


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''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''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''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''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
_snake_case             =     [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				{}
  with open(SCREAMING_SNAKE_CASE_      ,						"r"	) as file:
    for line_number, line in enumerate(SCREAMING_SNAKE_CASE_	):
      lowerCamelCase :					Any    =				line.strip()
      if line:
        lowerCamelCase :					Optional[Any]    =				line.split()
        lowerCamelCase :					Dict    =				line_number
        lowerCamelCase :					List[Any]    =				words[0]
        lowerCamelCase :					Optional[Any]    =				value
  return result






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for attribute in key.split("."	):
    lowerCamelCase :					Union[str, Any]    =				getattr(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					Optional[int]    =				None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(SCREAMING_SNAKE_CASE_	):
      lowerCamelCase :					Union[str, Any]    =				PARAM_MAPPING[full_name.split("."	)[-1]]
      lowerCamelCase :					Optional[Any]    =				"param"

  if weight_type is not None and weight_type != "param":
    lowerCamelCase :					str    =				getattr(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	).shape
  elif weight_type is not None and weight_type == "param":
    lowerCamelCase :					Optional[Any]    =				hf_pointer
    for attribute in hf_param_name.split("."	):
      lowerCamelCase :					int    =				getattr(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					Optional[int]    =				shape_pointer.shape

    # let's reduce dimension
    lowerCamelCase :					List[str]    =				value[0]
  else:
    lowerCamelCase :					List[str]    =				hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        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 :					Any    =				value
  elif weight_type == "weight_g":
    lowerCamelCase :					str    =				value
  elif weight_type == "weight_v":
    lowerCamelCase :					Dict    =				value
  elif weight_type == "bias":
    lowerCamelCase :					str    =				value
  elif weight_type == "param":
    for attribute in hf_param_name.split("."	):
      lowerCamelCase :					Dict    =				getattr(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					Union[str, Any]    =				value
  else:
    lowerCamelCase :					int    =				value

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(SCREAMING_SNAKE_CASE_	):
      lowerCamelCase :					int    =				PARAM_MAPPING[full_name.split("."	)[-1]]
      lowerCamelCase :					int    =				"param"

  if weight_type is not None and weight_type != "param":
    lowerCamelCase :					Union[str, Any]    =				".".join([key, weight_type]	)
  elif weight_type is not None and weight_type == "param":
    lowerCamelCase :					str    =				".".join([key, hf_param_name]	)
  else:
    lowerCamelCase :					Union[str, Any]    =				key

  lowerCamelCase :					Any    =				value if "lm_head" in full_key else value[0]


_snake_case             =     {
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=None      ,						SCREAMING_SNAKE_CASE_=None	):


  '''simple docstring'''

  lowerCamelCase :					Optional[int]    =				False
  for key, mapped_key in MAPPING.items():
    lowerCamelCase :					List[Any]    =				"wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split("w2v_model."	)[-1] == name.split("."	)[0]:
      lowerCamelCase :					List[Any]    =				True
      if "*" in mapped_key:
        lowerCamelCase :					List[str]    =				name.split(SCREAMING_SNAKE_CASE_	)[0].split("."	)[-2]
        lowerCamelCase :					List[Any]    =				mapped_key.replace("*"      ,						SCREAMING_SNAKE_CASE_	)
      if "weight_g" in name:
        lowerCamelCase :					Optional[Any]    =				"weight_g"
      elif "weight_v" in name:
        lowerCamelCase :					List[Any]    =				"weight_v"
      elif "bias" in name:
        lowerCamelCase :					Tuple    =				"bias"
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        lowerCamelCase :					Optional[Any]    =				"weight"
      else:
        lowerCamelCase :					Optional[int]    =				None
      if hf_dict is not None:
        rename_dict(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
      else:
        set_recursively(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
      return is_used
  return is_used






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				[]
  lowerCamelCase :					Optional[int]    =				fairseq_model.state_dict()

  lowerCamelCase :					Any    =				hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    lowerCamelCase :					List[str]    =				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 :					Union[str, Any]    =				True
    else:
      lowerCamelCase :					Optional[int]    =				load_wavaveca_layer(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
    if not is_used:
      unused_weights.append(SCREAMING_SNAKE_CASE_	)

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				full_name.split("conv_layers."	)[-1]
  lowerCamelCase :					Dict    =				name.split("."	)
  lowerCamelCase :					List[Any]    =				int(items[0]	)
  lowerCamelCase :					List[str]    =				int(items[1]	)

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            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:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""	)
      lowerCamelCase :					str    =				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:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""	)
      lowerCamelCase :					int    =				value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""	)
      lowerCamelCase :					List[str]    =				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_	)






@torch.no_grad()
def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=None      ,						SCREAMING_SNAKE_CASE_=None      ,						SCREAMING_SNAKE_CASE_=True      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  if config_path is not None:
    lowerCamelCase :					str    =				WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
  else:
    lowerCamelCase :					int    =				WavaVecaConfig()

  if is_seq_class:
    lowerCamelCase :					Union[str, Any]    =				read_txt_into_dict(SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					List[str]    =				idalabel
    lowerCamelCase :					List[Any]    =				WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					Union[str, Any]    =				WavaVecaFeatureExtractor(
        feature_size=1      ,						sampling_rate=16000      ,						padding_value=0      ,						do_normalize=SCREAMING_SNAKE_CASE_      ,						return_attention_mask=SCREAMING_SNAKE_CASE_      ,						)
    feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_	)

  elif is_finetuned:
    if dict_path:
      lowerCamelCase :					Optional[Any]    =				Dictionary.load(SCREAMING_SNAKE_CASE_	)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      lowerCamelCase :					str    =				target_dict.pad_index
      lowerCamelCase :					Any    =				target_dict.bos_index
      lowerCamelCase :					str    =				target_dict.eos_index
      lowerCamelCase :					List[Any]    =				len(target_dict.symbols	)
      lowerCamelCase :					Optional[Any]    =				os.path.join(SCREAMING_SNAKE_CASE_      ,						"vocab.json"	)
      if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(SCREAMING_SNAKE_CASE_	)	)
        return
      os.makedirs(SCREAMING_SNAKE_CASE_      ,						exist_ok=SCREAMING_SNAKE_CASE_	)
      lowerCamelCase :					Any    =				target_dict.indices

      # fairseq has the <pad> and <s> switched
      lowerCamelCase :					Tuple    =				0
      lowerCamelCase :					List[str]    =				1
      with open(SCREAMING_SNAKE_CASE_      ,						"w"      ,						encoding="utf-8"	) as vocab_handle:
        json.dump(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
      lowerCamelCase :					str    =				WavaVecaCTCTokenizer(
          SCREAMING_SNAKE_CASE_      ,						unk_token=target_dict.unk_word      ,						pad_token=target_dict.pad_word      ,						bos_token=target_dict.bos_word      ,						eos_token=target_dict.eos_word      ,						word_delimiter_token="|"      ,						do_lower_case=SCREAMING_SNAKE_CASE_      ,						)
      lowerCamelCase :					List[str]    =				True if config.feat_extract_norm == "layer" else False
      lowerCamelCase :					Tuple    =				WavaVecaFeatureExtractor(
          feature_size=1      ,						sampling_rate=16000      ,						padding_value=0      ,						do_normalize=SCREAMING_SNAKE_CASE_      ,						return_attention_mask=SCREAMING_SNAKE_CASE_      ,						)
      lowerCamelCase :					Dict    =				WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_      ,						tokenizer=SCREAMING_SNAKE_CASE_	)
      processor.save_pretrained(SCREAMING_SNAKE_CASE_	)

    lowerCamelCase :					Dict    =				WavaVecaForCTC(SCREAMING_SNAKE_CASE_	)
  else:
    lowerCamelCase :					Union[str, Any]    =				WavaVecaForPreTraining(SCREAMING_SNAKE_CASE_	)

  if is_finetuned or is_seq_class:
    lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase :					Dict    =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]      ,						arg_overrides={"data": "/".join(dict_path.split("/"	)[:-1]	)}	)
  else:
    lowerCamelCase :					Optional[Any]    =				argparse.Namespace(task="audio_pretraining"	)
    lowerCamelCase :					Any    =				fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE_	)

    lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase :					Optional[Any]    =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,						task=SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					List[Any]    =				model[0].eval()

  recursively_load_weights(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						not is_finetuned	)

  hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
   _snake_case             =     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_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
   parser.add_argument(
       '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
   )
   parser.add_argument(
       '''--is_seq_class''',
       action='''store_true''',
       help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
   )
   _snake_case             =     parser.parse_args()

   _snake_case             =     not args.not_finetuned and not args.is_seq_class
   convert_wavaveca_checkpoint(
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.dict_path,
       is_finetuned,
       args.is_seq_class,
   )







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


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      str     		=		"decision_transformer"
   __A :      Union[str, Any]     		=		["past_key_values"]
   __A :      Optional[int]     		=		{
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }






   def __init__(		self						,				__A=17						,				__A=4						,				__A=128						,				__A=4096						,				__A=True						,				__A=1						,				__A=1024						,				__A=3						,				__A=1						,				__A=None						,				__A="relu"						,				__A=0.1						,				__A=0.1						,				__A=0.1						,				__A=1e-5						,				__A=0.02						,				__A=True						,				__A=True						,				__A=5_0256						,				__A=5_0256						,				__A=False						,				__A=False						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				state_dim
     lowerCamelCase :					Tuple    =				act_dim
     lowerCamelCase :					List[str]    =				hidden_size
     lowerCamelCase :					Optional[Any]    =				max_ep_len
     lowerCamelCase :					Union[str, Any]    =				action_tanh
     lowerCamelCase :					int    =				vocab_size
     lowerCamelCase :					List[Any]    =				n_positions
     lowerCamelCase :					Dict    =				n_layer
     lowerCamelCase :					int    =				n_head
     lowerCamelCase :					List[Any]    =				n_inner
     lowerCamelCase :					Any    =				activation_function
     lowerCamelCase :					Optional[int]    =				resid_pdrop
     lowerCamelCase :					str    =				embd_pdrop
     lowerCamelCase :					Tuple    =				attn_pdrop
     lowerCamelCase :					List[Any]    =				layer_norm_epsilon
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					Optional[int]    =				scale_attn_weights
     lowerCamelCase :					List[Any]    =				use_cache
     lowerCamelCase :					Tuple    =				scale_attn_by_inverse_layer_idx
     lowerCamelCase :					Optional[int]    =				reorder_and_upcast_attn

     lowerCamelCase :					Dict    =				bos_token_id
     lowerCamelCase :					Any    =				eos_token_id

     super().__init__(bos_token_id=__A						,				eos_token_id=__A						,				**__A )



