

"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

UpperCAmelCase													={
    "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",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
UpperCAmelCase													=[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def   _A					(	_a	:						Optional[Any]     ,   _a	:						Tuple     ,   _a	:						List[Any]     ,   _a	:						List[Any]     ,   _a	:						Dict     ,   _a	:						Optional[int]				):



      """simple docstring"""






      for attribute in key.split("""."""				):
            if is_finetuned:
                  if attribute in ["quantizer", "project_q", "project_hid"]:
                        # those layers are only relevant for pretraining and should be dropped
                        return

                  if attribute == "ctc_proj":
                        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                        A				  =		"""lm_head"""

            A				  =		getattr(_a     ,   _a				)

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

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



def   _A					(	_a	:						List[str]     ,   _a	:						Tuple     ,   _a	:						int				):



      """simple docstring"""






      A				  =		[]
      A				  =		fairseq_model.state_dict()

      A				  =		hf_model.unispeech.feature_extractor

      for name, value in fairseq_dict.items():
            A				  =		False
            if "conv_layers" in name:
                  load_conv_layer(
                      _a     ,   _a     ,   _a     ,   _a     ,   hf_model.config.feat_extract_norm == """group"""     ,   )
                  A				  =		True
            else:
                  for key, mapped_key in MAPPING.items():
                        A				  =		"""unispeech.""" + 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]:
                              A				  =		True
                              if "*" in mapped_key:
                                    A				  =		name.split(_a				)[0].split("""."""				)[-2]
                                    A				  =		mapped_key.replace("""*"""     ,   _a				)
                              if "weight_g" in name:
                                    A				  =		"""weight_g"""
                              elif "weight_v" in name:
                                    A				  =		"""weight_v"""
                              elif "bias" in name:
                                    A				  =		"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    A				  =		"""weight"""
                              else:
                                    A				  =		None
                              set_recursively(_a     ,   _a     ,   _a     ,   _a     ,   _a     ,   _a				)
                        continue
            if not is_used:
                  unused_weights.append(_a				)

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



def   _A					(	_a	:						Optional[int]     ,   _a	:						Union[str, Any]     ,   _a	:						Union[str, Any]     ,   _a	:						Dict     ,   _a	:						Any				):



      """simple docstring"""






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



@torch.no_grad()
def   _A					(	_a	:						List[Any]     ,   _a	:						Tuple     ,   _a	:						Optional[Any]=None     ,   _a	:						Any=None     ,   _a	:						int=True				):



      """simple docstring"""






      if config_path is not None:
            A				  =		UniSpeechConfig.from_pretrained(_a				)
      else:
            A				  =		UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  A				  =		Dictionary.load_from_json(_a				)

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

                  # fairseq has the <pad> and <s> switched
                  A				  =		4_2
                  A				  =		4_3
                  with open(_a     ,   """w"""     ,   encoding="""utf-8"""				) as vocab_handle:
                        json.dump(_a     ,   _a				)
                  A				  =		WavaVecaPhonemeCTCTokenizer(
                      _a     ,   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=_a     ,   )
                  A				  =		True if config.feat_extract_norm == """layer""" else False
                  A				  =		WavaVecaFeatureExtractor(
                      feature_size=1     ,   sampling_rate=1_6_0_0_0     ,   padding_value=0     ,   do_normalize=_a     ,   return_attention_mask=_a     ,   )
                  A				  =		WavaVecaProcessor(feature_extractor=_a     ,   tokenizer=_a				)
                  processor.save_pretrained(_a				)

            A				  =		UniSpeechForCTC(_a				)
      else:
            A				  =		UniSpeechForPreTraining(_a				)

      if is_finetuned:
            A  ,			A  ,			A				  =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]     ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""				)[:-1]				), """w2v_path""": checkpoint_path}				)
      else:
            A  ,			A  ,			A				  =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

      A				  =		model[0].eval()

      recursively_load_weights(_a     ,   _a     ,   _a				)

      hf_unispeech.save_pretrained(_a				)


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_unispeech_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )







"""simple docstring"""


def   _A					(	_a	:						Optional[int]     ,   _a	:						str				):



      """simple docstring"""






      A				  =		0
      A				  =		len(_a				) - 1

      while left <= right:
            # avoid divided by 0 during interpolation
            if sorted_collection[left] == sorted_collection[right]:
                  if sorted_collection[left] == item:
                        return left
                  else:
                        return None

            A				  =		left + ((item - sorted_collection[left]) * (right - left)) // (
                sorted_collection[right] - sorted_collection[left]
            )

            # out of range check
            if point < 0 or point >= len(_a				):
                  return None

            A				  =		sorted_collection[point]
            if current_item == item:
                  return point
            else:
                  if point < left:
                        A				  =		left
                        A				  =		point
                  elif point > right:
                        A				  =		right
                        A				  =		point
                  else:
                        if item < current_item:
                              A				  =		point - 1
                        else:
                              A				  =		point + 1
      return None



def   _A					(	_a	:						Any     ,   _a	:						List[str]     ,   _a	:						Dict     ,   _a	:						List[str]				):



      """simple docstring"""






      if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                  return left
            else:
                  return None

      A				  =		left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

      # out of range check
      if point < 0 or point >= len(_a				):
            return None

      if sorted_collection[point] == item:
            return point
      elif point < left:
            return interpolation_search_by_recursion(_a     ,   _a     ,   _a     ,   _a				)
      elif point > right:
            return interpolation_search_by_recursion(_a     ,   _a     ,   _a     ,   _a				)
      else:
            if sorted_collection[point] > item:
                  return interpolation_search_by_recursion(
                      _a     ,   _a     ,   _a     ,   point - 1				)
            else:
                  return interpolation_search_by_recursion(
                      _a     ,   _a     ,   point + 1     ,   _a				)



def   _A					(	_a	:						List[Any]				):



      """simple docstring"""






      if collection != sorted(_a				):
            raise ValueError("""Collection must be ascending sorted"""				)
      return True


if __name__ == "__main__":
  import sys
  UpperCAmelCase													=0
  if debug == 1:
    UpperCAmelCase													=[10, 30, 40, 45, 50, 66, 77, 93]
    try:
      __assert_sorted(collection)
    except ValueError:
      sys.exit("Sequence must be ascending sorted to apply interpolation search")
    UpperCAmelCase													=67

  UpperCAmelCase													=interpolation_search(collection, target)
  if result is not None:
    print(f"""{target} found at positions: {result}""")
  else:
    print("Not found")




