


import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
UpperCAmelCase__     = logging.get_logger("transformers.models.speecht5")

UpperCAmelCase__     = {
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
UpperCAmelCase__     = {
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
UpperCAmelCase__     = {
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
UpperCAmelCase__     = {
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
UpperCAmelCase__     = {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
UpperCAmelCase__     = {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
UpperCAmelCase__     = {
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
UpperCAmelCase__     = {
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
UpperCAmelCase__     = {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
UpperCAmelCase__     = {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCAmelCase__     = {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCAmelCase__     = []
UpperCAmelCase__     = [
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
UpperCAmelCase__     = IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
UpperCAmelCase__     = IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
UpperCAmelCase__     = IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]





def 							A			(  _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     str					)       ->				Optional[Any]:







			'''simple docstring'''




			for attribute in key.split('.'					):
						_UpperCAmelCase	 =     getattr(__a							,     __a					)

			if weight_type is not None:
						_UpperCAmelCase	 =     getattr(__a							,     __a					).shape
			else:
						_UpperCAmelCase	 =     hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
						    F" {value.shape} for {full_name}"					)

			if weight_type == "weight":
						_UpperCAmelCase	 =     value
			elif weight_type == "weight_g":
						_UpperCAmelCase	 =     value
			elif weight_type == "weight_v":
						_UpperCAmelCase	 =     value
			elif weight_type == "bias":
						_UpperCAmelCase	 =     value
			elif weight_type == "running_mean":
						_UpperCAmelCase	 =     value
			elif weight_type == "running_var":
						_UpperCAmelCase	 =     value
			elif weight_type == "num_batches_tracked":
						_UpperCAmelCase	 =     value
			else:
						_UpperCAmelCase	 =     value

			logger.info(F"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."					)





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Any					)       ->				List[str]:







			'''simple docstring'''




			for key in ignore_keys:
						if key.endswith('.*'					):
									if name.startswith(key[:-1]					):
												return True
						elif ".*." in key:
									_UpperCAmelCase ,  _UpperCAmelCase	 =     key.split('.*.'					)
									if prefix in name and suffix in name:
												return True
						elif key in name:
									return True
			return False





def 							A			(  _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Dict					)       ->				List[str]:







			'''simple docstring'''
			_UpperCAmelCase	 =     []

			if task == "s2t":
						_UpperCAmelCase	 =     hf_model.speechta.encoder.prenet.feature_encoder
						_UpperCAmelCase	 =     MAPPING_S2T
						_UpperCAmelCase	 =     IGNORE_KEYS_S2T
			elif task == "t2s":
						_UpperCAmelCase	 =     None
						_UpperCAmelCase	 =     MAPPING_T2S
						_UpperCAmelCase	 =     IGNORE_KEYS_T2S
			elif task == "s2s":
						_UpperCAmelCase	 =     hf_model.speechta.encoder.prenet.feature_encoder
						_UpperCAmelCase	 =     MAPPING_S2S
						_UpperCAmelCase	 =     IGNORE_KEYS_S2S
			else:
						raise ValueError(F"Unsupported task: {task}"					)

			for name, value in fairseq_dict.items():
						if should_ignore(__a							,     __a					):
									logger.info(F"{name} was ignored"					)
									continue

						_UpperCAmelCase	 =     False
						if "conv_layers" in name:
									load_conv_layer(
									    __a							,     __a							,     __a							,     __a							,     hf_model.config.feat_extract_norm == 'group'							,     )
									_UpperCAmelCase	 =     True
						else:
									for key, mapped_key in MAPPING.items():
												# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

												if "*" in key:
															_UpperCAmelCase ,  _UpperCAmelCase	 =     key.split('.*.'					)
															if prefix in name and suffix in name:
																		_UpperCAmelCase	 =     suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
												if key in name:
															_UpperCAmelCase	 =     True
															if "*" in mapped_key:
																		_UpperCAmelCase	 =     name.split(__a					)[0].split('.'					)[-2]
																		_UpperCAmelCase	 =     mapped_key.replace('*'							,     __a					)
															if "weight_g" in name:
																		_UpperCAmelCase	 =     'weight_g'
															elif "weight_v" in name:
																		_UpperCAmelCase	 =     'weight_v'
															elif "bias" in name:
																		_UpperCAmelCase	 =     'bias'
															elif "weight" in name:
																		_UpperCAmelCase	 =     'weight'
															elif "running_mean" in name:
																		_UpperCAmelCase	 =     'running_mean'
															elif "running_var" in name:
																		_UpperCAmelCase	 =     'running_var'
															elif "num_batches_tracked" in name:
																		_UpperCAmelCase	 =     'num_batches_tracked'
															else:
																		_UpperCAmelCase	 =     None
															set_recursively(__a							,     __a							,     __a							,     __a							,     __a					)
												continue
						if not is_used:
									unused_weights.append(__a					)

			logger.warning(F"Unused weights: {unused_weights}"					)





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     List[Any]					)       ->				Any:







			'''simple docstring'''
			_UpperCAmelCase	 =     full_name.split('conv_layers.'					)[-1]
			_UpperCAmelCase	 =     name.split('.'					)
			_UpperCAmelCase	 =     int(items[0]					)
			_UpperCAmelCase	 =     int(items[1]					)

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    F"{full_name} has size {value.shape}, but"
												    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."					)
									_UpperCAmelCase	 =     value
									logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."					)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    F"{full_name} has size {value.shape}, but"
												    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."					)
									_UpperCAmelCase	 =     value
									logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."					)
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    F"{full_name} has size {value.shape}, but"
												    F" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."					)
									_UpperCAmelCase	 =     value
									logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    F"{full_name} has size {value.shape}, but"
												    F" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."					)
									_UpperCAmelCase	 =     value
									logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
			else:
						unused_weights.append(__a					)





@torch.no_grad()
def 							A			(  _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Any=None							,     _UpperCAmelCase       :     str=None							,     _UpperCAmelCase       :     Union[str, Any]=None							,     )       ->				str:







			'''simple docstring'''




			if config_path is not None:
						_UpperCAmelCase	 =     SpeechTaConfig.from_pretrained(__a					)
			else:
						_UpperCAmelCase	 =     SpeechTaConfig()

			if task == "s2t":
						_UpperCAmelCase	 =     config.max_text_positions
						_UpperCAmelCase	 =     SpeechTaForSpeechToText(__a					)
			elif task == "t2s":
						_UpperCAmelCase	 =     1_876
						_UpperCAmelCase	 =     600
						_UpperCAmelCase	 =     config.max_speech_positions
						_UpperCAmelCase	 =     SpeechTaForTextToSpeech(__a					)
			elif task == "s2s":
						_UpperCAmelCase	 =     1_876
						_UpperCAmelCase	 =     config.max_speech_positions
						_UpperCAmelCase	 =     SpeechTaForSpeechToSpeech(__a					)
			else:
						raise ValueError(F"Unknown task name: {task}"					)

			if vocab_path:
						_UpperCAmelCase	 =     SpeechTaTokenizer(__a							,     model_max_length=config.max_text_positions					)

						# Mask token behaves like a normal word, i.e. include the space before it
						_UpperCAmelCase	 =     AddedToken('<mask>'							,     lstrip=__a							,     rstrip=__a					)
						_UpperCAmelCase	 =     mask_token
						tokenizer.add_special_tokens({'mask_token': mask_token}					)
						tokenizer.add_tokens(['<ctc_blank>']					)

			_UpperCAmelCase	 =     SpeechTaFeatureExtractor()
			_UpperCAmelCase	 =     SpeechTaProcessor(tokenizer=__a							,     feature_extractor=__a					)
			processor.save_pretrained(__a					)

			_UpperCAmelCase	 =     torch.load(__a					)
			recursively_load_weights(fairseq_checkpoint['model']							,     __a							,     __a					)

			model.save_pretrained(__a					)

			if repo_id:
						print('Pushing to the hub...'					)
						processor.push_to_hub(__a					)
						model.push_to_hub(__a					)


if __name__ == "__main__":
							UpperCAmelCase__     = argparse.ArgumentParser()
							parser.add_argument(
							    "--task",
							    default="s2t",
							    type=str,
							    help="Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.",
							)
							parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
							parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
							parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
							parser.add_argument(
							    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
							)
							parser.add_argument(
							    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
							)

							UpperCAmelCase__     = parser.parse_args()
							convert_speechta_checkpoint(
							    args.task,
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.vocab_path,
							    args.push_to_hub,
							)





import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format="%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    level=os.environ.get("LOGLEVEL", "INFO").upper(),
    stream=sys.stdout,
)

UpperCAmelCase__     = logging.getLogger(__name__)

UpperCAmelCase__     = {"facebook/bart-base": BartForConditionalGeneration}
UpperCAmelCase__     = {"facebook/bart-base": BartTokenizer}





def 							A			(  )       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.'					)
			parser.add_argument(
			    '--validation_file'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help='A csv or a json file containing the validation data.'					)
			parser.add_argument(
			    '--max_length'							,     type=_UpperCAmelCase							,     default=5							,     help='The maximum total input sequence length after tokenization.'							,     )
			parser.add_argument(
			    '--num_beams'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help=(
			        'Number of beams to use for evaluation. This argument will be '
			        'passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'
			    )							,     )
			parser.add_argument(
			    '--model_name_or_path'							,     type=_UpperCAmelCase							,     help='Path to pretrained model or model identifier from huggingface.co/models.'							,     required=_UpperCAmelCase							,     )
			parser.add_argument(
			    '--config_name'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help='Pretrained config name or path if not the same as model_name'							,     )
			parser.add_argument(
			    '--device'							,     type=_UpperCAmelCase							,     default='cpu'							,     help='Device where the model will be run'							,     )
			parser.add_argument('--output_file_path'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help='Where to store the final ONNX file.'					)

			_UpperCAmelCase	 =     parser.parse_args()

			return args





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     List[Any]="cpu"					)       ->				Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase	 =     model_dict[model_name].from_pretrained(_UpperCAmelCase					).to(_UpperCAmelCase					)
			_UpperCAmelCase	 =     tokenizer_dict[model_name].from_pretrained(_UpperCAmelCase					)

			if model_name in ["facebook/bart-base"]:
						_UpperCAmelCase	 =     0
						_UpperCAmelCase	 =     None
						_UpperCAmelCase	 =     0

			return huggingface_model, tokenizer





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Tuple					)       ->				List[Any]:







			'''simple docstring'''
			model.eval()

			_UpperCAmelCase	 =     None
			_UpperCAmelCase	 =     torch.jit.script(BARTBeamSearchGenerator(_UpperCAmelCase					)					)

			with torch.no_grad():
						_UpperCAmelCase	 =     'My friends are cool but they eat too many carbs.'
						_UpperCAmelCase	 =     tokenizer([ARTICLE_TO_SUMMARIZE]							,     max_length=1_024							,     return_tensors='pt'					).to(model.device					)

						_UpperCAmelCase	 =     model.generate(
						    inputs['input_ids']							,     attention_mask=inputs['attention_mask']							,     num_beams=_UpperCAmelCase							,     max_length=_UpperCAmelCase							,     early_stopping=_UpperCAmelCase							,     decoder_start_token_id=model.config.decoder_start_token_id							,     )

						torch.onnx.export(
						    _UpperCAmelCase							,     (
						        inputs['input_ids'],
						        inputs['attention_mask'],
						        num_beams,
						        max_length,
						        model.config.decoder_start_token_id,
						    )							,     _UpperCAmelCase							,     opset_version=14							,     input_names=['input_ids', 'attention_mask', 'num_beams', 'max_length', 'decoder_start_token_id']							,     output_names=['output_ids']							,     dynamic_axes={
						        'input_ids': {0: 'batch', 1: 'seq'},
						        'output_ids': {0: 'batch', 1: 'seq_out'},
						    }							,     example_outputs=_UpperCAmelCase							,     )

						logger.info('Model exported to {}'.format(_UpperCAmelCase					)					)

						_UpperCAmelCase	 =     remove_dup_initializers(os.path.abspath(_UpperCAmelCase					)					)

						logger.info('Deduplicated and optimized model written to {}'.format(_UpperCAmelCase					)					)

						_UpperCAmelCase	 =     onnxruntime.InferenceSession(_UpperCAmelCase					)
						_UpperCAmelCase	 =     ort_sess.run(
						    _UpperCAmelCase							,     {
						        'input_ids': inputs['input_ids'].cpu().numpy(),
						        'attention_mask': inputs['attention_mask'].cpu().numpy(),
						        'num_beams': np.array(_UpperCAmelCase					),
						        'max_length': np.array(_UpperCAmelCase					),
						        'decoder_start_token_id': np.array(model.config.decoder_start_token_id					),
						    }							,     )

						np.testing.assert_allclose(summary_ids.cpu().numpy()							,     ort_out[0]							,     rtol=1E-3							,     atol=1E-3					)

						logger.info('Model outputs from torch and ONNX Runtime are similar.'					)
						logger.info('Success.'					)





def 							A			(  )       ->				Dict:







			'''simple docstring'''
			_UpperCAmelCase	 =     parse_args()
			_UpperCAmelCase	 =     5
			_UpperCAmelCase	 =     4

			# Make one log on every process with the configuration for debugging.
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,     datefmt='%m/%d/%Y %H:%M:%S'							,     level=logging.INFO							,     )

			logger.setLevel(logging.INFO					)
			transformers.utils.logging.set_verbosity_error()

			_UpperCAmelCase	 =     torch.device(args.device					)

			_UpperCAmelCase ,  _UpperCAmelCase	 =     load_model_tokenizer(args.model_name_or_path							,     _UpperCAmelCase					)

			if model.config.decoder_start_token_id is None:
						raise ValueError('Make sure that `config.decoder_start_token_id` is correctly defined'					)

			model.to(_UpperCAmelCase					)

			if args.max_length:
						_UpperCAmelCase	 =     args.max_length

			if args.num_beams:
						_UpperCAmelCase	 =     args.num_beams

			if args.output_file_path:
						_UpperCAmelCase	 =     args.output_file_path
			else:
						_UpperCAmelCase	 =     'BART.onnx'

			logger.info('Exporting model to ONNX'					)
			export_and_validate_model(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)


if __name__ == "__main__":
							main()
