




'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCAmelCase					:		Dict		=							logging.get_logger(__name__)

UpperCAmelCase					:		Any		=							{
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
UpperCAmelCase					:		Optional[int]		=							[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Tuple					     =     {}
      with open(lowerCAmelCase_			,      '''r'''	) as file:
            for line_number, line in enumerate(lowerCAmelCase_	):
                  _snake_case		:	Any					     =     line.strip()
                  if line:
                        _snake_case		:	List[Any]					     =     line.split()
                        _snake_case		:	List[Any]					     =     line_number
                        _snake_case		:	Optional[int]					     =     words[0]
                        _snake_case		:	int					     =     value
      return result




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      for attribute in key.split('''.'''	):
            _snake_case		:	Optional[int]					     =     getattr(lowerCAmelCase_			,      lowerCAmelCase_	)

      _snake_case		:	Union[str, Any]					     =     None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(lowerCAmelCase_	):
                  _snake_case		:	Optional[int]					     =     PARAM_MAPPING[full_name.split('''.'''	)[-1]]
                  _snake_case		:	Dict					     =     '''param'''

      if weight_type is not None and weight_type != "param":
            _snake_case		:	List[str]					     =     getattr(lowerCAmelCase_			,      lowerCAmelCase_	).shape
      elif weight_type is not None and weight_type == "param":
            _snake_case		:	int					     =     hf_pointer
            for attribute in hf_param_name.split('''.'''	):
                  _snake_case		:	str					     =     getattr(lowerCAmelCase_			,      lowerCAmelCase_	)
            _snake_case		:	Optional[int]					     =     shape_pointer.shape

            # let's reduce dimension
            _snake_case		:	Optional[int]					     =     value[0]
      else:
            _snake_case		:	Optional[Any]					     =     hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                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		:	Any					     =     value
      elif weight_type == "weight_g":
            _snake_case		:	Dict					     =     value
      elif weight_type == "weight_v":
            _snake_case		:	Dict					     =     value
      elif weight_type == "bias":
            _snake_case		:	Optional[Any]					     =     value
      elif weight_type == "param":
            for attribute in hf_param_name.split('''.'''	):
                  _snake_case		:	List[Any]					     =     getattr(lowerCAmelCase_			,      lowerCAmelCase_	)
            _snake_case		:	Optional[Any]					     =     value
      else:
            _snake_case		:	Tuple					     =     value

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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	str					     =     None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(lowerCAmelCase_	):
                  _snake_case		:	int					     =     PARAM_MAPPING[full_name.split('''.'''	)[-1]]
                  _snake_case		:	Dict					     =     '''param'''

      if weight_type is not None and weight_type != "param":
            _snake_case		:	str					     =     '''.'''.join([key, weight_type]	)
      elif weight_type is not None and weight_type == "param":
            _snake_case		:	int					     =     '''.'''.join([key, hf_param_name]	)
      else:
            _snake_case		:	List[str]					     =     key

      _snake_case		:	Optional[int]					     =     value if '''lm_head''' in full_key else value[0]


UpperCAmelCase					:		Optional[int]		=							{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     False
      for key, mapped_key in MAPPING.items():
            _snake_case		:	List[Any]					     =     '''wav2vec2.''' + 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]:
                  _snake_case		:	Dict					     =     True
                  if "*" in mapped_key:
                        _snake_case		:	Any					     =     name.split(lowerCAmelCase_	)[0].split('''.'''	)[-2]
                        _snake_case		:	str					     =     mapped_key.replace('''*'''			,      lowerCAmelCase_	)
                  if "weight_g" in name:
                        _snake_case		:	str					     =     '''weight_g'''
                  elif "weight_v" in name:
                        _snake_case		:	List[Any]					     =     '''weight_v'''
                  elif "bias" in name:
                        _snake_case		:	List[Any]					     =     '''bias'''
                  elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        _snake_case		:	int					     =     '''weight'''
                  else:
                        _snake_case		:	Optional[int]					     =     None
                  if hf_dict is not None:
                        rename_dict(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
                  else:
                        set_recursively(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
                  return is_used
      return is_used




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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

      _snake_case		:	Optional[int]					     =     hf_model.wavaveca.feature_extractor

      for name, value in fairseq_dict.items():
            _snake_case		:	Dict					     =     False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      hf_model.config.feat_extract_norm == '''group'''			,      )
                  _snake_case		:	Optional[int]					     =     True
            else:
                  _snake_case		:	Optional[Any]					     =     load_wavaveca_layer(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
            if not is_used:
                  unused_weights.append(lowerCAmelCase_	)

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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[str]					     =     full_name.split('''conv_layers.'''	)[-1]
      _snake_case		:	Tuple					     =     name.split('''.'''	)
      _snake_case		:	int					     =     int(items[0]	)
      _snake_case		:	Optional[int]					     =     int(items[1]	)

      if type_id == 0:
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            f'''{full_name} has size {value.shape}, but'''
                            f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''	)
                  _snake_case		:	Dict					     =     value
                  logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            f'''{full_name} has size {value.shape}, but'''
                            f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''	)
                  _snake_case		:	Dict					     =     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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            f'''{full_name} has size {value.shape}, but'''
                            f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''	)
                  _snake_case		:	int					     =     value
                  logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            f'''{full_name} has size {value.shape}, but'''
                            f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''	)
                  _snake_case		:	List[str]					     =     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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=True			,      lowerCAmelCase_=False	):





      """simple docstring"""



      if config_path is not None:
            _snake_case		:	Dict					     =     WavaVecaConfig.from_pretrained(lowerCAmelCase_	)
      else:
            _snake_case		:	Tuple					     =     WavaVecaConfig()

      if is_seq_class:
            _snake_case		:	List[str]					     =     read_txt_into_dict(lowerCAmelCase_	)
            _snake_case		:	Optional[Any]					     =     idalabel
            _snake_case		:	Dict					     =     WavaVecaForSequenceClassification(lowerCAmelCase_	)
            _snake_case		:	List[Any]					     =     WavaVecaFeatureExtractor(
                feature_size=1			,      sampling_rate=16_000			,      padding_value=0			,      do_normalize=lowerCAmelCase_			,      return_attention_mask=lowerCAmelCase_			,      )
            feature_extractor.save_pretrained(lowerCAmelCase_	)

      elif is_finetuned:
            if dict_path:
                  _snake_case		:	int					     =     Dictionary.load(lowerCAmelCase_	)

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

                  # fairseq has the <pad> and <s> switched
                  _snake_case		:	Any					     =     0
                  _snake_case		:	List[str]					     =     1
                  with open(lowerCAmelCase_			,      '''w'''			,      encoding='''utf-8'''	) as vocab_handle:
                        json.dump(lowerCAmelCase_			,      lowerCAmelCase_	)
                  _snake_case		:	str					     =     WavaVecaCTCTokenizer(
                      lowerCAmelCase_			,      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=lowerCAmelCase_			,      )
                  _snake_case		:	Any					     =     True if config.feat_extract_norm == '''layer''' else False
                  _snake_case		:	Any					     =     WavaVecaFeatureExtractor(
                      feature_size=1			,      sampling_rate=16_000			,      padding_value=0			,      do_normalize=lowerCAmelCase_			,      return_attention_mask=lowerCAmelCase_			,      )
                  _snake_case		:	Any					     =     WavaVecaProcessor(feature_extractor=lowerCAmelCase_			,      tokenizer=lowerCAmelCase_	)
                  processor.save_pretrained(lowerCAmelCase_	)

            _snake_case		:	List[Any]					     =     WavaVecaForCTC(lowerCAmelCase_	)
      else:
            _snake_case		:	str					     =     WavaVecaForPreTraining(lowerCAmelCase_	)

      if is_finetuned or is_seq_class:
            _snake_case		:	Tuple					     =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]			,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
      else:
            _snake_case		:	Tuple					     =     argparse.Namespace(task='''audio_pretraining'''	)
            _snake_case		:	str					     =     fairseq.tasks.setup_task(lowerCAmelCase_	)

            _snake_case		:	List[Any]					     =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,      task=lowerCAmelCase_	)

      _snake_case		:	Any					     =     model[0].eval()

      recursively_load_weights(lowerCAmelCase_			,      lowerCAmelCase_			,      not is_finetuned	)

      hf_wavavec.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
   UpperCAmelCase					:		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'
   )
   parser.add_argument(
       '--is_seq_class',
       action='store_true',
       help='Whether the model to convert is a fine-tuned sequence classification model or not',
   )
   UpperCAmelCase					:		Optional[int]		=							parser.parse_args()

   UpperCAmelCase					:		Optional[Any]		=							not args.not_finetuned and not args.is_seq_class
   convert_wavaveca_checkpoint(
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.dict_path,
       is_finetuned,
       args.is_seq_class,
   )









'''simple docstring'''

import math

from numpy import inf
from scipy.integrate import quad




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      if num <= 0:
            raise ValueError('''math domain error'''	)

      return quad(lowerCAmelCase_			,      0			,      lowerCAmelCase_			,      args=(lowerCAmelCase_)	)[0]




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      return math.pow(lowerCAmelCase_			,      z - 1	) * math.exp(-x	)


if __name__ == "__main__":
   from doctest import testmod

   testmod()


