





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_		  =			"""1.5.0"""

SCREAMING_SNAKE_CASE_		  =			["""vision-encoder-decoder"""]



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  Any:


      '''simple docstring'''


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

            SCREAMING_SNAKE_CASE						=		version.parse(_SCREAMING_SNAKE_CASE      )
            if parsed_optimum_version < version.parse(_SCREAMING_SNAKE_CASE      ):
                  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(_SCREAMING_SNAKE_CASE      )					,		stdout=subprocess.PIPE					,		shell=_SCREAMING_SNAKE_CASE      )
      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   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  Optional[int]:


      '''simple docstring'''


      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=_SCREAMING_SNAKE_CASE      )

      # 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(_SCREAMING_SNAKE_CASE					,		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(
                _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		args.opset					,		args.output.parent.joinpath("""encoder_model.onnx"""      )					,		)

            validate_model_outputs(
                _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		args.output.parent.joinpath("""encoder_model.onnx"""      )					,		_SCREAMING_SNAKE_CASE					,		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(
                _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		args.opset					,		args.output.parent.joinpath("""decoder_model.onnx"""      )					,		)

            validate_model_outputs(
                _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		args.output.parent.joinpath("""decoder_model.onnx"""      )					,		_SCREAMING_SNAKE_CASE					,		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(
                _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		args.opset					,		args.output					,		)

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

            validate_model_outputs(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		args.output					,		_SCREAMING_SNAKE_CASE					,		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."""					,		_SCREAMING_SNAKE_CASE					,		)



def   __lowercase  ( )					->  Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		ArgumentParser("""Hugging Face Transformers ONNX exporter"""      )
      parser.add_argument(
          """-m"""					,		"""--model"""					,		type=_SCREAMING_SNAKE_CASE					,		required=_SCREAMING_SNAKE_CASE					,		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=_SCREAMING_SNAKE_CASE					,		default=_SCREAMING_SNAKE_CASE					,		help="""ONNX opset version to export the model with."""      )
      parser.add_argument(
          """--atol"""					,		type=_SCREAMING_SNAKE_CASE					,		default=_SCREAMING_SNAKE_CASE					,		help="""Absolute difference tolerance when validating the model."""      )
      parser.add_argument(
          """--framework"""					,		type=_SCREAMING_SNAKE_CASE					,		choices=["""pt""", """tf"""]					,		default=_SCREAMING_SNAKE_CASE					,		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=_SCREAMING_SNAKE_CASE					,		help="""Path indicating where to store generated ONNX model."""      )
      parser.add_argument("""--cache_dir"""					,		type=_SCREAMING_SNAKE_CASE					,		default=_SCREAMING_SNAKE_CASE					,		help="""Path indicating where to store cache."""      )
      parser.add_argument(
          """--preprocessor"""					,		type=_SCREAMING_SNAKE_CASE					,		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(_SCREAMING_SNAKE_CASE      )
      else:
            export_with_optimum(_SCREAMING_SNAKE_CASE      )


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









import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						UpperCamelCase__					( lowerCAmelCase_				, unittest.TestCase  ):



     '''simple docstring'''

     __snake_case       :      Dict						      =     PreTrainedTokenizerFast
     __snake_case       :      Optional[int]						      =     False
     __snake_case       :      List[Any]						      =     True
     __snake_case       :      str						      =     "tokenizer_file"






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		False  # because we don't have pretrained_vocab_files_map
           super().setUp()
           SCREAMING_SNAKE_CASE						=		True

           SCREAMING_SNAKE_CASE						=		["""robot-test/dummy-tokenizer-fast""", """robot-test/dummy-tokenizer-wordlevel"""]
           SCREAMING_SNAKE_CASE						=		"""SaulLu/dummy-tokenizer-bytelevel-bpe"""

           # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
           SCREAMING_SNAKE_CASE						=		[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

           SCREAMING_SNAKE_CASE						=		PreTrainedTokenizerFast.from_pretrained(model_paths[0]			)
           tokenizer.save_pretrained(self.tmpdirname			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Tuple:




           '''simple docstring'''


           pass






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						List[Any]:




           '''simple docstring'''


           pass






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Union[str, Any]:




           '''simple docstring'''


           pass






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						List[Any]:




           '''simple docstring'''


           pass






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.tmpdirname
           # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
                       try:
                             SCREAMING_SNAKE_CASE						=		tempfile.mkdtemp()
                             SCREAMING_SNAKE_CASE						=		self.rust_tokenizer_class.from_pretrained(lowerCamelCase__		,**lowerCamelCase__			)

                             tokenizer.save_pretrained(self.tmpdirname			)
                             super().test_training_new_tokenizer()
                       finally:
                             # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                             # is restored
                             shutil.rmtree(self.tmpdirname			)
                             SCREAMING_SNAKE_CASE						=		tmpdirname_orig






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.tmpdirname
           # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
                       try:
                             SCREAMING_SNAKE_CASE						=		tempfile.mkdtemp()
                             SCREAMING_SNAKE_CASE						=		self.rust_tokenizer_class.from_pretrained(lowerCamelCase__		,**lowerCamelCase__			)

                             tokenizer.save_pretrained(self.tmpdirname			)
                             super().test_training_new_tokenizer_with_special_tokens_change()
                       finally:
                             # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                             # is restored
                             shutil.rmtree(self.tmpdirname			)
                             SCREAMING_SNAKE_CASE						=		tmpdirname_orig






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name			)

           SCREAMING_SNAKE_CASE						=		("""a""" for _ in range(1000			))
           SCREAMING_SNAKE_CASE						=		tokenizer.train_new_from_iterator(text_iterator=lowerCamelCase__		,length=1000		,vocab_size=50			)

           SCREAMING_SNAKE_CASE						=		new_tokenizer.encode("""a🤗"""			)
           self.assertEqual(lowerCamelCase__		,[64, 172, 253, 97, 245]			)

@require_tokenizers
class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		AutoTokenizer.from_pretrained("""bert-base-cased"""			)
           SCREAMING_SNAKE_CASE						=		json.loads(tokenizer._tokenizer.to_str()			)
           SCREAMING_SNAKE_CASE						=		len(lowerCamelCase__			)

           with tempfile.TemporaryDirectory() as tmp_dir:
                 # Hack to save this in the tokenizer_config.json
                 SCREAMING_SNAKE_CASE						=		["""tokenizer.4.0.0.json"""]
                 tokenizer.save_pretrained(lowerCamelCase__			)
                 json.dump(lowerCamelCase__		,open(os.path.join(lowerCamelCase__		,"""tokenizer.4.0.0.json"""			)		,"""w"""			)			)

                 # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
                 SCREAMING_SNAKE_CASE						=		AutoTokenizer.from_pretrained(lowerCamelCase__			)
                 self.assertEqual(len(lowerCamelCase__			)		,len(lowerCamelCase__			) + 1			)
                 SCREAMING_SNAKE_CASE						=		json.loads(new_tokenizer._tokenizer.to_str()			)
                 self.assertIn("""huggingface"""		,json_tokenizer["""model"""]["""vocab"""]			)

                 # Will need to be adjusted if we reach v42 and this test is still here.
                 # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
                 shutil.move(os.path.join(lowerCamelCase__		,"""tokenizer.4.0.0.json"""			)		,os.path.join(lowerCamelCase__		,"""tokenizer.42.0.0.json"""			)			)
                 SCREAMING_SNAKE_CASE						=		["""tokenizer.42.0.0.json"""]
                 tokenizer.save_pretrained(lowerCamelCase__			)
                 SCREAMING_SNAKE_CASE						=		AutoTokenizer.from_pretrained(lowerCamelCase__			)
                 self.assertEqual(len(lowerCamelCase__			)		,len(lowerCamelCase__			)			)
                 SCREAMING_SNAKE_CASE						=		json.loads(new_tokenizer._tokenizer.to_str()			)
                 self.assertNotIn("""huggingface"""		,json_tokenizer["""model"""]["""vocab"""]			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		"""hf-internal-testing/test-two-tokenizers"""

           # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
           SCREAMING_SNAKE_CASE						=		AutoTokenizer.from_pretrained(lowerCamelCase__			)
           self.assertEqual(len(lowerCamelCase__			)		,28997			)
           SCREAMING_SNAKE_CASE						=		json.loads(tokenizer._tokenizer.to_str()			)
           self.assertIn("""huggingface"""		,json_tokenizer["""model"""]["""vocab"""]			)

           # Testing an older version by monkey-patching the version in the module it's used.
           import transformers as old_transformers

           SCREAMING_SNAKE_CASE						=		"""3.0.0"""
           SCREAMING_SNAKE_CASE						=		old_transformers.models.auto.AutoTokenizer.from_pretrained(lowerCamelCase__			)
           self.assertEqual(len(lowerCamelCase__			)		,28996			)
           SCREAMING_SNAKE_CASE						=		json.loads(old_tokenizer._tokenizer.to_str()			)
           self.assertNotIn("""huggingface"""		,json_tokenizer["""model"""]["""vocab"""]			)

@require_tokenizers
class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		PreTrainedTokenizerFast.from_pretrained("""robot-test/dummy-tokenizer-wordlevel"""			)
           SCREAMING_SNAKE_CASE						=		"""The Matrix is a 1999 science fiction action film."""

           with concurrent.futures.ThreadPoolExecutor() as executor:
                 SCREAMING_SNAKE_CASE						=		[executor.submit(self.fetch		,lowerCamelCase__		,lowerCamelCase__			) for i in range(10			)]
                 SCREAMING_SNAKE_CASE						=		[future.result() for future in futures]
                 self.assertEqual(lowerCamelCase__		,[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10			)]			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       str			)	->						Union[str, Any]:




           '''simple docstring'''


           return tokenizer.encode(lowerCamelCase__		,truncation="""longest_first"""		,padding="""longest"""			)


