

'''simple docstring'''






def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	str = "mytext.txt"  ,  SCREAMING_SNAKE_CASE_     :	bool = False    )							->   None:
      """simple docstring"""

      import socket

      SCREAMING_SNAKE_CASE_      :			List[str]     =       1_2_3_1_2  # Reserve a port for your service.
      SCREAMING_SNAKE_CASE_      :			Any     =       socket.socket()  # Create a socket object
      SCREAMING_SNAKE_CASE_      :			List[str]     =       socket.gethostname()  # Get local machine name
      sock.bind((host, port)    )  # Bind to the port
      sock.listen(5    )  # Now wait for client connection.

      print("Server listening...."    )

      while True:
            SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Optional[int]     =       sock.accept()  # Establish connection with client.
            print(F"Got connection from {addr}"    )
            SCREAMING_SNAKE_CASE_      :			List[str]     =       conn.recv(1_0_2_4    )
            print(F"Server received: {data = }"    )

            with open(SCREAMING_SNAKE_CASE_  ,  "rb"    ) as in_file:
                  SCREAMING_SNAKE_CASE_      :			List[str]     =       in_file.read(1_0_2_4    )
                  while data:
                        conn.send(SCREAMING_SNAKE_CASE_    )
                        print(F"Sent {data!r}"    )
                        SCREAMING_SNAKE_CASE_      :			Any     =       in_file.read(1_0_2_4    )

            print("Done sending"    )
            conn.close()
            if testing:  # Allow the test to complete
                  break

      sock.shutdown(1    )
      sock.close()


if __name__ == "__main__":
      send_file()


'''simple docstring'''


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case__     :			Tuple      			=  logging.get_logger(__name__)

snake_case__     :			Tuple      			=  {
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}


def 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							Union[str, Any]	,     lowerCamelCase_					:							str	,     lowerCamelCase_					:							Any	,     lowerCamelCase_					:							Any   ):



     """simple docstring"""





     for attribute in key.split('.'   ):
          UpperCAmelCase_							:   List[Any]   =      getattr(lowerCamelCase_	,     lowerCamelCase_   )

     if weight_type is not None:
          UpperCAmelCase_							:   Any   =      getattr(lowerCamelCase_	,     lowerCamelCase_   ).shape
     else:
          UpperCAmelCase_							:   Optional[int]   =      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":
          UpperCAmelCase_							:   Optional[Any]   =      value
     elif weight_type == "weight_g":
          UpperCAmelCase_							:   str   =      value
     elif weight_type == "weight_v":
          UpperCAmelCase_							:   str   =      value
     elif weight_type == "bias":
          UpperCAmelCase_							:   Tuple   =      value
     else:
          UpperCAmelCase_							:   str   =      value

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


def 					_lowerCamelCase    (      lowerCamelCase_					:							Any	,     lowerCamelCase_					:							str	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     UpperCAmelCase_							:   str   =      []
     UpperCAmelCase_							:   Union[str, Any]   =      fairseq_model.state_dict()

     UpperCAmelCase_							:   Optional[Any]   =      hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          UpperCAmelCase_							:   Optional[Any]   =      False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     hf_model.config.feat_extract_norm == 'group'	,     )
               UpperCAmelCase_							:   Any   =      True
          else:
               for key, mapped_key in MAPPING.items():
                    UpperCAmelCase_							:   Optional[Any]   =      'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

                    if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
                         UpperCAmelCase_							:   int   =      True
                         if "*" in mapped_key:
                              UpperCAmelCase_							:   str   =      name.split(lowerCamelCase_   )[0].split('.'   )[-2]
                              if not layer_index.isnumeric():
                                   continue
                              UpperCAmelCase_							:   str   =      mapped_key.replace('*'	,     lowerCamelCase_   )
                         if "weight_g" in name:
                              UpperCAmelCase_							:   Dict   =      'weight_g'
                         elif "weight_v" in name:
                              UpperCAmelCase_							:   Optional[int]   =      'weight_v'
                         elif "weight" in name:
                              UpperCAmelCase_							:   Optional[int]   =      'weight'
                         elif "bias" in name:
                              UpperCAmelCase_							:   List[Any]   =      'bias'
                         else:
                              UpperCAmelCase_							:   Any   =      None
                         set_recursively(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )
                    continue
          if not is_used:
               unused_weights.append(lowerCamelCase_   )

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


def 					_lowerCamelCase    (      lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     UpperCAmelCase_							:   int   =      full_name.split('conv_layers.'   )[-1]
     UpperCAmelCase_							:   int   =      name.split('.'   )
     UpperCAmelCase_							:   int   =      int(items[0]   )
     UpperCAmelCase_							:   str   =      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.'''
               )
               UpperCAmelCase_							:   Optional[Any]   =      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.'''
               )
               UpperCAmelCase_							:   Optional[Any]   =      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."
               )
               UpperCAmelCase_							:   Any   =      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.'''
               )
               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(lowerCamelCase_   )


def 					_lowerCamelCase    (      lowerCamelCase_					:							Dict	,     lowerCamelCase_					:							str   ):



     """simple docstring"""





     UpperCAmelCase_							:   str   =      SEWDConfig()
     if is_finetuned:
          UpperCAmelCase_							:   List[Any]   =      model.wav_encoder.wav_model.cfg
     else:
          UpperCAmelCase_							:   str   =      model.cfg

     UpperCAmelCase_							:   Dict   =      fs_config.conv_bias
     UpperCAmelCase_							:   Tuple   =      eval(fs_config.conv_feature_layers   )
     UpperCAmelCase_							:   str   =      [x[0] for x in conv_layers]
     UpperCAmelCase_							:   Optional[Any]   =      [x[1] for x in conv_layers]
     UpperCAmelCase_							:   Any   =      [x[2] for x in conv_layers]
     UpperCAmelCase_							:   str   =      'gelu'
     UpperCAmelCase_							:   Dict   =      'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
     UpperCAmelCase_							:   List[Any]   =      0.0
     UpperCAmelCase_							:   List[str]   =      fs_config.activation_fn.name
     UpperCAmelCase_							:   Any   =      fs_config.encoder_embed_dim
     UpperCAmelCase_							:   str   =      0.02
     UpperCAmelCase_							:   List[str]   =      fs_config.encoder_ffn_embed_dim
     UpperCAmelCase_							:   List[Any]   =      1e-5
     UpperCAmelCase_							:   Union[str, Any]   =      fs_config.encoder_layerdrop
     UpperCAmelCase_							:   Optional[Any]   =      fs_config.encoder_attention_heads
     UpperCAmelCase_							:   List[Any]   =      fs_config.conv_pos_groups
     UpperCAmelCase_							:   Tuple   =      fs_config.conv_pos
     UpperCAmelCase_							:   Optional[Any]   =      len(lowerCamelCase_   )
     UpperCAmelCase_							:   int   =      fs_config.encoder_layers
     UpperCAmelCase_							:   Any   =      fs_config.squeeze_factor
     # DeBERTa-specific parameters:
     UpperCAmelCase_							:   Tuple   =      fs_config.max_position_embeddings
     UpperCAmelCase_							:   List[str]   =      fs_config.position_buckets
     UpperCAmelCase_							:   str   =      fs_config.share_att_key
     UpperCAmelCase_							:   Any   =      fs_config.relative_attention
     UpperCAmelCase_							:   Optional[Any]   =      fs_config.position_biased_input
     UpperCAmelCase_							:   Union[str, Any]   =      tuple(fs_config.pos_att_type.split('|'   )   )
     UpperCAmelCase_							:   str   =      fs_config.norm_rel_ebd

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          UpperCAmelCase_							:   str   =      model.cfg
          UpperCAmelCase_							:   Tuple   =      fs_config.final_dropout
          UpperCAmelCase_							:   str   =      fs_config.layerdrop
     UpperCAmelCase_							:   List[Any]   =      fs_config.activation_dropout
     UpperCAmelCase_							:   Dict   =      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     UpperCAmelCase_							:   Any   =      fs_config.attention_dropout
     UpperCAmelCase_							:   Optional[Any]   =      fs_config.dropout_input
     UpperCAmelCase_							:   Optional[Any]   =      fs_config.dropout
     UpperCAmelCase_							:   int   =      fs_config.mask_channel_length
     UpperCAmelCase_							:   str   =      fs_config.mask_channel_prob
     UpperCAmelCase_							:   int   =      fs_config.mask_length
     UpperCAmelCase_							:   Union[str, Any]   =      fs_config.mask_prob

     UpperCAmelCase_							:   Any   =      'Wav2Vec2FeatureExtractor'
     UpperCAmelCase_							:   Union[str, Any]   =      'Wav2Vec2CTCTokenizer'

     return config


@torch.no_grad()
def 					_lowerCamelCase    (      lowerCamelCase_					:							Dict	,     lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							int=None	,     lowerCamelCase_					:							int=None	,     lowerCamelCase_					:							Dict=True   ):



     """simple docstring"""





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

     if config_path is not None:
          UpperCAmelCase_							:   str   =      SEWDConfig.from_pretrained(lowerCamelCase_   )
     else:
          UpperCAmelCase_							:   Optional[Any]   =      convert_config(model[0]	,     lowerCamelCase_   )
     UpperCAmelCase_							:   List[Any]   =      model[0].eval()

     UpperCAmelCase_							:   Optional[int]   =      True if config.feat_extract_norm == 'layer' else False
     UpperCAmelCase_							:   Dict   =      WavaVecaFeatureExtractor(
         feature_size=1	,     sampling_rate=16000	,     padding_value=0	,     do_normalize=lowerCamelCase_	,     return_attention_mask=lowerCamelCase_	,     )

     if is_finetuned:
          if dict_path:
               UpperCAmelCase_							:   Union[str, Any]   =      Dictionary.load(lowerCamelCase_   )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               UpperCAmelCase_							:   Any   =      target_dict.pad_index
               UpperCAmelCase_							:   List[str]   =      target_dict.bos_index
               UpperCAmelCase_							:   Optional[int]   =      target_dict.pad_index
               UpperCAmelCase_							:   Any   =      target_dict.bos_index
               UpperCAmelCase_							:   Any   =      target_dict.eos_index
               UpperCAmelCase_							:   int   =      len(target_dict.symbols   )
               UpperCAmelCase_							:   Optional[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_   )
               with open(lowerCamelCase_	,     'w'	,     encoding='utf-8'   ) as vocab_handle:
                    json.dump(target_dict.indices	,     lowerCamelCase_   )
               UpperCAmelCase_							:   int   =      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_	,     )
               UpperCAmelCase_							:   Optional[Any]   =      WavaVecaProcessor(feature_extractor=lowerCamelCase_	,     tokenizer=lowerCamelCase_   )
               processor.save_pretrained(lowerCamelCase_   )

          UpperCAmelCase_							:   Optional[int]   =      SEWDForCTC(lowerCamelCase_   )
     else:
          UpperCAmelCase_							:   List[Any]   =      SEWDModel(lowerCamelCase_   )
          feature_extractor.save_pretrained(lowerCamelCase_   )

     recursively_load_weights(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )

     hf_model.save_pretrained(lowerCamelCase_   )


if __name__ == "__main__":
 snake_case__     :			List[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(
     '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
 )
 snake_case__     :			Any      			=  parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )






