




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


SCREAMING_SNAKE_CASE:    Tuple											=   '''1.5.0'''

SCREAMING_SNAKE_CASE:    int											=   ['''vision-encoder-decoder''']


def 				_a       (				lowerCAmelCase   )->  Dict:
  if is_optimum_available():
    from optimum.version import __version__ as optimum_version

    SCREAMING_SNAKE_CASE_							=       version.parse(lowercase__   )
    if parsed_optimum_version < version.parse(lowercase__   ):
      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]'   )
  SCREAMING_SNAKE_CASE_							=       [
      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}''',
  ]
  SCREAMING_SNAKE_CASE_							=       subprocess.Popen(' '.join(lowercase__   )						,	stdout=subprocess.PIPE						,	shell=lowercase__   )
  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 				_a       (				lowerCAmelCase   )->  Optional[Any]:
  SCREAMING_SNAKE_CASE_							=       args.output if args.output.is_file() else args.output.joinpath('model.onnx'   )
  if not args.output.parent.exists():
    args.output.parent.mkdir(parents=lowercase__   )

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

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

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

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

    if args.opset is None:
      SCREAMING_SNAKE_CASE_							=       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.'''   )

    SCREAMING_SNAKE_CASE_							=       AutoFeatureExtractor.from_pretrained(args.model   )

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

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

    SCREAMING_SNAKE_CASE_							=       AutoTokenizer.from_pretrained(args.model   )

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

    validate_model_outputs(
        lowercase__						,	lowercase__						,	lowercase__						,	args.output.parent.joinpath('decoder_model.onnx'   )						,	lowercase__						,	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":
      SCREAMING_SNAKE_CASE_							=       get_preprocessor(args.model   )
    elif args.preprocessor == "tokenizer":
      SCREAMING_SNAKE_CASE_							=       AutoTokenizer.from_pretrained(args.model   )
    elif args.preprocessor == "image_processor":
      SCREAMING_SNAKE_CASE_							=       AutoImageProcessor.from_pretrained(args.model   )
    elif args.preprocessor == "feature_extractor":
      SCREAMING_SNAKE_CASE_							=       AutoFeatureExtractor.from_pretrained(args.model   )
    elif args.preprocessor == "processor":
      SCREAMING_SNAKE_CASE_							=       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:
      SCREAMING_SNAKE_CASE_							=       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.'''   )

    SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       export(
        lowercase__						,	lowercase__						,	lowercase__						,	args.opset						,	args.output						,	)

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

    validate_model_outputs(lowercase__						,	lowercase__						,	lowercase__						,	args.output						,	lowercase__						,	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.'						,	lowercase__						,	)


def 				_a       (				)->  Optional[Any]:
  SCREAMING_SNAKE_CASE_							=       ArgumentParser('Hugging Face Transformers ONNX exporter'   )
  parser.add_argument(
      '-m'						,	'--model'						,	type=lowercase__						,	required=lowercase__						,	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=lowercase__						,	default=lowercase__						,	help='ONNX opset version to export the model with.'   )
  parser.add_argument(
      '--atol'						,	type=lowercase__						,	default=lowercase__						,	help='Absolute difference tolerance when validating the model.'   )
  parser.add_argument(
      '--framework'						,	type=lowercase__						,	choices=['pt', 'tf']						,	default=lowercase__						,	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=lowercase__						,	help='Path indicating where to store generated ONNX model.'   )
  parser.add_argument('--cache_dir'						,	type=lowercase__						,	default=lowercase__						,	help='Path indicating where to store cache.'   )
  parser.add_argument(
      '--preprocessor'						,	type=lowercase__						,	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.'
      )						,	)

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


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





from __future__ import annotations

import random
import string



class       lowercase_		:



						def __init__( self  :      str				,       snake_case__  :      str | None = None     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       passcode or self.__passcode_creator()
								SCREAMING_SNAKE_CASE_							=       self.__make_key_list()
								SCREAMING_SNAKE_CASE_							=       self.__make_shift_key()



						def __str__( self  :      List[Any]     ):

								"""simple docstring"""

								return "".join(self.__passcode     )



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      list[int]     ):

								"""simple docstring"""

								for i in range(1				,       len(snake_case__     )				,       2     ):
										iterlist[i] *= -1
								return iterlist



						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       string.ascii_letters + string.digits
								SCREAMING_SNAKE_CASE_							=       [random.choice(snake_case__     ) for _ in range(random.randint(10				,       20     )     )]
								return password



						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       (
								    string.ascii_letters + string.digits + string.punctuation + ' \t\n'
								)

								SCREAMING_SNAKE_CASE_							=       []

								# creates points known as breakpoints to break the key_list_options at those
								# points and pivot each substring
								SCREAMING_SNAKE_CASE_							=       sorted(set(self.__passcode     )     )
								SCREAMING_SNAKE_CASE_							=       []

								# algorithm for creating a new shuffled list, keys_l, out of key_list_options
								for i in key_list_options:
										temp_list.extend(snake_case__     )

										# checking breakpoints at which to pivot temporary sublist and add it into
										# keys_l
										if i in breakpoints or i == key_list_options[-1]:
												keys_l.extend(temp_list[::-1]     )
												temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
								return keys_l



						def   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       sum(self.__neg_pos([ord(snake_case__     ) for x in self.__passcode]     )     )
								return num if num > 0 else len(self.__passcode     )



						def   __a				( self  :      str				,       snake_case__  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ''

								# decoding shift like Caesar cipher algorithm implementing negative shift or
								# reverse shift or left shift
								for i in encoded_message:
										SCREAMING_SNAKE_CASE_							=       self.__key_list.index(snake_case__     )
										decoded_message += self.__key_list[
										    (position - self.__shift_key) % -len(self.__key_list     )
										]

								return decoded_message






						def   __a				( self  :      Optional[int]				,       snake_case__  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ''

								# encoding shift like Caesar cipher algorithm implementing positive shift or
								# forward shift or right shift
								for i in plaintext:
										SCREAMING_SNAKE_CASE_							=       self.__key_list.index(snake_case__     )
										encoded_message += self.__key_list[
										    (position + self.__shift_key) % len(self.__key_list     )
										]

								return encoded_message


def 				_a       (				lowerCAmelCase = "Hello, this is a modified Caesar cipher"   )->  str:
		SCREAMING_SNAKE_CASE_							=       ShuffledShiftCipher()
		return cipa.decrypt(cipa.encrypt(lowerCAmelCase   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()