


'''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()
__snake_case							   =  logging.get_logger(__name__)

__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''',
}
__snake_case							   =  [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def     a   (    __a				,      __a				,      __a				,      __a				,      __a     )						->			Optional[int]:



					'''simple docstring'''






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

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

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


def     a   (    __a				,      __a     )						->			List[str]:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				[]
					UpperCamelCase__     :Any							      =				fairseq_model.state_dict()

					UpperCamelCase__     :Optional[Any]							      =				hf_model.feature_extractor
					UpperCamelCase__     :str							      =				hf_model.adapter

					for name, value in fairseq_dict.items():
										UpperCamelCase__     :Dict							      =				False
										if "conv_layers" in name:
															load_conv_layer(
															    __a				,      __a				,      __a				,      __a				,      hf_model.config.feat_extract_norm == '''group'''				,      )
															UpperCamelCase__     :Optional[Any]							      =				True
										elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']     ):
															load_adapter(__a				,      __a				,      __a				,      __a     )
															UpperCamelCase__     :Optional[int]							      =				True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split('''w2v_model.'''     )[-1] == name.split('''.'''     )[0]:
																									UpperCamelCase__     :Any							      =				True
																									if "*" in mapped_key:
																														UpperCamelCase__     :Optional[int]							      =				name.split(__a     )[0].split('''.'''     )[-2]
																														UpperCamelCase__     :Optional[int]							      =				mapped_key.replace('''*'''				,      __a     )
																									if "weight_g" in name:
																														UpperCamelCase__     :Union[str, Any]							      =				'''weight_g'''
																									elif "weight_v" in name:
																														UpperCamelCase__     :Optional[Any]							      =				'''weight_v'''
																									elif "bias" in name:
																														UpperCamelCase__     :Dict							      =				'''bias'''
																									elif "weight" in name:
																														UpperCamelCase__     :Any							      =				'''weight'''
																									else:
																														UpperCamelCase__     :Optional[Any]							      =				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   (    __a				,      __a				,      __a				,      __a				,      __a     )						->			Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__     :List[str]							      =				full_name.split('''conv_layers.'''     )[-1]
					UpperCamelCase__     :int							      =				name.split('''.'''     )
					UpperCamelCase__     :str							      =				int(items[0]     )
					UpperCamelCase__     :Dict							      =				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__     :Optional[Any]							      =				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__     :Dict							      =				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__     :Dict							      =				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[str]							      =				value
															logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
					else:
										unused_weights.append(__a     )


def     a   (    __a				,      __a				,      __a				,      __a     )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :Any							      =				full_name.split('''adaptor.'''     )[-1]
					UpperCamelCase__     :Dict							      =				name.split('''.'''     )

					if items[1].isdigit():
										UpperCamelCase__     :Union[str, Any]							      =				int(items[1]     )
					else:
										UpperCamelCase__     :Tuple							      =				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__     :int							      =				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__     :str							      =				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__     :Optional[int]							      =				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__     :Tuple							      =				value
																				logger.info(f'''Adapter proj layer weight was initialized from {full_name}.'''     )
					elif isinstance(__a				,      __a     ):
										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__     :Tuple							      =				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(__a     )


def     a   (    __a     )						->			Dict:



					'''simple docstring'''






					UpperCamelCase__      ,       UpperCamelCase__     :List[str]							      =				emb.weight.shape
					UpperCamelCase__     :List[str]							      =				nn.Linear(__a				,      __a				,      bias=__a     )
					UpperCamelCase__     :Dict							      =				emb.weight.data
					return lin_layer


@torch.no_grad()
def     a   (    __a				,      __a				,      __a				,      __a				,      __a				,      __a				,      __a				,      __a				,      __a				,      __a				,      __a				,      )						->			Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__     :List[str]							      =				WavaVecaConfig.from_pretrained(
					    __a				,      add_adapter=__a				,      adapter_stride=__a				,      adapter_kernel_size=__a				,      use_auth_token=__a				,      output_hidden_size=__a				,      )
					UpperCamelCase__     :List[str]							      =				MBartConfig.from_pretrained(__a     )

					# load model
					UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Optional[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__     :Dict							      =				model[0].eval()

					# load feature extractor
					UpperCamelCase__     :Optional[Any]							      =				WavaVecaFeatureExtractor.from_pretrained(__a				,      use_auth_token=__a     )

					# set weights for wav2vec2 encoder
					UpperCamelCase__     :str							      =				WavaVecaModel(__a     )

					recursively_load_weights_wavaveca(model.encoder				,      __a     )

					# load decoder weights
					UpperCamelCase__     :str							      =				MBartForCausalLM(__a     )
					UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,      strict=__a     )
					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__     :Any							      =				SpeechEncoderDecoderModel(encoder=__a				,      decoder=__a     )
					UpperCamelCase__     :List[Any]							      =				False

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

					UpperCamelCase__     :Optional[int]							      =				hf_wavavec.config.to_dict()
					UpperCamelCase__     :Dict							      =				tokenizer.pad_token_id
					UpperCamelCase__     :Optional[Any]							      =				tokenizer.bos_token_id
					UpperCamelCase__     :Tuple							      =				tokenizer.eos_token_id
					UpperCamelCase__     :int							      =				'''mbart50'''
					UpperCamelCase__     :Optional[Any]							      =				'''wav2vec2'''

					UpperCamelCase__     :List[str]							      =				tokenizer.eos_token_id
					UpperCamelCase__     :Optional[Any]							      =				250004
					UpperCamelCase__     :str							      =				tokenizer.eos_token_id

					UpperCamelCase__     :Optional[int]							      =				SpeechEncoderDecoderConfig.from_dict(__a     )

					hf_wavavec.save_pretrained(__a     )
					feature_extractor.save_pretrained(__a     )


if __name__ == "__main__":
		__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=1024, type=int, help='''encoder output dim''')
		parser.add_argument('''--start_token_id''', default=250004, type=int, help='''`decoder_start_token_id` of model config''')

		__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,
		)



'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=None							):

							'''simple docstring'''


							if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

							UpperCamelCase__     :Optional[int]							      =				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							UpperCamelCase__     :Any							      =				beta_t.flatten()
							while len(beta_t.shape							) < len(x.shape							):
												UpperCamelCase__     :Any							      =				beta_t.unsqueeze(-1							)
							UpperCamelCase__     :Optional[int]							      =				-0.5 * beta_t * x

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps