



import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


UpperCAmelCase__     = logging.get_logger(__name__)

UpperCAmelCase__     = OrderedDict(
    [
        ("audio-spectrogram-transformer", "ASTFeatureExtractor"),
        ("beit", "BeitFeatureExtractor"),
        ("chinese_clip", "ChineseCLIPFeatureExtractor"),
        ("clap", "ClapFeatureExtractor"),
        ("clip", "CLIPFeatureExtractor"),
        ("clipseg", "ViTFeatureExtractor"),
        ("conditional_detr", "ConditionalDetrFeatureExtractor"),
        ("convnext", "ConvNextFeatureExtractor"),
        ("cvt", "ConvNextFeatureExtractor"),
        ("data2vec-audio", "Wav2Vec2FeatureExtractor"),
        ("data2vec-vision", "BeitFeatureExtractor"),
        ("deformable_detr", "DeformableDetrFeatureExtractor"),
        ("deit", "DeiTFeatureExtractor"),
        ("detr", "DetrFeatureExtractor"),
        ("dinat", "ViTFeatureExtractor"),
        ("donut-swin", "DonutFeatureExtractor"),
        ("dpt", "DPTFeatureExtractor"),
        ("encodec", "EncodecFeatureExtractor"),
        ("flava", "FlavaFeatureExtractor"),
        ("glpn", "GLPNFeatureExtractor"),
        ("groupvit", "CLIPFeatureExtractor"),
        ("hubert", "Wav2Vec2FeatureExtractor"),
        ("imagegpt", "ImageGPTFeatureExtractor"),
        ("layoutlmv2", "LayoutLMv2FeatureExtractor"),
        ("layoutlmv3", "LayoutLMv3FeatureExtractor"),
        ("levit", "LevitFeatureExtractor"),
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilenet_v1", "MobileNetV1FeatureExtractor"),
        ("mobilenet_v2", "MobileNetV2FeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("nat", "ViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
        ("resnet", "ConvNextFeatureExtractor"),
        ("segformer", "SegformerFeatureExtractor"),
        ("sew", "Wav2Vec2FeatureExtractor"),
        ("sew-d", "Wav2Vec2FeatureExtractor"),
        ("speech_to_text", "Speech2TextFeatureExtractor"),
        ("speecht5", "SpeechT5FeatureExtractor"),
        ("swiftformer", "ViTFeatureExtractor"),
        ("swin", "ViTFeatureExtractor"),
        ("swinv2", "ViTFeatureExtractor"),
        ("table-transformer", "DetrFeatureExtractor"),
        ("timesformer", "VideoMAEFeatureExtractor"),
        ("tvlt", "TvltFeatureExtractor"),
        ("unispeech", "Wav2Vec2FeatureExtractor"),
        ("unispeech-sat", "Wav2Vec2FeatureExtractor"),
        ("van", "ConvNextFeatureExtractor"),
        ("videomae", "VideoMAEFeatureExtractor"),
        ("vilt", "ViltFeatureExtractor"),
        ("vit", "ViTFeatureExtractor"),
        ("vit_mae", "ViTFeatureExtractor"),
        ("vit_msn", "ViTFeatureExtractor"),
        ("wav2vec2", "Wav2Vec2FeatureExtractor"),
        ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
        ("wavlm", "Wav2Vec2FeatureExtractor"),
        ("whisper", "WhisperFeatureExtractor"),
        ("xclip", "CLIPFeatureExtractor"),
        ("yolos", "YolosFeatureExtractor"),
    ]
)

UpperCAmelCase__     = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)





def 							A			(  _UpperCAmelCase       :     str					)       ->				Tuple:







			'''simple docstring'''
			for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
						if class_name in extractors:
									_UpperCAmelCase	 =     model_type_to_module_name(_UpperCAmelCase					)

									_UpperCAmelCase	 =     importlib.import_module(F".{module_name}"							,     'transformers.models'					)
									try:
												return getattr(_UpperCAmelCase							,     _UpperCAmelCase					)
									except AttributeError:
												continue

			for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
						if getattr(_UpperCAmelCase							,     '__name__'							,     _UpperCAmelCase					) == class_name:
									return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
			_UpperCAmelCase	 =     importlib.import_module('transformers'					)
			if hasattr(_UpperCAmelCase							,     _UpperCAmelCase					):
						return getattr(_UpperCAmelCase							,     _UpperCAmelCase					)

			return None





def 							A			(  _UpperCAmelCase       :     Union[str, os.PathLike]							,     _UpperCAmelCase       :     Optional[Union[str, os.PathLike]] = None							,     _UpperCAmelCase       :     bool = False							,     _UpperCAmelCase       :     bool = False							,     _UpperCAmelCase       :     Optional[Dict[str, str]] = None							,     _UpperCAmelCase       :     Optional[Union[bool, str]] = None							,     _UpperCAmelCase       :     Optional[str] = None							,     _UpperCAmelCase       :     bool = False							,     **_UpperCAmelCase       :     Tuple							,     )       ->				str:







			'''simple docstring'''
			_UpperCAmelCase	 =     get_file_from_repo(
			    _UpperCAmelCase							,     _UpperCAmelCase							,     cache_dir=_UpperCAmelCase							,     force_download=_UpperCAmelCase							,     resume_download=_UpperCAmelCase							,     proxies=_UpperCAmelCase							,     use_auth_token=_UpperCAmelCase							,     revision=_UpperCAmelCase							,     local_files_only=_UpperCAmelCase							,     )
			if resolved_config_file is None:
						logger.info(
						    'Could not locate the feature extractor configuration file, will try to use the model config instead.'					)
						return {}

			with open(_UpperCAmelCase							,     encoding='utf-8'					) as reader:
						return json.load(_UpperCAmelCase					)







class     __lowerCAmelCase					:



			def __init__(		self     :						Tuple)     ->					Tuple:



						"""simple docstring"""






						raise EnvironmentError(
						    'AutoFeatureExtractor is designed to be instantiated '
						    'using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.')



			@classmethod
			@replace_list_option_in_docstrings(A)
			def 					_lowerCamelCase (		cls     :						Tuple				,	A     :						Union[str, Any]				,	**A     :						Any)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     kwargs.pop('config'				,	A)
						_UpperCAmelCase	 =     kwargs.pop('trust_remote_code'				,	A)
						_UpperCAmelCase	 =     True

						_UpperCAmelCase ,  _UpperCAmelCase	 =     FeatureExtractionMixin.get_feature_extractor_dict(A				,	**A)
						_UpperCAmelCase	 =     config_dict.get('feature_extractor_type'				,	A)
						_UpperCAmelCase	 =     None
						if "AutoFeatureExtractor" in config_dict.get('auto_map'				,	{}):
									_UpperCAmelCase	 =     config_dict['auto_map']['AutoFeatureExtractor']

						# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
						if feature_extractor_class is None and feature_extractor_auto_map is None:
									if not isinstance(A				,	A):
												_UpperCAmelCase	 =     AutoConfig.from_pretrained(A				,	**A)
									# It could be in `config.feature_extractor_type``
									_UpperCAmelCase	 =     getattr(A				,	'feature_extractor_type'				,	A)
									if hasattr(A				,	'auto_map') and "AutoFeatureExtractor" in config.auto_map:
												_UpperCAmelCase	 =     config.auto_map['AutoFeatureExtractor']

						if feature_extractor_class is not None:
									_UpperCAmelCase	 =     feature_extractor_class_from_name(A)

						_UpperCAmelCase	 =     feature_extractor_auto_map is not None
						_UpperCAmelCase	 =     feature_extractor_class is not None or type(A) in FEATURE_EXTRACTOR_MAPPING
						_UpperCAmelCase	 =     resolve_trust_remote_code(
						    A				,	A				,	A				,	A)

						if has_remote_code and trust_remote_code:
									_UpperCAmelCase	 =     get_class_from_dynamic_module(
									    A				,	A				,	**A)
									_UpperCAmelCase	 =     kwargs.pop('code_revision'				,	A)
									if os.path.isdir(A):
												feature_extractor_class.register_for_auto_class()
									return feature_extractor_class.from_dict(A				,	**A)
						elif feature_extractor_class is not None:
									return feature_extractor_class.from_dict(A				,	**A)
						# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
						elif type(A) in FEATURE_EXTRACTOR_MAPPING:
									_UpperCAmelCase	 =     FEATURE_EXTRACTOR_MAPPING[type(A)]
									return feature_extractor_class.from_dict(A				,	**A)

						raise ValueError(
						    F"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a "
						    F"`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following "
						    F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys())}")



			@staticmethod
			def 					_lowerCamelCase (		A     :						int				,	A     :						Any)     ->					Union[str, Any]:



						"""simple docstring"""






						FEATURE_EXTRACTOR_MAPPING.register(A				,	A)





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()
