import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


lowerCAmelCase__					=				'1.5.0'

lowerCAmelCase__					=				['vision-encoder-decoder']






def    __lowerCamelCase	(		lowerCAmelCase__			):
       if is_optimum_available():
              from optimum.version import __version__ as optimum_version

              lowerCAmelCase__  			=      version.parse(lowerCAmelCase__			)
              if parsed_optimum_version < version.parse(lowerCAmelCase__			):
                     raise RuntimeError(
                         F"""transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You """
                         'can upgrade optimum by running: pip install -U optimum[exporters]'			)
       else:
              raise RuntimeError(
                  'transformers.onnx requires optimum to run, you can install the library by running: pip install '
                  'optimum[exporters]'			)
       lowerCAmelCase__  			=      [
           sys.executable,
           '-m',
           'optimum.exporters.onnx',
           F"""--model {args.model}""",
           F"""--task {args.feature}""",
           F"""--framework {args.framework}""" if args.framework is not None else '',
           F"""{args.output}""",
       ]
       lowerCAmelCase__  			=      subprocess.Popen(' '.join(lowerCAmelCase__			)			,						stdout=subprocess.PIPE			,						shell=lowerCAmelCase__			)
       proc.wait()

       logger.info(
           'The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as '
           'transformers.onnx is deprecated, and will be removed in v5. You can find more information here: '
           'https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.'			)






def    __lowerCamelCase	(		lowerCAmelCase__			):
       lowerCAmelCase__  			=      args.output if args.output.is_file() else args.output.joinpath('model.onnx'			)
       if not args.output.parent.exists():
              args.output.parent.mkdir(parents=lowerCAmelCase__			)

       # Allocate the model
       lowerCAmelCase__  			=      FeaturesManager.get_model_from_feature(
           args.feature			,						args.model			,						framework=args.framework			,						cache_dir=args.cache_dir			)

       lowerCAmelCase__			,      lowerCAmelCase__  			=      FeaturesManager.check_supported_model_or_raise(lowerCAmelCase__			,						feature=args.feature			)
       lowerCAmelCase__  			=      model_onnx_config(model.config			)

       if model_kind in ENCODER_DECODER_MODELS:
              lowerCAmelCase__  			=      model.get_encoder()
              lowerCAmelCase__  			=      model.get_decoder()

              lowerCAmelCase__  			=      onnx_config.get_encoder_config(encoder_model.config			)
              lowerCAmelCase__  			=      onnx_config.get_decoder_config(
                  encoder_model.config			,						decoder_model.config			,						feature=args.feature			)

              if args.opset is None:
                     lowerCAmelCase__  			=      max(encoder_onnx_config.default_onnx_opset			,						decoder_onnx_config.default_onnx_opset			)

              if args.opset < min(encoder_onnx_config.default_onnx_opset			,						decoder_onnx_config.default_onnx_opset			):
                     raise ValueError(
                         F"""Opset {args.opset} is not sufficient to export {model_kind}. At least """
                         F""" {min(encoder_onnx_config.default_onnx_opset			,						decoder_onnx_config.default_onnx_opset			)} is required."""			)

              lowerCAmelCase__  			=      AutoFeatureExtractor.from_pretrained(args.model			)

              lowerCAmelCase__			,      lowerCAmelCase__  			=      export(
                  lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						args.opset			,						args.output.parent.joinpath('encoder_model.onnx'			)			,						)

              validate_model_outputs(
                  lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						args.output.parent.joinpath('encoder_model.onnx'			)			,						lowerCAmelCase__			,						args.atol if args.atol else encoder_onnx_config.atol_for_validation			,						)

              lowerCAmelCase__  			=      AutoTokenizer.from_pretrained(args.model			)

              lowerCAmelCase__			,      lowerCAmelCase__  			=      export(
                  lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						args.opset			,						args.output.parent.joinpath('decoder_model.onnx'			)			,						)

              validate_model_outputs(
                  lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						args.output.parent.joinpath('decoder_model.onnx'			)			,						lowerCAmelCase__			,						args.atol if args.atol else decoder_onnx_config.atol_for_validation			,						)
              logger.info(
                  F"""All good, model saved at: {args.output.parent.joinpath('encoder_model.onnx'			).as_posix()},"""
                  F""" {args.output.parent.joinpath('decoder_model.onnx'			).as_posix()}"""			)

       else:
              # Instantiate the appropriate preprocessor
              if args.preprocessor == "auto":
                     lowerCAmelCase__  			=      get_preprocessor(args.model			)
              elif args.preprocessor == "tokenizer":
                     lowerCAmelCase__  			=      AutoTokenizer.from_pretrained(args.model			)
              elif args.preprocessor == "image_processor":
                     lowerCAmelCase__  			=      AutoImageProcessor.from_pretrained(args.model			)
              elif args.preprocessor == "feature_extractor":
                     lowerCAmelCase__  			=      AutoFeatureExtractor.from_pretrained(args.model			)
              elif args.preprocessor == "processor":
                     lowerCAmelCase__  			=      AutoProcessor.from_pretrained(args.model			)
              else:
                     raise ValueError(F"""Unknown preprocessor type '{args.preprocessor}'"""			)

              # Ensure the requested opset is sufficient
              if args.opset is None:
                     lowerCAmelCase__  			=      onnx_config.default_onnx_opset

              if args.opset < onnx_config.default_onnx_opset:
                     raise ValueError(
                         F"""Opset {args.opset} is not sufficient to export {model_kind}. """
                         F"""At least  {onnx_config.default_onnx_opset} is required."""			)

              lowerCAmelCase__			,      lowerCAmelCase__  			=      export(
                  lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						args.opset			,						args.output			,						)

              if args.atol is None:
                     lowerCAmelCase__  			=      onnx_config.atol_for_validation

              validate_model_outputs(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						args.output			,						lowerCAmelCase__			,						args.atol			)
              logger.info(F"""All good, model saved at: {args.output.as_posix()}"""			)
              warnings.warn(
                  'The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend'
                  ' using optimum.exporters.onnx in future. You can find more information here:'
                  ' https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.'			,						lowerCAmelCase__			,						)






def    __lowerCamelCase	(		):
       lowerCAmelCase__  			=      ArgumentParser('Hugging Face Transformers ONNX exporter'			)
       parser.add_argument(
           '-m'			,						'--model'			,						type=lowerCAmelCase__			,						required=lowerCAmelCase__			,						help='Model ID on huggingface.co or path on disk to load model from.'			)
       parser.add_argument(
           '--feature'			,						default='default'			,						help='The type of features to export the model with.'			,						)
       parser.add_argument('--opset'			,						type=lowerCAmelCase__			,						default=lowerCAmelCase__			,						help='ONNX opset version to export the model with.'			)
       parser.add_argument(
           '--atol'			,						type=lowerCAmelCase__			,						default=lowerCAmelCase__			,						help='Absolute difference tolerance when validating the model.'			)
       parser.add_argument(
           '--framework'			,						type=lowerCAmelCase__			,						choices=['pt', 'tf']			,						default=lowerCAmelCase__			,						help=(
               'The framework to use for the ONNX export.'
               ' If not provided, will attempt to use the local checkpoint\'s original framework'
               ' or what is available in the environment.'
           )			,						)
       parser.add_argument('output'			,						type=lowerCAmelCase__			,						help='Path indicating where to store generated ONNX model.'			)
       parser.add_argument('--cache_dir'			,						type=lowerCAmelCase__			,						default=lowerCAmelCase__			,						help='Path indicating where to store cache.'			)
       parser.add_argument(
           '--preprocessor'			,						type=lowerCAmelCase__			,						choices=['auto', 'tokenizer', 'feature_extractor', 'image_processor', 'processor']			,						default='auto'			,						help='Which type of preprocessor to use. \'auto\' tries to automatically detect it.'			,						)
       parser.add_argument(
           '--export_with_transformers'			,						action='store_true'			,						help=(
               'Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be '
               'useful when exporting a model supported in transformers but not in optimum, otherwise it is not '
               'recommended.'
           )			,						)

       lowerCAmelCase__  			=      parser.parse_args()
       if args.export_with_transformers or not is_optimum_available():
              export_with_transformers(lowerCAmelCase__			)
       else:
              export_with_optimum(lowerCAmelCase__			)


if __name__ == "__main__":
    lowerCAmelCase__					=				logging.get_logger('transformers.onnx')  # pylint: disable=invalid-name
    logger.setLevel(logging.INFO)
    main()

import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__					=				logging.get_logger(__name__)

lowerCAmelCase__					=				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowerCAmelCase__					=				{
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

lowerCAmelCase__					=				{
    'microsoft/biogpt': 1024,
}






def    __lowerCamelCase	(		lowerCAmelCase__			):
       lowerCAmelCase__  			=      set()
       lowerCAmelCase__  			=      word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)			)
              lowerCAmelCase__  			=      char
       return pairs



class        a_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






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




 def __init__(    self      :						List[str]    ,					lowercase__      :						Any    ,					lowercase__      :						int    ,					lowercase__      :						Union[str, Any]="<unk>"    ,					lowercase__      :						Optional[Any]="<s>"    ,					lowercase__      :						Dict="</s>"    ,					lowercase__      :						Tuple="</s>"    ,					lowercase__      :						Optional[Any]="<pad>"    ,					**lowercase__      :						Optional[int]    ,					):







        '''simple docstring'''



        super().__init__(
            bos_token=lowercase__    ,					eos_token=lowercase__    ,					sep_token=lowercase__    ,					unk_token=lowercase__    ,					pad_token=lowercase__    ,					**lowercase__    ,					)

        try:
               import sacremoses
        except ImportError:
               raise ImportError(
                   'You need to install sacremoses to use BioGptTokenizer. '
                   'See https://pypi.org/project/sacremoses/ for installation.')

        lowerCAmelCase__  			=      'en'
        lowerCAmelCase__  			=      sacremoses
        # cache of sm.MosesTokenizer instance
        lowerCAmelCase__  			=      {}
        lowerCAmelCase__  			=      {}
        with open(lowercase__    ,					encoding='utf-8') as vocab_handle:
               lowerCAmelCase__  			=      json.load(lowercase__)
        lowerCAmelCase__  			=      {v: k for k, v in self.encoder.items()}
        with open(lowercase__    ,					encoding='utf-8') as merges_handle:
               lowerCAmelCase__  			=      merges_handle.read().split('\n')[:-1]
        lowerCAmelCase__  			=      [tuple(merge.split()[:2]) for merge in merges]
        lowerCAmelCase__  			=      dict(zip(lowercase__    ,					range(len(lowercase__))))
        lowerCAmelCase__  			=      {}




 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        return len(self.encoder)




 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        return dict(self.encoder    ,					**self.added_tokens_encoder)




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						List[Any]    ,					lowercase__      :						int):







        '''simple docstring'''



        if lang not in self.cache_moses_tokenizer:
               lowerCAmelCase__  			=      self.sm.MosesTokenizer(lang=lowercase__)
               lowerCAmelCase__  			=      moses_tokenizer
        return self.cache_moses_tokenizer[lang].tokenize(
            lowercase__    ,					aggressive_dash_splits=lowercase__    ,					return_str=lowercase__    ,					escape=lowercase__)




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Tuple    ,					lowercase__      :						Any):







        '''simple docstring'''



        if lang not in self.cache_moses_detokenizer:
               lowerCAmelCase__  			=      self.sm.MosesDetokenizer(lang=lowercase__)
               lowerCAmelCase__  			=      moses_detokenizer
        return self.cache_moses_detokenizer[lang].detokenize(lowercase__)




 def 	__snake_case		(    self      :						Dict    ,					lowercase__      :						Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      tuple(token[:-1]) + (token[-1] + '</w>',)
        if token in self.cache:
               return self.cache[token]
        lowerCAmelCase__  			=      get_pairs(lowercase__)

        if not pairs:
               return token + "</w>"

        while True:
               lowerCAmelCase__  			=      min(lowercase__    ,					key=lambda lowercase__: self.bpe_ranks.get(lowercase__    ,					float('inf')))
               if bigram not in self.bpe_ranks:
                      break
               lowerCAmelCase__			,      lowerCAmelCase__  			=      bigram
               lowerCAmelCase__  			=      []
               lowerCAmelCase__  			=      0
               while i < len(lowercase__):
                      try:
                             lowerCAmelCase__  			=      word.index(lowercase__    ,					lowercase__)
                      except ValueError:
                             new_word.extend(word[i:])
                             break
                      else:
                             new_word.extend(word[i:j])
                             lowerCAmelCase__  			=      j

                      if word[i] == first and i < len(lowercase__) - 1 and word[i + 1] == second:
                             new_word.append(first + second)
                             i += 2
                      else:
                             new_word.append(word[i])
                             i += 1
               lowerCAmelCase__  			=      tuple(lowercase__)
               lowerCAmelCase__  			=      new_word
               if len(lowercase__) == 1:
                      break
               else:
                      lowerCAmelCase__  			=      get_pairs(lowercase__)
        lowerCAmelCase__  			=      ' '.join(lowercase__)
        if word == "\n  </w>":
               lowerCAmelCase__  			=      '\n</w>'
        lowerCAmelCase__  			=      word
        return word




 def 	__snake_case		(    self      :						Optional[Any]    ,					lowercase__      :						Any    ,					lowercase__      :						Dict=False):







        '''simple docstring'''



        if bypass_tokenizer:
               lowerCAmelCase__  			=      text.split()
        else:
               lowerCAmelCase__  			=      self.moses_tokenize(lowercase__    ,					self.lang)

        lowerCAmelCase__  			=      []
        for token in text:
               if token:
                      split_tokens.extend(list(self.bpe(lowercase__).split(' ')))

        return split_tokens




 def 	__snake_case		(    self      :						List[str]    ,					lowercase__      :						List[Any]):







        '''simple docstring'''



        return self.encoder.get(lowercase__    ,					self.encoder.get(self.unk_token))




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        return self.decoder.get(lowercase__    ,					self.unk_token)




 def 	__snake_case		(    self      :						Any    ,					lowercase__      :						int):







        '''simple docstring'''



        lowerCAmelCase__  			=      [t.replace(' '    ,					'').replace('</w>'    ,					' ') for t in tokens]
        lowerCAmelCase__  			=      ''.join(lowercase__).split()
        # detokenize
        lowerCAmelCase__  			=      self.moses_detokenize(lowercase__    ,					self.lang)
        return text




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						List[int]    ,					lowercase__      :						Optional[List[int]] = None):







        '''simple docstring'''



        if token_ids_a is None:
               return [self.sep_token_id] + token_ids_a
        lowerCAmelCase__  			=      [self.sep_token_id]
        return sep + token_ids_a + sep + token_ids_a




 def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						List[int]    ,					lowercase__      :						Optional[List[int]] = None    ,					lowercase__      :						bool = False):







        '''simple docstring'''



        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowercase__    ,					token_ids_a=lowercase__    ,					already_has_special_tokens=lowercase__)
        # no bos used in fairseq
        if token_ids_a is not None:
               return [1] + ([0] * len(lowercase__)) + [1] + ([0] * len(lowercase__))
        return [1] + ([0] * len(lowercase__))




 def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						List[int]    ,					lowercase__      :						Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__  			=      [self.sep_token_id]

        # no bos used in fairseq
        if token_ids_a is None:
               return len(token_ids_a + sep) * [0]
        return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]




 def 	__snake_case		(    self      :						Tuple    ,					lowercase__      :						str    ,					lowercase__      :						Optional[str] = None):







        '''simple docstring'''



        if not os.path.isdir(lowercase__):
               logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
               return
        lowerCAmelCase__  			=      os.path.join(
            lowercase__    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
        lowerCAmelCase__  			=      os.path.join(
            lowercase__    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

        with open(lowercase__    ,					'w'    ,					encoding='utf-8') as f:
               f.write(json.dumps(self.encoder    ,					indent=2    ,					sort_keys=lowercase__    ,					ensure_ascii=lowercase__) + '\n')

        lowerCAmelCase__  			=      0
        with open(lowercase__    ,					'w'    ,					encoding='utf-8') as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,					key=lambda lowercase__: kv[1]):
                      if index != token_index:
                             logger.warning(
                                 F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                                 ' Please check that the tokenizer is not corrupted!')
                             lowerCAmelCase__  			=      token_index
                      writer.write(' '.join(lowercase__) + '\n')
                      index += 1

        return vocab_file, merge_file




 def __getstate__(    self      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.__dict__.copy()
        lowerCAmelCase__  			=      None
        return state




 def __setstate__(    self      :						List[str]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      d

        try:
               import sacremoses
        except ImportError:
               raise ImportError(
                   'You need to install sacremoses to use XLMTokenizer. '
                   'See https://pypi.org/project/sacremoses/ for installation.')

        lowerCAmelCase__  			=      sacremoses
