






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

__UpperCamelCase      :       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""",
}
__UpperCamelCase      :       Tuple      			=      [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	str,					SCREAMING_SNAKE_CASE__:	Dict,					SCREAMING_SNAKE_CASE__:	str,					SCREAMING_SNAKE_CASE__:	str    )  ->			Any:

   """simple docstring"""
   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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Tuple,					SCREAMING_SNAKE_CASE__:	Dict    )  ->			Optional[int]:

   """simple docstring"""
   __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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Tuple,					SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	Dict,					SCREAMING_SNAKE_CASE__:	Dict    )  ->			Optional[int]:

   """simple docstring"""
   __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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Tuple,					SCREAMING_SNAKE_CASE__:	str,					SCREAMING_SNAKE_CASE__:	Optional[int],					SCREAMING_SNAKE_CASE__:	Optional[Any]    )  ->			Any:

   """simple docstring"""
   __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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			Optional[Any]:

   """simple docstring"""
   __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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	Optional[Any],					SCREAMING_SNAKE_CASE__:	Optional[int],					SCREAMING_SNAKE_CASE__:	List[Any],					SCREAMING_SNAKE_CASE__:	Tuple,					SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	List[Any],					SCREAMING_SNAKE_CASE__:	List[str],					SCREAMING_SNAKE_CASE__:	Tuple,					SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	Optional[int],					)  ->			Tuple:

   """simple docstring"""
   __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				     =    250004
   __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__":
 __UpperCamelCase      :       str      			=      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""")

 __UpperCamelCase      :       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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase      :       List[str]      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Tuple      			=      {
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="vit_msn"




      def __init__(  self ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-06 ,   lowerCamelCase=224 ,   lowerCamelCase=16 ,   lowerCamelCase=3 ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->Tuple:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )

         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    intermediate_size
         __a				     =    hidden_act
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    initializer_range
         __a				     =    layer_norm_eps
         __a				     =    image_size
         __a				     =    patch_size
         __a				     =    num_channels
         __a				     =    qkv_bias