


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

lowerCAmelCase__  :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''',
}
lowerCAmelCase__  :Optional[int]   			=							[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def        lowerCAmelCase__ (			a__:		int			,  a__:		List[str]			,  a__:		Tuple			,  a__:		Optional[Any]			,  a__:		List[Any]       )  ->		List[Any]:


							'''simple docstring'''





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

							if weight_type is not None:
														_UpperCAmelCase 		=			getattr(_SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE       ).shape
							else:
														_UpperCAmelCase 		=			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 		=			value
							elif weight_type == "weight_g":
														_UpperCAmelCase 		=			value
							elif weight_type == "weight_v":
														_UpperCAmelCase 		=			value
							elif weight_type == "bias":
														_UpperCAmelCase 		=			value
							else:
														_UpperCAmelCase 		=			value

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

def        lowerCAmelCase__ (			a__:		Optional[Any]			,  a__:		Dict       )  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase 		=			[]
							_UpperCAmelCase 		=			fairseq_model.state_dict()

							_UpperCAmelCase 		=			hf_model.feature_extractor
							_UpperCAmelCase 		=			hf_model.adapter

							for name, value in fairseq_dict.items():
														_UpperCAmelCase 		=			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'			,  )
																					_UpperCAmelCase 		=			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       )
																					_UpperCAmelCase 		=			True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('w2v_model.'       )[-1] == name.split('.'       )[0]:
																																			_UpperCAmelCase 		=			True
																																			if "*" in mapped_key:
																																										_UpperCAmelCase 		=			name.split(_SCREAMING_SNAKE_CASE       )[0].split('.'       )[-2]
																																										_UpperCAmelCase 		=			mapped_key.replace('*'			,  _SCREAMING_SNAKE_CASE       )
																																			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'
																																			else:
																																										_UpperCAmelCase 		=			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        lowerCAmelCase__ (			a__:		Optional[Any]			,  a__:		Optional[Any]			,  a__:		Dict			,  a__:		Optional[Any]			,  a__:		Any       )  ->		Optional[int]:


							'''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:
																					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 		=			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 		=			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 		=			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 		=			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        lowerCAmelCase__ (			a__:		str			,  a__:		Optional[Any]			,  a__:		Any			,  a__:		Dict       )  ->		str:


							'''simple docstring'''





							_UpperCAmelCase 		=			full_name.split('adaptor.'       )[-1]
							_UpperCAmelCase 		=			name.split('.'       )

							if items[1].isdigit():
														_UpperCAmelCase 		=			int(items[1]       )
							else:
														_UpperCAmelCase 		=			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 		=			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 		=			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 		=			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 		=			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.'''
																					_UpperCAmelCase 		=			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 		=			value
																					logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''       )
							else:
														unused_weights.append(_SCREAMING_SNAKE_CASE       )

def        lowerCAmelCase__ (			a__:		Optional[int]       )  ->		Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase 		=			emb.weight.shape
							_UpperCAmelCase 		=			nn.Linear(_SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  bias=_SCREAMING_SNAKE_CASE       )
							_UpperCAmelCase 		=			emb.weight.data
							return lin_layer

@torch.no_grad()
def        lowerCAmelCase__ (			a__:		int			,  a__:		List[Any]			,  a__:		Optional[Any]			,  a__:		Union[str, Any]			,  a__:		Any			,  a__:		Tuple			,  a__:		Dict			,  a__:		str			,  a__:		Optional[int]			,  a__:		Optional[Any]			,  a__:		int			,  )  ->		int:


							'''simple docstring'''





							_UpperCAmelCase 		=			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			,  )
							_UpperCAmelCase 		=			MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )

							# load model
							_UpperCAmelCase 		=			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 		=			model[0].eval()

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

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

							recursively_load_weights_wavaveca(model.encoder			,  _SCREAMING_SNAKE_CASE       )

							# load decoder weights
							_UpperCAmelCase 		=			MBartForCausalLM(_SCREAMING_SNAKE_CASE       )
							_UpperCAmelCase 		=			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}'''       )

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

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

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

							_UpperCAmelCase 		=			tokenizer.eos_token_id
							_UpperCAmelCase 		=			2_5_0_0_0_4
							_UpperCAmelCase 		=			tokenizer.eos_token_id

							_UpperCAmelCase 		=			SpeechEncoderDecoderConfig.from_dict(_SCREAMING_SNAKE_CASE       )

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


if __name__ == "__main__":
		lowerCAmelCase__  :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''')

		lowerCAmelCase__  :List[str]   			=							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"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""roberta-prelayernorm"""



      def __init__(						self    ,			lowercase=5_02_65    ,			lowercase=7_68    ,			lowercase=12    ,			lowercase=12    ,			lowercase=30_72    ,			lowercase="gelu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=5_12    ,			lowercase=2    ,			lowercase=0.02    ,			lowercase=1e-12    ,			lowercase=1    ,			lowercase=0    ,			lowercase=2    ,			lowercase="absolute"    ,			lowercase=True    ,			lowercase=None    ,			**lowercase    ,			)	->			Any:





        '''simple docstring'''





        super().__init__(pad_token_id=lowercase    ,			bos_token_id=lowercase    ,			eos_token_id=lowercase    ,			**lowercase)

        a__:     Union[str, Any]										=	vocab_size
        a__:     str										=	hidden_size
        a__:     Tuple										=	num_hidden_layers
        a__:     List[str]										=	num_attention_heads
        a__:     Dict										=	hidden_act
        a__:     int										=	intermediate_size
        a__:     Tuple										=	hidden_dropout_prob
        a__:     str										=	attention_probs_dropout_prob
        a__:     Tuple										=	max_position_embeddings
        a__:     Tuple										=	type_vocab_size
        a__:     Optional[Any]										=	initializer_range
        a__:     Tuple										=	layer_norm_eps
        a__:     Optional[int]										=	position_embedding_type
        a__:     Any										=	use_cache
        a__:     Dict										=	classifier_dropout







class    __snake_case       (     __lowerCAmelCase				):



      @property
      def   lowerCamelCase_   (						self)	->			Mapping[str, Mapping[int, str]]:





        '''simple docstring'''





        if self.task == "multiple-choice":
          a__:     str										=	{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
          a__:     Union[str, Any]										=	{0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ])


