'''simple docstring'''

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
      import torch




class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=14					,      lowerCAmelCase__=7					,      lowerCAmelCase__=True					,      lowerCAmelCase__=True					,      lowerCAmelCase__=False					,      lowerCAmelCase__=True					,      lowerCAmelCase__=99					,      lowerCAmelCase__=32					,      lowerCAmelCase__=5					,      lowerCAmelCase__=4					,      lowerCAmelCase__=37					,      lowerCAmelCase__="gelu"					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=512					,      lowerCAmelCase__=0.02					,      )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :int       =parent
        _UpperCamelCase  :int       =batch_size
        _UpperCamelCase  :str       =seq_length
        _UpperCamelCase  :int       =is_training
        _UpperCamelCase  :Dict       =use_input_mask
        _UpperCamelCase  :List[str]       =use_token_type_ids
        _UpperCamelCase  :int       =use_labels
        _UpperCamelCase  :Tuple       =vocab_size
        _UpperCamelCase  :Optional[Any]       =hidden_size
        _UpperCamelCase  :str       =num_hidden_layers
        _UpperCamelCase  :List[str]       =num_attention_heads
        _UpperCamelCase  :List[str]       =intermediate_size
        _UpperCamelCase  :Any       =hidden_act
        _UpperCamelCase  :Optional[Any]       =hidden_dropout_prob
        _UpperCamelCase  :int       =attention_probs_dropout_prob
        _UpperCamelCase  :str       =max_position_embeddings
        _UpperCamelCase  :Any       =initializer_range
        _UpperCamelCase  :Optional[Any]       =None
        _UpperCamelCase  :Optional[Any]       =vocab_size - 1
        _UpperCamelCase  :Optional[Any]       =vocab_size - 1
        _UpperCamelCase  :Tuple       =vocab_size - 1



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :Dict       =ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size     )

        _UpperCamelCase  :Optional[int]       =None
        if self.use_input_mask:
             _UpperCamelCase  :List[Any]       =random_attention_mask([self.batch_size, self.seq_length]     )

        _UpperCamelCase  :Optional[Any]       =GPTaConfig(
            vocab_size=self.vocab_size					,      n_embd=self.hidden_size					,      n_layer=self.num_hidden_layers					,      n_head=self.num_attention_heads					,      n_positions=self.max_position_embeddings					,      use_cache=lowerCAmelCase__					,      bos_token_id=self.bos_token_id					,      eos_token_id=self.eos_token_id					,      pad_token_id=self.pad_token_id					,      )

        return (config, input_ids, input_mask)



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.prepare_config_and_inputs()
        _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Any       =config_and_inputs
        _UpperCamelCase  :Union[str, Any]       ={"""input_ids""": input_ids, """attention_mask""": attention_mask}
        return config, inputs_dict



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =self.prepare_config_and_inputs()

        _UpperCamelCase  :int       =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
        _UpperCamelCase  :Union[str, Any]       =ids_tensor([self.batch_size, self.seq_length]					,      vocab_size=2     )

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )



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





        _UpperCamelCase  :List[str]       =20
        _UpperCamelCase  :Union[str, Any]       =model_class_name(lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =model.init_cache(input_ids.shape[0]					,      lowerCAmelCase__     )
        _UpperCamelCase  :Any       =jnp.ones((input_ids.shape[0], max_decoder_length)					,      dtype="""i4"""     )

        _UpperCamelCase  :Any       =jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1     )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1)     )
        _UpperCamelCase  :Tuple       =model(
            input_ids[:, :-1]					,      attention_mask=lowerCAmelCase__					,      past_key_values=lowerCAmelCase__					,      position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :str       =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,      dtype="""i4"""     )
        _UpperCamelCase  :List[str]       =model(
            input_ids[:, -1:]					,      attention_mask=lowerCAmelCase__					,      past_key_values=outputs_cache.past_key_values					,      position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :int       =model(lowerCAmelCase__     )

        _UpperCamelCase  :Dict       =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3					,      msg=f'''Max diff is {diff}'''     )




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :Dict       =20
        _UpperCamelCase  :List[str]       =model_class_name(lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])     )]					,      axis=-1					,      )

        _UpperCamelCase  :int       =model.init_cache(input_ids.shape[0]					,      lowerCAmelCase__     )
        _UpperCamelCase  :List[Any]       =jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1     )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1)     )

        _UpperCamelCase  :Dict       =model(
            input_ids[:, :-1]					,      attention_mask=lowerCAmelCase__					,      past_key_values=lowerCAmelCase__					,      position_ids=lowerCAmelCase__					,      )
        _UpperCamelCase  :Any       =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,      dtype="""i4"""     )
        _UpperCamelCase  :str       =model(
            input_ids[:, -1:]					,      past_key_values=outputs_cache.past_key_values					,      attention_mask=lowerCAmelCase__					,      position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :Tuple       =model(lowerCAmelCase__					,      attention_mask=lowerCAmelCase__     )

        _UpperCamelCase  :int       =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3					,      msg=f'''Max diff is {diff}'''     )




@require_flax
class    lowerCamelCase__  (   __snake_case       ,				__snake_case       ,				unittest.TestCase     ):
   __UpperCAmelCase           =						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
   __UpperCAmelCase           =						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()



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





        _UpperCamelCase  :int       =FlaxGPTaModelTester(self     )



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        for model_class_name in self.all_model_classes:
             _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        for model_class_name in self.all_model_classes:
             _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )



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





        _UpperCamelCase  :Optional[int]       =GPTaTokenizer.from_pretrained("""gpt2"""					,      pad_token="""</s>"""					,      padding_side="""left"""     )
        _UpperCamelCase  :Dict       =tokenizer(["""Hello this is a long string""", """Hey"""]					,      return_tensors="""np"""					,      padding=lowerCAmelCase__					,      truncation=lowerCAmelCase__     )

        _UpperCamelCase  :Dict       =FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""     )
        _UpperCamelCase  :int       =False
        _UpperCamelCase  :Optional[int]       =model.config.eos_token_id

        _UpperCamelCase  :Any       =jax.jit(model.generate     )

        _UpperCamelCase  :Dict       =jit_generate(inputs["""input_ids"""]					,      attention_mask=inputs["""attention_mask"""]     ).sequences

        _UpperCamelCase  :Optional[int]       =tokenizer.batch_decode(lowerCAmelCase__					,      skip_special_tokens=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[int]       =[
            """Hello this is a long string of words. I'm going to start with the first one.\n""",
            """Hey, I'm not sure if I'm going to be able to do""",
        ]

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )



   @is_pt_flax_cross_test
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :str       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__     ):
                  # prepare inputs
                  _UpperCamelCase  :Optional[Any]       =self._prepare_for_class(lowerCAmelCase__					,      lowerCAmelCase__     )
                  _UpperCamelCase  :Optional[int]       ={k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  _UpperCamelCase  :Union[str, Any]       =model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  _UpperCamelCase  :Dict       =getattr(lowerCAmelCase__					,      lowerCAmelCase__     )

                  _UpperCamelCase       ,      _UpperCamelCase  :List[Any]       =pt_inputs["""input_ids"""].shape
                  _UpperCamelCase  :List[str]       =np.random.randint(0					,      seq_length - 1					,      size=(batch_size,)     )
                  for batch_idx, start_index in enumerate(lowerCAmelCase__     ):
                       _UpperCamelCase  :Tuple       =0
                       _UpperCamelCase  :Dict       =1
                       _UpperCamelCase  :Tuple       =0
                       _UpperCamelCase  :str       =1
                  _UpperCamelCase  :Dict       =pt_model_class(lowerCAmelCase__     ).eval()
                  _UpperCamelCase  :int       =model_class(lowerCAmelCase__					,      dtype=jnp.floataa     )

                  _UpperCamelCase  :Any       =convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,      lowerCAmelCase__     )
                  _UpperCamelCase  :List[str]       =fx_state

                  with torch.no_grad():
                       _UpperCamelCase  :Optional[int]       =pt_model(**lowerCAmelCase__     ).to_tuple()

                  _UpperCamelCase  :List[Any]       =fx_model(**lowerCAmelCase__     ).to_tuple()
                  self.assertEqual(len(lowerCAmelCase__     )					,      len(lowerCAmelCase__     )					,      """Output lengths differ between Flax and PyTorch"""     )
                  for fx_output, pt_output in zip(lowerCAmelCase__					,      lowerCAmelCase__     ):
                       self.assert_almost_equals(fx_output[:, -1]					,      pt_output[:, -1].numpy()					,      4e-2     )

                  with tempfile.TemporaryDirectory() as tmpdirname:
                       pt_model.save_pretrained(lowerCAmelCase__     )
                       _UpperCamelCase  :Any       =model_class.from_pretrained(lowerCAmelCase__					,      from_pt=lowerCAmelCase__     )

                  _UpperCamelCase  :Optional[int]       =fx_model_loaded(**lowerCAmelCase__     ).to_tuple()
                  self.assertEqual(
                      len(lowerCAmelCase__     )					,      len(lowerCAmelCase__     )					,      """Output lengths differ between Flax and PyTorch"""     )
                  for fx_output_loaded, pt_output in zip(lowerCAmelCase__					,      lowerCAmelCase__     ):
                       self.assert_almost_equals(fx_output_loaded[:, -1]					,      pt_output[:, -1].numpy()					,      4e-2     )



   @is_pt_flax_cross_test
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__     ):
                  # prepare inputs
                  _UpperCamelCase  :Optional[int]       =self._prepare_for_class(lowerCAmelCase__					,      lowerCAmelCase__     )
                  _UpperCamelCase  :Tuple       ={k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  _UpperCamelCase  :str       =model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  _UpperCamelCase  :List[Any]       =getattr(lowerCAmelCase__					,      lowerCAmelCase__     )

                  _UpperCamelCase  :Union[str, Any]       =pt_model_class(lowerCAmelCase__     ).eval()
                  _UpperCamelCase  :Dict       =model_class(lowerCAmelCase__					,      dtype=jnp.floataa     )

                  _UpperCamelCase  :Optional[Any]       =load_flax_weights_in_pytorch_model(lowerCAmelCase__					,      fx_model.params     )
                  _UpperCamelCase       ,      _UpperCamelCase  :Optional[int]       =pt_inputs["""input_ids"""].shape
                  _UpperCamelCase  :Tuple       =np.random.randint(0					,      seq_length - 1					,      size=(batch_size,)     )
                  for batch_idx, start_index in enumerate(lowerCAmelCase__     ):
                       _UpperCamelCase  :List[Any]       =0
                       _UpperCamelCase  :Union[str, Any]       =1
                       _UpperCamelCase  :List[str]       =0
                       _UpperCamelCase  :List[Any]       =1

                  # make sure weights are tied in PyTorch
                  pt_model.tie_weights()

                  with torch.no_grad():
                       _UpperCamelCase  :Tuple       =pt_model(**lowerCAmelCase__     ).to_tuple()

                  _UpperCamelCase  :Tuple       =fx_model(**lowerCAmelCase__     ).to_tuple()
                  self.assertEqual(len(lowerCAmelCase__     )					,      len(lowerCAmelCase__     )					,      """Output lengths differ between Flax and PyTorch"""     )
                  for fx_output, pt_output in zip(lowerCAmelCase__					,      lowerCAmelCase__     ):
                       self.assert_almost_equals(fx_output[:, -1]					,      pt_output[:, -1].numpy()					,      4e-2     )

                  with tempfile.TemporaryDirectory() as tmpdirname:
                       fx_model.save_pretrained(lowerCAmelCase__     )
                       _UpperCamelCase  :Dict       =pt_model_class.from_pretrained(lowerCAmelCase__					,      from_flax=lowerCAmelCase__     )

                  with torch.no_grad():
                       _UpperCamelCase  :str       =pt_model_loaded(**lowerCAmelCase__     ).to_tuple()

                  self.assertEqual(
                      len(lowerCAmelCase__     )					,      len(lowerCAmelCase__     )					,      """Output lengths differ between Flax and PyTorch"""     )
                  for fx_output, pt_output in zip(lowerCAmelCase__					,      lowerCAmelCase__     ):
                       self.assert_almost_equals(fx_output[:, -1]					,      pt_output[:, -1].numpy()					,      4e-2     )




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





        for model_class_name in self.all_model_classes:
             _UpperCamelCase  :Optional[int]       =model_class_name.from_pretrained("""gpt2"""					,      from_pt=lowerCAmelCase__     )
             _UpperCamelCase  :Union[str, Any]       =model(np.ones((1, 1)     )     )
             self.assertIsNotNone(lowerCAmelCase__     )
'''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()