code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _A ( unittest.TestCase ):
"""simple docstring"""
... | 68 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class _A ( UpperCamelCase ):
"""simple docstring"""
lowerCamelCase : Tuple = 'ctr... | 68 | 1 |
import os
def lowercase__ ( ) -> Optional[Any]:
"""simple docstring"""
with open(os.path.dirname(A_ ) + """/p022_names.txt""" ) as file:
__UpperCAmelCase =str(file.readlines()[0] )
__UpperCAmelCase =names.replace("""\""... | 68 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = [
["attention", "attn"],
["encoder_attention", "encoder_attn"],
["q_lin"... | 68 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 68 |
from itertools import permutations
def lowercase__ ( A_: tuple ) -> bool:
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
... | 68 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
"processing_vision_text_dual_encoder... | 68 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__A = TypeVar("T")
def lowercase__ ( A_: int ) -> int:
"""simple docstring"""
return (position - 1) // 2
def lowercase__ ( A_: int ) -> ... | 68 | 1 |
from __future__ import annotations
import pandas as pd
def lowercase__ ( A_: list[int] , A_: list[int] , A_: int ) -> list[int]:
"""simple docstring"""
__UpperCAmelCase =[0] * no_of_processes
__UpperCAmelCase =[0] ... | 68 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__A = logging.get_logger(__name__)
@dataclass
class ... | 68 | 1 |
from __future__ import annotations
def lowercase__ ( A_: list[int] ) -> bool:
"""simple docstring"""
return len(set(A_ ) ) == len(A_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 68 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _A ( unittest.TestCase ):
"""simp... | 68 | 1 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__A = logging.get_logger(__name__)
__A = {
"vocab_file": ... | 68 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.confi... | 68 | 1 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import A... | 68 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffus... | 68 | 1 |
from math import factorial
class _A :
"""simple docstring"""
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : str ) -> Optional[int]:
__UpperCAmelCase =real
... | 68 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as com... | 68 | 1 |
import numpy as np
def lowercase__ ( A_: np.ndarray ) -> np.ndarray:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def lowercase__ ( A_: np.ndarray ) -> np.ndarray:
"""simple docstring"""
return ... | 68 |
def lowercase__ ( A_: int , A_: int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowercase__ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert... | 68 | 1 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def lowercase__ ( A_: dict[int, list[int]] , A_: int , A_: list[bool] ) -> list[int]:
"""simple docstring"""
__UpperCAm... | 68 |
from __future__ import annotations
import bisect
def lowercase__ ( A_: list[int] , A_: int , A_: int = 0 , A_: int = -1 ) -> int:
"""simple docstring"""
if hi < 0:
__UpperCAmelCase =len(A_ )
whil... | 68 | 1 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__A = TypeVar("KT")
__A = TypeVar("VT")
class _A ( Generic[KT, VT] ):
"""simple docstring"""
def __init__( self : int , __SCREAMING_SNAKE_CASE : KT | str = ... | 68 |
from typing import List
from .keymap import KEYMAP, get_character
def lowercase__ ( A_: str ) -> str:
"""simple docstring"""
def decorator(A_: int ):
__UpperCAmelCase =getattr(A_ , """handle_key""" , [] )
... | 68 | 1 |
def lowercase__ ( A_: int = 1 , A_: int = 1000 ) -> int:
"""simple docstring"""
__UpperCAmelCase =1
__UpperCAmelCase =0
for divide_by_number in range(A_ , digit + 1 ):
__UpperCAmelCase =[]
... | 68 |
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 ...... | 68 | 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 = {
"andreasmadsen/efficient_mlm_m0.40": (
"https://huggingface.co/andreasm... | 68 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.... | 68 | 1 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_avail... | 68 |
from __future__ import annotations
def lowercase__ ( A_: list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in rang... | 68 | 1 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.confi... | 68 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowercase__ ( A_: int , A_: int , A_: int , A_: int , A_: int , A_: int ) -> np.ndarray:
"""simple docstring""... | 68 | 1 |
def lowercase__ ( A_: float , A_: float , A_: float , A_: float , A_: float , ) -> float:
"""simple docstring"""
__UpperCAmelCase =[redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 ... | 68 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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 Confi... | 68 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from trans... | 68 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import to... | 68 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}
try:
if not i... | 68 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__A = logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.' )
@require_torch
@r... | 68 | 1 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from trans... | 68 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from trans... | 68 | 1 |
from __future__ import annotations
__A = [True] * 1_00_00_01
__A = 2
while i * i <= 1_00_00_00:
if seive[i]:
for j in range(i * i, 1_00_00_01, i):
__A = False
i += 1
def lowercase__ ( A_: int ) -> bool:
"""simple docstring"""
... | 68 |
from __future__ import annotations
from collections.abc import Iterator
class _A :
"""simple docstring"""
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : int ) -> None:
__UpperCAmelCase =value
__UpperCAmelCase ... | 68 | 1 |
def lowercase__ ( A_: list[int] ) -> list[int]:
"""simple docstring"""
__UpperCAmelCase =len(A_ )
for i in range(A_ ):
for j in range(i + 1 , A_ ):
if numbers[j] < numbers[i]:
__Up... | 68 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 68 | 1 |
from math import ceil
def lowercase__ ( A_: Optional[Any] , A_: Tuple ) -> Tuple:
"""simple docstring"""
__UpperCAmelCase =list(range(0 , A_ ) )
__UpperCAmelCase =[item for sublist in list(device_map.values() ) for i... | 68 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class _A ( UpperCamelCase ):
"""simple docstring"""
lowerCamelCase : Tuple = 'ctr... | 68 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _A ( UpperCamelCase ):
"""simp... | 68 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = [
["attention", "attn"],
["encoder_attention", "encoder_attn"],
["q_lin"... | 68 | 1 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import r... | 68 |
from itertools import permutations
def lowercase__ ( A_: tuple ) -> bool:
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
... | 68 | 1 |
def lowercase__ ( A_: int , A_: list ) -> Any:
"""simple docstring"""
_enforce_args(A_ , A_ )
if n == 0:
return 0
__UpperCAmelCase =float("""-inf""" )
for i in range(1 , n + 1 ):
__Upp... | 68 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__A = TypeVar("T")
def lowercase__ ( A_: int ) -> int:
"""simple docstring"""
return (position - 1) // 2
def lowercase__ ( A_: int ) -> ... | 68 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class _A ( UpperCamelCase ):
"""simple docstring"""
lowerCamelCase : Opt... | 68 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__A = logging.get_logger(__name__)
@dataclass
class ... | 68 | 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_barthez imp... | 68 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _A ( unittest.TestCase ):
"""simp... | 68 | 1 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
_... | 68 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.confi... | 68 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.... | 68 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffus... | 68 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
... | 68 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as com... | 68 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
__A = logging.get_logger(__name__)
__A = {
"Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
# See all DPT models at https://huggingface... | 68 |
def lowercase__ ( A_: int , A_: int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowercase__ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert... | 68 | 1 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def lowercase__ ( ) -> List[str]:
"""simple docstring"""
raise RuntimeError("""CUDA out ... | 68 |
from __future__ import annotations
import bisect
def lowercase__ ( A_: list[int] , A_: int , A_: int = 0 , A_: int = -1 ) -> int:
"""simple docstring"""
if hi < 0:
__UpperCAmelCase =len(A_ )
whil... | 68 | 1 |
def lowercase__ ( A_: list[list[int | float]] ) -> int:
"""simple docstring"""
__UpperCAmelCase =len(A_ )
__UpperCAmelCase =len(matrix[0] )
__UpperCAmelCase =min(A_ , A_ )
for row in range(A_ ):
# ... | 68 |
from typing import List
from .keymap import KEYMAP, get_character
def lowercase__ ( A_: str ) -> str:
"""simple docstring"""
def decorator(A_: int ):
__UpperCAmelCase =getattr(A_ , """handle_key""" , [] )
... | 68 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__A = logging.get_logger(__name__)
__A = {"vocab_file": "vocab.... | 68 |
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 ...... | 68 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__A = logging.get_logger(__name__)
@dataclass
class ... | 68 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.... | 68 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = "▁"
__A = {"vocab_file": "... | 68 |
from __future__ import annotations
def lowercase__ ( A_: list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in rang... | 68 | 1 |
import math
def lowercase__ ( ) -> None:
"""simple docstring"""
__UpperCAmelCase =input("""Enter message: """ )
__UpperCAmelCase =int(input(F'''Enter key [2-{len(A_ ) - 1}]: ''' ) )
__UpperCAmelCase =input("""Encryption/Decr... | 68 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowercase__ ( A_: int , A_: int , A_: int , A_: int , A_: int , A_: int ) -> np.ndarray:
"""simple docstring""... | 68 | 1 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSch... | 68 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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 Confi... | 68 | 1 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__A = logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.' )
@require_torch
@r... | 68 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import to... | 68 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyN... | 0 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__A = logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.' )
@require_torch
@r... | 68 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acceler... | 1 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from trans... | 68 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
"""configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG... | 2 |
from __future__ import annotations
from collections.abc import Iterator
class _A :
"""simple docstring"""
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : int ) -> None:
__UpperCAmelCase =value
__UpperCAmelCase ... | 68 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...t... | 3 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 68 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : float ):
if edge <= 0 or not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def _SCREAMING_SNAKE_CASE ... | 4 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class _A ( UpperCamelCase ):
"""simple docstring"""
lowerCamelCase : Tuple = 'ctr... | 68 | 0 |
'''simple docstring'''
def A (__lowerCamelCase :float ):
return 10 - x * x
def A (__lowerCamelCase :float , __lowerCamelCase :float ):
# Bolzano theory in order to find if there is a root between a and b
if equation(__lowerCamelCase ) * equation(__lowerCamelCase ) >=... | 5 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = [
["attention", "attn"],
["encoder_attention", "encoder_attn"],
["q_lin"... | 68 | 0 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class UpperCamelCase_ :
de... | 6 |
from itertools import permutations
def lowercase__ ( A_: tuple ) -> bool:
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
... | 68 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils ... | 7 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__A = TypeVar("T")
def lowercase__ ( A_: int ) -> int:
"""simple docstring"""
return (position - 1) // 2
def lowercase__ ( A_: int ) -> ... | 68 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( __snake_case : int , __snake_case : int ) -> list[list[int]]:
__A : list[list[int]] = []
create_all_state(1 , __snake_case , __snake_... | 8 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__A = logging.get_logger(__name__)
@dataclass
class ... | 68 | 0 |
from __future__ import annotations
from fractions import Fraction
def A ( __UpperCamelCase , __UpperCamelCase ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def A ( __UpperCamelCase ) -> list[str]:... | 9 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _A ( unittest.TestCase ):
"""simp... | 68 | 0 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _snake_case ( ):
_UpperCamelCase = HfArgumentParser(__snake_case )
_UpperCamelCase = parser.parse_args_into_dataclasses()[0]
_UpperCamelCase = TensorFlowBenchmark(a... | 10 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.confi... | 68 | 0 |
'''simple docstring'''
from math import loga
def lowerCAmelCase (__A):
"""simple docstring"""
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''')
... | 11 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffus... | 68 | 0 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 12 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as com... | 68 | 0 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
A__ : Optional[Any] = {
"""n_samples""": 64,
"""horizon""": 32,
"""num_inference_steps""": 20,
"""n_guide_steps""": 2, # can set to 0 for faster sampling, does... | 13 |
def lowercase__ ( A_: int , A_: int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowercase__ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert... | 68 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_... | 14 |
from __future__ import annotations
import bisect
def lowercase__ ( A_: list[int] , A_: int , A_: int = 0 , A_: int = -1 ) -> int:
"""simple docstring"""
if hi < 0:
__UpperCAmelCase =len(A_ )
whil... | 68 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
A : Optional[Any] = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
('kernel', '... | 15 |
from typing import List
from .keymap import KEYMAP, get_character
def lowercase__ ( A_: str ) -> str:
"""simple docstring"""
def decorator(A_: int ):
__UpperCAmelCase =getattr(A_ , """handle_key""" , [] )
... | 68 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
lo... | 16 |
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 ...... | 68 | 0 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.... | 17 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.... | 68 | 0 |
'''simple docstring'''
from __future__ import annotations
def __a(SCREAMING_SNAKE_CASE_ : list ):
'''simple docstring'''
if not nums:
raise ValueError("List is empty" )
return sum(SCREAMING_SNAKE_CASE_ ) / len(SCREAMING_SNAKE_CASE_ )
if __name__ == "__... | 18 |
from __future__ import annotations
def lowercase__ ( A_: list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in rang... | 68 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_a = {
"""configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MA... | 19 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowercase__ ( A_: int , A_: int , A_: int , A_: int , A_: int , A_: int ) -> np.ndarray:
"""simple docstring""... | 68 | 0 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_co... | 20 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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 Confi... | 68 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ : str = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP",... | 21 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import to... | 68 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case : str = logging.get_logger(__name__)
_snake_case : Tu... | 22 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__A = logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.' )
@require_torch
@r... | 68 | 0 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _snake_case (__lowercase , __lowercase , __lowercase):
UpperCamelCase_ = ('dense.weight', 'attention.self.query', 'attention.self.ke... | 23 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from trans... | 68 | 0 |
'''simple docstring'''
class lowerCAmelCase :
def __init__( self ) -> Any:
'''simple docstring'''
__snake_case = {}
def lowerCAmelCase ( self ) -> None:
'''simple docstring'''
print(self.vertex )
... | 24 |
from __future__ import annotations
from collections.abc import Iterator
class _A :
"""simple docstring"""
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : int ) -> None:
__UpperCAmelCase =value
__UpperCAmelCase ... | 68 | 0 |
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 s... | 25 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 68 | 0 |
'''simple docstring'''
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _A ( unittest.TestCase ):
def lowercase__ ( self : Tuple ) -> List[str]:
... | 26 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class _A ( UpperCamelCase ):
"""simple docstring"""
lowerCamelCase : Tuple = 'ctr... | 68 | 0 |
import numpy as np
__A : int = [
["a", "b", "c", "d", "e"],
["f", "g", "h", "i", "k"],
["l", "m", "n", "o", "p"],
["q", "r", "s", "t", "u"],
["v", "w", "x", "y", "z"],
]
class lowerCamelCase:
'''simple docstring'''
def __init... | 27 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = [
["attention", "attn"],
["encoder_attention", "encoder_attn"],
["q_lin"... | 68 | 0 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self, *A, **A ):
'''simple doc... | 28 |
from itertools import permutations
def lowercase__ ( A_: tuple ) -> bool:
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
... | 68 | 0 |
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
A_ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A_ = [ord(letter) for letter in string.ascii_lowercase]
A_ ... | 29 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__A = TypeVar("T")
def lowercase__ ( A_: int ) -> int:
"""simple docstring"""
return (position - 1) // 2
def lowercase__ ( A_: int ) -> ... | 68 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
if not is_tokenizers_available()... | 30 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__A = logging.get_logger(__name__)
@dataclass
class ... | 68 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Optional[int] = logging.get_logger(__name__)
lowerCamelCase__ : Any = {
'microsoft/git-base': 'https://huggingface.co/mi... | 31 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _A ( unittest.TestCase ):
"""simp... | 68 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
Upper... | 32 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.confi... | 68 | 0 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case__ = mf_knapsack(i - 1 , __lowerCAmelCas... | 33 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffus... | 68 | 0 |
"""simple docstring"""
import numpy as np
def __snake_case ( _lowercase ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __snake_case ( _lowercase ):
"""simple docstring"""
return vector * sigmoid(1.702 * vector )
if __... | 34 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as com... | 68 | 0 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 35 |
def lowercase__ ( A_: int , A_: int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowercase__ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert... | 68 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__lowercase : Union[str, Any] = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfi... | 36 |
from __future__ import annotations
import bisect
def lowercase__ ( A_: list[int] , A_: int , A_: int = 0 , A_: int = -1 ) -> int:
"""simple docstring"""
if hi < 0:
__UpperCAmelCase =len(A_ )
whil... | 68 | 0 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=A__ ):
"""simple docstring"""
_lowercase = ['transformers', 'torch', 'note_seq']
def __init__( self : Optional[int] , *lowerCamelCase__ : int , **lowerCamelCase__ : int ):
require... | 37 |
from typing import List
from .keymap import KEYMAP, get_character
def lowercase__ ( A_: str ) -> str:
"""simple docstring"""
def decorator(A_: int ):
__UpperCAmelCase =getattr(A_ , """handle_key""" , [] )
... | 68 | 0 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( ... | 38 |
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 ...... | 68 | 0 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if... | 39 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.... | 68 | 0 |
import math
import random
def UpperCamelCase ( snake_case__ : float , snake_case__ : bool = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
__UpperCAmelCase = 0.02
def UpperCamelCase ... | 40 |
from __future__ import annotations
def lowercase__ ( A_: list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in rang... | 68 | 0 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_conf... | 41 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowercase__ ( A_: int , A_: int , A_: int , A_: int , A_: int , A_: int ) -> np.ndarray:
"""simple docstring""... | 68 | 0 |
'''simple docstring'''
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
A_ = "scheduler_config.json"
class UpperCAmelCase ( UpperCAmel... | 42 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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 Confi... | 68 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path... | 43 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import to... | 68 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2... | 44 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__A = logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.' )
@require_torch
@r... | 68 | 0 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ..... | 45 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from trans... | 68 | 0 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lower... | 46 |
from __future__ import annotations
from collections.abc import Iterator
class _A :
"""simple docstring"""
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : int ) -> None:
__UpperCAmelCase =value
__UpperCAmelCase ... | 68 | 0 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
SCREAMING_SNAKE_C... | 47 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 68 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ : Any = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTra... | 48 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class _A ( UpperCamelCase ):
"""simple docstring"""
lowerCamelCase : Tuple = 'ctr... | 68 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
_lowercase : Optional[int] = TypeVar('KT')
_lowercase : Optional[int] = TypeVar('VT')
class _UpperCAmelCase ( Generic[KT, VT] ... | 49 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = [
["attention", "attn"],
["encoder_attention", "encoder_attn"],
["q_lin"... | 68 | 0 |
'''simple docstring'''
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = (DDIMParallelScheduler,)
_UpperCamelCase = (('eta', 0... | 50 |
from itertools import permutations
def lowercase__ ( A_: tuple ) -> bool:
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
... | 68 | 0 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __snake_case ( SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Any:
"""simple docstring"""
if not is_accelerate_availab... | 51 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__A = TypeVar("T")
def lowercase__ ( A_: int ) -> int:
"""simple docstring"""
return (position - 1) // 2
def lowercase__ ( A_: int ) -> ... | 68 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger... | 52 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__A = logging.get_logger(__name__)
@dataclass
class ... | 68 | 0 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def a_ ( lowerCAmelCase_ : str=N... | 53 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _A ( unittest.TestCase ):
"""simp... | 68 | 0 |
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =""
for i in table:
res += inp[i - 1]
return res
def a__ ( lowercase__ ):
'''simple docstring'''
retur... | 54 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.confi... | 68 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
class UpperCAmelCase :
'''simple do... | 55 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffus... | 68 | 0 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _lowercase ( pl.LightningModule ):
def __init__( self : Tuple , SCREAMING_SNAKE_CASE_ ... | 56 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as com... | 68 | 0 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTeste... | 57 |
def lowercase__ ( A_: int , A_: int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowercase__ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert... | 68 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : Optional[Any] = {
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFI... | 58 |
from __future__ import annotations
import bisect
def lowercase__ ( A_: list[int] , A_: int , A_: int = 0 , A_: int = -1 ) -> int:
"""simple docstring"""
if hi < 0:
__UpperCAmelCase =len(A_ )
whil... | 68 | 0 |
import inspect
import unittest
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->List[Any]:
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
a... | 59 |
from typing import List
from .keymap import KEYMAP, get_character
def lowercase__ ( A_: str ) -> str:
"""simple docstring"""
def decorator(A_: int ):
__UpperCAmelCase =getattr(A_ , """handle_key""" , [] )
... | 68 | 0 |
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