
import copy
import re




class 				lowerCamelCase__							:




	'''simple docstring'''




	_A			=  'hp'
	_A			=  {}
	_A			=  None







	@classmethod
	def      _lowerCamelCase       (      cls	:int  ,     a	:Union[str, Any]  ,     a	:str		)	->				Union[str, Any]:
								__UpperCamelCase			:     str         =      prefix
								__UpperCamelCase			:     Optional[Any]         =      defaults
								cls.build_naming_info()







	@staticmethod
	def      _lowerCamelCase       (      a	:Optional[int]  ,     a	:List[str]		)	->				List[Any]:
								if len(a		) == 0:
															return ""
								__UpperCamelCase			:     Any         =      None
								if any(char.isdigit() for char in word		):
															raise Exception(f'Parameters should not contain numbers: \'{word}\' contains a number'		)
								if word in info["short_word"]:
															return info["short_word"][word]
								for prefix_len in range(1  ,     len(a		) + 1		):
															__UpperCamelCase			:     List[str]         =      word[:prefix_len]
															if prefix in info["reverse_short_word"]:
																						continue
															else:
																						__UpperCamelCase			:     int         =      prefix
																						break

								if short_word is None:
															# Paranoid fallback
															def int_to_alphabetic(a	:Optional[Any]		):
																						__UpperCamelCase			:     Dict         =      ""
																						while integer != 0:
																													__UpperCamelCase			:     Any         =      chr(ord("A"		) + integer % 1_0		) + s
																													integer //= 1_0
																						return s

															__UpperCamelCase			:     Dict         =      0
															while True:
																						__UpperCamelCase			:     Any         =      word + "#" + int_to_alphabetic(a		)
																						if sword in info["reverse_short_word"]:
																													continue
																						else:
																													__UpperCamelCase			:     Dict         =      sword
																													break

								__UpperCamelCase			:     List[Any]         =      short_word
								__UpperCamelCase			:     Dict         =      word
								return short_word







	@staticmethod
	def      _lowerCamelCase       (      a	:int  ,     a	:str		)	->				Optional[int]:
								__UpperCamelCase			:     Optional[Any]         =      param_name.split("_"		)

								__UpperCamelCase			:     Optional[int]         =      [TrialShortNamer.shortname_for_word(a  ,     a		) for word in words]

								# We try to create a separatorless short name, but if there is a collision we have to fallback
								# to a separated short name
								__UpperCamelCase			:     Tuple         =      ["", "_"]

								for separator in separators:
															__UpperCamelCase			:     str         =      separator.join(a		)
															if shortname not in info["reverse_short_param"]:
																						__UpperCamelCase			:     str         =      shortname
																						__UpperCamelCase			:     int         =      param_name
																						return shortname

								return param_name







	@staticmethod
	def      _lowerCamelCase       (      a	:Union[str, Any]  ,     a	:Union[str, Any]		)	->				Dict:
								__UpperCamelCase			:     Tuple         =      TrialShortNamer.shortname_for_key(a  ,     a		)
								__UpperCamelCase			:     Tuple         =      short_name
								__UpperCamelCase			:     str         =      param_name







	@classmethod
	def      _lowerCamelCase       (      cls	:int		)	->				List[str]:
								if cls.NAMING_INFO is not None:
															return

								__UpperCamelCase			:     Optional[Any]         =      {
								    "short_word": {},
								    "reverse_short_word": {},
								    "short_param": {},
								    "reverse_short_param": {},
								}

								__UpperCamelCase			:     List[str]         =      list(cls.DEFAULTS.keys()		)

								for k in field_keys:
															cls.add_new_param_name(a  ,     a		)

								__UpperCamelCase			:     Union[str, Any]         =      info







	@classmethod
	def      _lowerCamelCase       (      cls	:int  ,     a	:Optional[Any]		)	->				Union[str, Any]:
								cls.build_naming_info()
								assert cls.PREFIX is not None
								__UpperCamelCase			:     Dict         =      [copy.copy(cls.PREFIX		)]

								for k, v in params.items():
															if k not in cls.DEFAULTS:
																						raise Exception(f'You should provide a default value for the param name {k} with value {v}'		)
															if v == cls.DEFAULTS[k]:
																						# The default value is not added to the name
																						continue

															__UpperCamelCase			:     Any         =      cls.NAMING_INFO["short_param"][k]

															if isinstance(a  ,     a		):
																						__UpperCamelCase			:     Tuple         =      1 if v else 0

															__UpperCamelCase			:     Tuple         =      "" if isinstance(a  ,     (int, float)		) else "-"
															__UpperCamelCase			:     List[Any]         =      f'{key}{sep}{v}'
															name.append(a		)

								return "_".join(a		)







	@classmethod
	def      _lowerCamelCase       (      cls	:str  ,     a	:int		)	->				List[Any]:
								__UpperCamelCase			:     Dict         =      repr[len(cls.PREFIX		) + 1 :]
								if repr == "":
															__UpperCamelCase			:     List[str]         =      []
								else:
															__UpperCamelCase			:     List[str]         =      repr.split("_"		)

								__UpperCamelCase			:     Optional[Any]         =      {}

								for value in values:
															if "-" in value:
																						__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      value.split("-"		)
															else:
																						__UpperCamelCase			:     Optional[int]         =      re.sub("[0-9.]"  ,     ""  ,     a		)
																						__UpperCamelCase			:     Optional[Any]         =      float(re.sub("[^0-9.]"  ,     ""  ,     a		)		)

															__UpperCamelCase			:     List[str]         =      cls.NAMING_INFO["reverse_short_param"][p_k]

															__UpperCamelCase			:     int         =      p_v

								for k in cls.DEFAULTS:
															if k not in parameters:
																						__UpperCamelCase			:     Union[str, Any]         =      cls.DEFAULTS[k]

								return parameters

# Copyright 2021 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 required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


lowercase						:      Any									= 'pytorch_model.bin'
lowercase						:      List[str]									= 'pytorch_model.bin.index.json'
lowercase						:      List[Any]									= 'adapter_config.json'
lowercase						:      str									= 'adapter_model.bin'
lowercase						:      List[str]									= 'adapter_model.safetensors'
lowercase						:      Any									= 'tf_model.h5'
lowercase						:      str									= 'tf_model.h5.index.json'
lowercase						:      Any									= 'model.ckpt'
lowercase						:      Optional[int]									= 'flax_model.msgpack'
lowercase						:      Dict									= 'flax_model.msgpack.index.json'
lowercase						:      Dict									= 'model.safetensors'
lowercase						:      Dict									= 'model.safetensors.index.json'
lowercase						:      Union[str, Any]									= 'config.json'
lowercase						:      Tuple									= 'preprocessor_config.json'
lowercase						:      Tuple									= FEATURE_EXTRACTOR_NAME
lowercase						:      Dict									= 'generation_config.json'
lowercase						:      Dict									= 'modelcard.json'

lowercase						:      Optional[int]									= '▁'
lowercase						:      Any									= SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

lowercase						:      List[Any]									= [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
lowercase						:      Tuple									= [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowercase						:      Any									= [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any])      ->					List[str]:






							'''simple docstring'''





							if version.parse(_lowerCamelCase) < version.parse(_lowerCamelCase):
														if "dev" in min_version:
																					__UpperCamelCase			:     List[str]         =      (
																					    "This example requires a source install from HuggingFace Transformers (see "
																					    "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
																					)
														else:
																					__UpperCamelCase			:     Optional[Any]         =      F'This example requires a minimum version of {min_version},'
														error_message += F' but the version found is {__version__}.\n'
														raise ImportError(
														    error_message
														    + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
														    "versions of HuggingFace Transformers.")