
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


lowercase						:      int									= logging.get_logger(__name__)

lowercase						:      Tuple									= {'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}

lowercase						:      List[Any]									= {
    'vocab_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt',
    },
    'emoji_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json',
    },
}

lowercase						:      Optional[Any]									= {
    'abeja/gpt-neox-japanese-2.7b': 2048,
}




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple       ,						_lowerCamelCase				:    Dict)      ->					int:






							'''simple docstring'''





							with open(__UpperCamelCase       ,						"r"       ,						encoding="utf-8") as f:
														__UpperCamelCase			:     Any         =      json.loads(f.read())

							__UpperCamelCase			:     Optional[int]         =      collections.OrderedDict()
							__UpperCamelCase			:     Dict         =      collections.OrderedDict()
							__UpperCamelCase			:     Optional[Any]         =      collections.OrderedDict()
							with open(__UpperCamelCase       ,						"r"       ,						encoding="utf-8") as f:
														__UpperCamelCase			:     int         =      f.readlines()
							__UpperCamelCase			:     Optional[Any]         =      [[t.rstrip("\n")] if (t == "," or "," not in t) else t.rstrip("\n").split(",") for t in token]
							for idx, b in enumerate(__UpperCamelCase):
														__UpperCamelCase			:     Optional[Any]         =      b
														__UpperCamelCase			:     List[str]         =      idx
														for wd in b:
																					__UpperCamelCase			:     Union[str, Any]         =      idx

							return vocab, raw_vocab, ids_to_tokens, emoji




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  VOCAB_FILES_NAMES
	_A			=  PRETRAINED_VOCAB_FILES_MAP
	_A			=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A			=  ['input_ids', 'attention_mask']







	def __init__(      self	:Tuple  ,     a	:int  ,     a	:Tuple  ,     a	:Optional[int]="<|endoftext|>"  ,     a	:List[Any]="<|endoftext|>"  ,     a	:Tuple="<|startoftext|>"  ,     a	:Optional[int]="<|endoftext|>"  ,     a	:Tuple=False  ,     **a	:Optional[int]  ,     )	->				Union[str, Any]:
								super().__init__(
								    unk_token=_SCREAMING_SNAKE_CASE  ,     pad_token=_SCREAMING_SNAKE_CASE  ,     bos_token=_SCREAMING_SNAKE_CASE  ,     eos_token=_SCREAMING_SNAKE_CASE  ,     do_clean_text=_SCREAMING_SNAKE_CASE  ,     **_SCREAMING_SNAKE_CASE  ,     )
								if not os.path.isfile(_SCREAMING_SNAKE_CASE		):
															raise ValueError(
															    f'Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'
															    " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"		)
								if not os.path.isfile(_SCREAMING_SNAKE_CASE		):
															raise ValueError(
															    f'Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'
															    " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"		)
								__UpperCamelCase			:     Optional[Any]         =      do_clean_text
								__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     List[str]         =      load_vocab_and_emoji(_SCREAMING_SNAKE_CASE  ,     _SCREAMING_SNAKE_CASE		)
								__UpperCamelCase			:     List[str]         =      SubWordJapaneseTokenizer(
								    vocab=self.vocab  ,     ids_to_tokens=self.ids_to_tokens  ,     emoji=self.emoji		)







	@property
	def      _lowerCamelCase       (      self	:str		)	->				List[str]:
								return len(self.raw_vocab		)







	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				Any:
								return dict(self.raw_vocab  ,     **self.added_tokens_encoder		)







	def      _lowerCamelCase       (      self	:int  ,     a	:Optional[int]		)	->				Union[str, Any]:
								return self.subword_tokenizer.tokenize(_SCREAMING_SNAKE_CASE  ,     clean=self.do_clean_text		)







	def      _lowerCamelCase       (      self	:Optional[int]  ,     a	:Dict		)	->				int:
								return self.vocab.get(_SCREAMING_SNAKE_CASE  ,     self.vocab.get(self.unk_token		)		)







	def      _lowerCamelCase       (      self	:int  ,     a	:Union[str, Any]		)	->				Optional[Any]:
								return self.subword_tokenizer.convert_id_to_token(_SCREAMING_SNAKE_CASE		)







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:str		)	->				Optional[Any]:
								__UpperCamelCase			:     Union[str, Any]         =      "".join(_SCREAMING_SNAKE_CASE		).strip()
								return out_string







	def      _lowerCamelCase       (      self	:Optional[int]  ,     a	:List[Any]		)	->				List[int]:
								__UpperCamelCase			:     Tuple         =      []
								for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE  ,     add_special_tokens=_SCREAMING_SNAKE_CASE		) + [self.eos_token_id]		)

								if len(_SCREAMING_SNAKE_CASE		) > self.model_max_length:
															__UpperCamelCase			:     int         =      input_ids[-self.model_max_length :]
								return input_ids







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:List[str]  ,     a	:Optional[int] = None		)	->				Tuple[str]:
								__UpperCamelCase			:     Dict         =      0
								if os.path.isdir(_SCREAMING_SNAKE_CASE		):
															__UpperCamelCase			:     Any         =      os.path.join(
															    _SCREAMING_SNAKE_CASE  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
															__UpperCamelCase			:     Optional[int]         =      os.path.join(
															    _SCREAMING_SNAKE_CASE  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]		)
								else:
															__UpperCamelCase			:     Dict         =      (
															    (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
															)
															__UpperCamelCase			:     Any         =      (
															    (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
															)
								with open(_SCREAMING_SNAKE_CASE  ,     "w"  ,     encoding="utf-8"		) as writer:
															for token_index, token in self.ids_to_tokens.items():
																						if index != token_index:
																													logger.warning(
																													    f'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
																													    " Please check that the vocabulary is not corrupted!"		)
																													__UpperCamelCase			:     Any         =      token_index
																						writer.write(",".join(_SCREAMING_SNAKE_CASE		) + "\n"		)
																						index += 1
								with open(_SCREAMING_SNAKE_CASE  ,     "w"  ,     encoding="utf-8"		) as writer:
															json.dump(self.emoji  ,     _SCREAMING_SNAKE_CASE		)
								return vocab_file, emoji_file




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __init__(      self	:List[Any]  ,     a	:str  ,     a	:List[str]  ,     a	:Union[str, Any]		)	->				Tuple:
								__UpperCamelCase			:     List[str]         =      vocab  # same as swe
								__UpperCamelCase			:     Union[str, Any]         =      ids_to_tokens  # same as bpe
								__UpperCamelCase			:     List[Any]         =      emoji
								__UpperCamelCase			:     Union[str, Any]         =      np.max([len(_SCREAMING_SNAKE_CASE		) for w in self.vocab.keys()]		)
								__UpperCamelCase			:     Optional[int]         =      re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"		)
								__UpperCamelCase			:     List[Any]         =      re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"		)
								__UpperCamelCase			:     Tuple         =      re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"		)
								__UpperCamelCase			:     Union[str, Any]         =      re.compile(
								    r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"		)
								__UpperCamelCase			:     Dict         =      re.compile(
								    r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"		)
								__UpperCamelCase			:     List[Any]         =      re.compile(
								    r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"		)
								__UpperCamelCase			:     List[str]         =      "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
								__UpperCamelCase			:     str         =      "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
								__UpperCamelCase			:     Tuple         =      str.maketrans({k: "<BLOCK>" for k in keisen + blocks}		)







	def __len__(      self	:int		)	->				List[Any]:
								return len(self.ids_to_tokens		)







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:Dict		)	->				Dict:
								__UpperCamelCase			:     int         =      self.content_repattera.sub("<URL>"  ,     _SCREAMING_SNAKE_CASE		)
								__UpperCamelCase			:     Union[str, Any]         =      self.content_repattera.sub("<EMAIL>"  ,     _SCREAMING_SNAKE_CASE		)
								__UpperCamelCase			:     Any         =      self.content_repattera.sub("<TEL>"  ,     _SCREAMING_SNAKE_CASE		)
								__UpperCamelCase			:     Optional[Any]         =      self.content_repattera.sub("<DATE>"  ,     _SCREAMING_SNAKE_CASE		)
								__UpperCamelCase			:     int         =      self.content_repattera.sub("<DATE>"  ,     _SCREAMING_SNAKE_CASE		)
								__UpperCamelCase			:     Optional[Any]         =      self.content_repattera.sub("<PRICE>"  ,     _SCREAMING_SNAKE_CASE		)
								__UpperCamelCase			:     Tuple         =      content.translate(self.content_transa		)
								while "<BLOCK><BLOCK>" in content:
															__UpperCamelCase			:     Tuple         =      content.replace("<BLOCK><BLOCK>"  ,     "<BLOCK>"		)
								return content







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:Dict  ,     a	:Any=False		)	->				int:
								__UpperCamelCase			:     Any         =      text.replace(" "  ,     "<SP>"		)
								__UpperCamelCase			:     str         =      text.replace("　"  ,     "<SP>"		)
								__UpperCamelCase			:     Union[str, Any]         =      text.replace("\r\n"  ,     "<BR>"		)
								__UpperCamelCase			:     Dict         =      text.replace("\n"  ,     "<BR>"		)
								__UpperCamelCase			:     int         =      text.replace("\r"  ,     "<BR>"		)
								__UpperCamelCase			:     Optional[int]         =      text.replace("\t"  ,     "<TAB>"		)
								__UpperCamelCase			:     List[str]         =      text.replace("—"  ,     "ー"		)
								__UpperCamelCase			:     Optional[Any]         =      text.replace("−"  ,     "ー"		)
								for k, v in self.emoji["emoji"].items():
															if k in text:
																						__UpperCamelCase			:     List[Any]         =      text.replace(_SCREAMING_SNAKE_CASE  ,     _SCREAMING_SNAKE_CASE		)
								if clean:
															__UpperCamelCase			:     int         =      self.clean_text(_SCREAMING_SNAKE_CASE		)

								def check_simbol(a	:Union[str, Any]		):
															__UpperCamelCase			:     Tuple         =      x.encode()
															if len(_SCREAMING_SNAKE_CASE		) == 1 and len(_SCREAMING_SNAKE_CASE		) == 2:
																						__UpperCamelCase			:     Dict         =      (int(e[0]		) << 8) + int(e[1]		)
																						if (
																						    (c >= 0xc2_a1 and c <= 0xc2_bf)
																						    or (c >= 0xc7_80 and c <= 0xc7_83)
																						    or (c >= 0xca_b9 and c <= 0xcb_bf)
																						    or (c >= 0xcc_80 and c <= 0xcd_a2)
																						):
																													return True
															return False

								def checkuae(a	:Optional[int]		):
															__UpperCamelCase			:     List[Any]         =      x.encode()
															if len(_SCREAMING_SNAKE_CASE		) == 1 and len(_SCREAMING_SNAKE_CASE		) == 3:
																						__UpperCamelCase			:     Optional[Any]         =      (int(e[0]		) << 1_6) + (int(e[1]		) << 8) + int(e[2]		)
																						if c >= 0xe2_80_80 and c <= 0xe2_b0_7f:
																													return True
															return False

								__UpperCamelCase			:     str         =      0
								__UpperCamelCase			:     Optional[int]         =      []
								while pos < len(_SCREAMING_SNAKE_CASE		):
															__UpperCamelCase			:     List[Any]         =      min(len(_SCREAMING_SNAKE_CASE		)  ,     pos + self.maxlen + 1		) if text[pos] == "<" else pos + 3
															__UpperCamelCase			:     Optional[Any]         =      []  # (token_id, token, pos)
															for e in range(_SCREAMING_SNAKE_CASE  ,     _SCREAMING_SNAKE_CASE  ,     -1		):
																						__UpperCamelCase			:     Optional[Any]         =      text[pos:e]
																						if wd in self.vocab:
																													if wd[0] == "<" and len(_SCREAMING_SNAKE_CASE		) > 2:
																																				__UpperCamelCase			:     Union[str, Any]         =      [(self.vocab[wd], wd, e)]
																																				break
																													else:
																																				candidates.append((self.vocab[wd], wd, e)		)
															if len(_SCREAMING_SNAKE_CASE		) > 0:
																						# the smallest token_id is adopted
																						__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Optional[Any]         =      sorted(_SCREAMING_SNAKE_CASE  ,     key=lambda a		: x[0]		)[0]
																						result.append(_SCREAMING_SNAKE_CASE		)
																						__UpperCamelCase			:     Union[str, Any]         =      e
															else:
																						__UpperCamelCase			:     List[str]         =      pos + 1
																						__UpperCamelCase			:     int         =      text[pos:end]
																						if check_simbol(_SCREAMING_SNAKE_CASE		):
																													result.append("<KIGOU>"		)
																						elif checkuae(_SCREAMING_SNAKE_CASE		):
																													result.append("<U2000U2BFF>"		)
																						else:
																													for i in wd.encode("utf-8"		):
																																				result.append("<|byte%d|>" % i		)
																						__UpperCamelCase			:     Optional[Any]         =      end
								return result







	def      _lowerCamelCase       (      self	:str  ,     a	:Dict  ,     a	:Optional[int]="\n"		)	->				List[Any]:
								__UpperCamelCase			:     Optional[int]         =      []
								__UpperCamelCase			:     List[Any]         =      []
								__UpperCamelCase			:     List[str]         =      self.ids_to_tokens[index][0]
								if word[:6] == "<|byte" and word[-2:] == "|>":
															byte_tokens.append(int(word[6:-2]		)		)
								else:
															if len(_SCREAMING_SNAKE_CASE		) > 0:
																						words.append(bytearray(_SCREAMING_SNAKE_CASE		).decode("utf-8"  ,     errors="replace"		)		)
																						__UpperCamelCase			:     Dict         =      []
															if word[:7] == "<|emoji" and word[-2:] == "|>":
																						words.append(self.emoji["emoji_inv"][word]		)
															elif word == "<SP>":
																						words.append(" "		)
															elif word == "<BR>":
																						words.append(_SCREAMING_SNAKE_CASE		)
															elif word == "<TAB>":
																						words.append("\t"		)
															elif word == "<BLOCK>":
																						words.append("▀"		)
															elif word == "<KIGOU>":
																						words.append("ǀ"		)
															elif word == "<U2000U2BFF>":
																						words.append("‖"		)
															else:
																						words.append(_SCREAMING_SNAKE_CASE		)
								if len(_SCREAMING_SNAKE_CASE		) > 0:
															words.append(bytearray(_SCREAMING_SNAKE_CASE		).decode("utf-8"  ,     errors="replace"		)		)
								__UpperCamelCase			:     Optional[Any]         =      "".join(_SCREAMING_SNAKE_CASE		)
								return text

# 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.")