"""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()
SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

SCREAMING_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",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				Union[str, Any] ,			_UpperCamelCase       :				str ,			_UpperCamelCase       :				int ,			_UpperCamelCase       :				str					)					->				List[str]:





      """simple docstring"""






      for attribute in key.split('.'					):
            snake_case								=			getattr(_UpperCamelCase ,			_UpperCamelCase					)

      if weight_type is not None:
            snake_case								=			getattr(_UpperCamelCase ,			_UpperCamelCase					).shape
      else:
            snake_case								=			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								=			value
      elif weight_type == "weight_g":
            snake_case								=			value
      elif weight_type == "weight_v":
            snake_case								=			value
      elif weight_type == "bias":
            snake_case								=			value
      else:
            snake_case								=			value

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





def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				List[str]					)					->				str:





      """simple docstring"""






      snake_case								=			[]
      snake_case								=			fairseq_model.state_dict()

      snake_case								=			hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            snake_case								=			False
            if "conv_layers" in name:
                  load_conv_layer(
                      _UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase ,			hf_model.config.feat_extract_norm == 'group' ,			)
                  snake_case								=			True
            else:
                  for key, mapped_key in MAPPING.items():
                        snake_case								=			'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):
                              snake_case								=			True
                              if "*" in mapped_key:
                                    snake_case								=			name.split(_UpperCamelCase					)[0].split('.'					)[-2]
                                    snake_case								=			mapped_key.replace('*' ,			_UpperCamelCase					)
                              if "weight_g" in name:
                                    snake_case								=			'weight_g'
                              elif "weight_v" in name:
                                    snake_case								=			'weight_v'
                              elif "weight" in name:
                                    snake_case								=			'weight'
                              elif "bias" in name:
                                    snake_case								=			'bias'
                              else:
                                    snake_case								=			None
                              set_recursively(_UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase					)
                        continue
            if not is_used:
                  unused_weights.append(_UpperCamelCase					)

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





def    lowerCAmelCase__    (			_UpperCamelCase       :				Dict ,			_UpperCamelCase       :				str ,			_UpperCamelCase       :				Optional[int] ,			_UpperCamelCase       :				str ,			_UpperCamelCase       :				str					)					->				Optional[Any]:





      """simple docstring"""






      snake_case								=			full_name.split('conv_layers.'					)[-1]
      snake_case								=			name.split('.'					)
      snake_case								=			int(items[0]					)
      snake_case								=			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								=			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								=			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								=			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								=			value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
      else:
            unused_weights.append(_UpperCamelCase					)





@torch.no_grad()
def    lowerCAmelCase__    (			_UpperCamelCase       :				List[Any] ,			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				Dict=None ,			_UpperCamelCase       :				Optional[int]=None ,			_UpperCamelCase       :				Any=True					)					->				Dict:





      """simple docstring"""






      if config_path is not None:
            snake_case								=			HubertConfig.from_pretrained(_UpperCamelCase					)
      else:
            snake_case								=			HubertConfig()

      if is_finetuned:
            if dict_path:
                  snake_case								=			Dictionary.load(_UpperCamelCase					)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  snake_case								=			target_dict.pad_index
                  snake_case								=			target_dict.bos_index
                  snake_case								=			target_dict.eos_index
                  snake_case								=			len(target_dict.symbols					)
                  snake_case								=			os.path.join(_UpperCamelCase ,			'vocab.json'					)
                  if not os.path.isdir(_UpperCamelCase					):
                        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_UpperCamelCase					)					)
                        return
                  os.makedirs(_UpperCamelCase ,			exist_ok=_UpperCamelCase					)
                  with open(_UpperCamelCase ,			'w' ,			encoding='utf-8'					) as vocab_handle:
                        json.dump(target_dict.indices ,			_UpperCamelCase					)
                  snake_case								=			WavaVecaCTCTokenizer(
                      _UpperCamelCase ,			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=_UpperCamelCase ,			)
                  snake_case								=			True if config.feat_extract_norm == 'layer' else False
                  snake_case								=			WavaVecaFeatureExtractor(
                      feature_size=1 ,			sampling_rate=1_6_0_0_0 ,			padding_value=0 ,			do_normalize=_UpperCamelCase ,			return_attention_mask=_UpperCamelCase ,			)
                  snake_case								=			WavaVecaProcessor(feature_extractor=_UpperCamelCase ,			tokenizer=_UpperCamelCase					)
                  processor.save_pretrained(_UpperCamelCase					)

            snake_case								=			HubertForCTC(_UpperCamelCase					)
      else:
            snake_case								=			HubertModel(_UpperCamelCase					)

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

      snake_case								=			model[0].eval()

      recursively_load_weights(_UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase					)

      hf_wavavec.save_pretrained(_UpperCamelCase					)


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



"""simple docstring"""
class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				pass





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				pass





class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			[
										    [],
										    [],
										    [],
										]

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										try:
																if len(self.queues[priority]    ) >= 1_00:
																						raise OverflowError('Maximum queue size is 100'    )
																self.queues[priority].append(lowerCAmelCase    )
										except IndexError:
																raise ValueError('Valid priorities are 0, 1, and 2'    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										for queue in self.queues:
																if queue:
																						return queue.pop(0    )
										raise UnderFlowError('All queues are empty'    )

				def __str__(				self    ):






										"""simple docstring"""

										return "\n".join(F"""Priority {i}: {q}""" for i, q in enumerate(self.queues    )    )





class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			[]

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if len(self.queue    ) == 1_00:
																raise OverFlowError('Maximum queue size is 100'    )
										self.queue.append(lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										if not self.queue:
																raise UnderFlowError('The queue is empty'    )
										else:
																snake_case								=			min(self.queue    )
																self.queue.remove(lowerCAmelCase    )
																return data

				def __str__(				self    ):






										"""simple docstring"""

										return str(self.queue    )





def    lowerCAmelCase__    (			)					->				int:





						"""simple docstring"""






						snake_case								=			FixedPriorityQueue()
						fpq.enqueue(0 ,			1_0					)
						fpq.enqueue(1 ,			7_0					)
						fpq.enqueue(0 ,			1_0_0					)
						fpq.enqueue(2 ,			1					)
						fpq.enqueue(2 ,			5					)
						fpq.enqueue(1 ,			7					)
						fpq.enqueue(2 ,			4					)
						fpq.enqueue(1 ,			6_4					)
						fpq.enqueue(0 ,			1_2_8					)
						print(_UpperCamelCase					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(_UpperCamelCase					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)





def    lowerCAmelCase__    (			)					->				List[str]:





						"""simple docstring"""






						snake_case								=			ElementPriorityQueue()
						epq.enqueue(1_0					)
						epq.enqueue(7_0					)
						epq.enqueue(1_0_0					)
						epq.enqueue(1					)
						epq.enqueue(5					)
						epq.enqueue(7					)
						epq.enqueue(4					)
						epq.enqueue(6_4					)
						epq.enqueue(1_2_8					)
						print(_UpperCamelCase					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(_UpperCamelCase					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)


if __name__ == "__main__":
					fixed_priority_queue()
					element_priority_queue()


