

'''simple docstring'''






import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

snake_case_					  =	{
    "post_extract_proj": "feature_projection.projection",
    "models.0.layer_norm": "feature_projection.layer_norm",
    "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",
}
snake_case_					  =	[
    "lm_head",
]


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Tuple  ,  SCREAMING_SNAKE_CASE_					:     int  ,  SCREAMING_SNAKE_CASE_					:     List[str]  ,  SCREAMING_SNAKE_CASE_					:     Any  ,  SCREAMING_SNAKE_CASE_					:     Any   )							->       Union[str, Any]:
      """simple docstring"""

      for attribute in key.split("."   ):
            SCREAMING_SNAKE_CASE_				:      Tuple					=	getattr(_lowerCAmelCase  ,  _lowerCAmelCase   )

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

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


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Dict  ,  SCREAMING_SNAKE_CASE_					:     List[str]  ,  SCREAMING_SNAKE_CASE_					:     int   )							->       Union[str, Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Any					=	[]
      SCREAMING_SNAKE_CASE_				:      Tuple					=	fairseq_model.state_dict()

      if not is_headless:
            SCREAMING_SNAKE_CASE_				:      Optional[int]					=	hf_model.dataavec_audio.feature_extractor
            SCREAMING_SNAKE_CASE_				:      Optional[int]					=	hf_model.dataavec_audio.encoder.pos_conv_embed

      else:
            SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	hf_model.feature_extractor
            SCREAMING_SNAKE_CASE_				:      List[str]					=	hf_model.encoder.pos_conv_embed

      for name, value in fairseq_dict.items():
            SCREAMING_SNAKE_CASE_				:      Dict					=	False
            if "conv_layers" in name:
                  load_conv_layer(
                      _lowerCAmelCase  ,  _lowerCAmelCase  ,  _lowerCAmelCase  ,  _lowerCAmelCase  ,  )
                  SCREAMING_SNAKE_CASE_				:      str					=	True
            elif "pos_conv" in name:
                  load_pos_conv_layer(
                      _lowerCAmelCase  ,  _lowerCAmelCase  ,  _lowerCAmelCase  ,  _lowerCAmelCase  ,  )
                  SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	True
            else:
                  for key, mapped_key in MAPPING.items():
                        if not is_headless:
                              SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	'''data2vec_audio.''' + 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]:
                              SCREAMING_SNAKE_CASE_				:      Optional[int]					=	True
                              if "*" in mapped_key:
                                    SCREAMING_SNAKE_CASE_				:      str					=	name.split(_lowerCAmelCase   )[0].split("."   )[-2]
                                    SCREAMING_SNAKE_CASE_				:      List[str]					=	mapped_key.replace("*"  ,  _lowerCAmelCase   )
                              if "weight_g" in name:
                                    SCREAMING_SNAKE_CASE_				:      Tuple					=	'''weight_g'''
                              elif "weight_v" in name:
                                    SCREAMING_SNAKE_CASE_				:      List[Any]					=	'''weight_v'''
                              elif "bias" in name:
                                    SCREAMING_SNAKE_CASE_				:      List[str]					=	'''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	'''weight'''
                              else:
                                    SCREAMING_SNAKE_CASE_				:      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  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[Any]  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       Any:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Tuple					=	path.split("."   )
      return reduce(_lowerCAmelCase  ,  _lowerCAmelCase  ,  _lowerCAmelCase   )


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Dict  ,  SCREAMING_SNAKE_CASE_					:     Union[str, Any]  ,  SCREAMING_SNAKE_CASE_					:     Union[str, Any]  ,  SCREAMING_SNAKE_CASE_					:     str   )							->       Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      str					=	access_by_string(_lowerCAmelCase  ,  _lowerCAmelCase   )
      SCREAMING_SNAKE_CASE_				:      Any					=	hf_weight.data

      if fsq_value.shape != hf_value.shape:
            raise ValueError(F"{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."   )
      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	fsq_value
      logger.info(F"{full_name} was correctly initialized from {hf_weight_path}."   )


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Tuple  ,  SCREAMING_SNAKE_CASE_					:     Optional[Any]  ,  SCREAMING_SNAKE_CASE_					:     Dict  ,  SCREAMING_SNAKE_CASE_					:     Dict   )							->       Optional[int]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Any					=	full_name.split("conv_layers."   )[-1]
      SCREAMING_SNAKE_CASE_				:      int					=	name.split("."   )
      SCREAMING_SNAKE_CASE_				:      Dict					=	int(items[0]   )
      SCREAMING_SNAKE_CASE_				:      Tuple					=	int(items[1]   )

      SCREAMING_SNAKE_CASE_				:      List[Any]					=	name.split("."   )[-1]
      if type_id == 0:
            SCREAMING_SNAKE_CASE_				:      List[str]					=	'''conv'''
      elif type_id == 2:
            SCREAMING_SNAKE_CASE_				:      int					=	'''layer_norm'''
      else:
            unused_weights.append(_lowerCAmelCase   )
            return

      set_weights(_lowerCAmelCase  ,  _lowerCAmelCase  ,  _lowerCAmelCase  ,  F"conv_layers.{layer_id}.{layer_type}.{weight_type}"   )


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[str]  ,  SCREAMING_SNAKE_CASE_					:     Optional[Any]  ,  SCREAMING_SNAKE_CASE_					:     Tuple  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       Tuple:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      str					=	full_name.split("pos_conv."   )[-1]
      SCREAMING_SNAKE_CASE_				:      List[str]					=	name.split("."   )
      SCREAMING_SNAKE_CASE_				:      List[str]					=	int(items[0]   )
      SCREAMING_SNAKE_CASE_				:      Dict					=	int(items[1]   )

      SCREAMING_SNAKE_CASE_				:      str					=	name.split("."   )[-1]
      if type_id != 0:
            unused_weights.append(_lowerCAmelCase   )
            return
      else:
            SCREAMING_SNAKE_CASE_				:      Tuple					=	'''conv'''

      set_weights(_lowerCAmelCase  ,  _lowerCAmelCase  ,  _lowerCAmelCase  ,  F"layers.{layer_id}.{layer_type}.{weight_type}"   )


@torch.no_grad()
def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     str  ,  SCREAMING_SNAKE_CASE_					:     str  ,  SCREAMING_SNAKE_CASE_					:     List[str]=None  ,  SCREAMING_SNAKE_CASE_					:     Union[str, Any]=None  ,  SCREAMING_SNAKE_CASE_					:     List[Any]=True   )							->       str:
      """simple docstring"""

      if config_path is not None:
            SCREAMING_SNAKE_CASE_				:      Dict					=	DataaVecAudioConfig.from_pretrained(_lowerCAmelCase   )
      else:
            SCREAMING_SNAKE_CASE_				:      Any					=	DataaVecAudioConfig()

      if not is_finetuned:
            # Modify final_proj layer name
            SCREAMING_SNAKE_CASE_				:      Dict					=	DataaVecAudioModel(_lowerCAmelCase   )
            SCREAMING_SNAKE_CASE_				:      Dict					=	os.path.dirname(_lowerCAmelCase   )

            SCREAMING_SNAKE_CASE_				:      str					=	torch.load(_lowerCAmelCase   )
            SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	state_dict['''model'''].pop("final_proj.0.weight"   )
            SCREAMING_SNAKE_CASE_				:      Tuple					=	state_dict['''model'''].pop("final_proj.0.bias"   )
            SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	os.path.join(_lowerCAmelCase  ,  "converted.pt"   )
            torch.save(_lowerCAmelCase  ,  _lowerCAmelCase   )
      else:
            SCREAMING_SNAKE_CASE_				:      Any					=	DataaVecAudioForCTC(_lowerCAmelCase   )
            SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	checkpoint_path

      def load_dataavec(SCREAMING_SNAKE_CASE_					:     str   ):
            SCREAMING_SNAKE_CASE_				:      int					=	fairseq.checkpoint_utils.load_model_ensemble_and_task([path]   )
            return model[0].eval()

      SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	load_dataavec(_lowerCAmelCase   )

      recursively_load_weights(_lowerCAmelCase  ,  _lowerCAmelCase  ,  not is_finetuned   )

      SCREAMING_SNAKE_CASE_				:      Dict					=	WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"   )

      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	load_dataset("patrickvonplaten/librispeech_asr_dummy"  ,  "clean"  ,  split="validation"   )
      SCREAMING_SNAKE_CASE_				:      Dict					=	[x['''array'''] for x in ds[:4]['''audio''']]

      SCREAMING_SNAKE_CASE_				:      int					=	processor(_lowerCAmelCase  ,  return_tensors="pt"  ,  padding=_lowerCAmelCase   )

      SCREAMING_SNAKE_CASE_				:      Any					=	inputs.input_values
      SCREAMING_SNAKE_CASE_				:      Dict					=	inputs.attention_mask
      #    input_values = inputs.input_values[:, :-1]
      #    attention_mask = inputs.attention_mask[:, :-1]

      hf_wavavec.eval()
      model.eval()
      if is_finetuned:
            SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	model(source=_lowerCAmelCase  ,  padding_mask=(1 - attention_mask)  ,  mask=_lowerCAmelCase  ,  features_only=_lowerCAmelCase   )[
                '''encoder_out'''
            ].transpose(0  ,  1   )
            SCREAMING_SNAKE_CASE_				:      str					=	hf_wavavec(_lowerCAmelCase  ,  attention_mask=_lowerCAmelCase   )['''logits''']

            SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	torch.argmax(_lowerCAmelCase  ,  dim=-1   )
            SCREAMING_SNAKE_CASE_				:      List[str]					=	processor.batch_decode(_lowerCAmelCase   )

            print(F"Expected Output: {ds[:4]['text']}, Pred: {output_string}"   )
      else:
            SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	model(source=_lowerCAmelCase  ,  padding_mask=(1 - attention_mask)  ,  mask=_lowerCAmelCase  ,  features_only=_lowerCAmelCase   )[
                '''layer_results'''
            ][-1][0].transpose(0  ,  1   )
            SCREAMING_SNAKE_CASE_				:      List[str]					=	hf_wavavec(_lowerCAmelCase  ,  attention_mask=_lowerCAmelCase   )['''last_hidden_state''']

      print(our_output.shape  ,  their_output.shape   )
      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	torch.max(torch.abs(our_output - their_output   )   ).item()
      print(F"max_absolute_diff = {max_absolute_diff}"   )  # ~ 1e-7
      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	torch.allclose(_lowerCAmelCase  ,  _lowerCAmelCase  ,  atol=1E-3   )
      print("Do both models output the same tensors?"  ,  "🔥" if success else "💩"   )
      if not success:
            raise Exception("Something went wRoNg"   )

      hf_wavavec.save_pretrained(_lowerCAmelCase   )

      if is_finetuned:
            processor.save_pretrained(_lowerCAmelCase   )
      else:
            processor.feature_extractor.save_pretrained(_lowerCAmelCase   )


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'
 )
 snake_case_					  =	parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )




'''simple docstring'''






import heapq


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[str]  ,  SCREAMING_SNAKE_CASE_					:     Any  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       Union[str, Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	[(0, start)]  # cost from start node,end node
      SCREAMING_SNAKE_CASE_				:      str					=	set()
      while heap:
            ((SCREAMING_SNAKE_CASE_),						(SCREAMING_SNAKE_CASE_))				:      Optional[int]					=	heapq.heappop(SCREAMING_SNAKE_CASE_   )
            if u in visited:
                  continue
            visited.add(SCREAMING_SNAKE_CASE_   )
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  SCREAMING_SNAKE_CASE_				:      int					=	cost + c
                  heapq.heappush(SCREAMING_SNAKE_CASE_  ,  (next_item, v)   )
      return -1


snake_case_					  =	{
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

snake_case_					  =	dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

snake_case_					  =	dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

snake_case_					  =	dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()

