype == "binary":
SCREAMING_SNAKE_CASE : Optional[int] = min(_a , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS)
_visit(_a , _a)
return None if batch_size is np.inf else batch_size
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Union[str, Any] , a : NestedDataStructureLike[PathLike] , a : Optional[NamedSplit] = None , a : Optional[Features] = None , a : str = None , a : bool = False , a : bool = False , a : Optional[int] = None , **a : str , ) -> int:
"""simple docstring"""
super().__init__(
a , split=a , features=a , cache_dir=a , keep_in_memory=a , streaming=a , num_proc=a , **a , )
SCREAMING_SNAKE_CASE : Optional[int] = path_or_paths if isinstance(a , a ) else {self.split: path_or_paths}
SCREAMING_SNAKE_CASE : Any = _PACKAGED_DATASETS_MODULES["parquet"][1]
SCREAMING_SNAKE_CASE : Optional[Any] = Parquet(
cache_dir=a , data_files=a , features=a , hash=a , **a , )
def __UpperCamelCase ( self : int ) -> int:
"""simple docstring"""
if self.streaming:
SCREAMING_SNAKE_CASE : List[str] = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : Any = None
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Optional[int] = None
self.builder.download_and_prepare(
download_config=a , download_mode=a , verification_mode=a , base_path=a , num_proc=self.num_proc , )
SCREAMING_SNAKE_CASE : Optional[int] = self.builder.as_dataset(
split=self.split , verification_mode=a , in_memory=self.keep_in_memory )
return dataset
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Union[str, Any] , a : Dataset , a : Union[PathLike, BinaryIO] , a : Optional[int] = None , **a : Optional[Any] , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = dataset
SCREAMING_SNAKE_CASE : Union[str, Any] = path_or_buf
SCREAMING_SNAKE_CASE : str = batch_size or get_writer_batch_size(dataset.features )
SCREAMING_SNAKE_CASE : List[Any] = parquet_writer_kwargs
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , "wb+" ) as buffer:
SCREAMING_SNAKE_CASE : Optional[int] = self._write(file_obj=a , batch_size=a , **self.parquet_writer_kwargs )
else:
SCREAMING_SNAKE_CASE : Dict = self._write(file_obj=self.path_or_buf , batch_size=a , **self.parquet_writer_kwargs )
return written
def __UpperCamelCase ( self : Optional[Any] , a : BinaryIO , a : int , **a : str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = 0
SCREAMING_SNAKE_CASE : Tuple = parquet_writer_kwargs.pop("path_or_buf" , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.dataset.features.arrow_schema
SCREAMING_SNAKE_CASE : Optional[Any] = pq.ParquetWriter(a , schema=a , **a )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , a ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating parquet from Arrow format" , ):
SCREAMING_SNAKE_CASE : Dict = query_table(
table=self.dataset._data , key=slice(a , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(a )
written += batch.nbytes
writer.close()
return written | 25 |
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 _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Union[str, Any] = False
return options
def __UpperCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=10 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = 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 __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Dict = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=20 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[str] = output.images
SCREAMING_SNAKE_CASE : Optional[int] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Any = 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 | 25 | 1 |
from ...processing_utils import ProcessorMixin
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='SpeechT5FeatureExtractor'
lowerCamelCase__ ='SpeechT5Tokenizer'
def __init__( self : Optional[Any] , a : str , a : List[str] ) -> Optional[Any]:
"""simple docstring"""
super().__init__(a , a )
def __call__( self : Optional[int] , *a : Optional[Any] , **a : List[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = kwargs.pop("audio" , a )
SCREAMING_SNAKE_CASE : Any = kwargs.pop("text" , a )
SCREAMING_SNAKE_CASE : List[Any] = kwargs.pop("text_target" , a )
SCREAMING_SNAKE_CASE : str = kwargs.pop("audio_target" , a )
SCREAMING_SNAKE_CASE : List[str] = kwargs.pop("sampling_rate" , a )
if audio is not None and text is not None:
raise ValueError(
"Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?" )
if audio_target is not None and text_target is not None:
raise ValueError(
"Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?" )
if audio is None and audio_target is None and text is None and text_target is None:
raise ValueError(
"You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process." )
if audio is not None:
SCREAMING_SNAKE_CASE : Tuple = self.feature_extractor(a , *a , sampling_rate=a , **a )
elif text is not None:
SCREAMING_SNAKE_CASE : Optional[int] = self.tokenizer(a , **a )
else:
SCREAMING_SNAKE_CASE : Any = None
if audio_target is not None:
SCREAMING_SNAKE_CASE : Dict = self.feature_extractor(audio_target=a , *a , sampling_rate=a , **a )
SCREAMING_SNAKE_CASE : str = targets["input_values"]
elif text_target is not None:
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer(a , **a )
SCREAMING_SNAKE_CASE : Optional[Any] = targets["input_ids"]
else:
SCREAMING_SNAKE_CASE : List[str] = None
if inputs is None:
return targets
if targets is not None:
SCREAMING_SNAKE_CASE : str = labels
SCREAMING_SNAKE_CASE : List[Any] = targets.get("attention_mask" )
if decoder_attention_mask is not None:
SCREAMING_SNAKE_CASE : Optional[int] = decoder_attention_mask
return inputs
def __UpperCamelCase ( self : Tuple , *a : int , **a : int ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = kwargs.pop("input_values" , a )
SCREAMING_SNAKE_CASE : Tuple = kwargs.pop("input_ids" , a )
SCREAMING_SNAKE_CASE : List[str] = kwargs.pop("labels" , a )
if input_values is not None and input_ids is not None:
raise ValueError("Cannot process both `input_values` and `input_ids` inputs." )
if input_values is None and input_ids is None and labels is None:
raise ValueError(
"You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded." )
if input_values is not None:
SCREAMING_SNAKE_CASE : Optional[int] = self.feature_extractor.pad(a , *a , **a )
elif input_ids is not None:
SCREAMING_SNAKE_CASE : str = self.tokenizer.pad(a , **a )
else:
SCREAMING_SNAKE_CASE : Tuple = None
if labels is not None:
if "input_ids" in labels or (isinstance(a , a ) and "input_ids" in labels[0]):
SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer.pad(a , **a )
SCREAMING_SNAKE_CASE : List[str] = targets["input_ids"]
else:
SCREAMING_SNAKE_CASE : Dict = self.feature_extractor.feature_size
SCREAMING_SNAKE_CASE : List[Any] = self.feature_extractor.num_mel_bins
SCREAMING_SNAKE_CASE : str = self.feature_extractor.pad(a , *a , **a )
SCREAMING_SNAKE_CASE : Dict = feature_size_hack
SCREAMING_SNAKE_CASE : Tuple = targets["input_values"]
else:
SCREAMING_SNAKE_CASE : List[str] = None
if inputs is None:
return targets
if targets is not None:
SCREAMING_SNAKE_CASE : int = labels
SCREAMING_SNAKE_CASE : Dict = targets.get("attention_mask" )
if decoder_attention_mask is not None:
SCREAMING_SNAKE_CASE : Dict = decoder_attention_mask
return inputs
def __UpperCamelCase ( self : List[Any] , *a : int , **a : List[Any] ) -> Optional[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*a , **a )
def __UpperCamelCase ( self : Optional[int] , *a : Any , **a : Dict ) -> List[str]:
"""simple docstring"""
return self.tokenizer.decode(*a , **a ) | 25 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase__ ( _a):
return getitem, k
def lowerCamelCase__ ( _a , _a):
return setitem, k, v
def lowerCamelCase__ ( _a):
return delitem, k
def lowerCamelCase__ ( _a , _a , *_a):
try:
return fun(_a , *_a), None
except Exception as e:
return None, e
a_ = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
a_ = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
a_ = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
a_ = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items"),
pytest.param(_overwrite_items , id="overwrite items"),
pytest.param(_delete_items , id="delete items"),
pytest.param(_access_absent_items , id="access absent items"),
pytest.param(_add_with_resize_up , id="add with resize up"),
pytest.param(_add_with_resize_down , id="add with resize down"),
) , )
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Dict = HashMap(initial_block_size=4)
SCREAMING_SNAKE_CASE : List[str] = {}
for _, (fun, *args) in enumerate(_a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = _run_operation(_a , _a , *_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = _run_operation(_a , _a , *_a)
assert my_res == py_res
assert str(_a) == str(_a)
assert set(_a) == set(_a)
assert len(_a) == len(_a)
assert set(my.items()) == set(py.items())
def lowerCamelCase__ ( ):
def is_public(_a) -> bool:
return not name.startswith("_")
SCREAMING_SNAKE_CASE : List[str] = {name for name in dir({}) if is_public(_a)}
SCREAMING_SNAKE_CASE : Union[str, Any] = {name for name in dir(HashMap()) if is_public(_a)}
assert dict_public_names > hash_public_names | 25 | 1 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='char'
lowerCamelCase__ ='bpe'
lowerCamelCase__ ='wp'
a_ = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =['image_processor', 'char_tokenizer']
lowerCamelCase__ ='ViTImageProcessor'
lowerCamelCase__ ='MgpstrTokenizer'
def __init__( self : Any , a : Any=None , a : str=None , **a : List[Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , a , )
SCREAMING_SNAKE_CASE : Dict = kwargs.pop("feature_extractor" )
SCREAMING_SNAKE_CASE : int = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
SCREAMING_SNAKE_CASE : str = tokenizer
SCREAMING_SNAKE_CASE : List[str] = AutoTokenizer.from_pretrained("gpt2" )
SCREAMING_SNAKE_CASE : List[Any] = AutoTokenizer.from_pretrained("bert-base-uncased" )
super().__init__(a , a )
def __call__( self : Optional[Any] , a : List[Any]=None , a : List[Any]=None , a : Union[str, Any]=None , **a : int ) -> List[Any]:
"""simple docstring"""
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process." )
if images is not None:
SCREAMING_SNAKE_CASE : Tuple = self.image_processor(a , return_tensors=a , **a )
if text is not None:
SCREAMING_SNAKE_CASE : List[str] = self.char_tokenizer(a , return_tensors=a , **a )
if text is None:
return inputs
elif images is None:
return encodings
else:
SCREAMING_SNAKE_CASE : Tuple = encodings["input_ids"]
return inputs
def __UpperCamelCase ( self : Union[str, Any] , a : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[str] = sequences
SCREAMING_SNAKE_CASE : Optional[int] = char_preds.size(0 )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = self._decode_helper(a , "char" )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[int] = self._decode_helper(a , "bpe" )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = self._decode_helper(a , "wp" )
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : List[str] = []
for i in range(a ):
SCREAMING_SNAKE_CASE : Dict = [char_scores[i], bpe_scores[i], wp_scores[i]]
SCREAMING_SNAKE_CASE : int = [char_strs[i], bpe_strs[i], wp_strs[i]]
SCREAMING_SNAKE_CASE : Optional[Any] = scores.index(max(a ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : List[str] = final_strs
SCREAMING_SNAKE_CASE : Union[str, Any] = final_scores
SCREAMING_SNAKE_CASE : Any = char_strs
SCREAMING_SNAKE_CASE : Tuple = bpe_strs
SCREAMING_SNAKE_CASE : Union[str, Any] = wp_strs
return out
def __UpperCamelCase ( self : Dict , a : str , a : Optional[Any] ) -> Dict:
"""simple docstring"""
if format == DecodeType.CHARACTER:
SCREAMING_SNAKE_CASE : Union[str, Any] = self.char_decode
SCREAMING_SNAKE_CASE : int = 1
SCREAMING_SNAKE_CASE : Tuple = "[s]"
elif format == DecodeType.BPE:
SCREAMING_SNAKE_CASE : Any = self.bpe_decode
SCREAMING_SNAKE_CASE : Union[str, Any] = 2
SCREAMING_SNAKE_CASE : Optional[Any] = "#"
elif format == DecodeType.WORDPIECE:
SCREAMING_SNAKE_CASE : Any = self.wp_decode
SCREAMING_SNAKE_CASE : Tuple = 102
SCREAMING_SNAKE_CASE : List[str] = "[SEP]"
else:
raise ValueError(F"Format {format} is not supported." )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = [], []
SCREAMING_SNAKE_CASE : Union[str, Any] = pred_logits.size(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = pred_logits.size(1 )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = pred_logits.topk(1 , dim=-1 , largest=a , sorted=a )
SCREAMING_SNAKE_CASE : Dict = preds_index.view(-1 , a )[:, 1:]
SCREAMING_SNAKE_CASE : Any = decoder(a )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Any = torch.nn.functional.softmax(a , dim=2 ).max(dim=2 )
SCREAMING_SNAKE_CASE : Tuple = preds_max_prob[:, 1:]
for index in range(a ):
SCREAMING_SNAKE_CASE : List[Any] = preds_str[index].find(a )
SCREAMING_SNAKE_CASE : Tuple = preds_str[index][:pred_eos]
SCREAMING_SNAKE_CASE : int = preds_index[index].cpu().tolist()
SCREAMING_SNAKE_CASE : Union[str, Any] = pred_index.index(a ) if eos_token in pred_index else -1
SCREAMING_SNAKE_CASE : int = preds_max_prob[index][: pred_eos_index + 1]
SCREAMING_SNAKE_CASE : List[Any] = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(a )
conf_scores.append(a )
return dec_strs, conf_scores
def __UpperCamelCase ( self : int , a : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = [seq.replace(" " , "" ) for seq in self.char_tokenizer.batch_decode(a )]
return decode_strs
def __UpperCamelCase ( self : Dict , a : List[str] ) -> Union[str, Any]:
"""simple docstring"""
return self.bpe_tokenizer.batch_decode(a )
def __UpperCamelCase ( self : Union[str, Any] , a : str ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = [seq.replace(" " , "" ) for seq in self.wp_tokenizer.batch_decode(a )]
return decode_strs | 25 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
'VanForImageClassification',
'VanModel',
'VanPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 1 |
from __future__ import annotations
from statistics import mean
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = [0] * no_of_processes
SCREAMING_SNAKE_CASE : Tuple = [0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(_a):
SCREAMING_SNAKE_CASE : Tuple = burst_time[i]
SCREAMING_SNAKE_CASE : list[int] = []
SCREAMING_SNAKE_CASE : List[str] = 0
SCREAMING_SNAKE_CASE : Dict = 0
# When processes are not completed,
# A process whose arrival time has passed \
# and has remaining execution time is put into the ready_process.
# The shortest process in the ready_process, target_process is executed.
while completed != no_of_processes:
SCREAMING_SNAKE_CASE : Optional[Any] = []
SCREAMING_SNAKE_CASE : List[str] = -1
for i in range(_a):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(_a)
if len(_a) > 0:
SCREAMING_SNAKE_CASE : Optional[int] = ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
SCREAMING_SNAKE_CASE : List[str] = i
total_time += burst_time[target_process]
completed += 1
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = (
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Any = [0] * no_of_processes
for i in range(_a):
SCREAMING_SNAKE_CASE : Tuple = burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print('[TEST CASE 01]')
a_ = 4
a_ = [2, 5, 3, 7]
a_ = [0, 0, 0, 0]
a_ = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
a_ = calculate_turnaroundtime(
burst_time, no_of_processes, waiting_time
)
# Printing the Result
print('PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time')
for i, process_id in enumerate(list(range(1, 5))):
print(
F'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
F'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
)
print(F'''\nAverage waiting time = {mean(waiting_time):.5f}''')
print(F'''Average turnaround time = {mean(turn_around_time):.5f}''') | 25 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
if len(_a) == 0:
return []
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = min(_a), max(_a)
SCREAMING_SNAKE_CASE : Dict = int(max_value - min_value) + 1
SCREAMING_SNAKE_CASE : list[list] = [[] for _ in range(_a)]
for i in my_list:
buckets[int(i - min_value)].append(_a)
return [v for bucket in buckets for v in sorted(_a)]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15] | 25 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a_ = logging.get_logger(__name__)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : List[str] , *a : Optional[int] , **a : Dict ) -> None:
"""simple docstring"""
warnings.warn(
"The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use LayoutLMv2ImageProcessor instead." , a , )
super().__init__(*a , **a ) | 25 |
a_ = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset([])
a_ = frozenset(['image'])
a_ = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image'])
a_ = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'negative_prompt'])
a_ = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
a_ = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image', 'mask_image'])
a_ = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['example_image', 'image', 'mask_image'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset(['input_tokens'])
a_ = frozenset(['input_tokens']) | 25 | 1 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : int , a : Callable , a : Optional[Features] = None , a : str = None , a : bool = False , a : bool = False , a : Optional[dict] = None , a : Optional[int] = None , **a : Union[str, Any] , ) -> int:
"""simple docstring"""
super().__init__(
features=a , cache_dir=a , keep_in_memory=a , streaming=a , num_proc=a , **a , )
SCREAMING_SNAKE_CASE : Tuple = Generator(
cache_dir=a , features=a , generator=a , gen_kwargs=a , **a , )
def __UpperCamelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
if self.streaming:
SCREAMING_SNAKE_CASE : Dict = self.builder.as_streaming_dataset(split="train" )
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : Any = None
self.builder.download_and_prepare(
download_config=a , download_mode=a , verification_mode=a , base_path=a , num_proc=self.num_proc , )
SCREAMING_SNAKE_CASE : int = self.builder.as_dataset(
split="train" , verification_mode=a , in_memory=self.keep_in_memory )
return dataset | 25 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
a_ = get_logger()
a_ = None
class _UpperCamelCase ( TensorFormatter[Mapping, 'jax.Array', Mapping] ):
'''simple docstring'''
def __init__( self : Optional[Any] , a : str=None , a : List[Any]=None , **a : Any ) -> Optional[Any]:
"""simple docstring"""
super().__init__(features=a )
import jax
from jaxlib.xla_client import Device
if isinstance(a , a ):
raise ValueError(
F"Expected {device} to be a `str` not {type(a )}, as `jaxlib.xla_extension.Device` "
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
SCREAMING_SNAKE_CASE : List[str] = device if isinstance(a , a ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : str = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
F"Device with string identifier {self.device} not listed among the available "
F"devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default "
F"device: {str(jax.devices()[0] )}." )
SCREAMING_SNAKE_CASE : Any = str(jax.devices()[0] )
SCREAMING_SNAKE_CASE : Any = jnp_array_kwargs
@staticmethod
def __UpperCamelCase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
"""simple docstring"""
import jax
return {str(a ): device for device in jax.devices()}
def __UpperCamelCase ( self : Dict , a : Tuple ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(a , a ) and column:
if all(
isinstance(a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(a , axis=0 )
return column
def __UpperCamelCase ( self : Dict , a : str ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
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()
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
if isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
SCREAMING_SNAKE_CASE : Dict = {"dtype": jnp.intaa}
else:
SCREAMING_SNAKE_CASE : str = {"dtype": jnp.intaa}
elif isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
SCREAMING_SNAKE_CASE : int = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(a , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : Dict = np.asarray(a )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : Optional[Any] = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(a , **{**default_dtype, **self.jnp_array_kwargs} )
def __UpperCamelCase ( self : Any , a : List[str] ) -> Dict:
"""simple docstring"""
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(a , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(a , "__array__" ) and not isinstance(a , jax.Array ):
SCREAMING_SNAKE_CASE : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(a , np.ndarray ):
if data_struct.dtype == object: # jax arrays 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 __UpperCamelCase ( self : Optional[Any] , a : dict ) -> Dict:
"""simple docstring"""
return map_nested(self._recursive_tensorize , a , map_list=a )
def __UpperCamelCase ( self : Dict , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.numpy_arrow_extractor().extract_row(a )
SCREAMING_SNAKE_CASE : List[Any] = self.python_features_decoder.decode_row(a )
return self.recursive_tensorize(a )
def __UpperCamelCase ( self : Optional[int] , a : pa.Table ) -> "jax.Array":
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.numpy_arrow_extractor().extract_column(a )
SCREAMING_SNAKE_CASE : Optional[Any] = self.python_features_decoder.decode_column(a , pa_table.column_names[0] )
SCREAMING_SNAKE_CASE : Tuple = self.recursive_tensorize(a )
SCREAMING_SNAKE_CASE : Optional[int] = self._consolidate(a )
return column
def __UpperCamelCase ( self : List[Any] , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.numpy_arrow_extractor().extract_batch(a )
SCREAMING_SNAKE_CASE : str = self.python_features_decoder.decode_batch(a )
SCREAMING_SNAKE_CASE : List[Any] = self.recursive_tensorize(a )
for column_name in batch:
SCREAMING_SNAKE_CASE : List[Any] = self._consolidate(batch[column_name] )
return batch | 25 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
a_ = random.Random()
def lowerCamelCase__ ( _a , _a=1.0 , _a=None , _a=None):
if rng is None:
SCREAMING_SNAKE_CASE : List[str] = global_rng
SCREAMING_SNAKE_CASE : Optional[int] = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : str , a : Any , a : Union[str, Any]=7 , a : List[Any]=400 , a : str=2000 , a : Dict=2048 , a : List[Any]=128 , a : Tuple=1 , a : Union[str, Any]=512 , a : List[str]=30 , a : Tuple=4_4100 , ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = parent
SCREAMING_SNAKE_CASE : List[str] = batch_size
SCREAMING_SNAKE_CASE : List[str] = min_seq_length
SCREAMING_SNAKE_CASE : List[str] = max_seq_length
SCREAMING_SNAKE_CASE : Optional[Any] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE : Dict = spectrogram_length
SCREAMING_SNAKE_CASE : Optional[int] = feature_size
SCREAMING_SNAKE_CASE : List[Any] = num_audio_channels
SCREAMING_SNAKE_CASE : Optional[Any] = hop_length
SCREAMING_SNAKE_CASE : List[Any] = chunk_length
SCREAMING_SNAKE_CASE : List[str] = sampling_rate
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def __UpperCamelCase ( self : Optional[int] , a : int=False , a : Tuple=False ) -> Union[str, Any]:
"""simple docstring"""
def _flatten(a : Any ):
return list(itertools.chain(*a ) )
if equal_length:
SCREAMING_SNAKE_CASE : Optional[int] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE : Optional[int] = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE : Any = [np.asarray(a ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =TvltFeatureExtractor
def __UpperCamelCase ( self : int ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = TvltFeatureExtractionTester(self )
def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(a , "spectrogram_length" ) )
self.assertTrue(hasattr(a , "feature_size" ) )
self.assertTrue(hasattr(a , "num_audio_channels" ) )
self.assertTrue(hasattr(a , "hop_length" ) )
self.assertTrue(hasattr(a , "chunk_length" ) )
self.assertTrue(hasattr(a , "sampling_rate" ) )
def __UpperCamelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : List[Any] = feat_extract_first.save_pretrained(a )[0]
check_json_file_has_correct_format(a )
SCREAMING_SNAKE_CASE : Any = self.feature_extraction_class.from_pretrained(a )
SCREAMING_SNAKE_CASE : Optional[Any] = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : int = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : str = dict_first.pop("mel_filters" )
SCREAMING_SNAKE_CASE : str = dict_second.pop("mel_filters" )
self.assertTrue(np.allclose(a , a ) )
self.assertEqual(a , a )
def __UpperCamelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join(a , "feat_extract.json" )
feat_extract_first.to_json_file(a )
SCREAMING_SNAKE_CASE : Optional[Any] = self.feature_extraction_class.from_json_file(a )
SCREAMING_SNAKE_CASE : Optional[Any] = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : Tuple = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : str = dict_first.pop("mel_filters" )
SCREAMING_SNAKE_CASE : Optional[Any] = dict_second.pop("mel_filters" )
self.assertTrue(np.allclose(a , a ) )
self.assertEqual(a , a )
def __UpperCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE : str = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE : str = [np.asarray(a ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE : str = feature_extractor(np_speech_inputs[0] , return_tensors="np" , sampling_rate=4_4100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
SCREAMING_SNAKE_CASE : Tuple = feature_extractor(a , return_tensors="np" , sampling_rate=4_4100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
SCREAMING_SNAKE_CASE : Union[str, Any] = feature_extractor(
a , return_tensors="np" , sampling_rate=4_4100 , mask_audio=a ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE : Any = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(a )
SCREAMING_SNAKE_CASE : Optional[int] = feature_extractor(a , return_tensors="np" , sampling_rate=4_4100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def __UpperCamelCase ( self : List[Any] , a : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE : Dict = ds.sort("id" ).select(range(a ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE : Tuple = TvltFeatureExtractor()
SCREAMING_SNAKE_CASE : Any = feature_extractor(a , return_tensors="pt" ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 192, 128) )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , a , atol=1e-4 ) ) | 25 |
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 _UpperCamelCase :
'''simple docstring'''
@staticmethod
def __UpperCamelCase ( *a : str , **a : int ) -> str:
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =MODEL_FOR_OBJECT_DETECTION_MAPPING
def __UpperCamelCase ( self : Optional[Any] , a : str , a : Optional[Any] , a : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ObjectDetectionPipeline(model=a , image_processor=a )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def __UpperCamelCase ( self : List[Any] , a : Optional[int] , a : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" , threshold=0.0 )
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
import datasets
SCREAMING_SNAKE_CASE : Any = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
SCREAMING_SNAKE_CASE : Dict = [
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"],
]
SCREAMING_SNAKE_CASE : Tuple = object_detector(a , threshold=0.0 )
self.assertEqual(len(a ) , len(a ) )
for outputs in batch_outputs:
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
@require_tf
@unittest.skip("Object detection not implemented in TF" )
def __UpperCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
pass
@require_torch
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = "hf-internal-testing/tiny-detr-mobilenetsv3"
SCREAMING_SNAKE_CASE : Dict = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : Tuple = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : int = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=0.0 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
] , )
SCREAMING_SNAKE_CASE : Dict = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : List[str] = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : int = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : int = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Tuple = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : Tuple = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : str = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = 0.9985
SCREAMING_SNAKE_CASE : int = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : List[str] = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : str = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=a )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
@require_torch
@require_pytesseract
@slow
def __UpperCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = "Narsil/layoutlmv3-finetuned-funsd"
SCREAMING_SNAKE_CASE : Dict = 0.9993
SCREAMING_SNAKE_CASE : str = pipeline("object-detection" , model=a , threshold=a )
SCREAMING_SNAKE_CASE : List[Any] = object_detector(
"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
] , ) | 25 | 1 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def lowerCamelCase__ ( _a):
random.seed(_a)
np.random.seed(_a)
torch.manual_seed(_a)
torch.cuda.manual_seed_all(_a)
# ^^ safe to call this function even if cuda is not available
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , a : Iterable[torch.nn.Parameter] , a : float = 0.9999 , a : float = 0.0 , a : int = 0 , a : bool = False , a : Union[float, int] = 1.0 , a : Union[float, int] = 2 / 3 , a : Optional[Any] = None , a : Dict[str, Any] = None , **a : Dict , ) -> Tuple:
"""simple docstring"""
if isinstance(a , torch.nn.Module ):
SCREAMING_SNAKE_CASE : Optional[Any] = (
"Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
"Please pass the parameters of the module instead."
)
deprecate(
"passing a `torch.nn.Module` to `ExponentialMovingAverage`" , "1.0.0" , a , standard_warn=a , )
SCREAMING_SNAKE_CASE : Optional[Any] = parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
SCREAMING_SNAKE_CASE : Tuple = True
if kwargs.get("max_value" , a ) is not None:
SCREAMING_SNAKE_CASE : List[Any] = "The `max_value` argument is deprecated. Please use `decay` instead."
deprecate("max_value" , "1.0.0" , a , standard_warn=a )
SCREAMING_SNAKE_CASE : str = kwargs["max_value"]
if kwargs.get("min_value" , a ) is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = "The `min_value` argument is deprecated. Please use `min_decay` instead."
deprecate("min_value" , "1.0.0" , a , standard_warn=a )
SCREAMING_SNAKE_CASE : List[Any] = kwargs["min_value"]
SCREAMING_SNAKE_CASE : Optional[int] = list(a )
SCREAMING_SNAKE_CASE : str = [p.clone().detach() for p in parameters]
if kwargs.get("device" , a ) is not None:
SCREAMING_SNAKE_CASE : int = "The `device` argument is deprecated. Please use `to` instead."
deprecate("device" , "1.0.0" , a , standard_warn=a )
self.to(device=kwargs["device"] )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = decay
SCREAMING_SNAKE_CASE : Tuple = min_decay
SCREAMING_SNAKE_CASE : Tuple = update_after_step
SCREAMING_SNAKE_CASE : Tuple = use_ema_warmup
SCREAMING_SNAKE_CASE : List[Any] = inv_gamma
SCREAMING_SNAKE_CASE : int = power
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : List[Any] = None # set in `step()`
SCREAMING_SNAKE_CASE : Union[str, Any] = model_cls
SCREAMING_SNAKE_CASE : Dict = model_config
@classmethod
def __UpperCamelCase ( cls : Optional[Any] , a : Optional[int] , a : Tuple ) -> "EMAModel":
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = model_cls.load_config(a , return_unused_kwargs=a )
SCREAMING_SNAKE_CASE : Any = model_cls.from_pretrained(a )
SCREAMING_SNAKE_CASE : Tuple = cls(model.parameters() , model_cls=a , model_config=model.config )
ema_model.load_state_dict(a )
return ema_model
def __UpperCamelCase ( self : List[str] , a : Any ) -> List[Any]:
"""simple docstring"""
if self.model_cls is None:
raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__." )
if self.model_config is None:
raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__." )
SCREAMING_SNAKE_CASE : List[Any] = self.model_cls.from_config(self.model_config )
SCREAMING_SNAKE_CASE : List[str] = self.state_dict()
state_dict.pop("shadow_params" , a )
model.register_to_config(**a )
self.copy_to(model.parameters() )
model.save_pretrained(a )
def __UpperCamelCase ( self : List[str] , a : int ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = max(0 , optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
SCREAMING_SNAKE_CASE : Any = 1 - (1 + step / self.inv_gamma) ** -self.power
else:
SCREAMING_SNAKE_CASE : List[str] = (1 + step) / (10 + step)
SCREAMING_SNAKE_CASE : Optional[Any] = min(a , self.decay )
# make sure decay is not smaller than min_decay
SCREAMING_SNAKE_CASE : str = max(a , self.min_decay )
return cur_decay_value
@torch.no_grad()
def __UpperCamelCase ( self : Optional[int] , a : Iterable[torch.nn.Parameter] ) -> int:
"""simple docstring"""
if isinstance(a , torch.nn.Module ):
SCREAMING_SNAKE_CASE : Tuple = (
"Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
"Please pass the parameters of the module instead."
)
deprecate(
"passing a `torch.nn.Module` to `ExponentialMovingAverage.step`" , "1.0.0" , a , standard_warn=a , )
SCREAMING_SNAKE_CASE : List[Any] = parameters.parameters()
SCREAMING_SNAKE_CASE : List[str] = list(a )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_decay(self.optimization_step )
SCREAMING_SNAKE_CASE : Any = decay
SCREAMING_SNAKE_CASE : Union[str, Any] = 1 - decay
SCREAMING_SNAKE_CASE : Union[str, Any] = contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params , a ):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
SCREAMING_SNAKE_CASE : List[str] = deepspeed.zero.GatheredParameters(a , modifier_rank=a )
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param) )
else:
s_param.copy_(a )
def __UpperCamelCase ( self : Optional[Any] , a : Iterable[torch.nn.Parameter] ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = list(a )
for s_param, param in zip(self.shadow_params , a ):
param.data.copy_(s_param.to(param.device ).data )
def __UpperCamelCase ( self : List[str] , a : int=None , a : Union[str, Any]=None ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = [
p.to(device=a , dtype=a ) if p.is_floating_point() else p.to(device=a )
for p in self.shadow_params
]
def __UpperCamelCase ( self : Dict ) -> dict:
"""simple docstring"""
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def __UpperCamelCase ( self : Tuple , a : Iterable[torch.nn.Parameter] ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = [param.detach().cpu().clone() for param in parameters]
def __UpperCamelCase ( self : Tuple , a : Iterable[torch.nn.Parameter] ) -> None:
"""simple docstring"""
if self.temp_stored_params is None:
raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`" )
for c_param, param in zip(self.temp_stored_params , a ):
param.data.copy_(c_param.data )
# Better memory-wise.
SCREAMING_SNAKE_CASE : Tuple = None
def __UpperCamelCase ( self : Optional[int] , a : dict ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = copy.deepcopy(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = state_dict.get("decay" , self.decay )
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError("Decay must be between 0 and 1" )
SCREAMING_SNAKE_CASE : Optional[int] = state_dict.get("min_decay" , self.min_decay )
if not isinstance(self.min_decay , a ):
raise ValueError("Invalid min_decay" )
SCREAMING_SNAKE_CASE : List[str] = state_dict.get("optimization_step" , self.optimization_step )
if not isinstance(self.optimization_step , a ):
raise ValueError("Invalid optimization_step" )
SCREAMING_SNAKE_CASE : Dict = state_dict.get("update_after_step" , self.update_after_step )
if not isinstance(self.update_after_step , a ):
raise ValueError("Invalid update_after_step" )
SCREAMING_SNAKE_CASE : List[Any] = state_dict.get("use_ema_warmup" , self.use_ema_warmup )
if not isinstance(self.use_ema_warmup , a ):
raise ValueError("Invalid use_ema_warmup" )
SCREAMING_SNAKE_CASE : Union[str, Any] = state_dict.get("inv_gamma" , self.inv_gamma )
if not isinstance(self.inv_gamma , (float, int) ):
raise ValueError("Invalid inv_gamma" )
SCREAMING_SNAKE_CASE : List[Any] = state_dict.get("power" , self.power )
if not isinstance(self.power , (float, int) ):
raise ValueError("Invalid power" )
SCREAMING_SNAKE_CASE : Tuple = state_dict.get("shadow_params" , a )
if shadow_params is not None:
SCREAMING_SNAKE_CASE : List[Any] = shadow_params
if not isinstance(self.shadow_params , a ):
raise ValueError("shadow_params must be a list" )
if not all(isinstance(a , torch.Tensor ) for p in self.shadow_params ):
raise ValueError("shadow_params must all be Tensors" ) | 25 |
def lowerCamelCase__ ( _a):
if not isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Tuple = f"Input value of [number={number}] must be an integer"
raise TypeError(_a)
if number < 0:
return False
SCREAMING_SNAKE_CASE : Union[str, Any] = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='roformer'
def __init__( self : Dict , a : Any=5_0000 , a : List[Any]=None , a : str=768 , a : str=12 , a : Tuple=12 , a : Optional[Any]=3072 , a : List[str]="gelu" , a : List[Any]=0.1 , a : Union[str, Any]=0.1 , a : Tuple=1536 , a : List[str]=2 , a : Tuple=0.02 , a : Any=1e-12 , a : Optional[int]=0 , a : Union[str, Any]=False , a : int=True , **a : str , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=a , **a )
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size if embedding_size is None else embedding_size
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = type_vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE : List[str] = rotary_value
SCREAMING_SNAKE_CASE : int = use_cache
class _UpperCamelCase ( __A ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE : str = {0: "batch", 1: "sequence"}
SCREAMING_SNAKE_CASE : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] ) | 25 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Dict , a : Tuple , a : Any=13 , a : Any=7 , a : Union[str, Any]=True , a : List[Any]=True , a : List[str]=False , a : List[str]=True , a : Any=99 , a : str=32 , a : Any=5 , a : Optional[int]=4 , a : Union[str, Any]=37 , a : Dict="gelu" , a : List[Any]=0.1 , a : Optional[Any]=0.1 , a : List[str]=512 , a : Union[str, Any]=16 , a : str=2 , a : Dict=0.02 , a : Optional[int]=3 , a : Union[str, Any]=4 , a : int=None , ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = parent
SCREAMING_SNAKE_CASE : Any = batch_size
SCREAMING_SNAKE_CASE : Optional[int] = seq_length
SCREAMING_SNAKE_CASE : List[Any] = is_training
SCREAMING_SNAKE_CASE : int = use_input_mask
SCREAMING_SNAKE_CASE : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE : str = use_labels
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_size
SCREAMING_SNAKE_CASE : str = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = intermediate_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE : List[str] = type_vocab_size
SCREAMING_SNAKE_CASE : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : Tuple = num_labels
SCREAMING_SNAKE_CASE : Tuple = num_choices
SCREAMING_SNAKE_CASE : Optional[Any] = scope
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : str = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE : int = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self : Dict ) -> str:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : Optional[int] , a : Optional[int] , a : Dict , a : str , a : str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = DistilBertModel(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , a )
SCREAMING_SNAKE_CASE : Optional[Any] = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self : Tuple , a : Optional[int] , a : Dict , a : Tuple , a : int , a : int , a : Any ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = DistilBertForMaskedLM(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : str = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self : List[Any] , a : int , a : Optional[Any] , a : Optional[Any] , a : str , a : str , a : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertForQuestionAnswering(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(
a , attention_mask=a , start_positions=a , end_positions=a )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase ( self : Optional[int] , a : str , a : Any , a : int , a : Optional[Any] , a : int , a : str ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.num_labels
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertForSequenceClassification(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase ( self : Optional[Any] , a : List[Any] , a : Optional[int] , a : Union[str, Any] , a : Dict , a : Any , a : Optional[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.num_labels
SCREAMING_SNAKE_CASE : List[str] = DistilBertForTokenClassification(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase ( self : int , a : Any , a : Optional[int] , a : Union[str, Any] , a : Tuple , a : Optional[int] , a : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.num_choices
SCREAMING_SNAKE_CASE : Any = DistilBertForMultipleChoice(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Optional[Any] = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE)) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE : int = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCamelCase__ =(
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
def __UpperCamelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = DistilBertModelTester(self )
SCREAMING_SNAKE_CASE : List[str] = ConfigTester(self , config_class=a , dim=37 )
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a )
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a )
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a )
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a )
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a )
def __UpperCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a )
@slow
def __UpperCamelCase ( self : int ) -> Any:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertModel.from_pretrained(a )
self.assertIsNotNone(a )
@slow
@require_torch_gpu
def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : Any = model_class(config=a )
SCREAMING_SNAKE_CASE : List[Any] = self._prepare_for_class(a , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.jit.trace(
a , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a , os.path.join(a , "traced_model.pt" ) )
SCREAMING_SNAKE_CASE : Tuple = torch.jit.load(os.path.join(a , "traced_model.pt" ) , map_location=a )
loaded(inputs_dict["input_ids"].to(a ) , inputs_dict["attention_mask"].to(a ) )
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertModel.from_pretrained("distilbert-base-uncased" )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , attention_mask=a )[0]
SCREAMING_SNAKE_CASE : List[str] = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) ) | 25 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
a_ = {
'vocab_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
a_ = {
'vocab_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
a_ = {
'vocab_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
),
},
}
a_ = {
'facebook/dpr-ctx_encoder-single-nq-base': 512,
'facebook/dpr-ctx_encoder-multiset-base': 512,
}
a_ = {
'facebook/dpr-question_encoder-single-nq-base': 512,
'facebook/dpr-question_encoder-multiset-base': 512,
}
a_ = {
'facebook/dpr-reader-single-nq-base': 512,
'facebook/dpr-reader-multiset-base': 512,
}
a_ = {
'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
a_ = {
'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
a_ = {
'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =VOCAB_FILES_NAMES
lowerCamelCase__ =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =VOCAB_FILES_NAMES
lowerCamelCase__ =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
a_ = collections.namedtuple(
'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)
a_ = collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])
a_ = r'\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n ```\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n ```\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n - `\'np\'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Returns:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n '
@add_start_docstrings(__A )
class _UpperCamelCase :
'''simple docstring'''
def __call__( self : Union[str, Any] , a : str , a : Optional[str] = None , a : Optional[str] = None , a : Union[bool, str] = False , a : Union[bool, str] = False , a : Optional[int] = None , a : Optional[Union[str, TensorType]] = None , a : Optional[bool] = None , **a : Any , ) -> BatchEncoding:
"""simple docstring"""
if titles is None and texts is None:
return super().__call__(
a , padding=a , truncation=a , max_length=a , return_tensors=a , return_attention_mask=a , **a , )
elif titles is None or texts is None:
SCREAMING_SNAKE_CASE : Tuple = titles if texts is None else texts
return super().__call__(
a , a , padding=a , truncation=a , max_length=a , return_tensors=a , return_attention_mask=a , **a , )
SCREAMING_SNAKE_CASE : Dict = titles if not isinstance(a , a ) else [titles]
SCREAMING_SNAKE_CASE : Optional[int] = texts if not isinstance(a , a ) else [texts]
SCREAMING_SNAKE_CASE : str = len(a )
SCREAMING_SNAKE_CASE : Optional[Any] = questions if not isinstance(a , a ) else [questions] * n_passages
if len(a ) != len(a ):
raise ValueError(
F"There should be as many titles than texts but got {len(a )} titles and {len(a )} texts." )
SCREAMING_SNAKE_CASE : Tuple = super().__call__(a , a , padding=a , truncation=a )["input_ids"]
SCREAMING_SNAKE_CASE : Optional[int] = super().__call__(a , add_special_tokens=a , padding=a , truncation=a )["input_ids"]
SCREAMING_SNAKE_CASE : int = {
"input_ids": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(a , a )
]
}
if return_attention_mask is not False:
SCREAMING_SNAKE_CASE : Optional[Any] = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
SCREAMING_SNAKE_CASE : Dict = attention_mask
return self.pad(a , padding=a , max_length=a , return_tensors=a )
def __UpperCamelCase ( self : List[str] , a : BatchEncoding , a : DPRReaderOutput , a : int = 16 , a : int = 64 , a : int = 4 , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = reader_input["input_ids"]
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = reader_output[:3]
SCREAMING_SNAKE_CASE : Any = len(a )
SCREAMING_SNAKE_CASE : Any = sorted(range(a ) , reverse=a , key=relevance_logits.__getitem__ )
SCREAMING_SNAKE_CASE : List[DPRReaderOutput] = []
for doc_id in sorted_docs:
SCREAMING_SNAKE_CASE : Union[str, Any] = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
SCREAMING_SNAKE_CASE : Tuple = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
SCREAMING_SNAKE_CASE : Union[str, Any] = sequence_ids.index(self.pad_token_id )
else:
SCREAMING_SNAKE_CASE : Optional[int] = len(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=a , top_spans=a , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=a , start_index=a , end_index=a , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(a ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def __UpperCamelCase ( self : str , a : List[int] , a : List[int] , a : int , a : int , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = []
for start_index, start_score in enumerate(a ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
SCREAMING_SNAKE_CASE : Tuple = sorted(a , key=lambda a : x[1] , reverse=a )
SCREAMING_SNAKE_CASE : str = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(F"Wrong span indices: [{start_index}:{end_index}]" )
SCREAMING_SNAKE_CASE : int = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(F"Span is too long: {length} > {max_answer_length}" )
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(a ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(__A )
class _UpperCamelCase ( __A , __A ):
'''simple docstring'''
lowerCamelCase__ =VOCAB_FILES_NAMES
lowerCamelCase__ =READER_PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ =READER_PRETRAINED_INIT_CONFIGURATION
lowerCamelCase__ =['input_ids', 'attention_mask'] | 25 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 1 |
import math
import sys
def lowerCamelCase__ ( _a):
if number != int(_a):
raise ValueError("the value of input must be a natural number")
if number < 0:
raise ValueError("the value of input must not be a negative number")
if number == 0:
return 1
SCREAMING_SNAKE_CASE : List[str] = [-1] * (number + 1)
SCREAMING_SNAKE_CASE : Optional[int] = 0
for i in range(1 , number + 1):
SCREAMING_SNAKE_CASE : str = sys.maxsize
SCREAMING_SNAKE_CASE : Dict = int(math.sqrt(_a))
for j in range(1 , root + 1):
SCREAMING_SNAKE_CASE : Tuple = 1 + answers[i - (j**2)]
SCREAMING_SNAKE_CASE : int = min(_a , _a)
SCREAMING_SNAKE_CASE : List[str] = answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
def __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE : Union[str, Any] = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Any = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" , return_dict=a )[0]
SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "google/ncsnpp-celebahq-256"
SCREAMING_SNAKE_CASE : List[Any] = UNetaDModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Optional[Any] = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = pipe(num_inference_steps=20 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 1 |
import inspect
import unittest
from transformers import MobileViTConfig
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 transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __UpperCamelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(a , "hidden_sizes" ) )
self.parent.assertTrue(hasattr(a , "neck_hidden_sizes" ) )
self.parent.assertTrue(hasattr(a , "num_attention_heads" ) )
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : int , a : str , a : Union[str, Any]=13 , a : Dict=32 , a : Optional[Any]=2 , a : str=3 , a : Optional[Any]=640 , a : List[str]=4 , a : Optional[int]="silu" , a : Optional[int]=3 , a : str=32 , a : List[Any]=0.1 , a : Optional[Any]=0.1 , a : Optional[int]=0.1 , a : Any=0.02 , a : int=True , a : Dict=True , a : Dict=10 , a : Optional[int]=None , ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = parent
SCREAMING_SNAKE_CASE : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE : Any = image_size
SCREAMING_SNAKE_CASE : Dict = patch_size
SCREAMING_SNAKE_CASE : Optional[Any] = num_channels
SCREAMING_SNAKE_CASE : int = last_hidden_size
SCREAMING_SNAKE_CASE : str = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : Tuple = conv_kernel_size
SCREAMING_SNAKE_CASE : List[Any] = output_stride
SCREAMING_SNAKE_CASE : List[str] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : List[str] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : List[Any] = classifier_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = use_labels
SCREAMING_SNAKE_CASE : Optional[int] = is_training
SCREAMING_SNAKE_CASE : Any = num_labels
SCREAMING_SNAKE_CASE : Dict = initializer_range
SCREAMING_SNAKE_CASE : List[Any] = scope
def __UpperCamelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE : Any = None
SCREAMING_SNAKE_CASE : Any = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
SCREAMING_SNAKE_CASE : List[str] = self.get_config()
return config, pixel_values, labels, pixel_labels
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self : Optional[Any] , a : Optional[Any] , a : Optional[int] , a : Optional[int] , a : Tuple ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = MobileViTModel(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(a )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __UpperCamelCase ( self : Any , a : Tuple , a : Any , a : List[Any] , a : Union[str, Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.num_labels
SCREAMING_SNAKE_CASE : Union[str, Any] = MobileViTForImageClassification(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = model(a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase ( self : Optional[int] , a : List[str] , a : Tuple , a : Optional[int] , a : Dict ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.num_labels
SCREAMING_SNAKE_CASE : Union[str, Any] = MobileViTForSemanticSegmentation(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Tuple = model(a )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
SCREAMING_SNAKE_CASE : Tuple = model(a , labels=a )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE : Any = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
lowerCamelCase__ =(
{
'feature-extraction': MobileViTModel,
'image-classification': MobileViTForImageClassification,
'image-segmentation': MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowerCamelCase__ =False
lowerCamelCase__ =False
lowerCamelCase__ =False
lowerCamelCase__ =False
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = MobileViTModelTester(self )
SCREAMING_SNAKE_CASE : Tuple = MobileViTConfigTester(self , config_class=a , has_text_modality=a )
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileViT does not use inputs_embeds" )
def __UpperCamelCase ( self : List[Any] ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="MobileViT does not support input and output embeddings" )
def __UpperCamelCase ( self : Any ) -> Tuple:
"""simple docstring"""
pass
@unittest.skip(reason="MobileViT does not output attentions" )
def __UpperCamelCase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
pass
def __UpperCamelCase ( self : Any ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Tuple = model_class(a )
SCREAMING_SNAKE_CASE : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE : Dict = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE : Union[str, Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , a )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def __UpperCamelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
pass
def __UpperCamelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def __UpperCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(a : Tuple , a : Tuple , a : Optional[Any] ):
SCREAMING_SNAKE_CASE : str = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[Any] = model(**self._prepare_for_class(a , a ) )
SCREAMING_SNAKE_CASE : List[str] = outputs.hidden_states
SCREAMING_SNAKE_CASE : Optional[Any] = 5
self.assertEqual(len(a ) , a )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
SCREAMING_SNAKE_CASE : Union[str, Any] = 2
for i in range(len(a ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Dict = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE : List[Any] = True
check_hidden_states_output(a , a , a )
def __UpperCamelCase ( self : Tuple ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a )
def __UpperCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*a )
@slow
def __UpperCamelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : List[Any] = MobileViTModel.from_pretrained(a )
self.assertIsNotNone(a )
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : Tuple = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small" ) if is_vision_available() else None
@slow
def __UpperCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small" ).to(a )
SCREAMING_SNAKE_CASE : Dict = self.default_image_processor
SCREAMING_SNAKE_CASE : List[Any] = prepare_img()
SCREAMING_SNAKE_CASE : str = image_processor(images=a , return_tensors="pt" ).to(a )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[str] = model(**a )
# verify the logits
SCREAMING_SNAKE_CASE : Tuple = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([-1.9364, -1.2327, -0.4653] ).to(a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a , atol=1e-4 ) )
@slow
def __UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small" )
SCREAMING_SNAKE_CASE : Optional[int] = model.to(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small" )
SCREAMING_SNAKE_CASE : Tuple = prepare_img()
SCREAMING_SNAKE_CASE : Tuple = image_processor(images=a , return_tensors="pt" ).to(a )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[int] = model(**a )
SCREAMING_SNAKE_CASE : Optional[int] = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE : List[Any] = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , a )
SCREAMING_SNAKE_CASE : Any = torch.tensor(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
] , device=a , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , a , atol=1e-4 ) )
@slow
def __UpperCamelCase ( self : Dict ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small" )
SCREAMING_SNAKE_CASE : List[str] = model.to(a )
SCREAMING_SNAKE_CASE : Dict = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small" )
SCREAMING_SNAKE_CASE : Optional[int] = prepare_img()
SCREAMING_SNAKE_CASE : List[str] = image_processor(images=a , return_tensors="pt" ).to(a )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[int] = model(**a )
SCREAMING_SNAKE_CASE : int = outputs.logits.detach().cpu()
SCREAMING_SNAKE_CASE : int = image_processor.post_process_semantic_segmentation(outputs=a , target_sizes=[(50, 60)] )
SCREAMING_SNAKE_CASE : List[str] = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , a )
SCREAMING_SNAKE_CASE : int = image_processor.post_process_semantic_segmentation(outputs=a )
SCREAMING_SNAKE_CASE : Tuple = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , a ) | 25 |
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
SCREAMING_SNAKE_CASE : Optional[Any] = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res | 25 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}
# See all BART models at https://huggingface.co/models?filter=bart
a_ = {
'vocab_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
},
'merges_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
},
}
a_ = {
'facebook/bart-base': 1024,
'facebook/bart-large': 1024,
'facebook/bart-large-mnli': 1024,
'facebook/bart-large-cnn': 1024,
'facebook/bart-large-xsum': 1024,
'yjernite/bart_eli5': 1024,
}
@lru_cache()
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : int = (
list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1))
)
SCREAMING_SNAKE_CASE : Union[str, Any] = bs[:]
SCREAMING_SNAKE_CASE : List[str] = 0
for b in range(2**8):
if b not in bs:
bs.append(_a)
cs.append(2**8 + n)
n += 1
SCREAMING_SNAKE_CASE : Any = [chr(_a) for n in cs]
return dict(zip(_a , _a))
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : str = set()
SCREAMING_SNAKE_CASE : str = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
SCREAMING_SNAKE_CASE : Any = char
return pairs
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =VOCAB_FILES_NAMES
lowerCamelCase__ =PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ =['input_ids', 'attention_mask']
def __init__( self : List[str] , a : int , a : str , a : str="replace" , a : Tuple="<s>" , a : int="</s>" , a : Any="</s>" , a : Any="<s>" , a : List[str]="<unk>" , a : List[Any]="<pad>" , a : List[str]="<mask>" , a : Union[str, Any]=False , **a : Any , ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else bos_token
SCREAMING_SNAKE_CASE : Tuple = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else eos_token
SCREAMING_SNAKE_CASE : Union[str, Any] = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else sep_token
SCREAMING_SNAKE_CASE : Optional[Any] = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else cls_token
SCREAMING_SNAKE_CASE : str = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else unk_token
SCREAMING_SNAKE_CASE : Optional[Any] = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE : Union[str, Any] = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else mask_token
super().__init__(
errors=a , bos_token=a , eos_token=a , unk_token=a , sep_token=a , cls_token=a , pad_token=a , mask_token=a , add_prefix_space=a , **a , )
with open(a , encoding="utf-8" ) as vocab_handle:
SCREAMING_SNAKE_CASE : int = json.load(a )
SCREAMING_SNAKE_CASE : str = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE : Optional[Any] = errors # how to handle errors in decoding
SCREAMING_SNAKE_CASE : Optional[int] = bytes_to_unicode()
SCREAMING_SNAKE_CASE : str = {v: k for k, v in self.byte_encoder.items()}
with open(a , encoding="utf-8" ) as merges_handle:
SCREAMING_SNAKE_CASE : Tuple = merges_handle.read().split("\n" )[1:-1]
SCREAMING_SNAKE_CASE : Optional[Any] = [tuple(merge.split() ) for merge in bpe_merges]
SCREAMING_SNAKE_CASE : Union[str, Any] = dict(zip(a , range(len(a ) ) ) )
SCREAMING_SNAKE_CASE : Optional[int] = {}
SCREAMING_SNAKE_CASE : Dict = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
SCREAMING_SNAKE_CASE : Tuple = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def __UpperCamelCase ( self : int ) -> List[Any]:
"""simple docstring"""
return len(self.encoder )
def __UpperCamelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def __UpperCamelCase ( self : Dict , a : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
SCREAMING_SNAKE_CASE : int = tuple(a )
SCREAMING_SNAKE_CASE : Optional[Any] = get_pairs(a )
if not pairs:
return token
while True:
SCREAMING_SNAKE_CASE : Optional[Any] = min(a , key=lambda a : self.bpe_ranks.get(a , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[int] = bigram
SCREAMING_SNAKE_CASE : Optional[Any] = []
SCREAMING_SNAKE_CASE : Any = 0
while i < len(a ):
try:
SCREAMING_SNAKE_CASE : List[Any] = word.index(a , a )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
SCREAMING_SNAKE_CASE : Tuple = j
if word[i] == first and i < len(a ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
SCREAMING_SNAKE_CASE : Optional[Any] = tuple(a )
SCREAMING_SNAKE_CASE : Tuple = new_word
if len(a ) == 1:
break
else:
SCREAMING_SNAKE_CASE : str = get_pairs(a )
SCREAMING_SNAKE_CASE : str = " ".join(a )
SCREAMING_SNAKE_CASE : List[str] = word
return word
def __UpperCamelCase ( self : Any , a : Tuple ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = []
for token in re.findall(self.pat , a ):
SCREAMING_SNAKE_CASE : int = "".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(a ).split(" " ) )
return bpe_tokens
def __UpperCamelCase ( self : Tuple , a : Any ) -> str:
"""simple docstring"""
return self.encoder.get(a , self.encoder.get(self.unk_token ) )
def __UpperCamelCase ( self : List[str] , a : List[str] ) -> Union[str, Any]:
"""simple docstring"""
return self.decoder.get(a )
def __UpperCamelCase ( self : List[str] , a : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "".join(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def __UpperCamelCase ( self : Union[str, Any] , a : str , a : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(a ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
SCREAMING_SNAKE_CASE : Tuple = os.path.join(
a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(
a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(a , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=a , ensure_ascii=a ) + "\n" )
SCREAMING_SNAKE_CASE : List[Any] = 0
with open(a , "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 a : 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!" )
SCREAMING_SNAKE_CASE : Any = token_index
writer.write(" ".join(a ) + "\n" )
index += 1
return vocab_file, merge_file
def __UpperCamelCase ( self : List[str] , a : List[int] , a : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE : Any = [self.cls_token_id]
SCREAMING_SNAKE_CASE : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __UpperCamelCase ( self : Tuple , a : List[int] , a : Optional[List[int]] = None , a : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=a , token_ids_a=a , already_has_special_tokens=a )
if token_ids_a is None:
return [1] + ([0] * len(a )) + [1]
return [1] + ([0] * len(a )) + [1, 1] + ([0] * len(a )) + [1]
def __UpperCamelCase ( self : List[Any] , a : List[int] , a : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Optional[Any] = [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 __UpperCamelCase ( self : int , a : Tuple , a : List[str]=False , **a : List[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(a ) > 0 and not text[0].isspace()):
SCREAMING_SNAKE_CASE : Tuple = " " + text
return (text, kwargs) | 25 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='roformer'
def __init__( self : Dict , a : Any=5_0000 , a : List[Any]=None , a : str=768 , a : str=12 , a : Tuple=12 , a : Optional[Any]=3072 , a : List[str]="gelu" , a : List[Any]=0.1 , a : Union[str, Any]=0.1 , a : Tuple=1536 , a : List[str]=2 , a : Tuple=0.02 , a : Any=1e-12 , a : Optional[int]=0 , a : Union[str, Any]=False , a : int=True , **a : str , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=a , **a )
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size if embedding_size is None else embedding_size
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = type_vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE : List[str] = rotary_value
SCREAMING_SNAKE_CASE : int = use_cache
class _UpperCamelCase ( __A ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE : str = {0: "batch", 1: "sequence"}
SCREAMING_SNAKE_CASE : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] ) | 25 | 1 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
a_ = '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n'
a_ = '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'
a_ = '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: "c" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric(\'mauve\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCamelCase ( datasets.Metric ):
'''simple docstring'''
def __UpperCamelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="https://github.com/krishnap25/mauve" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/krishnap25/mauve"] , reference_urls=[
"https://arxiv.org/abs/2102.01454",
"https://github.com/krishnap25/mauve",
] , )
def __UpperCamelCase ( self : Tuple , a : Union[str, Any] , a : Dict , a : Optional[int]=None , a : int=None , a : str=None , a : str=None , a : int="auto" , a : str=-1 , a : Union[str, Any]=0.9 , a : Optional[Any]=5 , a : Dict=500 , a : int="gpt2-large" , a : List[Any]=-1 , a : Union[str, Any]=1024 , a : Union[str, Any]=25 , a : Any=5 , a : str=True , a : Any=25 , ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = compute_mauve(
p_text=a , q_text=a , p_features=a , q_features=a , p_tokens=a , q_tokens=a , num_buckets=a , pca_max_data=a , kmeans_explained_var=a , kmeans_num_redo=a , kmeans_max_iter=a , featurize_model_name=a , device_id=a , max_text_length=a , divergence_curve_discretization_size=a , mauve_scaling_factor=a , verbose=a , seed=a , )
return out | 25 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__name__)
a_ = 'Hello world! cécé herlolip'
a_ = namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = BertAbsConfig(
temp_dir="." , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder="bert" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : Dict = torch.load(_a , lambda _a , _a: storage)
SCREAMING_SNAKE_CASE : str = AbsSummarizer(_a , torch.device("cpu") , _a)
original.eval()
SCREAMING_SNAKE_CASE : List[str] = BertAbsSummarizer(_a , torch.device("cpu"))
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model")
new_model.bert.load_state_dict(original.bert.state_dict())
new_model.decoder.load_state_dict(original.decoder.state_dict())
new_model.generator.load_state_dict(original.generator.state_dict())
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical")
SCREAMING_SNAKE_CASE : List[str] = BertTokenizer.from_pretrained("bert-base-uncased")
# prepare the model inputs
SCREAMING_SNAKE_CASE : List[str] = tokenizer.encode("This is sample éàalj'-.")
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode("This is sample 3 éàalj'-.")
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0
# forward pass
SCREAMING_SNAKE_CASE : List[Any] = encoder_input_ids
SCREAMING_SNAKE_CASE : List[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Dict = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : Optional[int] = original(_a , _a , _a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Dict = original.generator(_a)
SCREAMING_SNAKE_CASE : Any = new_model(
_a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Tuple = new_model.generator(_a)
SCREAMING_SNAKE_CASE : List[Any] = torch.max(torch.abs(output_converted_model - output_original_model)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : int = torch.allclose(_a , _a , atol=1E-3)
if are_identical:
logging.info("all weights are equal up to 1e-3")
else:
raise ValueError("the weights are different. The new model is likely different from the original one.")
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary")
torch.save(
new_model.state_dict() , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin")
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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.',
)
a_ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
) | 25 | 1 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : int = float(_a)
SCREAMING_SNAKE_CASE : Dict = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_cos() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_cos() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : str = float(_a)
SCREAMING_SNAKE_CASE : Any = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15)) | 25 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
a_ = parser.parse_args()
a_ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
a_ = CLIPImageProcessor()
a_ = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
a_ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path) | 25 | 1 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
a_ = parse(importlib.metadata.version('torch'))
def lowerCamelCase__ ( _a , _a , _a):
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(f"`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys())}, received {operation}")
SCREAMING_SNAKE_CASE : Union[str, Any] = STR_OPERATION_TO_FUNC[operation]
if isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Any = parse(importlib.metadata.version(_a))
return operation(_a , parse(_a))
def lowerCamelCase__ ( _a , _a):
return compare_versions(_a , _a , _a) | 25 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Swinv2ForImageClassification',
'Swinv2ForMaskedImageModeling',
'Swinv2Model',
'Swinv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 1 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
raise Exception('requires fairseq >= 1.0.0a')
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = 'Hello world! cécé herlolip'
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Union[str, Any] = FairseqRobertaModel.from_pretrained(_a)
roberta.eval() # disable dropout
SCREAMING_SNAKE_CASE : int = roberta.model.encoder.sentence_encoder
SCREAMING_SNAKE_CASE : List[str] = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings , hidden_size=roberta.cfg.model.encoder_embed_dim , num_hidden_layers=roberta.cfg.model.encoder_layers , num_attention_heads=roberta.cfg.model.encoder_attention_heads , intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1E-5 , )
if classification_head:
SCREAMING_SNAKE_CASE : List[str] = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our RoBERTa config:" , _a)
SCREAMING_SNAKE_CASE : Any = XLMRobertaXLForSequenceClassification(_a) if classification_head else XLMRobertaXLForMaskedLM(_a)
model.eval()
# Now let's copy all the weights.
# Embeddings
SCREAMING_SNAKE_CASE : Tuple = roberta_sent_encoder.embed_tokens.weight
SCREAMING_SNAKE_CASE : Tuple = roberta_sent_encoder.embed_positions.weight
SCREAMING_SNAKE_CASE : Any = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight) # just zero them out b/c RoBERTa doesn't use them.
SCREAMING_SNAKE_CASE : Any = roberta_sent_encoder.layer_norm.weight
SCREAMING_SNAKE_CASE : Union[str, Any] = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers):
# Encoder: start of layer
SCREAMING_SNAKE_CASE : BertLayer = model.roberta.encoder.layer[i]
SCREAMING_SNAKE_CASE : TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
SCREAMING_SNAKE_CASE : RobertaAttention = layer.attention
SCREAMING_SNAKE_CASE : Dict = roberta_layer.self_attn_layer_norm.weight
SCREAMING_SNAKE_CASE : str = roberta_layer.self_attn_layer_norm.bias
# self attention
SCREAMING_SNAKE_CASE : BertSelfAttention = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size))
)
SCREAMING_SNAKE_CASE : Tuple = roberta_layer.self_attn.q_proj.weight
SCREAMING_SNAKE_CASE : Optional[int] = roberta_layer.self_attn.q_proj.bias
SCREAMING_SNAKE_CASE : Dict = roberta_layer.self_attn.k_proj.weight
SCREAMING_SNAKE_CASE : str = roberta_layer.self_attn.k_proj.bias
SCREAMING_SNAKE_CASE : Union[str, Any] = roberta_layer.self_attn.v_proj.weight
SCREAMING_SNAKE_CASE : Optional[Any] = roberta_layer.self_attn.v_proj.bias
# self-attention output
SCREAMING_SNAKE_CASE : BertSelfOutput = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
SCREAMING_SNAKE_CASE : Dict = roberta_layer.self_attn.out_proj.weight
SCREAMING_SNAKE_CASE : List[Any] = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
SCREAMING_SNAKE_CASE : Optional[int] = roberta_layer.final_layer_norm.weight
SCREAMING_SNAKE_CASE : Dict = roberta_layer.final_layer_norm.bias
# intermediate
SCREAMING_SNAKE_CASE : BertIntermediate = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
SCREAMING_SNAKE_CASE : Optional[int] = roberta_layer.fca.weight
SCREAMING_SNAKE_CASE : str = roberta_layer.fca.bias
# output
SCREAMING_SNAKE_CASE : BertOutput = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
SCREAMING_SNAKE_CASE : Tuple = roberta_layer.fca.weight
SCREAMING_SNAKE_CASE : int = roberta_layer.fca.bias
# end of layer
if classification_head:
SCREAMING_SNAKE_CASE : Optional[Any] = roberta.model.classification_heads["mnli"].dense.weight
SCREAMING_SNAKE_CASE : Optional[Any] = roberta.model.classification_heads["mnli"].dense.bias
SCREAMING_SNAKE_CASE : List[Any] = roberta.model.classification_heads["mnli"].out_proj.weight
SCREAMING_SNAKE_CASE : Tuple = roberta.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
SCREAMING_SNAKE_CASE : Union[str, Any] = roberta.model.encoder.lm_head.dense.weight
SCREAMING_SNAKE_CASE : int = roberta.model.encoder.lm_head.dense.bias
SCREAMING_SNAKE_CASE : Dict = roberta.model.encoder.lm_head.layer_norm.weight
SCREAMING_SNAKE_CASE : Union[str, Any] = roberta.model.encoder.lm_head.layer_norm.bias
SCREAMING_SNAKE_CASE : Dict = roberta.model.encoder.lm_head.weight
SCREAMING_SNAKE_CASE : Tuple = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
SCREAMING_SNAKE_CASE : torch.Tensor = roberta.encode(_a).unsqueeze(0) # batch of size 1
SCREAMING_SNAKE_CASE : List[str] = model(_a)[0]
if classification_head:
SCREAMING_SNAKE_CASE : Optional[int] = roberta.model.classification_heads["mnli"](roberta.extract_features(_a))
else:
SCREAMING_SNAKE_CASE : Optional[int] = roberta.model(_a)[0]
print(our_output.shape , their_output.shape)
SCREAMING_SNAKE_CASE : str = torch.max(torch.abs(our_output - their_output)).item()
print(f"max_absolute_diff = {max_absolute_diff}") # ~ 1e-7
SCREAMING_SNAKE_CASE : Optional[Any] = torch.allclose(_a , _a , atol=1E-3)
print("Do both models output the same tensors?" , "🔥" if success else "💩")
if not success:
raise Exception("Something went wRoNg")
pathlib.Path(_a).mkdir(parents=_a , exist_ok=_a)
print(f"Saving model to {pytorch_dump_folder_path}")
model.save_pretrained(_a)
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--roberta_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_ = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
) | 25 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values")
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values")
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase__ ( _a , _a , _a):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values")
SCREAMING_SNAKE_CASE : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def lowerCamelCase__ ( _a , _a):
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values")
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori")
return 4 * pow(_a , 2) * torus_radius * tube_radius
def lowerCamelCase__ ( _a , _a):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def lowerCamelCase__ ( _a , _a , _a):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values")
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("Given three sides do not form a triangle")
SCREAMING_SNAKE_CASE : List[str] = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE : Optional[int] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea))
return area
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def lowerCamelCase__ ( _a , _a , _a):
if basea < 0 or basea < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values")
return 1 / 2 * (basea + basea) * height
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def lowerCamelCase__ ( _a , _a):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("area_rhombus() only accepts non-negative values")
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase__ ( _a , _a):
if not isinstance(_a , _a) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides")
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side")
return (sides * length**2) / (4 * tan(pi / sides))
return (sides * length**2) / (4 * tan(pi / sides))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('[DEMO] Areas of various geometric shapes: \n')
print(F'''Rectangle: {area_rectangle(10, 20) = }''')
print(F'''Square: {area_square(10) = }''')
print(F'''Triangle: {area_triangle(10, 10) = }''')
print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(F'''Rhombus: {area_rhombus(10, 20) = }''')
print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(F'''Circle: {area_circle(20) = }''')
print(F'''Ellipse: {area_ellipse(10, 20) = }''')
print('\nSurface Areas of various geometric shapes: \n')
print(F'''Cube: {surface_area_cube(20) = }''')
print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(F'''Sphere: {surface_area_sphere(20) = }''')
print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(F'''Cone: {surface_area_cone(10, 20) = }''')
print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(F'''Torus: {surface_area_torus(20, 10) = }''')
print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(F'''Square: {area_reg_polygon(4, 10) = }''')
print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''') | 25 | 1 |
from math import sqrt
def lowerCamelCase__ ( _a):
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(sqrt(_a) + 1) , 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCamelCase__ ( _a = 10001):
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
SCREAMING_SNAKE_CASE : List[Any] = 1
while count != nth and number < 3:
number += 1
if is_prime(_a):
count += 1
while count != nth:
number += 2
if is_prime(_a):
count += 1
return number
if __name__ == "__main__":
print(F'''{solution() = }''') | 25 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 1 |
from __future__ import annotations
import math
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = size
# approximate the overall size of segment tree with given value
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
SCREAMING_SNAKE_CASE : Union[str, Any] = [0 for i in range(0 , 4 * size )]
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )] # flag for lazy update
def __UpperCamelCase ( self : Tuple , a : int ) -> int:
"""simple docstring"""
return idx * 2
def __UpperCamelCase ( self : str , a : int ) -> int:
"""simple docstring"""
return idx * 2 + 1
def __UpperCamelCase ( self : int , a : int , a : int , a : int , a : list[int] ) -> None:
"""simple docstring"""
if left_element == right_element:
SCREAMING_SNAKE_CASE : int = a[left_element - 1]
else:
SCREAMING_SNAKE_CASE : Optional[int] = (left_element + right_element) // 2
self.build(self.left(a ) , a , a , a )
self.build(self.right(a ) , mid + 1 , a , a )
SCREAMING_SNAKE_CASE : List[Any] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : int , a : int , a : int , a : int , a : int ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : Any = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[str] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : List[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
SCREAMING_SNAKE_CASE : Optional[Any] = val
if left_element != right_element:
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Optional[Any] = True
return True
SCREAMING_SNAKE_CASE : int = (left_element + right_element) // 2
self.update(self.left(a ) , a , a , a , a , a )
self.update(self.right(a ) , mid + 1 , a , a , a , a )
SCREAMING_SNAKE_CASE : Optional[int] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
return True
def __UpperCamelCase ( self : Dict , a : int , a : int , a : int , a : int , a : int ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[Any] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
SCREAMING_SNAKE_CASE : Dict = (left_element + right_element) // 2
SCREAMING_SNAKE_CASE : Tuple = self.query(self.left(a ) , a , a , a , a )
SCREAMING_SNAKE_CASE : Tuple = self.query(self.right(a ) , mid + 1 , a , a , a )
return max(a , a )
def __str__( self : str ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , a , a ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
a_ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
a_ = 15
a_ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 25 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_a)
if n > 1:
factors.append(_a)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
from .pipeline_kandinsky import KandinskyPipeline
from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
from .text_encoder import MultilingualCLIP | 25 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : int = float(_a)
SCREAMING_SNAKE_CASE : Dict = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_cos() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_cos() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : str = float(_a)
SCREAMING_SNAKE_CASE : Any = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15)) | 25 | 1 |
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : List[str] = 0
for ch in input_str:
SCREAMING_SNAKE_CASE : Optional[int] = ord(_a)
SCREAMING_SNAKE_CASE : str = pow(2 , _a)
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 |
from __future__ import annotations
import math
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = size
# approximate the overall size of segment tree with given value
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
SCREAMING_SNAKE_CASE : Union[str, Any] = [0 for i in range(0 , 4 * size )]
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )] # flag for lazy update
def __UpperCamelCase ( self : Tuple , a : int ) -> int:
"""simple docstring"""
return idx * 2
def __UpperCamelCase ( self : str , a : int ) -> int:
"""simple docstring"""
return idx * 2 + 1
def __UpperCamelCase ( self : int , a : int , a : int , a : int , a : list[int] ) -> None:
"""simple docstring"""
if left_element == right_element:
SCREAMING_SNAKE_CASE : int = a[left_element - 1]
else:
SCREAMING_SNAKE_CASE : Optional[int] = (left_element + right_element) // 2
self.build(self.left(a ) , a , a , a )
self.build(self.right(a ) , mid + 1 , a , a )
SCREAMING_SNAKE_CASE : List[Any] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : int , a : int , a : int , a : int , a : int ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : Any = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[str] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : List[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
SCREAMING_SNAKE_CASE : Optional[Any] = val
if left_element != right_element:
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Optional[Any] = True
return True
SCREAMING_SNAKE_CASE : int = (left_element + right_element) // 2
self.update(self.left(a ) , a , a , a , a , a )
self.update(self.right(a ) , mid + 1 , a , a , a , a )
SCREAMING_SNAKE_CASE : Optional[int] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
return True
def __UpperCamelCase ( self : Dict , a : int , a : int , a : int , a : int , a : int ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[Any] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
SCREAMING_SNAKE_CASE : Dict = (left_element + right_element) // 2
SCREAMING_SNAKE_CASE : Tuple = self.query(self.left(a ) , a , a , a , a )
SCREAMING_SNAKE_CASE : Tuple = self.query(self.right(a ) , mid + 1 , a , a , a )
return max(a , a )
def __str__( self : str ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , a , a ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
a_ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
a_ = 15
a_ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 25 | 1 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
a_ = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class _UpperCamelCase ( datasets.BuilderConfig ):
'''simple docstring'''
lowerCamelCase__ =None
def lowerCamelCase__ ( _a , _a , ):
import pyspark
def generate_fn():
SCREAMING_SNAKE_CASE : str = df.select("*" , pyspark.sql.functions.spark_partition_id().alias("part_id"))
for partition_id in partition_order:
SCREAMING_SNAKE_CASE : int = df_with_partition_id.select("*").where(f"part_id = {partition_id}").drop("part_id")
SCREAMING_SNAKE_CASE : Tuple = partition_df.collect()
SCREAMING_SNAKE_CASE : int = 0
for row in rows:
yield f"{partition_id}_{row_id}", row.asDict()
row_id += 1
return generate_fn
class _UpperCamelCase ( _BaseExamplesIterable ):
'''simple docstring'''
def __init__( self : Dict , a : "pyspark.sql.DataFrame" , a : str=None , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = df
SCREAMING_SNAKE_CASE : Any = partition_order or range(self.df.rdd.getNumPartitions() )
SCREAMING_SNAKE_CASE : List[str] = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
yield from self.generate_examples_fn()
def __UpperCamelCase ( self : Optional[int] , a : np.random.Generator ) -> "SparkExamplesIterable":
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(a )
return SparkExamplesIterable(self.df , partition_order=a )
def __UpperCamelCase ( self : List[Any] , a : int , a : int ) -> "SparkExamplesIterable":
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = self.split_shard_indices_by_worker(a , a )
return SparkExamplesIterable(self.df , partition_order=a )
@property
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
return len(self.partition_order )
class _UpperCamelCase ( datasets.DatasetBuilder ):
'''simple docstring'''
lowerCamelCase__ =SparkConfig
def __init__( self : Tuple , a : "pyspark.sql.DataFrame" , a : str = None , a : str = None , **a : Optional[int] , ) -> Tuple:
"""simple docstring"""
import pyspark
SCREAMING_SNAKE_CASE : Tuple = pyspark.sql.SparkSession.builder.getOrCreate()
SCREAMING_SNAKE_CASE : int = df
SCREAMING_SNAKE_CASE : Optional[Any] = working_dir
super().__init__(
cache_dir=a , config_name=str(self.df.semanticHash() ) , **a , )
def __UpperCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
def create_cache_and_write_probe(a : Optional[int] ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=a )
SCREAMING_SNAKE_CASE : Tuple = os.path.join(self._cache_dir , "fs_test" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(a , "a" )
return [probe_file]
if self._spark.conf.get("spark.master" , "" ).startswith("local" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
SCREAMING_SNAKE_CASE : Optional[Any] = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(a ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir" )
def __UpperCamelCase ( self : Any ) -> Dict:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def __UpperCamelCase ( self : Optional[Any] , a : datasets.download.download_manager.DownloadManager ) -> Any:
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def __UpperCamelCase ( self : int , a : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
import pyspark
def get_arrow_batch_size(a : Dict ):
for batch in it:
yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]} )
SCREAMING_SNAKE_CASE : Tuple = self.df.count()
SCREAMING_SNAKE_CASE : int = df_num_rows if df_num_rows <= 100 else 100
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
SCREAMING_SNAKE_CASE : str = (
self.df.limit(a )
.repartition(1 )
.mapInArrow(a , "batch_bytes: long" )
.agg(pyspark.sql.functions.sum("batch_bytes" ).alias("sample_bytes" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
SCREAMING_SNAKE_CASE : Dict = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
SCREAMING_SNAKE_CASE : Tuple = min(a , int(approx_total_size / max_shard_size ) )
SCREAMING_SNAKE_CASE : Tuple = self.df.repartition(a )
def __UpperCamelCase ( self : Any , a : str , a : str , a : int , ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
"""simple docstring"""
import pyspark
SCREAMING_SNAKE_CASE : Dict = ParquetWriter if file_format == "parquet" else ArrowWriter
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(self._working_dir , os.path.basename(a ) ) if self._working_dir else fpath
SCREAMING_SNAKE_CASE : List[Any] = file_format == "parquet"
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
SCREAMING_SNAKE_CASE : List[Any] = self.config.features
SCREAMING_SNAKE_CASE : Dict = self._writer_batch_size
SCREAMING_SNAKE_CASE : int = self._fs.storage_options
def write_arrow(a : List[Any] ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
SCREAMING_SNAKE_CASE : List[str] = pyspark.TaskContext().taskAttemptId()
SCREAMING_SNAKE_CASE : List[str] = next(a , a )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=["task_id", "num_examples", "num_bytes"] , )
SCREAMING_SNAKE_CASE : Tuple = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = writer_class(
features=a , path=working_fpath.replace("SSSSS" , F"{shard_id:05d}" ).replace("TTTTT" , F"{task_id:05d}" ) , writer_batch_size=a , storage_options=a , embed_local_files=a , )
SCREAMING_SNAKE_CASE : Dict = pa.Table.from_batches([first_batch] )
writer.write_table(a )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=["task_id", "num_examples", "num_bytes"] , )
shard_id += 1
SCREAMING_SNAKE_CASE : Any = writer_class(
features=writer._features , path=working_fpath.replace("SSSSS" , F"{shard_id:05d}" ).replace("TTTTT" , F"{task_id:05d}" ) , writer_batch_size=a , storage_options=a , embed_local_files=a , )
SCREAMING_SNAKE_CASE : str = pa.Table.from_batches([batch] )
writer.write_table(a )
if writer._num_bytes > 0:
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=["task_id", "num_examples", "num_bytes"] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(a ) ):
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(os.path.dirname(a ) , os.path.basename(a ) )
shutil.move(a , a )
SCREAMING_SNAKE_CASE : Optional[int] = (
self.df.mapInArrow(a , "task_id: long, num_examples: long, num_bytes: long" )
.groupBy("task_id" )
.agg(
pyspark.sql.functions.sum("num_examples" ).alias("total_num_examples" ) , pyspark.sql.functions.sum("num_bytes" ).alias("total_num_bytes" ) , pyspark.sql.functions.count("num_bytes" ).alias("num_shards" ) , pyspark.sql.functions.collect_list("num_examples" ).alias("shard_lengths" ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def __UpperCamelCase ( self : Optional[Any] , a : "datasets.SplitGenerator" , a : str = "arrow" , a : Optional[Union[str, int]] = None , a : Optional[int] = None , **a : str , ) -> str:
"""simple docstring"""
self._validate_cache_dir()
SCREAMING_SNAKE_CASE : int = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(a )
SCREAMING_SNAKE_CASE : Dict = not is_remote_filesystem(self._fs )
SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join if is_local else posixpath.join
SCREAMING_SNAKE_CASE : Optional[int] = "-TTTTT-SSSSS-of-NNNNN"
SCREAMING_SNAKE_CASE : Dict = F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
SCREAMING_SNAKE_CASE : Dict = path_join(self._output_dir , a )
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : str = 0
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = []
SCREAMING_SNAKE_CASE : List[Any] = []
for task_id, content in self._prepare_split_single(a , a , a ):
(
(
SCREAMING_SNAKE_CASE
) ,(
SCREAMING_SNAKE_CASE
) ,(
SCREAMING_SNAKE_CASE
) ,(
SCREAMING_SNAKE_CASE
) ,
) : Optional[Any] = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(a )
SCREAMING_SNAKE_CASE : List[str] = total_num_examples
SCREAMING_SNAKE_CASE : str = total_num_bytes
# should rename everything at the end
logger.debug(F"Renaming {total_shards} shards." )
if total_shards > 1:
SCREAMING_SNAKE_CASE : List[Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
SCREAMING_SNAKE_CASE : Optional[Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
a : int , a : int , a : int , ):
rename(
a , fpath.replace("SSSSS" , F"{shard_id:05d}" ).replace("TTTTT" , F"{task_id:05d}" ) , fpath.replace("TTTTT-SSSSS" , F"{global_shard_id:05d}" ).replace("NNNNN" , F"{total_shards:05d}" ) , )
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : str = 0
for i in range(len(a ) ):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[int] = task_id_and_num_shards[i]
for shard_id in range(a ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(a , len(a ) ).map(lambda a : _rename_shard(*a ) ).collect()
else:
# don't use any pattern
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("SSSSS" , F"{shard_id:05d}" ).replace("TTTTT" , F"{task_id:05d}" ) , fpath.replace(a , "" ) , )
def __UpperCamelCase ( self : int , a : "datasets.SplitGenerator" , ) -> SparkExamplesIterable:
"""simple docstring"""
return SparkExamplesIterable(self.df ) | 25 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
SCREAMING_SNAKE_CASE : str = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : Optional[int] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : Tuple = output.images
SCREAMING_SNAKE_CASE : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Tuple = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : List[str] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_dpmpp_2m" )
SCREAMING_SNAKE_CASE : str = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = sd_pipe(
[prompt] , generator=a , guidance_scale=7.5 , num_inference_steps=15 , output_type="np" , use_karras_sigmas=a , )
SCREAMING_SNAKE_CASE : str = output.images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 1 |
import datasets
from .evaluate import evaluate
a_ = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
a_ = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
a_ = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the CUAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCamelCase ( datasets.Metric ):
'''simple docstring'''
def __UpperCamelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": {
"id": datasets.Value("string" ),
"prediction_text": datasets.features.Sequence(datasets.Value("string" ) ),
},
"references": {
"id": datasets.Value("string" ),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
},
} ) , codebase_urls=["https://www.atticusprojectai.org/cuad"] , reference_urls=["https://www.atticusprojectai.org/cuad"] , )
def __UpperCamelCase ( self : int , a : Any , a : Any ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
SCREAMING_SNAKE_CASE : List[str] = [
{
"paragraphs": [
{
"qas": [
{
"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
"id": ref["id"],
}
for ref in references
]
}
]
}
]
SCREAMING_SNAKE_CASE : Tuple = evaluate(dataset=a , predictions=a )
return score | 25 |
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 _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Union[str, Any] = False
return options
def __UpperCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=10 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = 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 __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Dict = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=20 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[str] = output.images
SCREAMING_SNAKE_CASE : Optional[int] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Any = 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 | 25 | 1 |
import os
from datetime import datetime as dt
from github import Github
a_ = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : Union[str, Any] = Github(os.environ["GITHUB_TOKEN"])
SCREAMING_SNAKE_CASE : Tuple = g.get_repo("huggingface/diffusers")
SCREAMING_SNAKE_CASE : Tuple = repo.get_issues(state="open")
for issue in open_issues:
SCREAMING_SNAKE_CASE : Optional[Any] = sorted(issue.get_comments() , key=lambda _a: i.created_at , reverse=_a)
SCREAMING_SNAKE_CASE : Tuple = comments[0] if len(_a) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state="closed")
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state="open")
issue.remove_from_labels("stale")
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
"This issue has been automatically marked as stale because it has not had "
"recent activity. If you think this still needs to be addressed "
"please comment on this thread.\n\nPlease note that issues that do not follow the "
"[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) "
"are likely to be ignored.")
issue.add_to_labels("stale")
if __name__ == "__main__":
main() | 25 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase__ ( _a):
return getitem, k
def lowerCamelCase__ ( _a , _a):
return setitem, k, v
def lowerCamelCase__ ( _a):
return delitem, k
def lowerCamelCase__ ( _a , _a , *_a):
try:
return fun(_a , *_a), None
except Exception as e:
return None, e
a_ = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
a_ = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
a_ = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
a_ = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items"),
pytest.param(_overwrite_items , id="overwrite items"),
pytest.param(_delete_items , id="delete items"),
pytest.param(_access_absent_items , id="access absent items"),
pytest.param(_add_with_resize_up , id="add with resize up"),
pytest.param(_add_with_resize_down , id="add with resize down"),
) , )
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Dict = HashMap(initial_block_size=4)
SCREAMING_SNAKE_CASE : List[str] = {}
for _, (fun, *args) in enumerate(_a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = _run_operation(_a , _a , *_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = _run_operation(_a , _a , *_a)
assert my_res == py_res
assert str(_a) == str(_a)
assert set(_a) == set(_a)
assert len(_a) == len(_a)
assert set(my.items()) == set(py.items())
def lowerCamelCase__ ( ):
def is_public(_a) -> bool:
return not name.startswith("_")
SCREAMING_SNAKE_CASE : List[str] = {name for name in dir({}) if is_public(_a)}
SCREAMING_SNAKE_CASE : Union[str, Any] = {name for name in dir(HashMap()) if is_public(_a)}
assert dict_public_names > hash_public_names | 25 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[str] = analyze_text(_a)
SCREAMING_SNAKE_CASE : Tuple = list(" " + ascii_lowercase)
# what is our total sum of probabilities.
SCREAMING_SNAKE_CASE : List[Any] = sum(single_char_strings.values())
# one length string
SCREAMING_SNAKE_CASE : Optional[Any] = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
SCREAMING_SNAKE_CASE : Tuple = single_char_strings[ch]
SCREAMING_SNAKE_CASE : List[str] = my_str / all_sum
my_fir_sum += prob * math.loga(_a) # entropy formula.
# print entropy
print(f"{round(-1 * my_fir_sum):.1f}")
# two len string
SCREAMING_SNAKE_CASE : Dict = sum(two_char_strings.values())
SCREAMING_SNAKE_CASE : Optional[int] = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
SCREAMING_SNAKE_CASE : List[Any] = cha + cha
if sequence in two_char_strings:
SCREAMING_SNAKE_CASE : List[Any] = two_char_strings[sequence]
SCREAMING_SNAKE_CASE : List[str] = int(_a) / all_sum
my_sec_sum += prob * math.loga(_a)
# print second entropy
print(f"{round(-1 * my_sec_sum):.1f}")
# print the difference between them
print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum)):.1f}")
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Tuple = Counter() # type: ignore
SCREAMING_SNAKE_CASE : Tuple = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 , len(_a) - 1):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def lowerCamelCase__ ( ):
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main() | 25 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
'VanForImageClassification',
'VanModel',
'VanPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
'VanForImageClassification',
'VanModel',
'VanPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
if len(_a) == 0:
return []
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = min(_a), max(_a)
SCREAMING_SNAKE_CASE : Dict = int(max_value - min_value) + 1
SCREAMING_SNAKE_CASE : list[list] = [[] for _ in range(_a)]
for i in my_list:
buckets[int(i - min_value)].append(_a)
return [v for bucket in buckets for v in sorted(_a)]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15] | 25 | 1 |
import sys
a_ = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def lowerCamelCase__ ( _a = N):
SCREAMING_SNAKE_CASE : Optional[Any] = -sys.maxsize - 1
for i in range(len(_a) - 12):
SCREAMING_SNAKE_CASE : List[Any] = 1
for j in range(13):
product *= int(n[i + j])
if product > largest_product:
SCREAMING_SNAKE_CASE : Union[str, Any] = product
return largest_product
if __name__ == "__main__":
print(F'''{solution() = }''') | 25 |
a_ = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset([])
a_ = frozenset(['image'])
a_ = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image'])
a_ = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'negative_prompt'])
a_ = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
a_ = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image', 'mask_image'])
a_ = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['example_image', 'image', 'mask_image'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset(['input_tokens'])
a_ = frozenset(['input_tokens']) | 25 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =42
lowerCamelCase__ =42
class _UpperCamelCase ( __A , __A ):
'''simple docstring'''
lowerCamelCase__ =1
@register_to_config
def __init__( self : Dict , a : int = 2000 , a : float = 0.15 , a : float = 0.01 , a : float = 1348.0 , a : float = 1e-5 , a : int = 1 , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = sigma_max
# setable values
SCREAMING_SNAKE_CASE : str = None
self.set_sigmas(a , a , a , a )
def __UpperCamelCase ( self : Optional[int] , a : torch.FloatTensor , a : Optional[int] = None ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def __UpperCamelCase ( self : Optional[int] , a : int , a : float = None , a : Union[str, torch.device] = None ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = sampling_eps if sampling_eps is not None else self.config.sampling_eps
SCREAMING_SNAKE_CASE : List[Any] = torch.linspace(1 , a , a , device=a )
def __UpperCamelCase ( self : str , a : int , a : float = None , a : float = None , a : float = None ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = sigma_min if sigma_min is not None else self.config.sigma_min
SCREAMING_SNAKE_CASE : List[str] = sigma_max if sigma_max is not None else self.config.sigma_max
SCREAMING_SNAKE_CASE : Tuple = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(a , a )
SCREAMING_SNAKE_CASE : Tuple = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
SCREAMING_SNAKE_CASE : Optional[Any] = torch.exp(torch.linspace(math.log(a ) , math.log(a ) , a ) )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )
def __UpperCamelCase ( self : Dict , a : int , a : Dict ) -> str:
"""simple docstring"""
return torch.where(
timesteps == 0 , torch.zeros_like(t.to(timesteps.device ) ) , self.discrete_sigmas[timesteps - 1].to(timesteps.device ) , )
def __UpperCamelCase ( self : str , a : torch.FloatTensor , a : int , a : torch.FloatTensor , a : Optional[torch.Generator] = None , a : bool = True , ) -> Union[SdeVeOutput, Tuple]:
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" )
SCREAMING_SNAKE_CASE : int = timestep * torch.ones(
sample.shape[0] , device=sample.device ) # torch.repeat_interleave(timestep, sample.shape[0])
SCREAMING_SNAKE_CASE : Tuple = (timestep * (len(self.timesteps ) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
SCREAMING_SNAKE_CASE : int = timesteps.to(self.discrete_sigmas.device )
SCREAMING_SNAKE_CASE : Optional[Any] = self.discrete_sigmas[timesteps].to(sample.device )
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_adjacent_sigma(a , a ).to(sample.device )
SCREAMING_SNAKE_CASE : Tuple = torch.zeros_like(a )
SCREAMING_SNAKE_CASE : List[Any] = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
SCREAMING_SNAKE_CASE : int = diffusion.flatten()
while len(diffusion.shape ) < len(sample.shape ):
SCREAMING_SNAKE_CASE : List[Any] = diffusion.unsqueeze(-1 )
SCREAMING_SNAKE_CASE : List[str] = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
SCREAMING_SNAKE_CASE : Optional[Any] = randn_tensor(
sample.shape , layout=sample.layout , generator=a , device=sample.device , dtype=sample.dtype )
SCREAMING_SNAKE_CASE : Optional[Any] = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
SCREAMING_SNAKE_CASE : Any = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=a , prev_sample_mean=a )
def __UpperCamelCase ( self : str , a : torch.FloatTensor , a : torch.FloatTensor , a : Optional[torch.Generator] = None , a : bool = True , ) -> Union[SchedulerOutput, Tuple]:
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" )
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
SCREAMING_SNAKE_CASE : List[str] = randn_tensor(sample.shape , layout=sample.layout , generator=a ).to(sample.device )
# compute step size from the model_output, the noise, and the snr
SCREAMING_SNAKE_CASE : Dict = torch.norm(model_output.reshape(model_output.shape[0] , -1 ) , dim=-1 ).mean()
SCREAMING_SNAKE_CASE : str = torch.norm(noise.reshape(noise.shape[0] , -1 ) , dim=-1 ).mean()
SCREAMING_SNAKE_CASE : int = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
SCREAMING_SNAKE_CASE : Dict = step_size * torch.ones(sample.shape[0] ).to(sample.device )
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
SCREAMING_SNAKE_CASE : Optional[int] = step_size.flatten()
while len(step_size.shape ) < len(sample.shape ):
SCREAMING_SNAKE_CASE : Union[str, Any] = step_size.unsqueeze(-1 )
SCREAMING_SNAKE_CASE : str = sample + step_size * model_output
SCREAMING_SNAKE_CASE : List[Any] = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=a )
def __UpperCamelCase ( self : int , a : torch.FloatTensor , a : torch.FloatTensor , a : torch.FloatTensor , ) -> torch.FloatTensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = timesteps.to(original_samples.device )
SCREAMING_SNAKE_CASE : Optional[int] = self.discrete_sigmas.to(original_samples.device )[timesteps]
SCREAMING_SNAKE_CASE : str = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(a ) * sigmas[:, None, None, None]
)
SCREAMING_SNAKE_CASE : Optional[Any] = noise + original_samples
return noisy_samples
def __len__( self : Optional[Any] ) -> int:
"""simple docstring"""
return self.config.num_train_timesteps | 25 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
a_ = get_logger()
a_ = None
class _UpperCamelCase ( TensorFormatter[Mapping, 'jax.Array', Mapping] ):
'''simple docstring'''
def __init__( self : Optional[Any] , a : str=None , a : List[Any]=None , **a : Any ) -> Optional[Any]:
"""simple docstring"""
super().__init__(features=a )
import jax
from jaxlib.xla_client import Device
if isinstance(a , a ):
raise ValueError(
F"Expected {device} to be a `str` not {type(a )}, as `jaxlib.xla_extension.Device` "
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
SCREAMING_SNAKE_CASE : List[str] = device if isinstance(a , a ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : str = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
F"Device with string identifier {self.device} not listed among the available "
F"devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default "
F"device: {str(jax.devices()[0] )}." )
SCREAMING_SNAKE_CASE : Any = str(jax.devices()[0] )
SCREAMING_SNAKE_CASE : Any = jnp_array_kwargs
@staticmethod
def __UpperCamelCase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
"""simple docstring"""
import jax
return {str(a ): device for device in jax.devices()}
def __UpperCamelCase ( self : Dict , a : Tuple ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(a , a ) and column:
if all(
isinstance(a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(a , axis=0 )
return column
def __UpperCamelCase ( self : Dict , a : str ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
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()
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
if isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
SCREAMING_SNAKE_CASE : Dict = {"dtype": jnp.intaa}
else:
SCREAMING_SNAKE_CASE : str = {"dtype": jnp.intaa}
elif isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
SCREAMING_SNAKE_CASE : int = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(a , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : Dict = np.asarray(a )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : Optional[Any] = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(a , **{**default_dtype, **self.jnp_array_kwargs} )
def __UpperCamelCase ( self : Any , a : List[str] ) -> Dict:
"""simple docstring"""
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(a , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(a , "__array__" ) and not isinstance(a , jax.Array ):
SCREAMING_SNAKE_CASE : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(a , np.ndarray ):
if data_struct.dtype == object: # jax arrays 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 __UpperCamelCase ( self : Optional[Any] , a : dict ) -> Dict:
"""simple docstring"""
return map_nested(self._recursive_tensorize , a , map_list=a )
def __UpperCamelCase ( self : Dict , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.numpy_arrow_extractor().extract_row(a )
SCREAMING_SNAKE_CASE : List[Any] = self.python_features_decoder.decode_row(a )
return self.recursive_tensorize(a )
def __UpperCamelCase ( self : Optional[int] , a : pa.Table ) -> "jax.Array":
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.numpy_arrow_extractor().extract_column(a )
SCREAMING_SNAKE_CASE : Optional[Any] = self.python_features_decoder.decode_column(a , pa_table.column_names[0] )
SCREAMING_SNAKE_CASE : Tuple = self.recursive_tensorize(a )
SCREAMING_SNAKE_CASE : Optional[int] = self._consolidate(a )
return column
def __UpperCamelCase ( self : List[Any] , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.numpy_arrow_extractor().extract_batch(a )
SCREAMING_SNAKE_CASE : str = self.python_features_decoder.decode_batch(a )
SCREAMING_SNAKE_CASE : List[Any] = self.recursive_tensorize(a )
for column_name in batch:
SCREAMING_SNAKE_CASE : List[Any] = self._consolidate(batch[column_name] )
return batch | 25 | 1 |
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_ = logging.get_logger(__name__)
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Dict = nn.ModuleList([src_layers[i] for i in layers_to_copy])
assert len(_a) == len(_a), f"{len(_a)} != {len(_a)}"
dest_layers.load_state_dict(layers_to_copy.state_dict())
a_ = {
# 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_ = {
# 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 lowerCamelCase__ ( _a , _a):
try:
SCREAMING_SNAKE_CASE : Any = 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(_a))
def lowerCamelCase__ ( _a , _a):
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(_a))
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def lowerCamelCase__ ( _a , _a = "student" , _a = None , _a = None , _a=False , _a=None , _a=None , **_a , ):
SCREAMING_SNAKE_CASE : Dict = "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(_a , _a):
AutoTokenizer.from_pretrained(_a).save_pretrained(_a) # purely for convenience
SCREAMING_SNAKE_CASE : str = AutoModelForSeqaSeqLM.from_pretrained(_a).eval()
else:
assert isinstance(_a , _a), f"teacher must be a model or string got type {type(_a)}"
SCREAMING_SNAKE_CASE : Optional[int] = teacher.config.to_diff_dict()
try:
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
SCREAMING_SNAKE_CASE : List[str] = teacher_e
if d is None:
SCREAMING_SNAKE_CASE : Optional[Any] = teacher_d
init_kwargs.update({"encoder_layers": e, "decoder_layers": d})
except AttributeError: # T5
if hasattr(teacher.config , "num_encoder_layers"):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
SCREAMING_SNAKE_CASE : Optional[Any] = teacher_e
if d is None:
SCREAMING_SNAKE_CASE : List[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(_a)
# Copy weights
SCREAMING_SNAKE_CASE : Tuple = teacher.config_class(**_a)
SCREAMING_SNAKE_CASE : Any = AutoModelForSeqaSeqLM.from_config(_a)
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
SCREAMING_SNAKE_CASE : List[str] = student.load_state_dict(teacher.state_dict() , strict=_a)
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
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[int] = list(range(_a)), list(range(_a))
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(_a)
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:
SCREAMING_SNAKE_CASE : List[int] = pick_layers_to_copy(_a , _a)
if d_layers_to_copy is None:
SCREAMING_SNAKE_CASE : List[int] = pick_layers_to_copy(_a , _a)
try:
if hasattr(
_a , "prophetnet"): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , _a)
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , _a)
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , _a)
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , _a)
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , _a)
copy_layers(teacher.decoder.block , student.decoder.block , _a)
logger.info(
f"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}")
SCREAMING_SNAKE_CASE : Optional[int] = {
"teacher_type": teacher.config.model_type,
"copied_encoder_layers": e_layers_to_copy,
"copied_decoder_layers": d_layers_to_copy,
}
student.save_pretrained(_a)
# 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) | 25 |
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 _UpperCamelCase :
'''simple docstring'''
@staticmethod
def __UpperCamelCase ( *a : str , **a : int ) -> str:
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =MODEL_FOR_OBJECT_DETECTION_MAPPING
def __UpperCamelCase ( self : Optional[Any] , a : str , a : Optional[Any] , a : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ObjectDetectionPipeline(model=a , image_processor=a )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def __UpperCamelCase ( self : List[Any] , a : Optional[int] , a : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" , threshold=0.0 )
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
import datasets
SCREAMING_SNAKE_CASE : Any = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
SCREAMING_SNAKE_CASE : Dict = [
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"],
]
SCREAMING_SNAKE_CASE : Tuple = object_detector(a , threshold=0.0 )
self.assertEqual(len(a ) , len(a ) )
for outputs in batch_outputs:
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
@require_tf
@unittest.skip("Object detection not implemented in TF" )
def __UpperCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
pass
@require_torch
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = "hf-internal-testing/tiny-detr-mobilenetsv3"
SCREAMING_SNAKE_CASE : Dict = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : Tuple = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : int = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=0.0 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
] , )
SCREAMING_SNAKE_CASE : Dict = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : List[str] = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : int = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : int = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Tuple = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : Tuple = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : str = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = 0.9985
SCREAMING_SNAKE_CASE : int = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : List[str] = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : str = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=a )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
@require_torch
@require_pytesseract
@slow
def __UpperCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = "Narsil/layoutlmv3-finetuned-funsd"
SCREAMING_SNAKE_CASE : Dict = 0.9993
SCREAMING_SNAKE_CASE : str = pipeline("object-detection" , model=a , threshold=a )
SCREAMING_SNAKE_CASE : List[Any] = object_detector(
"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
] , ) | 25 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _UpperCamelCase ( __A , __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =StableDiffusionInpaintPipeline
lowerCamelCase__ =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
lowerCamelCase__ =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowerCamelCase__ =frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowerCamelCase__ =frozenset([] )
def __UpperCamelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Dict = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , 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=a , )
SCREAMING_SNAKE_CASE : List[str] = PNDMScheduler(skip_prk_steps=a )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = 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=128 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Dict = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="gelu" , projection_dim=512 , )
SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPTextModel(a )
SCREAMING_SNAKE_CASE : Tuple = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
SCREAMING_SNAKE_CASE : List[str] = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def __UpperCamelCase ( self : List[Any] , a : Tuple , a : List[Any]=0 ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(a ) ).to(a )
SCREAMING_SNAKE_CASE : str = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE : Optional[int] = Image.fromarray(np.uinta(a ) ).convert("RGB" ).resize((64, 64) )
SCREAMING_SNAKE_CASE : Any = Image.fromarray(np.uinta(image + 4 ) ).convert("RGB" ).resize((64, 64) )
if str(a ).startswith("mps" ):
SCREAMING_SNAKE_CASE : Dict = torch.manual_seed(a )
else:
SCREAMING_SNAKE_CASE : Dict = torch.Generator(device=a ).manual_seed(a )
SCREAMING_SNAKE_CASE : Optional[Any] = {
"prompt": "A painting of a squirrel eating a burger",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def __UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = "cpu" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE : Tuple = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Tuple = StableDiffusionInpaintPipeline(**a )
SCREAMING_SNAKE_CASE : int = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[Any] = self.get_dummy_inputs(a )
SCREAMING_SNAKE_CASE : Dict = sd_pipe(**a ).images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE : Dict = np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : Dict ) -> List[str]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png" )
SCREAMING_SNAKE_CASE : str = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" )
SCREAMING_SNAKE_CASE : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
"/yellow_cat_sitting_on_a_park_bench.npy" )
SCREAMING_SNAKE_CASE : Tuple = "stabilityai/stable-diffusion-2-inpainting"
SCREAMING_SNAKE_CASE : Union[str, Any] = StableDiffusionInpaintPipeline.from_pretrained(a , safety_checker=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE : Union[str, Any] = "Face of a yellow cat, high resolution, sitting on a park bench"
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = pipe(
prompt=a , image=a , mask_image=a , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : Optional[int] = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 9e-3
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png" )
SCREAMING_SNAKE_CASE : Dict = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" )
SCREAMING_SNAKE_CASE : Tuple = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
"/yellow_cat_sitting_on_a_park_bench_fp16.npy" )
SCREAMING_SNAKE_CASE : Any = "stabilityai/stable-diffusion-2-inpainting"
SCREAMING_SNAKE_CASE : Optional[int] = StableDiffusionInpaintPipeline.from_pretrained(
a , torch_dtype=torch.floataa , safety_checker=a , )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE : Dict = "Face of a yellow cat, high resolution, sitting on a park bench"
SCREAMING_SNAKE_CASE : List[Any] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = pipe(
prompt=a , image=a , mask_image=a , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[str] = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 5e-1
def __UpperCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png" )
SCREAMING_SNAKE_CASE : str = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" )
SCREAMING_SNAKE_CASE : Dict = "stabilityai/stable-diffusion-2-inpainting"
SCREAMING_SNAKE_CASE : Any = PNDMScheduler.from_pretrained(a , subfolder="scheduler" )
SCREAMING_SNAKE_CASE : str = StableDiffusionInpaintPipeline.from_pretrained(
a , safety_checker=a , scheduler=a , torch_dtype=torch.floataa , )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
SCREAMING_SNAKE_CASE : Optional[int] = "Face of a yellow cat, high resolution, sitting on a park bench"
SCREAMING_SNAKE_CASE : Dict = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(
prompt=a , image=a , mask_image=a , generator=a , num_inference_steps=2 , output_type="np" , )
SCREAMING_SNAKE_CASE : Dict = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 10**9 | 25 |
def lowerCamelCase__ ( _a):
if not isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Tuple = f"Input value of [number={number}] must be an integer"
raise TypeError(_a)
if number < 0:
return False
SCREAMING_SNAKE_CASE : Union[str, Any] = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCamelCase__ ( _a):
return (data["data"], data["target"])
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Tuple = XGBClassifier()
classifier.fit(_a , _a)
return classifier
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : str = load_iris()
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Any = data_handling(_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : int = train_test_split(
_a , _a , test_size=0.25)
SCREAMING_SNAKE_CASE : str = iris["target_names"]
# Create an XGBoost Classifier from the training data
SCREAMING_SNAKE_CASE : Any = xgboost(_a , _a)
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
_a , _a , _a , display_labels=_a , cmap="Blues" , normalize="true" , )
plt.title("Normalized Confusion Matrix - IRIS Dataset")
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main() | 25 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Dict , a : Tuple , a : Any=13 , a : Any=7 , a : Union[str, Any]=True , a : List[Any]=True , a : List[str]=False , a : List[str]=True , a : Any=99 , a : str=32 , a : Any=5 , a : Optional[int]=4 , a : Union[str, Any]=37 , a : Dict="gelu" , a : List[Any]=0.1 , a : Optional[Any]=0.1 , a : List[str]=512 , a : Union[str, Any]=16 , a : str=2 , a : Dict=0.02 , a : Optional[int]=3 , a : Union[str, Any]=4 , a : int=None , ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = parent
SCREAMING_SNAKE_CASE : Any = batch_size
SCREAMING_SNAKE_CASE : Optional[int] = seq_length
SCREAMING_SNAKE_CASE : List[Any] = is_training
SCREAMING_SNAKE_CASE : int = use_input_mask
SCREAMING_SNAKE_CASE : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE : str = use_labels
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_size
SCREAMING_SNAKE_CASE : str = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = intermediate_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE : List[str] = type_vocab_size
SCREAMING_SNAKE_CASE : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : Tuple = num_labels
SCREAMING_SNAKE_CASE : Tuple = num_choices
SCREAMING_SNAKE_CASE : Optional[Any] = scope
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : str = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE : int = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self : Dict ) -> str:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : Optional[int] , a : Optional[int] , a : Dict , a : str , a : str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = DistilBertModel(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , a )
SCREAMING_SNAKE_CASE : Optional[Any] = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self : Tuple , a : Optional[int] , a : Dict , a : Tuple , a : int , a : int , a : Any ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = DistilBertForMaskedLM(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : str = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self : List[Any] , a : int , a : Optional[Any] , a : Optional[Any] , a : str , a : str , a : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertForQuestionAnswering(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(
a , attention_mask=a , start_positions=a , end_positions=a )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase ( self : Optional[int] , a : str , a : Any , a : int , a : Optional[Any] , a : int , a : str ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.num_labels
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertForSequenceClassification(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase ( self : Optional[Any] , a : List[Any] , a : Optional[int] , a : Union[str, Any] , a : Dict , a : Any , a : Optional[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.num_labels
SCREAMING_SNAKE_CASE : List[str] = DistilBertForTokenClassification(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase ( self : int , a : Any , a : Optional[int] , a : Union[str, Any] , a : Tuple , a : Optional[int] , a : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.num_choices
SCREAMING_SNAKE_CASE : Any = DistilBertForMultipleChoice(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Optional[Any] = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE)) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE : int = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCamelCase__ =(
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
def __UpperCamelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = DistilBertModelTester(self )
SCREAMING_SNAKE_CASE : List[str] = ConfigTester(self , config_class=a , dim=37 )
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a )
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a )
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a )
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a )
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a )
def __UpperCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a )
@slow
def __UpperCamelCase ( self : int ) -> Any:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertModel.from_pretrained(a )
self.assertIsNotNone(a )
@slow
@require_torch_gpu
def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : Any = model_class(config=a )
SCREAMING_SNAKE_CASE : List[Any] = self._prepare_for_class(a , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.jit.trace(
a , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a , os.path.join(a , "traced_model.pt" ) )
SCREAMING_SNAKE_CASE : Tuple = torch.jit.load(os.path.join(a , "traced_model.pt" ) , map_location=a )
loaded(inputs_dict["input_ids"].to(a ) , inputs_dict["attention_mask"].to(a ) )
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertModel.from_pretrained("distilbert-base-uncased" )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , attention_mask=a )[0]
SCREAMING_SNAKE_CASE : List[str] = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) ) | 25 | 1 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
a_ = 'base_with_context'
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]))
SCREAMING_SNAKE_CASE : int = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"]) , requires_grad=_a)
for lyr_num, lyr in enumerate(model.encoders):
SCREAMING_SNAKE_CASE : List[Any] = weights[f"layers_{lyr_num}"]
SCREAMING_SNAKE_CASE : Dict = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]))
SCREAMING_SNAKE_CASE : int = ly_weight["attention"]
SCREAMING_SNAKE_CASE : List[str] = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
SCREAMING_SNAKE_CASE : List[str] = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
SCREAMING_SNAKE_CASE : Any = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
SCREAMING_SNAKE_CASE : Dict = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
SCREAMING_SNAKE_CASE : Optional[Any] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
SCREAMING_SNAKE_CASE : int = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))
SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]))
return model
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T))
SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"]) , requires_grad=_a)
for lyr_num, lyr in enumerate(model.encoders):
SCREAMING_SNAKE_CASE : List[Any] = weights[f"layers_{lyr_num}"]
SCREAMING_SNAKE_CASE : int = ly_weight["attention"]
SCREAMING_SNAKE_CASE : int = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
SCREAMING_SNAKE_CASE : int = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
SCREAMING_SNAKE_CASE : Dict = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
SCREAMING_SNAKE_CASE : Any = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]))
SCREAMING_SNAKE_CASE : Dict = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
SCREAMING_SNAKE_CASE : int = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))
SCREAMING_SNAKE_CASE : str = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]))
return model
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Any = nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T))
SCREAMING_SNAKE_CASE : Dict = nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T))
SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"]) , requires_grad=_a)
SCREAMING_SNAKE_CASE : int = nn.Parameter(
torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T))
for lyr_num, lyr in enumerate(model.decoders):
SCREAMING_SNAKE_CASE : Tuple = weights[f"layers_{lyr_num}"]
SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(
torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"]))
SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T))
SCREAMING_SNAKE_CASE : List[Any] = ly_weight["self_attention"]
SCREAMING_SNAKE_CASE : Any = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
SCREAMING_SNAKE_CASE : Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
SCREAMING_SNAKE_CASE : str = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
SCREAMING_SNAKE_CASE : Dict = ly_weight["MultiHeadDotProductAttention_0"]
SCREAMING_SNAKE_CASE : List[str] = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
SCREAMING_SNAKE_CASE : Dict = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
SCREAMING_SNAKE_CASE : Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(
torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]))
SCREAMING_SNAKE_CASE : List[str] = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
SCREAMING_SNAKE_CASE : List[str] = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T))
SCREAMING_SNAKE_CASE : Dict = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
SCREAMING_SNAKE_CASE : List[str] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
SCREAMING_SNAKE_CASE : str = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))
SCREAMING_SNAKE_CASE : Any = nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]))
SCREAMING_SNAKE_CASE : Optional[Any] = nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T))
return model
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = checkpoints.load_tax_checkpoint(args.checkpoint_path)
SCREAMING_SNAKE_CASE : int = jnp.tree_util.tree_map(onp.array , _a)
SCREAMING_SNAKE_CASE : Union[str, Any] = [
"from __gin__ import dynamic_registration",
"from music_spectrogram_diffusion.models.diffusion import diffusion_utils",
"diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0",
"diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()",
]
SCREAMING_SNAKE_CASE : List[str] = os.path.join(args.checkpoint_path , ".." , "config.gin")
SCREAMING_SNAKE_CASE : Optional[Any] = inference.parse_training_gin_file(_a , _a)
SCREAMING_SNAKE_CASE : List[str] = inference.InferenceModel(args.checkpoint_path , _a)
SCREAMING_SNAKE_CASE : str = DDPMScheduler(beta_schedule="squaredcos_cap_v2" , variance_type="fixed_large")
SCREAMING_SNAKE_CASE : Optional[Any] = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length["inputs"] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="gated-gelu" , )
SCREAMING_SNAKE_CASE : Any = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length["targets_context"] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="gated-gelu" , )
SCREAMING_SNAKE_CASE : Any = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length["targets_context"] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
SCREAMING_SNAKE_CASE : int = load_notes_encoder(ta_checkpoint["target"]["token_encoder"] , _a)
SCREAMING_SNAKE_CASE : Optional[Any] = load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"] , _a)
SCREAMING_SNAKE_CASE : List[str] = load_decoder(ta_checkpoint["target"]["decoder"] , _a)
SCREAMING_SNAKE_CASE : Tuple = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder")
SCREAMING_SNAKE_CASE : List[Any] = SpectrogramDiffusionPipeline(
notes_encoder=_a , continuous_encoder=_a , decoder=_a , scheduler=_a , melgan=_a , )
if args.save:
pipe.save_pretrained(args.output_path)
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
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=F'''{MODEL}/checkpoint_500000''',
type=str,
required=False,
help='Path to the original jax model checkpoint.',
)
a_ = parser.parse_args()
main(args) | 25 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 1 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class _UpperCamelCase :
'''simple docstring'''
@staticmethod
def __UpperCamelCase ( *a : Any , **a : str ) -> Any:
"""simple docstring"""
pass
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : str = hashlib.mda(image.tobytes())
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def __UpperCamelCase ( self : Tuple , a : int , a : Any , a : Any ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = DepthEstimationPipeline(model=a , image_processor=a )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def __UpperCamelCase ( self : int , a : List[str] , a : List[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png" )
self.assertEqual({"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )} , a )
import datasets
SCREAMING_SNAKE_CASE : Tuple = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
SCREAMING_SNAKE_CASE : List[Any] = depth_estimator(
[
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"],
] )
self.assertEqual(
[
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
] , a , )
@require_tf
@unittest.skip("Depth estimation is not implemented in TF" )
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
pass
@slow
@require_torch
def __UpperCamelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = "Intel/dpt-large"
SCREAMING_SNAKE_CASE : Optional[int] = pipeline("depth-estimation" , model=a )
SCREAMING_SNAKE_CASE : str = depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg" )
SCREAMING_SNAKE_CASE : Union[str, Any] = hashimage(outputs["depth"] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item() ) , 2.662 )
@require_torch
def __UpperCamelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT" ) | 25 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
def __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE : Union[str, Any] = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Any = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" , return_dict=a )[0]
SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "google/ncsnpp-celebahq-256"
SCREAMING_SNAKE_CASE : List[Any] = UNetaDModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Optional[Any] = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = pipe(num_inference_steps=20 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 1 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def lowerCamelCase__ ( _a):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , a : nn.Module , a : int ) -> Dict:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE : str = module
SCREAMING_SNAKE_CASE : List[Any] = nn.Sequential(
nn.Linear(module.in_features , a , bias=a ) , nn.Linear(a , module.out_features , bias=a ) , )
SCREAMING_SNAKE_CASE : Optional[Any] = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=a )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def __UpperCamelCase ( self : str , a : Optional[Any] , *a : str , **a : List[str] ) -> Optional[int]:
"""simple docstring"""
return self.module(a , *a , **a ) + self.adapter(a )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ ='bigscience/bloom-1b7'
# Constant values
lowerCamelCase__ =2.109659552692574
lowerCamelCase__ ='Hello my name is'
lowerCamelCase__ =set()
EXPECTED_OUTPUTS.add('Hello my name is John and I am a professional photographer. I' )
EXPECTED_OUTPUTS.add('Hello my name is John.\nI am a friend of your father.\n' )
EXPECTED_OUTPUTS.add('Hello my name is John Doe, I am a student at the University' )
lowerCamelCase__ =10
def __UpperCamelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = AutoTokenizer.from_pretrained(self.model_name )
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> int:
"""simple docstring"""
super().setUp()
# Models and tokenizer
SCREAMING_SNAKE_CASE : Dict = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map="auto" )
SCREAMING_SNAKE_CASE : int = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=a , device_map="auto" )
def __UpperCamelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.model_abit.config
self.assertTrue(hasattr(a , "quantization_config" ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = config.to_dict()
SCREAMING_SNAKE_CASE : str = config.to_diff_dict()
SCREAMING_SNAKE_CASE : Union[str, Any] = config.to_json_string()
def __UpperCamelCase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
from bitsandbytes.nn import Paramsabit
SCREAMING_SNAKE_CASE : int = self.model_fpaa.get_memory_footprint()
SCREAMING_SNAKE_CASE : int = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
SCREAMING_SNAKE_CASE : Union[str, Any] = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(a , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def __UpperCamelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.tokenizer(self.input_text , return_tensors="pt" )
SCREAMING_SNAKE_CASE : Tuple = self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=a ) , self.EXPECTED_OUTPUTS )
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = BitsAndBytesConfig()
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : int = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=a , device_map="auto" )
SCREAMING_SNAKE_CASE : int = self.tokenizer(self.input_text , return_tensors="pt" )
SCREAMING_SNAKE_CASE : Tuple = model_abit_from_config.generate(
input_ids=encoded_input["input_ids"].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=a ) , self.EXPECTED_OUTPUTS )
def __UpperCamelCase ( self : Any ) -> str:
"""simple docstring"""
with self.assertRaises(a ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(a )
def __UpperCamelCase ( self : List[str] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = BitsAndBytesConfig()
with self.assertRaises(a ):
SCREAMING_SNAKE_CASE : List[str] = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=a , load_in_abit=a , device_map="auto" , bnb_abit_quant_type="nf4" , )
def __UpperCamelCase ( self : Any ) -> Tuple:
"""simple docstring"""
with self.assertRaises(a ):
# Tries with `str`
self.model_abit.to("cpu" )
with self.assertRaises(a ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(a ):
# Tries with a `device`
self.model_abit.to(torch.device("cuda:0" ) )
with self.assertRaises(a ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(a ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer(self.input_text , return_tensors="pt" )
SCREAMING_SNAKE_CASE : Optional[Any] = self.model_fpaa.to(torch.floataa )
SCREAMING_SNAKE_CASE : Optional[Any] = self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
SCREAMING_SNAKE_CASE : Tuple = self.model_fpaa.to("cpu" )
# Check this does not throw an error
SCREAMING_SNAKE_CASE : Dict = self.model_fpaa.half()
# Check this does not throw an error
SCREAMING_SNAKE_CASE : str = self.model_fpaa.float()
def __UpperCamelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = AutoModelForSeqaSeqLM.from_pretrained("t5-small" , load_in_abit=a , device_map="auto" )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def __UpperCamelCase ( cls : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = "t5-small"
SCREAMING_SNAKE_CASE : Optional[Any] = "google/flan-t5-small" # flan-t5 uses dense-act instead of dense-relu-dense
SCREAMING_SNAKE_CASE : Any = AutoTokenizer.from_pretrained(cls.model_name )
SCREAMING_SNAKE_CASE : Optional[Any] = "Translate in German: Hello, my dog is cute"
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
from transformers import TaForConditionalGeneration
SCREAMING_SNAKE_CASE : Any = TaForConditionalGeneration._keep_in_fpaa_modules
SCREAMING_SNAKE_CASE : Dict = None
# test with `t5-small`
SCREAMING_SNAKE_CASE : List[Any] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=a , device_map="auto" )
SCREAMING_SNAKE_CASE : int = self.tokenizer(self.input_text , return_tensors="pt" ).to(0 )
SCREAMING_SNAKE_CASE : Any = model.generate(**a )
# test with `flan-t5-small`
SCREAMING_SNAKE_CASE : Dict = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=a , device_map="auto" )
SCREAMING_SNAKE_CASE : int = self.tokenizer(self.input_text , return_tensors="pt" ).to(0 )
SCREAMING_SNAKE_CASE : Optional[int] = model.generate(**a )
SCREAMING_SNAKE_CASE : Tuple = modules
def __UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
SCREAMING_SNAKE_CASE : Optional[Any] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=a , device_map="auto" )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer(self.input_text , return_tensors="pt" ).to(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = model.generate(**a )
# test with `flan-t5-small`
SCREAMING_SNAKE_CASE : List[str] = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=a , device_map="auto" )
SCREAMING_SNAKE_CASE : Dict = self.tokenizer(self.input_text , return_tensors="pt" ).to(0 )
SCREAMING_SNAKE_CASE : Tuple = model.generate(**a )
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __UpperCamelCase ( self : Any ) -> Tuple:
"""simple docstring"""
super().setUp()
# model_name
SCREAMING_SNAKE_CASE : Optional[int] = "bigscience/bloom-560m"
SCREAMING_SNAKE_CASE : List[str] = "t5-small"
# Different types of model
SCREAMING_SNAKE_CASE : str = AutoModel.from_pretrained(self.model_name , load_in_abit=a , device_map="auto" )
# Sequence classification model
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=a , device_map="auto" )
# CausalLM model
SCREAMING_SNAKE_CASE : List[Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=a , device_map="auto" )
# Seq2seq model
SCREAMING_SNAKE_CASE : List[str] = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=a , device_map="auto" )
def __UpperCamelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Dict:
"""simple docstring"""
super().setUp()
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Dict ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = pipeline(
"text-generation" , model=self.model_name , model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
SCREAMING_SNAKE_CASE : Any = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]["generated_text"] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
super().setUp()
def __UpperCamelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=a , device_map="balanced" )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
SCREAMING_SNAKE_CASE : Tuple = self.tokenizer(self.input_text , return_tensors="pt" )
# Second real batch
SCREAMING_SNAKE_CASE : Tuple = model_parallel.generate(input_ids=encoded_input["input_ids"].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=a ) , self.EXPECTED_OUTPUTS )
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __UpperCamelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = "facebook/opt-350m"
super().setUp()
def __UpperCamelCase ( self : int ) -> List[Any]:
"""simple docstring"""
if version.parse(importlib.metadata.version("bitsandbytes" ) ) < version.parse("0.37.0" ):
return
# Step 1: freeze all parameters
SCREAMING_SNAKE_CASE : Dict = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=a )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
SCREAMING_SNAKE_CASE : Any = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
SCREAMING_SNAKE_CASE : Union[str, Any] = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(a ) ):
SCREAMING_SNAKE_CASE : Optional[Any] = LoRALayer(module.q_proj , rank=16 )
SCREAMING_SNAKE_CASE : Optional[int] = LoRALayer(module.k_proj , rank=16 )
SCREAMING_SNAKE_CASE : Optional[int] = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
SCREAMING_SNAKE_CASE : Dict = self.tokenizer("Test batch " , return_tensors="pt" ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
SCREAMING_SNAKE_CASE : str = model.forward(**a )
out.logits.norm().backward()
for module in model.modules():
if isinstance(a , a ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(a , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='gpt2-xl'
lowerCamelCase__ =3.3191854854152187 | 25 |
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
SCREAMING_SNAKE_CASE : Optional[Any] = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res | 25 | 1 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCamelCase__ ( _a , _a , _a = None):
if version.parse(hfh.__version__).release < version.parse("0.11.0").release:
# old versions of hfh don't url-encode the file path
SCREAMING_SNAKE_CASE : Dict = quote(_a)
return hfh.hf_hub_url(_a , _a , repo_type="dataset" , revision=_a) | 25 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='roformer'
def __init__( self : Dict , a : Any=5_0000 , a : List[Any]=None , a : str=768 , a : str=12 , a : Tuple=12 , a : Optional[Any]=3072 , a : List[str]="gelu" , a : List[Any]=0.1 , a : Union[str, Any]=0.1 , a : Tuple=1536 , a : List[str]=2 , a : Tuple=0.02 , a : Any=1e-12 , a : Optional[int]=0 , a : Union[str, Any]=False , a : int=True , **a : str , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=a , **a )
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size if embedding_size is None else embedding_size
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = type_vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE : List[str] = rotary_value
SCREAMING_SNAKE_CASE : int = use_cache
class _UpperCamelCase ( __A ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE : str = {0: "batch", 1: "sequence"}
SCREAMING_SNAKE_CASE : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] ) | 25 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
a_ = {
'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='instructblip_vision_model'
def __init__( self : List[str] , a : Optional[int]=1408 , a : List[str]=6144 , a : str=39 , a : Optional[int]=16 , a : List[Any]=224 , a : Dict=14 , a : Union[str, Any]="gelu" , a : Tuple=1e-6 , a : Any=0.0 , a : str=1e-10 , a : Tuple=True , **a : Dict , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**a )
SCREAMING_SNAKE_CASE : List[Any] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = intermediate_size
SCREAMING_SNAKE_CASE : Optional[int] = num_hidden_layers
SCREAMING_SNAKE_CASE : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE : Dict = patch_size
SCREAMING_SNAKE_CASE : List[str] = image_size
SCREAMING_SNAKE_CASE : Dict = initializer_range
SCREAMING_SNAKE_CASE : str = attention_dropout
SCREAMING_SNAKE_CASE : str = layer_norm_eps
SCREAMING_SNAKE_CASE : str = hidden_act
SCREAMING_SNAKE_CASE : Tuple = qkv_bias
@classmethod
def __UpperCamelCase ( cls : int , a : Union[str, os.PathLike] , **a : Tuple ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(a )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = cls.get_config_dict(a , **a )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("model_type" ) == "instructblip":
SCREAMING_SNAKE_CASE : 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(a , **a )
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='instructblip_qformer'
def __init__( self : List[Any] , a : str=3_0522 , a : List[Any]=768 , a : Tuple=12 , a : Dict=12 , a : List[Any]=3072 , a : List[Any]="gelu" , a : Optional[Any]=0.1 , a : List[str]=0.1 , a : List[Any]=512 , a : List[str]=0.02 , a : List[str]=1e-12 , a : Optional[Any]=0 , a : Union[str, Any]="absolute" , a : Any=2 , a : Union[str, Any]=1408 , **a : Tuple , ) -> str:
"""simple docstring"""
super().__init__(pad_token_id=a , **a )
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : Any = hidden_size
SCREAMING_SNAKE_CASE : Dict = num_hidden_layers
SCREAMING_SNAKE_CASE : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE : str = hidden_act
SCREAMING_SNAKE_CASE : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : List[str] = max_position_embeddings
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : List[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : Optional[int] = position_embedding_type
SCREAMING_SNAKE_CASE : Dict = cross_attention_frequency
SCREAMING_SNAKE_CASE : int = encoder_hidden_size
@classmethod
def __UpperCamelCase ( cls : Dict , a : Union[str, os.PathLike] , **a : Optional[int] ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(a )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = cls.get_config_dict(a , **a )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("model_type" ) == "instructblip":
SCREAMING_SNAKE_CASE : int = config_dict["qformer_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(a , **a )
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='instructblip'
lowerCamelCase__ =True
def __init__( self : int , a : Optional[Any]=None , a : str=None , a : List[Any]=None , a : Optional[int]=32 , **a : List[Any] ) -> List[str]:
"""simple docstring"""
super().__init__(**a )
if vision_config is None:
SCREAMING_SNAKE_CASE : Optional[int] = {}
logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values." )
if qformer_config is None:
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values." )
if text_config is None:
SCREAMING_SNAKE_CASE : Tuple = {}
logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)." )
SCREAMING_SNAKE_CASE : int = InstructBlipVisionConfig(**a )
SCREAMING_SNAKE_CASE : Union[str, Any] = InstructBlipQFormerConfig(**a )
SCREAMING_SNAKE_CASE : Union[str, Any] = text_config["model_type"] if "model_type" in text_config else "opt"
SCREAMING_SNAKE_CASE : Optional[Any] = CONFIG_MAPPING[text_model_type](**a )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.text_config.tie_word_embeddings
SCREAMING_SNAKE_CASE : Union[str, Any] = self.text_config.is_encoder_decoder
SCREAMING_SNAKE_CASE : str = num_query_tokens
SCREAMING_SNAKE_CASE : Optional[Any] = self.vision_config.hidden_size
SCREAMING_SNAKE_CASE : Optional[Any] = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
SCREAMING_SNAKE_CASE : Optional[Any] = 1.0
SCREAMING_SNAKE_CASE : Tuple = 0.02
@classmethod
def __UpperCamelCase ( cls : int , a : InstructBlipVisionConfig , a : InstructBlipQFormerConfig , a : PretrainedConfig , **a : int , ) -> List[str]:
"""simple docstring"""
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **a , )
def __UpperCamelCase ( self : List[str] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE : Any = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE : Union[str, Any] = self.qformer_config.to_dict()
SCREAMING_SNAKE_CASE : Tuple = self.text_config.to_dict()
SCREAMING_SNAKE_CASE : int = self.__class__.model_type
return output | 25 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__name__)
a_ = 'Hello world! cécé herlolip'
a_ = namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = BertAbsConfig(
temp_dir="." , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder="bert" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : Dict = torch.load(_a , lambda _a , _a: storage)
SCREAMING_SNAKE_CASE : str = AbsSummarizer(_a , torch.device("cpu") , _a)
original.eval()
SCREAMING_SNAKE_CASE : List[str] = BertAbsSummarizer(_a , torch.device("cpu"))
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model")
new_model.bert.load_state_dict(original.bert.state_dict())
new_model.decoder.load_state_dict(original.decoder.state_dict())
new_model.generator.load_state_dict(original.generator.state_dict())
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical")
SCREAMING_SNAKE_CASE : List[str] = BertTokenizer.from_pretrained("bert-base-uncased")
# prepare the model inputs
SCREAMING_SNAKE_CASE : List[str] = tokenizer.encode("This is sample éàalj'-.")
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode("This is sample 3 éàalj'-.")
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0
# forward pass
SCREAMING_SNAKE_CASE : List[Any] = encoder_input_ids
SCREAMING_SNAKE_CASE : List[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Dict = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : Optional[int] = original(_a , _a , _a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Dict = original.generator(_a)
SCREAMING_SNAKE_CASE : Any = new_model(
_a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Tuple = new_model.generator(_a)
SCREAMING_SNAKE_CASE : List[Any] = torch.max(torch.abs(output_converted_model - output_original_model)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : int = torch.allclose(_a , _a , atol=1E-3)
if are_identical:
logging.info("all weights are equal up to 1e-3")
else:
raise ValueError("the weights are different. The new model is likely different from the original one.")
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary")
torch.save(
new_model.state_dict() , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin")
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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.',
)
a_ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
) | 25 | 1 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCamelCase__ ( _a):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set())
@pytest.fixture
def lowerCamelCase__ ( _a):
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : int , a : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = metric_id
class _UpperCamelCase :
'''simple docstring'''
lowerCamelCase__ =[MetricMock(__A ) for metric_id in ['accuracy', 'mse', 'precision', 'codeparrot/apps_metric']]
def __UpperCamelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
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 lowerCamelCase__ ( _a , _a , _a , _a , _a):
if "tmp_path" in args:
SCREAMING_SNAKE_CASE : int = tuple(arg if arg != "tmp_path" else tmp_path for arg in args)
with pytest.warns(_a , match="https://huggingface.co/docs/evaluate"):
func(*_a) | 25 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
a_ = parser.parse_args()
a_ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
a_ = CLIPImageProcessor()
a_ = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
a_ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path) | 25 | 1 |
def lowerCamelCase__ ( _a):
return credit_card_number.startswith(("34", "35", "37", "4", "5", "6"))
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = credit_card_number
SCREAMING_SNAKE_CASE : str = 0
SCREAMING_SNAKE_CASE : Dict = len(_a) - 2
for i in range(_a , -1 , -2):
# double the value of every second digit
SCREAMING_SNAKE_CASE : int = int(cc_number[i])
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
SCREAMING_SNAKE_CASE : int = cc_number[:i] + str(_a) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_a) - 1 , -1 , -2):
total += int(cc_number[i])
return total % 10 == 0
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : str = f"{credit_card_number} is an invalid credit card number because"
if not credit_card_number.isdigit():
print(f"{error_message} it has nonnumerical characters.")
return False
if not 13 <= len(_a) <= 16:
print(f"{error_message} of its length.")
return False
if not validate_initial_digits(_a):
print(f"{error_message} of its first two digits.")
return False
if not luhn_validation(_a):
print(f"{error_message} it fails the Luhn check.")
return False
print(f"{credit_card_number} is a valid credit card number.")
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number('4111111111111111')
validate_credit_card_number('32323') | 25 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Swinv2ForImageClassification',
'Swinv2ForMaskedImageModeling',
'Swinv2Model',
'Swinv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 1 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values")
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values")
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase__ ( _a , _a , _a):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values")
SCREAMING_SNAKE_CASE : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def lowerCamelCase__ ( _a , _a):
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values")
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori")
return 4 * pow(_a , 2) * torus_radius * tube_radius
def lowerCamelCase__ ( _a , _a):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def lowerCamelCase__ ( _a , _a , _a):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values")
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("Given three sides do not form a triangle")
SCREAMING_SNAKE_CASE : List[str] = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE : Optional[int] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea))
return area
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def lowerCamelCase__ ( _a , _a , _a):
if basea < 0 or basea < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values")
return 1 / 2 * (basea + basea) * height
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def lowerCamelCase__ ( _a , _a):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("area_rhombus() only accepts non-negative values")
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase__ ( _a , _a):
if not isinstance(_a , _a) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides")
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side")
return (sides * length**2) / (4 * tan(pi / sides))
return (sides * length**2) / (4 * tan(pi / sides))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('[DEMO] Areas of various geometric shapes: \n')
print(F'''Rectangle: {area_rectangle(10, 20) = }''')
print(F'''Square: {area_square(10) = }''')
print(F'''Triangle: {area_triangle(10, 10) = }''')
print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(F'''Rhombus: {area_rhombus(10, 20) = }''')
print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(F'''Circle: {area_circle(20) = }''')
print(F'''Ellipse: {area_ellipse(10, 20) = }''')
print('\nSurface Areas of various geometric shapes: \n')
print(F'''Cube: {surface_area_cube(20) = }''')
print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(F'''Sphere: {surface_area_sphere(20) = }''')
print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(F'''Cone: {surface_area_cone(10, 20) = }''')
print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(F'''Torus: {surface_area_torus(20, 10) = }''')
print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(F'''Square: {area_reg_polygon(4, 10) = }''')
print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''') | 25 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values")
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values")
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase__ ( _a , _a , _a):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values")
SCREAMING_SNAKE_CASE : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def lowerCamelCase__ ( _a , _a):
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values")
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori")
return 4 * pow(_a , 2) * torus_radius * tube_radius
def lowerCamelCase__ ( _a , _a):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def lowerCamelCase__ ( _a , _a , _a):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values")
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("Given three sides do not form a triangle")
SCREAMING_SNAKE_CASE : List[str] = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE : Optional[int] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea))
return area
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def lowerCamelCase__ ( _a , _a , _a):
if basea < 0 or basea < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values")
return 1 / 2 * (basea + basea) * height
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def lowerCamelCase__ ( _a , _a):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("area_rhombus() only accepts non-negative values")
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase__ ( _a , _a):
if not isinstance(_a , _a) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides")
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side")
return (sides * length**2) / (4 * tan(pi / sides))
return (sides * length**2) / (4 * tan(pi / sides))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('[DEMO] Areas of various geometric shapes: \n')
print(F'''Rectangle: {area_rectangle(10, 20) = }''')
print(F'''Square: {area_square(10) = }''')
print(F'''Triangle: {area_triangle(10, 10) = }''')
print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(F'''Rhombus: {area_rhombus(10, 20) = }''')
print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(F'''Circle: {area_circle(20) = }''')
print(F'''Ellipse: {area_ellipse(10, 20) = }''')
print('\nSurface Areas of various geometric shapes: \n')
print(F'''Cube: {surface_area_cube(20) = }''')
print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(F'''Sphere: {surface_area_sphere(20) = }''')
print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(F'''Cone: {surface_area_cone(10, 20) = }''')
print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(F'''Torus: {surface_area_torus(20, 10) = }''')
print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(F'''Square: {area_reg_polygon(4, 10) = }''')
print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''') | 25 | 1 |
import math
def lowerCamelCase__ ( _a , _a):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(_a)
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
raise AssertionError("This should never happen")
if __name__ == "__main__": # Main function
# Read two numbers from input and typecast them to int using map function.
# Here x is the base and y is the power.
a_ = 'Enter the base and the power separated by a comma: '
a_ , a_ = map(int, input(prompt).split(','))
a_ , a_ = map(int, input(prompt).split(','))
# We find the log of each number, using the function res(), which takes two
# arguments.
a_ = res(xa, ya)
a_ = res(xa, ya)
# We check for the largest number
if resa > resa:
print('Largest number is', xa, '^', ya)
elif resa > resa:
print('Largest number is', xa, '^', ya)
else:
print('Both are equal') | 25 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 1 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.json'}
a_ = {
'vocab_file': {
'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
}
}
a_ = {'mgp-str': 27}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =VOCAB_FILES_NAMES
lowerCamelCase__ =PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : str , a : Tuple , a : Any="[GO]" , a : Dict="[GO]" , a : List[Any]="[s]" , a : Tuple="[GO]" , **a : Any ) -> Optional[Any]:
"""simple docstring"""
super().__init__(
unk_token=a , bos_token=a , eos_token=a , pad_token=a , **a , )
with open(a , encoding="utf-8" ) as vocab_handle:
SCREAMING_SNAKE_CASE : Any = json.load(a )
SCREAMING_SNAKE_CASE : Dict = {v: k for k, v in self.vocab.items()}
@property
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
return len(self.vocab )
def __UpperCamelCase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
return dict(self.vocab , **self.added_tokens_encoder )
def __UpperCamelCase ( self : Union[str, Any] , a : Optional[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = []
for s in text:
char_tokens.extend(a )
return char_tokens
def __UpperCamelCase ( self : Optional[Any] , a : Optional[int] ) -> int:
"""simple docstring"""
return self.vocab.get(a , self.vocab.get(self.unk_token ) )
def __UpperCamelCase ( self : List[str] , a : List[Any] ) -> List[str]:
"""simple docstring"""
return self.decoder.get(a )
def __UpperCamelCase ( self : Union[str, Any] , a : str , a : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(a ):
logger.error("Vocabulary path ({}) should be a directory".format(a ) )
return
SCREAMING_SNAKE_CASE : List[str] = os.path.join(
a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
with open(a , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.vocab , indent=2 , sort_keys=a , ensure_ascii=a ) + "\n" )
return (vocab_file,) | 25 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_a)
if n > 1:
factors.append(_a)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( _a , _a):
if len(_a) == 0:
return False
SCREAMING_SNAKE_CASE : Optional[Any] = len(_a) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , _a)
else:
return binary_search(a_list[midpoint + 1 :] , _a)
if __name__ == "__main__":
a_ = input('Enter numbers separated by comma:\n').strip()
a_ = [int(item.strip()) for item in user_input.split(',')]
a_ = int(input('Enter the number to be found in the list:\n').strip())
a_ = '' if binary_search(sequence, target) else 'not '
print(F'''{target} was {not_str}found in {sequence}''') | 25 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : int = float(_a)
SCREAMING_SNAKE_CASE : Dict = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_cos() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_cos() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : str = float(_a)
SCREAMING_SNAKE_CASE : Any = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15)) | 25 | 1 |
import numpy as np
def lowerCamelCase__ ( _a):
return 1 / (1 + np.exp(-vector))
def lowerCamelCase__ ( _a):
return vector * sigmoid(_a)
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 |
from __future__ import annotations
import math
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = size
# approximate the overall size of segment tree with given value
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
SCREAMING_SNAKE_CASE : Union[str, Any] = [0 for i in range(0 , 4 * size )]
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )] # flag for lazy update
def __UpperCamelCase ( self : Tuple , a : int ) -> int:
"""simple docstring"""
return idx * 2
def __UpperCamelCase ( self : str , a : int ) -> int:
"""simple docstring"""
return idx * 2 + 1
def __UpperCamelCase ( self : int , a : int , a : int , a : int , a : list[int] ) -> None:
"""simple docstring"""
if left_element == right_element:
SCREAMING_SNAKE_CASE : int = a[left_element - 1]
else:
SCREAMING_SNAKE_CASE : Optional[int] = (left_element + right_element) // 2
self.build(self.left(a ) , a , a , a )
self.build(self.right(a ) , mid + 1 , a , a )
SCREAMING_SNAKE_CASE : List[Any] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : int , a : int , a : int , a : int , a : int ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : Any = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[str] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : List[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
SCREAMING_SNAKE_CASE : Optional[Any] = val
if left_element != right_element:
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Optional[Any] = True
return True
SCREAMING_SNAKE_CASE : int = (left_element + right_element) // 2
self.update(self.left(a ) , a , a , a , a , a )
self.update(self.right(a ) , mid + 1 , a , a , a , a )
SCREAMING_SNAKE_CASE : Optional[int] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
return True
def __UpperCamelCase ( self : Dict , a : int , a : int , a : int , a : int , a : int ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[Any] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
SCREAMING_SNAKE_CASE : Dict = (left_element + right_element) // 2
SCREAMING_SNAKE_CASE : Tuple = self.query(self.left(a ) , a , a , a , a )
SCREAMING_SNAKE_CASE : Tuple = self.query(self.right(a ) , mid + 1 , a , a , a )
return max(a , a )
def __str__( self : str ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , a , a ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
a_ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
a_ = 15
a_ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 25 | 1 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a_ = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
a_ = importlib.util.spec_from_file_location(
'transformers',
os.path.join(PATH_TO_TRANSFORMERS, '__init__.py'),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
a_ = spec.loader.load_module()
a_ = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
a_ = re.compile('\[(.+?)\]\((https://huggingface\.co/.+?)\)')
a_ = {
'CLIPConfigMixin',
'DecisionTransformerConfigMixin',
'EncoderDecoderConfigMixin',
'RagConfigMixin',
'SpeechEncoderDecoderConfigMixin',
'VisionEncoderDecoderConfigMixin',
'VisionTextDualEncoderConfigMixin',
}
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : int = []
for config_class in list(CONFIG_MAPPING.values()):
SCREAMING_SNAKE_CASE : int = False
# source code of `config_class`
SCREAMING_SNAKE_CASE : Any = inspect.getsource(_a)
SCREAMING_SNAKE_CASE : List[Any] = _re_checkpoint.findall(_a)
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : int = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
SCREAMING_SNAKE_CASE : Optional[int] = f"https://huggingface.co/{ckpt_name}"
if ckpt_link == ckpt_link_from_name:
SCREAMING_SNAKE_CASE : int = True
break
SCREAMING_SNAKE_CASE : str = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(_a)
if len(_a) > 0:
SCREAMING_SNAKE_CASE : List[Any] = "\n".join(sorted(_a))
raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}")
if __name__ == "__main__":
check_config_docstrings_have_checkpoints() | 25 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
SCREAMING_SNAKE_CASE : str = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : Optional[int] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : Tuple = output.images
SCREAMING_SNAKE_CASE : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Tuple = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : List[str] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_dpmpp_2m" )
SCREAMING_SNAKE_CASE : str = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = sd_pipe(
[prompt] , generator=a , guidance_scale=7.5 , num_inference_steps=15 , output_type="np" , use_karras_sigmas=a , )
SCREAMING_SNAKE_CASE : str = output.images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
),
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='swin2sr'
lowerCamelCase__ ={
'hidden_size': 'embed_dim',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Any , a : List[Any]=64 , a : List[str]=1 , a : int=3 , a : Union[str, Any]=180 , a : Union[str, Any]=[6, 6, 6, 6, 6, 6] , a : Union[str, Any]=[6, 6, 6, 6, 6, 6] , a : Any=8 , a : List[Any]=2.0 , a : List[Any]=True , a : Optional[Any]=0.0 , a : Union[str, Any]=0.0 , a : Union[str, Any]=0.1 , a : List[Any]="gelu" , a : Any=False , a : Any=0.02 , a : Tuple=1e-5 , a : Optional[int]=2 , a : List[str]=1.0 , a : int="1conv" , a : Dict="pixelshuffle" , **a : str , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**a )
SCREAMING_SNAKE_CASE : Tuple = image_size
SCREAMING_SNAKE_CASE : int = patch_size
SCREAMING_SNAKE_CASE : Optional[Any] = num_channels
SCREAMING_SNAKE_CASE : Tuple = embed_dim
SCREAMING_SNAKE_CASE : Dict = depths
SCREAMING_SNAKE_CASE : Tuple = len(a )
SCREAMING_SNAKE_CASE : Tuple = num_heads
SCREAMING_SNAKE_CASE : Dict = window_size
SCREAMING_SNAKE_CASE : Optional[int] = mlp_ratio
SCREAMING_SNAKE_CASE : str = qkv_bias
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : List[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[Any] = drop_path_rate
SCREAMING_SNAKE_CASE : str = hidden_act
SCREAMING_SNAKE_CASE : Optional[int] = use_absolute_embeddings
SCREAMING_SNAKE_CASE : Tuple = layer_norm_eps
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : Any = upscale
SCREAMING_SNAKE_CASE : int = img_range
SCREAMING_SNAKE_CASE : List[Any] = resi_connection
SCREAMING_SNAKE_CASE : List[str] = upsampler | 25 |
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 _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Union[str, Any] = False
return options
def __UpperCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=10 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = 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 __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Dict = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=20 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[str] = output.images
SCREAMING_SNAKE_CASE : Optional[int] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Any = 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 | 25 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Any ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(a ) )
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(a ) )
def __UpperCamelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(a ) )
def __UpperCamelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
self.assertTrue(is_safetensors_compatible(a ) )
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
# Removed: 'text_encoder/model.safetensors',
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertFalse(is_safetensors_compatible(a ) )
def __UpperCamelCase ( self : Any ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
SCREAMING_SNAKE_CASE : str = "fp16"
self.assertTrue(is_safetensors_compatible(a , variant=a ) )
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = [
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
SCREAMING_SNAKE_CASE : int = "fp16"
self.assertTrue(is_safetensors_compatible(a , variant=a ) )
def __UpperCamelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
SCREAMING_SNAKE_CASE : Tuple = "fp16"
self.assertTrue(is_safetensors_compatible(a , variant=a ) )
def __UpperCamelCase ( self : str ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
SCREAMING_SNAKE_CASE : List[Any] = "fp16"
self.assertFalse(is_safetensors_compatible(a , variant=a ) )
def __UpperCamelCase ( self : List[str] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = [
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
]
SCREAMING_SNAKE_CASE : Optional[int] = "fp16"
self.assertTrue(is_safetensors_compatible(a , variant=a ) )
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
SCREAMING_SNAKE_CASE : int = "fp16"
self.assertTrue(is_safetensors_compatible(a , variant=a ) )
def __UpperCamelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
# 'text_encoder/model.fp16.safetensors',
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
SCREAMING_SNAKE_CASE : List[str] = "fp16"
self.assertFalse(is_safetensors_compatible(a , variant=a ) ) | 25 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase__ ( _a):
return getitem, k
def lowerCamelCase__ ( _a , _a):
return setitem, k, v
def lowerCamelCase__ ( _a):
return delitem, k
def lowerCamelCase__ ( _a , _a , *_a):
try:
return fun(_a , *_a), None
except Exception as e:
return None, e
a_ = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
a_ = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
a_ = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
a_ = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items"),
pytest.param(_overwrite_items , id="overwrite items"),
pytest.param(_delete_items , id="delete items"),
pytest.param(_access_absent_items , id="access absent items"),
pytest.param(_add_with_resize_up , id="add with resize up"),
pytest.param(_add_with_resize_down , id="add with resize down"),
) , )
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Dict = HashMap(initial_block_size=4)
SCREAMING_SNAKE_CASE : List[str] = {}
for _, (fun, *args) in enumerate(_a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = _run_operation(_a , _a , *_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = _run_operation(_a , _a , *_a)
assert my_res == py_res
assert str(_a) == str(_a)
assert set(_a) == set(_a)
assert len(_a) == len(_a)
assert set(my.items()) == set(py.items())
def lowerCamelCase__ ( ):
def is_public(_a) -> bool:
return not name.startswith("_")
SCREAMING_SNAKE_CASE : List[str] = {name for name in dir({}) if is_public(_a)}
SCREAMING_SNAKE_CASE : Union[str, Any] = {name for name in dir(HashMap()) if is_public(_a)}
assert dict_public_names > hash_public_names | 25 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
a_ = logging.get_logger(__name__)
class _UpperCamelCase :
'''simple docstring'''
lowerCamelCase__ =42
lowerCamelCase__ =None
@staticmethod
def __UpperCamelCase ( ) -> Any:
"""simple docstring"""
raise NotImplementedError
def __UpperCamelCase ( self : str , a : Optional[Any] , a : int , a : str , **a : int ) -> Union[str, Any]:
"""simple docstring"""
raise NotImplementedError
def __UpperCamelCase ( self : Any , a : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
raise NotImplementedError
def __UpperCamelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
if not self.is_available():
raise RuntimeError(
F"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}." )
@classmethod
def __UpperCamelCase ( cls : str ) -> Optional[Any]:
"""simple docstring"""
return F"`pip install {cls.pip_package or cls.name}`"
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='optuna'
@staticmethod
def __UpperCamelCase ( ) -> Tuple:
"""simple docstring"""
return is_optuna_available()
def __UpperCamelCase ( self : List[str] , a : Tuple , a : int , a : str , **a : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
return run_hp_search_optuna(a , a , a , **a )
def __UpperCamelCase ( self : Optional[int] , a : Optional[Any] ) -> Tuple:
"""simple docstring"""
return default_hp_space_optuna(a )
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='ray'
lowerCamelCase__ ='\'ray[tune]\''
@staticmethod
def __UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
return is_ray_available()
def __UpperCamelCase ( self : Tuple , a : str , a : int , a : str , **a : str ) -> Tuple:
"""simple docstring"""
return run_hp_search_ray(a , a , a , **a )
def __UpperCamelCase ( self : int , a : Optional[int] ) -> List[Any]:
"""simple docstring"""
return default_hp_space_ray(a )
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='sigopt'
@staticmethod
def __UpperCamelCase ( ) -> int:
"""simple docstring"""
return is_sigopt_available()
def __UpperCamelCase ( self : List[Any] , a : Any , a : int , a : str , **a : int ) -> Optional[int]:
"""simple docstring"""
return run_hp_search_sigopt(a , a , a , **a )
def __UpperCamelCase ( self : Optional[Any] , a : Dict ) -> str:
"""simple docstring"""
return default_hp_space_sigopt(a )
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='wandb'
@staticmethod
def __UpperCamelCase ( ) -> Dict:
"""simple docstring"""
return is_wandb_available()
def __UpperCamelCase ( self : List[str] , a : Optional[Any] , a : int , a : str , **a : List[Any] ) -> List[str]:
"""simple docstring"""
return run_hp_search_wandb(a , a , a , **a )
def __UpperCamelCase ( self : Tuple , a : Optional[int] ) -> int:
"""simple docstring"""
return default_hp_space_wandb(a )
a_ = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : int = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_a) > 0:
SCREAMING_SNAKE_CASE : Tuple = available_backends[0].name
if len(_a) > 1:
logger.info(
f"{len(_a)} hyperparameter search backends available. Using {name} as the default.")
return name
raise RuntimeError(
"No hyperparameter search backend available.\n"
+ "\n".join(
f" - To install {backend.name} run {backend.pip_install()}"
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values())) | 25 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
'VanForImageClassification',
'VanModel',
'VanPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 1 |
from __future__ import annotations
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[int] , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = order
# a_{0} ... a_{k}
SCREAMING_SNAKE_CASE : List[Any] = [1.0] + [0.0] * order
# b_{0} ... b_{k}
SCREAMING_SNAKE_CASE : List[str] = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
SCREAMING_SNAKE_CASE : Dict = [0.0] * self.order
# y[n-1] ... y[n-k]
SCREAMING_SNAKE_CASE : Tuple = [0.0] * self.order
def __UpperCamelCase ( self : List[str] , a : list[float] , a : list[float] ) -> None:
"""simple docstring"""
if len(a ) < self.order:
SCREAMING_SNAKE_CASE : int = [1.0, *a_coeffs]
if len(a ) != self.order + 1:
SCREAMING_SNAKE_CASE : List[Any] = (
F"Expected a_coeffs to have {self.order + 1} elements "
F"for {self.order}-order filter, got {len(a )}"
)
raise ValueError(a )
if len(a ) != self.order + 1:
SCREAMING_SNAKE_CASE : str = (
F"Expected b_coeffs to have {self.order + 1} elements "
F"for {self.order}-order filter, got {len(a )}"
)
raise ValueError(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = a_coeffs
SCREAMING_SNAKE_CASE : List[Any] = b_coeffs
def __UpperCamelCase ( self : Optional[int] , a : float ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
SCREAMING_SNAKE_CASE : int = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
SCREAMING_SNAKE_CASE : List[Any] = self.input_history[:-1]
SCREAMING_SNAKE_CASE : List[str] = self.output_history[:-1]
SCREAMING_SNAKE_CASE : Any = sample
SCREAMING_SNAKE_CASE : List[str] = result
return result | 25 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
if len(_a) == 0:
return []
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = min(_a), max(_a)
SCREAMING_SNAKE_CASE : Dict = int(max_value - min_value) + 1
SCREAMING_SNAKE_CASE : list[list] = [[] for _ in range(_a)]
for i in my_list:
buckets[int(i - min_value)].append(_a)
return [v for bucket in buckets for v in sorted(_a)]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15] | 25 | 1 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
a_ = get_logger(__name__)
class _UpperCamelCase :
'''simple docstring'''
lowerCamelCase__ ='dummy_data'
lowerCamelCase__ ='datasets'
lowerCamelCase__ =False
def __init__( self : Tuple , a : str , a : str , a : Union[Version, str] , a : Optional[str] = None , a : bool = False , a : bool = True , a : Optional[List[Callable]] = None , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = dataset_name
SCREAMING_SNAKE_CASE : int = cache_dir
SCREAMING_SNAKE_CASE : Optional[Any] = use_local_dummy_data
SCREAMING_SNAKE_CASE : str = config
# download_callbacks take a single url as input
SCREAMING_SNAKE_CASE : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
SCREAMING_SNAKE_CASE : Optional[Any] = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
SCREAMING_SNAKE_CASE : List[str] = str(a )
# to be downloaded
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Dict = None
@property
def __UpperCamelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
if self._dummy_file is None:
SCREAMING_SNAKE_CASE : Optional[int] = self.download_dummy_data()
return self._dummy_file
@property
def __UpperCamelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("dummy" , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join("dummy" , self.version_name )
@property
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
return os.path.join(self.dummy_data_folder , "dummy_data.zip" )
def __UpperCamelCase ( self : Any ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
SCREAMING_SNAKE_CASE : str = cached_path(
a , cache_dir=self.cache_dir , extract_compressed_file=a , force_extract=a )
return os.path.join(a , self.dummy_file_name )
@property
def __UpperCamelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def __UpperCamelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
if self._bucket_url is None:
SCREAMING_SNAKE_CASE : Dict = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , "/" ) )
return self._bucket_url
@property
def __UpperCamelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , "/" ).split("/" )[:-1] )
def __UpperCamelCase ( self : Any , a : Union[str, Any] , *a : str ) -> Dict:
"""simple docstring"""
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
SCREAMING_SNAKE_CASE : Tuple = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
SCREAMING_SNAKE_CASE : Dict = self.dummy_file_name
# special case when data_url is a dict
if isinstance(a , a ):
return self.create_dummy_data_dict(a , a )
elif isinstance(a , (list, tuple) ):
return self.create_dummy_data_list(a , a )
else:
return self.create_dummy_data_single(a , a )
def __UpperCamelCase ( self : Tuple , a : List[str] , *a : List[str] ) -> Optional[Any]:
"""simple docstring"""
return self.download_and_extract(a )
def __UpperCamelCase ( self : str , a : str , a : List[Any] ) -> List[Any]:
"""simple docstring"""
return self.download_and_extract(a )
def __UpperCamelCase ( self : str , a : Any , *a : str , **a : int ) -> Optional[int]:
"""simple docstring"""
return path
def __UpperCamelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
return {}
def __UpperCamelCase ( self : int , a : str , a : List[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(a , a ):
for single_url in single_urls:
download_callback(a )
else:
SCREAMING_SNAKE_CASE : str = single_urls
download_callback(a )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(a , a ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [os.path.join(a , urllib.parse.quote_plus(Path(a ).name ) ) for x in single_urls]
else:
SCREAMING_SNAKE_CASE : Optional[int] = single_urls
SCREAMING_SNAKE_CASE : List[str] = os.path.join(a , urllib.parse.quote_plus(Path(a ).name ) )
SCREAMING_SNAKE_CASE : Optional[int] = value
# make sure that values are unique
if all(isinstance(a , a ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
SCREAMING_SNAKE_CASE : List[str] = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def __UpperCamelCase ( self : Dict , a : Tuple , a : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
SCREAMING_SNAKE_CASE : Union[str, Any] = all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}" , a ) ) for url in data_url )
SCREAMING_SNAKE_CASE : Union[str, Any] = all(
url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
SCREAMING_SNAKE_CASE : List[str] = [data_url[0]] * len(a )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(a )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
SCREAMING_SNAKE_CASE : Dict = os.path.join(a , urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
dummy_data_list.append(a )
return dummy_data_list
def __UpperCamelCase ( self : Dict , a : Optional[Any] , a : Optional[int] ) -> int:
"""simple docstring"""
for download_callback in self.download_callbacks:
download_callback(a )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
SCREAMING_SNAKE_CASE : int = os.path.join(a , urllib.parse.quote_plus(data_url.split("/" )[-1] ) )
if os.path.exists(a ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def __UpperCamelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
pass
def __UpperCamelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
pass
def __UpperCamelCase ( self : List[str] , a : str ) -> List[Any]:
"""simple docstring"""
def _iter_archive_members(a : Union[str, Any] ):
# this preserves the order of the members inside the ZIP archive
SCREAMING_SNAKE_CASE : Optional[int] = Path(self.dummy_file ).parent
SCREAMING_SNAKE_CASE : Union[str, Any] = path.relative_to(a )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
SCREAMING_SNAKE_CASE : Optional[Any] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(a )
SCREAMING_SNAKE_CASE : Optional[Any] = Path(a )
SCREAMING_SNAKE_CASE : Dict = _iter_archive_members(a ) if self.use_local_dummy_data else path.rglob("*" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((".", "__") ):
yield file_path.relative_to(a ).as_posix(), file_path.open("rb" )
def __UpperCamelCase ( self : Dict , a : Optional[Any] ) -> List[str]:
"""simple docstring"""
if not isinstance(a , a ):
SCREAMING_SNAKE_CASE : List[str] = [paths]
for path in paths:
if os.path.isfile(a ):
if os.path.basename(a ).startswith((".", "__") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(a ):
if os.path.basename(a ).startswith((".", "__") ):
continue
dirnames.sort()
for filename in sorted(a ):
if filename.startswith((".", "__") ):
continue
yield os.path.join(a , a ) | 25 |
a_ = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset([])
a_ = frozenset(['image'])
a_ = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image'])
a_ = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'negative_prompt'])
a_ = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
a_ = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image', 'mask_image'])
a_ = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['example_image', 'image', 'mask_image'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset(['input_tokens'])
a_ = frozenset(['input_tokens']) | 25 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}
class _UpperCamelCase ( __A , __A ):
'''simple docstring'''
lowerCamelCase__ ='convnextv2'
def __init__( self : List[Any] , a : List[str]=3 , a : List[str]=4 , a : Union[str, Any]=4 , a : Optional[Any]=None , a : Tuple=None , a : Optional[int]="gelu" , a : int=0.02 , a : Optional[int]=1e-12 , a : int=0.0 , a : Optional[Any]=224 , a : Union[str, Any]=None , a : Union[str, Any]=None , **a : List[Any] , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**a )
SCREAMING_SNAKE_CASE : List[str] = num_channels
SCREAMING_SNAKE_CASE : Dict = patch_size
SCREAMING_SNAKE_CASE : List[Any] = num_stages
SCREAMING_SNAKE_CASE : Tuple = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
SCREAMING_SNAKE_CASE : Any = [3, 3, 9, 3] if depths is None else depths
SCREAMING_SNAKE_CASE : str = hidden_act
SCREAMING_SNAKE_CASE : Any = initializer_range
SCREAMING_SNAKE_CASE : int = layer_norm_eps
SCREAMING_SNAKE_CASE : Any = drop_path_rate
SCREAMING_SNAKE_CASE : List[str] = image_size
SCREAMING_SNAKE_CASE : List[str] = ["stem"] + [F"stage{idx}" for idx in range(1 , len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = get_aligned_output_features_output_indices(
out_features=a , out_indices=a , stage_names=self.stage_names ) | 25 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
a_ = get_logger()
a_ = None
class _UpperCamelCase ( TensorFormatter[Mapping, 'jax.Array', Mapping] ):
'''simple docstring'''
def __init__( self : Optional[Any] , a : str=None , a : List[Any]=None , **a : Any ) -> Optional[Any]:
"""simple docstring"""
super().__init__(features=a )
import jax
from jaxlib.xla_client import Device
if isinstance(a , a ):
raise ValueError(
F"Expected {device} to be a `str` not {type(a )}, as `jaxlib.xla_extension.Device` "
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
SCREAMING_SNAKE_CASE : List[str] = device if isinstance(a , a ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : str = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
F"Device with string identifier {self.device} not listed among the available "
F"devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default "
F"device: {str(jax.devices()[0] )}." )
SCREAMING_SNAKE_CASE : Any = str(jax.devices()[0] )
SCREAMING_SNAKE_CASE : Any = jnp_array_kwargs
@staticmethod
def __UpperCamelCase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
"""simple docstring"""
import jax
return {str(a ): device for device in jax.devices()}
def __UpperCamelCase ( self : Dict , a : Tuple ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(a , a ) and column:
if all(
isinstance(a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(a , axis=0 )
return column
def __UpperCamelCase ( self : Dict , a : str ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
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()
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
if isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
SCREAMING_SNAKE_CASE : Dict = {"dtype": jnp.intaa}
else:
SCREAMING_SNAKE_CASE : str = {"dtype": jnp.intaa}
elif isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
SCREAMING_SNAKE_CASE : int = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(a , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : Dict = np.asarray(a )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : Optional[Any] = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(a , **{**default_dtype, **self.jnp_array_kwargs} )
def __UpperCamelCase ( self : Any , a : List[str] ) -> Dict:
"""simple docstring"""
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(a , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(a , "__array__" ) and not isinstance(a , jax.Array ):
SCREAMING_SNAKE_CASE : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(a , np.ndarray ):
if data_struct.dtype == object: # jax arrays 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 __UpperCamelCase ( self : Optional[Any] , a : dict ) -> Dict:
"""simple docstring"""
return map_nested(self._recursive_tensorize , a , map_list=a )
def __UpperCamelCase ( self : Dict , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.numpy_arrow_extractor().extract_row(a )
SCREAMING_SNAKE_CASE : List[Any] = self.python_features_decoder.decode_row(a )
return self.recursive_tensorize(a )
def __UpperCamelCase ( self : Optional[int] , a : pa.Table ) -> "jax.Array":
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.numpy_arrow_extractor().extract_column(a )
SCREAMING_SNAKE_CASE : Optional[Any] = self.python_features_decoder.decode_column(a , pa_table.column_names[0] )
SCREAMING_SNAKE_CASE : Tuple = self.recursive_tensorize(a )
SCREAMING_SNAKE_CASE : Optional[int] = self._consolidate(a )
return column
def __UpperCamelCase ( self : List[Any] , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.numpy_arrow_extractor().extract_batch(a )
SCREAMING_SNAKE_CASE : str = self.python_features_decoder.decode_batch(a )
SCREAMING_SNAKE_CASE : List[Any] = self.recursive_tensorize(a )
for column_name in batch:
SCREAMING_SNAKE_CASE : List[Any] = self._consolidate(batch[column_name] )
return batch | 25 | 1 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Tuple , a : int , a : Optional[int]=13 , a : Dict=30 , a : Optional[int]=2 , a : Dict=3 , a : str=True , a : Tuple=True , a : int=32 , a : List[Any]=5 , a : Tuple=4 , a : Tuple=37 , a : Optional[Any]="gelu" , a : int=0.1 , a : Optional[int]=0.1 , a : Optional[int]=10 , a : List[str]=0.02 , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = parent
SCREAMING_SNAKE_CASE : Tuple = batch_size
SCREAMING_SNAKE_CASE : int = image_size
SCREAMING_SNAKE_CASE : Union[str, Any] = patch_size
SCREAMING_SNAKE_CASE : Dict = num_channels
SCREAMING_SNAKE_CASE : str = is_training
SCREAMING_SNAKE_CASE : List[str] = use_labels
SCREAMING_SNAKE_CASE : Tuple = hidden_size
SCREAMING_SNAKE_CASE : Any = num_hidden_layers
SCREAMING_SNAKE_CASE : Any = num_attention_heads
SCREAMING_SNAKE_CASE : str = intermediate_size
SCREAMING_SNAKE_CASE : Any = hidden_act
SCREAMING_SNAKE_CASE : Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Tuple = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE : List[str] = (image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE : int = num_patches + 1
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE : Optional[int] = 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=a , initializer_range=self.initializer_range , )
return config, pixel_values
def __UpperCamelCase ( self : Optional[Any] , a : str , a : Union[str, Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = FlaxViTModel(config=a )
SCREAMING_SNAKE_CASE : Optional[int] = model(a )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE : Dict = (self.image_size, self.image_size)
SCREAMING_SNAKE_CASE : int = (self.patch_size, self.patch_size)
SCREAMING_SNAKE_CASE : int = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def __UpperCamelCase ( self : Union[str, Any] , a : Union[str, Any] , a : Any ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[Any] = FlaxViTForImageClassification(config=a )
SCREAMING_SNAKE_CASE : Any = model(a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE : str = 1
SCREAMING_SNAKE_CASE : Union[str, Any] = FlaxViTForImageClassification(a )
SCREAMING_SNAKE_CASE : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE : Dict = model(a )
def __UpperCamelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE
) ,(
SCREAMING_SNAKE_CASE
) ,
) : Any = config_and_inputs
SCREAMING_SNAKE_CASE : Optional[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def __UpperCamelCase ( self : Tuple ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = FlaxViTModelTester(self )
SCREAMING_SNAKE_CASE : Union[str, Any] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=37 )
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : str ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a )
def __UpperCamelCase ( self : List[str] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Optional[int] = model_class(a )
SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE : Tuple = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE : List[Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , a )
def __UpperCamelCase ( self : Dict ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE : List[Any] = self._prepare_for_class(a , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = model_class(a )
@jax.jit
def model_jitted(a : Any , **a : Any ):
return model(pixel_values=a , **a )
with self.subTest("JIT Enabled" ):
SCREAMING_SNAKE_CASE : Union[str, Any] = model_jitted(**a ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE : Any = model_jitted(**a ).to_tuple()
self.assertEqual(len(a ) , len(a ) )
for jitted_output, output in zip(a , a ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def __UpperCamelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE : Optional[int] = model_class_name.from_pretrained("google/vit-base-patch16-224" )
SCREAMING_SNAKE_CASE : Optional[int] = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(a ) | 25 |
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 _UpperCamelCase :
'''simple docstring'''
@staticmethod
def __UpperCamelCase ( *a : str , **a : int ) -> str:
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =MODEL_FOR_OBJECT_DETECTION_MAPPING
def __UpperCamelCase ( self : Optional[Any] , a : str , a : Optional[Any] , a : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ObjectDetectionPipeline(model=a , image_processor=a )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def __UpperCamelCase ( self : List[Any] , a : Optional[int] , a : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" , threshold=0.0 )
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
import datasets
SCREAMING_SNAKE_CASE : Any = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
SCREAMING_SNAKE_CASE : Dict = [
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"],
]
SCREAMING_SNAKE_CASE : Tuple = object_detector(a , threshold=0.0 )
self.assertEqual(len(a ) , len(a ) )
for outputs in batch_outputs:
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
@require_tf
@unittest.skip("Object detection not implemented in TF" )
def __UpperCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
pass
@require_torch
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = "hf-internal-testing/tiny-detr-mobilenetsv3"
SCREAMING_SNAKE_CASE : Dict = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : Tuple = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : int = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=0.0 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
] , )
SCREAMING_SNAKE_CASE : Dict = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : List[str] = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : int = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : int = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Tuple = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : Tuple = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : str = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = 0.9985
SCREAMING_SNAKE_CASE : int = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : List[str] = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : str = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=a )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
@require_torch
@require_pytesseract
@slow
def __UpperCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = "Narsil/layoutlmv3-finetuned-funsd"
SCREAMING_SNAKE_CASE : Dict = 0.9993
SCREAMING_SNAKE_CASE : str = pipeline("object-detection" , model=a , threshold=a )
SCREAMING_SNAKE_CASE : List[Any] = object_detector(
"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
] , ) | 25 | 1 |
def lowerCamelCase__ ( _a , _a , _a):
def update_area_of_max_square(_a , _a) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
SCREAMING_SNAKE_CASE : Tuple = update_area_of_max_square(_a , col + 1)
SCREAMING_SNAKE_CASE : str = update_area_of_max_square(row + 1 , col + 1)
SCREAMING_SNAKE_CASE : Dict = update_area_of_max_square(row + 1 , _a)
if mat[row][col]:
SCREAMING_SNAKE_CASE : Optional[int] = 1 + min([right, diagonal, down])
SCREAMING_SNAKE_CASE : Optional[Any] = max(largest_square_area[0] , _a)
return sub_problem_sol
else:
return 0
SCREAMING_SNAKE_CASE : Dict = [0]
update_area_of_max_square(0 , 0)
return largest_square_area[0]
def lowerCamelCase__ ( _a , _a , _a):
def update_area_of_max_square_using_dp_array(
_a , _a , _a) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
SCREAMING_SNAKE_CASE : List[Any] = update_area_of_max_square_using_dp_array(_a , col + 1 , _a)
SCREAMING_SNAKE_CASE : Dict = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , _a)
SCREAMING_SNAKE_CASE : Optional[Any] = update_area_of_max_square_using_dp_array(row + 1 , _a , _a)
if mat[row][col]:
SCREAMING_SNAKE_CASE : str = 1 + min([right, diagonal, down])
SCREAMING_SNAKE_CASE : Any = max(largest_square_area[0] , _a)
SCREAMING_SNAKE_CASE : str = sub_problem_sol
return sub_problem_sol
else:
return 0
SCREAMING_SNAKE_CASE : Dict = [0]
SCREAMING_SNAKE_CASE : Optional[Any] = [[-1] * cols for _ in range(_a)]
update_area_of_max_square_using_dp_array(0 , 0 , _a)
return largest_square_area[0]
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : str = [[0] * (cols + 1) for _ in range(rows + 1)]
SCREAMING_SNAKE_CASE : Any = 0
for row in range(rows - 1 , -1 , -1):
for col in range(cols - 1 , -1 , -1):
SCREAMING_SNAKE_CASE : int = dp_array[row][col + 1]
SCREAMING_SNAKE_CASE : str = dp_array[row + 1][col + 1]
SCREAMING_SNAKE_CASE : Optional[int] = dp_array[row + 1][col]
if mat[row][col] == 1:
SCREAMING_SNAKE_CASE : Optional[int] = 1 + min(_a , _a , _a)
SCREAMING_SNAKE_CASE : int = max(dp_array[row][col] , _a)
else:
SCREAMING_SNAKE_CASE : Optional[int] = 0
return largest_square_area
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : int = [0] * (cols + 1)
SCREAMING_SNAKE_CASE : Dict = [0] * (cols + 1)
SCREAMING_SNAKE_CASE : List[str] = 0
for row in range(rows - 1 , -1 , -1):
for col in range(cols - 1 , -1 , -1):
SCREAMING_SNAKE_CASE : str = current_row[col + 1]
SCREAMING_SNAKE_CASE : Union[str, Any] = next_row[col + 1]
SCREAMING_SNAKE_CASE : Dict = next_row[col]
if mat[row][col] == 1:
SCREAMING_SNAKE_CASE : List[str] = 1 + min(_a , _a , _a)
SCREAMING_SNAKE_CASE : int = max(current_row[col] , _a)
else:
SCREAMING_SNAKE_CASE : List[str] = 0
SCREAMING_SNAKE_CASE : int = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]])) | 25 |
def lowerCamelCase__ ( _a):
if not isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Tuple = f"Input value of [number={number}] must be an integer"
raise TypeError(_a)
if number < 0:
return False
SCREAMING_SNAKE_CASE : Union[str, Any] = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 1 |
def lowerCamelCase__ ( _a , _a , _a=False):
if isinstance(_a , _a) and isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Tuple = len(set_a.intersection(_a))
if alternative_union:
SCREAMING_SNAKE_CASE : Dict = len(_a) + len(_a)
else:
SCREAMING_SNAKE_CASE : str = len(set_a.union(_a))
return intersection / union
if isinstance(_a , (list, tuple)) and isinstance(_a , (list, tuple)):
SCREAMING_SNAKE_CASE : Dict = [element for element in set_a if element in set_b]
if alternative_union:
SCREAMING_SNAKE_CASE : List[Any] = len(_a) + len(_a)
return len(_a) / union
else:
SCREAMING_SNAKE_CASE : Optional[Any] = set_a + [element for element in set_b if element not in set_a]
return len(_a) / len(_a)
return len(_a) / len(_a)
return None
if __name__ == "__main__":
a_ = {'a', 'b', 'c', 'd', 'e'}
a_ = {'c', 'd', 'e', 'f', 'h', 'i'}
print(jaccard_similarity(set_a, set_b)) | 25 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Dict , a : Tuple , a : Any=13 , a : Any=7 , a : Union[str, Any]=True , a : List[Any]=True , a : List[str]=False , a : List[str]=True , a : Any=99 , a : str=32 , a : Any=5 , a : Optional[int]=4 , a : Union[str, Any]=37 , a : Dict="gelu" , a : List[Any]=0.1 , a : Optional[Any]=0.1 , a : List[str]=512 , a : Union[str, Any]=16 , a : str=2 , a : Dict=0.02 , a : Optional[int]=3 , a : Union[str, Any]=4 , a : int=None , ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = parent
SCREAMING_SNAKE_CASE : Any = batch_size
SCREAMING_SNAKE_CASE : Optional[int] = seq_length
SCREAMING_SNAKE_CASE : List[Any] = is_training
SCREAMING_SNAKE_CASE : int = use_input_mask
SCREAMING_SNAKE_CASE : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE : str = use_labels
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_size
SCREAMING_SNAKE_CASE : str = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = intermediate_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE : List[str] = type_vocab_size
SCREAMING_SNAKE_CASE : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : Tuple = num_labels
SCREAMING_SNAKE_CASE : Tuple = num_choices
SCREAMING_SNAKE_CASE : Optional[Any] = scope
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : str = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE : int = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self : Dict ) -> str:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : Optional[int] , a : Optional[int] , a : Dict , a : str , a : str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = DistilBertModel(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , a )
SCREAMING_SNAKE_CASE : Optional[Any] = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self : Tuple , a : Optional[int] , a : Dict , a : Tuple , a : int , a : int , a : Any ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = DistilBertForMaskedLM(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : str = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self : List[Any] , a : int , a : Optional[Any] , a : Optional[Any] , a : str , a : str , a : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertForQuestionAnswering(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(
a , attention_mask=a , start_positions=a , end_positions=a )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase ( self : Optional[int] , a : str , a : Any , a : int , a : Optional[Any] , a : int , a : str ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.num_labels
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertForSequenceClassification(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase ( self : Optional[Any] , a : List[Any] , a : Optional[int] , a : Union[str, Any] , a : Dict , a : Any , a : Optional[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.num_labels
SCREAMING_SNAKE_CASE : List[str] = DistilBertForTokenClassification(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase ( self : int , a : Any , a : Optional[int] , a : Union[str, Any] , a : Tuple , a : Optional[int] , a : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.num_choices
SCREAMING_SNAKE_CASE : Any = DistilBertForMultipleChoice(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Optional[Any] = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE)) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE : int = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCamelCase__ =(
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
def __UpperCamelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = DistilBertModelTester(self )
SCREAMING_SNAKE_CASE : List[str] = ConfigTester(self , config_class=a , dim=37 )
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a )
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a )
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a )
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a )
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a )
def __UpperCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a )
@slow
def __UpperCamelCase ( self : int ) -> Any:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertModel.from_pretrained(a )
self.assertIsNotNone(a )
@slow
@require_torch_gpu
def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : Any = model_class(config=a )
SCREAMING_SNAKE_CASE : List[Any] = self._prepare_for_class(a , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.jit.trace(
a , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a , os.path.join(a , "traced_model.pt" ) )
SCREAMING_SNAKE_CASE : Tuple = torch.jit.load(os.path.join(a , "traced_model.pt" ) , map_location=a )
loaded(inputs_dict["input_ids"].to(a ) , inputs_dict["attention_mask"].to(a ) )
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertModel.from_pretrained("distilbert-base-uncased" )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , attention_mask=a )[0]
SCREAMING_SNAKE_CASE : List[str] = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) ) | 25 | 1 |
from __future__ import annotations
import time
a_ = list[tuple[int, int]]
a_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
a_ = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , a : int , a : int , a : int , a : int , a : Node | None ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = pos_x
SCREAMING_SNAKE_CASE : Dict = pos_y
SCREAMING_SNAKE_CASE : int = (pos_y, pos_x)
SCREAMING_SNAKE_CASE : Any = goal_x
SCREAMING_SNAKE_CASE : Dict = goal_y
SCREAMING_SNAKE_CASE : str = parent
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : tuple[int, int] , a : tuple[int, int] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = Node(start[1] , start[0] , goal[1] , goal[0] , a )
SCREAMING_SNAKE_CASE : Tuple = Node(goal[1] , goal[0] , goal[1] , goal[0] , a )
SCREAMING_SNAKE_CASE : str = [self.start]
SCREAMING_SNAKE_CASE : Any = False
def __UpperCamelCase ( self : str ) -> Path | None:
"""simple docstring"""
while self.node_queue:
SCREAMING_SNAKE_CASE : List[str] = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
SCREAMING_SNAKE_CASE : Tuple = True
return self.retrace_path(a )
SCREAMING_SNAKE_CASE : str = self.get_successors(a )
for node in successors:
self.node_queue.append(a )
if not self.reached:
return [self.start.pos]
return None
def __UpperCamelCase ( self : Dict , a : Node ) -> list[Node]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = []
for action in delta:
SCREAMING_SNAKE_CASE : int = parent.pos_x + action[1]
SCREAMING_SNAKE_CASE : Dict = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(a ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(a , a , self.target.pos_y , self.target.pos_x , a ) )
return successors
def __UpperCamelCase ( self : Union[str, Any] , a : Node | None ) -> Path:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = node
SCREAMING_SNAKE_CASE : List[Any] = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
SCREAMING_SNAKE_CASE : Dict = current_node.parent
path.reverse()
return path
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : str , a : Optional[int] , a : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = BreadthFirstSearch(a , a )
SCREAMING_SNAKE_CASE : Any = BreadthFirstSearch(a , a )
SCREAMING_SNAKE_CASE : Dict = False
def __UpperCamelCase ( self : List[Any] ) -> Path | None:
"""simple docstring"""
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
SCREAMING_SNAKE_CASE : Optional[Any] = self.fwd_bfs.node_queue.pop(0 )
SCREAMING_SNAKE_CASE : Any = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
SCREAMING_SNAKE_CASE : int = True
return self.retrace_bidirectional_path(
a , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = current_bwd_node
SCREAMING_SNAKE_CASE : str = current_fwd_node
SCREAMING_SNAKE_CASE : Union[str, Any] = {
self.fwd_bfs: self.fwd_bfs.get_successors(a ),
self.bwd_bfs: self.bwd_bfs.get_successors(a ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(a )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def __UpperCamelCase ( self : int , a : Node , a : Node ) -> Path:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = self.fwd_bfs.retrace_path(a )
SCREAMING_SNAKE_CASE : str = self.bwd_bfs.retrace_path(a )
bwd_path.pop()
bwd_path.reverse()
SCREAMING_SNAKE_CASE : Any = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
a_ = (0, 0)
a_ = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
a_ = time.time()
a_ = BreadthFirstSearch(init, goal)
a_ = bfs.search()
a_ = time.time() - start_bfs_time
print('Unidirectional BFS computation time : ', bfs_time)
a_ = time.time()
a_ = BidirectionalBreadthFirstSearch(init, goal)
a_ = bd_bfs.search()
a_ = time.time() - start_bd_bfs_time
print('Bidirectional BFS computation time : ', bd_bfs_time) | 25 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 1 |
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[int] = [0] * len(_a)
for i in range(1 , len(_a)):
# use last results for better performance - dynamic programming
SCREAMING_SNAKE_CASE : Dict = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
SCREAMING_SNAKE_CASE : List[str] = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
SCREAMING_SNAKE_CASE : List[Any] = j
return prefix_result
def lowerCamelCase__ ( _a):
return max(prefix_function(_a))
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
def __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE : Union[str, Any] = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Any = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" , return_dict=a )[0]
SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "google/ncsnpp-celebahq-256"
SCREAMING_SNAKE_CASE : List[Any] = UNetaDModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Optional[Any] = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = pipe(num_inference_steps=20 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 1 |
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_big_bird import BigBirdTokenizer
else:
a_ = None
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
a_ = {
'vocab_file': {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
),
},
'tokenizer_file': {
'google/bigbird-roberta-base': (
'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
),
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
),
},
}
a_ = {
'google/bigbird-roberta-base': 4096,
'google/bigbird-roberta-large': 4096,
'google/bigbird-base-trivia-itc': 4096,
}
a_ = '▁'
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =VOCAB_FILES_NAMES
lowerCamelCase__ =PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ =BigBirdTokenizer
lowerCamelCase__ =['input_ids', 'attention_mask']
lowerCamelCase__ =[]
def __init__( self : str , a : int=None , a : Optional[int]=None , a : Union[str, Any]="<unk>" , a : int="<s>" , a : Any="</s>" , a : List[str]="<pad>" , a : List[Any]="[SEP]" , a : Optional[Any]="[MASK]" , a : Optional[int]="[CLS]" , **a : List[str] , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else bos_token
SCREAMING_SNAKE_CASE : Tuple = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else eos_token
SCREAMING_SNAKE_CASE : List[Any] = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else unk_token
SCREAMING_SNAKE_CASE : Optional[int] = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else pad_token
SCREAMING_SNAKE_CASE : Optional[Any] = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else cls_token
SCREAMING_SNAKE_CASE : int = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE : Tuple = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else mask_token
super().__init__(
a , tokenizer_file=a , bos_token=a , eos_token=a , unk_token=a , sep_token=a , pad_token=a , cls_token=a , mask_token=a , **a , )
SCREAMING_SNAKE_CASE : Tuple = vocab_file
SCREAMING_SNAKE_CASE : Tuple = False if not self.vocab_file else True
def __UpperCamelCase ( self : Dict , a : List[int] , a : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __UpperCamelCase ( self : List[Any] , a : List[int] , a : Optional[List[int]] = None , a : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(a )) + [1]
return [1] + ([0] * len(a )) + [1] + ([0] * len(a )) + [1]
def __UpperCamelCase ( self : List[str] , a : List[int] , a : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : 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 ) * [0] + len(token_ids_a + sep ) * [1]
def __UpperCamelCase ( self : Optional[Any] , a : str , a : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(a ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(
a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a ):
copyfile(self.vocab_file , a )
return (out_vocab_file,) | 25 |
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
SCREAMING_SNAKE_CASE : Optional[Any] = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res | 25 | 1 |
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 _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Union[str, Any] = False
return options
def __UpperCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=10 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = 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 __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Dict = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=20 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[str] = output.images
SCREAMING_SNAKE_CASE : Optional[int] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Any = 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 | 25 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='roformer'
def __init__( self : Dict , a : Any=5_0000 , a : List[Any]=None , a : str=768 , a : str=12 , a : Tuple=12 , a : Optional[Any]=3072 , a : List[str]="gelu" , a : List[Any]=0.1 , a : Union[str, Any]=0.1 , a : Tuple=1536 , a : List[str]=2 , a : Tuple=0.02 , a : Any=1e-12 , a : Optional[int]=0 , a : Union[str, Any]=False , a : int=True , **a : str , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=a , **a )
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size if embedding_size is None else embedding_size
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = type_vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE : List[str] = rotary_value
SCREAMING_SNAKE_CASE : int = use_cache
class _UpperCamelCase ( __A ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE : str = {0: "batch", 1: "sequence"}
SCREAMING_SNAKE_CASE : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] ) | 25 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
'BridgeTower/bridgetower-base-itm-mlm': (
'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
),
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='bridgetower_vision_model'
def __init__( self : Tuple , a : Any=768 , a : Optional[Any]=12 , a : Dict=3 , a : Dict=16 , a : Optional[Any]=288 , a : List[Any]=1 , a : Tuple=1e-05 , a : Union[str, Any]=False , a : Any=True , a : Optional[int]=False , **a : Optional[Any] , ) -> Optional[int]:
"""simple docstring"""
super().__init__(**a )
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE : Dict = num_hidden_layers
SCREAMING_SNAKE_CASE : Any = num_channels
SCREAMING_SNAKE_CASE : int = patch_size
SCREAMING_SNAKE_CASE : Tuple = image_size
SCREAMING_SNAKE_CASE : Dict = initializer_factor
SCREAMING_SNAKE_CASE : Any = layer_norm_eps
SCREAMING_SNAKE_CASE : Union[str, Any] = stop_gradient
SCREAMING_SNAKE_CASE : Tuple = share_layernorm
SCREAMING_SNAKE_CASE : Any = remove_last_layer
@classmethod
def __UpperCamelCase ( cls : str , a : Union[str, os.PathLike] , **a : Dict ) -> "PretrainedConfig":
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Any = cls.get_config_dict(a , **a )
if config_dict.get("model_type" ) == "bridgetower":
SCREAMING_SNAKE_CASE : Dict = 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(a , **a )
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='bridgetower_text_model'
def __init__( self : str , a : int=5_0265 , a : List[Any]=768 , a : int=12 , a : Optional[Any]=12 , a : Union[str, Any]=1 , a : str=3072 , a : List[Any]="gelu" , a : Any=0.1 , a : List[str]=0.1 , a : Tuple=514 , a : Optional[int]=1 , a : Tuple=1e-05 , a : Union[str, Any]=1 , a : Any=0 , a : str=2 , a : str="absolute" , a : Any=True , **a : Dict , ) -> Optional[int]:
"""simple docstring"""
super().__init__(**a )
SCREAMING_SNAKE_CASE : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE : Any = hidden_size
SCREAMING_SNAKE_CASE : Optional[int] = num_hidden_layers
SCREAMING_SNAKE_CASE : List[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : int = hidden_act
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_factor
SCREAMING_SNAKE_CASE : List[str] = intermediate_size
SCREAMING_SNAKE_CASE : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Any = max_position_embeddings
SCREAMING_SNAKE_CASE : List[Any] = type_vocab_size
SCREAMING_SNAKE_CASE : List[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : Union[str, Any] = position_embedding_type
SCREAMING_SNAKE_CASE : List[str] = use_cache
SCREAMING_SNAKE_CASE : List[str] = pad_token_id
SCREAMING_SNAKE_CASE : Tuple = bos_token_id
SCREAMING_SNAKE_CASE : Optional[int] = eos_token_id
@classmethod
def __UpperCamelCase ( cls : Tuple , a : Union[str, os.PathLike] , **a : List[str] ) -> "PretrainedConfig":
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = cls.get_config_dict(a , **a )
if config_dict.get("model_type" ) == "bridgetower":
SCREAMING_SNAKE_CASE : 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(a , **a )
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='bridgetower'
def __init__( self : List[str] , a : List[Any]=True , a : List[Any]="gelu" , a : Union[str, Any]=768 , a : Tuple=1 , a : List[Any]=1e-05 , a : Dict=False , a : Optional[int]="add" , a : Any=12 , a : Optional[int]=6 , a : int=False , a : Optional[int]=False , a : Dict=None , a : Tuple=None , **a : Union[str, Any] , ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = kwargs.pop("text_config_dict" , a )
SCREAMING_SNAKE_CASE : Optional[Any] = kwargs.pop("vision_config_dict" , a )
super().__init__(**a )
SCREAMING_SNAKE_CASE : Any = share_cross_modal_transformer_layers
SCREAMING_SNAKE_CASE : Any = hidden_act
SCREAMING_SNAKE_CASE : Any = hidden_size
SCREAMING_SNAKE_CASE : Tuple = initializer_factor
SCREAMING_SNAKE_CASE : Union[str, Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : int = share_link_tower_layers
SCREAMING_SNAKE_CASE : str = link_tower_type
SCREAMING_SNAKE_CASE : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE : Dict = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = tie_word_embeddings
SCREAMING_SNAKE_CASE : Any = init_layernorm_from_vision_encoder
if text_config is None:
SCREAMING_SNAKE_CASE : Any = {}
logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values." )
if vision_config is None:
SCREAMING_SNAKE_CASE : Optional[Any] = {}
logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values." )
SCREAMING_SNAKE_CASE : Union[str, Any] = BridgeTowerTextConfig(**a )
SCREAMING_SNAKE_CASE : Optional[Any] = BridgeTowerVisionConfig(**a )
@classmethod
def __UpperCamelCase ( cls : List[Any] , a : BridgeTowerTextConfig , a : BridgeTowerVisionConfig , **a : List[str] ) -> Optional[int]:
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **a )
def __UpperCamelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE : str = self.text_config.to_dict()
SCREAMING_SNAKE_CASE : Optional[int] = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE : Dict = self.__class__.model_type
return output | 25 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__name__)
a_ = 'Hello world! cécé herlolip'
a_ = namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = BertAbsConfig(
temp_dir="." , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder="bert" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : Dict = torch.load(_a , lambda _a , _a: storage)
SCREAMING_SNAKE_CASE : str = AbsSummarizer(_a , torch.device("cpu") , _a)
original.eval()
SCREAMING_SNAKE_CASE : List[str] = BertAbsSummarizer(_a , torch.device("cpu"))
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model")
new_model.bert.load_state_dict(original.bert.state_dict())
new_model.decoder.load_state_dict(original.decoder.state_dict())
new_model.generator.load_state_dict(original.generator.state_dict())
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical")
SCREAMING_SNAKE_CASE : List[str] = BertTokenizer.from_pretrained("bert-base-uncased")
# prepare the model inputs
SCREAMING_SNAKE_CASE : List[str] = tokenizer.encode("This is sample éàalj'-.")
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode("This is sample 3 éàalj'-.")
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0
# forward pass
SCREAMING_SNAKE_CASE : List[Any] = encoder_input_ids
SCREAMING_SNAKE_CASE : List[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Dict = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : Optional[int] = original(_a , _a , _a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Dict = original.generator(_a)
SCREAMING_SNAKE_CASE : Any = new_model(
_a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Tuple = new_model.generator(_a)
SCREAMING_SNAKE_CASE : List[Any] = torch.max(torch.abs(output_converted_model - output_original_model)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : int = torch.allclose(_a , _a , atol=1E-3)
if are_identical:
logging.info("all weights are equal up to 1e-3")
else:
raise ValueError("the weights are different. The new model is likely different from the original one.")
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary")
torch.save(
new_model.state_dict() , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin")
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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.',
)
a_ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
) | 25 | 1 |
from math import ceil, sqrt
def lowerCamelCase__ ( _a = 1000000):
SCREAMING_SNAKE_CASE : List[Any] = 0
for outer_width in range(3 , (limit // 4) + 2):
if outer_width**2 > limit:
SCREAMING_SNAKE_CASE : str = max(ceil(sqrt(outer_width**2 - limit)) , 1)
else:
SCREAMING_SNAKE_CASE : str = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F'''{solution() = }''') | 25 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
a_ = parser.parse_args()
a_ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
a_ = CLIPImageProcessor()
a_ = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
a_ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path) | 25 | 1 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE : List[Any] = ya
SCREAMING_SNAKE_CASE : str = xa
for k in range(_a):
SCREAMING_SNAKE_CASE : Optional[int] = y[k] + step_size * ode_func(_a , y[k])
SCREAMING_SNAKE_CASE : Union[str, Any] = y[k] + (
(step_size / 2) * (ode_func(_a , y[k]) + ode_func(x + step_size , _a))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Swinv2ForImageClassification',
'Swinv2ForMaskedImageModeling',
'Swinv2Model',
'Swinv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 1 |
def lowerCamelCase__ ( _a):
if a < 0:
raise ValueError("Input value must be a positive integer")
elif isinstance(_a , _a):
raise TypeError("Input value must be a 'int' type")
return bin(_a).count("1")
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values")
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values")
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase__ ( _a , _a , _a):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values")
SCREAMING_SNAKE_CASE : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def lowerCamelCase__ ( _a , _a):
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values")
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori")
return 4 * pow(_a , 2) * torus_radius * tube_radius
def lowerCamelCase__ ( _a , _a):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def lowerCamelCase__ ( _a , _a , _a):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values")
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("Given three sides do not form a triangle")
SCREAMING_SNAKE_CASE : List[str] = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE : Optional[int] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea))
return area
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def lowerCamelCase__ ( _a , _a , _a):
if basea < 0 or basea < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values")
return 1 / 2 * (basea + basea) * height
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def lowerCamelCase__ ( _a , _a):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("area_rhombus() only accepts non-negative values")
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase__ ( _a , _a):
if not isinstance(_a , _a) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides")
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side")
return (sides * length**2) / (4 * tan(pi / sides))
return (sides * length**2) / (4 * tan(pi / sides))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('[DEMO] Areas of various geometric shapes: \n')
print(F'''Rectangle: {area_rectangle(10, 20) = }''')
print(F'''Square: {area_square(10) = }''')
print(F'''Triangle: {area_triangle(10, 10) = }''')
print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(F'''Rhombus: {area_rhombus(10, 20) = }''')
print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(F'''Circle: {area_circle(20) = }''')
print(F'''Ellipse: {area_ellipse(10, 20) = }''')
print('\nSurface Areas of various geometric shapes: \n')
print(F'''Cube: {surface_area_cube(20) = }''')
print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(F'''Sphere: {surface_area_sphere(20) = }''')
print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(F'''Cone: {surface_area_cone(10, 20) = }''')
print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(F'''Torus: {surface_area_torus(20, 10) = }''')
print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(F'''Square: {area_reg_polygon(4, 10) = }''')
print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''') | 25 | 1 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
a_ = parser.parse_args()
a_ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
a_ = CLIPImageProcessor()
a_ = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
a_ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path) | 25 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 1 |
from ..utils import DummyObject, requires_backends
class _UpperCamelCase ( metaclass=__A ):
'''simple docstring'''
lowerCamelCase__ =['torch', 'torchsde']
def __init__( self : Optional[int] , *a : Optional[Any] , **a : Any ) -> List[Any]:
"""simple docstring"""
requires_backends(self , ["torch", "torchsde"] )
@classmethod
def __UpperCamelCase ( cls : List[str] , *a : int , **a : Tuple ) -> int:
"""simple docstring"""
requires_backends(cls , ["torch", "torchsde"] )
@classmethod
def __UpperCamelCase ( cls : Optional[Any] , *a : Dict , **a : Optional[int] ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ["torch", "torchsde"] ) | 25 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_a)
if n > 1:
factors.append(_a)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 1 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
a_ = logging.get_logger(__name__)
class _UpperCamelCase :
'''simple docstring'''
lowerCamelCase__ =None
@experimental
def lowerCamelCase__ ( _a , _a , _a , _a , _a , _a , _a):
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
_a , _a , _a , _a , _a , _a , _a)
return _map_with_joblib(_a , _a , _a , _a , _a , _a , _a)
def lowerCamelCase__ ( _a , _a , _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Dict = num_proc if num_proc <= len(_a) else len(_a)
SCREAMING_SNAKE_CASE : Optional[int] = [] # We organize the splits ourselve (contiguous splits)
for index in range(_a):
SCREAMING_SNAKE_CASE : Any = len(_a) // num_proc
SCREAMING_SNAKE_CASE : int = len(_a) % num_proc
SCREAMING_SNAKE_CASE : List[Any] = div * index + min(_a , _a)
SCREAMING_SNAKE_CASE : List[str] = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc))
if len(_a) != sum(len(i[1]) for i in split_kwds):
raise ValueError(
f"Error dividing inputs iterable among processes. "
f"Total number of objects {len(_a)}, "
f"length: {sum(len(i[1]) for i in split_kwds)}")
logger.info(
f"Spawning {num_proc} processes for {len(_a)} objects in slices of {[len(i[1]) for i in split_kwds]}")
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = None, None
if not disable_tqdm:
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[int] = (RLock(),), tqdm.set_lock
with Pool(_a , initargs=_a , initializer=_a) as pool:
SCREAMING_SNAKE_CASE : List[str] = pool.map(_a , _a)
logger.info(f"Finished {num_proc} processes")
SCREAMING_SNAKE_CASE : str = [obj for proc_res in mapped for obj in proc_res]
logger.info(f"Unpacked {len(_a)} objects")
return mapped
def lowerCamelCase__ ( _a , _a , _a , _a , _a , _a , _a):
# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
# and it requires monkey-patching joblib internal classes which is subject to change
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name , n_jobs=_a):
return joblib.Parallel()(
joblib.delayed(_a)((function, obj, types, None, True, None)) for obj in iterable)
@experimental
@contextlib.contextmanager
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
SCREAMING_SNAKE_CASE : Optional[int] = None | 25 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : int = float(_a)
SCREAMING_SNAKE_CASE : Dict = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_cos() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_cos() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : str = float(_a)
SCREAMING_SNAKE_CASE : Any = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15)) | 25 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
if len(_a) == 0:
return []
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = min(_a), max(_a)
SCREAMING_SNAKE_CASE : Dict = int(max_value - min_value) + 1
SCREAMING_SNAKE_CASE : list[list] = [[] for _ in range(_a)]
for i in my_list:
buckets[int(i - min_value)].append(_a)
return [v for bucket in buckets for v in sorted(_a)]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15] | 25 |
from __future__ import annotations
import math
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = size
# approximate the overall size of segment tree with given value
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
SCREAMING_SNAKE_CASE : Union[str, Any] = [0 for i in range(0 , 4 * size )]
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )] # flag for lazy update
def __UpperCamelCase ( self : Tuple , a : int ) -> int:
"""simple docstring"""
return idx * 2
def __UpperCamelCase ( self : str , a : int ) -> int:
"""simple docstring"""
return idx * 2 + 1
def __UpperCamelCase ( self : int , a : int , a : int , a : int , a : list[int] ) -> None:
"""simple docstring"""
if left_element == right_element:
SCREAMING_SNAKE_CASE : int = a[left_element - 1]
else:
SCREAMING_SNAKE_CASE : Optional[int] = (left_element + right_element) // 2
self.build(self.left(a ) , a , a , a )
self.build(self.right(a ) , mid + 1 , a , a )
SCREAMING_SNAKE_CASE : List[Any] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : int , a : int , a : int , a : int , a : int ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : Any = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[str] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : List[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
SCREAMING_SNAKE_CASE : Optional[Any] = val
if left_element != right_element:
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Optional[Any] = True
return True
SCREAMING_SNAKE_CASE : int = (left_element + right_element) // 2
self.update(self.left(a ) , a , a , a , a , a )
self.update(self.right(a ) , mid + 1 , a , a , a , a )
SCREAMING_SNAKE_CASE : Optional[int] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
return True
def __UpperCamelCase ( self : Dict , a : int , a : int , a : int , a : int , a : int ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[Any] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
SCREAMING_SNAKE_CASE : Dict = (left_element + right_element) // 2
SCREAMING_SNAKE_CASE : Tuple = self.query(self.left(a ) , a , a , a , a )
SCREAMING_SNAKE_CASE : Tuple = self.query(self.right(a ) , mid + 1 , a , a , a )
return max(a , a )
def __str__( self : str ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , a , a ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
a_ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
a_ = 15
a_ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 25 | 1 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Union[str, Any] , a : int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = n
SCREAMING_SNAKE_CASE : str = [None] * self.n
SCREAMING_SNAKE_CASE : List[str] = 0 # index of the first element
SCREAMING_SNAKE_CASE : Tuple = 0
SCREAMING_SNAKE_CASE : str = 0
def __len__( self : Union[str, Any] ) -> int:
"""simple docstring"""
return self.size
def __UpperCamelCase ( self : Any ) -> bool:
"""simple docstring"""
return self.size == 0
def __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
return False if self.is_empty() else self.array[self.front]
def __UpperCamelCase ( self : Tuple , a : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
if self.size >= self.n:
raise Exception("QUEUE IS FULL" )
SCREAMING_SNAKE_CASE : Union[str, Any] = data
SCREAMING_SNAKE_CASE : Optional[Any] = (self.rear + 1) % self.n
self.size += 1
return self
def __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
if self.size == 0:
raise Exception("UNDERFLOW" )
SCREAMING_SNAKE_CASE : Tuple = self.array[self.front]
SCREAMING_SNAKE_CASE : List[str] = None
SCREAMING_SNAKE_CASE : Any = (self.front + 1) % self.n
self.size -= 1
return temp | 25 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
SCREAMING_SNAKE_CASE : str = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : Optional[int] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : Tuple = output.images
SCREAMING_SNAKE_CASE : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Tuple = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : List[str] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_dpmpp_2m" )
SCREAMING_SNAKE_CASE : str = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = sd_pipe(
[prompt] , generator=a , guidance_scale=7.5 , num_inference_steps=15 , output_type="np" , use_karras_sigmas=a , )
SCREAMING_SNAKE_CASE : str = output.images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 1 |
def lowerCamelCase__ ( _a):
if len(_a) <= 1:
return lst
SCREAMING_SNAKE_CASE : List[str] = 1
while i < len(_a):
if lst[i - 1] <= lst[i]:
i += 1
else:
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[str] = lst[i], lst[i - 1]
i -= 1
if i == 0:
SCREAMING_SNAKE_CASE : Optional[Any] = 1
return lst
if __name__ == "__main__":
a_ = input('Enter numbers separated by a comma:\n').strip()
a_ = [int(item) for item in user_input.split(',')]
print(gnome_sort(unsorted)) | 25 |
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 _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Union[str, Any] = False
return options
def __UpperCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=10 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = 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 __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Dict = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=20 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[str] = output.images
SCREAMING_SNAKE_CASE : Optional[int] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Any = 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 | 25 | 1 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
a_ = ['text', 'image', 'audio']
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : List[str] = []
for input_type in input_types:
if input_type == "text":
inputs.append("Text input")
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png").resize((512, 512)))
elif input_type == "audio":
inputs.append(torch.ones(3000))
elif isinstance(_a , _a):
inputs.append(create_inputs(_a))
else:
raise ValueError(f"Invalid type requested: {input_type}")
return inputs
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : List[Any] = []
for output in outputs:
if isinstance(_a , (str, AgentText)):
output_types.append("text")
elif isinstance(_a , (Image.Image, AgentImage)):
output_types.append("image")
elif isinstance(_a , (torch.Tensor, AgentAudio)):
output_types.append("audio")
else:
raise ValueError(f"Invalid output: {output}")
return output_types
@is_tool_test
class _UpperCamelCase :
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> List[Any]:
"""simple docstring"""
self.assertTrue(hasattr(self.tool , "inputs" ) )
self.assertTrue(hasattr(self.tool , "outputs" ) )
SCREAMING_SNAKE_CASE : List[str] = self.tool.inputs
for _input in inputs:
if isinstance(_input , a ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
SCREAMING_SNAKE_CASE : Optional[Any] = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def __UpperCamelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = create_inputs(self.tool.inputs )
SCREAMING_SNAKE_CASE : Dict = self.tool(*a )
# There is a single output
if len(self.tool.outputs ) == 1:
SCREAMING_SNAKE_CASE : Union[str, Any] = [outputs]
self.assertListEqual(output_types(a ) , self.tool.outputs )
def __UpperCamelCase ( self : List[str] ) -> int:
"""simple docstring"""
self.assertTrue(hasattr(self.tool , "description" ) )
self.assertTrue(hasattr(self.tool , "default_checkpoint" ) )
self.assertTrue(self.tool.description.startswith("This is a tool that" ) )
def __UpperCamelCase ( self : str ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = create_inputs(self.tool.inputs )
SCREAMING_SNAKE_CASE : Any = self.tool(*a )
if not isinstance(a , a ):
SCREAMING_SNAKE_CASE : Dict = [outputs]
self.assertEqual(len(a ) , len(self.tool.outputs ) )
for output, output_type in zip(a , self.tool.outputs ):
SCREAMING_SNAKE_CASE : Any = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(a , a ) )
def __UpperCamelCase ( self : List[str] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = create_inputs(self.tool.inputs )
SCREAMING_SNAKE_CASE : List[Any] = []
for _input, input_type in zip(a , self.tool.inputs ):
if isinstance(a , a ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
SCREAMING_SNAKE_CASE : Tuple = self.tool(*a )
if not isinstance(a , a ):
SCREAMING_SNAKE_CASE : List[str] = [outputs]
self.assertEqual(len(a ) , len(self.tool.outputs ) ) | 25 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase__ ( _a):
return getitem, k
def lowerCamelCase__ ( _a , _a):
return setitem, k, v
def lowerCamelCase__ ( _a):
return delitem, k
def lowerCamelCase__ ( _a , _a , *_a):
try:
return fun(_a , *_a), None
except Exception as e:
return None, e
a_ = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
a_ = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
a_ = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
a_ = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items"),
pytest.param(_overwrite_items , id="overwrite items"),
pytest.param(_delete_items , id="delete items"),
pytest.param(_access_absent_items , id="access absent items"),
pytest.param(_add_with_resize_up , id="add with resize up"),
pytest.param(_add_with_resize_down , id="add with resize down"),
) , )
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Dict = HashMap(initial_block_size=4)
SCREAMING_SNAKE_CASE : List[str] = {}
for _, (fun, *args) in enumerate(_a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = _run_operation(_a , _a , *_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = _run_operation(_a , _a , *_a)
assert my_res == py_res
assert str(_a) == str(_a)
assert set(_a) == set(_a)
assert len(_a) == len(_a)
assert set(my.items()) == set(py.items())
def lowerCamelCase__ ( ):
def is_public(_a) -> bool:
return not name.startswith("_")
SCREAMING_SNAKE_CASE : List[str] = {name for name in dir({}) if is_public(_a)}
SCREAMING_SNAKE_CASE : Union[str, Any] = {name for name in dir(HashMap()) if is_public(_a)}
assert dict_public_names > hash_public_names | 25 | 1 |
def lowerCamelCase__ ( _a = 10**12):
SCREAMING_SNAKE_CASE : Tuple = 1
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Optional[int] = 1
SCREAMING_SNAKE_CASE : Optional[Any] = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev_denominator += 2 * denominator
denominator += 2 * prev_denominator
return (denominator + 1) // 2
if __name__ == "__main__":
print(F'''{solution() = }''') | 25 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
'VanForImageClassification',
'VanModel',
'VanPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 1 |
from PIL import Image
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_a) -> int:
return int(128 + factor * (c - 128))
return img.point(_a)
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change contrast to 170
a_ = change_contrast(img, 170)
cont_img.save('image_data/lena_high_contrast.png', format='png') | 25 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
if len(_a) == 0:
return []
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = min(_a), max(_a)
SCREAMING_SNAKE_CASE : Dict = int(max_value - min_value) + 1
SCREAMING_SNAKE_CASE : list[list] = [[] for _ in range(_a)]
for i in my_list:
buckets[int(i - min_value)].append(_a)
return [v for bucket in buckets for v in sorted(_a)]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15] | 25 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
SCREAMING_SNAKE_CASE : Dict = get_activation("gelu" )
self.assertTrue(torch.allclose(gelu_python(a ) , torch_builtin(a ) ) )
self.assertFalse(torch.allclose(gelu_python(a ) , gelu_new(a ) ) )
def __UpperCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
SCREAMING_SNAKE_CASE : int = get_activation("gelu" )
SCREAMING_SNAKE_CASE : str = get_activation("gelu_10" )
SCREAMING_SNAKE_CASE : Optional[Any] = torch_builtin(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = geluaa(a )
SCREAMING_SNAKE_CASE : Optional[int] = torch.where(y_gelu_aa < 10.0 , 1 , 0 )
self.assertTrue(torch.max(a ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def __UpperCamelCase ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
get_activation("gelu" )
get_activation("gelu_10" )
get_activation("gelu_fast" )
get_activation("gelu_new" )
get_activation("gelu_python" )
get_activation("gelu_pytorch_tanh" )
get_activation("linear" )
get_activation("mish" )
get_activation("quick_gelu" )
get_activation("relu" )
get_activation("sigmoid" )
get_activation("silu" )
get_activation("swish" )
get_activation("tanh" )
with self.assertRaises(a ):
get_activation("bogus" )
with self.assertRaises(a ):
get_activation(a )
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = get_activation("gelu" )
SCREAMING_SNAKE_CASE : Dict = 1
SCREAMING_SNAKE_CASE : Optional[Any] = get_activation("gelu" )
self.assertEqual(acta.a , 1 )
with self.assertRaises(a ):
SCREAMING_SNAKE_CASE : Union[str, Any] = acta.a | 25 |
a_ = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset([])
a_ = frozenset(['image'])
a_ = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image'])
a_ = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'negative_prompt'])
a_ = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
a_ = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image', 'mask_image'])
a_ = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['example_image', 'image', 'mask_image'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset(['input_tokens'])
a_ = frozenset(['input_tokens']) | 25 | 1 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = []
for part_id in partition_order:
SCREAMING_SNAKE_CASE : str = df.where(f"SPARK_PARTITION_ID() = {part_id}").collect()
for row_idx, row in enumerate(_a):
expected_row_ids_and_row_dicts.append((f"{part_id}_{row_idx}", row.asDict()))
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : str = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
SCREAMING_SNAKE_CASE : Union[str, Any] = spark.range(100).repartition(1)
SCREAMING_SNAKE_CASE : Dict = Spark(_a)
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16)
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : Tuple = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
SCREAMING_SNAKE_CASE : Any = spark.range(10).repartition(2)
SCREAMING_SNAKE_CASE : Tuple = [1, 0]
SCREAMING_SNAKE_CASE : Union[str, Any] = _generate_iterable_examples(_a , _a) # Reverse the partitions.
SCREAMING_SNAKE_CASE : Optional[Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , _a)
for i, (row_id, row_dict) in enumerate(generate_fn()):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : Optional[int] = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
SCREAMING_SNAKE_CASE : List[Any] = spark.range(10).repartition(1)
SCREAMING_SNAKE_CASE : Tuple = SparkExamplesIterable(_a)
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(_a):
assert row_id == f"0_{i}"
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : int = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
SCREAMING_SNAKE_CASE : int = spark.range(30).repartition(3)
# Mock the generator so that shuffle reverses the partition indices.
with patch("numpy.random.Generator") as generator_mock:
SCREAMING_SNAKE_CASE : int = lambda _a: x.reverse()
SCREAMING_SNAKE_CASE : List[Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [2, 1, 0])
SCREAMING_SNAKE_CASE : str = SparkExamplesIterable(_a).shuffle_data_sources(_a)
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(_a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[int] = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : Optional[int] = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
SCREAMING_SNAKE_CASE : Optional[Any] = spark.range(20).repartition(4)
# Partitions 0 and 2
SCREAMING_SNAKE_CASE : int = SparkExamplesIterable(_a).shard_data_sources(worker_id=0 , num_workers=2)
assert shard_it_a.n_shards == 2
SCREAMING_SNAKE_CASE : str = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [0, 2])
for i, (row_id, row_dict) in enumerate(_a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
SCREAMING_SNAKE_CASE : Any = SparkExamplesIterable(_a).shard_data_sources(worker_id=1 , num_workers=2)
assert shard_it_a.n_shards == 2
SCREAMING_SNAKE_CASE : List[Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [1, 3])
for i, (row_id, row_dict) in enumerate(_a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : str = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
SCREAMING_SNAKE_CASE : List[str] = spark.range(100).repartition(1)
SCREAMING_SNAKE_CASE : int = Spark(_a)
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1)
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 100 | 25 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
a_ = get_logger()
a_ = None
class _UpperCamelCase ( TensorFormatter[Mapping, 'jax.Array', Mapping] ):
'''simple docstring'''
def __init__( self : Optional[Any] , a : str=None , a : List[Any]=None , **a : Any ) -> Optional[Any]:
"""simple docstring"""
super().__init__(features=a )
import jax
from jaxlib.xla_client import Device
if isinstance(a , a ):
raise ValueError(
F"Expected {device} to be a `str` not {type(a )}, as `jaxlib.xla_extension.Device` "
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
SCREAMING_SNAKE_CASE : List[str] = device if isinstance(a , a ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : str = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
F"Device with string identifier {self.device} not listed among the available "
F"devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default "
F"device: {str(jax.devices()[0] )}." )
SCREAMING_SNAKE_CASE : Any = str(jax.devices()[0] )
SCREAMING_SNAKE_CASE : Any = jnp_array_kwargs
@staticmethod
def __UpperCamelCase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
"""simple docstring"""
import jax
return {str(a ): device for device in jax.devices()}
def __UpperCamelCase ( self : Dict , a : Tuple ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(a , a ) and column:
if all(
isinstance(a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(a , axis=0 )
return column
def __UpperCamelCase ( self : Dict , a : str ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
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()
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
if isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
SCREAMING_SNAKE_CASE : Dict = {"dtype": jnp.intaa}
else:
SCREAMING_SNAKE_CASE : str = {"dtype": jnp.intaa}
elif isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
SCREAMING_SNAKE_CASE : int = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(a , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : Dict = np.asarray(a )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : Optional[Any] = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(a , **{**default_dtype, **self.jnp_array_kwargs} )
def __UpperCamelCase ( self : Any , a : List[str] ) -> Dict:
"""simple docstring"""
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(a , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(a , "__array__" ) and not isinstance(a , jax.Array ):
SCREAMING_SNAKE_CASE : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(a , np.ndarray ):
if data_struct.dtype == object: # jax arrays 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 __UpperCamelCase ( self : Optional[Any] , a : dict ) -> Dict:
"""simple docstring"""
return map_nested(self._recursive_tensorize , a , map_list=a )
def __UpperCamelCase ( self : Dict , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.numpy_arrow_extractor().extract_row(a )
SCREAMING_SNAKE_CASE : List[Any] = self.python_features_decoder.decode_row(a )
return self.recursive_tensorize(a )
def __UpperCamelCase ( self : Optional[int] , a : pa.Table ) -> "jax.Array":
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.numpy_arrow_extractor().extract_column(a )
SCREAMING_SNAKE_CASE : Optional[Any] = self.python_features_decoder.decode_column(a , pa_table.column_names[0] )
SCREAMING_SNAKE_CASE : Tuple = self.recursive_tensorize(a )
SCREAMING_SNAKE_CASE : Optional[int] = self._consolidate(a )
return column
def __UpperCamelCase ( self : List[Any] , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.numpy_arrow_extractor().extract_batch(a )
SCREAMING_SNAKE_CASE : str = self.python_features_decoder.decode_batch(a )
SCREAMING_SNAKE_CASE : List[Any] = self.recursive_tensorize(a )
for column_name in batch:
SCREAMING_SNAKE_CASE : List[Any] = self._consolidate(batch[column_name] )
return batch | 25 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 |
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 _UpperCamelCase :
'''simple docstring'''
@staticmethod
def __UpperCamelCase ( *a : str , **a : int ) -> str:
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =MODEL_FOR_OBJECT_DETECTION_MAPPING
def __UpperCamelCase ( self : Optional[Any] , a : str , a : Optional[Any] , a : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ObjectDetectionPipeline(model=a , image_processor=a )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def __UpperCamelCase ( self : List[Any] , a : Optional[int] , a : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" , threshold=0.0 )
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
import datasets
SCREAMING_SNAKE_CASE : Any = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
SCREAMING_SNAKE_CASE : Dict = [
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"],
]
SCREAMING_SNAKE_CASE : Tuple = object_detector(a , threshold=0.0 )
self.assertEqual(len(a ) , len(a ) )
for outputs in batch_outputs:
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
@require_tf
@unittest.skip("Object detection not implemented in TF" )
def __UpperCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
pass
@require_torch
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = "hf-internal-testing/tiny-detr-mobilenetsv3"
SCREAMING_SNAKE_CASE : Dict = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : Tuple = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : int = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=0.0 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
] , )
SCREAMING_SNAKE_CASE : Dict = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : List[str] = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : int = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : int = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Tuple = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : Tuple = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : str = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = 0.9985
SCREAMING_SNAKE_CASE : int = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : List[str] = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : str = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=a )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
@require_torch
@require_pytesseract
@slow
def __UpperCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = "Narsil/layoutlmv3-finetuned-funsd"
SCREAMING_SNAKE_CASE : Dict = 0.9993
SCREAMING_SNAKE_CASE : str = pipeline("object-detection" , model=a , threshold=a )
SCREAMING_SNAKE_CASE : List[Any] = object_detector(
"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
] , ) | 25 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
# See all MVP models at https://huggingface.co/models?filter=mvp
a_ = {
'vocab_file': {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
},
'added_tokens.json': {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
},
'merges_file': {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
},
'tokenizer_file': {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
},
}
a_ = {
'RUCAIBox/mvp': 1024,
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =VOCAB_FILES_NAMES
lowerCamelCase__ =PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ =['input_ids', 'attention_mask']
lowerCamelCase__ =MvpTokenizer
def __init__( self : int , a : List[str]=None , a : List[str]=None , a : List[str]=None , a : int="replace" , a : Tuple="<s>" , a : List[Any]="</s>" , a : int="</s>" , a : Optional[Any]="<s>" , a : int="<unk>" , a : int="<pad>" , a : List[Any]="<mask>" , a : Union[str, Any]=False , a : str=True , **a : Optional[int] , ) -> int:
"""simple docstring"""
super().__init__(
a , a , tokenizer_file=a , errors=a , bos_token=a , eos_token=a , sep_token=a , cls_token=a , unk_token=a , pad_token=a , mask_token=a , add_prefix_space=a , trim_offsets=a , **a , )
SCREAMING_SNAKE_CASE : Optional[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , a ) != add_prefix_space:
SCREAMING_SNAKE_CASE : Dict = getattr(a , pre_tok_state.pop("type" ) )
SCREAMING_SNAKE_CASE : str = add_prefix_space
SCREAMING_SNAKE_CASE : List[str] = pre_tok_class(**a )
SCREAMING_SNAKE_CASE : Optional[int] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
SCREAMING_SNAKE_CASE : List[str] = "post_processor"
SCREAMING_SNAKE_CASE : Optional[Any] = getattr(self.backend_tokenizer , a , a )
if tokenizer_component_instance:
SCREAMING_SNAKE_CASE : Any = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
SCREAMING_SNAKE_CASE : str = tuple(state["sep"] )
if "cls" in state:
SCREAMING_SNAKE_CASE : Optional[int] = tuple(state["cls"] )
SCREAMING_SNAKE_CASE : Optional[Any] = False
if state.get("add_prefix_space" , a ) != add_prefix_space:
SCREAMING_SNAKE_CASE : List[str] = add_prefix_space
SCREAMING_SNAKE_CASE : Optional[int] = True
if state.get("trim_offsets" , a ) != trim_offsets:
SCREAMING_SNAKE_CASE : str = trim_offsets
SCREAMING_SNAKE_CASE : Union[str, Any] = True
if changes_to_apply:
SCREAMING_SNAKE_CASE : Tuple = getattr(a , state.pop("type" ) )
SCREAMING_SNAKE_CASE : Tuple = component_class(**a )
setattr(self.backend_tokenizer , a , a )
@property
def __UpperCamelCase ( self : Tuple ) -> str:
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def __UpperCamelCase ( self : str , a : Tuple ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else value
SCREAMING_SNAKE_CASE : str = value
def __UpperCamelCase ( self : Tuple , *a : List[Any] , **a : List[str] ) -> BatchEncoding:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = kwargs.get("is_split_into_words" , a )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs." )
return super()._batch_encode_plus(*a , **a )
def __UpperCamelCase ( self : List[Any] , *a : Union[str, Any] , **a : Dict ) -> BatchEncoding:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = kwargs.get("is_split_into_words" , a )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs." )
return super()._encode_plus(*a , **a )
def __UpperCamelCase ( self : int , a : str , a : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self._tokenizer.model.save(a , name=a )
return tuple(a )
def __UpperCamelCase ( self : str , a : str , a : Optional[Any]=None ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __UpperCamelCase ( self : Optional[int] , a : List[int] , a : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Union[str, Any] = [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] | 25 |
def lowerCamelCase__ ( _a):
if not isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Tuple = f"Input value of [number={number}] must be an integer"
raise TypeError(_a)
if number < 0:
return False
SCREAMING_SNAKE_CASE : Union[str, Any] = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 1 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
a_ = HfArgumentParser(InitializationArguments)
a_ = parser.parse_args()
# Load codeparrot tokenizer trained for Python code tokenization
a_ = AutoTokenizer.from_pretrained(args.tokenizer_name)
# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
a_ = {
'vocab_size': len(tokenizer),
'scale_attn_by_inverse_layer_idx': True,
'reorder_and_upcast_attn': True,
}
# Load model config (GPT-2 large in this case)
a_ = AutoConfig.from_pretrained(args.config_name, **config_kwargs)
# Initialize new model with config
a_ = AutoModelForCausalLM.from_config(config)
# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub) | 25 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Dict , a : Tuple , a : Any=13 , a : Any=7 , a : Union[str, Any]=True , a : List[Any]=True , a : List[str]=False , a : List[str]=True , a : Any=99 , a : str=32 , a : Any=5 , a : Optional[int]=4 , a : Union[str, Any]=37 , a : Dict="gelu" , a : List[Any]=0.1 , a : Optional[Any]=0.1 , a : List[str]=512 , a : Union[str, Any]=16 , a : str=2 , a : Dict=0.02 , a : Optional[int]=3 , a : Union[str, Any]=4 , a : int=None , ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = parent
SCREAMING_SNAKE_CASE : Any = batch_size
SCREAMING_SNAKE_CASE : Optional[int] = seq_length
SCREAMING_SNAKE_CASE : List[Any] = is_training
SCREAMING_SNAKE_CASE : int = use_input_mask
SCREAMING_SNAKE_CASE : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE : str = use_labels
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_size
SCREAMING_SNAKE_CASE : str = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = intermediate_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE : List[str] = type_vocab_size
SCREAMING_SNAKE_CASE : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : Tuple = num_labels
SCREAMING_SNAKE_CASE : Tuple = num_choices
SCREAMING_SNAKE_CASE : Optional[Any] = scope
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : str = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE : int = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self : Dict ) -> str:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : Optional[int] , a : Optional[int] , a : Dict , a : str , a : str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = DistilBertModel(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , a )
SCREAMING_SNAKE_CASE : Optional[Any] = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self : Tuple , a : Optional[int] , a : Dict , a : Tuple , a : int , a : int , a : Any ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = DistilBertForMaskedLM(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : str = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self : List[Any] , a : int , a : Optional[Any] , a : Optional[Any] , a : str , a : str , a : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertForQuestionAnswering(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(
a , attention_mask=a , start_positions=a , end_positions=a )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase ( self : Optional[int] , a : str , a : Any , a : int , a : Optional[Any] , a : int , a : str ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.num_labels
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertForSequenceClassification(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase ( self : Optional[Any] , a : List[Any] , a : Optional[int] , a : Union[str, Any] , a : Dict , a : Any , a : Optional[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.num_labels
SCREAMING_SNAKE_CASE : List[str] = DistilBertForTokenClassification(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase ( self : int , a : Any , a : Optional[int] , a : Union[str, Any] , a : Tuple , a : Optional[int] , a : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.num_choices
SCREAMING_SNAKE_CASE : Any = DistilBertForMultipleChoice(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Optional[Any] = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE)) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE : int = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCamelCase__ =(
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
def __UpperCamelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = DistilBertModelTester(self )
SCREAMING_SNAKE_CASE : List[str] = ConfigTester(self , config_class=a , dim=37 )
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a )
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a )
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a )
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a )
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a )
def __UpperCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a )
@slow
def __UpperCamelCase ( self : int ) -> Any:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertModel.from_pretrained(a )
self.assertIsNotNone(a )
@slow
@require_torch_gpu
def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : Any = model_class(config=a )
SCREAMING_SNAKE_CASE : List[Any] = self._prepare_for_class(a , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.jit.trace(
a , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a , os.path.join(a , "traced_model.pt" ) )
SCREAMING_SNAKE_CASE : Tuple = torch.jit.load(os.path.join(a , "traced_model.pt" ) , map_location=a )
loaded(inputs_dict["input_ids"].to(a ) , inputs_dict["attention_mask"].to(a ) )
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertModel.from_pretrained("distilbert-base-uncased" )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , attention_mask=a )[0]
SCREAMING_SNAKE_CASE : List[str] = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) ) | 25 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 1 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__name__)
a_ = 'Hello world! cécé herlolip'
a_ = namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = BertAbsConfig(
temp_dir="." , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder="bert" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : Dict = torch.load(_a , lambda _a , _a: storage)
SCREAMING_SNAKE_CASE : str = AbsSummarizer(_a , torch.device("cpu") , _a)
original.eval()
SCREAMING_SNAKE_CASE : List[str] = BertAbsSummarizer(_a , torch.device("cpu"))
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model")
new_model.bert.load_state_dict(original.bert.state_dict())
new_model.decoder.load_state_dict(original.decoder.state_dict())
new_model.generator.load_state_dict(original.generator.state_dict())
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical")
SCREAMING_SNAKE_CASE : List[str] = BertTokenizer.from_pretrained("bert-base-uncased")
# prepare the model inputs
SCREAMING_SNAKE_CASE : List[str] = tokenizer.encode("This is sample éàalj'-.")
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode("This is sample 3 éàalj'-.")
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0
# forward pass
SCREAMING_SNAKE_CASE : List[Any] = encoder_input_ids
SCREAMING_SNAKE_CASE : List[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Dict = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : Optional[int] = original(_a , _a , _a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Dict = original.generator(_a)
SCREAMING_SNAKE_CASE : Any = new_model(
_a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Tuple = new_model.generator(_a)
SCREAMING_SNAKE_CASE : List[Any] = torch.max(torch.abs(output_converted_model - output_original_model)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : int = torch.allclose(_a , _a , atol=1E-3)
if are_identical:
logging.info("all weights are equal up to 1e-3")
else:
raise ValueError("the weights are different. The new model is likely different from the original one.")
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary")
torch.save(
new_model.state_dict() , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin")
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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.',
)
a_ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
) | 25 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
def __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE : Union[str, Any] = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Any = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" , return_dict=a )[0]
SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "google/ncsnpp-celebahq-256"
SCREAMING_SNAKE_CASE : List[Any] = UNetaDModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Optional[Any] = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = pipe(num_inference_steps=20 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 1 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def lowerCamelCase__ ( _a = True , *_a , **_a):
if not is_tqdm_available():
raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.")
SCREAMING_SNAKE_CASE : List[Any] = False
if main_process_only:
SCREAMING_SNAKE_CASE : Optional[int] = PartialState().local_process_index == 0
return _tqdm(*_a , **_a , disable=_a) | 25 |
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
SCREAMING_SNAKE_CASE : Optional[Any] = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res | 25 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket")
@patch("builtins.open")
def lowerCamelCase__ ( _a , _a):
# ===== initialization =====
SCREAMING_SNAKE_CASE : int = Mock()
SCREAMING_SNAKE_CASE : List[str] = conn, Mock()
SCREAMING_SNAKE_CASE : str = iter([1, None])
SCREAMING_SNAKE_CASE : str = lambda _a: next(_a)
# ===== invoke =====
send_file(filename="mytext.txt" , testing=_a)
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once() | 25 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='roformer'
def __init__( self : Dict , a : Any=5_0000 , a : List[Any]=None , a : str=768 , a : str=12 , a : Tuple=12 , a : Optional[Any]=3072 , a : List[str]="gelu" , a : List[Any]=0.1 , a : Union[str, Any]=0.1 , a : Tuple=1536 , a : List[str]=2 , a : Tuple=0.02 , a : Any=1e-12 , a : Optional[int]=0 , a : Union[str, Any]=False , a : int=True , **a : str , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=a , **a )
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size if embedding_size is None else embedding_size
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = type_vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE : List[str] = rotary_value
SCREAMING_SNAKE_CASE : int = use_cache
class _UpperCamelCase ( __A ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE : str = {0: "batch", 1: "sequence"}
SCREAMING_SNAKE_CASE : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] ) | 25 | 1 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =42
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : int , a : List[Any]=3 , a : Optional[int]=3 , a : Dict=("DownEncoderBlock2D",) , a : Any=(64,) , a : List[Any]=2 , a : Dict=32 , a : str="silu" , a : Optional[Any]=True , ) -> int:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE : Optional[int] = layers_per_block
SCREAMING_SNAKE_CASE : Tuple = torch.nn.Convad(
a , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : Any = nn.ModuleList([] )
# down
SCREAMING_SNAKE_CASE : Optional[int] = block_out_channels[0]
for i, down_block_type in enumerate(a ):
SCREAMING_SNAKE_CASE : str = output_channel
SCREAMING_SNAKE_CASE : int = block_out_channels[i]
SCREAMING_SNAKE_CASE : Union[str, Any] = i == len(a ) - 1
SCREAMING_SNAKE_CASE : Optional[int] = get_down_block(
a , num_layers=self.layers_per_block , in_channels=a , out_channels=a , add_downsample=not is_final_block , resnet_eps=1e-6 , downsample_padding=0 , resnet_act_fn=a , resnet_groups=a , attention_head_dim=a , temb_channels=a , )
self.down_blocks.append(a )
# mid
SCREAMING_SNAKE_CASE : str = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=a , output_scale_factor=1 , resnet_time_scale_shift="default" , attention_head_dim=block_out_channels[-1] , resnet_groups=a , temb_channels=a , )
# out
SCREAMING_SNAKE_CASE : str = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=a , eps=1e-6 )
SCREAMING_SNAKE_CASE : Dict = nn.SiLU()
SCREAMING_SNAKE_CASE : Union[str, Any] = 2 * out_channels if double_z else out_channels
SCREAMING_SNAKE_CASE : Tuple = nn.Convad(block_out_channels[-1] , a , 3 , padding=1 )
SCREAMING_SNAKE_CASE : Any = False
def __UpperCamelCase ( self : List[Any] , a : str ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = x
SCREAMING_SNAKE_CASE : List[str] = self.conv_in(a )
if self.training and self.gradient_checkpointing:
def create_custom_forward(a : Any ):
def custom_forward(*a : Optional[int] ):
return module(*a )
return custom_forward
# down
if is_torch_version(">=" , "1.11.0" ):
for down_block in self.down_blocks:
SCREAMING_SNAKE_CASE : str = torch.utils.checkpoint.checkpoint(
create_custom_forward(a ) , a , use_reentrant=a )
# middle
SCREAMING_SNAKE_CASE : Tuple = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , a , use_reentrant=a )
else:
for down_block in self.down_blocks:
SCREAMING_SNAKE_CASE : Any = torch.utils.checkpoint.checkpoint(create_custom_forward(a ) , a )
# middle
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , a )
else:
# down
for down_block in self.down_blocks:
SCREAMING_SNAKE_CASE : List[Any] = down_block(a )
# middle
SCREAMING_SNAKE_CASE : Optional[Any] = self.mid_block(a )
# post-process
SCREAMING_SNAKE_CASE : Optional[int] = self.conv_norm_out(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.conv_act(a )
SCREAMING_SNAKE_CASE : str = self.conv_out(a )
return sample
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : List[Any] , a : Dict=3 , a : int=3 , a : Optional[Any]=("UpDecoderBlock2D",) , a : Tuple=(64,) , a : int=2 , a : Any=32 , a : Optional[int]="silu" , a : int="group" , ) -> int:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE : Optional[Any] = layers_per_block
SCREAMING_SNAKE_CASE : Any = nn.Convad(
a , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Union[str, Any] = nn.ModuleList([] )
SCREAMING_SNAKE_CASE : Optional[Any] = in_channels if norm_type == "spatial" else None
# mid
SCREAMING_SNAKE_CASE : Optional[Any] = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=a , output_scale_factor=1 , resnet_time_scale_shift="default" if norm_type == "group" else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=a , temb_channels=a , )
# up
SCREAMING_SNAKE_CASE : Any = list(reversed(a ) )
SCREAMING_SNAKE_CASE : Optional[Any] = reversed_block_out_channels[0]
for i, up_block_type in enumerate(a ):
SCREAMING_SNAKE_CASE : Tuple = output_channel
SCREAMING_SNAKE_CASE : List[str] = reversed_block_out_channels[i]
SCREAMING_SNAKE_CASE : int = i == len(a ) - 1
SCREAMING_SNAKE_CASE : List[str] = get_up_block(
a , num_layers=self.layers_per_block + 1 , in_channels=a , out_channels=a , prev_output_channel=a , add_upsample=not is_final_block , resnet_eps=1e-6 , resnet_act_fn=a , resnet_groups=a , attention_head_dim=a , temb_channels=a , resnet_time_scale_shift=a , )
self.up_blocks.append(a )
SCREAMING_SNAKE_CASE : Any = output_channel
# out
if norm_type == "spatial":
SCREAMING_SNAKE_CASE : Optional[int] = SpatialNorm(block_out_channels[0] , a )
else:
SCREAMING_SNAKE_CASE : List[str] = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=a , eps=1e-6 )
SCREAMING_SNAKE_CASE : Optional[int] = nn.SiLU()
SCREAMING_SNAKE_CASE : Any = nn.Convad(block_out_channels[0] , a , 3 , padding=1 )
SCREAMING_SNAKE_CASE : int = False
def __UpperCamelCase ( self : Tuple , a : Union[str, Any] , a : List[Any]=None ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = z
SCREAMING_SNAKE_CASE : int = self.conv_in(a )
SCREAMING_SNAKE_CASE : str = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(a : Dict ):
def custom_forward(*a : List[Any] ):
return module(*a )
return custom_forward
if is_torch_version(">=" , "1.11.0" ):
# middle
SCREAMING_SNAKE_CASE : str = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , a , a , use_reentrant=a )
SCREAMING_SNAKE_CASE : List[Any] = sample.to(a )
# up
for up_block in self.up_blocks:
SCREAMING_SNAKE_CASE : Optional[int] = torch.utils.checkpoint.checkpoint(
create_custom_forward(a ) , a , a , use_reentrant=a )
else:
# middle
SCREAMING_SNAKE_CASE : Any = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , a , a )
SCREAMING_SNAKE_CASE : Dict = sample.to(a )
# up
for up_block in self.up_blocks:
SCREAMING_SNAKE_CASE : int = torch.utils.checkpoint.checkpoint(create_custom_forward(a ) , a , a )
else:
# middle
SCREAMING_SNAKE_CASE : Optional[int] = self.mid_block(a , a )
SCREAMING_SNAKE_CASE : List[Any] = sample.to(a )
# up
for up_block in self.up_blocks:
SCREAMING_SNAKE_CASE : Dict = up_block(a , a )
# post-process
if latent_embeds is None:
SCREAMING_SNAKE_CASE : Optional[int] = self.conv_norm_out(a )
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = self.conv_norm_out(a , a )
SCREAMING_SNAKE_CASE : List[Any] = self.conv_act(a )
SCREAMING_SNAKE_CASE : Optional[int] = self.conv_out(a )
return sample
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , a : str , a : Optional[int] , a : Optional[int] , a : Tuple=None , a : Optional[Any]="random" , a : Any=False , a : List[str]=True ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE : List[Any] = n_e
SCREAMING_SNAKE_CASE : str = vq_embed_dim
SCREAMING_SNAKE_CASE : Optional[int] = beta
SCREAMING_SNAKE_CASE : Any = legacy
SCREAMING_SNAKE_CASE : List[Any] = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
SCREAMING_SNAKE_CASE : List[Any] = remap
if self.remap is not None:
self.register_buffer("used" , torch.tensor(np.load(self.remap ) ) )
SCREAMING_SNAKE_CASE : Dict = self.used.shape[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
SCREAMING_SNAKE_CASE : Union[str, Any] = self.re_embed
SCREAMING_SNAKE_CASE : List[Any] = self.re_embed + 1
print(
F"Remapping {self.n_e} indices to {self.re_embed} indices. "
F"Using {self.unknown_index} for unknown indices." )
else:
SCREAMING_SNAKE_CASE : List[str] = n_e
SCREAMING_SNAKE_CASE : Any = sane_index_shape
def __UpperCamelCase ( self : Tuple , a : Any ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = inds.shape
assert len(a ) > 1
SCREAMING_SNAKE_CASE : Dict = inds.reshape(ishape[0] , -1 )
SCREAMING_SNAKE_CASE : List[str] = self.used.to(a )
SCREAMING_SNAKE_CASE : List[str] = (inds[:, :, None] == used[None, None, ...]).long()
SCREAMING_SNAKE_CASE : Tuple = match.argmax(-1 )
SCREAMING_SNAKE_CASE : Dict = match.sum(2 ) < 1
if self.unknown_index == "random":
SCREAMING_SNAKE_CASE : Tuple = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
SCREAMING_SNAKE_CASE : Optional[int] = self.unknown_index
return new.reshape(a )
def __UpperCamelCase ( self : str , a : List[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = inds.shape
assert len(a ) > 1
SCREAMING_SNAKE_CASE : List[str] = inds.reshape(ishape[0] , -1 )
SCREAMING_SNAKE_CASE : List[str] = self.used.to(a )
if self.re_embed > self.used.shape[0]: # extra token
SCREAMING_SNAKE_CASE : Dict = 0 # simply set to zero
SCREAMING_SNAKE_CASE : Any = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , a )
return back.reshape(a )
def __UpperCamelCase ( self : Union[str, Any] , a : Tuple ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = z.permute(0 , 2 , 3 , 1 ).contiguous()
SCREAMING_SNAKE_CASE : str = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
SCREAMING_SNAKE_CASE : str = torch.argmin(torch.cdist(a , self.embedding.weight ) , dim=1 )
SCREAMING_SNAKE_CASE : Optional[Any] = self.embedding(a ).view(z.shape )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
SCREAMING_SNAKE_CASE : Union[str, Any] = None
# compute loss for embedding
if not self.legacy:
SCREAMING_SNAKE_CASE : Optional[Any] = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
SCREAMING_SNAKE_CASE : Any = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
SCREAMING_SNAKE_CASE : Union[str, Any] = z + (z_q - z).detach()
# reshape back to match original input shape
SCREAMING_SNAKE_CASE : str = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
SCREAMING_SNAKE_CASE : Optional[Any] = self.remap_to_used(a )
SCREAMING_SNAKE_CASE : Optional[int] = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
SCREAMING_SNAKE_CASE : int = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def __UpperCamelCase ( self : Any , a : Dict , a : str ) -> List[str]:
"""simple docstring"""
if self.remap is not None:
SCREAMING_SNAKE_CASE : Any = indices.reshape(shape[0] , -1 ) # add batch axis
SCREAMING_SNAKE_CASE : int = self.unmap_to_all(a )
SCREAMING_SNAKE_CASE : str = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
SCREAMING_SNAKE_CASE : List[Any] = self.embedding(a )
if shape is not None:
SCREAMING_SNAKE_CASE : Any = z_q.view(a )
# reshape back to match original input shape
SCREAMING_SNAKE_CASE : int = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : List[str] , a : List[str] , a : Dict=False ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = parameters
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Any = torch.chunk(a , 2 , dim=1 )
SCREAMING_SNAKE_CASE : Any = torch.clamp(self.logvar , -30.0 , 20.0 )
SCREAMING_SNAKE_CASE : str = deterministic
SCREAMING_SNAKE_CASE : int = torch.exp(0.5 * self.logvar )
SCREAMING_SNAKE_CASE : Tuple = torch.exp(self.logvar )
if self.deterministic:
SCREAMING_SNAKE_CASE : Tuple = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def __UpperCamelCase ( self : Optional[int] , a : Optional[torch.Generator] = None ) -> torch.FloatTensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = randn_tensor(
self.mean.shape , generator=a , device=self.parameters.device , dtype=self.parameters.dtype )
SCREAMING_SNAKE_CASE : int = self.mean + self.std * sample
return x
def __UpperCamelCase ( self : Optional[Any] , a : str=None ) -> Any:
"""simple docstring"""
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def __UpperCamelCase ( self : List[Any] , a : Union[str, Any] , a : Dict=[1, 2, 3] ) -> int:
"""simple docstring"""
if self.deterministic:
return torch.Tensor([0.0] )
SCREAMING_SNAKE_CASE : List[str] = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=a )
def __UpperCamelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
return self.mean | 25 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__name__)
a_ = 'Hello world! cécé herlolip'
a_ = namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = BertAbsConfig(
temp_dir="." , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder="bert" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : Dict = torch.load(_a , lambda _a , _a: storage)
SCREAMING_SNAKE_CASE : str = AbsSummarizer(_a , torch.device("cpu") , _a)
original.eval()
SCREAMING_SNAKE_CASE : List[str] = BertAbsSummarizer(_a , torch.device("cpu"))
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model")
new_model.bert.load_state_dict(original.bert.state_dict())
new_model.decoder.load_state_dict(original.decoder.state_dict())
new_model.generator.load_state_dict(original.generator.state_dict())
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical")
SCREAMING_SNAKE_CASE : List[str] = BertTokenizer.from_pretrained("bert-base-uncased")
# prepare the model inputs
SCREAMING_SNAKE_CASE : List[str] = tokenizer.encode("This is sample éàalj'-.")
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode("This is sample 3 éàalj'-.")
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0
# forward pass
SCREAMING_SNAKE_CASE : List[Any] = encoder_input_ids
SCREAMING_SNAKE_CASE : List[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Dict = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : Optional[int] = original(_a , _a , _a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Dict = original.generator(_a)
SCREAMING_SNAKE_CASE : Any = new_model(
_a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Tuple = new_model.generator(_a)
SCREAMING_SNAKE_CASE : List[Any] = torch.max(torch.abs(output_converted_model - output_original_model)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : int = torch.allclose(_a , _a , atol=1E-3)
if are_identical:
logging.info("all weights are equal up to 1e-3")
else:
raise ValueError("the weights are different. The new model is likely different from the original one.")
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary")
torch.save(
new_model.state_dict() , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin")
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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.',
)
a_ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
) | 25 | 1 |
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : str = 1
SCREAMING_SNAKE_CASE : str = 2
while i * i <= n:
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_divisors
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : str = 1
SCREAMING_SNAKE_CASE : List[Any] = 1
while True:
i += 1
t_num += i
if count_divisors(_a) > 500:
break
return t_num
if __name__ == "__main__":
print(solution()) | 25 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
a_ = parser.parse_args()
a_ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
a_ = CLIPImageProcessor()
a_ = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
a_ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path) | 25 | 1 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any] , a : List[Any] , a : List[Any]=13 , a : Union[str, Any]=7 , a : Optional[int]=True , a : Optional[int]=True , a : int=True , a : Any=True , a : Dict=99 , a : Tuple=32 , a : Optional[int]=5 , a : List[Any]=4 , a : Optional[int]=4 , a : List[str]="gelu" , a : Optional[int]=0.0 , a : int=0.1 , a : List[Any]=True , a : Union[str, Any]=512 , a : Tuple=16 , a : Union[str, Any]=2 , a : List[str]=0.02 , a : Any=3 , a : int=4 , a : int=None , ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = parent
SCREAMING_SNAKE_CASE : Any = batch_size
SCREAMING_SNAKE_CASE : Dict = seq_length
SCREAMING_SNAKE_CASE : str = is_training
SCREAMING_SNAKE_CASE : Optional[int] = use_input_mask
SCREAMING_SNAKE_CASE : Optional[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE : List[str] = use_labels
SCREAMING_SNAKE_CASE : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE : Tuple = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : str = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = intermediate_multiple_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_act
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_dropout
SCREAMING_SNAKE_CASE : Any = attention_dropout
SCREAMING_SNAKE_CASE : str = weight_tying
SCREAMING_SNAKE_CASE : Any = max_position_embeddings
SCREAMING_SNAKE_CASE : int = type_vocab_size
SCREAMING_SNAKE_CASE : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : Any = num_labels
SCREAMING_SNAKE_CASE : List[Any] = num_choices
SCREAMING_SNAKE_CASE : List[str] = scope
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Optional[Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : Optional[int] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : List[Any] = self.get_config()
return config, input_ids, input_mask, token_labels
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
return GPTNeoXJapaneseConfig(
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_multiple_size=self.intermediate_multiple_size , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , weight_tying=self.weight_tying , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=a , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE : Optional[int] = True
return config, input_ids, input_mask, token_labels
def __UpperCamelCase ( self : List[str] , a : Union[str, Any] , a : Optional[Any] , a : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = GPTNeoXJapaneseModel(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : str = model(a , attention_mask=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self : Tuple , a : Optional[int] , a : List[str] , a : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = True
SCREAMING_SNAKE_CASE : Dict = GPTNeoXJapaneseModel(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[Any] = model(a , attention_mask=a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self : Optional[Any] , a : Tuple , a : Union[str, Any] , a : Union[str, Any] , a : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = GPTNeoXJapaneseForCausalLM(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self : Optional[int] , a : Tuple , a : Tuple , a : Dict ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : str = GPTNeoXJapaneseForCausalLM(config=a )
model.to(a )
model.eval()
# first forward pass
SCREAMING_SNAKE_CASE : List[str] = model(a , attention_mask=a , use_cache=a )
SCREAMING_SNAKE_CASE : Any = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE : Dict = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE : str = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE : Any = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cat([input_mask, next_mask] , dim=-1 )
SCREAMING_SNAKE_CASE : List[Any] = model(a , attention_mask=a , output_hidden_states=a )
SCREAMING_SNAKE_CASE : Any = output_from_no_past["hidden_states"][0]
SCREAMING_SNAKE_CASE : Dict = model(
a , attention_mask=a , past_key_values=a , output_hidden_states=a , )["hidden_states"][0]
# select random slice
SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE : List[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE : List[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a , a , atol=1e-3 ) )
def __UpperCamelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[int] = config_and_inputs
SCREAMING_SNAKE_CASE : Any = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
lowerCamelCase__ =(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
lowerCamelCase__ =(
{'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
lowerCamelCase__ =False
lowerCamelCase__ =False
lowerCamelCase__ =False
lowerCamelCase__ =False
def __UpperCamelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = GPTNeoXJapaneseModelTester(self )
SCREAMING_SNAKE_CASE : List[str] = ConfigTester(self , config_class=a , hidden_size=37 )
def __UpperCamelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(a , a , a )
def __UpperCamelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(a , a , a )
def __UpperCamelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs_for_decoder()
SCREAMING_SNAKE_CASE : Dict = None
self.model_tester.create_and_check_model_as_decoder(a , a , a )
def __UpperCamelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(a , a , a )
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*a )
@slow
def __UpperCamelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = "abeja/gpt-neox-japanese-2.7b"
SCREAMING_SNAKE_CASE : Optional[int] = ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]
SCREAMING_SNAKE_CASE : List[Any] = [
"データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
"100年後に必要とされる会社は、「人」が中心の会社です。",
"フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
"国境の長いトンネルを抜けると、そこは雪国だった。",
"美味しい日本食といえば、やっぱりお寿司ですよね。",
]
SCREAMING_SNAKE_CASE : List[str] = GPTNeoXJapaneseTokenizer.from_pretrained(a )
SCREAMING_SNAKE_CASE : Tuple = GPTNeoXJapaneseForCausalLM.from_pretrained(a )
SCREAMING_SNAKE_CASE : Optional[Any] = []
for prompt in prompts:
SCREAMING_SNAKE_CASE : str = tokenizer(a , return_tensors="pt" ).input_ids
SCREAMING_SNAKE_CASE : Any = model.generate(a , max_length=50 )
SCREAMING_SNAKE_CASE : Tuple = tokenizer.batch_decode(a , skip_special_tokens=a )
predicted_outputs += generated_string
self.assertListEqual(a , a ) | 25 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Swinv2ForImageClassification',
'Swinv2ForMaskedImageModeling',
'Swinv2Model',
'Swinv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 1 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
a_ = TypeVar('KEY')
a_ = TypeVar('VAL')
@dataclass(frozen=__A , slots=__A )
class _UpperCamelCase ( Generic[KEY, VAL] ):
'''simple docstring'''
lowerCamelCase__ =42
lowerCamelCase__ =42
class _UpperCamelCase ( _Item ):
'''simple docstring'''
def __init__( self : Dict ) -> None:
"""simple docstring"""
super().__init__(a , a )
def __bool__( self : str ) -> bool:
"""simple docstring"""
return False
a_ = _DeletedItem()
class _UpperCamelCase ( MutableMapping[KEY, VAL] ):
'''simple docstring'''
def __init__( self : Union[str, Any] , a : int = 8 , a : float = 0.75 ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = initial_block_size
SCREAMING_SNAKE_CASE : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
SCREAMING_SNAKE_CASE : List[str] = capacity_factor
SCREAMING_SNAKE_CASE : List[Any] = 0
def __UpperCamelCase ( self : str , a : KEY ) -> int:
"""simple docstring"""
return hash(a ) % len(self._buckets )
def __UpperCamelCase ( self : Optional[Any] , a : int ) -> int:
"""simple docstring"""
return (ind + 1) % len(self._buckets )
def __UpperCamelCase ( self : List[Any] , a : int , a : KEY , a : VAL ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = self._buckets[ind]
if not stored:
SCREAMING_SNAKE_CASE : int = _Item(a , a )
self._len += 1
return True
elif stored.key == key:
SCREAMING_SNAKE_CASE : Dict = _Item(a , a )
return True
else:
return False
def __UpperCamelCase ( self : Union[str, Any] ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(a )
def __UpperCamelCase ( self : Tuple ) -> bool:
"""simple docstring"""
if len(self._buckets ) <= self._initial_block_size:
return False
SCREAMING_SNAKE_CASE : Optional[int] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def __UpperCamelCase ( self : Any , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = self._buckets
SCREAMING_SNAKE_CASE : Optional[int] = [None] * new_size
SCREAMING_SNAKE_CASE : Dict = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def __UpperCamelCase ( self : Tuple ) -> None:
"""simple docstring"""
self._resize(len(self._buckets ) * 2 )
def __UpperCamelCase ( self : List[str] ) -> None:
"""simple docstring"""
self._resize(len(self._buckets ) // 2 )
def __UpperCamelCase ( self : List[str] , a : KEY ) -> Iterator[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self._get_bucket_index(a )
for _ in range(len(self._buckets ) ):
yield ind
SCREAMING_SNAKE_CASE : int = self._get_next_ind(a )
def __UpperCamelCase ( self : List[Any] , a : KEY , a : VAL ) -> None:
"""simple docstring"""
for ind in self._iterate_buckets(a ):
if self._try_set(a , a , a ):
break
def __setitem__( self : List[str] , a : KEY , a : VAL ) -> None:
"""simple docstring"""
if self._is_full():
self._size_up()
self._add_item(a , a )
def __delitem__( self : int , a : KEY ) -> None:
"""simple docstring"""
for ind in self._iterate_buckets(a ):
SCREAMING_SNAKE_CASE : List[str] = self._buckets[ind]
if item is None:
raise KeyError(a )
if item is _deleted:
continue
if item.key == key:
SCREAMING_SNAKE_CASE : int = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self : List[str] , a : KEY ) -> VAL:
"""simple docstring"""
for ind in self._iterate_buckets(a ):
SCREAMING_SNAKE_CASE : List[Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(a )
def __len__( self : Optional[Any] ) -> int:
"""simple docstring"""
return self._len
def __iter__( self : Any ) -> Iterator[KEY]:
"""simple docstring"""
yield from (item.key for item in self._buckets if item)
def __repr__( self : Optional[int] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = " ,".join(
F"{item.key}: {item.val}" for item in self._buckets if item )
return F"HashMap({val_string})" | 25 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values")
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values")
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase__ ( _a , _a , _a):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values")
SCREAMING_SNAKE_CASE : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def lowerCamelCase__ ( _a , _a):
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values")
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori")
return 4 * pow(_a , 2) * torus_radius * tube_radius
def lowerCamelCase__ ( _a , _a):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def lowerCamelCase__ ( _a , _a , _a):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values")
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("Given three sides do not form a triangle")
SCREAMING_SNAKE_CASE : List[str] = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE : Optional[int] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea))
return area
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def lowerCamelCase__ ( _a , _a , _a):
if basea < 0 or basea < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values")
return 1 / 2 * (basea + basea) * height
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def lowerCamelCase__ ( _a , _a):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("area_rhombus() only accepts non-negative values")
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase__ ( _a , _a):
if not isinstance(_a , _a) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides")
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side")
return (sides * length**2) / (4 * tan(pi / sides))
return (sides * length**2) / (4 * tan(pi / sides))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('[DEMO] Areas of various geometric shapes: \n')
print(F'''Rectangle: {area_rectangle(10, 20) = }''')
print(F'''Square: {area_square(10) = }''')
print(F'''Triangle: {area_triangle(10, 10) = }''')
print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(F'''Rhombus: {area_rhombus(10, 20) = }''')
print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(F'''Circle: {area_circle(20) = }''')
print(F'''Ellipse: {area_ellipse(10, 20) = }''')
print('\nSurface Areas of various geometric shapes: \n')
print(F'''Cube: {surface_area_cube(20) = }''')
print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(F'''Sphere: {surface_area_sphere(20) = }''')
print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(F'''Cone: {surface_area_cone(10, 20) = }''')
print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(F'''Torus: {surface_area_torus(20, 10) = }''')
print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(F'''Square: {area_reg_polygon(4, 10) = }''')
print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''') | 25 | 1 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
a_ = 'true'
def lowerCamelCase__ ( _a , _a=82 , _a=16):
set_seed(42)
SCREAMING_SNAKE_CASE : List[Any] = RegressionModel()
SCREAMING_SNAKE_CASE : Optional[Any] = deepcopy(_a)
SCREAMING_SNAKE_CASE : Any = RegressionDataset(length=_a)
SCREAMING_SNAKE_CASE : List[Any] = DataLoader(_a , batch_size=_a)
model.to(accelerator.device)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[int] = accelerator.prepare(_a , _a)
return model, ddp_model, dataloader
def lowerCamelCase__ ( _a , _a=False):
SCREAMING_SNAKE_CASE : Optional[Any] = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased")
SCREAMING_SNAKE_CASE : Optional[int] = load_dataset("glue" , "mrpc" , split="validation")
def tokenize_function(_a):
SCREAMING_SNAKE_CASE : str = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=_a , max_length=_a)
return outputs
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE : Union[str, Any] = dataset.map(
_a , batched=_a , remove_columns=["idx", "sentence1", "sentence2"] , )
SCREAMING_SNAKE_CASE : Any = tokenized_datasets.rename_column("label" , "labels")
def collate_fn(_a):
if use_longest:
return tokenizer.pad(_a , padding="longest" , return_tensors="pt")
return tokenizer.pad(_a , padding="max_length" , max_length=128 , return_tensors="pt")
return DataLoader(_a , shuffle=_a , collate_fn=_a , batch_size=16)
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Any = Accelerator(dispatch_batches=_a , split_batches=_a)
SCREAMING_SNAKE_CASE : str = get_dataloader(_a , not dispatch_batches)
SCREAMING_SNAKE_CASE : Any = AutoModelForSequenceClassification.from_pretrained(
"hf-internal-testing/mrpc-bert-base-cased" , return_dict=_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = accelerator.prepare(_a , _a)
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : int = []
for batch in dataloader:
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = batch.values()
with torch.no_grad():
SCREAMING_SNAKE_CASE : str = model(_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Any = accelerator.gather_for_metrics((logit, target))
logits_and_targets.append((logit, target))
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = [], []
for logit, targ in logits_and_targets:
logits.append(_a)
targs.append(_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[str] = torch.cat(_a), torch.cat(_a)
return logits, targs
def lowerCamelCase__ ( _a , _a=82 , _a=False , _a=False , _a=16):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : int = get_basic_setup(_a , _a , _a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[int] = generate_predictions(_a , _a , _a)
assert (
len(_a) == num_samples
), f"Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(_a)}"
def lowerCamelCase__ ( _a = False , _a = False):
SCREAMING_SNAKE_CASE : List[str] = evaluate.load("glue" , "mrpc")
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : int = get_mrpc_setup(_a , _a)
# First do baseline
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : int = setup["no"]
model.to(_a)
model.eval()
for batch in dataloader:
batch.to(_a)
with torch.inference_mode():
SCREAMING_SNAKE_CASE : Union[str, Any] = model(**_a)
SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.logits.argmax(dim=-1)
metric.add_batch(predictions=_a , references=batch["labels"])
SCREAMING_SNAKE_CASE : Dict = metric.compute()
# Then do distributed
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
SCREAMING_SNAKE_CASE : Optional[int] = model(**_a)
SCREAMING_SNAKE_CASE : Tuple = outputs.logits.argmax(dim=-1)
SCREAMING_SNAKE_CASE : Union[str, Any] = batch["labels"]
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = accelerator.gather_for_metrics((preds, references))
metric.add_batch(predictions=_a , references=_a)
SCREAMING_SNAKE_CASE : Dict = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key]), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : Tuple = Accelerator(split_batches=_a , dispatch_batches=_a)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("**Testing gather_for_metrics**")
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`")
test_mrpc(_a , _a)
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test torch metrics**")
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
SCREAMING_SNAKE_CASE : Tuple = Accelerator(split_batches=_a , dispatch_batches=_a)
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99")
test_torch_metrics(_a , 99)
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test last batch is not dropped when perfectly divisible**")
SCREAMING_SNAKE_CASE : str = Accelerator()
test_torch_metrics(_a , 512)
accelerator.state._reset_state()
def lowerCamelCase__ ( _a):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main() | 25 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 1 |
from __future__ import annotations
from math import pi
def lowerCamelCase__ ( _a , _a , _a):
if (inductance, frequency, reactance).count(0) != 1:
raise ValueError("One and only one argument must be 0")
if inductance < 0:
raise ValueError("Inductance cannot be negative")
if frequency < 0:
raise ValueError("Frequency cannot be negative")
if reactance < 0:
raise ValueError("Inductive reactance cannot be negative")
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("Exactly one argument must be 0")
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_a)
if n > 1:
factors.append(_a)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
a_ = logging.get_logger(__name__)
@add_end_docstrings(__A )
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : List[Any] , *a : Optional[Any] , **a : Optional[Any] ) -> List[Any]:
"""simple docstring"""
super().__init__(*a , **a )
requires_backends(self , "vision" )
self.check_model_type(a )
def __call__( self : int , a : Union[str, List[str], "Image.Image", List["Image.Image"]] , **a : Optional[int] ) -> List[Any]:
"""simple docstring"""
return super().__call__(a , **a )
def __UpperCamelCase ( self : Tuple , **a : Any ) -> Optional[Any]:
"""simple docstring"""
return {}, {}, {}
def __UpperCamelCase ( self : int , a : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = load_image(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = image.size
SCREAMING_SNAKE_CASE : List[str] = self.image_processor(images=a , return_tensors=self.framework )
return model_inputs
def __UpperCamelCase ( self : Optional[int] , a : Any ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model(**a )
return model_outputs
def __UpperCamelCase ( self : List[str] , a : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = model_outputs.predicted_depth
SCREAMING_SNAKE_CASE : List[str] = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode="bicubic" , align_corners=a )
SCREAMING_SNAKE_CASE : List[Any] = prediction.squeeze().cpu().numpy()
SCREAMING_SNAKE_CASE : List[Any] = (output * 255 / np.max(a )).astype("uint8" )
SCREAMING_SNAKE_CASE : Any = Image.fromarray(a )
SCREAMING_SNAKE_CASE : Optional[int] = {}
SCREAMING_SNAKE_CASE : Optional[int] = predicted_depth
SCREAMING_SNAKE_CASE : Optional[int] = depth
return output_dict | 25 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : int = float(_a)
SCREAMING_SNAKE_CASE : Dict = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_cos() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_cos() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : str = float(_a)
SCREAMING_SNAKE_CASE : Any = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15)) | 25 | 1 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Tuple , a : Union[str, Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = arr.split("," )
def __UpperCamelCase ( self : Any ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = [int(self.array[0] )] * len(self.array )
SCREAMING_SNAKE_CASE : Dict = [int(self.array[0] )] * len(self.array )
for i in range(1 , len(self.array ) ):
SCREAMING_SNAKE_CASE : Dict = max(
int(self.array[i] ) + sum_value[i - 1] , int(self.array[i] ) )
SCREAMING_SNAKE_CASE : List[Any] = max(sum_value[i] , rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
a_ = input('please input some numbers:')
a_ = SubArray(whole_array)
a_ = array.solve_sub_array()
print(('the results is:', re)) | 25 |
from __future__ import annotations
import math
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = size
# approximate the overall size of segment tree with given value
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
SCREAMING_SNAKE_CASE : Union[str, Any] = [0 for i in range(0 , 4 * size )]
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )] # flag for lazy update
def __UpperCamelCase ( self : Tuple , a : int ) -> int:
"""simple docstring"""
return idx * 2
def __UpperCamelCase ( self : str , a : int ) -> int:
"""simple docstring"""
return idx * 2 + 1
def __UpperCamelCase ( self : int , a : int , a : int , a : int , a : list[int] ) -> None:
"""simple docstring"""
if left_element == right_element:
SCREAMING_SNAKE_CASE : int = a[left_element - 1]
else:
SCREAMING_SNAKE_CASE : Optional[int] = (left_element + right_element) // 2
self.build(self.left(a ) , a , a , a )
self.build(self.right(a ) , mid + 1 , a , a )
SCREAMING_SNAKE_CASE : List[Any] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : int , a : int , a : int , a : int , a : int ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : Any = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[str] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : List[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
SCREAMING_SNAKE_CASE : Optional[Any] = val
if left_element != right_element:
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Optional[Any] = True
return True
SCREAMING_SNAKE_CASE : int = (left_element + right_element) // 2
self.update(self.left(a ) , a , a , a , a , a )
self.update(self.right(a ) , mid + 1 , a , a , a , a )
SCREAMING_SNAKE_CASE : Optional[int] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
return True
def __UpperCamelCase ( self : Dict , a : int , a : int , a : int , a : int , a : int ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[Any] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
SCREAMING_SNAKE_CASE : Dict = (left_element + right_element) // 2
SCREAMING_SNAKE_CASE : Tuple = self.query(self.left(a ) , a , a , a , a )
SCREAMING_SNAKE_CASE : Tuple = self.query(self.right(a ) , mid + 1 , a , a , a )
return max(a , a )
def __str__( self : str ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , a , a ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
a_ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
a_ = 15
a_ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 25 | 1 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ ='hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
def __UpperCamelCase ( self : Any , a : Optional[int]=0 ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = floats_tensor((1, 3, 128, 128) , rng=random.Random(a ) )
SCREAMING_SNAKE_CASE : str = np.random.RandomState(a )
SCREAMING_SNAKE_CASE : int = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def __UpperCamelCase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[str] = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE : Tuple = pipe(**a ).images
SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
SCREAMING_SNAKE_CASE : Optional[Any] = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE : List[Any] = pipe(**a ).images
SCREAMING_SNAKE_CASE : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
SCREAMING_SNAKE_CASE : List[str] = np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __UpperCamelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
SCREAMING_SNAKE_CASE : Dict = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=a )
# warmup pass to apply optimizations
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(**self.get_dummy_inputs() )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE : List[Any] = pipe(**a ).images
SCREAMING_SNAKE_CASE : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
SCREAMING_SNAKE_CASE : Any = np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __UpperCamelCase ( self : Any ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
SCREAMING_SNAKE_CASE : List[str] = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[str] = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(**a ).images
SCREAMING_SNAKE_CASE : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
SCREAMING_SNAKE_CASE : Dict = np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __UpperCamelCase ( self : str ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
SCREAMING_SNAKE_CASE : Optional[int] = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(**a ).images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
SCREAMING_SNAKE_CASE : Tuple = np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __UpperCamelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
SCREAMING_SNAKE_CASE : str = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE : List[str] = pipe(**a ).images
SCREAMING_SNAKE_CASE : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
SCREAMING_SNAKE_CASE : Optional[int] = np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : Any ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = ort.SessionOptions()
SCREAMING_SNAKE_CASE : str = False
return options
def __UpperCamelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
SCREAMING_SNAKE_CASE : Optional[int] = init_image.resize((768, 512) )
# using the PNDM scheduler by default
SCREAMING_SNAKE_CASE : str = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Tuple = "A fantasy landscape, trending on artstation"
SCREAMING_SNAKE_CASE : Optional[int] = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Dict = pipe(
prompt=a , image=a , strength=0.75 , guidance_scale=7.5 , num_inference_steps=10 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : Any = output.images
SCREAMING_SNAKE_CASE : str = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
SCREAMING_SNAKE_CASE : Tuple = np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def __UpperCamelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
SCREAMING_SNAKE_CASE : Dict = init_image.resize((768, 512) )
SCREAMING_SNAKE_CASE : Any = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5" , subfolder="scheduler" , revision="onnx" )
SCREAMING_SNAKE_CASE : List[str] = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[str] = "A fantasy landscape, trending on artstation"
SCREAMING_SNAKE_CASE : List[Any] = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Dict = pipe(
prompt=a , image=a , strength=0.75 , guidance_scale=7.5 , num_inference_steps=20 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : Union[str, Any] = output.images
SCREAMING_SNAKE_CASE : Any = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2 | 25 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
SCREAMING_SNAKE_CASE : str = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : Optional[int] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : Tuple = output.images
SCREAMING_SNAKE_CASE : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Tuple = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : List[str] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_dpmpp_2m" )
SCREAMING_SNAKE_CASE : str = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = sd_pipe(
[prompt] , generator=a , guidance_scale=7.5 , num_inference_steps=15 , output_type="np" , use_karras_sigmas=a , )
SCREAMING_SNAKE_CASE : str = output.images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 1 |
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