


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

__UpperCAmelCase    				=      {
    "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",
}
__UpperCAmelCase    				=      [
    "lm_head",
]





def     lowercase__	(  lowerCamelCase     :  Optional[Any]				,	lowerCamelCase     :  Tuple				,	lowerCamelCase     :  Union[str, Any]				,	lowerCamelCase     :  Optional[Any]				,	lowerCamelCase     :  str				)					->     List[str]:
  for attribute in key.split("."				):
    lowerCAmelCase__    :    List[Any]								=					getattr(lowerCamelCase				,	lowerCamelCase				)

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

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





def     lowercase__	(  lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     str:
  lowerCAmelCase__    :    Union[str, Any]								=					[]
  lowerCAmelCase__    :    List[Any]								=					fairseq_model.state_dict()

  if not is_headless:
    lowerCAmelCase__    :    Optional[Any]								=					hf_model.dataavec_audio.feature_extractor
    lowerCAmelCase__    :    Tuple								=					hf_model.dataavec_audio.encoder.pos_conv_embed

  else:
    lowerCAmelCase__    :    str								=					hf_model.feature_extractor
    lowerCAmelCase__    :    str								=					hf_model.encoder.pos_conv_embed

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





def     lowercase__	(  lowerCamelCase     :  Dict				,	lowerCamelCase     :  List[str]				,	lowerCamelCase     :  Optional[Any]				,	lowerCamelCase     :  Dict				)					->     Optional[int]:
  lowerCAmelCase__    :    Optional[int]								=					access_by_string(lowerCamelCase				,	lowerCamelCase				)
  lowerCAmelCase__    :    int								=					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."				)
  lowerCAmelCase__    :    Dict								=					fsq_value
  logger.info(F"{full_name} was correctly initialized from {hf_weight_path}."				)





def     lowercase__	(  lowerCamelCase     :  List[str]				,	lowerCamelCase     :  Union[str, Any]				,	lowerCamelCase     :  Optional[int]				,	lowerCamelCase     :  List[str]				)					->     str:
  lowerCAmelCase__    :    List[str]								=					full_name.split("conv_layers."				)[-1]
  lowerCAmelCase__    :    Dict								=					name.split("."				)
  lowerCAmelCase__    :    Any								=					int(items[0]				)
  lowerCAmelCase__    :    Optional[int]								=					int(items[1]				)

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

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





def     lowercase__	(  lowerCamelCase     :  Tuple				,	lowerCamelCase     :  int				,	lowerCamelCase     :  List[Any]				,	lowerCamelCase     :  Any				)					->     int:
  lowerCAmelCase__    :    Tuple								=					full_name.split("pos_conv."				)[-1]
  lowerCAmelCase__    :    int								=					name.split("."				)
  lowerCAmelCase__    :    str								=					int(items[0]				)
  lowerCAmelCase__    :    Dict								=					int(items[1]				)

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

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





@torch.no_grad()
def     lowercase__	(  lowerCamelCase     :  Optional[int]				,	lowerCamelCase     :  List[str]				,	lowerCamelCase     :  Tuple=None				,	lowerCamelCase     :  str=None				,	lowerCamelCase     :  int=True				)					->     int:
  if config_path is not None:
    lowerCAmelCase__    :    Dict								=					DataaVecAudioConfig.from_pretrained(lowerCamelCase				)
  else:
    lowerCAmelCase__    :    int								=					DataaVecAudioConfig()

  if not is_finetuned:
    # Modify final_proj layer name
    lowerCAmelCase__    :    List[Any]								=					DataaVecAudioModel(lowerCamelCase				)
    lowerCAmelCase__    :    Dict								=					os.path.dirname(lowerCamelCase				)

    lowerCAmelCase__    :    int								=					torch.load(lowerCamelCase				)
    lowerCAmelCase__    :    str								=					state_dict["model"].pop("final_proj.0.weight"				)
    lowerCAmelCase__    :    Any								=					state_dict["model"].pop("final_proj.0.bias"				)
    lowerCAmelCase__    :    str								=					os.path.join(lowerCamelCase				,	"converted.pt"				)
    torch.save(lowerCamelCase				,	lowerCamelCase				)
  else:
    lowerCAmelCase__    :    Any								=					DataaVecAudioForCTC(lowerCamelCase				)
    lowerCAmelCase__    :    Dict								=					checkpoint_path

  def load_dataavec(lowerCamelCase     :  int				):
    lowerCAmelCase__				,		lowerCAmelCase__				,		lowerCAmelCase__    :    Tuple								=					fairseq.checkpoint_utils.load_model_ensemble_and_task([path]				)
    return model[0].eval()

  lowerCAmelCase__    :    List[str]								=					load_dataavec(lowerCamelCase				)

  recursively_load_weights(lowerCamelCase				,	lowerCamelCase				,	not is_finetuned				)

  lowerCAmelCase__    :    Union[str, Any]								=					WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"				)

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

  lowerCAmelCase__    :    Dict								=					processor(lowerCamelCase				,	return_tensors="pt"				,	padding=lowerCamelCase				)

  lowerCAmelCase__    :    Any								=					inputs.input_values
  lowerCAmelCase__    :    Optional[int]								=					inputs.attention_mask
  #    input_values = inputs.input_values[:, :-1]
  #    attention_mask = inputs.attention_mask[:, :-1]

  hf_wavavec.eval()
  model.eval()
  if is_finetuned:
    lowerCAmelCase__    :    List[str]								=					model(source=lowerCamelCase				,	padding_mask=(1 - attention_mask)				,	mask=lowerCamelCase				,	features_only=lowerCamelCase				)[
        "encoder_out"
    ].transpose(0				,	1				)
    lowerCAmelCase__    :    Optional[Any]								=					hf_wavavec(lowerCamelCase				,	attention_mask=lowerCamelCase				)["logits"]

    lowerCAmelCase__    :    str								=					torch.argmax(lowerCamelCase				,	dim=-1				)
    lowerCAmelCase__    :    int								=					processor.batch_decode(lowerCamelCase				)

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

  print(our_output.shape				,	their_output.shape				)
  lowerCAmelCase__    :    int								=					torch.max(torch.abs(our_output - their_output				)				).item()
  print(F"max_absolute_diff = {max_absolute_diff}"				)  # ~ 1e-7
  lowerCAmelCase__    :    int								=					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__":
   __UpperCAmelCase    				=      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"
   )
   __UpperCAmelCase    				=      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"""





from __future__ import annotations

__UpperCAmelCase    				=      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]

class  lowercase_  :







      def __init__(      self :							int		,  _lowercase :							int    ):
        lowerCAmelCase__    :    int								=					-1
        lowerCAmelCase__    :    int								=					-1
        lowerCAmelCase__    :    list[int]								=					[-1] * length
        lowerCAmelCase__    :    Node | None								=					None
        lowerCAmelCase__    :    Node | None								=					None







      def __repr__(      self :							List[Any]    ):
        return f"Node(min_value={self.minn} max_value={self.maxx})"





def     lowercase__	(  lowerCamelCase     :  list[int]				)					->     Node | None:
  lowerCAmelCase__    :    Any								=					Node(len(lowerCamelCase				)				)
  lowerCAmelCase__				,		lowerCAmelCase__    :    Any								=					min(lowerCamelCase				), max(lowerCamelCase				)
  # Leaf node case where the node contains only one unique value
  if root.minn == root.maxx:
    return root
  lowerCAmelCase__    :    str								=					(root.minn + root.maxx) // 2

  lowerCAmelCase__    :    list[int]								=					[]
  lowerCAmelCase__    :    list[int]								=					[]

  for index, num in enumerate(lowerCamelCase				):
    if num <= pivot:
      left_arr.append(lowerCamelCase				)
    else:
      right_arr.append(lowerCamelCase				)
    lowerCAmelCase__    :    Optional[Any]								=					len(lowerCamelCase				)
  lowerCAmelCase__    :    List[str]								=					build_tree(lowerCamelCase				)
  lowerCAmelCase__    :    List[Any]								=					build_tree(lowerCamelCase				)
  return root





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     int:
  if index < 0 or node is None:
    return 0
  # Leaf node cases
  if node.minn == node.maxx:
    return index + 1 if node.minn == num else 0
  lowerCAmelCase__    :    int								=					(node.minn + node.maxx) // 2
  if num <= pivot:
    # go the left subtree and map index to the left subtree
    return rank_till_index(node.left				,	lowerCamelCase				,	node.map_left[index] - 1				)
  else:
    # go to the right subtree and map index to the right subtree
    return rank_till_index(node.right				,	lowerCamelCase				,	index - node.map_left[index]				)





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     int:
  if start > end:
    return 0
  lowerCAmelCase__    :    int								=					rank_till_index(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)
  lowerCAmelCase__    :    Optional[Any]								=					rank_till_index(lowerCamelCase				,	lowerCamelCase				,	start - 1				)
  return rank_till_end - rank_before_start





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     int:
  if index > (end - start) or start > end or node is None:
    return -1
  # Leaf node case
  if node.minn == node.maxx:
    return node.minn
  # Number of elements in the left subtree in interval [start, end]
  lowerCAmelCase__    :    List[str]								=					node.map_left[end] - (
      node.map_left[start - 1] if start else 0
  )
  if num_elements_in_left_tree > index:
    return quantile(
        node.left				,	lowerCamelCase				,	(node.map_left[start - 1] if start else 0)				,	node.map_left[end] - 1				,	)
  else:
    return quantile(
        node.right				,	index - num_elements_in_left_tree				,	start - (node.map_left[start - 1] if start else 0)				,	end - node.map_left[end]				,	)





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     int:
  if (
      start > end
      or node is None
      or start_num > end_num
      or node.minn > end_num
      or node.maxx < start_num
  ):
    return 0
  if start_num <= node.minn and node.maxx <= end_num:
    return end - start + 1
  lowerCAmelCase__    :    List[str]								=					range_counting(
      node.left				,	(node.map_left[start - 1] if start else 0)				,	node.map_left[end] - 1				,	lowerCamelCase				,	lowerCamelCase				,	)
  lowerCAmelCase__    :    List[str]								=					range_counting(
      node.right				,	start - (node.map_left[start - 1] if start else 0)				,	end - node.map_left[end]				,	lowerCamelCase				,	lowerCamelCase				,	)
  return left + right


if __name__ == "__main__":
   import doctest

   doctest.testmod()
