"""simple docstring"""





import argparse
import hashlib  # hashlib is only used inside the Test class
import struct


class   UpperCAmelCase_			:


      def __init__(     self	,    a							) ->      Optional[Any]:
             lowercase__    :	List[Any]     					=			data
             lowercase__    :	Optional[int]     					=			[0X67_452_301, 0XEF_CDA_B89, 0X98_BAD_CFE, 0X10_325_476, 0XC3_D2E_1F0]


      @staticmethod
      def 	_UpperCAmelCase (     a	,    a							) ->      Tuple:
             return ((n << b) | (n >> (3_2 - b))) & 0XFF_FFF_FFF


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Dict     					=			b'\x80' + b'\x00' * (6_3 - (len(self.data							) + 8) % 6_4)
             lowercase__    :	Dict     					=			self.data + padding + struct.pack('>Q'	,    8 * len(self.data							)							)
             return padded_data


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             return [
                 self.padded_data[i : i + 6_4] for i in range(0	,    len(self.padded_data							)	,    6_4							)
             ]


      def 	_UpperCAmelCase (     self	,    a							) ->      Optional[Any]:
             lowercase__    :	Optional[Any]     					=			list(struct.unpack('>16L'	,    a							)							) + [0] * 6_4
             for i in range(1_6	,    8_0							):
                    lowercase__    :	Optional[Any]     					=			self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 1_4] ^ w[i - 1_6])	,    1							)
             return w


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	Union[str, Any]     					=			self.padding()
             lowercase__    :	int     					=			self.split_blocks()
             for block in self.blocks:
                    lowercase__    :	Tuple     					=			self.expand_block(a							)
                    lowercase__					,   lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	List[Any]     					=			self.h
                    for i in range(0	,    8_0							):
                           if 0 <= i < 2_0:
                                  lowercase__    :	Tuple     					=			(b & c) | ((~b) & d)
                                  lowercase__    :	Any     					=			0X5A_827_999
                           elif 2_0 <= i < 4_0:
                                  lowercase__    :	Tuple     					=			b ^ c ^ d
                                  lowercase__    :	int     					=			0X6E_D9E_BA1
                           elif 4_0 <= i < 6_0:
                                  lowercase__    :	Any     					=			(b & c) | (b & d) | (c & d)
                                  lowercase__    :	Optional[int]     					=			0X8F_1BB_CDC
                           elif 6_0 <= i < 8_0:
                                  lowercase__    :	Any     					=			b ^ c ^ d
                                  lowercase__    :	List[Any]     					=			0XCA_62C_1D6
                           lowercase__					,   lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	Dict     					=			(
                               self.rotate(a	,    5							) + f + e + k + expanded_block[i] & 0XFF_FFF_FFF,
                               a,
                               self.rotate(a	,    3_0							),
                               c,
                               d,
                           )
                    lowercase__    :	Optional[int]     					=			(
                        self.h[0] + a & 0XFF_FFF_FFF,
                        self.h[1] + b & 0XFF_FFF_FFF,
                        self.h[2] + c & 0XFF_FFF_FFF,
                        self.h[3] + d & 0XFF_FFF_FFF,
                        self.h[4] + e & 0XFF_FFF_FFF,
                    )
             return ("{:08x}" * 5).format(*self.h							)





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	int     					=			B'Test String'
       assert SHAaHash(_lowerCAmelCase  ).final_hash() == hashlib.shaa(_lowerCAmelCase  ).hexdigest()  # noqa: S324





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			argparse.ArgumentParser(description='Process some strings or files'  )
       parser.add_argument(
           '--string'     ,				dest='input_string'     ,				default='Hello World!! Welcome to Cryptography'     ,				help='Hash the string'     ,				)
       parser.add_argument('--file'     ,				dest='input_file'     ,				help='Hash contents of a file'  )
       lowercase__    :	List[str]     					=			parser.parse_args()
       lowercase__    :	Dict     					=			args.input_string
       # In any case hash input should be a bytestring
       if args.input_file:
              with open(args.input_file     ,				'rb'  ) as f:
                     lowercase__    :	Optional[Any]     					=			f.read()
       else:
              lowercase__    :	Optional[Any]     					=			bytes(_lowerCAmelCase     ,				'utf-8'  )
       print(SHAaHash(_lowerCAmelCase  ).final_hash()  )


if __name__ == "__main__":
       main()
       import doctest

       doctest.testmod()


"""simple docstring"""





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

_UpperCamelCase     :		List[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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
_UpperCamelCase     :		List[str]											=					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Tuple  ):





       '''simple docstring'''

       for attribute in key.split('.'  ):
              lowercase__    :	Dict     					=			getattr(_lowerCAmelCase     ,				_lowerCAmelCase  )

       if weight_type is not None:
              lowercase__    :	Optional[int]     					=			getattr(_lowerCAmelCase     ,				_lowerCAmelCase  ).shape
       else:
              lowercase__    :	Optional[int]     					=			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":
              lowercase__    :	Optional[Any]     					=			value
       elif weight_type == "weight_g":
              lowercase__    :	Dict     					=			value
       elif weight_type == "weight_v":
              lowercase__    :	List[str]     					=			value
       elif weight_type == "bias":
              lowercase__    :	Optional[Any]     					=			value
       else:
              lowercase__    :	List[str]     					=			value

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





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[Any]  ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			[]
       lowercase__    :	List[str]     					=			fairseq_model.state_dict()

       lowercase__    :	Union[str, Any]     					=			hf_model.unispeech_sat.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase__    :	Optional[int]     					=			False
              if "conv_layers" in name:
                     load_conv_layer(
                         _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				hf_model.config.feat_extract_norm == 'group'     ,				)
                     lowercase__    :	Optional[Any]     					=			True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase__    :	List[Any]     					=			'unispeech_sat.' + 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]:
                                   if "layer_norm_for_extract" in name and (".".join(name.split('.'  )[:-1]  ) != key):
                                          # special case since naming is very similar
                                          continue
                                   lowercase__    :	int     					=			True
                                   if "*" in mapped_key:
                                          lowercase__    :	Optional[int]     					=			name.split(_lowerCAmelCase  )[0].split('.'  )[-2]
                                          lowercase__    :	List[str]     					=			mapped_key.replace('*'     ,				_lowerCAmelCase  )
                                   if "weight_g" in name:
                                          lowercase__    :	List[Any]     					=			'weight_g'
                                   elif "weight_v" in name:
                                          lowercase__    :	int     					=			'weight_v'
                                   elif "bias" in name:
                                          lowercase__    :	Dict     					=			'bias'
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          lowercase__    :	Union[str, Any]     					=			'weight'
                                   else:
                                          lowercase__    :	int     					=			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        a_  (   _lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Dict  ):





       '''simple docstring'''

       lowercase__    :	int     					=			full_name.split('conv_layers.'  )[-1]
       lowercase__    :	int     					=			name.split('.'  )
       lowercase__    :	int     					=			int(items[0]  )
       lowercase__    :	Dict     					=			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."""  )
                     lowercase__    :	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."""  )
                     lowercase__    :	Optional[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[layer_id].layer_norm.bias.data.shape} was found."""  )
                     lowercase__    :	List[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[layer_id].layer_norm.weight.data.shape} was found."""  )
                     lowercase__    :	int     					=			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						:				Union[str, Any]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				List[Any]=None     ,				_lowerCAmelCase						:				str=None     ,				_lowerCAmelCase						:				Tuple=True  ):





       '''simple docstring'''

       if config_path is not None:
              lowercase__    :	Any     					=			UniSpeechSatConfig.from_pretrained(_lowerCAmelCase  )
       else:
              lowercase__    :	Any     					=			UniSpeechSatConfig()

       lowercase__    :	Union[str, Any]     					=			''

       if is_finetuned:
              lowercase__    :	Optional[Any]     					=			UniSpeechSatForCTC(_lowerCAmelCase  )
       else:
              lowercase__    :	List[Any]     					=			UniSpeechSatForPreTraining(_lowerCAmelCase  )

       lowercase__					,   lowercase__					,   lowercase__    :	int     					=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]     ,				arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  )}  )
       lowercase__    :	Union[str, Any]     					=			model[0].eval()

       recursively_load_weights(_lowerCAmelCase     ,				_lowerCAmelCase  )

       hf_wavavec.save_pretrained(_lowerCAmelCase  )


if __name__ == "__main__":
       _UpperCamelCase     :		Tuple											=					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"
       )
       _UpperCamelCase     :		str											=					parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )

