



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json""",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   List[Any]       =       """m2m_100"""
							UpperCamelCase__					:   Union[str, Any]       =       ["""past_key_values"""]
							UpperCamelCase__					:   Any       =       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


							def __init__(				self	,	a_=128_112	,	a_=1_024	,	a_=12	,	a_=4_096	,	a_=16	,	a_=12	,	a_=4_096	,	a_=16	,	a_=0.05	,	a_=0.05	,	a_=True	,	a_=True	,	a_="relu"	,	a_=1_024	,	a_=0.1	,	a_=0.1	,	a_=0.0	,	a_=0.02	,	a_=2	,	a_=True	,	a_=1	,	a_=0	,	a_=2	,	**a_	,	):
											a__					   =		vocab_size
											a__					   =		max_position_embeddings
											a__					   =		d_model
											a__					   =		encoder_ffn_dim
											a__					   =		encoder_layers
											a__					   =		encoder_attention_heads
											a__					   =		decoder_ffn_dim
											a__					   =		decoder_layers
											a__					   =		decoder_attention_heads
											a__					   =		dropout
											a__					   =		attention_dropout
											a__					   =		activation_dropout
											a__					   =		activation_function
											a__					   =		init_std
											a__					   =		encoder_layerdrop
											a__					   =		decoder_layerdrop
											a__					   =		use_cache
											a__					   =		encoder_layers
											a__					   =		scale_embedding  # scale factor will be sqrt(d_model) if True

											super().__init__(
											    pad_token_id=a_	,	bos_token_id=a_	,	eos_token_id=a_	,	is_encoder_decoder=a_	,	decoder_start_token_id=a_	,	**a_	,	)




class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@property
							def _a   (				self		):
											a__					   =		OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											    ]		)

											if self.use_past:
															a__					   =		{0: """batch"""}
															a__					   =		{0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
															a__					   =		{0: """batch""", 1: """decoder_sequence"""}
															a__					   =		{0: """batch""", 1: """decoder_sequence"""}

											if self.use_past:
															self.fill_with_past_key_values_(a_	,	direction="""inputs"""		)
											return common_inputs


							def _a   (				self	,	a_	,	a_ = -1	,	a_ = -1	,	a_ = False	,	a_ = None	,	):
											# Copied from OnnxConfig.generate_dummy_inputs
											# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											a__					   =		compute_effective_axis_dimension(
											    a_	,	fixed_dimension=OnnxConfig.default_fixed_batch	,	num_token_to_add=0		)

											# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
											a__					   =		tokenizer.num_special_tokens_to_add(a_		)
											a__					   =		compute_effective_axis_dimension(
											    a_	,	fixed_dimension=OnnxConfig.default_fixed_sequence	,	num_token_to_add=a_		)

											# Generate dummy inputs according to compute batch and sequence
											a__					   =		[""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
											a__					   =		dict(tokenizer(a_	,	return_tensors=a_		)		)
											return common_inputs


							def _a   (				self	,	a_	,	a_ = -1	,	a_ = -1	,	a_ = False	,	a_ = None	,	):
											a__					   =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    a_	,	a_	,	a_	,	a_	,	a_		)

											# Generate decoder inputs
											a__					   =		seq_length if not self.use_past else 1
											a__					   =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    a_	,	a_	,	a_	,	a_	,	a_		)
											a__					   =		{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
											a__					   =		dict(**a_	,	**a_		)

											if self.use_past:
															if not is_torch_available():
																			raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""		)
															else:
																			import torch
															a__   ,      a__					   =		common_inputs["""input_ids"""].shape
															a__					   =		common_inputs["""decoder_input_ids"""].shape[1]
															a__   ,      a__					   =		self.num_attention_heads
															a__					   =		(
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															a__					   =		decoder_seq_length + 3
															a__					   =		(
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)

															a__					   =		torch.cat(
															    [common_inputs["""decoder_attention_mask"""], torch.ones(a_	,	a_		)]	,	dim=1		)

															a__					   =		[]
															# If the number of encoder and decoder layers are present in the model configuration, both are considered
															a__   ,      a__					   =		self.num_layers
															a__					   =		min(a_	,	a_		)
															a__					   =		max(a_	,	a_		) - min_num_layers
															a__					   =		"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

															for _ in range(a_		):
																			common_inputs["past_key_values"].append(
																			    (
																			        torch.zeros(a_		),
																			        torch.zeros(a_		),
																			        torch.zeros(a_		),
																			        torch.zeros(a_		),
																			    )		)
															# TODO: test this.
															a__					   =		encoder_shape if remaining_side_name == """encoder""" else decoder_shape
															for _ in range(a_	,	a_		):
																			common_inputs["past_key_values"].append((torch.zeros(a_		), torch.zeros(a_		))		)
											return common_inputs







							UpperCamelCase__					:   Tuple       =       _generate_dummy_inputs_for_default_and_seqaseq_lm


'''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()