



'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase      						=       logging.get_logger(__name__)

UpperCAmelCase      						=       {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}

class __snake_case( UpperCamelCase_   ):


						'''simple docstring'''



						UpperCAmelCase				:       Tuple 	=						'swinv2'

						UpperCAmelCase				:       str 	=						{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}


						def __init__(   self      ,			A_=224      ,			A_=4      ,			A_=3      ,			A_=96      ,			A_=[2, 2, 6, 2]      ,			A_=[3, 6, 12, 24]      ,			A_=7      ,			A_=4.0      ,			A_=True      ,			A_=0.0      ,			A_=0.0      ,			A_=0.1      ,			A_="gelu"      ,			A_=False      ,			A_=0.0_2      ,			A_=1e-5      ,			A_=32      ,			**A_      ,			)      ->      Optional[int]:
							super().__init__(**lowercase_ )

							lowerCAmelCase       =      image_size
							lowerCAmelCase       =      patch_size
							lowerCAmelCase       =      num_channels
							lowerCAmelCase       =      embed_dim
							lowerCAmelCase       =      depths
							lowerCAmelCase       =      len(lowercase_ )
							lowerCAmelCase       =      num_heads
							lowerCAmelCase       =      window_size
							lowerCAmelCase       =      mlp_ratio
							lowerCAmelCase       =      qkv_bias
							lowerCAmelCase       =      hidden_dropout_prob
							lowerCAmelCase       =      attention_probs_dropout_prob
							lowerCAmelCase       =      drop_path_rate
							lowerCAmelCase       =      hidden_act
							lowerCAmelCase       =      use_absolute_embeddings
							lowerCAmelCase       =      layer_norm_eps
							lowerCAmelCase       =      initializer_range
							lowerCAmelCase       =      encoder_stride
							# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							lowerCAmelCase       =      int(embed_dim * 2 ** (len(lowercase_ ) - 1) )
							lowerCAmelCase       =      (0, 0, 0, 0)



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		   =			logging.get_logger(__name__)

lowerCamelCase		   =			{
    '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		   =			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def       a_ (       SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : Dict						,     SCREAMING_SNAKE_CASE__   : List[str]						,     SCREAMING_SNAKE_CASE__   : Optional[Any]						,     SCREAMING_SNAKE_CASE__   : Union[str, Any]	):
       '''simple docstring'''





       for attribute in key.split('.'	):
              _lowerCamelCase			:						Optional[Any]            =getattr(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)

       if weight_type is not None:
              _lowerCamelCase			:						str            =getattr(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	).shape
       else:
              _lowerCamelCase			:						Optional[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":
              _lowerCamelCase			:						Tuple            =value
       elif weight_type == "weight_g":
              _lowerCamelCase			:						Any            =value
       elif weight_type == "weight_v":
              _lowerCamelCase			:						Any            =value
       elif weight_type == "bias":
              _lowerCamelCase			:						Dict            =value
       else:
              _lowerCamelCase			:						Union[str, Any]            =value

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




def       a_ (       SCREAMING_SNAKE_CASE__   : int						,     SCREAMING_SNAKE_CASE__   : Optional[Any]	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =[]
       _lowerCamelCase			:						Optional[Any]            =fairseq_model.state_dict()

       _lowerCamelCase			:						Tuple            =hf_model.feature_extractor
       _lowerCamelCase			:						int            =hf_model.adapter

       for name, value in fairseq_dict.items():
              _lowerCamelCase			:						Optional[Any]            =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'						,     )
                     _lowerCamelCase			:						Optional[int]            =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__	)
                     _lowerCamelCase			:						int            =True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split('w2v_model.'	)[-1] == name.split('.'	)[0]:
                                   _lowerCamelCase			:						Dict            =True
                                   if "*" in mapped_key:
                                          _lowerCamelCase			:						List[str]            =name.split(SCREAMING_SNAKE_CASE__	)[0].split('.'	)[-2]
                                          _lowerCamelCase			:						Dict            =mapped_key.replace('*'						,     SCREAMING_SNAKE_CASE__	)
                                   if "weight_g" in name:
                                          _lowerCamelCase			:						List[Any]            ='weight_g'
                                   elif "weight_v" in name:
                                          _lowerCamelCase			:						int            ='weight_v'
                                   elif "bias" in name:
                                          _lowerCamelCase			:						int            ='bias'
                                   elif "weight" in name:
                                          _lowerCamelCase			:						Optional[int]            ='weight'
                                   else:
                                          _lowerCamelCase			:						Any            =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_ (       SCREAMING_SNAKE_CASE__   : Optional[int]						,     SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : List[Any]						,     SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Any	):
       '''simple docstring'''





       _lowerCamelCase			:						str            =full_name.split('conv_layers.'	)[-1]
       _lowerCamelCase			:						List[Any]            =name.split('.'	)
       _lowerCamelCase			:						Optional[int]            =int(items[0]	)
       _lowerCamelCase			:						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.'''
                     )
                     _lowerCamelCase			:						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.'''
                     )
                     _lowerCamelCase			:						int            =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."
                     )
                     _lowerCamelCase			:						List[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.'''
                     )
                     _lowerCamelCase			:						Tuple            =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_ (       SCREAMING_SNAKE_CASE__   : Optional[Any]						,     SCREAMING_SNAKE_CASE__   : List[str]						,     SCREAMING_SNAKE_CASE__   : Optional[int]						,     SCREAMING_SNAKE_CASE__   : Dict	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =full_name.split('adaptor.'	)[-1]
       _lowerCamelCase			:						str            =name.split('.'	)

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




def       a_ (       SCREAMING_SNAKE_CASE__   : Tuple	):
       '''simple docstring'''





       _lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =emb.weight.shape
       _lowerCamelCase			:						str            =nn.Linear(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     bias=SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						Optional[Any]            =emb.weight.data
       return lin_layer




@torch.no_grad()
def       a_ (       SCREAMING_SNAKE_CASE__   : int						,     SCREAMING_SNAKE_CASE__   : List[str]						,     SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : List[Any]						,     SCREAMING_SNAKE_CASE__   : List[Any]						,     SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : Dict						,     SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : List[Any]						,     ):
       '''simple docstring'''





       _lowerCamelCase			:						int            =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__						,     )
       _lowerCamelCase			:						str            =MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__	)

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

       # load feature extractor
       _lowerCamelCase			:						List[Any]            =WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__						,     use_auth_token=SCREAMING_SNAKE_CASE__	)

       # set weights for wav2vec2 encoder
       _lowerCamelCase			:						List[Any]            =WavaVecaModel(SCREAMING_SNAKE_CASE__	)

       recursively_load_weights_wavaveca(model.encoder						,     SCREAMING_SNAKE_CASE__	)

       # load decoder weights
       _lowerCamelCase			:						int            =MBartForCausalLM(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =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}'''	)

       _lowerCamelCase			:						Dict            =SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__						,     decoder=SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						List[Any]            =False

       _lowerCamelCase			:						Any            =MBartaaTokenizer(SCREAMING_SNAKE_CASE__	)
       tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__	)

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

       _lowerCamelCase			:						Tuple            =tokenizer.eos_token_id
       _lowerCamelCase			:						Dict            =250_004
       _lowerCamelCase			:						List[str]            =tokenizer.eos_token_id

       _lowerCamelCase			:						Dict            =SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE__	)

       hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__	)
       feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
 lowerCamelCase		   =			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=10_24, type=int, help='encoder output dim')
 parser.add_argument('--start_token_id', default=25_00_04, type=int, help='`decoder_start_token_id` of model config')

 lowerCamelCase		   =			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,
 )


