def       __magic_name__(  __UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      _lowerCamelCase			    =				min(__UpperCAmelCase		)  # min() finds the minimum value
      _lowerCamelCase			    =				max(__UpperCAmelCase		)  # max() finds the maximum value

      _lowerCamelCase			    =				max_val - min_val + 1  # size is difference of max and min values plus one

      # list of pigeonholes of size equal to the variable size
      _lowerCamelCase			    =				[0] * size

      # Populate the pigeonholes.
      for x in a:
            assert isinstance(__UpperCAmelCase ,						__UpperCAmelCase		), "integers only please"
            holes[x - min_val] += 1

      # Putting the elements back into the array in an order.
      _lowerCamelCase			    =				0
      for count in range(__UpperCAmelCase		):
            while holes[count] > 0:
                  holes[count] -= 1
                  _lowerCamelCase			    =				count + min_val
                  i += 1
def       __magic_name__(  )						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				[8, 3, 2, 7, 4, 6, 8]
      pigeonhole_sort(__UpperCAmelCase		)
      print('''Sorted order is:''' ,						''' '''.join(__UpperCAmelCase		)		)


if __name__ == "__main__":
     main()


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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Optional[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
                        _lowerCamelCase			    =				'''lm_head'''

            _lowerCamelCase			    =				getattr(__UpperCAmelCase ,						__UpperCAmelCase		)

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

      logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      _lowerCamelCase			    =				fairseq_model.state_dict()

      _lowerCamelCase			    =				hf_model.unispeech.feature_extractor

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

      logger.warning(F'Unused weights: {unused_weights}'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				full_name.split('''conv_layers.'''		)[-1]
      _lowerCamelCase			    =				name.split('''.'''		)
      _lowerCamelCase			    =				int(items[0]		)
      _lowerCamelCase			    =				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.'
                  )
                  _lowerCamelCase			    =				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.'
                  )
                  _lowerCamelCase			    =				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."
                  )
                  _lowerCamelCase			    =				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.'
                  )
                  _lowerCamelCase			    =				value
                  logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
      else:
            unused_weights.append(__UpperCAmelCase		)
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						__UpperCAmelCase=True		)						->    Union[str, Any]:



      '''simple docstring'''

      if config_path is not None:
            _lowerCamelCase			    =				UniSpeechConfig.from_pretrained(__UpperCAmelCase		)
      else:
            _lowerCamelCase			    =				UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  _lowerCamelCase			    =				Dictionary.load_from_json(__UpperCAmelCase		)

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

                  # fairseq has the <pad> and <s> switched
                  _lowerCamelCase			    =				42
                  _lowerCamelCase			    =				43
                  with open(__UpperCAmelCase ,						'''w''' ,						encoding='''utf-8'''		) as vocab_handle:
                        json.dump(__UpperCAmelCase ,						__UpperCAmelCase		)
                  _lowerCamelCase			    =				WavaVecaPhonemeCTCTokenizer(
                      __UpperCAmelCase ,						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=__UpperCAmelCase ,						)
                  _lowerCamelCase			    =				True if config.feat_extract_norm == '''layer''' else False
                  _lowerCamelCase			    =				WavaVecaFeatureExtractor(
                      feature_size=1 ,						sampling_rate=1_6000 ,						padding_value=0 ,						do_normalize=__UpperCAmelCase ,						return_attention_mask=__UpperCAmelCase ,						)
                  _lowerCamelCase			    =				WavaVecaProcessor(feature_extractor=__UpperCAmelCase ,						tokenizer=__UpperCAmelCase		)
                  processor.save_pretrained(__UpperCAmelCase		)

            _lowerCamelCase			    =				UniSpeechForCTC(__UpperCAmelCase		)
      else:
            _lowerCamelCase			    =				UniSpeechForPreTraining(__UpperCAmelCase		)

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

      _lowerCamelCase			    =				model[0].eval()

      recursively_load_weights(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)

      hf_unispeech.save_pretrained(__UpperCAmelCase		)


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
     )