code: 
  
  import unittest
  
  import numpy as np
  import timeout_decorator  # noqa
  
  from transformers import OPTConfig, is_flax_available
  from transformers.testing_utils import require_flax, require_sentencepiece, slow
  
  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     :			List[str]    ="""platform"""
  
  					import jax
  					import jax.numpy as jnp
  
  					from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE=None  ,SCREAMING_SNAKE_CASE=None			):
  	if attention_mask is None:
  		UpperCAmelCase__:  Dict		       =   np.where(input_ids != config.pad_token_id  ,1  ,0			)
  	return {
  	    "input_ids": input_ids,
  	    "attention_mask": attention_mask,
  	}
  
  @require_flax
  class __UpperCamelCase    :
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__=1_3							,			lowerCamelCase__=7							,			lowerCamelCase__=True							,			lowerCamelCase__=False							,			lowerCamelCase__=9_9							,			lowerCamelCase__=1_6							,			lowerCamelCase__=2							,			lowerCamelCase__=4							,			lowerCamelCase__=4							,			lowerCamelCase__="gelu"							,			lowerCamelCase__=0.1							,			lowerCamelCase__=0.1							,			lowerCamelCase__=2_0							,			lowerCamelCase__=2							,			lowerCamelCase__=1							,			lowerCamelCase__=0							,			lowerCamelCase__=1_6							,			lowerCamelCase__=1_6							,			lowerCamelCase__=0.02							,			):
  						UpperCAmelCase__:  Tuple		       =   parent
  						UpperCAmelCase__:  int		       =   batch_size
  						UpperCAmelCase__:  Dict		       =   seq_length
  						UpperCAmelCase__:  Optional[int]		       =   is_training
  						UpperCAmelCase__:  List[str]		       =   use_labels
  						UpperCAmelCase__:  Optional[int]		       =   vocab_size
  						UpperCAmelCase__:  Optional[Any]		       =   hidden_size
  						UpperCAmelCase__:  str		       =   num_hidden_layers
  						UpperCAmelCase__:  List[Any]		       =   num_attention_heads
  						UpperCAmelCase__:  Any		       =   intermediate_size
  						UpperCAmelCase__:  Any		       =   hidden_act
  						UpperCAmelCase__:  List[Any]		       =   hidden_dropout_prob
  						UpperCAmelCase__:  Optional[int]		       =   attention_probs_dropout_prob
  						UpperCAmelCase__:  List[Any]		       =   max_position_embeddings
  						UpperCAmelCase__:  Tuple		       =   eos_token_id
  						UpperCAmelCase__:  Optional[int]		       =   pad_token_id
  						UpperCAmelCase__:  Tuple		       =   bos_token_id
  						UpperCAmelCase__:  int		       =   embed_dim
  						UpperCAmelCase__:  Any		       =   word_embed_proj_dim
  						UpperCAmelCase__:  str		       =   initializer_range
  						UpperCAmelCase__:  Optional[Any]		       =   False
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[Any]		       =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,			self.vocab_size	)							,			3							,			self.vocab_size	)
  						UpperCAmelCase__:  int		       =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,			dtype=np.intaa	))							,			-1	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   OPTConfig(
  						    vocab_size=self.vocab_size							,			hidden_size=self.hidden_size							,			num_hidden_layers=self.num_hidden_layers							,			num_attention_heads=self.num_attention_heads							,			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							,			embed_dim=self.embed_dim							,			is_encoder_decoder=lowerCamelCase__							,			word_embed_proj_dim=self.word_embed_proj_dim							,			initializer_range=self.initializer_range							,			use_cache=lowerCamelCase__							,			)
  						UpperCAmelCase__:  Any		       =   prepare_opt_inputs_dict(lowerCamelCase__							,			lowerCamelCase__	)
  						return config, inputs_dict
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Optional[int]		       =   self.prepare_config_and_inputs()
  						return config, inputs_dict
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Tuple		       =   2_0
  						UpperCAmelCase__:  Any		       =   model_class_name(lowerCamelCase__	)
  
  						UpperCAmelCase__:  str		       =   inputs_dict["input_ids"]
  						UpperCAmelCase__:  str		       =   inputs_dict["attention_mask"]
  
  						UpperCAmelCase__:  List[str]		       =   model.init_cache(input_ids.shape[0]							,			lowerCamelCase__	)
  						UpperCAmelCase__:  Optional[int]		       =   jnp.ones((input_ids.shape[0], max_length)							,			dtype="i4"	)
  
  						UpperCAmelCase__:  Dict		       =   jnp.broadcast_to(
  						    jnp.arange(input_ids.shape[-1] - 1	)[None, :]							,			(input_ids.shape[0], input_ids.shape[-1] - 1)							,			)
  						UpperCAmelCase__:  List[Any]		       =   model(
  						    input_ids[:, :-1]							,			attention_mask=lowerCamelCase__							,			past_key_values=lowerCamelCase__							,			position_ids=lowerCamelCase__							,			)
  
  						UpperCAmelCase__:  Tuple		       =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,			dtype="i4"	)
  						UpperCAmelCase__:  Any		       =   model(
  						    input_ids[:, -1:]							,			attention_mask=lowerCamelCase__							,			past_key_values=outputs_cache.past_key_values							,			position_ids=lowerCamelCase__							,			)
  
  						UpperCAmelCase__:  str		       =   model(lowerCamelCase__	)
  
  						UpperCAmelCase__:  List[str]		       =   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__	):
  						UpperCAmelCase__:  Optional[Any]		       =   2_0
  						UpperCAmelCase__:  Tuple		       =   model_class_name(lowerCamelCase__	)
  
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Any		       =   (
  						    inputs_dict["input_ids"],
  						    inputs_dict["attention_mask"],
  						)
  
  						UpperCAmelCase__:  int		       =   jnp.concatenate(
  						    [
  						        attention_mask,
  						        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])	),
  						    ]							,			axis=-1							,			)
  
  						UpperCAmelCase__:  Tuple		       =   model.init_cache(input_ids.shape[0]							,			lowerCamelCase__	)
  						UpperCAmelCase__:  str		       =   jnp.broadcast_to(
  						    jnp.arange(input_ids.shape[-1] - 1	)[None, :]							,			(input_ids.shape[0], input_ids.shape[-1] - 1)							,			)
  
  						UpperCAmelCase__:  Optional[int]		       =   model(
  						    input_ids[:, :-1]							,			attention_mask=lowerCamelCase__							,			past_key_values=lowerCamelCase__							,			position_ids=lowerCamelCase__							,			)
  						UpperCAmelCase__:  Tuple		       =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,			dtype="i4"	)
  						UpperCAmelCase__:  Dict		       =   model(
  						    input_ids[:, -1:]							,			past_key_values=outputs_cache.past_key_values							,			attention_mask=lowerCamelCase__							,			position_ids=lowerCamelCase__							,			)
  
  						UpperCAmelCase__:  int		       =   model(lowerCamelCase__							,			attention_mask=lowerCamelCase__	)
  
  						UpperCAmelCase__:  List[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 __UpperCamelCase    (							_a				,unittest.TestCase				,_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  					__magic_name__     			=      () if is_flax_available() else ()
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[Any]		       =   FlaxOPTModelTester(self	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   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       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__:  int		       =   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__	)
  
  
  
  
  
  					@slow
  					def       _UpperCAmelCase						(							self	):
  						for model_class_name in self.all_model_classes:
  							UpperCAmelCase__:  Union[str, Any]		       =   model_class_name.from_pretrained("facebook/opt-125m"	)
  							UpperCAmelCase__:  int		       =   np.ones((1, 1)	) * model.config.eos_token_id
  							UpperCAmelCase__:  Optional[int]		       =   model(lowerCamelCase__	)
  							self.assertIsNotNone(lowerCamelCase__	)
  
  @require_sentencepiece
  @require_flax
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					@slow
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[Any]		       =   FlaxOPTModel.from_pretrained("facebook/opt-350m"	)
  						UpperCAmelCase__:  Tuple		       =   jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]	)
  						UpperCAmelCase__:  int		       =   model(input_ids=lowerCamelCase__	).last_hidden_state
  						UpperCAmelCase__:  Optional[int]		       =   (1, 1_1, 5_1_2)
  						self.assertEqual(output.shape							,			lowerCamelCase__	)
  						UpperCAmelCase__:  List[Any]		       =   jnp.array(
  						    [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]	)
  						self.assertTrue(jnp.allclose(output[:, :3, :3]							,			lowerCamelCase__							,			atol=4e-2	)	)
  
  @require_flax
  @slow
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						super().setUp()
  						UpperCAmelCase__:  Optional[Any]		       =   "facebook/opt-350m"
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Union[str, Any]		       =   FlaxOPTForCausalLM.from_pretrained(self.path_model	)
  						UpperCAmelCase__:  Dict		       =   GPTaTokenizer.from_pretrained(self.path_model	)
  
  						UpperCAmelCase__:  List[Any]		       =   [
  						    "Today is a beautiful day and I want to",
  						    "In the city of",
  						    "Paris is the capital of France and",
  						    "Computers and mobile phones have taken",
  						]
  						# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
  						UpperCAmelCase__:  List[Any]		       =   tokenizer(lowerCamelCase__							,			return_tensors="jax"							,			padding=lowerCamelCase__							,			add_special_tokens=lowerCamelCase__	)
  						UpperCAmelCase__:  List[Any]		       =   model(inputs.input_ids							,			attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
  						UpperCAmelCase__:  Dict		       =   jnp.array(
  						    [
  						        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
  						        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
  						        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
  						        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
  						    ]	)
  						self.assertTrue(jnp.allclose(lowerCamelCase__							,			lowerCamelCase__							,			atol=4e-2	)	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   jax.jit(lowerCamelCase__	)
  						UpperCAmelCase__:  List[Any]		       =   model(inputs.input_ids							,			attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
  						self.assertTrue(jnp.allclose(lowerCamelCase__							,			lowerCamelCase__							,			atol=4e-2	)	)
  
  @require_flax
  @slow
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					@property
  					def       _UpperCAmelCase						(							self	):
  						return [
  						    "Today is a beautiful day and I want",
  						    "In the city of",
  						    "Paris is the capital of France and",
  						    "Computers and mobile phones have taken",
  						]
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[Any]		       =   "facebook/opt-125m"
  
  						UpperCAmelCase__:  Tuple		       =   [
  						    "Today is a beautiful day and I want to",
  						    "In the city of New York, the city",
  						    "Paris is the capital of France and the capital",
  						    "Computers and mobile phones have taken over the",
  						]
  
  						UpperCAmelCase__:  Tuple		       =   []
  
  						UpperCAmelCase__:  int		       =   FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__	)
  						UpperCAmelCase__:  int		       =   GPTaTokenizer.from_pretrained(lowerCamelCase__	)
  
  						for prompt in self.prompts:
  							UpperCAmelCase__:  Tuple		       =   tokenizer(lowerCamelCase__							,			return_tensors="jax"	).input_ids
  
  							UpperCAmelCase__:  Optional[Any]		       =   model.generate(lowerCamelCase__							,			max_length=1_0	)
  							UpperCAmelCase__:  Optional[int]		       =   generated_ids[0]
  
  							UpperCAmelCase__:  List[str]		       =   tokenizer.batch_decode(lowerCamelCase__							,			skip_special_tokens=lowerCamelCase__	)
  							predicted_outputs += generated_string
  
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Union[str, Any]		       =   "facebook/opt-350m"
  
  						UpperCAmelCase__:  List[str]		       =   [
  						    "Today is a beautiful day and I want to",
  						    "In the city of San Francisco, the city",
  						    "Paris is the capital of France and the capital",
  						    "Computers and mobile phones have taken over the",
  						]
  
  						UpperCAmelCase__:  Tuple		       =   []
  						UpperCAmelCase__:  List[Any]		       =   FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__	)
  						UpperCAmelCase__:  Any		       =   GPTaTokenizer.from_pretrained(lowerCamelCase__	)
  
  						for prompt in self.prompts:
  							UpperCAmelCase__:  Dict		       =   tokenizer(lowerCamelCase__							,			return_tensors="jax"	).input_ids
  
  							UpperCAmelCase__:  Dict		       =   model.generate(lowerCamelCase__							,			max_length=1_0	)
  							UpperCAmelCase__:  str		       =   generated_ids[0]
  
  							UpperCAmelCase__:  str		       =   tokenizer.batch_decode(lowerCamelCase__							,			skip_special_tokens=lowerCamelCase__	)
  							predicted_outputs += generated_string
  
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Dict		       =   "facebook/opt-125m"
  						UpperCAmelCase__:  Optional[Any]		       =   [
  						    "Today is a beautiful day and I want to thank",
  						    "In the city of Rome Canaver Canaver Canaver Canaver",
  						]
  						UpperCAmelCase__:  int		       =   FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__	)
  						UpperCAmelCase__:  List[Any]		       =   GPTaTokenizer.from_pretrained(lowerCamelCase__	)
  						UpperCAmelCase__:  List[str]		       =   tokenizer(
  						    [
  						        "Today is a beautiful day and I want to",
  						        "In the city of",
  						    ]							,			return_tensors="jax"							,			padding=lowerCamelCase__							,			)
  
  						UpperCAmelCase__:  int		       =   jax.jit(model.generate	)
  
  						UpperCAmelCase__:  str		       =   jit_generate(inputs["input_ids"]							,			attention_mask=inputs["attention_mask"]	).sequences
  
  						UpperCAmelCase__:  Optional[int]		       =   tokenizer.batch_decode(lowerCamelCase__							,			skip_special_tokens=lowerCamelCase__	)
  
  						self.assertIsNotNone(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Tuple		       =   "facebook/opt-350m"
  
  						UpperCAmelCase__:  List[Any]		       =   GPTaTokenizer.from_pretrained(lowerCamelCase__	)
  						UpperCAmelCase__:  Union[str, Any]		       =   FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__	)
  
  						UpperCAmelCase__:  List[str]		       =   "left"
  
  						# use different length sentences to test batching
  						UpperCAmelCase__:  Tuple		       =   [
  						    "Hello, my dog is a little",
  						    "Today, I",
  						]
  
  						UpperCAmelCase__:  int		       =   tokenizer(lowerCamelCase__							,			return_tensors="jax"							,			padding=lowerCamelCase__	)
  						UpperCAmelCase__:  List[str]		       =   inputs["input_ids"]
  
  						UpperCAmelCase__:  Tuple		       =   model.generate(input_ids=lowerCamelCase__							,			attention_mask=inputs["attention_mask"]							,			trace=lowerCamelCase__	)
  
  						UpperCAmelCase__:  List[Any]		       =   tokenizer(sentences[0]							,			return_tensors="jax"	).input_ids
  						UpperCAmelCase__:  Union[str, Any]		       =   model.generate(input_ids=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Tuple		       =   inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
  						UpperCAmelCase__:  Dict		       =   tokenizer(sentences[1]							,			return_tensors="jax"	).input_ids
  						UpperCAmelCase__:  int		       =   model.generate(input_ids=lowerCamelCase__							,			max_length=model.config.max_length - num_paddings	)
  
  						UpperCAmelCase__:  List[str]		       =   tokenizer.batch_decode(outputs[0]							,			skip_special_tokens=lowerCamelCase__	)
  						UpperCAmelCase__:  Tuple		       =   tokenizer.decode(output_non_padded[0][0]							,			skip_special_tokens=lowerCamelCase__	)
  						UpperCAmelCase__:  Tuple		       =   tokenizer.decode(output_padded[0][0]							,			skip_special_tokens=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   [
  						    "Hello, my dog is a little bit of a dork.\nI'm a little bit",
  						    "Today, I was in the middle of a conversation with a friend about the",
  						]
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  						self.assertListEqual(lowerCamelCase__							,			[non_padded_sentence, padded_sentence]	)
style_context: 
  
  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     :			int    ="""1.5.0"""
  
  _lowerCAmelCase     :			Optional[Any]    =["""vision-encoder-decoder"""]
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	if is_optimum_available():
  		from optimum.version import __version__ as optimum_version
  
  		UpperCAmelCase__:  Optional[int]		       =   version.parse(SCREAMING_SNAKE_CASE			)
  		if parsed_optimum_version < version.parse(SCREAMING_SNAKE_CASE			):
  			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__:  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}",
  	]
  	UpperCAmelCase__:  str		       =   subprocess.Popen(" ".join(SCREAMING_SNAKE_CASE			)  ,stdout=subprocess.PIPE  ,shell=SCREAMING_SNAKE_CASE			)
  	proc.wait()
  
  	logger.info(
  	    "The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as "
  	    "transformers.onnx is deprecated, and will be removed in v5. You can find more information here: "
  	    "https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."			)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	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=SCREAMING_SNAKE_CASE			)
  
  	# Allocate the model
  	UpperCAmelCase__:  int		       =   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(SCREAMING_SNAKE_CASE  ,feature=args.feature			)
  	UpperCAmelCase__:  str		       =   model_onnx_config(model.config			)
  
  	if model_kind in ENCODER_DECODER_MODELS:
  		UpperCAmelCase__:  List[str]		       =   model.get_encoder()
  		UpperCAmelCase__:  Optional[int]		       =   model.get_decoder()
  
  		UpperCAmelCase__:  List[str]		       =   onnx_config.get_encoder_config(encoder_model.config			)
  		UpperCAmelCase__:  Any		       =   onnx_config.get_decoder_config(
  		    encoder_model.config  ,decoder_model.config  ,feature=args.feature			)
  
  		if args.opset is None:
  			UpperCAmelCase__:  Tuple		       =   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__:  str		       =   AutoFeatureExtractor.from_pretrained(args.model			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output.parent.joinpath("encoder_model.onnx"			)  ,)
  
  		validate_model_outputs(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output.parent.joinpath("encoder_model.onnx"			)  ,SCREAMING_SNAKE_CASE  ,args.atol if args.atol else encoder_onnx_config.atol_for_validation  ,)
  
  		UpperCAmelCase__:  Dict		       =   AutoTokenizer.from_pretrained(args.model			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  str		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output.parent.joinpath("decoder_model.onnx"			)  ,)
  
  		validate_model_outputs(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output.parent.joinpath("decoder_model.onnx"			)  ,SCREAMING_SNAKE_CASE  ,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__:  str		       =   get_preprocessor(args.model			)
  		elif args.preprocessor == "tokenizer":
  			UpperCAmelCase__:  Any		       =   AutoTokenizer.from_pretrained(args.model			)
  		elif args.preprocessor == "image_processor":
  			UpperCAmelCase__:  Optional[int]		       =   AutoImageProcessor.from_pretrained(args.model			)
  		elif args.preprocessor == "feature_extractor":
  			UpperCAmelCase__:  int		       =   AutoFeatureExtractor.from_pretrained(args.model			)
  		elif args.preprocessor == "processor":
  			UpperCAmelCase__:  Optional[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:
  			UpperCAmelCase__:  List[str]		       =   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__:  Tuple		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output  ,)
  
  		if args.atol is None:
  			UpperCAmelCase__:  List[Any]		       =   onnx_config.atol_for_validation
  
  		validate_model_outputs(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output  ,SCREAMING_SNAKE_CASE  ,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."  ,SCREAMING_SNAKE_CASE  ,)
  
  
  
  def  _A  (			):
  	UpperCAmelCase__:  str		       =   ArgumentParser("Hugging Face Transformers ONNX exporter"			)
  	parser.add_argument(
  	    "-m"  ,"--model"  ,type=SCREAMING_SNAKE_CASE  ,required=SCREAMING_SNAKE_CASE  ,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=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="ONNX opset version to export the model with."			)
  	parser.add_argument(
  	    "--atol"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="Absolute difference tolerance when validating the model."			)
  	parser.add_argument(
  	    "--framework"  ,type=SCREAMING_SNAKE_CASE  ,choices=["pt", "tf"]  ,default=SCREAMING_SNAKE_CASE  ,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=SCREAMING_SNAKE_CASE  ,help="Path indicating where to store generated ONNX model."			)
  	parser.add_argument("--cache_dir"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="Path indicating where to store cache."			)
  	parser.add_argument(
  	    "--preprocessor"  ,type=SCREAMING_SNAKE_CASE  ,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__:  Optional[int]		       =   parser.parse_args()
  	if args.export_with_transformers or not is_optimum_available():
  		export_with_transformers(SCREAMING_SNAKE_CASE			)
  	else:
  		export_with_optimum(SCREAMING_SNAKE_CASE			)
  
  
  if __name__ == "__main__":
  					_lowerCAmelCase     :			int    =logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
  					logger.setLevel(logging.INFO)
  					main()