





"""simple docstring"""


import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


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

lowerCamelCase__				  =   {
    "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.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "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",
}
lowerCamelCase__				  =   [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_			)					->		int:




      """simple docstring"""

      for attribute in key.split("."			):
            _UpperCamelCase      :	Tuple						=       getattr(lowerCAmelCase__		,lowerCAmelCase__			)

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

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




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase      :	Optional[int]						=       []
      _UpperCamelCase      :	List[str]						=       fairseq_model.state_dict()

      _UpperCamelCase      :	List[Any]						=       hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            _UpperCamelCase      :	List[Any]						=       False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,hf_model.config.feat_extract_norm == "group"		,)
                  _UpperCamelCase      :	int						=       True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("w2v_model."			)[-1] == name.split("."			)[0]:
                              _UpperCamelCase      :	List[Any]						=       True
                              if "*" in mapped_key:
                                    _UpperCamelCase      :	List[Any]						=       name.split(lowerCAmelCase__			)[0].split("."			)[-2]
                                    _UpperCamelCase      :	Union[str, Any]						=       mapped_key.replace("*"		,lowerCAmelCase__			)
                              if "weight_g" in name:
                                    _UpperCamelCase      :	int						=       "weight_g"
                              elif "weight_v" in name:
                                    _UpperCamelCase      :	List[str]						=       "weight_v"
                              elif "bias" in name and "relative_attention_bias" not in name:
                                    _UpperCamelCase      :	Any						=       "bias"
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    _UpperCamelCase      :	Optional[int]						=       "weight"
                              else:
                                    _UpperCamelCase      :	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 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	Any						=       full_name.split("conv_layers."			)[-1]
      _UpperCamelCase      :	Any						=       name.split("."			)
      _UpperCamelCase      :	Union[str, Any]						=       int(items[0]			)
      _UpperCamelCase      :	int						=       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.'''
                  )
                  _UpperCamelCase      :	List[str]						=       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.'''
                  )
                  _UpperCamelCase      :	List[Any]						=       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."
                  )
                  _UpperCamelCase      :	Optional[Any]						=       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.'''
                  )
                  _UpperCamelCase      :	Any						=       value
                  logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
      else:
            unused_weights.append(lowerCAmelCase__			)




@torch.no_grad()
def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_=None			)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       torch.load(lowerCAmelCase__			)
      _UpperCamelCase      :	List[Any]						=       WavLMConfigOrig(checkpoint["cfg"]			)
      _UpperCamelCase      :	Tuple						=       WavLMOrig(lowerCAmelCase__			)
      model.load_state_dict(checkpoint["model"]			)
      model.eval()

      if config_path is not None:
            _UpperCamelCase      :	int						=       WavLMConfig.from_pretrained(lowerCAmelCase__			)
      else:
            _UpperCamelCase      :	List[str]						=       WavLMConfig()

      _UpperCamelCase      :	Union[str, Any]						=       WavLMModel(lowerCAmelCase__			)

      recursively_load_weights(lowerCAmelCase__		,lowerCAmelCase__			)

      hf_wavlm.save_pretrained(lowerCAmelCase__			)


if __name__ == "__main__":
  lowerCamelCase__				  =   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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  lowerCamelCase__				  =   parser.parse_args()
  convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







"""simple docstring"""


from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase__				  =   200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowerCamelCase__				  =   50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowerCamelCase__				  =   0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))




def 					lowercase__    (					lowercase_		,lowercase_			)					->		tuple[str, float]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       len([g for position, g in enumerate(lowercase_			) if g == main_target[position]]			)
      return (item, float(lowercase_			))




def 					lowercase__    (					lowercase_		,lowercase_			)					->		tuple[str, str]:




      """simple docstring"""

      _UpperCamelCase      :	Tuple						=       random.randint(0		,len(lowercase_			) - 1			)
      _UpperCamelCase      :	Dict						=       parent_a[:random_slice] + parent_a[random_slice:]
      _UpperCamelCase      :	Tuple						=       parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)




def 					lowercase__    (					lowercase_		,lowercase_			)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	int						=       list(lowercase_			)
      if random.uniform(0		,1			) < MUTATION_PROBABILITY:
            _UpperCamelCase      :	int						=       random.choice(lowercase_			)
      return "".join(lowercase_			)




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,)					->		list[str]:




      """simple docstring"""

      _UpperCamelCase      :	Optional[Any]						=       []
      # Generate more children proportionally to the fitness score.
      _UpperCamelCase      :	List[str]						=       int(parent_a[1] * 100			) + 1
      _UpperCamelCase      :	Union[str, Any]						=       10 if child_n >= 10 else child_n
      for _ in range(lowercase_			):
            _UpperCamelCase      :	Dict						=       population_score[random.randint(0		,lowercase_			)][0]

            _UpperCamelCase,  _UpperCamelCase      :	Dict						=       crossover(parent_a[0]		,lowercase_			)
            # Append new string to the population list.
            pop.append(mutate(lowercase_		,lowercase_			)			)
            pop.append(mutate(lowercase_		,lowercase_			)			)
      return pop




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_ = True			)					->		tuple[int, int, str]:




      """simple docstring"""

      if N_POPULATION < N_SELECTED:
            _UpperCamelCase      :	List[str]						=       F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
            raise ValueError(lowercase_			)
      # Verify that the target contains no genes besides the ones inside genes variable.
      _UpperCamelCase      :	int						=       sorted({c for c in target if c not in genes}			)
      if not_in_genes_list:
            _UpperCamelCase      :	int						=       F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
            raise ValueError(lowercase_			)

      # Generate random starting population.
      _UpperCamelCase      :	Union[str, Any]						=       []
      for _ in range(lowercase_			):
            population.append("".join([random.choice(lowercase_			) for i in range(len(lowercase_			)			)]			)			)

      # Just some logs to know what the algorithms is doing.
      _UpperCamelCase,  _UpperCamelCase      :	str						=       0, 0

      # This loop will end when we find a perfect match for our target.
      while True:
            generation += 1
            total_population += len(lowercase_			)

            # Random population created. Now it's time to evaluate.

            # Adding a bit of concurrency can make everything faster,
            #
            # import concurrent.futures
            # population_score: list[tuple[str, float]] = []
            # with concurrent.futures.ThreadPoolExecutor(
            #                                   max_workers=NUM_WORKERS) as executor:
            #     futures = {executor.submit(evaluate, item) for item in population}
            #     concurrent.futures.wait(futures)
            #     population_score = [item.result() for item in futures]
            #
            # but with a simple algorithm like this, it will probably be slower.
            # We just need to call evaluate for every item inside the population.
            _UpperCamelCase      :	int						=       [evaluate(lowercase_		,lowercase_			) for item in population]

            # Check if there is a matching evolution.
            _UpperCamelCase      :	Optional[Any]						=       sorted(lowercase_		,key=lambda lowercase_			: x[1]		,reverse=lowercase_			)
            if population_score[0][0] == target:
                  return (generation, total_population, population_score[0][0])

            # Print the best result every 10 generation.
            # Just to know that the algorithm is working.
            if debug and generation % 10 == 0:
                  print(
                      F'''\nGeneration: {generation}'''
                      F'''\nTotal Population:{total_population}'''
                      F'''\nBest score: {population_score[0][1]}'''
                      F'''\nBest string: {population_score[0][0]}'''			)

            # Flush the old population, keeping some of the best evolutions.
            # Keeping this avoid regression of evolution.
            _UpperCamelCase      :	str						=       population[: int(N_POPULATION / 3			)]
            population.clear()
            population.extend(lowercase_			)
            # Normalize population score to be between 0 and 1.
            _UpperCamelCase      :	str						=       [
                (item, score / len(lowercase_			)) for item, score in population_score
            ]

            # This is selection
            for i in range(lowercase_			):
                  population.extend(select(population_score[int(lowercase_			)]		,lowercase_		,lowercase_			)			)
                  # Check if the population has already reached the maximum value and if so,
                  # break the cycle.  If this check is disabled, the algorithm will take
                  # forever to compute large strings, but will also calculate small strings in
                  # a far fewer generations.
                  if len(lowercase_			) > N_POPULATION:
                        break


if __name__ == "__main__":
  lowerCamelCase__				  =   (
      "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
  )
  lowerCamelCase__				  =   list(
      " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
      "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
  )
  lowerCamelCase__  ,    lowerCamelCase__  ,    lowerCamelCase__				  =   basic(target_str, genes_list)
  print(
      f"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
  )
