




import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCamelCase							:						Optional[int]					       =  "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase							:						int					       =  direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCamelCase							:						Union[str, Any]					       =  re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowerCamelCase							:						Optional[int]					       =  re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCamelCase							:						List[str]					       =  re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCamelCase							:						Any					       =  [
    ("pretraining", "MODEL_FOR_PRETRAINING_MAPPING_NAMES", "AutoModelForPreTraining"),
    ("feature-extraction", "MODEL_MAPPING_NAMES", "AutoModel"),
    ("audio-classification", "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForAudioClassification"),
    ("text-generation", "MODEL_FOR_CAUSAL_LM_MAPPING_NAMES", "AutoModelForCausalLM"),
    ("automatic-speech-recognition", "MODEL_FOR_CTC_MAPPING_NAMES", "AutoModelForCTC"),
    ("image-classification", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForImageClassification"),
    ("image-segmentation", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES", "AutoModelForImageSegmentation"),
    ("fill-mask", "MODEL_FOR_MASKED_LM_MAPPING_NAMES", "AutoModelForMaskedLM"),
    ("object-detection", "MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES", "AutoModelForObjectDetection"),
    (
        "zero-shot-object-detection",
        "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES",
        "AutoModelForZeroShotObjectDetection",
    ),
    ("question-answering", "MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForQuestionAnswering"),
    ("text2text-generation", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES", "AutoModelForSeq2SeqLM"),
    ("text-classification", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForSequenceClassification"),
    ("automatic-speech-recognition", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES", "AutoModelForSpeechSeq2Seq"),
    (
        "table-question-answering",
        "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForTableQuestionAnswering",
    ),
    ("token-classification", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES", "AutoModelForTokenClassification"),
    ("multiple-choice", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES", "AutoModelForMultipleChoice"),
    (
        "next-sentence-prediction",
        "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES",
        "AutoModelForNextSentencePrediction",
    ),
    (
        "audio-frame-classification",
        "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForAudioFrameClassification",
    ),
    ("audio-xvector", "MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES", "AutoModelForAudioXVector"),
    (
        "document-question-answering",
        "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForDocumentQuestionAnswering",
    ),
    (
        "visual-question-answering",
        "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForVisualQuestionAnswering",
    ),
    ("image-to-text", "MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES", "AutoModelForVision2Seq"),
    (
        "zero-shot-image-classification",
        "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForZeroShotImageClassification",
    ),
    ("depth-estimation", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES", "AutoModelForDepthEstimation"),
    ("video-classification", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForVideoClassification"),
    ("mask-generation", "MODEL_FOR_MASK_GENERATION_MAPPING_NAMES", "AutoModelForMaskGeneration"),
]

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Tuple	):


      '''simple docstring'''



      lowerCamelCase_							=       re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'      ,				lowercase	)
      return [m.group(0	) for m in matches]

def      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



      lowerCamelCase_							=       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      lowerCamelCase_							=       {
          config.replace('Config'      ,				''	): model_type for model_type, config in config_maping_names.items()
      }

      # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
      lowerCamelCase_							=       collections.defaultdict(lowercase	)
      lowerCamelCase_							=       collections.defaultdict(lowercase	)
      lowerCamelCase_							=       collections.defaultdict(lowercase	)

      # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
      for attr_name in dir(lowercase	):
            lowerCamelCase_							=       None
            if _re_tf_models.match(lowercase	) is not None:
                  lowerCamelCase_							=       tf_models
                  lowerCamelCase_							=       _re_tf_models.match(lowercase	).groups()[0]
            elif _re_flax_models.match(lowercase	) is not None:
                  lowerCamelCase_							=       flax_models
                  lowerCamelCase_							=       _re_flax_models.match(lowercase	).groups()[0]
            elif _re_pt_models.match(lowercase	) is not None:
                  lowerCamelCase_							=       pt_models
                  lowerCamelCase_							=       _re_pt_models.match(lowercase	).groups()[0]

            if lookup_dict is not None:
                  while len(lowercase	) > 0:
                        if attr_name in model_prefix_to_model_type:
                              lowerCamelCase_							=       True
                              break
                        # Try again after removing the last word in the name
                        lowerCamelCase_							=       ''.join(camel_case_split(lowercase	)[:-1]	)

      lowerCamelCase_							=       set(list(pt_models.keys()	) + list(tf_models.keys()	) + list(flax_models.keys()	)	)
      lowerCamelCase_							=       list(lowercase	)
      all_models.sort()

      lowerCamelCase_							=       {'model_type': all_models}
      lowerCamelCase_							=       [pt_models[t] for t in all_models]
      lowerCamelCase_							=       [tf_models[t] for t in all_models]
      lowerCamelCase_							=       [flax_models[t] for t in all_models]

      # Now let's use the auto-mapping names to make sure
      lowerCamelCase_							=       {}
      for t in all_models:
            if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
                  lowerCamelCase_							=       'AutoProcessor'
            elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
                  lowerCamelCase_							=       'AutoTokenizer'
            elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
                  lowerCamelCase_							=       'AutoFeatureExtractor'
            else:
                  # Default to AutoTokenizer if a model has nothing, for backward compatibility.
                  lowerCamelCase_							=       'AutoTokenizer'

      lowerCamelCase_							=       [processors[t] for t in all_models]

      return pd.DataFrame(lowercase	)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Dict	):


      '''simple docstring'''



      lowerCamelCase_							=       [
          transformers_module.models.auto.modeling_auto,
          transformers_module.models.auto.modeling_tf_auto,
          transformers_module.models.auto.modeling_flax_auto,
      ]
      for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
            lowerCamelCase_							=       [model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
            lowerCamelCase_							=       [auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
            # Loop through all three frameworks
            for module, cls, mapping in zip(lowercase      ,				lowercase      ,				lowercase	):
                  # The type of pipeline may not exist in this framework
                  if not hasattr(lowercase      ,				lowercase	):
                        continue
                  # First extract all model_names
                  lowerCamelCase_							=       []
                  for name in getattr(lowercase      ,				lowercase	).values():
                        if isinstance(lowercase      ,				lowercase	):
                              model_names.append(lowercase	)
                        else:
                              model_names.extend(list(lowercase	)	)

            # Add pipeline tag and auto model class for those models
                  table.update({model_name: (pipeline_tag, cls) for model_name in model_names}	)

      return table

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			List[str]      ,				lowercase      :			str	):


      '''simple docstring'''



      lowerCamelCase_							=       get_frameworks_table()
      lowerCamelCase_							=       Dataset.from_pandas(lowercase	)

      lowerCamelCase_							=       hf_hub_download(
          'huggingface/transformers-metadata'      ,				'pipeline_tags.json'      ,				repo_type='dataset'      ,				token=lowercase	)
      lowerCamelCase_							=       Dataset.from_json(lowercase	)
      lowerCamelCase_							=       {
          tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
          for i in range(len(lowercase	)	)
      }
      lowerCamelCase_							=       update_pipeline_and_auto_class_table(lowercase	)

      # Sort the model classes to avoid some nondeterministic updates to create false update commits.
      lowerCamelCase_							=       sorted(table.keys()	)
      lowerCamelCase_							=       pd.DataFrame(
          {
              'model_class': model_classes,
              'pipeline_tag': [table[m][0] for m in model_classes],
              'auto_class': [table[m][1] for m in model_classes],
          }	)
      lowerCamelCase_							=       Dataset.from_pandas(lowercase	)

      with tempfile.TemporaryDirectory() as tmp_dir:
            frameworks_dataset.to_json(os.path.join(lowercase      ,				'frameworks.json'	)	)
            tags_dataset.to_json(os.path.join(lowercase      ,				'pipeline_tags.json'	)	)

            if commit_sha is not None:
                  lowerCamelCase_							=       (
                      f"""Update with commit {commit_sha}\n\nSee: """
                      f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
                  )
            else:
                  lowerCamelCase_							=       'Update'

            upload_folder(
                repo_id='huggingface/transformers-metadata'      ,				folder_path=lowercase      ,				repo_type='dataset'      ,				token=lowercase      ,				commit_message=lowercase      ,				)

def      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



      lowerCamelCase_							=       {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
      lowerCamelCase_							=       transformers_module.pipelines.SUPPORTED_TASKS
      lowerCamelCase_							=       []
      for key in pipeline_tasks:
            if key not in in_table:
                  lowerCamelCase_							=       pipeline_tasks[key]['pt']
                  if isinstance(lowercase      ,				(list, tuple)	):
                        lowerCamelCase_							=       model[0]
                  lowerCamelCase_							=       model.__name__
                  if model not in in_table.values():
                        missing.append(lowercase	)

      if len(lowercase	) > 0:
            lowerCamelCase_							=       ', '.join(lowercase	)
            raise ValueError(
                'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
                f"""`utils/update_metadata.py`: {msg}. Please add them!"""	)


if __name__ == "__main__":
 lowerCamelCase							:						Dict					       =  argparse.ArgumentParser()
 parser.add_argument("--token", type=str, help="The token to use to push to the transformers-metadata dataset.")
 parser.add_argument("--commit_sha", type=str, help="The sha of the commit going with this update.")
 parser.add_argument("--check-only", action="store_true", help="Activate to just check all pipelines are present.")
 lowerCamelCase							:						Dict					       =  parser.parse_args()

 if args.check_only:
  check_pipeline_tags()
 else:
  update_metadata(args.token, args.commit_sha)








'''simple docstring'''

import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


a							   =				logging.get_logger(__name__)

a							   =				'▁'

a							   =				{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

a							   =				{
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

a							   =				{
    'facebook/m2m100_418M': 1_024,
}

# fmt: off
a							   =				{
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		int        =				VOCAB_FILES_NAMES
							UpperCAmelCase			:		Tuple        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase			:		Any        =				PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase			:		Tuple        =				["""input_ids""", """attention_mask"""]

							UpperCAmelCase			:		List[int]        =				[]
							UpperCAmelCase			:		List[int]        =				[]


							def __init__(				self   :						int      ,						a_   :						Union[str, Any]      ,						a_   :						Union[str, Any]      ,						a_   :						Optional[Any]=None      ,						a_   :						str=None      ,						a_   :						Tuple="<s>"      ,						a_   :						int="</s>"      ,						a_   :						int="</s>"      ,						a_   :						Any="<pad>"      ,						a_   :						Union[str, Any]="<unk>"      ,						a_   :						Optional[Any]="m2m100"      ,						a_   :						Optional[Dict[str, Any]] = None      ,						a_   :						Dict=8      ,						**a_   :						Union[str, Any]      ,						)			->					None:
										snake_case:    Optional[Any]      							={} if sp_model_kwargs is None else sp_model_kwargs

										snake_case:    Union[str, Any]      							=language_codes
										snake_case:    Optional[int]      							=FAIRSEQ_LANGUAGE_CODES[language_codes]
										snake_case:    Dict      							={lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

										snake_case:    Optional[int]      							=kwargs.get('additional_special_tokens'      ,						[]			)
										kwargs["additional_special_tokens"] += [
										    self.get_lang_token(a_			)
										    for lang_code in fairseq_language_code
										    if self.get_lang_token(a_			) not in kwargs["additional_special_tokens"]
										]

										super().__init__(
										    src_lang=a_      ,						tgt_lang=a_      ,						bos_token=a_      ,						eos_token=a_      ,						sep_token=a_      ,						unk_token=a_      ,						pad_token=a_      ,						language_codes=a_      ,						sp_model_kwargs=self.sp_model_kwargs      ,						num_madeup_words=a_      ,						**a_      ,						)

										snake_case:    int      							=vocab_file
										snake_case:    int      							=load_json(a_			)
										snake_case:    Tuple      							={v: k for k, v in self.encoder.items()}
										snake_case:    Any      							=spm_file
										snake_case:    int      							=load_spm(a_      ,						self.sp_model_kwargs			)

										snake_case:    List[Any]      							=len(self.encoder			)

										snake_case:    Optional[Any]      							={
										    self.get_lang_token(a_			): self.encoder_size + i for i, lang_code in enumerate(a_			)
										}
										snake_case:    List[str]      							={lang_code: self.encoder_size + i for i, lang_code in enumerate(a_			)}
										snake_case:    int      							={v: k for k, v in self.lang_token_to_id.items()}

										snake_case:    Any      							=src_lang if src_lang is not None else 'en'
										snake_case:    Optional[int]      							=tgt_lang
										snake_case:    Optional[int]      							=self.get_lang_id(self._src_lang			)
										self.set_src_lang_special_tokens(self._src_lang			)

										snake_case:    str      							=num_madeup_words


							@property
							def    UpperCamelCase     (				self   :						int			)			->					int:
										return len(self.encoder			) + len(self.lang_token_to_id			)


							@property
							def    UpperCamelCase     (				self   :						Optional[int]			)			->					str:
										return self._src_lang


							@src_lang.setter
							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						str			)			->					None:
										snake_case:    Optional[Any]      							=new_src_lang
										self.set_src_lang_special_tokens(self._src_lang			)


							def    UpperCamelCase     (				self   :						Optional[int]      ,						a_   :						str			)			->					List[str]:
										return self.sp_model.encode(a_      ,						out_type=a_			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						Optional[int]			)			->					Union[str, Any]:
										if token in self.lang_token_to_id:
													return self.lang_token_to_id[token]
										return self.encoder.get(a_      ,						self.encoder[self.unk_token]			)


							def    UpperCamelCase     (				self   :						str      ,						a_   :						int			)			->					str:
										if index in self.id_to_lang_token:
													return self.id_to_lang_token[index]
										return self.decoder.get(a_      ,						self.unk_token			)


							def    UpperCamelCase     (				self   :						int      ,						a_   :						Optional[Any]			)			->					Tuple:
										snake_case:    List[Any]      							=[]
										snake_case:    List[str]      							=''
										for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																out_string += self.sp_model.decode(a_			) + token
																snake_case:    Optional[Any]      							=[]
													else:
																current_sub_tokens.append(a_			)
										out_string += self.sp_model.decode(a_			)
										return out_string.strip()


							def    UpperCamelCase     (				self   :						Dict      ,						a_   :						List[int]      ,						a_   :						Optional[List[int]] = None      ,						a_   :						bool = False			)			->					List[int]:

										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=a_      ,						token_ids_a=a_      ,						already_has_special_tokens=a_			)

										snake_case:    List[Any]      							=[1] * len(self.prefix_tokens			)
										snake_case:    Optional[int]      							=[1] * len(self.suffix_tokens			)
										if token_ids_a is None:
													return prefix_ones + ([0] * len(a_			)) + suffix_ones
										return prefix_ones + ([0] * len(a_			)) + ([0] * len(a_			)) + suffix_ones


							def    UpperCamelCase     (				self   :						str      ,						a_   :						List[int]      ,						a_   :						Optional[List[int]] = None			)			->					List[int]:
										if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										snake_case:    List[str]      							={self.convert_ids_to_tokens(a_			): i for i in range(self.vocab_size			)}
										vocab.update(self.added_tokens_encoder			)
										return vocab


							def __getstate__(				self   :						Union[str, Any]			)			->					Dict:
										snake_case:    Optional[int]      							=self.__dict__.copy()
										snake_case:    List[Any]      							=None
										return state


							def __setstate__(				self   :						int      ,						a_   :						Dict			)			->					None:
										snake_case:    Any      							=d

										# for backward compatibility
										if not hasattr(self      ,						'sp_model_kwargs'			):
													snake_case:    Optional[int]      							={}

										snake_case:    List[str]      							=load_spm(self.spm_file      ,						self.sp_model_kwargs			)


							def    UpperCamelCase     (				self   :						str      ,						a_   :						str      ,						a_   :						Optional[str] = None			)			->					Tuple[str]:
										snake_case:    Tuple      							=Path(a_			)
										if not save_dir.is_dir():
													raise OSError(F'''{save_directory} should be a directory'''			)
										snake_case:    Union[str, Any]      							=save_dir / (
										    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
										)
										snake_case:    List[Any]      							=save_dir / (
										    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
										)

										save_json(self.encoder      ,						a_			)

										if os.path.abspath(self.spm_file			) != os.path.abspath(a_			) and os.path.isfile(self.spm_file			):
													copyfile(self.spm_file      ,						a_			)
										elif not os.path.isfile(self.spm_file			):
													with open(a_      ,						'wb'			) as fi:
																snake_case:    Any      							=self.sp_model.serialized_model_proto()
																fi.write(a_			)

										return (str(a_			), str(a_			))


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						a_   :						List[str]      ,						a_   :						str = "en"      ,						a_   :						Optional[List[str]] = None      ,						a_   :						str = "ro"      ,						**a_   :						List[str]      ,						)			->					BatchEncoding:
										snake_case:    List[str]      							=src_lang
										snake_case:    Optional[int]      							=tgt_lang
										self.set_src_lang_special_tokens(self.src_lang			)
										return super().prepare_seqaseq_batch(a_      ,						a_      ,						**a_			)


							def    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						int      ,						a_   :						Optional[str]      ,						a_   :						Optional[str]      ,						**a_   :						Union[str, Any]			)			->					Any:
										if src_lang is None or tgt_lang is None:
													raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
										snake_case:    List[str]      							=src_lang
										snake_case:    List[str]      							=self(a_      ,						add_special_tokens=a_      ,						**a_			)
										snake_case:    List[Any]      							=self.get_lang_id(a_			)
										snake_case:    List[str]      							=tgt_lang_id
										return inputs


							def    UpperCamelCase     (				self   :						str			)			->					List[str]:
										self.set_src_lang_special_tokens(self.src_lang			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Tuple:
										self.set_tgt_lang_special_tokens(self.tgt_lang			)


							def    UpperCamelCase     (				self   :						List[str]      ,						a_   :						str			)			->					None:
										snake_case:    Optional[Any]      							=self.get_lang_token(a_			)
										snake_case:    Optional[Any]      							=self.lang_token_to_id[lang_token]
										snake_case:    Optional[Any]      							=[self.cur_lang_id]
										snake_case:    Optional[int]      							=[self.eos_token_id]


							def    UpperCamelCase     (				self   :						Optional[int]      ,						a_   :						str			)			->					None:
										snake_case:    Dict      							=self.get_lang_token(a_			)
										snake_case:    Tuple      							=self.lang_token_to_id[lang_token]
										snake_case:    Optional[int]      							=[self.cur_lang_id]
										snake_case:    List[str]      							=[self.eos_token_id]


							def    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						str			)			->					str:
										return self.lang_code_to_token[lang]




							def    UpperCamelCase     (				self   :						int      ,						a_   :						str			)			->					int:
										snake_case:    Union[str, Any]      							=self.get_lang_token(a_			)
										return self.lang_token_to_id[lang_token]







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					sentencepiece.SentencePieceProcessor:





			"""simple docstring"""







			snake_case:    Dict      							=sentencepiece.SentencePieceProcessor(**__UpperCAmelCase					)
			spm.Load(str(__UpperCAmelCase					)					)
			return spm







def a_     (    __UpperCAmelCase					)				->					Union[Dict, List]:





			"""simple docstring"""







			with open(__UpperCAmelCase				,							'r'					) as f:
						return json.load(__UpperCAmelCase					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					None:





			"""simple docstring"""







			with open(__UpperCAmelCase				,							'w'					) as f:
						json.dump(__UpperCAmelCase				,							__UpperCAmelCase				,							indent=2					)




