
"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_a		       =							{
    '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',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   for attribute in key.split("."  ):
      UpperCAmelCase_					:					List[str]			     =    getattr(__lowerCamelCase,		__lowerCamelCase  )

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

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







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					str			     =    []
   UpperCAmelCase_					:					int			     =    fairseq_model.state_dict()

   UpperCAmelCase_					:					int			     =    hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      UpperCAmelCase_					:					int			     =    False
      if "conv_layers" in name:
         load_conv_layer(
             __lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		hf_model.config.feat_extract_norm == "group",		)
         UpperCAmelCase_					:					int			     =    True
      else:
         for key, mapped_key in MAPPING.items():
            UpperCAmelCase_					:					List[str]			     =    "hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

            if key in name or (key.split("w2v_model."  )[-1] == name.split("."  )[0] and not is_finetuned):
               UpperCAmelCase_					:					Union[str, Any]			     =    True
               if "*" in mapped_key:
                  UpperCAmelCase_					:					Tuple			     =    name.split(__lowerCamelCase  )[0].split("."  )[-2]
                  UpperCAmelCase_					:					List[Any]			     =    mapped_key.replace("*",		__lowerCamelCase  )
               if "weight_g" in name:
                  UpperCAmelCase_					:					Optional[Any]			     =    "weight_g"
               elif "weight_v" in name:
                  UpperCAmelCase_					:					int			     =    "weight_v"
               elif "weight" in name:
                  UpperCAmelCase_					:					List[Any]			     =    "weight"
               elif "bias" in name:
                  UpperCAmelCase_					:					Union[str, Any]			     =    "bias"
               else:
                  UpperCAmelCase_					:					Optional[int]			     =    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 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Optional[Any]			     =    full_name.split("conv_layers."  )[-1]
   UpperCAmelCase_					:					Tuple			     =    name.split("."  )
   UpperCAmelCase_					:					int			     =    int(items[0]  )
   UpperCAmelCase_					:					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."""
         )
         UpperCAmelCase_					:					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."""
         )
         UpperCAmelCase_					:					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."
         )
         UpperCAmelCase_					:					Optional[int]			     =    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."""
         )
         UpperCAmelCase_					:					Any			     =    value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
   else:
      unused_weights.append(__lowerCamelCase  )







@torch.no_grad()
def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase=None,		__lowerCamelCase=None,		__lowerCamelCase=True  ):
   if config_path is not None:
      UpperCAmelCase_					:					Any			     =    HubertConfig.from_pretrained(__lowerCamelCase  )
   else:
      UpperCAmelCase_					:					Dict			     =    HubertConfig()

   if is_finetuned:
      if dict_path:
         UpperCAmelCase_					:					str			     =    Dictionary.load(__lowerCamelCase  )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         UpperCAmelCase_					:					str			     =    target_dict.pad_index
         UpperCAmelCase_					:					int			     =    target_dict.bos_index
         UpperCAmelCase_					:					List[str]			     =    target_dict.eos_index
         UpperCAmelCase_					:					Optional[Any]			     =    len(target_dict.symbols  )
         UpperCAmelCase_					:					int			     =    os.path.join(__lowerCamelCase,		"vocab.json"  )
         if not os.path.isdir(__lowerCamelCase  ):
            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__lowerCamelCase  )  )
            return
         os.makedirs(__lowerCamelCase,		exist_ok=__lowerCamelCase  )
         with open(__lowerCamelCase,		"w",		encoding="utf-8"  ) as vocab_handle:
            json.dump(target_dict.indices,		__lowerCamelCase  )
         UpperCAmelCase_					:					List[Any]			     =    WavaVecaCTCTokenizer(
             __lowerCamelCase,		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=__lowerCamelCase,		)
         UpperCAmelCase_					:					int			     =    True if config.feat_extract_norm == "layer" else False
         UpperCAmelCase_					:					Dict			     =    WavaVecaFeatureExtractor(
             feature_size=1,		sampling_rate=1_6000,		padding_value=0,		do_normalize=__lowerCamelCase,		return_attention_mask=__lowerCamelCase,		)
         UpperCAmelCase_					:					Tuple			     =    WavaVecaProcessor(feature_extractor=__lowerCamelCase,		tokenizer=__lowerCamelCase  )
         processor.save_pretrained(__lowerCamelCase  )

      UpperCAmelCase_					:					List[Any]			     =    HubertForCTC(__lowerCamelCase  )
   else:
      UpperCAmelCase_					:					str			     =    HubertModel(__lowerCamelCase  )

   if is_finetuned:
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path],		arg_overrides={"data": "/".join(dict_path.split("/"  )[:-1]  )}  )
   else:
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  )

   UpperCAmelCase_					:					int			     =    model[0].eval()

   recursively_load_weights(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )

   hf_wavavec.save_pretrained(__lowerCamelCase  )


if __name__ == "__main__":
    _a		       =							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'
    )
    _a		       =							parser.parse_args()
    convert_hubert_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )




class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:int						)		-> Dict:
								__UpperCamelCase	:   Union[str, Any]			   =					{}







	def    _lowerCamelCase				(					self		:str						)		-> None:
								print(self.vertex						)
								for i in self.vertex:
															print(a     ,   " -> "     ,   " -> ".join([str(a						) for j in self.vertex[i]]						)						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:int     ,   a		:int						)		-> None:
								# check if vertex is already present,
								if from_vertex in self.vertex:
															self.vertex[from_vertex].append(a						)
								else:
															# else make a new vertex
															__UpperCamelCase	:   Optional[Any]			   =					[to_vertex]







	def    _lowerCamelCase				(					self		:Tuple						)		-> None:
								# visited array for storing already visited nodes
								__UpperCamelCase	:   Dict			   =					[False] * len(self.vertex						)

								# call the recursive helper function
								for i in range(len(self.vertex						)						):
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)







	def    _lowerCamelCase				(					self		:Any     ,   a		:int     ,   a		:list						)		-> None:
								# mark start vertex as visited
								__UpperCamelCase	:   int			   =					True

								print(a     ,   end=" "						)

								# Recur for all the vertices that are adjacent to this node
								for i in self.vertex:
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)


if __name__ == "__main__":
		lowercase  :					Dict													=       Graph()
		g.add_edge(0, 1)
		g.add_edge(0, 2)
		g.add_edge(1, 2)
		g.add_edge(2, 0)
		g.add_edge(2, 3)
		g.add_edge(3, 3)

		g.print_graph()
		print('DFS:')
		g.dfs()

		# OUTPUT:
		# 0  ->  1 -> 2
		# 1  ->  2
		# 2  ->  0 -> 3
		# 3  ->  3
		# DFS:
		#  0 1 2 3