'''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()
__snake_case						  =		logging.get_logger(__name__)

__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""",
    """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""",
}
__snake_case						  =		[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[str]:
  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
        lowercase_     =       """lm_head"""

    lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

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

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

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  lowercase_     =       []
  lowercase_     =       fairseq_model.state_dict()

  lowercase_     =       hf_model.unispeech.feature_extractor

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

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

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     =       full_name.split("""conv_layers."""						)[-1]
  lowercase_     =       name.split("""."""						)
  lowercase_     =       int(items[0]						)
  lowercase_     =       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."""
      )
      lowercase_     =       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."""
      )
      lowercase_     =       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."
      )
      lowercase_     =       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."""
      )
      lowercase_     =       value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
  else:
    unused_weights.append(SCREAMING_SNAKE_CASE_						)

@torch.no_grad()
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=True						)			->Optional[Any]:
  if config_path is not None:
    lowercase_     =       UniSpeechConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
  else:
    lowercase_     =       UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      lowercase_     =       Dictionary.load_from_json(SCREAMING_SNAKE_CASE_						)

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

      # fairseq has the <pad> and <s> switched
      lowercase_     =       42
      lowercase_     =       43
      with open(SCREAMING_SNAKE_CASE_ ,	"""w""" ,	encoding="""utf-8"""						) as vocab_handle:
        json.dump(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
      lowercase_     =       WavaVecaPhonemeCTCTokenizer(
          SCREAMING_SNAKE_CASE_ ,	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=SCREAMING_SNAKE_CASE_ ,	)
      lowercase_     =       True if config.feat_extract_norm == """layer""" else False
      lowercase_     =       WavaVecaFeatureExtractor(
          feature_size=1 ,	sampling_rate=1_60_00 ,	padding_value=0 ,	do_normalize=SCREAMING_SNAKE_CASE_ ,	return_attention_mask=SCREAMING_SNAKE_CASE_ ,	)
      lowercase_     =       WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_ ,	tokenizer=SCREAMING_SNAKE_CASE_						)
      processor.save_pretrained(SCREAMING_SNAKE_CASE_						)

    lowercase_     =       UniSpeechForCTC(SCREAMING_SNAKE_CASE_						)
  else:
    lowercase_     =       UniSpeechForPreTraining(SCREAMING_SNAKE_CASE_						)

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

  lowercase_     =       model[0].eval()

  recursively_load_weights(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  hf_unispeech.save_pretrained(SCREAMING_SNAKE_CASE_						)


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_unispeech_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

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__snake_case						  =		10

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  for i in range(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    if array[i] == target:
      return i
  return -1

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  lowercase_     =       0
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						)
  while left <= right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

    lowercase_     =       (left + right) // 3 + 1
    lowercase_     =       2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      lowercase_     =       one_third - 1
    elif array[two_third] < target:
      lowercase_     =       two_third + 1

    else:
      lowercase_     =       one_third + 1
      lowercase_     =       two_third - 1
  else:
    return -1

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  if left < right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       (left + right) // 3 + 1
    lowercase_     =       2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      return rec_ternary_search(SCREAMING_SNAKE_CASE_ ,	one_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    elif array[two_third] < target:
      return rec_ternary_search(two_third + 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    else:
      return rec_ternary_search(one_third + 1 ,	two_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  else:
    return -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __snake_case						  =		input("""Enter numbers separated by comma:\n""").strip()
       __snake_case						  =		[int(item.strip()) for item in user_input.split(""",""")]
       assert collection == sorted(collection), f"List must be ordered.\n{collection}."
       __snake_case						  =		int(input("""Enter the number to be found in the list:\n""").strip())
       __snake_case						  =		ite_ternary_search(collection, target)
       __snake_case						  =		rec_ternary_search(0, len(collection) - 1, collection, target)
       if resulta != -1:
              print(f'''Iterative search: {target} found at positions: {resulta}''')
              print(f'''Recursive search: {target} found at positions: {resulta}''')
       else:
              print("""Not found""")

