"""simple docstring"""



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowercase		:		Optional[int]														=							logging.get_logger(__name__)

__lowercase		:		int														=							{
    "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",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case,      snake_case):
     for attribute in key.split('''.'''):
          __snake_case				=							getattr(snake_case,      snake_case)

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

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





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     __snake_case				=							[]
     __snake_case				=							fairseq_model.state_dict()

     __snake_case				=							hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          __snake_case				=							False
          if "conv_layers" in name:
               load_conv_layer(
                   snake_case,      snake_case,      snake_case,      snake_case,      hf_model.config.feat_extract_norm == '''group''',      )
               __snake_case				=							True
          else:
               for key, mapped_key in MAPPING.items():
                    __snake_case				=							'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                    if key in name or (key.split('''w2v_model.''')[-1] == name.split('''.''')[0] and not is_finetuned):
                         __snake_case				=							True
                         if "*" in mapped_key:
                              __snake_case				=							name.split(snake_case)[0].split('''.''')[-2]
                              __snake_case				=							mapped_key.replace('''*''',      snake_case)
                         if "weight_g" in name:
                              __snake_case				=							'''weight_g'''
                         elif "weight_v" in name:
                              __snake_case				=							'''weight_v'''
                         elif "weight" in name:
                              __snake_case				=							'''weight'''
                         elif "bias" in name:
                              __snake_case				=							'''bias'''
                         else:
                              __snake_case				=							None
                         set_recursively(snake_case,      snake_case,      snake_case,      snake_case,      snake_case)
                    continue
          if not is_used:
               unused_weights.append(snake_case)

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





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





@torch.no_grad()
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case=None,      snake_case=None,      snake_case=True):
     if config_path is not None:
          __snake_case				=							HubertConfig.from_pretrained(snake_case)
     else:
          __snake_case				=							HubertConfig()

     if is_finetuned:
          if dict_path:
               __snake_case				=							Dictionary.load(snake_case)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               __snake_case				=							target_dict.pad_index
               __snake_case				=							target_dict.bos_index
               __snake_case				=							target_dict.eos_index
               __snake_case				=							len(target_dict.symbols)
               __snake_case				=							os.path.join(snake_case,      '''vocab.json''')
               if not os.path.isdir(snake_case):
                    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case))
                    return
               os.makedirs(snake_case,      exist_ok=snake_case)
               with open(snake_case,      '''w''',      encoding='''utf-8''') as vocab_handle:
                    json.dump(target_dict.indices,      snake_case)
               __snake_case				=							WavaVecaCTCTokenizer(
                   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=snake_case,      )
               __snake_case				=							True if config.feat_extract_norm == '''layer''' else False
               __snake_case				=							WavaVecaFeatureExtractor(
                   feature_size=1,      sampling_rate=1_60_00,      padding_value=0,      do_normalize=snake_case,      return_attention_mask=snake_case,      )
               __snake_case				=							WavaVecaProcessor(feature_extractor=snake_case,      tokenizer=snake_case)
               processor.save_pretrained(snake_case)

          __snake_case				=							HubertForCTC(snake_case)
     else:
          __snake_case				=							HubertModel(snake_case)

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

     __snake_case				=							model[0].eval()

     recursively_load_weights(snake_case,      snake_case,      snake_case)

     hf_wavavec.save_pretrained(snake_case)


if __name__ == "__main__":
       __lowercase		:		str														=							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"
       )
       __lowercase		:		Optional[int]														=							parser.parse_args()
       convert_hubert_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )
"""simple docstring"""



import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case = True,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = False,      snake_case = 1_00,      snake_case = 0.01,      snake_case = 1,      ):
     __snake_case				=							False
     __snake_case				=							search_prob
     __snake_case				=							start_temperate
     __snake_case				=							[]
     __snake_case				=							0
     __snake_case				=							None

     while not search_end:
          __snake_case				=							current_state.score()
          if best_state is None or current_score > best_state.score():
               __snake_case				=							current_state
          scores.append(snake_case)
          iterations += 1
          __snake_case				=							None
          __snake_case				=							current_state.get_neighbors()
          while (
              next_state is None and neighbors
          ):  # till we do not find a neighbor that we can move to
               __snake_case				=							random.randint(0,      len(snake_case) - 1)  # picking a random neighbor
               __snake_case				=							neighbors.pop(snake_case)
               __snake_case				=							picked_neighbor.score() - current_score

               if (
                   picked_neighbor.x > max_x
                   or picked_neighbor.x < min_x
                   or picked_neighbor.y > max_y
                   or picked_neighbor.y < min_y
               ):
                    continue  # neighbor outside our bounds

               if not find_max:
                    __snake_case				=							change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    __snake_case				=							picked_neighbor
               else:
                    __snake_case				=							(math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         __snake_case				=							picked_neighbor
          __snake_case				=							current_temp - (current_temp * rate_of_decrease)

          if current_temp < threshold_temp or next_state is None:
               # temperature below threshold, or could not find a suitable neighbor
               __snake_case				=							True
          else:
               __snake_case				=							next_state

     if visualization:
          from matplotlib import pyplot as plt

          plt.plot(range(snake_case),      snake_case)
          plt.xlabel('''Iterations''')
          plt.ylabel('''Function values''')
          plt.show()
     return best_state


if __name__ == "__main__":





       def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (x**2) + (y**2)

       # starting the problem with initial coordinates (12, 47)
       __lowercase		:		Dict														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Union[str, Any]														=							simulated_annealing(
           prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
       )
       print(
           "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
           F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
       )

       # starting the problem with initial coordinates (12, 47)
       __lowercase		:		Any														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		int														=							simulated_annealing(
           prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
       )
       print(
           "The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
           F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
       )





       def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (3 * x**2) - (6 * y)

       __lowercase		:		List[str]														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Dict														=							simulated_annealing(prob, find_max=False, visualization=True)
       print(
           "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
           F"""{local_min.score()}"""
       )

       __lowercase		:		Any														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Tuple														=							simulated_annealing(prob, find_max=True, visualization=True)




       print(
           "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
           F"""{local_min.score()}"""
       )