

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]	)


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()