




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	:	Dict				=   logging.get_logger(__name__)

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




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase						,lowercase					)  ->       str:
   for attribute in key.split("""."""					):
      snake_case				:    Tuple           =	getattr(lowercase						,lowercase					)

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

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




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Any:
   snake_case				:    Dict           =	[]
   snake_case				:    str           =	fairseq_model.state_dict()

   snake_case				:    Optional[Any]           =	hf_model.feature_extractor
   snake_case				:    List[Any]           =	hf_model.adapter

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

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




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




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase					)  ->       int:
   snake_case				:    Optional[Any]           =	full_name.split("""adaptor."""					)[-1]
   snake_case				:    List[str]           =	name.split("""."""					)

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




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       int:
   snake_case				:    Tuple           =	emb.weight.shape
   snake_case				:    List[str]           =	nn.Linear(lowercase						,lowercase						,bias=lowercase					)
   snake_case				:    str           =	emb.weight.data
   return lin_layer




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase						,lowercase						,lowercase						,lowercase						,lowercase						,lowercase						,lowercase						,lowercase						,)  ->       Tuple:
   snake_case				:    List[str]           =	WavaVecaConfig.from_pretrained(
       lowercase						,add_adapter=lowercase						,adapter_stride=lowercase						,adapter_kernel_size=lowercase						,use_auth_token=lowercase						,output_hidden_size=lowercase						,)
   snake_case				:    Union[str, Any]           =	MBartConfig.from_pretrained(lowercase					)

   # load model
   snake_case				:    Optional[int]           =	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,
       }						,)
   snake_case				:    Optional[Any]           =	model[0].eval()

   # load feature extractor
   snake_case				:    Optional[int]           =	WavaVecaFeatureExtractor.from_pretrained(lowercase						,use_auth_token=lowercase					)

   # set weights for wav2vec2 encoder
   snake_case				:    List[str]           =	WavaVecaModel(lowercase					)

   recursively_load_weights_wavaveca(model.encoder						,lowercase					)

   # load decoder weights
   snake_case				:    str           =	MBartForCausalLM(lowercase					)
   snake_case				:    int           =	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,strict=lowercase					)
   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}"""					)

   snake_case				:    Any           =	SpeechEncoderDecoderModel(encoder=lowercase						,decoder=lowercase					)
   snake_case				:    Union[str, Any]           =	False

   snake_case				:    str           =	MBartaaTokenizer(lowercase					)
   tokenizer.save_pretrained(lowercase					)

   snake_case				:    Tuple           =	hf_wavavec.config.to_dict()
   snake_case				:    int           =	tokenizer.pad_token_id
   snake_case				:    Union[str, Any]           =	tokenizer.bos_token_id
   snake_case				:    str           =	tokenizer.eos_token_id
   snake_case				:    List[str]           =	"""mbart50"""
   snake_case				:    int           =	"""wav2vec2"""

   snake_case				:    Tuple           =	tokenizer.eos_token_id
   snake_case				:    str           =	250004
   snake_case				:    Union[str, Any]           =	tokenizer.eos_token_id

   snake_case				:    List[str]           =	SpeechEncoderDecoderConfig.from_dict(lowercase					)

   hf_wavavec.save_pretrained(lowercase					)
   feature_extractor.save_pretrained(lowercase					)


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	:	Any				=   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,
       )











import functools




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:

   # Validation
   if not isinstance(lowercase						,lowercase					) or not all(isinstance(lowercase						,lowercase					) for day in days					):
      raise ValueError("""The parameter days should be a list of integers"""					)

   if len(lowercase					) != 3 or not all(isinstance(lowercase						,lowercase					) for cost in costs					):
      raise ValueError("""The parameter costs should be a list of three integers"""					)

   if len(lowercase					) == 0:
      return 0

   if min(lowercase					) <= 0:
      raise ValueError("""All days elements should be greater than 0"""					)

   if max(lowercase					) >= 366:
      raise ValueError("""All days elements should be less than 366"""					)

   snake_case				:    List[str]           =	set(lowercase					)

   @functools.cache
   def dynamic_programming(lowercase					) -> int:
      if index > 365:
         return 0

      if index not in days_set:
         return dynamic_programming(index + 1					)

      return min(
          costs[0] + dynamic_programming(index + 1					)						,costs[1] + dynamic_programming(index + 7					)						,costs[2] + dynamic_programming(index + 30					)						,)

   return dynamic_programming(1					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





