code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=__lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Any = ["note_seq"]
def __init__( self , *_a , **_a ) -> Optional... | 235 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ..... | 235 | 1 |
def __UpperCAmelCase ( __a : list ) -> list:
"""simple docstring"""
for i in range(len(__a ) - 1 ,0 ,-1 ):
_a : Union[str, Any] = False
for j in range(__a ,0 ,-1 ):
if unsorted[j] < unsorted[j - 1]:
_a ... | 235 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : str = ["image_processor", "tokenizer"]
UpperCAm... | 235 | 1 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class UpperCAmelCase_ ( ... | 235 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
p... | 235 | 1 |
from datetime import datetime as dt
import os
from github import Github
a__ = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def __UpperCAmelCase ( ) -> Union[str, Any]:
... | 235 |
import requests
a__ = '''YOUR API KEY'''
def __UpperCAmelCase ( __a : str ,__a : str = giphy_api_key ) -> list:
"""simple docstring"""
_a : Optional[Any] = '''+'''.join(query.split() )
_a : Union[str, Any] = F"""https://api.giphy.co... | 235 | 1 |
def __UpperCAmelCase ( __a : List[str] ,__a : int ,__a : Optional[Any]=False ) -> Optional[Any]:
"""simple docstring"""
if isinstance(__a ,__a ) and isinstance(__a ,__a ):
_a : Union[str, Any] = len(set_a.intersection(__a ) )
if a... | 235 |
from collections.abc import Callable
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a = None ) -> None:
# Stores actual heap items.
_a : list = []
# Stores indexes of each item fo... | 235 | 1 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xform... | 235 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : Dict ) -> Tuple:
"""simple docstring"""
_a ... | 235 | 1 |
import logging
from transformers import PretrainedConfig
a__ = logging.getLogger(__name__)
a__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __... | 235 |
import logging
from transformers import PretrainedConfig
a__ = logging.getLogger(__name__)
a__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __... | 235 | 1 |
import math
def __UpperCAmelCase ( __a : Optional[Any] ,__a : List[str] ) -> Optional[int]:
"""simple docstring"""
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(__a )
else:
... | 235 |
def __UpperCAmelCase ( __a : float ) -> float:
"""simple docstring"""
return 10 - x * x
def __UpperCAmelCase ( __a : float ,__a : float ) -> float:
"""simple docstring"""
if equation(__a ) * equation(__a ) >= 0:
raise ValueErr... | 235 | 1 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
a__ = logging.getLogger(__name__)
if __name__ == "__main__":
a__ = ... | 235 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a__ = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation ... | 235 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCAmelCase ( __a : Optional[Any] ) -> Unio... | 235 |
a__ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_n... | 235 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __UpperCAmelCase ( __a : str ) -> Optional[int]:
"""simple docstring"""
def wrapper(*__a : Optional[Any] ,**__a ... | 235 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils imp... | 235 | 1 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 235 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 235 | 1 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __UpperCAmelCase ( __a : Tuple ) -> int:
"""simple ... | 235 |
from math import ceil
def __UpperCAmelCase ( __a : int = 1_001 ) -> int:
"""simple docstring"""
_a : List[Any] = 1
for i in range(1 ,int(ceil(n / 2.0 ) ) ):
_a : Optional[Any] = 2 * i + 1
_a : Optional[int] = 2... | 235 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text,... | 235 |
def __UpperCAmelCase ( __a : int ,__a : list[int] ,__a : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(__a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 235 | 1 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcess... | 235 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ = logging.get_logger(__name__)
a__ = {
'''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/... | 235 | 1 |
def __UpperCAmelCase ( __a : list ) -> list:
"""simple docstring"""
_a : str = False
while is_sorted is False: # Until all the indices are traversed keep looping
_a : int = True
for i in range(0 ,len(__a ) - 1 ,2 ): # iterating ... | 235 |
def __UpperCAmelCase ( __a : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
_a : Optional[Any] = 0
_a : List[Any] = len(__a ) - 1
_a : List[str] = 0
while index >= 0:
_a : Dict = ... | 235 | 1 |
def __UpperCAmelCase ( __a : list[int] ,__a : list[int] ,__a : int ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__a ) )
def __UpperCAmelCase ( __a : ... | 235 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/r... | 235 | 1 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import f... | 235 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAG... | 235 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
a__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCAmelCase_ ( datasets.Bui... | 235 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = ["image_processor", "tokenizer"]
... | 235 | 1 |
from PIL import Image
def __UpperCAmelCase ( __a : Image ,__a : int ) -> Image:
"""simple docstring"""
_a : List[str] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(__a : int ) -> int:
return int(128 + factor * (c - 1... | 235 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = (DDPMScheduler,)
def __lowercase ( sel... | 235 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 235 |
import operator as op
a__ = '''scaler.pt'''
a__ = '''pytorch_model'''
a__ = '''random_states'''
a__ = '''optimizer'''
a__ = '''scheduler'''
a__ = '''pytorch_model.bin'''
a__ = '''pytorch_model.bin.index.json'''
a__ = '''model.safetensors'''
a__ = '''model.safetensors... | 235 | 1 |
def __UpperCAmelCase ( __a : int = 3 ,__a : int = 7 ,__a : int = 1_000_000 ) -> int:
"""simple docstring"""
_a : Union[str, Any] = 0
_a : Union[str, Any] = 1
for current_denominator in range(1 ,limit + 1 ):
_a : Any... | 235 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCAmelCase_ :
"""simple docstring"""
pass
| 235 | 1 |
def __UpperCAmelCase ( __a : int ) -> None:
"""simple docstring"""
_a : int = generate_pascal_triangle(__a )
for row_idx in range(__a ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=''' ''' )
... | 235 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ..... | 235 | 1 |
a__ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_n... | 235 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : str = ["image_processor", "tokenizer"]
UpperCAm... | 235 | 1 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def __UpperCAmelCase ( __a : str ,__a : float | Decimal ,__a : float = 10**-10 ) -> float:
"""simple docstring"""
_a : Any =... | 235 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
p... | 235 | 1 |
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_mo... | 235 |
import requests
a__ = '''YOUR API KEY'''
def __UpperCAmelCase ( __a : str ,__a : str = giphy_api_key ) -> list:
"""simple docstring"""
_a : Optional[Any] = '''+'''.join(query.split() )
_a : Union[str, Any] = F"""https://api.giphy.co... | 235 | 1 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = (DDIMParallelScheduler,)
UpperCAmelCase__ ... | 235 |
from collections.abc import Callable
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a = None ) -> None:
# Stores actual heap items.
_a : list = []
# Stores indexes of each item fo... | 235 | 1 |
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = "SpeechT5FeatureExtractor"
UpperCAmelCase__ : Optional[int] = "SpeechT5Tokenizer"
... | 235 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : Dict ) -> Tuple:
"""simple docstring"""
_a ... | 235 | 1 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
a__ = _symbol_database.Def... | 235 |
import logging
from transformers import PretrainedConfig
a__ = logging.getLogger(__name__)
a__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __... | 235 | 1 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate ... | 235 |
def __UpperCAmelCase ( __a : float ) -> float:
"""simple docstring"""
return 10 - x * x
def __UpperCAmelCase ( __a : float ,__a : float ) -> float:
"""simple docstring"""
if equation(__a ) * equation(__a ) >= 0:
raise ValueErr... | 235 | 1 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
a__ = logging.get_logger(__name__)
a__ = {
'''post_extract_proj''': '''feature_projection.projection''',
'''... | 235 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a__ = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation ... | 235 | 1 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __UpperCAmelCase ( __a : int ,__a : int ,__a : str ) ... | 235 |
a__ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_n... | 235 | 1 |
import itertools
import math
def __UpperCAmelCase ( __a : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even number... | 235 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils imp... | 235 | 1 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __UpperCAmelCase ( __a : Dict ,__a : str ,__a : Union[str, Any] ) -> ... | 235 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 235 | 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 ... | 235 |
from math import ceil
def __UpperCAmelCase ( __a : int = 1_001 ) -> int:
"""simple docstring"""
_a : List[Any] = 1
for i in range(1 ,int(ceil(n / 2.0 ) ) ):
_a : Optional[Any] = 2 * i + 1
_a : Optional[int] = 2... | 235 | 1 |
from __future__ import annotations
from typing import Any
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a = 0 ) -> None:
_a , _a : str = row, column
... | 235 |
def __UpperCAmelCase ( __a : int ,__a : list[int] ,__a : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(__a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 235 | 1 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 235 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ = logging.get_logger(__name__)
a__ = {
'''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/... | 235 | 1 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def __UpperCAmelCase ( __a : Any ) -> Union[str, Any]:
"""simple docstring"""
_a : ... | 235 |
def __UpperCAmelCase ( __a : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
_a : Optional[Any] = 0
_a : List[Any] = len(__a ) - 1
_a : List[str] = 0
while index >= 0:
_a : Dict = ... | 235 | 1 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __UpperCAmelCase ( __a : str ,__a : Union[str, Any]=None ) -> int:
"""simple docstring"""
_a : Optional[int] ... | 235 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/r... | 235 | 1 |
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,
)
a__ = {
'''configuration_xlm_roberta''': [
... | 235 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAG... | 235 | 1 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# 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
a__ = '''.'''
# Internal TensorFlow ops that can be safely... | 235 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = ["image_processor", "tokenizer"]
... | 235 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax i... | 235 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = (DDPMScheduler,)
def __lowercase ( sel... | 235 | 1 |
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 ... | 235 |
import operator as op
a__ = '''scaler.pt'''
a__ = '''pytorch_model'''
a__ = '''random_states'''
a__ = '''optimizer'''
a__ = '''scheduler'''
a__ = '''pytorch_model.bin'''
a__ = '''pytorch_model.bin.index.json'''
a__ = '''model.safetensors'''
a__ = '''model.safetensors... | 235 | 1 |
import qiskit
def __UpperCAmelCase ( __a : int ,__a : int ) -> qiskit.result.counts.Counts:
"""simple docstring"""
_a : str = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
_a : int ... | 235 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCAmelCase_ :
"""simple docstring"""
pass
| 235 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils imp... | 235 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ..... | 235 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.uti... | 235 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : str = ["image_processor", "tokenizer"]
UpperCAm... | 235 | 1 |
from __future__ import annotations
from typing import Generic, TypeVar
a__ = TypeVar('''T''')
class UpperCAmelCase_ ( Generic[T] ):
"""simple docstring"""
def __init__( self , _a ) -> None:
_a : Any = data
... | 235 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
p... | 235 | 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
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, i... | 235 |
import requests
a__ = '''YOUR API KEY'''
def __UpperCAmelCase ( __a : str ,__a : str = giphy_api_key ) -> list:
"""simple docstring"""
_a : Optional[Any] = '''+'''.join(query.split() )
_a : Union[str, Any] = F"""https://api.giphy.co... | 235 | 1 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : int = (IPNDMScheduler,)
UpperCAmelCase__ ... | 235 |
from collections.abc import Callable
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a = None ) -> None:
# Stores actual heap items.
_a : list = []
# Stores indexes of each item fo... | 235 | 1 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ..... | 235 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : Dict ) -> Tuple:
"""simple docstring"""
_a ... | 235 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_enco... | 235 |
import logging
from transformers import PretrainedConfig
a__ = logging.getLogger(__name__)
a__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __... | 235 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : str = ["image_processor", "tokenizer"]
UpperCAm... | 235 |
def __UpperCAmelCase ( __a : float ) -> float:
"""simple docstring"""
return 10 - x * x
def __UpperCAmelCase ( __a : float ,__a : float ) -> float:
"""simple docstring"""
if equation(__a ) * equation(__a ) >= 0:
raise ValueErr... | 235 | 1 |
import os
import sys
import unittest
a__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_mode... | 235 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a__ = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation ... | 235 | 1 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from .... | 235 |
a__ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_n... | 235 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
a__ ... | 235 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils imp... | 235 | 1 |
def __UpperCAmelCase ( __a : int ) -> int:
"""simple docstring"""
assert isinstance(__a ,__a ), F"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
_a : Optional[Any] = F"""The input value... | 235 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 235 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
'''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
),
}
clas... | 235 |
from math import ceil
def __UpperCAmelCase ( __a : int = 1_001 ) -> int:
"""simple docstring"""
_a : List[Any] = 1
for i in range(1 ,int(ceil(n / 2.0 ) ) ):
_a : Optional[Any] = 2 * i + 1
_a : Optional[int] = 2... | 235 | 1 |
a__ = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def __UpperCAmelCase ( __a : str ) -> int:
"""simple docstring"""
_a : Union[str, Any] = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
_a : S... | 235 |
def __UpperCAmelCase ( __a : int ,__a : list[int] ,__a : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(__a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 235 | 1 |
import operator as op
a__ = '''scaler.pt'''
a__ = '''pytorch_model'''
a__ = '''random_states'''
a__ = '''optimizer'''
a__ = '''scheduler'''
a__ = '''pytorch_model.bin'''
a__ = '''pytorch_model.bin.index.json'''
a__ = '''model.safetensors'''
a__ = '''model.safetensors... | 235 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ = logging.get_logger(__name__)
a__ = {
'''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/... | 235 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a__ = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_available()... | 235 |
def __UpperCAmelCase ( __a : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
_a : Optional[Any] = 0
_a : List[Any] = len(__a ) - 1
_a : List[str] = 0
while index >= 0:
_a : Dict = ... | 235 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers... | 235 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/r... | 235 | 1 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __UpperCAmelCase ( __a : Union[str, Any] ) -> int:
"""simple docstring"""
_a : List[str] = FileLock(str(tmpdir / '''foo.lock''' ) )
_a : Tuple = F... | 235 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAG... | 235 | 1 |
# using dfs for finding eulerian path traversal
def __UpperCAmelCase ( __a : Any ,__a : Dict ,__a : List[Any] ,__a : Tuple=None ) -> Optional[Any]:
"""simple docstring"""
_a : str = (path or []) + [u]
for v in graph[u]:
if v... | 235 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = ["image_processor", "tokenizer"]
... | 235 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : Dict ) -> Tuple:
"""simple docstring"""
_a ... | 235 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = (DDPMScheduler,)
def __lowercase ( sel... | 235 | 1 |
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_... | 235 |
import operator as op
a__ = '''scaler.pt'''
a__ = '''pytorch_model'''
a__ = '''random_states'''
a__ = '''optimizer'''
a__ = '''scheduler'''
a__ = '''pytorch_model.bin'''
a__ = '''pytorch_model.bin.index.json'''
a__ = '''model.safetensors'''
a__ = '''model.safetensors... | 235 | 1 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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 ... | 235 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCAmelCase_ :
"""simple docstring"""
pass
| 235 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {}
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = "llama"
Upper... | 235 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ..... | 235 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_availa... | 235 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : str = ["image_processor", "tokenizer"]
UpperCAm... | 235 | 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__ = {... | 235 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
p... | 235 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ = logging.get_logger(__name__)
a__ = {
'''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/... | 235 |
import requests
a__ = '''YOUR API KEY'''
def __UpperCAmelCase ( __a : str ,__a : str = giphy_api_key ) -> list:
"""simple docstring"""
_a : Optional[Any] = '''+'''.join(query.split() )
_a : Union[str, Any] = F"""https://api.giphy.co... | 235 | 1 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def __UpperCAmelCase ( __a : List[str] ) -> Optional[Any]:
"""simple docstring"""
return DownloadCommand(args.model ,args.cache_dir ,args.force ,args.trust_remote_code )
class ... | 235 |
from collections.abc import Callable
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a = None ) -> None:
# Stores actual heap items.
_a : list = []
# Stores indexes of each item fo... | 235 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDete... | 235 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : Dict ) -> Tuple:
"""simple docstring"""
_a ... | 235 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, sl... | 235 |
import logging
from transformers import PretrainedConfig
a__ = logging.getLogger(__name__)
a__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __... | 235 | 1 |
import numpy as np
def __UpperCAmelCase ( __a : np.array ) -> np.array:
"""simple docstring"""
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 235 |
def __UpperCAmelCase ( __a : float ) -> float:
"""simple docstring"""
return 10 - x * x
def __UpperCAmelCase ( __a : float ,__a : float ) -> float:
"""simple docstring"""
if equation(__a ) * equation(__a ) >= 0:
raise ValueErr... | 235 | 1 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ..... | 235 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a__ = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation ... | 235 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
a__ = loggi... | 235 |
a__ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_n... | 235 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __UpperCAmelCase ( __a : dict ) -> tuple:
"... | 235 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils imp... | 235 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
a__ = namedtuple('''covid_data''', '''cases deaths recovered''')
def __UpperCAmelCase ( __a : str = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
"""simple docstring"""
... | 235 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 235 | 1 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSe... | 235 |
from math import ceil
def __UpperCAmelCase ( __a : int = 1_001 ) -> int:
"""simple docstring"""
_a : List[Any] = 1
for i in range(1 ,int(ceil(n / 2.0 ) ) ):
_a : Optional[Any] = 2 * i + 1
_a : Optional[int] = 2... | 235 | 1 |
from __future__ import annotations
def __UpperCAmelCase ( __a : int | float | str ,__a : int | float | str ) -> list[str]:
"""simple docstring"""
if nth_term == "":
return [""]
_a : List[Any] = int(__a )
_a : Tuple = int(_... | 235 |
def __UpperCAmelCase ( __a : int ,__a : list[int] ,__a : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(__a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 235 | 1 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a__ = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation ... | 235 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ = logging.get_logger(__name__)
a__ = {
'''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/... | 235 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def __UpperCAmelCase ( __a : str ) -> Dict:
"""simple docstring"""
def decorator(__a : List[Any] ):
_a : Dict = getattr(__a ,'''handle_key''' ,[] )
handle += [key]
... | 235 |
def __UpperCAmelCase ( __a : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
_a : Optional[Any] = 0
_a : List[Any] = len(__a ) - 1
_a : List[str] = 0
while index >= 0:
_a : Dict = ... | 235 | 1 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __UpperCAmelCase ( __a : Union[... | 235 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/r... | 235 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class UpperCAmelCase_ ( pl.LightningModule ):
"""simple docstring"""
def __init__( self , _a ... | 235 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAG... | 235 | 1 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
a__ = logging.get_logger(__name__)
class UpperCAmelCase_ ( __lowercase... | 235 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = ["image_processor", "tokenizer"]
... | 235 | 1 |
def __UpperCAmelCase ( __a : Union[str, Any] ,__a : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(__a ):
for j in range(__a ):
if dist[i][j] !=... | 235 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = (DDPMScheduler,)
def __lowercase ( sel... | 235 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
a__ = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. Read the documentati... | 235 |
import operator as op
a__ = '''scaler.pt'''
a__ = '''pytorch_model'''
a__ = '''random_states'''
a__ = '''optimizer'''
a__ = '''scheduler'''
a__ = '''pytorch_model.bin'''
a__ = '''pytorch_model.bin.index.json'''
a__ = '''model.safetensors'''
a__ = '''model.safetensors... | 235 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __UpperCAmelCase ( ) -> str:
"""simple docstring"""
_a : Optional[int] = HfArgumentParser(__a )
_a : List[str] = parser.parse_args_into_dataclasses()[0]
... | 235 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCAmelCase_ :
"""simple docstring"""
pass
| 235 | 1 |
# 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-2.0
#
# Unless requ... | 235 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ..... | 235 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_f... | 235 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : str = ["image_processor", "tokenizer"]
UpperCAm... | 235 | 1 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
Compu... | 235 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
p... | 235 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = ["image_processor", "tokenizer"]
... | 235 |
import requests
a__ = '''YOUR API KEY'''
def __UpperCAmelCase ( __a : str ,__a : str = giphy_api_key ) -> list:
"""simple docstring"""
_a : Optional[Any] = '''+'''.join(query.split() )
_a : Union[str, Any] = F"""https://api.giphy.co... | 235 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_ ( __lowercase ):
"""simp... | 235 |
from collections.abc import Callable
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a = None ) -> None:
# Stores actual heap items.
_a : list = []
# Stores indexes of each item fo... | 235 | 1 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.... | 235 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : Dict ) -> Tuple:
"""simple docstring"""
_a ... | 235 | 1 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
fr... | 235 |
import logging
from transformers import PretrainedConfig
a__ = logging.getLogger(__name__)
a__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __... | 235 | 1 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
a__ = logging.get_logger(__name__)
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
def __init__( self , *_a , **_a... | 235 |
def __UpperCAmelCase ( __a : float ) -> float:
"""simple docstring"""
return 10 - x * x
def __UpperCAmelCase ( __a : float ,__a : float ) -> float:
"""simple docstring"""
if equation(__a ) * equation(__a ) >= 0:
raise ValueErr... | 235 | 1 |
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 logg... | 235 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a__ = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation ... | 235 | 1 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
a__ = logging.getLogger()
def __UpperCAmelCa... | 235 |
a__ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_n... | 235 | 1 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a ) -> None:
_a : Any = size
_a : Optional[Any] = [0] * size
_a : Optional[Any] = [0] * size
@stati... | 235 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils imp... | 235 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''microsoft/beit-base-patch16-224-pt2... | 235 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 235 | 1 |
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