
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()
lowercase__          =logging.get_logger(__name__)

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Any  ):
 for attribute in key.split('''.'''  ):
  __a			:  Optional[Any]		    =  getattr(lowerCAmelCase__  ,       lowerCAmelCase__  )

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

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    List[Any]  ):
 __a			:  Tuple		    =  []
 __a			:  Optional[Any]		    =  fairseq_model.state_dict()

 __a			:  Tuple		    =  hf_model.feature_extractor
 __a			:  Optional[Any]		    =  hf_model.adapter

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

 logger.warning(f"Unused weights: {unused_weights}"  )




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




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    Any  ):
 __a			:  Union[str, Any]		    =  full_name.split('''adaptor.'''  )[-1]
 __a			:  Dict		    =  name.split('''.'''  )

 if items[1].isdigit():
  __a			:  Union[str, Any]		    =  int(items[1]  )
 else:
  __a			:  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."
    __a			:  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."
    __a			:  int		    =  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			:  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."
    __a			:  Optional[Any]		    =  value
    logger.info(f"Adapter proj layer weight was initialized from {full_name}."  )
 elif isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  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			:  Union[str, 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."
   __a			:  Union[str, Any]		    =  value
   logger.info(f"Adapter layer {layer_id} bias was initialized from {full_name}."  )
 else:
  unused_weights.append(lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ):
 __a    ,	__a			:  Optional[int]		    =  emb.weight.shape
 __a			:  Optional[int]		    =  nn.Linear(lowerCAmelCase__  ,       lowerCAmelCase__  ,       bias=lowerCAmelCase__  )
 __a			:  Optional[Any]		    =  emb.weight.data
 return lin_layer




@torch.no_grad()
def        __UpperCamelCase					(    lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    Any  ,       ):
 __a			:  Tuple		    =  WavaVecaConfig.from_pretrained(
     lowerCAmelCase__  ,       add_adapter=lowerCAmelCase__  ,       adapter_stride=lowerCAmelCase__  ,       adapter_kernel_size=lowerCAmelCase__  ,       use_auth_token=lowerCAmelCase__  ,       output_hidden_size=lowerCAmelCase__  ,       )
 __a			:  int		    =  MBartConfig.from_pretrained(lowerCAmelCase__  )

 # load model
 __a    ,	__a    ,	__a			:  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,
     }  ,       )
 __a			:  List[Any]		    =  model[0].eval()

 # load feature extractor
 __a			:  Dict		    =  WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase__  ,       use_auth_token=lowerCAmelCase__  )

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

 recursively_load_weights_wavaveca(model.encoder  ,       lowerCAmelCase__  )

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

 __a			:  int		    =  MBartaaTokenizer(lowerCAmelCase__  )
 tokenizer.save_pretrained(lowerCAmelCase__  )

 __a			:  Dict		    =  hf_wavavec.config.to_dict()
 __a			:  str		    =  tokenizer.pad_token_id
 __a			:  Union[str, Any]		    =  tokenizer.bos_token_id
 __a			:  str		    =  tokenizer.eos_token_id
 __a			:  Optional[int]		    =  '''mbart50'''
 __a			:  Optional[int]		    =  '''wav2vec2'''

 __a			:  Any		    =  tokenizer.eos_token_id
 __a			:  Tuple		    =  2_5_0_0_0_4
 __a			:  str		    =  tokenizer.eos_token_id

 __a			:  Tuple		    =  SpeechEncoderDecoderConfig.from_dict(lowerCAmelCase__  )

 hf_wavavec.save_pretrained(lowerCAmelCase__  )
 feature_extractor.save_pretrained(lowerCAmelCase__  )


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

      lowercase__          =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,
      )


from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


lowercase__          =logging.get_logger(__name__)

lowercase__          ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__          ={
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

lowercase__          ={'allegro/herbert-base-cased': 514}
lowercase__          ={}



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE				:							Dict     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				HerbertTokenizer
   def __init__(self			: str ,    snake_case_			: Optional[Any]=None ,    snake_case_			: Tuple=None ,    snake_case_			: Dict=None ,    snake_case_			: List[str]="<s>" ,    snake_case_			: Any="<unk>" ,    snake_case_			: Optional[int]="<pad>" ,    snake_case_			: Optional[Any]="<mask>" ,    snake_case_			: List[Any]="</s>" ,    **snake_case_			: Optional[int] ,    ):
    super().__init__(
        snake_case_ ,    snake_case_ ,    tokenizer_file=snake_case_ ,    cls_token=snake_case_ ,    unk_token=snake_case_ ,    pad_token=snake_case_ ,    mask_token=snake_case_ ,    sep_token=snake_case_ ,    **snake_case_ ,    )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  int		    =  [self.cls_token_id]
    __a			:  Union[str, Any]		    =  [self.sep_token_id]
    if token_ids_a is None:
     return cls + token_ids_a + sep

    return cls + token_ids_a + sep + token_ids_a + sep
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: bool = False       ):
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=snake_case_ ,    token_ids_a=snake_case_ ,    already_has_special_tokens=snake_case_       )

    if token_ids_a is None:
     return [1] + ([0] * len(snake_case_       )) + [1]
    return [1] + ([0] * len(snake_case_       )) + [1] + ([0] * len(snake_case_       )) + [1]
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  Optional[int]		    =  [self.sep_token_id]
    __a			:  int		    =  [self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep       ) * [0]
    return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]







   def 				lowerCAmelCase      (self			: int ,    snake_case_			: str ,    snake_case_			: Optional[str] = None       ):
    __a			:  Any		    =  self._tokenizer.model.save(snake_case_ ,    name=snake_case_       )
    return tuple(snake_case_       )
