


'''simple docstring'''
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     lowerCAmelCase__  =     'platform'

     import jax
     import jax.numpy as jnp

     from transformers import AutoTokenizer
     from transformers.models.mbart.modeling_flax_mbart import (
         FlaxMBartForConditionalGeneration,
         FlaxMBartForQuestionAnswering,
         FlaxMBartForSequenceClassification,
         FlaxMBartModel,
         shift_tokens_right,
     )



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase=None    ,       UpperCAmelCase=None    ,       UpperCAmelCase=None    ,       UpperCAmelCase=None    ,       UpperCAmelCase=None    ,       UpperCAmelCase=None    ,       ):






       """simple docstring"""





       if attention_mask is None:
              snake_case__					:	int          =					np.where(input_ids != config.pad_token_id    ,       1    ,       0     )
       if decoder_attention_mask is None:
              snake_case__					:	Union[str, Any]          =					np.where(decoder_input_ids != config.pad_token_id    ,       1    ,       0     )
       if head_mask is None:
              snake_case__					:	Optional[Any]          =					np.ones((config.encoder_layers, config.encoder_attention_heads)     )
       if decoder_head_mask is None:
              snake_case__					:	str          =					np.ones((config.decoder_layers, config.decoder_attention_heads)     )
       if cross_attn_head_mask is None:
              snake_case__					:	Optional[Any]          =					np.ones((config.decoder_layers, config.decoder_attention_heads)     )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
       }







class 					_A						(						unittest.TestCase							):



   '''simple docstring'''






   def __init__(	self	:					Optional[int]					, lowerCamelCase	:					List[Any]					, lowerCamelCase	:					str=13					, lowerCamelCase	:					List[Any]=7					, lowerCamelCase	:					str=True					, lowerCamelCase	:					Dict=False					, lowerCamelCase	:					str=99					, lowerCamelCase	:					Any=16					, lowerCamelCase	:					List[Any]=2					, lowerCamelCase	:					List[str]=4					, lowerCamelCase	:					List[str]=4					, lowerCamelCase	:					Union[str, Any]="gelu"					, lowerCamelCase	:					Optional[Any]=0.1					, lowerCamelCase	:					Dict=0.1					, lowerCamelCase	:					Optional[int]=32					, lowerCamelCase	:					Dict=2					, lowerCamelCase	:					int=1					, lowerCamelCase	:					str=0					, lowerCamelCase	:					Union[str, Any]=2					, lowerCamelCase	:					Union[str, Any]=0.02					, )->	List[str]:
          snake_case__					:	Tuple          =					parent
          snake_case__					:	List[str]          =					batch_size
          snake_case__					:	int          =					seq_length
          snake_case__					:	Any          =					is_training
          snake_case__					:	Union[str, Any]          =					use_labels
          snake_case__					:	Union[str, Any]          =					vocab_size
          snake_case__					:	int          =					hidden_size
          snake_case__					:	int          =					num_hidden_layers
          snake_case__					:	Union[str, Any]          =					num_attention_heads
          snake_case__					:	Tuple          =					intermediate_size
          snake_case__					:	Any          =					hidden_act
          snake_case__					:	str          =					hidden_dropout_prob
          snake_case__					:	Dict          =					attention_probs_dropout_prob
          snake_case__					:	Union[str, Any]          =					max_position_embeddings
          snake_case__					:	Optional[int]          =					eos_token_id
          snake_case__					:	List[str]          =					pad_token_id
          snake_case__					:	Optional[Any]          =					bos_token_id
          snake_case__					:	List[Any]          =					decoder_start_token_id
          snake_case__					:	Optional[Any]          =					initializer_range




   def   __lowerCAmelCase						(	self	:					int						)->	Union[str, Any]:
          snake_case__					:	List[str]          =					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					, self.vocab_size						)					, 3					, self.vocab_size						)
          snake_case__					:	Tuple          =					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					, dtype=np.intaa						))					, -1						)

          snake_case__					:	Optional[Any]          =					shift_tokens_right(lowerCamelCase					, 1						)

          snake_case__					:	Optional[int]          =					MBartConfig(
              vocab_size=self.vocab_size					, d_model=self.hidden_size					, encoder_layers=self.num_hidden_layers					, decoder_layers=self.num_hidden_layers					, encoder_attention_heads=self.num_attention_heads					, decoder_attention_heads=self.num_attention_heads					, encoder_ffn_dim=self.intermediate_size					, decoder_ffn_dim=self.intermediate_size					, dropout=self.hidden_dropout_prob					, attention_dropout=self.attention_probs_dropout_prob					, max_position_embeddings=self.max_position_embeddings					, eos_token_id=self.eos_token_id					, bos_token_id=self.bos_token_id					, pad_token_id=self.pad_token_id					, decoder_start_token_id=self.decoder_start_token_id					, initializer_range=self.initializer_range					, use_cache=lowerCamelCase					, )
          snake_case__					:	Optional[int]          =					prepare_mbart_inputs_dict(lowerCamelCase					, lowerCamelCase					, lowerCamelCase						)
          return config, inputs_dict




   def   __lowerCAmelCase						(	self	:					Dict						)->	Any:
          snake_case__       ,   snake_case__					:	List[str]          =					self.prepare_config_and_inputs()
          return config, inputs_dict




   def   __lowerCAmelCase						(	self	:					Tuple					, lowerCamelCase	:					Optional[int]					, lowerCamelCase	:					Union[str, Any]					, lowerCamelCase	:					Any						)->	List[str]:
          snake_case__					:	Any          =					20
          snake_case__					:	List[Any]          =					model_class_name(lowerCamelCase						)

          snake_case__					:	Tuple          =					model.encode(inputs_dict["""input_ids"""]						)

          snake_case__       ,   snake_case__					:	str          =					(
              inputs_dict["""decoder_input_ids"""],
              inputs_dict["""decoder_attention_mask"""],
          )

          snake_case__					:	int          =					model.init_cache(decoder_input_ids.shape[0]					, lowerCamelCase					, lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					, dtype="""i4"""						)

          snake_case__					:	int          =					jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]					, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					, )
          snake_case__					:	Any          =					model.decode(
              decoder_input_ids[:, :-1]					, lowerCamelCase					, decoder_attention_mask=lowerCamelCase					, past_key_values=lowerCamelCase					, decoder_position_ids=lowerCamelCase					, )

          snake_case__					:	Tuple          =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					, dtype="""i4"""						)
          snake_case__					:	List[Any]          =					model.decode(
              decoder_input_ids[:, -1:]					, lowerCamelCase					, decoder_attention_mask=lowerCamelCase					, past_key_values=outputs_cache.past_key_values					, decoder_position_ids=lowerCamelCase					, )

          snake_case__					:	Optional[int]          =					model.decode(lowerCamelCase					, lowerCamelCase						)

          snake_case__					:	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   __lowerCAmelCase						(	self	:					Optional[Any]					, lowerCamelCase	:					int					, lowerCamelCase	:					Any					, lowerCamelCase	:					Union[str, Any]						)->	Optional[int]:
          snake_case__					:	int          =					20
          snake_case__					:	Tuple          =					model_class_name(lowerCamelCase						)

          snake_case__					:	Tuple          =					model.encode(inputs_dict["""input_ids"""]						)

          snake_case__       ,   snake_case__					:	Optional[int]          =					(
              inputs_dict["""decoder_input_ids"""],
              inputs_dict["""decoder_attention_mask"""],
          )

          snake_case__					:	str          =					jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
              ]					, axis=-1					, )

          snake_case__					:	List[Any]          =					model.init_cache(decoder_input_ids.shape[0]					, lowerCamelCase					, lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]					, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					, )

          snake_case__					:	str          =					model.decode(
              decoder_input_ids[:, :-1]					, lowerCamelCase					, decoder_attention_mask=lowerCamelCase					, past_key_values=lowerCamelCase					, decoder_position_ids=lowerCamelCase					, )
          snake_case__					:	Any          =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					, dtype="""i4"""						)
          snake_case__					:	List[Any]          =					model.decode(
              decoder_input_ids[:, -1:]					, lowerCamelCase					, past_key_values=outputs_cache.past_key_values					, decoder_attention_mask=lowerCamelCase					, decoder_position_ids=lowerCamelCase					, )

          snake_case__					:	Optional[Any]          =					model.decode(lowerCamelCase					, lowerCamelCase					, decoder_attention_mask=lowerCamelCase						)

          snake_case__					:	Optional[Any]          =					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 					_A						(						unittest.TestCase							):



   '''simple docstring'''






   _lowercase    						= 99




   def   __lowerCAmelCase						(	self	:					str						)->	Tuple:
          snake_case__					:	List[Any]          =					np.array(
              [
                  [71, 82, 18, 33, 46, 91, 2],
                  [68, 34, 26, 58, 30, 82, 2],
                  [5, 97, 17, 39, 94, 40, 2],
                  [76, 83, 94, 25, 70, 78, 2],
                  [87, 59, 41, 35, 48, 66, 2],
                  [55, 13, 16, 58, 5, 2, 1],  # note padding
                  [64, 27, 31, 51, 12, 75, 2],
                  [52, 64, 86, 17, 83, 39, 2],
                  [48, 61, 9, 24, 71, 82, 2],
                  [26, 1, 60, 48, 22, 13, 2],
                  [21, 5, 62, 28, 14, 76, 2],
                  [45, 98, 37, 86, 59, 48, 2],
                  [70, 70, 50, 9, 28, 0, 2],
              ]					, dtype=np.intaa					, )

          snake_case__					:	Dict          =					input_ids.shape[0]
          snake_case__					:	List[str]          =					MBartConfig(
              vocab_size=self.vocab_size					, d_model=24					, encoder_layers=2					, decoder_layers=2					, encoder_attention_heads=2					, decoder_attention_heads=2					, encoder_ffn_dim=32					, decoder_ffn_dim=32					, max_position_embeddings=48					, eos_token_id=2					, pad_token_id=1					, bos_token_id=0					, )
          return config, input_ids, batch_size




   def   __lowerCAmelCase						(	self	:					List[Any]						)->	Optional[Any]:
          snake_case__       ,   snake_case__       ,   snake_case__					:	str          =					self._get_config_and_data()
          snake_case__					:	Dict          =					FlaxMBartForSequenceClassification(lowerCamelCase						)
          snake_case__					:	Any          =					model(input_ids=lowerCamelCase					, decoder_input_ids=lowerCamelCase						)
          snake_case__					:	List[Any]          =					(batch_size, config.num_labels)
          self.assertEqual(outputs["""logits"""].shape					, lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					List[Any]						)->	Tuple:
          snake_case__       ,   snake_case__       ,   snake_case__					:	Tuple          =					self._get_config_and_data()
          snake_case__					:	Tuple          =					FlaxMBartForQuestionAnswering(lowerCamelCase						)
          snake_case__					:	Any          =					model(input_ids=lowerCamelCase						)

          self.assertEqual(outputs["""start_logits"""].shape					, input_ids.shape						)
          self.assertEqual(outputs["""end_logits"""].shape					, input_ids.shape						)




   def   __lowerCAmelCase						(	self	:					int						)->	Optional[int]:
          snake_case__       ,   snake_case__       ,   snake_case__					:	List[str]          =					self._get_config_and_data()
          snake_case__					:	str          =					FlaxMBartForConditionalGeneration(lowerCamelCase						)
          snake_case__					:	Dict          =					lm_model(input_ids=lowerCamelCase						)
          snake_case__					:	int          =					(batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs["""logits"""].shape					, lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					Any						)->	Optional[Any]:
          snake_case__					:	str          =					MBartConfig(
              vocab_size=self.vocab_size					, d_model=14					, encoder_layers=2					, decoder_layers=2					, encoder_attention_heads=2					, decoder_attention_heads=2					, encoder_ffn_dim=8					, decoder_ffn_dim=8					, max_position_embeddings=48					, )
          snake_case__					:	Dict          =					FlaxMBartForConditionalGeneration(lowerCamelCase						)
          snake_case__					:	List[Any]          =					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					, dtype=np.intaa						)
          snake_case__					:	List[str]          =					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					, dtype=np.intaa						)
          snake_case__					:	Any          =					lm_model(input_ids=lowerCamelCase					, decoder_input_ids=lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					(*summary.shape, config.vocab_size)
          self.assertEqual(outputs["""logits"""].shape					, lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					Any						)->	List[str]:
          snake_case__					:	Union[str, Any]          =					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					, dtype=np.intaa						)
          snake_case__					:	List[Any]          =					shift_tokens_right(lowerCamelCase					, 1						)
          snake_case__					:	str          =					np.equal(lowerCamelCase					, 1						).astype(np.floataa						).sum()
          snake_case__					:	Dict          =					np.equal(lowerCamelCase					, 1						).astype(np.floataa						).sum()
          self.assertEqual(shifted.shape					, input_ids.shape						)
          self.assertEqual(lowerCamelCase					, n_pad_before - 1						)
          self.assertTrue(np.equal(shifted[:, 0]					, 2						).all()						)







@require_flax
class 					_A						(						UpperCamelCase    ,  unittest.TestCase    ,  UpperCamelCase							):



   '''simple docstring'''






   _lowercase    						= True
   _lowercase    						= (
       (
           FlaxMBartModel,
           FlaxMBartForConditionalGeneration,
           FlaxMBartForSequenceClassification,
           FlaxMBartForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   _lowercase    						= (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	List[str]:
          snake_case__					:	Union[str, Any]          =					FlaxMBartModelTester(self						)




   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	Optional[Any]:
          snake_case__       ,   snake_case__					:	List[Any]          =					self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(lowerCamelCase					, lowerCamelCase					, lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					int						)->	Optional[Any]:
          snake_case__       ,   snake_case__					:	List[str]          =					self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase					, lowerCamelCase					, lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					int						)->	int:
          snake_case__       ,   snake_case__					:	List[Any]          =					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__						):
                        snake_case__					:	str          =					self._prepare_for_class(lowerCamelCase					, lowerCamelCase						)
                        snake_case__					:	Optional[int]          =					model_class(lowerCamelCase						)

                        @jax.jit
                        def encode_jitted(lowerCamelCase	:					str					, lowerCamelCase	:					List[str]=None					, **lowerCamelCase	:					Dict						):
                               return model.encode(input_ids=lowerCamelCase					, attention_mask=lowerCamelCase						)

                        with self.subTest("""JIT Enabled"""						):
                               snake_case__					:	Tuple          =					encode_jitted(**lowerCamelCase						).to_tuple()

                        with self.subTest("""JIT Disabled"""						):
                               with jax.disable_jit():
                                      snake_case__					:	Any          =					encode_jitted(**lowerCamelCase						).to_tuple()

                        self.assertEqual(len(lowerCamelCase						)					, len(lowerCamelCase						)						)
                        for jitted_output, output in zip(lowerCamelCase					, lowerCamelCase						):
                               self.assertEqual(jitted_output.shape					, output.shape						)




   def   __lowerCAmelCase						(	self	:					Dict						)->	List[Any]:
          snake_case__       ,   snake_case__					:	Optional[int]          =					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__						):
                        snake_case__					:	Dict          =					model_class(lowerCamelCase						)
                        snake_case__					:	Any          =					model.encode(inputs_dict["""input_ids"""]					, inputs_dict["""attention_mask"""]						)

                        snake_case__					:	Optional[Any]          =					{
                            """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                            """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                            """encoder_outputs""": encoder_outputs,
                        }

                        @jax.jit
                        def decode_jitted(lowerCamelCase	:					Optional[int]					, lowerCamelCase	:					List[str]					, lowerCamelCase	:					List[Any]						):
                               return model.decode(
                                   decoder_input_ids=lowerCamelCase					, decoder_attention_mask=lowerCamelCase					, encoder_outputs=lowerCamelCase					, )

                        with self.subTest("""JIT Enabled"""						):
                               snake_case__					:	Dict          =					decode_jitted(**lowerCamelCase						).to_tuple()

                        with self.subTest("""JIT Disabled"""						):
                               with jax.disable_jit():
                                      snake_case__					:	Tuple          =					decode_jitted(**lowerCamelCase						).to_tuple()

                        self.assertEqual(len(lowerCamelCase						)					, len(lowerCamelCase						)						)
                        for jitted_output, output in zip(lowerCamelCase					, lowerCamelCase						):
                               self.assertEqual(jitted_output.shape					, output.shape						)




   @slow
   def   __lowerCAmelCase						(	self	:					Optional[Any]						)->	Union[str, Any]:
          for model_class_name in self.all_model_classes:
                 snake_case__					:	List[str]          =					model_class_name.from_pretrained("""facebook/mbart-large-cc25"""					, from_pt=lowerCamelCase						)
                 # FlaxMBartForSequenceClassification expects eos token in input_ids
                 snake_case__					:	Dict          =					np.ones((1, 1)						) * model.config.eos_token_id
                 snake_case__					:	Dict          =					model(lowerCamelCase						)
                 self.assertIsNotNone(lowerCamelCase						)







@require_flax
@require_sentencepiece
@require_tokenizers
class 					_A						(						unittest.TestCase							):



   '''simple docstring'''






   _lowercase    						= [
       ' UN Chief Says There Is No Military Solution in Syria',
   ]
   _lowercase    						= [
       'Şeful ONU declară că nu există o soluţie militară în Siria',
   ]
   _lowercase    						= 'facebook/mbart-large-en-ro'




   @cached_property
   def   __lowerCAmelCase						(	self	:					Optional[Any]						)->	Optional[Any]:
          return AutoTokenizer.from_pretrained(self.model_name						)




   @cached_property
   def   __lowerCAmelCase						(	self	:					Optional[Any]						)->	Union[str, Any]:
          snake_case__					:	Tuple          =					FlaxMBartForConditionalGeneration.from_pretrained(self.model_name					, from_pt=lowerCamelCase						)
          return model




   def   __lowerCAmelCase						(	self	:					str					, **lowerCamelCase	:					Optional[Any]						)->	str:
          snake_case__					:	Union[str, Any]          =					self.translate_src_text(**lowerCamelCase						)
          self.assertListEqual(self.expected_text					, lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					str					, **lowerCamelCase	:					Optional[Any]						)->	int:
          snake_case__					:	Optional[int]          =					self.tokenizer(self.src_text					, **lowerCamelCase					, return_tensors="""np"""						)
          snake_case__					:	Optional[int]          =					self.model.generate(
              model_inputs.input_ids					, attention_mask=model_inputs.attention_mask					, decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""]					, early_stopping=lowerCamelCase					, num_beams=2					, ).sequences
          snake_case__					:	Optional[int]          =					self.tokenizer.batch_decode(lowerCamelCase					, skip_special_tokens=lowerCamelCase						)
          return generated_words




   @slow
   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	Dict:
          self._assert_generated_batch_equal_expected()








'''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__  =     '1.5.0'

lowerCAmelCase__  =     ['vision-encoder-decoder']



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





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

              snake_case__					:	Dict          =					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__					:	Union[str, Any]          =					[
           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__					:	Union[str, Any]          =					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 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	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=UpperCAmelCase     )

       # Allocate the model
       snake_case__					:	Dict          =					FeaturesManager.get_model_from_feature(
           args.feature    ,       args.model    ,       framework=args.framework    ,       cache_dir=args.cache_dir     )

       snake_case__       ,   snake_case__					:	List[Any]          =					FeaturesManager.check_supported_model_or_raise(UpperCAmelCase    ,       feature=args.feature     )
       snake_case__					:	Any          =					model_onnx_config(model.config     )

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

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

              if args.opset is None:
                     snake_case__					:	List[Any]          =					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__					:	Dict          =					AutoFeatureExtractor.from_pretrained(args.model     )

              snake_case__       ,   snake_case__					:	List[str]          =					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__					:	List[Any]          =					AutoTokenizer.from_pretrained(args.model     )

              snake_case__       ,   snake_case__					:	Dict          =					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__					:	int          =					get_preprocessor(args.model     )
              elif args.preprocessor == "tokenizer":
                     snake_case__					:	List[str]          =					AutoTokenizer.from_pretrained(args.model     )
              elif args.preprocessor == "image_processor":
                     snake_case__					:	Optional[int]          =					AutoImageProcessor.from_pretrained(args.model     )
              elif args.preprocessor == "feature_extractor":
                     snake_case__					:	Tuple          =					AutoFeatureExtractor.from_pretrained(args.model     )
              elif args.preprocessor == "processor":
                     snake_case__					:	int          =					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__					:	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."""     )

              snake_case__       ,   snake_case__					:	Dict          =					export(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.opset    ,       args.output    ,       )

              if args.atol is None:
                     snake_case__					:	int          =					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 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	List[str]          =					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__					:	str          =					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__":
     lowerCAmelCase__  =     logging.get_logger('transformers.onnx')  # pylint: disable=invalid-name
     logger.setLevel(logging.INFO)
     main()




