



'''simple docstring'''




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()
a   :			List[Any]					  =    logging.get_logger(__name__)

a   :			Optional[Any]					  =    {
    """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""",
}
a   :			Dict					  =    [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)      ->			List[Any]:






							for attribute in key.split("""."""		):
														UpperCAmelCase     :		Optional[int]							     =		getattr(UpperCamelCase__	,				UpperCamelCase__		)

							if weight_type is not None:
														UpperCAmelCase     :		List[str]							     =		getattr(UpperCamelCase__	,				UpperCamelCase__		).shape
							else:
														UpperCAmelCase     :		List[Any]							     =		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":
														UpperCAmelCase     :		List[str]							     =		value
							elif weight_type == "weight_g":
														UpperCAmelCase     :		str							     =		value
							elif weight_type == "weight_v":
														UpperCAmelCase     :		int							     =		value
							elif weight_type == "bias":
														UpperCAmelCase     :		Optional[Any]							     =		value
							else:
														UpperCAmelCase     :		List[Any]							     =		value

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



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			List[Any]:
							UpperCAmelCase     :		List[Any]							     =		[]
							UpperCAmelCase     :		Optional[Any]							     =		fairseq_model.state_dict()

							UpperCAmelCase     :		Dict							     =		hf_model.feature_extractor
							UpperCAmelCase     :		int							     =		hf_model.adapter

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

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



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[Any]:
							UpperCAmelCase     :		Tuple							     =		full_name.split("""conv_layers."""		)[-1]
							UpperCAmelCase     :		Tuple							     =		name.split("""."""		)
							UpperCAmelCase     :		Dict							     =		int(items[0]		)
							UpperCAmelCase     :		List[Any]							     =		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.'''
																					)
																					UpperCAmelCase     :		Tuple							     =		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.'''
																					)
																					UpperCAmelCase     :		Any							     =		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."
																					)
																					UpperCAmelCase     :		Union[str, Any]							     =		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.'''
																					)
																					UpperCAmelCase     :		List[Any]							     =		value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
							else:
														unused_weights.append(UpperCamelCase__		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)      ->			Union[str, Any]:
							UpperCAmelCase     :		Dict							     =		full_name.split("""adaptor."""		)[-1]
							UpperCAmelCase     :		int							     =		name.split("""."""		)

							if items[1].isdigit():
														UpperCAmelCase     :		Optional[Any]							     =		int(items[1]		)
							else:
														UpperCAmelCase     :		Optional[Any]							     =		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.'''
																												UpperCAmelCase     :		List[str]							     =		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.'''
																												UpperCAmelCase     :		Any							     =		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.'''
																												UpperCAmelCase     :		Tuple							     =		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.'''
																												UpperCAmelCase     :		List[Any]							     =		value
																												logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''		)
							elif isinstance(UpperCamelCase__	,				UpperCamelCase__		):
														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.'''
																					UpperCAmelCase     :		List[Any]							     =		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.'''
																					UpperCAmelCase     :		Dict							     =		value
																					logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''		)
							else:
														unused_weights.append(UpperCamelCase__		)



def       __lowerCamelCase  (					_lowercase		)      ->			int:
							UpperCAmelCase     ,     UpperCAmelCase     :		str							     =		emb.weight.shape
							UpperCAmelCase     :		List[Any]							     =		nn.Linear(UpperCamelCase__	,				UpperCamelCase__	,				bias=UpperCamelCase__		)
							UpperCAmelCase     :		List[Any]							     =		emb.weight.data
							return lin_layer



@torch.no_grad()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				)      ->			Optional[Any]:
							UpperCAmelCase     :		List[str]							     =		WavaVecaConfig.from_pretrained(
							    UpperCamelCase__	,				add_adapter=UpperCamelCase__	,				adapter_stride=UpperCamelCase__	,				adapter_kernel_size=UpperCamelCase__	,				use_auth_token=UpperCamelCase__	,				output_hidden_size=UpperCamelCase__	,				)
							UpperCAmelCase     :		Tuple							     =		MBartConfig.from_pretrained(UpperCamelCase__		)

							# load model
							UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		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,
							    }	,				)
							UpperCAmelCase     :		Union[str, Any]							     =		model[0].eval()

							# load feature extractor
							UpperCAmelCase     :		List[str]							     =		WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase__	,				use_auth_token=UpperCamelCase__		)

							# set weights for wav2vec2 encoder
							UpperCAmelCase     :		List[str]							     =		WavaVecaModel(UpperCamelCase__		)

							recursively_load_weights_wavaveca(model.encoder	,				UpperCamelCase__		)

							# load decoder weights
							UpperCAmelCase     :		Optional[Any]							     =		MBartForCausalLM(UpperCamelCase__		)
							UpperCAmelCase     ,     UpperCAmelCase     :		int							     =		hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,				strict=UpperCamelCase__		)
							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}'''		)

							UpperCAmelCase     :		Optional[int]							     =		SpeechEncoderDecoderModel(encoder=UpperCamelCase__	,				decoder=UpperCamelCase__		)
							UpperCAmelCase     :		List[Any]							     =		False

							UpperCAmelCase     :		Optional[int]							     =		MBartaaTokenizer(UpperCamelCase__		)
							tokenizer.save_pretrained(UpperCamelCase__		)

							UpperCAmelCase     :		Dict							     =		hf_wavavec.config.to_dict()
							UpperCAmelCase     :		Tuple							     =		tokenizer.pad_token_id
							UpperCAmelCase     :		int							     =		tokenizer.bos_token_id
							UpperCAmelCase     :		List[str]							     =		tokenizer.eos_token_id
							UpperCAmelCase     :		Optional[int]							     =		"""mbart50"""
							UpperCAmelCase     :		Optional[Any]							     =		"""wav2vec2"""

							UpperCAmelCase     :		int							     =		tokenizer.eos_token_id
							UpperCAmelCase     :		Tuple							     =		2_5_0_0_0_4
							UpperCAmelCase     :		Tuple							     =		tokenizer.eos_token_id

							UpperCAmelCase     :		int							     =		SpeechEncoderDecoderConfig.from_dict(UpperCamelCase__		)

							hf_wavavec.save_pretrained(UpperCamelCase__		)
							feature_extractor.save_pretrained(UpperCamelCase__		)


if __name__ == "__main__":
							a   :			Dict					  =    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""")

							a   :			Optional[int]					  =    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,
							)




import numpy as np





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return 1 / (1 + np.exp(-vector      ))





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return vector * sigmoid(UpperCamelCase__      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




