

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()
lowercase_									=   logging.get_logger(__name__)

lowercase_									=   {
    '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',
}
lowercase_									=   [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def      a      (			A__	:   Optional[Any]      ,   A__	:   Optional[Any]      ,   A__	:   Union[str, Any]      ,   A__	:   Optional[Any]      ,   A__	:   List[str]      ,   A__	:   Optional[Any]     )    ->      List[Any]:





      """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
                        _lowercase	       ='lm_head'

            _lowercase	       =getattr(__A      ,   __A     )

      if weight_type is not None:
            _lowercase	       =getattr(__A      ,   __A     ).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      (			A__	:   Any      ,   A__	:   Optional[int]      ,   A__	:   Tuple     )    ->      Tuple:





      """simple docstring"""



      _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(
                      __A      ,   __A      ,   __A      ,   __A      ,   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(__A     )[0].split('.'     )[-2]
                                    _lowercase	       =mapped_key.replace('*'      ,   __A     )
                              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(__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__	:   Any      ,   A__	:   Optional[int]      ,   A__	:   List[Any]      ,   A__	:   List[str]      ,   A__	:   List[str]     )    ->      Tuple:





      """simple docstring"""



      _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(__A     )
@torch.no_grad()
def      a      (			A__	:   Tuple      ,   A__	:   Dict      ,   A__	:   Tuple=None      ,   A__	:   Dict=None      ,   A__	:   List[Any]=True     )    ->      Tuple:





      """simple docstring"""



      if config_path is not None:
            _lowercase	       =UniSpeechConfig.from_pretrained(__A     )
      else:
            _lowercase	       =UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  _lowercase	       =Dictionary.load_from_json(__A     )

                  # 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(__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     )
                  _lowercase	       =target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  _lowercase	       =42
                  _lowercase	       =43
                  with open(__A      ,   'w'      ,   encoding='utf-8'     ) as vocab_handle:
                        json.dump(__A      ,   __A     )
                  _lowercase	       =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      ,   )
                  _lowercase	       =True if config.feat_extract_norm == 'layer' else False
                  _lowercase	       =WavaVecaFeatureExtractor(
                      feature_size=1      ,   sampling_rate=16000      ,   padding_value=0      ,   do_normalize=__A      ,   return_attention_mask=__A      ,   )
                  _lowercase	       =WavaVecaProcessor(feature_extractor=__A      ,   tokenizer=__A     )
                  processor.save_pretrained(__A     )

            _lowercase	       =UniSpeechForCTC(__A     )
      else:
            _lowercase	       =UniSpeechForPreTraining(__A     )

      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(__A      ,   __A      ,   __A     )

      hf_unispeech.save_pretrained(__A     )


if __name__ == "__main__":
  lowercase_									=   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_									=   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 math




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  u
     for i in range(1  ,	__A):
          _a      =  temp * (u - i)
     return temp




def lowerCAmelCase  ():



     """simple docstring"""



     _a      =  int(input('''enter the numbers of values: '''))
     _a      =  []
     for _ in range(__A):
          y.append([])
     for i in range(__A):
          for j in range(__A):
               y[i].append(__A)
               _a      =  0

     print('''enter the values of parameters in a list: ''')
     _a      =  list(map(__A  ,	input().split()))

     print('''enter the values of corresponding parameters: ''')
     for i in range(__A):
          _a      =  float(input())

     _a      =  int(input('''enter the value to interpolate: '''))
     _a      =  (value - x[0]) / (x[1] - x[0])

     # for calculating forward difference table

     for i in range(1  ,	__A):
          for j in range(n - i):
               _a      =  y[j + 1][i - 1] - y[j][i - 1]

     _a      =  y[0][0]
     for i in range(1  ,	__A):
          summ += (ucal(__A  ,	__A) * y[0][i]) / math.factorial(__A)

     print(F'''the value at {value} is {summ}''')


if __name__ == "__main__":
  main()



