
"""simple docstring"""


def        lowerCAmelCase_       (		snake_case_		:      str							,   snake_case_		:      str      )       ->str:
			if not isinstance(snake_case_							,   snake_case_      ):
						raise TypeError('plaintext must be a string'      )
			if not isinstance(snake_case_							,   snake_case_      ):
						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
			lowerCamelCase__     :					Optional[int]		=					plaintext.lower()
			lowerCamelCase__     :					Union[str, Any]		=					key.lower()
			lowerCamelCase__     :					Tuple		=					0
			lowerCamelCase__     :					Optional[Any]		=					0
			lowerCamelCase__     :					Tuple		=					''
			while plaintext_iterator < len(snake_case_      ):
						if (
						    ord(plaintext[plaintext_iterator]      ) < 9_7
						    or ord(plaintext[plaintext_iterator]      ) > 1_2_2
						):
									ciphertext += plaintext[plaintext_iterator]
									plaintext_iterator += 1
						elif ord(key[key_iterator]      ) < 9_7 or ord(key[key_iterator]      ) > 1_2_2:
									key_iterator += 1
						else:
									ciphertext += chr(
									    (
									        (ord(plaintext[plaintext_iterator]      ) - 9_7 + ord(key[key_iterator]      ))
									        - 9_7
									    )
									    % 2_6
									    + 9_7      )
									key_iterator += 1
									plaintext_iterator += 1
			return ciphertext






def        lowerCAmelCase_       (		snake_case_		:      str							,   snake_case_		:      str      )       ->str:
			if not isinstance(snake_case_							,   snake_case_      ):
						raise TypeError('ciphertext must be a string'      )
			if not isinstance(snake_case_							,   snake_case_      ):
						raise TypeError('key must be a string'      )

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

			lowerCamelCase__     :					Tuple		=					key.lower()
			lowerCamelCase__     :					Tuple		=					0
			lowerCamelCase__     :					List[Any]		=					0
			lowerCamelCase__     :					Optional[Any]		=					''
			while ciphertext_iterator < len(snake_case_      ):
						if (
						    ord(ciphertext[ciphertext_iterator]      ) < 9_7
						    or ord(ciphertext[ciphertext_iterator]      ) > 1_2_2
						):
									plaintext += ciphertext[ciphertext_iterator]
						else:
									plaintext += chr(
									    (ord(ciphertext[ciphertext_iterator]      ) - ord(key[key_iterator]      )) % 2_6
									    + 9_7      )
									key += chr(
									    (ord(ciphertext[ciphertext_iterator]      ) - ord(key[key_iterator]      )) % 2_6
									    + 9_7      )
									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""")

"""simple docstring"""


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowerCAmelCase								=						{
    """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_       (		snake_case_		:      int							,   snake_case_		:      List[str]							,   snake_case_		:      Optional[Any]							,   snake_case_		:      Optional[int]							,   snake_case_		:      Optional[Any]      )       ->str:
			for attribute in key.split('.'      ):
						lowerCamelCase__     :					str		=getattr(snake_case_							,   snake_case_      )

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

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






def        lowerCAmelCase_       (		snake_case_		:      Union[str, Any]							,   snake_case_		:      List[str]      )       ->Tuple:
			lowerCamelCase__     :					int		=[]
			lowerCamelCase__     :					List[Any]		=fairseq_model.state_dict()

			lowerCamelCase__     :					List[Any]		=hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						lowerCamelCase__     :					Dict		=False
						if "conv_layers" in name:
									load_conv_layer(
									    snake_case_							,   snake_case_							,   snake_case_							,   snake_case_							,   hf_model.config.feat_extract_norm == 'group'							,   )
									lowerCamelCase__     :					Optional[Any]		=True
						else:
									for key, mapped_key in MAPPING.items():
												lowerCamelCase__     :					Optional[int]		=mapped_key

												if key in name:
															lowerCamelCase__     :					List[Any]		=True
															if "*" in mapped_key:
																		lowerCamelCase__     :					int		=name.split(snake_case_      )[0].split('.'      )[-2]
																		lowerCamelCase__     :					List[Any]		=mapped_key.replace('*'							,   snake_case_      )
															if "weight_g" in name:
																		lowerCamelCase__     :					Dict		='weight_g'
															elif "weight_v" in name:
																		lowerCamelCase__     :					List[Any]		='weight_v'
															elif "weight" in name:
																		lowerCamelCase__     :					int		='weight'
															elif "bias" in name:
																		lowerCamelCase__     :					Union[str, Any]		='bias'
															else:
																		lowerCamelCase__     :					Any		=None
															set_recursively(snake_case_							,   snake_case_							,   snake_case_							,   snake_case_							,   snake_case_      )
												continue
						if not is_used:
									unused_weights.append(snake_case_      )

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






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






def        lowerCAmelCase_       (		snake_case_		:      List[str]      )       ->Union[str, Any]:
			lowerCamelCase__     :					Tuple		=HubertConfig()
			lowerCamelCase__     :					int		=model.config

			lowerCamelCase__     :					Optional[Any]		=fs_config.activation_dropout
			lowerCamelCase__     :					Optional[int]		=False
			lowerCamelCase__     :					int		=fs_config.attention_dropout
			lowerCamelCase__     :					Optional[Any]		=False
			lowerCamelCase__     :					Tuple		=eval(fs_config.extractor_conv_feature_layers      )
			lowerCamelCase__     :					Tuple		=[x[0] for x in conv_layers]
			lowerCamelCase__     :					Optional[int]		=[x[1] for x in conv_layers]
			lowerCamelCase__     :					Dict		=[x[2] for x in conv_layers]
			lowerCamelCase__     :					Union[str, Any]		='gelu'
			lowerCamelCase__     :					Tuple		='layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
			lowerCamelCase__     :					Any		=False
			lowerCamelCase__     :					List[str]		=0.0
			lowerCamelCase__     :					Any		=0.0
			lowerCamelCase__     :					str		=fs_config.activation_fn
			lowerCamelCase__     :					List[str]		=fs_config.dropout
			lowerCamelCase__     :					Union[str, Any]		=fs_config.encoder_embed_dim
			lowerCamelCase__     :					Dict		=0.02
			lowerCamelCase__     :					Optional[int]		=fs_config.encoder_ffn_embed_dim
			lowerCamelCase__     :					str		=1E-5
			lowerCamelCase__     :					Optional[int]		=0.0
			lowerCamelCase__     :					Any		=fs_config.encoder_attention_heads
			lowerCamelCase__     :					Dict		=fs_config.conv_pos_groups
			lowerCamelCase__     :					Optional[int]		=fs_config.conv_pos
			lowerCamelCase__     :					int		=len(snake_case_      )
			lowerCamelCase__     :					Optional[int]		=fs_config.encoder_layers

			return config






@torch.no_grad()
def        lowerCAmelCase_       (		snake_case_		:      Union[str, Any]							,   snake_case_		:      Dict=None      )       ->int:
			lowerCamelCase__     :					int		=distilhubert().model.model

			if config_path is not None:
						lowerCamelCase__     :					Union[str, Any]		=HubertConfig.from_pretrained(snake_case_      )
			else:
						lowerCamelCase__     :					List[str]		=convert_config(snake_case_      )
			lowerCamelCase__     :					Union[str, Any]		=model.eval()

			lowerCamelCase__     :					Tuple		=WavaVecaFeatureExtractor(
			    feature_size=1							,   sampling_rate=1_6_0_0_0							,   padding_value=0							,   do_normalize=snake_case_							,   return_attention_mask=snake_case_							,   )
			lowerCamelCase__     :					Optional[int]		=HubertModel(snake_case_      )

			recursively_load_weights(snake_case_							,   snake_case_      )

			feature_extractor.save_pretrained(snake_case_      )
			hf_model.save_pretrained(snake_case_      )


if __name__ == "__main__":
							lowerCAmelCase								=						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""")
							lowerCAmelCase								=						parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)