



import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =      {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
SCREAMING_SNAKE_CASE__       =      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      List[str]:
			for attribute in key.split('.'					):
						A__       =    getattr(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE					)

			if weight_type is not None:
						A__       =    getattr(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE					).shape
			else:
						A__       =    hf_pointer.shape

			assert hf_shape == value.shape, (
			    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":
						A__       =    value
			elif weight_type == "weight_g":
						A__       =    value
			elif weight_type == "weight_v":
						A__       =    value
			elif weight_type == "bias":
						A__       =    value
			else:
						A__       =    value

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


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Any:
			A__       =    []
			A__       =    fairseq_model.state_dict()

			A__       =    hf_model.feature_extractor
			A__       =    hf_model.adapter

			for name, value in fairseq_dict.items():
						A__       =    False
						if "conv_layers" in name:
									load_conv_layer(
									    _SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	hf_model.config.feat_extract_norm == 'group' ,	)
									A__       =    True
						elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']					):
									load_adapter(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE					)
									A__       =    True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split('w2v_model.'					)[-1] == name.split('.'					)[0]:
															A__       =    True
															if "*" in mapped_key:
																		A__       =    name.split(_SCREAMING_SNAKE_CASE					)[0].split('.'					)[-2]
																		A__       =    mapped_key.replace('*' ,	_SCREAMING_SNAKE_CASE					)
															if "weight_g" in name:
																		A__       =    'weight_g'
															elif "weight_v" in name:
																		A__       =    'weight_v'
															elif "bias" in name:
																		A__       =    'bias'
															elif "weight" in name:
																		A__       =    'weight'
															else:
																		A__       =    None
															set_recursively(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE					)
												continue
						if not is_used:
									unused_weights.append(_SCREAMING_SNAKE_CASE					)

			logger.warning(f'''Unused weights: {unused_weights}'''					)


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Any:
			A__       =    full_name.split('conv_layers.'					)[-1]
			A__       =    name.split('.'					)
			A__       =    int(items[0]					)
			A__       =    int(items[1]					)

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    f'''{full_name} has size {value.shape}, but'''
									    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
									)
									A__       =    value
									logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''					)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    f'''{full_name} has size {value.shape}, but'''
									    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
									)
									A__       =    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:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
									    " found."
									)
									A__       =    value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    f'''{full_name} has size {value.shape}, but'''
									    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
									)
									A__       =    value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
			else:
						unused_weights.append(_SCREAMING_SNAKE_CASE					)


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      List[str]:
			A__       =    full_name.split('adaptor.'					)[-1]
			A__       =    name.split('.'					)

			if items[1].isdigit():
						A__       =    int(items[1]					)
			else:
						A__       =    None

			if "adaptor" not in full_name:
						if "proj_ln" in full_name:
									# has to be layer norm
									if "bias" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.bias.data.shape
												), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
												A__       =    value
												logger.info(f'''Adapter proj layer norm bias was initialized from {full_name}.'''					)
									if "weight" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.weight.data.shape
												), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
												A__       =    value
						else:
									# has to be projection layer
									if "bias" in name:
												assert (
												    value.shape == adapter.proj.bias.data.shape
												), f'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
												A__       =    value
												logger.info(f'''Adapter proj layer bias was initialized from {full_name}.'''					)
									if "weight" in name:
												assert (
												    value.shape == adapter.proj.weight.data.shape
												), f'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
												A__       =    value
												logger.info(f'''Adapter proj layer weight was initialized from {full_name}.'''					)
			elif isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE					):
						if "bias" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.bias.data.shape
									), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
									A__       =    value
									logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''					)
						elif "weight" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.weight.data.shape
									), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
									A__       =    value
									logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''					)
			else:
						unused_weights.append(_SCREAMING_SNAKE_CASE					)


def A						(       __UpperCamelCase					)						->      Optional[Any]:
			A__     , A__       =    emb.weight.shape
			A__       =    nn.Linear(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	bias=_SCREAMING_SNAKE_CASE					)
			A__       =    emb.weight.data
			return lin_layer


@torch.no_grad()
def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	)						->      List[str]:
			A__       =    WavaVecaConfig.from_pretrained(
			    _SCREAMING_SNAKE_CASE ,	add_adapter=_SCREAMING_SNAKE_CASE ,	adapter_stride=_SCREAMING_SNAKE_CASE ,	adapter_kernel_size=_SCREAMING_SNAKE_CASE ,	use_auth_token=_SCREAMING_SNAKE_CASE ,	output_hidden_size=_SCREAMING_SNAKE_CASE ,	)
			A__       =    MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE					)

			# load model
			A__     , A__     , A__       =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path] ,	arg_overrides={
			        'config_yaml': config_yaml_path,
			        'data': '/'.join(dict_path.split('/'					)[:-1]					),
			        'w2v_path': checkpoint_path,
			        'load_pretrained_decoder_from': None,
			    } ,	)
			A__       =    model[0].eval()

			# load feature extractor
			A__       =    WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE ,	use_auth_token=_SCREAMING_SNAKE_CASE					)

			# set weights for wav2vec2 encoder
			A__       =    WavaVecaModel(_SCREAMING_SNAKE_CASE					)

			recursively_load_weights_wavaveca(model.encoder ,	_SCREAMING_SNAKE_CASE					)

			# load decoder weights
			A__       =    MBartForCausalLM(_SCREAMING_SNAKE_CASE					)
			A__     , A__       =    hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,	strict=_SCREAMING_SNAKE_CASE					)
			logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''					)
			logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''					)

			A__       =    SpeechEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE ,	decoder=_SCREAMING_SNAKE_CASE					)
			A__       =    False

			A__       =    MBartaaTokenizer(_SCREAMING_SNAKE_CASE					)
			tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE					)

			A__       =    hf_wavavec.config.to_dict()
			A__       =    tokenizer.pad_token_id
			A__       =    tokenizer.bos_token_id
			A__       =    tokenizer.eos_token_id
			A__       =    'mbart50'
			A__       =    'wav2vec2'

			A__       =    tokenizer.eos_token_id
			A__       =    250_004
			A__       =    tokenizer.eos_token_id

			A__       =    SpeechEncoderDecoderConfig.from_dict(_SCREAMING_SNAKE_CASE					)

			hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE					)
			feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      argparse.ArgumentParser()
							parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
							parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
							parser.add_argument('''--config_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
							parser.add_argument(
							    '''--encoder_config_path''',
							    default='''facebook/wav2vec2-xls-r-1b''',
							    type=str,
							    help='''Path to hf encoder wav2vec2 checkpoint config''',
							)
							parser.add_argument(
							    '''--decoder_config_path''',
							    default='''facebook/mbart-large-50-one-to-many-mmt''',
							    type=str,
							    help='''Path to hf decoder checkpoint config''',
							)
							parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
							parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
							parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
							parser.add_argument('''--encoder_output_dim''', default=1_0_2_4, type=int, help='''encoder output dim''')
							parser.add_argument('''--start_token_id''', default=2_5_0_0_0_4, type=int, help='''`decoder_start_token_id` of model config''')

							SCREAMING_SNAKE_CASE__       =      parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    args.config_yaml_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    add_adapter=args.add_adapter,
							    adapter_kernel_size=args.adapter_kernel_size,
							    adapter_stride=args.adapter_stride,
							    decoder_start_token_id=args.start_token_id,
							    encoder_output_dim=args.encoder_output_dim,
							)









from typing import Dict

from .base import GenericTensor, Pipeline


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def     _a							(     self					:       Any				,					_snake_case					:       str=None				,					_snake_case					:       Dict=None				,					_snake_case					:       Any=None				,					**_snake_case					:       str					):






								"""simple docstring"""


								if tokenize_kwargs is None:
											A__       =    {}

								if truncation is not None:
											if "truncation" in tokenize_kwargs:
														raise ValueError(
														    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'					)
											A__       =    truncation

								A__       =    tokenize_kwargs

								A__       =    {}
								if return_tensors is not None:
											A__       =    return_tensors

								return preprocess_params, {}, postprocess_params



					def     _a							(     self					:       Any				,					_snake_case					:       Dict				,					**_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.framework
								A__       =    self.tokenizer(_snake_case				,					return_tensors=_snake_case				,					**_snake_case					)
								return model_inputs



					def     _a							(     self					:       List[Any]				,					_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    self.model(**_snake_case					)
								return model_outputs



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       List[Any]				,					_snake_case					:       str=False					):






								"""simple docstring"""


								if return_tensors:
											return model_outputs[0]
								if self.framework == "pt":
											return model_outputs[0].tolist()
								elif self.framework == "tf":
											return model_outputs[0].numpy().tolist()



					def __call__(     self					:       Dict				,					*_snake_case					:       int				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


								return super().__call__(*_snake_case				,					**_snake_case					)




