






"""simple docstring"""





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A__       :			str=						logging.get_logger(__name__)

A__       :			List[str]=						{
    """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""",
    """mask_emb""": """masked_spec_embed""",
}







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Dict:
    """simple docstring"""

    for attribute in key.split('.'       ):
        UpperCamelCase__								=     getattr(lowerCAmelCase_			,						lowerCAmelCase_       )

    if weight_type is not None:
        UpperCamelCase__								=     getattr(lowerCAmelCase_			,						lowerCAmelCase_       ).shape
    else:
        UpperCamelCase__								=     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__								=     value
    elif weight_type == "weight_g":
        UpperCamelCase__								=     value
    elif weight_type == "weight_v":
        UpperCamelCase__								=     value
    elif weight_type == "bias":
        UpperCamelCase__								=     value
    else:
        UpperCamelCase__								=     value

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Optional[Any]:
    """simple docstring"""

    UpperCamelCase__								=     []
    UpperCamelCase__								=     fairseq_model.state_dict()

    UpperCamelCase__								=     hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCamelCase__								=     False
        if "conv_layers" in name:
            load_conv_layer(
                lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_			,						hf_model.config.feat_extract_norm == 'group'			,						)
            UpperCamelCase__								=     True
        else:
            for key, mapped_key in MAPPING.items():
                UpperCamelCase__								=     mapped_key

                if key in name:
                    UpperCamelCase__								=     True
                    if "*" in mapped_key:
                        UpperCamelCase__								=     name.split(lowerCAmelCase_       )[0].split('.'       )[-2]
                        UpperCamelCase__								=     mapped_key.replace('*'			,						lowerCAmelCase_       )
                    if "weight_g" in name:
                        UpperCamelCase__								=     'weight_g'
                    elif "weight_v" in name:
                        UpperCamelCase__								=     'weight_v'
                    elif "weight" in name:
                        UpperCamelCase__								=     'weight'
                    elif "bias" in name:
                        UpperCamelCase__								=     'bias'
                    else:
                        UpperCamelCase__								=     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_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						str:
    """simple docstring"""

    UpperCamelCase__								=     full_name.split('conv_layers.'       )[-1]
    UpperCamelCase__								=     name.split('.'       )
    UpperCamelCase__								=     int(items[0]       )
    UpperCamelCase__								=     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__								=     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__								=     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__								=     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__								=     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_(			SCREAMING_SNAKE_CASE       )       ->						Optional[Any]:
    """simple docstring"""

    UpperCamelCase__								=     HubertConfig()
    UpperCamelCase__								=     model.config

    UpperCamelCase__								=     fs_config.activation_dropout
    UpperCamelCase__								=     False
    UpperCamelCase__								=     fs_config.attention_dropout
    UpperCamelCase__								=     False
    UpperCamelCase__								=     eval(fs_config.extractor_conv_feature_layers       )
    UpperCamelCase__								=     [x[0] for x in conv_layers]
    UpperCamelCase__								=     [x[1] for x in conv_layers]
    UpperCamelCase__								=     [x[2] for x in conv_layers]
    UpperCamelCase__								=     'gelu'
    UpperCamelCase__								=     'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
    UpperCamelCase__								=     False
    UpperCamelCase__								=     0.0
    UpperCamelCase__								=     0.0
    UpperCamelCase__								=     fs_config.activation_fn
    UpperCamelCase__								=     fs_config.dropout
    UpperCamelCase__								=     fs_config.encoder_embed_dim
    UpperCamelCase__								=     0.02
    UpperCamelCase__								=     fs_config.encoder_ffn_embed_dim
    UpperCamelCase__								=     1E-5
    UpperCamelCase__								=     0.0
    UpperCamelCase__								=     fs_config.encoder_attention_heads
    UpperCamelCase__								=     fs_config.conv_pos_groups
    UpperCamelCase__								=     fs_config.conv_pos
    UpperCamelCase__								=     len(lowerCAmelCase_       )
    UpperCamelCase__								=     fs_config.encoder_layers

    return config







@torch.no_grad()
def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE=None       )       ->						Optional[int]:
    """simple docstring"""

    UpperCamelCase__								=     distilhubert().model.model

    if config_path is not None:
        UpperCamelCase__								=     HubertConfig.from_pretrained(lowerCAmelCase_       )
    else:
        UpperCamelCase__								=     convert_config(lowerCAmelCase_       )
    UpperCamelCase__								=     model.eval()

    UpperCamelCase__								=     WavaVecaFeatureExtractor(
        feature_size=1			,						sampling_rate=1_60_00			,						padding_value=0			,						do_normalize=lowerCAmelCase_			,						return_attention_mask=lowerCAmelCase_			,						)
    UpperCamelCase__								=     HubertModel(lowerCAmelCase_       )

    recursively_load_weights(lowerCAmelCase_			,						lowerCAmelCase_       )

    feature_extractor.save_pretrained(lowerCAmelCase_       )
    hf_model.save_pretrained(lowerCAmelCase_       )


if __name__ == "__main__":
  A__       :			List[Any]=						argparse.ArgumentParser()
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
  A__       :			Tuple=						parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     if not isinstance(lowerCAmelCase_,       lowerCAmelCase_     ):
          raise TypeError('plaintext must be a string'     )
     if not isinstance(lowerCAmelCase_,       lowerCAmelCase_     ):
          raise TypeError('key must be a string'     )

     if not plaintext:
          raise ValueError('plaintext is empty'     )
     if not key:
          raise ValueError('key is empty'     )

     key += plaintext
     SCREAMING_SNAKE_CASE 		=plaintext.lower()
     SCREAMING_SNAKE_CASE 		=key.lower()
     SCREAMING_SNAKE_CASE 		=0
     SCREAMING_SNAKE_CASE 		=0
     SCREAMING_SNAKE_CASE 		=''
     while plaintext_iterator < len(lowerCAmelCase_     ):
          if (
              ord(plaintext[plaintext_iterator]     ) < 97
              or ord(plaintext[plaintext_iterator]     ) > 122
          ):
               ciphertext += plaintext[plaintext_iterator]
               plaintext_iterator += 1
          elif ord(key[key_iterator]     ) < 97 or ord(key[key_iterator]     ) > 122:
               key_iterator += 1
          else:
               ciphertext += chr(
                   (
                       (ord(plaintext[plaintext_iterator]     ) - 97 + ord(key[key_iterator]     ))
                       - 97
                   )
                   % 26
                   + 97     )
               key_iterator += 1
               plaintext_iterator += 1
     return ciphertext







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     if not isinstance(lowerCAmelCase_,       lowerCAmelCase_     ):
          raise TypeError('ciphertext must be a string'     )
     if not isinstance(lowerCAmelCase_,       lowerCAmelCase_     ):
          raise TypeError('key must be a string'     )

     if not ciphertext:
          raise ValueError('ciphertext is empty'     )
     if not key:
          raise ValueError('key is empty'     )

     SCREAMING_SNAKE_CASE 		=key.lower()
     SCREAMING_SNAKE_CASE 		=0
     SCREAMING_SNAKE_CASE 		=0
     SCREAMING_SNAKE_CASE 		=''
     while ciphertext_iterator < len(lowerCAmelCase_     ):
          if (
              ord(ciphertext[ciphertext_iterator]     ) < 97
              or ord(ciphertext[ciphertext_iterator]     ) > 122
          ):
               plaintext += ciphertext[ciphertext_iterator]
          else:
               plaintext += chr(
                   (ord(ciphertext[ciphertext_iterator]     ) - ord(key[key_iterator]     )) % 26
                   + 97     )
               key += chr(
                   (ord(ciphertext[ciphertext_iterator]     ) - ord(key[key_iterator]     )) % 26
                   + 97     )
               key_iterator += 1
          ciphertext_iterator += 1
     return plaintext


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _lowerCamelCase							=int(input("Type 1 to encrypt or 2 to decrypt:"))
 if operation == 1:
  _lowerCamelCase							=input("Typeplaintext to be encrypted:\n")
  _lowerCamelCase							=input("Type the key:\n")
  print(encrypt(plaintext, key))
 elif operation == 2:
  _lowerCamelCase							=input("Type the ciphertext to be decrypted:\n")
  _lowerCamelCase							=input("Type the key:\n")
  print(decrypt(ciphertext, key))
 decrypt("jsqqs avvwo", "coffee")

