






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()
A_						:						Tuple						      =					logging.get_logger(__name__)

A_						:						Optional[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',
    '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',
}
A_						:						int						      =					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]

def     snake_case   (UpperCAmelCase__    )			->							str:
  UpperCamelCase_:   Tuple        =							{}
  with open(UpperCAmelCase__      ,       'r'    ) as file:
    for line_number, line in enumerate(UpperCAmelCase__    ):
      UpperCamelCase_:   List[Any]        =							line.strip()
      if line:
        UpperCamelCase_:   List[Any]        =							line.split()
        UpperCamelCase_:   Optional[Any]        =							line_number
        UpperCamelCase_:   Any        =							words[0]
        UpperCamelCase_:   List[Any]        =							value
  return result

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							List[str]:
  for attribute in key.split('.'    ):
    UpperCamelCase_:   str        =							getattr(UpperCAmelCase__      ,       UpperCAmelCase__    )

  UpperCamelCase_:   str        =							None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(UpperCAmelCase__    ):
      UpperCamelCase_:   Any        =							PARAM_MAPPING[full_name.split('.'    )[-1]]
      UpperCamelCase_:   Dict        =							'param'

  if weight_type is not None and weight_type != "param":
    UpperCamelCase_:   Optional[Any]        =							getattr(UpperCAmelCase__      ,       UpperCAmelCase__    ).shape
  elif weight_type is not None and weight_type == "param":
    UpperCamelCase_:   Optional[Any]        =							hf_pointer
    for attribute in hf_param_name.split('.'    ):
      UpperCamelCase_:   str        =							getattr(UpperCAmelCase__      ,       UpperCAmelCase__    )
    UpperCamelCase_:   Tuple        =							shape_pointer.shape

    # let's reduce dimension
    UpperCamelCase_:   int        =							value[0]
  else:
    UpperCamelCase_:   Union[str, 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":
    UpperCamelCase_:   Optional[int]        =							value
  elif weight_type == "weight_g":
    UpperCamelCase_:   Any        =							value
  elif weight_type == "weight_v":
    UpperCamelCase_:   Union[str, Any]        =							value
  elif weight_type == "bias":
    UpperCamelCase_:   Union[str, Any]        =							value
  elif weight_type == "param":
    for attribute in hf_param_name.split('.'    ):
      UpperCamelCase_:   Dict        =							getattr(UpperCAmelCase__      ,       UpperCAmelCase__    )
    UpperCamelCase_:   Optional[Any]        =							value
  else:
    UpperCamelCase_:   int        =							value

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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Any:
  UpperCamelCase_:   Union[str, Any]        =							None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(UpperCAmelCase__    ):
      UpperCamelCase_:   Dict        =							PARAM_MAPPING[full_name.split('.'    )[-1]]
      UpperCamelCase_:   List[Any]        =							'param'

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

  UpperCamelCase_:   Any        =							value if 'lm_head' in full_key else value[0]


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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							List[str]:
  UpperCamelCase_:   List[Any]        =							[]
  UpperCamelCase_:   Dict        =							fairseq_model.state_dict()

  UpperCamelCase_:   Optional[Any]        =							hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    UpperCamelCase_:   Union[str, Any]        =							False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       hf_model.config.feat_extract_norm == 'group'      ,       )
      UpperCamelCase_:   List[Any]        =							True
    else:
      UpperCamelCase_:   Tuple        =							load_wavaveca_layer(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )
    if not is_used:
      unused_weights.append(UpperCAmelCase__    )

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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Any:
  UpperCamelCase_:   Any        =							full_name.split('conv_layers.'    )[-1]
  UpperCamelCase_:   int        =							name.split('.'    )
  UpperCamelCase_:   int        =							int(items[0]    )
  UpperCamelCase_:   Union[str, Any]        =							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.'''    )
      UpperCamelCase_:   Union[str, Any]        =							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.'''    )
      UpperCamelCase_:   int        =							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.'''    )
      UpperCamelCase_:   Union[str, Any]        =							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.'''    )
      UpperCamelCase_:   List[Any]        =							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     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__=None      ,       UpperCAmelCase__=None      ,       UpperCAmelCase__=True      ,       UpperCAmelCase__=False    )			->							Dict:
  if config_path is not None:
    UpperCamelCase_:   Tuple        =							WavaVecaConfig.from_pretrained(UpperCAmelCase__    )
  else:
    UpperCamelCase_:   List[str]        =							WavaVecaConfig()

  if is_seq_class:
    UpperCamelCase_:   int        =							read_txt_into_dict(UpperCAmelCase__    )
    UpperCamelCase_:   Tuple        =							idalabel
    UpperCamelCase_:   str        =							WavaVecaForSequenceClassification(UpperCAmelCase__    )
    UpperCamelCase_:   Optional[int]        =							WavaVecaFeatureExtractor(
        feature_size=1      ,       sampling_rate=1_6_0_0_0      ,       padding_value=0      ,       do_normalize=UpperCAmelCase__      ,       return_attention_mask=UpperCAmelCase__      ,       )
    feature_extractor.save_pretrained(UpperCAmelCase__    )

  elif is_finetuned:
    if dict_path:
      UpperCamelCase_:   List[Any]        =							Dictionary.load(UpperCAmelCase__    )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      UpperCamelCase_:   Dict        =							target_dict.pad_index
      UpperCamelCase_:   Tuple        =							target_dict.bos_index
      UpperCamelCase_:   Optional[Any]        =							target_dict.eos_index
      UpperCamelCase_:   Union[str, Any]        =							len(target_dict.symbols    )
      UpperCamelCase_:   int        =							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__    )
      UpperCamelCase_:   str        =							target_dict.indices

      # fairseq has the <pad> and <s> switched
      UpperCamelCase_:   List[str]        =							0
      UpperCamelCase_:   List[Any]        =							1
      with open(UpperCAmelCase__      ,       'w'      ,       encoding='utf-8'    ) as vocab_handle:
        json.dump(UpperCAmelCase__      ,       UpperCAmelCase__    )
      UpperCamelCase_:   Union[str, Any]        =							WavaVecaCTCTokenizer(
          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__      ,       )
      UpperCamelCase_:   Any        =							True if config.feat_extract_norm == 'layer' else False
      UpperCamelCase_:   Tuple        =							WavaVecaFeatureExtractor(
          feature_size=1      ,       sampling_rate=1_6_0_0_0      ,       padding_value=0      ,       do_normalize=UpperCAmelCase__      ,       return_attention_mask=UpperCAmelCase__      ,       )
      UpperCamelCase_:   Dict        =							WavaVecaProcessor(feature_extractor=UpperCAmelCase__      ,       tokenizer=UpperCAmelCase__    )
      processor.save_pretrained(UpperCAmelCase__    )

    UpperCamelCase_:   Any        =							WavaVecaForCTC(UpperCAmelCase__    )
  else:
    UpperCamelCase_:   Any        =							WavaVecaForPreTraining(UpperCAmelCase__    )

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

    UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   Union[str, Any]        =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,       task=UpperCAmelCase__    )

  UpperCamelCase_:   str        =							model[0].eval()

  recursively_load_weights(UpperCAmelCase__      ,       UpperCAmelCase__      ,       not is_finetuned    )

  hf_wavavec.save_pretrained(UpperCAmelCase__    )


if __name__ == "__main__":
   A_						:						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',
   )
   A_						:						int						      =					parser.parse_args()

   A_						:						str						      =					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 re






def      UpperCamelCase_				(      A__						):
	a_      				=						re.compile(r"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$"""						)
	if match := re.search(A__  ,      A__						):
		return match.string == phone
	return False


if __name__ == "__main__":
		print(indian_phone_validator('+918827897895'))
