





'''simple docstring'''







import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType


@require_torch
class        __a   :
							__a       : Any          =							()
							__a       : List[Any]          =							True

							def      UpperCAmelCase__						(				self				:  Union[str, Any] )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_      :   List[str]      =	self.config_class

													# test default config
													UpperCAmelCase_      :   List[Any]      =	config_class()
													self.assertIsNotNone(lowerCAmelCase__ )
													UpperCAmelCase_      :   List[str]      =	["""stem"""] + [F"""stage{idx}""" for idx in range(1							,  len(config.depths ) + 1 )]
													self.assertEqual(config.stage_names							,  lowerCAmelCase__ )
													self.assertTrue(set(config.out_features ).issubset(set(config.stage_names ) ) )

													# Test out_features and out_indices are correctly set
													# out_features and out_indices both None
													UpperCAmelCase_      :   Any      =	config_class(out_features=lowerCAmelCase__							,  out_indices=lowerCAmelCase__ )
													self.assertEqual(config.out_features							,  [config.stage_names[-1]] )
													self.assertEqual(config.out_indices							,  [len(config.stage_names ) - 1] )

													# out_features and out_indices both set
													UpperCAmelCase_      :   int      =	config_class(out_features=['''stem''', '''stage1''']							,  out_indices=[0, 1] )
													self.assertEqual(config.out_features							,  ['''stem''', '''stage1'''] )
													self.assertEqual(config.out_indices							,  [0, 1] )

													# Only out_features set
													UpperCAmelCase_      :   Dict      =	config_class(out_features=['''stage1''', '''stage3'''] )
													self.assertEqual(config.out_features							,  ['''stage1''', '''stage3'''] )
													self.assertEqual(config.out_indices							,  [1, 3] )

													# Only out_indices set
													UpperCAmelCase_      :   Tuple      =	config_class(out_indices=[0, 2] )
													self.assertEqual(config.out_features							,  [config.stage_names[0], config.stage_names[2]] )
													self.assertEqual(config.out_indices							,  [0, 2] )

													# Error raised when out_indices do not correspond to out_features
													with self.assertRaises(lowerCAmelCase__ ):
																			UpperCAmelCase_      :   List[Any]      =	config_class(out_features=['''stage1''', '''stage2''']							,  out_indices=[0, 2] )

							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			UpperCAmelCase_      :   Union[str, Any]      =	model_class(lowerCAmelCase__ )
																			UpperCAmelCase_      :   Union[str, Any]      =	inspect.signature(model.forward )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			UpperCAmelCase_      :   Tuple      =	[*signature.parameters.keys()]
																			UpperCAmelCase_      :   Any      =	["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]							,  lowerCAmelCase__ )

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							int:







													"""simple docstring"""
													UpperCAmelCase_      :   List[str]      =	self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			UpperCAmelCase_      :   int      =	model_class(lowerCAmelCase__ )
																			self.assertEqual(len(model.channels )							,  len(config.out_features ) )
																			UpperCAmelCase_      :   List[str]      =	model.num_features
																			UpperCAmelCase_      :   int      =	[config.stage_names.index(lowerCAmelCase__ ) for feat in config.out_features]
																			UpperCAmelCase_      :   List[str]      =	[num_features[idx] for idx in out_indices]
																			self.assertListEqual(model.channels							,  lowerCAmelCase__ )

																			UpperCAmelCase_      :   Any      =	copy.deepcopy(lowerCAmelCase__ )
																			UpperCAmelCase_      :   List[Any]      =	None
																			UpperCAmelCase_      :   Optional[Any]      =	model_class(lowerCAmelCase__ )
																			self.assertEqual(len(model.channels )							,  1 )
																			self.assertListEqual(model.channels							,  [num_features[-1]] )

																			UpperCAmelCase_      :   Tuple      =	copy.deepcopy(lowerCAmelCase__ )
																			UpperCAmelCase_      :   int      =	None
																			UpperCAmelCase_      :   int      =	model_class(lowerCAmelCase__ )
																			self.assertEqual(len(model.channels )							,  1 )
																			self.assertListEqual(model.channels							,  [num_features[-1]] )

							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							str:







													"""simple docstring"""
													UpperCAmelCase_      :   Any      =	self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			UpperCAmelCase_      :   List[Any]      =	model_class(lowerCAmelCase__ )
																			model.to(lowerCAmelCase__ )
																			model.eval()
																			UpperCAmelCase_      :   str      =	model(**lowerCAmelCase__ )

																			self.assertEqual(len(result.feature_maps )							,  len(config.out_features ) )
																			self.assertEqual(len(model.channels )							,  len(config.out_features ) )
																			self.assertEqual(len(result.feature_maps )							,  len(config.out_indices ) )
																			self.assertEqual(len(model.channels )							,  len(config.out_indices ) )

																			# Check output of last stage is taken if out_features=None, out_indices=None
																			UpperCAmelCase_      :   Any      =	copy.deepcopy(lowerCAmelCase__ )
																			UpperCAmelCase_      :   List[Any]      =	None
																			UpperCAmelCase_      :   Dict      =	model_class(lowerCAmelCase__ )
																			model.to(lowerCAmelCase__ )
																			model.eval()
																			UpperCAmelCase_      :   str      =	model(**lowerCAmelCase__ )

																			self.assertEqual(len(result.feature_maps )							,  1 )
																			self.assertEqual(len(model.channels )							,  1 )

																			UpperCAmelCase_      :   str      =	copy.deepcopy(lowerCAmelCase__ )
																			UpperCAmelCase_      :   Dict      =	None
																			UpperCAmelCase_      :   Tuple      =	model_class(lowerCAmelCase__ )
																			model.to(lowerCAmelCase__ )
																			model.eval()
																			UpperCAmelCase_      :   Dict      =	model(**lowerCAmelCase__ )

																			self.assertEqual(len(result.feature_maps )							,  1 )
																			self.assertEqual(len(model.channels )							,  1 )

																			# Check backbone can be initialized with fresh weights
																			UpperCAmelCase_      :   Any      =	copy.deepcopy(lowerCAmelCase__ )
																			UpperCAmelCase_      :   Optional[int]      =	False
																			UpperCAmelCase_      :   Any      =	model_class(lowerCAmelCase__ )
																			model.to(lowerCAmelCase__ )
																			model.eval()
																			UpperCAmelCase_      :   Optional[int]      =	model(**lowerCAmelCase__ )

							def      UpperCAmelCase__						(				self				:  List[str] )       ->							Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	self.model_tester.prepare_config_and_inputs_for_common()

													for backbone_class in self.all_model_classes:
																			UpperCAmelCase_      :   Optional[int]      =	backbone_class(lowerCAmelCase__ )

																			self.assertTrue(hasattr(lowerCAmelCase__							,  '''backbone_type''' ) )
																			self.assertTrue(hasattr(lowerCAmelCase__							,  '''stage_names''' ) )
																			self.assertTrue(hasattr(lowerCAmelCase__							,  '''num_features''' ) )
																			self.assertTrue(hasattr(lowerCAmelCase__							,  '''out_indices''' ) )
																			self.assertTrue(hasattr(lowerCAmelCase__							,  '''out_features''' ) )
																			self.assertTrue(hasattr(lowerCAmelCase__							,  '''out_feature_channels''' ) )
																			self.assertTrue(hasattr(lowerCAmelCase__							,  '''channels''' ) )

																			self.assertIsInstance(backbone.backbone_type							,  lowerCAmelCase__ )
																			# Verify num_features has been initialized in the backbone init
																			self.assertIsNotNone(backbone.num_features )
																			self.assertTrue(len(backbone.channels ) == len(backbone.out_indices ) )
																			self.assertTrue(len(backbone.stage_names ) == len(backbone.num_features ) )
																			self.assertTrue(len(backbone.channels ) <= len(backbone.num_features ) )
																			self.assertTrue(len(backbone.out_feature_channels ) == len(backbone.stage_names ) )



							def      UpperCAmelCase__						(				self				:  List[str] )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	self.model_tester.prepare_config_and_inputs_for_common()
													UpperCAmelCase_      :   Union[str, Any]      =	inputs_dict["""pixel_values"""].shape[0]

													for backbone_class in self.all_model_classes:
																			UpperCAmelCase_      :   str      =	backbone_class(lowerCAmelCase__ )
																			backbone.to(lowerCAmelCase__ )
																			backbone.eval()

																			UpperCAmelCase_      :   Any      =	backbone(**lowerCAmelCase__ )

																			# Test default outputs and verify feature maps
																			self.assertIsInstance(outputs.feature_maps							,  lowerCAmelCase__ )
																			self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
																			for feature_map, n_channels in zip(outputs.feature_maps							,  backbone.channels ):
																									self.assertTrue(feature_map.shape[:2]							,  (batch_size, n_channels) )
																			self.assertIsNone(outputs.hidden_states )
																			self.assertIsNone(outputs.attentions )

																			# Test output_hidden_states=True
																			UpperCAmelCase_      :   Any      =	backbone(**lowerCAmelCase__							,  output_hidden_states=lowerCAmelCase__ )
																			self.assertIsNotNone(outputs.hidden_states )
																			self.assertTrue(len(outputs.hidden_states )							,  len(backbone.stage_names ) )
																			for hidden_state, n_channels in zip(outputs.hidden_states							,  backbone.channels ):
																									self.assertTrue(hidden_state.shape[:2]							,  (batch_size, n_channels) )

																			# Test output_attentions=True
																			if self.has_attentions:
																									UpperCAmelCase_      :   int      =	backbone(**lowerCAmelCase__							,  output_attentions=lowerCAmelCase__ )
																									self.assertIsNotNone(outputs.attentions )






'''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


_lowerCamelCase :				List[Any]														= """1.5.0"""

_lowerCamelCase :				List[Any]														= ["""vision-encoder-decoder"""]



def  _lowerCAmelCase				(				__a      )  ->      Any:
     '''simple docstring'''







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

          _UpperCamelCase  :Dict       =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]"""      )
     _UpperCamelCase  :Tuple       =[
         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}''',
     ]
     _UpperCamelCase  :Union[str, Any]       =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  _lowerCAmelCase				(				__a      )  ->      Optional[int]:
     '''simple docstring'''







     _UpperCamelCase  :Optional[int]       =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
     _UpperCamelCase  :Dict       =FeaturesManager.get_model_from_feature(
         args.feature			,      args.model			,      framework=args.framework			,      cache_dir=args.cache_dir      )

     _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =FeaturesManager.check_supported_model_or_raise(__a			,      feature=args.feature      )
     _UpperCamelCase  :int       =model_onnx_config(model.config      )

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

          _UpperCamelCase  :Dict       =onnx_config.get_encoder_config(encoder_model.config      )
          _UpperCamelCase  :str       =onnx_config.get_decoder_config(
              encoder_model.config			,      decoder_model.config			,      feature=args.feature      )

          if args.opset is None:
               _UpperCamelCase  :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.'''      )

          _UpperCamelCase  :Optional[Any]       =AutoFeatureExtractor.from_pretrained(args.model      )

          _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =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			,      )

          _UpperCamelCase  :str       =AutoTokenizer.from_pretrained(args.model      )

          _UpperCamelCase       ,      _UpperCamelCase  :str       =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":
               _UpperCamelCase  :Union[str, Any]       =get_preprocessor(args.model      )
          elif args.preprocessor == "tokenizer":
               _UpperCamelCase  :Dict       =AutoTokenizer.from_pretrained(args.model      )
          elif args.preprocessor == "image_processor":
               _UpperCamelCase  :Union[str, Any]       =AutoImageProcessor.from_pretrained(args.model      )
          elif args.preprocessor == "feature_extractor":
               _UpperCamelCase  :int       =AutoFeatureExtractor.from_pretrained(args.model      )
          elif args.preprocessor == "processor":
               _UpperCamelCase  :Dict       =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:
               _UpperCamelCase  :Optional[int]       =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.'''      )

          _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =export(
              __a			,      __a			,      __a			,      args.opset			,      args.output			,      )

          if args.atol is None:
               _UpperCamelCase  :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  _lowerCAmelCase				(				)  ->      List[str]:
     '''simple docstring'''







     _UpperCamelCase  :Union[str, 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."""
         )			,      )

     _UpperCamelCase  :str       =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__":
      _lowerCamelCase :				Optional[int]														= logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
      logger.setLevel(logging.INFO)
      main()