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 copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : int = "upernet"
... | 40 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( _lowercase , ... | 91 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 41 |
"""simple docstring"""
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 lowerCAmelCase_ ( _lowercase ... | 91 | 0 |
'''simple docstring'''
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 UpperCAmelCase ( U... | 42 |
"""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, PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = '''▁'... | 91 | 0 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import Vide... | 43 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureEx... | 91 | 0 |
'''simple docstring'''
UpperCAmelCase_ : List[Any] = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
UpperC... | 44 |
"""simple docstring"""
from torch import nn
def _snake_case ( snake_case__ : Union[str, Any] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'Unsupported activation function: {act_f... | 91 | 0 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
... | 45 |
"""simple docstring"""
import copy
import re
class lowerCAmelCase_ :
'''simple docstring'''
_lowerCamelCase: str = '''hp'''
_lowerCamelCase: List[Any] = {}
_lowerCamelCase: List[Any] = None
@classmethod
def _SCREAMING_SNAKE_... | 91 | 0 |
"""simple docstring"""
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_ ( ... | 46 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with p... | 91 | 0 |
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_availa... | 47 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( _lowercase ,... | 91 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class A ( SCREAMING_SN... | 48 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier... | 91 | 0 |
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale... | 49 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distribu... | 91 | 0 |
'''simple docstring'''
# 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... | 50 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
... | 91 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[int] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
... | 51 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig''',
'''XLM... | 91 | 0 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_... | 52 |
"""simple docstring"""
# 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
#
# Un... | 91 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_snake_case : Optional[int] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvaila... | 53 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common im... | 91 | 0 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__lowercase : int =None
try:
import msvcrt
except ImportError:
__lowercase : List[Any] =None
try:
import fcntl
except ImportError:
__lowercase : Uni... | 54 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
_lowercase = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def _snake_case ( ):
A = os.path.dirname(os.path.realpath(snake_case__ ) )
A = os.path.join(snake_case__ , 'words.txt' )
... | 91 | 0 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
f... | 55 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''goog... | 91 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _lowercase :
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : int ) -> None:
__snake_case = num_of_nodes
__snake_case = [... | 56 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase = {
'''configuration_electra''': ['''ELECTRA_PRETRAINED_CONF... | 91 | 0 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
A_ : Optional[Any] = re.compile(r'\b(a|an|the)\b', re.UNICODE)
A_ : Dict = None
def snake_case () -> str:
Up... | 57 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
f... | 91 | 0 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : float = 1 / sqrt(2 ) ):
'''simpl... | 58 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
def __init__( self : ... | 91 | 0 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.... | 59 |
"""simple docstring"""
from maths.prime_check import is_prime
def _snake_case ( snake_case__ : int ):
if not isinstance(snake_case__ , snake_case__ ):
A = F'Input value of [number={number}] must be an integer'
raise TypeError(snake_case__ )
if is_prime(snake_case__ ) and is_prime(... | 91 | 0 |
# 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 app... | 60 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats... | 91 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transfo... | 61 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( snake_case__ : tuple[int, int] , snake_case__ : int ):
A , A = position
A = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
... | 91 | 0 |
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = len(lowercase ), len(grid[0] )
if (
min(lowercase , lowercase ) < 0
... | 62 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( _lowercase , ... | 91 | 0 |
import os
def lowerCamelCase__ ( __lowerCamelCase : Any ):
__UpperCAmelCase : Tuple = len(grid[0] )
__UpperCAmelCase : int = len(__lowerCamelCase )
__UpperCAmelCase : Dict = 0
__UpperCAmelCase : List[str... | 63 |
"""simple docstring"""
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 lowerCAmelCase_ ( _lowercase ... | 91 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False)... | 64 |
"""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, PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = '''▁'... | 91 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
if depth < 0:
raise ValueError("""De... | 65 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureEx... | 91 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.uti... | 66 |
"""simple docstring"""
from torch import nn
def _snake_case ( snake_case__ : Union[str, Any] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'Unsupported activation function: {act_f... | 91 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not ... | 67 |
"""simple docstring"""
import copy
import re
class lowerCAmelCase_ :
'''simple docstring'''
_lowerCamelCase: str = '''hp'''
_lowerCamelCase: List[Any] = {}
_lowerCamelCase: List[Any] = None
@classmethod
def _SCREAMING_SNAKE_... | 91 | 0 |
import re
from filelock import FileLock
try:
import nltk
__A = True
except (ImportError, ModuleNotFoundError):
__A = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def lowercase__ ( A_: str ) -> str:
... | 68 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with p... | 91 | 0 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
a : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a : list[int] = [ord(letter)... | 69 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( _lowercase ,... | 91 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def _SCREAMING_SNAKE_CASE ( lowercase : Optional[Any] ):
'''simple docstring'''
lowerCamelCase_ = args.pruning... | 70 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier... | 91 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
class _snake_case :
def __init__( self ,_snake_case ):
UpperCAmelCase_ : Tuple = size
# approximate the overall size of segment tree with given value
UpperCAmelCase_ : str ... | 71 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distribu... | 91 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( lowercase_ : list[float] ) -> bool:
'''simple docstring'''
if len(lowercase_ ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
... | 72 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
... | 91 | 0 |
import numpy as np
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1e-12 , _UpperCAmelCase = 100 , ):
assert np.shape(_UpperCAmelCase)[0] == np.shape(_UpperCAmelCase)[1]
# Ensure proper dimensionality.
assert np.shape(_UpperC... | 73 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig''',
'''XLM... | 91 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def a__ ( snake_case ):
"""simple doc... | 74 |
"""simple docstring"""
# 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
#
# Un... | 91 | 0 |
'''simple docstring'''
# 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-... | 75 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common im... | 91 | 0 |
"""simple docstring"""
import math
def __UpperCAmelCase ( __UpperCamelCase ):
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 n... | 76 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
_lowercase = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def _snake_case ( ):
A = os.path.dirname(os.path.realpath(snake_case__ ) )
A = os.path.join(snake_case__ , 'words.txt' )
... | 91 | 0 |
"""simple docstring"""
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,... | 77 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''goog... | 91 | 0 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
SCREAMING_SNAKE_CASE_: int =6378137.0
SCREAMING_SNAKE_CASE_: List[Any] =6356752.314245
SCREAMING_SNAKE_CASE_: Dict =6_37_81_37
def lowerCAmelCase_ ( snake_case_ : float , snake_case_ : ... | 78 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase = {
'''configuration_electra''': ['''ELECTRA_PRETRAINED_CONF... | 91 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Any = {"""configuration_plbart""": ["""PLBART_PRETRAINED_... | 79 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
f... | 91 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Dict = {
"""configuration_xlm_roberta_xl""": [
"""XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XLMRobertaXLConfig""",
"""XLMRo... | 80 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
def __init__( self : ... | 91 | 0 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : int = logging.get_logger(__name__)
# TODO Update this
_snake_case : Tuple = {
"facebook/esm-1b": "https://... | 81 |
"""simple docstring"""
from maths.prime_check import is_prime
def _snake_case ( snake_case__ : int ):
if not isinstance(snake_case__ , snake_case__ ):
A = F'Input value of [number={number}] must be an integer'
raise TypeError(snake_case__ )
if is_prime(snake_case__ ) and is_prime(... | 91 | 0 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class lowercase__ :
'''simple docstring'''
def __init__( self : Tuple ) -> Dict:
'''simple docstring'''
Uppe... | 82 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats... | 91 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCH... | 83 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( snake_case__ : tuple[int, int] , snake_case__ : int ):
A , A = position
A = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
... | 91 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {'''configuration_mbart''': ['''MBART_PRETRAINED_... | 84 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( _lowercase , ... | 91 | 0 |
def _a ( lowercase__ : list[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = len(lowercase__ )
for i in range(lowercase__ ):
for j in range(i + 1 , lowercase__ ):
if numbers[j] < numbers[i]:
SCREAMIN... | 85 |
"""simple docstring"""
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 lowerCAmelCase_ ( _lowercase ... | 91 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a :Optional[int] = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProcessor'],
}
try:
i... | 86 |
"""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, PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = '''▁'... | 91 | 0 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : Tuple = {
"""kakaobrain/al... | 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureEx... | 91 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _snake_case ( ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args... | 88 |
"""simple docstring"""
from torch import nn
def _snake_case ( snake_case__ : Union[str, Any] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'Unsupported activation function: {act_f... | 91 | 0 |
import torch
from diffusers import StableDiffusionPipeline
SCREAMING_SNAKE_CASE : List[str] = "path-to-your-trained-model"
SCREAMING_SNAKE_CASE : List[Any] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
SCREAMING_SNAKE_CASE : List[Any] = ... | 89 |
"""simple docstring"""
import copy
import re
class lowerCAmelCase_ :
'''simple docstring'''
_lowerCamelCase: str = '''hp'''
_lowerCamelCase: List[Any] = {}
_lowerCamelCase: List[Any] = None
@classmethod
def _SCREAMING_SNAKE_... | 91 | 0 |
'''simple docstring'''
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ ) -> List[str]:
lowerCAmelCase__ = n
lowerCAmelCase__ = [None] * self.n
lo... | 90 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with p... | 91 | 0 |
'''simple docstring'''
import numpy as np
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : Optional[Any] , __magic_name__ : Optional[int] , __magic_name__ : int , __magic_name__ : str ) -> List[str]:
lowercase : ... | 92 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( _lowercase ,... | 91 | 0 |
"""simple docstring"""
from torch import nn
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
super().__init__()
lowerCAmelCase__... | 93 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier... | 91 | 0 |
'''simple docstring'''
import math
def lowercase_ ( ) -> None:
"""simple docstring"""
lowercase : Union[str, Any] =input('''Enter message: ''' )
lowercase : List[Any] =int(input(F'Enter key [2-{len(__A ) - 1}]: ' ) )
lowercase : ... | 94 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distribu... | 91 | 0 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_opti... | 95 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
... | 91 | 0 |
"""simple docstring"""
def a ( __UpperCAmelCase : list[int] ) -> float:
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
__magic_name__: Dict = sum(__UpperCAmelCase ) / len(__UpperC... | 96 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig''',
'''XLM... | 91 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
__a = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def a ( ):
'''simple docstring'''
lowercase_ = os.path.dirname(os.path.realpath(snake_case__ ) )
lowercase_ = os.path.join(sna... | 97 |
"""simple docstring"""
# 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
#
# Un... | 91 | 0 |
'''simple docstring'''
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a__ ( lowercase : bool = True, *lowercase : Tuple, **lowercase : Dict ) -> str:
"""simple docstr... | 98 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common im... | 91 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_... | 99 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
_lowercase = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def _snake_case ( ):
A = os.path.dirname(os.path.realpath(snake_case__ ) )
A = os.path.join(snake_case__ , 'words.txt' )
... | 91 | 0 |
from collections.abc import Callable
import numpy as np
def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> np.array:
SCREAMING_SNAKE_CASE__ = int(np.ceil((x_end - xa) / step_size ) )
... | 100 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''goog... | 91 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from .... | 101 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase = {
'''configuration_electra''': ['''ELECTRA_PRETRAINED_CONF... | 91 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__magic_name__ : Optional[int] = {
"""configuration_speecht5""": [
""... | 102 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
f... | 91 | 0 |
"""simple docstring"""
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 UpperCAmelC... | 103 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
def __init__( self : ... | 91 | 0 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _lowerCamelCase ( UpperCAmelCase_ : bool = True, *UpperCAmelCase_ : List[Any], **UpperCAmelCase_... | 104 |
"""simple docstring"""
from maths.prime_check import is_prime
def _snake_case ( snake_case__ : int ):
if not isinstance(snake_case__ , snake_case__ ):
A = F'Input value of [number={number}] must be an integer'
raise TypeError(snake_case__ )
if is_prime(snake_case__ ) and is_prime(... | 91 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez im... | 105 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats... | 91 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCAmelCase__ : list[int] ) -> list[int]:
'''simple docstring'''
if len(lowerCAmelCase__ ) == 0:
return array
A , A = min(lowerCAmelCase__ ), max(lowerCAmelCas... | 106 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( snake_case__ : tuple[int, int] , snake_case__ : int ):
A , A = position
A = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
... | 91 | 0 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
_UpperCAmelCase : Union[str, Any] = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def _SCREAMING_SNAKE_CASE ( __snake_case : Tuple , __snake... | 107 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( _lowercase , ... | 91 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a: Union[str, Any] = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}
try:
if not is_torch_a... | 108 |
"""simple docstring"""
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 lowerCAmelCase_ ( _lowercase ... | 91 | 0 |
'''simple docstring'''
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# -... | 109 |
"""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, PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = '''▁'... | 91 | 0 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case ):
UpperCAmelCase__ : Union[str, Any] = ('dense.weight', 'attention.self.q... | 110 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureEx... | 91 | 0 |
class _lowerCAmelCase:
"""simple docstring"""
def __init__( self ):
UpperCamelCase_: Optional[int] = 0
UpperCamelCase_: Tuple = 0
UpperCamelCase_: Dict = {}
def ... | 57 |
"""simple docstring"""
from torch import nn
def _snake_case ( snake_case__ : Union[str, Any] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'Unsupported activation function: {act_f... | 91 | 0 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
lowercase__ =logging.get_logger(__name__)
class UpperCamelCase__ ( _lowercase ):
def __init__(self : Union[str, Any] , *snake_case_ : int , **snake_case_ ... | 521 |
"""simple docstring"""
import copy
import re
class lowerCAmelCase_ :
'''simple docstring'''
_lowerCamelCase: str = '''hp'''
_lowerCamelCase: List[Any] = {}
_lowerCamelCase: List[Any] = None
@classmethod
def _SCREAMING_SNAKE_... | 91 | 0 |
import inspect
import unittest
from transformers import BitConfig
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_backbone_common import BackboneTesterMixi... | 117 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with p... | 91 | 0 |
"""simple docstring"""
def lowerCamelCase_ (UpperCamelCase__ : int ):
if number < 0:
raise ValueError('''number must not be negative''' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 506 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( _lowercase ,... | 91 | 0 |
import re
import string
import numpy as np
import datasets
A : Optional[int] = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
A : Dict = '''
Args:
predictions: List of predi... | 287 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier... | 91 | 0 |
'''simple docstring'''
from math import sqrt
def _snake_case ( _SCREAMING_SNAKE_CASE : int ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase = 0
for i in range(1 , int(sqrt(snake_case__ ) + 1 ) ):
if n % i == 0 an... | 433 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distribu... | 91 | 0 |
def _UpperCAmelCase (UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : int ):
if len(snake_case__ ) != len(snake_case__ ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
rai... | 503 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
... | 91 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {}
try:
if not is_sentencepiece_available()... | 695 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig''',
'''XLM... | 91 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
l... | 45 |
"""simple docstring"""
# 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
#
# Un... | 91 | 0 |
"""simple docstring"""
def lowercase_ ( _lowerCamelCase: int = 10 ) -> Optional[Any]:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ) or n < 0:
raise ValueError("Invalid input" )
__lowerCamelCase : List[str] = 10**n
_... | 646 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common im... | 91 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"camember... | 539 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
_lowercase = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def _snake_case ( ):
A = os.path.dirname(os.path.realpath(snake_case__ ) )
A = os.path.join(snake_case__ , 'words.txt' )
... | 91 | 0 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _lowerCAmelCase( _lowercase ... | 57 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''goog... | 91 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowercase__ ={}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
low... | 521 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase = {
'''configuration_electra''': ['''ELECTRA_PRETRAINED_CONF... | 91 | 0 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_availabl... | 117 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
f... | 91 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class _UpperCAmelCase :
'''simple docstring'''
a__ =field(... | 506 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
def __init__( self : ... | 91 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import... | 287 |
"""simple docstring"""
from maths.prime_check import is_prime
def _snake_case ( snake_case__ : int ):
if not isinstance(snake_case__ , snake_case__ ):
A = F'Input value of [number={number}] must be an integer'
raise TypeError(snake_case__ )
if is_prime(snake_case__ ) and is_prime(... | 91 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTe... | 433 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats... | 91 | 0 |
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArgument... | 503 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( snake_case__ : tuple[int, int] , snake_case__ : int ):
A , A = position
A = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
... | 91 | 0 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowercase ( lowerCAmelCase__ : str , lowerCAmelCase__ : float | Decimal , lowerCAmelCase__ : float = 10**-10 ... | 695 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( _lowercase , ... | 91 | 0 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
UpperCamelCase = "__DUMMY_TRANSFORMERS_USER__"
UpperCamelCase = "Dummy User"
UpperCamelCase = "hf_hZEmnoOEYISjraJtbySaKCNnSu... | 45 |
"""simple docstring"""
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 lowerCAmelCase_ ( _lowercase ... | 91 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floa... | 646 |
"""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, PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = '''▁'... | 91 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_f... | 539 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureEx... | 91 | 0 |
def snake_case () -> Any:
UpperCamelCase_: Tuple = 0
for i in range(1 , 1_0_0_1 ):
total += i**i
return str(snake_case__ )[-1_0:]
if __name__ == "__main__":
print(solution()) | 57 |
"""simple docstring"""
from torch import nn
def _snake_case ( snake_case__ : Union[str, Any] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'Unsupported activation function: {act_f... | 91 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
Segfor... | 521 |
"""simple docstring"""
import copy
import re
class lowerCAmelCase_ :
'''simple docstring'''
_lowerCamelCase: str = '''hp'''
_lowerCamelCase: List[Any] = {}
_lowerCamelCase: List[Any] = None
@classmethod
def _SCREAMING_SNAKE_... | 91 | 0 |
def _a ( a :list ) -> Optional[int]:
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
a = grid[0]
... | 117 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with p... | 91 | 0 |
"""simple docstring"""
def lowerCamelCase_ (UpperCamelCase__ : int , UpperCamelCase__ : int ):
while b:
_UpperCAmelCase , _UpperCAmelCase : Any = b, a % b
return a
def lowerCamelCase_ (UpperCamelCase__ : int , Upper... | 506 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( _lowercase ,... | 91 | 0 |
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 287 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier... | 91 | 0 |
'''simple docstring'''
UpperCAmelCase = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
raise ImportWarning(
'To use `datasets`, Python>=3.7 is required, and the current versio... | 433 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distribu... | 91 | 0 |
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , __lowerCamelCase) -> Optional[Any]:
_A : Any = val
_A : Tuple = None
_A : Dict = None
def _lowerCamelCase ... | 503 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
... | 91 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/... | 695 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig''',
'''XLM... | 91 | 0 |
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