




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

a         =      {
    '''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''',
}
a         =      [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 						_snake_case					( _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: Tuple    ,  _snake_case							: Optional[int]    ,  _snake_case							: int    ,  _snake_case							: Union[str, Any]							)   -> List[str]:






      '''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(_snake_case    ,  _snake_case							)

      if weight_type is not None:
            _A        =       getattr(_snake_case    ,  _snake_case							).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 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: int    ,  _snake_case							: Optional[Any]							)   -> Any:






      '''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(
                      _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  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(_snake_case							)[0].split('.'							)[-2]
                                    _A        =       mapped_key.replace('*'    ,  _snake_case							)
                              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(_snake_case    ,  _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 						_snake_case					( _snake_case							: Optional[Any]    ,  _snake_case							: List[str]    ,  _snake_case							: Optional[int]    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]							)   -> List[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(_snake_case							)






@torch.no_grad()
def 						_snake_case					( _snake_case							: Optional[Any]    ,  _snake_case							: int    ,  _snake_case							: Optional[Any]=None    ,  _snake_case							: Tuple=None    ,  _snake_case							: int=True							)   -> Dict:






      '''simple docstring'''


      if config_path is not None:
            _A        =       UniSpeechConfig.from_pretrained(_snake_case							)
      else:
            _A        =       UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  _A        =       Dictionary.load_from_json(_snake_case							)

                  # 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(_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							)
                  _A        =       target_dict.indices

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

            _A        =       UniSpeechForCTC(_snake_case							)
      else:
            _A        =       UniSpeechForPreTraining(_snake_case							)

      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(_snake_case    ,  _snake_case    ,  _snake_case							)

      hf_unispeech.save_pretrained(_snake_case							)


if __name__ == "__main__":
       a         =      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'''
       )
       a         =      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

import time

import numpy as np

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





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	str   ,							_UpperCAmelCase	:	list[int]   ,							_UpperCAmelCase	:	list[list[int]]   ,							_UpperCAmelCase	:	list[list[int]]   ,							):
       _A        =       claim_vector
       _A        =       allocated_resources_table
       _A        =       maximum_claim_table

 def       lowerCAmelCase_							(			self	:	Tuple		):
       return [
           sum(p_item[i] for p_item in self.__allocated_resources_table		)
           for i in range(len(self.__allocated_resources_table[0]		)		)
       ]

 def       lowerCAmelCase_							(			self	:	Tuple		):
       return np.array(self.__claim_vector		) - np.array(
           self.__processes_resource_summation()		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return [
           list(np.array(self.__maximum_claim_table[i]		) - np.array(_UpperCAmelCase		)		)
           for i, allocated_resource in enumerate(self.__allocated_resources_table		)
       ]

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return {self.__need().index(_UpperCAmelCase		): i for i in self.__need()}

 def       lowerCAmelCase_							(			self	:	List[str]   ,							**_UpperCAmelCase	:	int		):
       _A        =       self.__need()
       _A        =       self.__allocated_resources_table
       _A        =       self.__available_resources()
       _A        =       self.__need_index_manager()
       for kw, val in kwargs.items():
             if kw and val is True:
                   self.__pretty_data()
       print('_' * 50 + '\n'		)
       while need_list:
             _A        =       False
             for each_need in need_list:
                   _A        =       True
                   for index, need in enumerate(_UpperCAmelCase		):
                         if need > available_resources[index]:
                               _A        =       False
                               break
                   if execution:
                         _A        =       True
                         # get the original index of the process from ind_ctrl db
                         for original_need_index, need_clone in need_index_manager.items():
                               if each_need == need_clone:
                                     _A        =       original_need_index
                         print(F'''Process {process_number + 1} is executing.'''		)
                         # remove the process run from stack
                         need_list.remove(_UpperCAmelCase		)
                         # update available/freed resources stack
                         _A        =       np.array(_UpperCAmelCase		) + np.array(
                             alloc_resources_table[process_number]		)
                         print(
                             'Updated available resource stack for processes: '
                             + ' '.join([str(_UpperCAmelCase		) for x in available_resources]		)		)
                         break
             if safe:
                   print('The process is in a safe state.\n'		)
             else:
                   print('System in unsafe state. Aborting...\n'		)
                   break



 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       print(' ' * 9 + 'Allocated Resource Table'		)
       for item in self.__allocated_resources_table:
             print(
                 F'''P{self.__allocated_resources_table.index(_UpperCAmelCase		) + 1}'''
                 + ' '.join(F'''{it:>8}''' for it in item		)
                 + '\n'		)
       print(' ' * 9 + 'System Resource Table'		)
       for item in self.__maximum_claim_table:
             print(
                 F'''P{self.__maximum_claim_table.index(_UpperCAmelCase		) + 1}'''
                 + ' '.join(F'''{it:>8}''' for it in item		)
                 + '\n'		)
       print(
           'Current Usage by Active Processes: '
           + ' '.join(str(_UpperCAmelCase		) for x in self.__claim_vector		)		)
       print(
           'Initial Available Resources:       '
           + ' '.join(str(_UpperCAmelCase		) for x in self.__available_resources()		)		)
       time.sleep(1		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

