


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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__a       =					logging.get_logger(__name__)

__a       =					{
    '''post_extract_proj''': '''feature_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.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 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase   )			->Union[str, Any]:







      """simple docstring"""

      for attribute in key.split('''.'''   ):
            lowercase	:      Tuple          =						getattr(__SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE   )

      if weight_type is not None:
            lowercase	:      List[Any]          =						getattr(__SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE   ).shape
      else:
            lowercase	:      str          =						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	:      Optional[Any]          =						value
      elif weight_type == "weight_g":
            lowercase	:      Optional[Any]          =						value
      elif weight_type == "weight_v":
            lowercase	:      Optional[Any]          =						value
      elif weight_type == "bias":
            lowercase	:      Union[str, Any]          =						value
      else:
            lowercase	:      Any          =						value

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




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase   )			->Tuple:







      """simple docstring"""

      lowercase	:      Optional[int]          =						[]
      lowercase	:      Optional[int]          =						fairseq_model.state_dict()

      lowercase	:      List[str]          =						hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            lowercase	:      List[str]          =						False
            if "conv_layers" in name:
                  load_conv_layer(
                      __SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE,			hf_model.config.feat_extract_norm == '''group''',			)
                  lowercase	:      str          =						True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowercase	:      int          =						'sew.' + 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]:
                              lowercase	:      str          =						True
                              if "*" in mapped_key:
                                    lowercase	:      int          =						name.split(__SCREAMING_SNAKE_CASE   )[0].split('''.'''   )[-2]
                                    lowercase	:      Optional[Any]          =						mapped_key.replace('''*''',			__SCREAMING_SNAKE_CASE   )
                              if "weight_g" in name:
                                    lowercase	:      Any          =						'weight_g'
                              elif "weight_v" in name:
                                    lowercase	:      Tuple          =						'weight_v'
                              elif "weight" in name:
                                    lowercase	:      int          =						'weight'
                              elif "bias" in name:
                                    lowercase	:      List[Any]          =						'bias'
                              else:
                                    lowercase	:      Optional[Any]          =						None
                              set_recursively(__SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE   )
                        continue
            if not is_used:
                  unused_weights.append(__SCREAMING_SNAKE_CASE   )

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




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase   )			->Optional[Any]:







      """simple docstring"""

      lowercase	:      Dict          =						full_name.split('''conv_layers.'''   )[-1]
      lowercase	:      int          =						name.split('''.'''   )
      lowercase	:      Any          =						int(items[0]   )
      lowercase	:      Tuple          =						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	:      List[str]          =						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	:      List[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."
                  )
                  lowercase	:      Optional[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."""
                  )
                  lowercase	:      List[str]          =						value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
      else:
            unused_weights.append(__SCREAMING_SNAKE_CASE   )




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase   )			->Tuple:







      """simple docstring"""

      lowercase	:      str          =						SEWConfig()
      if is_finetuned:
            lowercase	:      List[Any]          =						model.wav_encoder.wav_model.cfg
      else:
            lowercase	:      Tuple          =						model.cfg

      lowercase	:      Any          =						fs_config.conv_bias
      lowercase	:      Optional[Any]          =						eval(fs_config.conv_feature_layers   )
      lowercase	:      int          =						[x[0] for x in conv_layers]
      lowercase	:      Any          =						[x[1] for x in conv_layers]
      lowercase	:      Optional[Any]          =						[x[2] for x in conv_layers]
      lowercase	:      Tuple          =						'gelu'
      lowercase	:      str          =						'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
      lowercase	:      int          =						0.0
      lowercase	:      Any          =						fs_config.activation_fn.name
      lowercase	:      Tuple          =						fs_config.encoder_embed_dim
      lowercase	:      int          =						0.0_2
      lowercase	:      Union[str, Any]          =						fs_config.encoder_ffn_embed_dim
      lowercase	:      Tuple          =						1e-5
      lowercase	:      Union[str, Any]          =						fs_config.encoder_layerdrop
      lowercase	:      Tuple          =						fs_config.encoder_attention_heads
      lowercase	:      List[str]          =						fs_config.conv_pos_groups
      lowercase	:      Union[str, Any]          =						fs_config.conv_pos
      lowercase	:      str          =						len(__SCREAMING_SNAKE_CASE   )
      lowercase	:      Optional[Any]          =						fs_config.encoder_layers
      lowercase	:      str          =						fs_config.squeeze_factor

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

      lowercase	:      List[Any]          =						'Wav2Vec2FeatureExtractor'
      lowercase	:      Union[str, Any]          =						'Wav2Vec2CTCTokenizer'

      return config




@torch.no_grad()
def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase=None,			_UpperCamelCase=None,			_UpperCamelCase=True   )			->List[str]:







      """simple docstring"""

      if is_finetuned:
            lowercase	:      Optional[int]          =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path],			arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''   )[:-1]   )}   )
      else:
            lowercase	:      Union[str, Any]          =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

      if config_path is not None:
            lowercase	:      List[str]          =						SEWConfig.from_pretrained(__SCREAMING_SNAKE_CASE   )
      else:
            lowercase	:      Tuple          =						convert_config(model[0],			__SCREAMING_SNAKE_CASE   )
      lowercase	:      Optional[Any]          =						model[0].eval()

      lowercase	:      List[Any]          =						True if config.feat_extract_norm == 'layer' else False
      lowercase	:      Optional[Any]          =						WavaVecaFeatureExtractor(
          feature_size=1,			sampling_rate=16000,			padding_value=0,			do_normalize=__SCREAMING_SNAKE_CASE,			return_attention_mask=__SCREAMING_SNAKE_CASE,			)

      if is_finetuned:
            if dict_path:
                  lowercase	:      Dict          =						Dictionary.load(__SCREAMING_SNAKE_CASE   )

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  lowercase	:      str          =						target_dict.pad_index
                  lowercase	:      Union[str, Any]          =						target_dict.bos_index
                  lowercase	:      Tuple          =						target_dict.pad_index
                  lowercase	:      List[Any]          =						target_dict.bos_index
                  lowercase	:      Any          =						target_dict.eos_index
                  lowercase	:      str          =						len(target_dict.symbols   )
                  lowercase	:      Optional[Any]          =						os.path.join(__SCREAMING_SNAKE_CASE,			'''vocab.json'''   )
                  if not os.path.isdir(__SCREAMING_SNAKE_CASE   ):
                        logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__SCREAMING_SNAKE_CASE   )   )
                        return
                  os.makedirs(__SCREAMING_SNAKE_CASE,			exist_ok=__SCREAMING_SNAKE_CASE   )
                  with open(__SCREAMING_SNAKE_CASE,			'''w''',			encoding='''utf-8'''   ) as vocab_handle:
                        json.dump(target_dict.indices,			__SCREAMING_SNAKE_CASE   )
                  lowercase	:      Union[str, Any]          =						WavaVecaCTCTokenizer(
                      __SCREAMING_SNAKE_CASE,			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=__SCREAMING_SNAKE_CASE,			)
                  lowercase	:      Tuple          =						WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE,			tokenizer=__SCREAMING_SNAKE_CASE   )
                  processor.save_pretrained(__SCREAMING_SNAKE_CASE   )

            lowercase	:      List[str]          =						SEWForCTC(__SCREAMING_SNAKE_CASE   )
      else:
            lowercase	:      Any          =						SEWModel(__SCREAMING_SNAKE_CASE   )
            feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE   )

      recursively_load_weights(__SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE   )

      hf_model.save_pretrained(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
  __a       =					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'''
  )
  __a       =					parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )


"""simple docstring"""







import argparse
import struct
import unittest







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				str													=	data

        # Initialize hash values
        lowercase_	:				Optional[int]													=	[
            0X6_A_0_9_E_6_6_7,
            0XB_B_6_7_A_E_8_5,
            0X3_C_6_E_F_3_7_2,
            0XA_5_4_F_F_5_3_A,
            0X5_1_0_E_5_2_7_F,
            0X9_B_0_5_6_8_8_C,
            0X1_F_8_3_D_9_A_B,
            0X5_B_E_0_C_D_1_9,
        ]

        # Initialize round constants
        lowercase_	:				Tuple													=	[
            0X4_2_8_A_2_F_9_8,
            0X7_1_3_7_4_4_9_1,
            0XB_5_C_0_F_B_C_F,
            0XE_9_B_5_D_B_A_5,
            0X3_9_5_6_C_2_5_B,
            0X5_9_F_1_1_1_F_1,
            0X9_2_3_F_8_2_A_4,
            0XA_B_1_C_5_E_D_5,
            0XD_8_0_7_A_A_9_8,
            0X1_2_8_3_5_B_0_1,
            0X2_4_3_1_8_5_B_E,
            0X5_5_0_C_7_D_C_3,
            0X7_2_B_E_5_D_7_4,
            0X8_0_D_E_B_1_F_E,
            0X9_B_D_C_0_6_A_7,
            0XC_1_9_B_F_1_7_4,
            0XE_4_9_B_6_9_C_1,
            0XE_F_B_E_4_7_8_6,
            0X0_F_C_1_9_D_C_6,
            0X2_4_0_C_A_1_C_C,
            0X2_D_E_9_2_C_6_F,
            0X4_A_7_4_8_4_A_A,
            0X5_C_B_0_A_9_D_C,
            0X7_6_F_9_8_8_D_A,
            0X9_8_3_E_5_1_5_2,
            0XA_8_3_1_C_6_6_D,
            0XB_0_0_3_2_7_C_8,
            0XB_F_5_9_7_F_C_7,
            0XC_6_E_0_0_B_F_3,
            0XD_5_A_7_9_1_4_7,
            0X0_6_C_A_6_3_5_1,
            0X1_4_2_9_2_9_6_7,
            0X2_7_B_7_0_A_8_5,
            0X2_E_1_B_2_1_3_8,
            0X4_D_2_C_6_D_F_C,
            0X5_3_3_8_0_D_1_3,
            0X6_5_0_A_7_3_5_4,
            0X7_6_6_A_0_A_B_B,
            0X8_1_C_2_C_9_2_E,
            0X9_2_7_2_2_C_8_5,
            0XA_2_B_F_E_8_A_1,
            0XA_8_1_A_6_6_4_B,
            0XC_2_4_B_8_B_7_0,
            0XC_7_6_C_5_1_A_3,
            0XD_1_9_2_E_8_1_9,
            0XD_6_9_9_0_6_2_4,
            0XF_4_0_E_3_5_8_5,
            0X1_0_6_A_A_0_7_0,
            0X1_9_A_4_C_1_1_6,
            0X1_E_3_7_6_C_0_8,
            0X2_7_4_8_7_7_4_C,
            0X3_4_B_0_B_C_B_5,
            0X3_9_1_C_0_C_B_3,
            0X4_E_D_8_A_A_4_A,
            0X5_B_9_C_C_A_4_F,
            0X6_8_2_E_6_F_F_3,
            0X7_4_8_F_8_2_E_E,
            0X7_8_A_5_6_3_6_F,
            0X8_4_C_8_7_8_1_4,
            0X8_C_C_7_0_2_0_8,
            0X9_0_B_E_F_F_F_A,
            0XA_4_5_0_6_C_E_B,
            0XB_E_F_9_A_3_F_7,
            0XC_6_7_1_7_8_F_2,
        ]

        lowercase_	:				Tuple													=	self.preprocessing(self.data			)
        self.final_hash()
   @staticmethod
   def 			_UpperCAmelCase   (			__UpperCamelCase			)	->      bytes:


        '''simple docstring'''


        lowercase_	:				str													=	B'\x80' + (B'\x00' * (63 - (len(__UpperCamelCase			) + 8) % 64))
        lowercase_	:				str													=	struct.pack('>Q'     ,(len(__UpperCamelCase			) * 8)			)
        return data + padding + big_endian_integer
   def 			_UpperCAmelCase   (			self			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	[
            self.preprocessed_data[x : x + 64]
            for x in range(0     ,len(self.preprocessed_data			)     ,64			)
        ]

        for block in self.blocks:
             # Convert the given block into a list of 4 byte integers
             lowercase_	:				Any													=	list(struct.unpack('>16L'     ,__UpperCamelCase			)			)
             # add 48 0-ed integers
             words += [0] * 48

             lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				Optional[int]													=	self.hashes

             for index in range(0     ,64			):
                  if index > 15:
                       # modify the zero-ed indexes at the end of the array
                       lowercase_	:				str													=	(
                           self.ror(words[index - 15]     ,7			)
                           ^ self.ror(words[index - 15]     ,18			)
                           ^ (words[index - 15] >> 3)
                       )
                       lowercase_	:				int													=	(
                           self.ror(words[index - 2]     ,17			)
                           ^ self.ror(words[index - 2]     ,19			)
                           ^ (words[index - 2] >> 10)
                       )

                       lowercase_	:				Optional[Any]													=	(
                           words[index - 16] + sa + words[index - 7] + sa
                       ) % 0X1_0_0_0_0_0_0_0_0

                  # Compression
                  lowercase_	:				Tuple													=	self.ror(__UpperCamelCase     ,6			) ^ self.ror(__UpperCamelCase     ,11			) ^ self.ror(__UpperCamelCase     ,25			)
                  lowercase_	:				Union[str, Any]													=	(e & f) ^ ((~e & 0XF_F_F_F_F_F_F_F) & g)
                  lowercase_	:				str													=	(
                      h + sa + ch + self.round_constants[index] + words[index]
                  ) % 0X1_0_0_0_0_0_0_0_0
                  lowercase_	:				Optional[int]													=	self.ror(__UpperCamelCase     ,2			) ^ self.ror(__UpperCamelCase     ,13			) ^ self.ror(__UpperCamelCase     ,22			)
                  lowercase_	:				Optional[Any]													=	(a & b) ^ (a & c) ^ (b & c)
                  lowercase_	:				Any													=	(sa + maj) % 0X1_0_0_0_0_0_0_0_0

                  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				Tuple													=	(
                      g,
                      f,
                      e,
                      ((d + tempa) % 0X1_0_0_0_0_0_0_0_0),
                      c,
                      b,
                      a,
                      ((tempa + tempa) % 0X1_0_0_0_0_0_0_0_0),
                  )

             lowercase_	:				str													=	[a, b, c, d, e, f, g, h]

             # Modify final values
             lowercase_	:				Dict													=	[
                 ((element + mutated_hash_values[index]) % 0X1_0_0_0_0_0_0_0_0)
                 for index, element in enumerate(self.hashes			)
             ]

        lowercase_	:				Any													=	''.join([hex(__UpperCamelCase			)[2:].zfill(8			) for value in self.hashes]			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      int:


        '''simple docstring'''


        return 0XF_F_F_F_F_F_F_F & (value << (32 - rotations)) | (value >> rotations)







class        UpperCamelCase		(			unittest.TestCase  ):
   def 			_UpperCAmelCase   (			self			)	->      None:


        '''simple docstring'''


        import hashlib

        lowercase_	:				Union[str, Any]													=	bytes('Test String'     ,'utf-8'			)
        self.assertEqual(SHAaaa(__UpperCamelCase			).hash     ,hashlib.shaaaa(__UpperCamelCase			).hexdigest()			)






def 			lowercase__(       ):
     import doctest

     doctest.testmod()

     lowercase_	:				Tuple													=	argparse.ArgumentParser()
     parser.add_argument(
         '-s'     ,			'--string'     ,			dest='input_string'     ,			default='Hello World!! Welcome to Cryptography'     ,			help='Hash the string'     ,			)
     parser.add_argument(
         '-f'     ,			'--file'     ,			dest='input_file'     ,			help='Hash contents of a file'      )

     lowercase_	:				Any													=	parser.parse_args()

     lowercase_	:				int													=	args.input_string

     # hash input should be a bytestring
     if args.input_file:
          with open(args.input_file     ,			'rb'      ) as f:
               lowercase_	:				str													=	f.read()
     else:
          lowercase_	:				Optional[int]													=	bytes(__SCREAMING_SNAKE_CASE     ,			'utf-8'      )

     print(SHAaaa(__SCREAMING_SNAKE_CASE      ).hash      )


if __name__ == "__main__":
       main()

