




'''simple docstring'''





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_		:    Optional[Any]									=		["""vision-encoder-decoder"""]





def 			__a			(     _UpperCamelCase:					int					)		->				Optional[Any]:

     """simple docstring"""




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

          _snake_case							=   version.parse(_UpperCamelCase					)
          if parsed_optimum_version < version.parse(_UpperCamelCase					):
               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]"					)
     _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}""",
     ]
     _snake_case							=   subprocess.Popen(" ".join(_UpperCamelCase					)      ,		stdout=subprocess.PIPE      ,		shell=_UpperCamelCase					)
     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			(     _UpperCamelCase:					Dict					)		->				Any:

     """simple docstring"""




     _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=_UpperCamelCase					)

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

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

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

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

          if args.opset is None:
               _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."""					)

          _snake_case							=   AutoFeatureExtractor.from_pretrained(args.model					)

          _snake_case							=   export(
              _UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		args.opset      ,		args.output.parent.joinpath("encoder_model.onnx"					)      ,		)

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

          _snake_case							=   AutoTokenizer.from_pretrained(args.model					)

          _snake_case							=   export(
              _UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		args.opset      ,		args.output.parent.joinpath("decoder_model.onnx"					)      ,		)

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

          _snake_case							=   export(
              _UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		args.opset      ,		args.output      ,		)

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

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





def 			__a			(     )		->				int:

     """simple docstring"""




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

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


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








'''simple docstring'''





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union

class      _a       (		enum.Enum							):
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		"""timm"""
     SCREAMING_SNAKE_CASE_							:					int					       =		"""transformers"""







def 			__a			(     _UpperCamelCase:					Optional[Iterable[str]]      ,		_UpperCamelCase:					Optional[Iterable[int]]      ,		_UpperCamelCase:					Optional[Iterable[str]]					)		->				Union[str, Any]:

     """simple docstring"""




     if stage_names is None:
          raise ValueError("Stage_names must be set for transformers backbones"					)

     if out_features is not None:
          if not isinstance(_UpperCamelCase      ,		(list,)					):
               raise ValueError(F"""out_features must be a list {type(_UpperCamelCase					)}"""					)
          if any(feat not in stage_names for feat in out_features					):
               raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}"""					)

     if out_indices is not None:
          if not isinstance(_UpperCamelCase      ,		(list, tuple)					):
               raise ValueError(F"""out_indices must be a list or tuple, got {type(_UpperCamelCase					)}"""					)
          if any(idx >= len(_UpperCamelCase					) for idx in out_indices					):
               raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"					)

     if out_features is not None and out_indices is not None:
          if len(_UpperCamelCase					) != len(_UpperCamelCase					):
               raise ValueError("out_features and out_indices should have the same length if both are set"					)
          if out_features != [stage_names[idx] for idx in out_indices]:
               raise ValueError("out_features and out_indices should correspond to the same stages if both are set"					)





def 			__a			(     _UpperCamelCase:					Optional[List[str]]      ,		_UpperCamelCase:					Optional[Union[List[int], Tuple[int]]]      ,		_UpperCamelCase:					List[str]      ,		)		->				Any:

     """simple docstring"""




     if out_indices is None and out_features is None:
          _snake_case							=   [len(_UpperCamelCase					) - 1]
          _snake_case							=   [stage_names[-1]]
     elif out_indices is None and out_features is not None:
          _snake_case							=   [stage_names.index(_UpperCamelCase					) for layer in out_features]
     elif out_features is None and out_indices is not None:
          _snake_case							=   [stage_names[idx] for idx in out_indices]
     return out_features, out_indices





def 			__a			(     _UpperCamelCase:					Optional[List[str]]      ,		_UpperCamelCase:					Optional[Union[List[int], Tuple[int]]]      ,		_UpperCamelCase:					List[str]      ,		)		->				Tuple[List[str], List[int]]:

     """simple docstring"""




     verify_out_features_out_indices(out_features=_UpperCamelCase      ,		out_indices=_UpperCamelCase      ,		stage_names=_UpperCamelCase					)
     _snake_case							,      _snake_case							=   _align_output_features_output_indices(
         out_features=_UpperCamelCase      ,		out_indices=_UpperCamelCase      ,		stage_names=_UpperCamelCase					)
     # Verify that the aligned out_features and out_indices are valid
     verify_out_features_out_indices(out_features=_UpperCamelCase      ,		out_indices=_UpperCamelCase      ,		stage_names=_UpperCamelCase					)
     return output_features, output_indices

class      _a       :
     SCREAMING_SNAKE_CASE_							:					Optional[BackboneType]					       =		None






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          if getattr(self      ,"_backbone"      ,_SCREAMING_SNAKE_CASE					) is None:
               raise ValueError("self._backbone must be set before calling _init_timm_backbone"					)

          # These will diagree with the defaults for the transformers models e.g. for resnet50
          # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
          # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
          _snake_case							=   [stage["module"] for stage in self._backbone.feature_info.info]
          _snake_case							=   [stage["num_chs"] for stage in self._backbone.feature_info.info]
          _snake_case							=   self._backbone.feature_info.out_indices
          _snake_case							=   self._backbone.feature_info.module_name()

          # We verify the out indices and out features are valid
          verify_out_features_out_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)
          _snake_case							,      _snake_case							=   out_features, out_indices






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"stage_names"					)
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"out_features"      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"out_indices"      ,_SCREAMING_SNAKE_CASE					)

          _snake_case							=   stage_names
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=_SCREAMING_SNAKE_CASE					)
          # Number of channels for each stage. This is set in the transformer backbone model init
          _snake_case							=   None






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          _snake_case							=   config

          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"use_timm_backbone"      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

          if self.backbone_type == BackboneType.TIMM:
               self._init_timm_backbone(_SCREAMING_SNAKE_CASE					)
          elif self.backbone_type == BackboneType.TRANSFORMERS:
               self._init_transformers_backbone(_SCREAMING_SNAKE_CASE					)
          else:
               raise ValueError(f"""backbone_type {self.backbone_type} not supported."""					)






     @property
     def        _lowercase			(				self					)							->    int:
          return self._out_features






     @out_features.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    Any:
          return self._out_indices






     @out_indices.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    int:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    List[str]:
          # the current backbones will output the number of channels for each stage
          # even if that stage is not in the out_features list.
          return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names					)}






     @property
     def        _lowercase			(				self					)							->    Any:
          return [self.out_feature_channels[name] for name in self.out_features]






     def        _lowercase			(				self      ,*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							=   dict(inspect.signature(self.forward					).parameters					)
          _snake_case							=   {k: v for k, v in kwargs.items() if k in signature}
          return self(*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,)							->    Any:
          raise NotImplementedError("This method should be implemented by the derived class."					)






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							=   super().to_dict()
          _snake_case							=   output.pop("_out_features"					)
          _snake_case							=   output.pop("_out_indices"					)
          return output







class      _a       :






     @property
     def        _lowercase			(				self					)							->    List[str]:
          return self._out_features






     @out_features.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    Any:
          return self._out_indices






     @out_indices.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    Optional[Any]:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   super().to_dict()
          _snake_case							=   output.pop("_out_features"					)
          _snake_case							=   output.pop("_out_indices"					)
          return output


