

import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCAmelCase__	:       Union[str, Any]			      =      [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-generation""",
        """text-classification""",
        """token-classification""",
        """language-modeling""",
        """multiple-choice""",
        """question-answering""",
        """summarization""",
        """translation""",
        """image-classification""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
  import run_clm
  import run_image_classification
  import run_mlm
  import run_ner
  import run_qa as run_squad
  import run_summarization
  import run_swag
  import run_text_classification
  import run_translation


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase__	:       Union[str, Any]			      =      logging.getLogger()






def    __lowercase (       )       ->  List[str]:
      SCREAMING_SNAKE_CASE	: int					       =				argparse.ArgumentParser()
      parser.add_argument("""-f"""     )
      SCREAMING_SNAKE_CASE	: Dict					       =				parser.parse_args()
      return args.f






def    __lowercase (       _A     )       ->  str:
      SCREAMING_SNAKE_CASE	: Tuple					       =				{}
      SCREAMING_SNAKE_CASE	: List[Any]					       =				os.path.join(_A  ,      """all_results.json"""     )
      if os.path.exists(_A     ):
            with open(_A  ,      """r"""     ) as f:
                  SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				json.load(_A     )
      else:
            raise ValueError(F"can't find {path}"     )
      return results






def    __lowercase (       )       ->  Any:
      return bool(tf.config.list_physical_devices("""GPU"""     )     )


UpperCAmelCase__	:       Optional[int]			      =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class     a__    (				UpperCAmelCase    ):







  """simple docstring"""




  @skip("""Skipping until shape inference for to_tf_dataset PR is merged."""						)
  def 	_lowercase (     self      :	Optional[int]						)   ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[Any]					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: Any					       =				f"\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

        if is_cuda_available():
              testargs.append("""--fp16"""						)

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_text_classification.main()
              # Reset the mixed precision policy so we don't break other tests
              tf.keras.mixed_precision.set_global_policy("""float32"""						)
              SCREAMING_SNAKE_CASE	: int					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""eval_accuracy"""]			,			0.75						)


  def 	_lowercase (     self      :	Optional[Any]						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: int					       =				f"\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

        if len(tf.config.list_physical_devices("""GPU"""						)						) > 1:
              # Skipping because there are not enough batches to train the model + would need a drop_last to work.
              return

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_clm.main()
              SCREAMING_SNAKE_CASE	: Tuple					       =				get_results(UpperCAmelCase__						)
              self.assertLess(result["""eval_perplexity"""]			,			1_0_0						)


  def 	_lowercase (     self      :	Union[str, Any]						)   ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: str					       =				f"\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        ".split()

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_mlm.main()
              SCREAMING_SNAKE_CASE	: Dict					       =				get_results(UpperCAmelCase__						)
              self.assertLess(result["""eval_perplexity"""]			,			4_2						)


  def 	_lowercase (     self      :	Optional[Any]						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				7 if get_gpu_count() > 1 else 2

        SCREAMING_SNAKE_CASE	: str					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: Tuple					       =				f"\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        ".split()

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_ner.main()
              SCREAMING_SNAKE_CASE	: int					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""accuracy"""]			,			0.75						)


  def 	_lowercase (     self      :	Tuple						)   ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Dict					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				f"\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_squad.main()
              SCREAMING_SNAKE_CASE	: int					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""f1"""]			,			3_0						)
              self.assertGreaterEqual(result["""exact"""]			,			3_0						)


  def 	_lowercase (     self      :	Any						)   ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: List[Any]					       =				f"\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_swag.main()
              SCREAMING_SNAKE_CASE	: str					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""val_accuracy"""]			,			0.8						)


  @slow
  def 	_lowercase (     self      :	Any						)   ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: str					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				f"\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_summarization.main()
              SCREAMING_SNAKE_CASE	: Any					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""rouge1"""]			,			1_0						)
              self.assertGreaterEqual(result["""rouge2"""]			,			2						)
              self.assertGreaterEqual(result["""rougeL"""]			,			7						)
              self.assertGreaterEqual(result["""rougeLsum"""]			,			7						)


  @slow
  def 	_lowercase (     self      :	List[str]						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: Any					       =				f"\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        ".split()

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_translation.main()
              SCREAMING_SNAKE_CASE	: Dict					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""bleu"""]			,			3_0						)




  def 	_lowercase (     self      :	Optional[int]						)   ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				f"\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            ".split()

        with patch.object(UpperCAmelCase__			,			"""argv"""			,			UpperCAmelCase__						):
              run_image_classification.main()
              SCREAMING_SNAKE_CASE	: List[Any]					       =				get_results(UpperCAmelCase__						)
              self.assertGreaterEqual(result["""accuracy"""]			,			0.7						)







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


UpperCAmelCase__	:       int			      =      """1.5.0"""

UpperCAmelCase__	:       Any			      =      ["""vision-encoder-decoder"""]






def    __lowercase (       _A     )       ->  Any:
      if is_optimum_available():
            from optimum.version import __version__ as optimum_version

            SCREAMING_SNAKE_CASE	: int					       =				version.parse(_A     )
            if parsed_optimum_version < version.parse(_A     ):
                  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	: List[str]					       =				[
          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	: Dict					       =				subprocess.Popen(""" """.join(_A     )  ,      stdout=subprocess.PIPE  ,      shell=_A     )
      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 (       _A     )       ->  Any:
      SCREAMING_SNAKE_CASE	: List[str]					       =				args.output if args.output.is_file() else args.output.joinpath("""model.onnx"""     )
      if not args.output.parent.exists():
            args.output.parent.mkdir(parents=_A     )

      # Allocate the model
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				FeaturesManager.get_model_from_feature(
          args.feature  ,      args.model  ,      framework=args.framework  ,      cache_dir=args.cache_dir     )

      SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Any					       =				FeaturesManager.check_supported_model_or_raise(_A  ,      feature=args.feature     )
      SCREAMING_SNAKE_CASE	: str					       =				model_onnx_config(model.config     )

      if model_kind in ENCODER_DECODER_MODELS:
            SCREAMING_SNAKE_CASE	: str					       =				model.get_encoder()
            SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				model.get_decoder()

            SCREAMING_SNAKE_CASE	: Optional[int]					       =				onnx_config.get_encoder_config(encoder_model.config     )
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				onnx_config.get_decoder_config(
                encoder_model.config  ,      decoder_model.config  ,      feature=args.feature     )

            if args.opset is None:
                  SCREAMING_SNAKE_CASE	: Optional[int]					       =				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	: List[str]					       =				AutoFeatureExtractor.from_pretrained(args.model     )

            SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Optional[int]					       =				export(
                _A  ,      _A  ,      _A  ,      args.opset  ,      args.output.parent.joinpath("""encoder_model.onnx"""     )  ,      )

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

            SCREAMING_SNAKE_CASE	: Optional[int]					       =				AutoTokenizer.from_pretrained(args.model     )

            SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: List[Any]					       =				export(
                _A  ,      _A  ,      _A  ,      args.opset  ,      args.output.parent.joinpath("""decoder_model.onnx"""     )  ,      )

            validate_model_outputs(
                _A  ,      _A  ,      _A  ,      args.output.parent.joinpath("""decoder_model.onnx"""     )  ,      _A  ,      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	: List[Any]					       =				get_preprocessor(args.model     )
            elif args.preprocessor == "tokenizer":
                  SCREAMING_SNAKE_CASE	: Optional[Any]					       =				AutoTokenizer.from_pretrained(args.model     )
            elif args.preprocessor == "image_processor":
                  SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				AutoImageProcessor.from_pretrained(args.model     )
            elif args.preprocessor == "feature_extractor":
                  SCREAMING_SNAKE_CASE	: int					       =				AutoFeatureExtractor.from_pretrained(args.model     )
            elif args.preprocessor == "processor":
                  SCREAMING_SNAKE_CASE	: List[Any]					       =				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	: Dict					       =				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	: Optional[Any]					       =				export(
                _A  ,      _A  ,      _A  ,      args.opset  ,      args.output  ,      )

            if args.atol is None:
                  SCREAMING_SNAKE_CASE	: List[Any]					       =				onnx_config.atol_for_validation

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






def    __lowercase (       )       ->  List[str]:
      SCREAMING_SNAKE_CASE	: List[Any]					       =				ArgumentParser("""Hugging Face Transformers ONNX exporter"""     )
      parser.add_argument(
          """-m"""  ,      """--model"""  ,      type=_A  ,      required=_A  ,      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=_A  ,      default=_A  ,      help="""ONNX opset version to export the model with."""     )
      parser.add_argument(
          """--atol"""  ,      type=_A  ,      default=_A  ,      help="""Absolute difference tolerance when validating the model."""     )
      parser.add_argument(
          """--framework"""  ,      type=_A  ,      choices=["""pt""", """tf"""]  ,      default=_A  ,      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=_A  ,      help="""Path indicating where to store generated ONNX model."""     )
      parser.add_argument("""--cache_dir"""  ,      type=_A  ,      default=_A  ,      help="""Path indicating where to store cache."""     )
      parser.add_argument(
          """--preprocessor"""  ,      type=_A  ,      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	: Tuple					       =				parser.parse_args()
      if args.export_with_transformers or not is_optimum_available():
            export_with_transformers(_A     )
      else:
            export_with_optimum(_A     )


if __name__ == "__main__":
  UpperCAmelCase__	:       Optional[Any]			      =      logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
  logger.setLevel(logging.INFO)
  main()




