



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

UpperCAmelCase__       =	{
    "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",
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
UpperCAmelCase__       =	[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 							A     (					_UpperCAmelCase    :       int							,       _UpperCAmelCase    :       Dict							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       List[str]   )    ->			Union[str, Any]:







			'''simple docstring'''
			for attribute in key.split('.'   ):
						if is_finetuned:
									if attribute in ["quantizer", "project_q", "project_hid"]:
												# those layers are only relevant for pretraining and should be dropped
												return

									if attribute == "ctc_proj":
												# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
												_UpperCAmelCase			=       'lm_head'

						_UpperCAmelCase			=       getattr(_UpperCAmelCase							,       _UpperCAmelCase   )

			if weight_type is not None:
						_UpperCAmelCase			=       getattr(_UpperCAmelCase							,       _UpperCAmelCase   ).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 							A     (					_UpperCAmelCase    :       int							,       _UpperCAmelCase    :       str							,       _UpperCAmelCase    :       List[str]   )    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       []
			_UpperCAmelCase			=       fairseq_model.state_dict()

			_UpperCAmelCase			=       hf_model.unispeech.feature_extractor

			for name, value in fairseq_dict.items():
						_UpperCAmelCase			=       False
						if "conv_layers" in name:
									load_conv_layer(
									    _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       hf_model.config.feat_extract_norm == 'group'							,       )
									_UpperCAmelCase			=       True
						else:
									for key, mapped_key in MAPPING.items():
												_UpperCAmelCase			=       'unispeech.' + 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]:
															_UpperCAmelCase			=       True
															if "*" in mapped_key:
																		_UpperCAmelCase			=       name.split(_UpperCAmelCase   )[0].split('.'   )[-2]
																		_UpperCAmelCase			=       mapped_key.replace('*'							,       _UpperCAmelCase   )
															if "weight_g" in name:
																		_UpperCAmelCase			=       'weight_g'
															elif "weight_v" in name:
																		_UpperCAmelCase			=       'weight_v'
															elif "bias" in name:
																		_UpperCAmelCase			=       'bias'
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		_UpperCAmelCase			=       'weight'
															else:
																		_UpperCAmelCase			=       None
															set_recursively(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )
												continue
						if not is_used:
									unused_weights.append(_UpperCAmelCase   )

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





def 							A     (					_UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       int							,       _UpperCAmelCase    :       Dict   )    ->			int:







			'''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(_UpperCAmelCase   )





@torch.no_grad()
def 							A     (					_UpperCAmelCase    :       Dict							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       Tuple=None							,       _UpperCAmelCase    :       List[Any]=None							,       _UpperCAmelCase    :       Optional[Any]=True   )    ->			Optional[int]:







			'''simple docstring'''
			if config_path is not None:
						_UpperCAmelCase			=       UniSpeechConfig.from_pretrained(_UpperCAmelCase   )
			else:
						_UpperCAmelCase			=       UniSpeechConfig()

			if is_finetuned:
						if dict_path:
									_UpperCAmelCase			=       Dictionary.load_from_json(_UpperCAmelCase   )

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									_UpperCAmelCase			=       target_dict.pad_index
									_UpperCAmelCase			=       target_dict.bos_index
									_UpperCAmelCase			=       target_dict.eos_index
									_UpperCAmelCase			=       len(target_dict.symbols   )
									_UpperCAmelCase			=       os.path.join(_UpperCAmelCase							,       'vocab.json'   )
									if not os.path.isdir(_UpperCAmelCase   ):
												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_UpperCAmelCase   )   )
												return
									os.makedirs(_UpperCAmelCase							,       exist_ok=_UpperCAmelCase   )
									_UpperCAmelCase			=       target_dict.indices

									# fairseq has the <pad> and <s> switched
									_UpperCAmelCase			=       42
									_UpperCAmelCase			=       43
									with open(_UpperCAmelCase							,       'w'							,       encoding='utf-8'   ) as vocab_handle:
												json.dump(_UpperCAmelCase							,       _UpperCAmelCase   )
									_UpperCAmelCase			=       WavaVecaPhonemeCTCTokenizer(
									    _UpperCAmelCase							,       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=_UpperCAmelCase							,       )
									_UpperCAmelCase			=       True if config.feat_extract_norm == 'layer' else False
									_UpperCAmelCase			=       WavaVecaFeatureExtractor(
									    feature_size=1							,       sampling_rate=16_000							,       padding_value=0							,       do_normalize=_UpperCAmelCase							,       return_attention_mask=_UpperCAmelCase							,       )
									_UpperCAmelCase			=       WavaVecaProcessor(feature_extractor=_UpperCAmelCase							,       tokenizer=_UpperCAmelCase   )
									processor.save_pretrained(_UpperCAmelCase   )

						_UpperCAmelCase			=       UniSpeechForCTC(_UpperCAmelCase   )
			else:
						_UpperCAmelCase			=       UniSpeechForPreTraining(_UpperCAmelCase   )

			if is_finetuned:
						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]							,       arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   ), 'w2v_path': checkpoint_path}   )
			else:
						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

			_UpperCAmelCase			=       model[0].eval()

			recursively_load_weights(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )

			hf_unispeech.save_pretrained(_UpperCAmelCase   )


if __name__ == "__main__":
						UpperCAmelCase__       =	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__       =	parser.parse_args()
						convert_unispeech_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)





import string

import numpy





def 							A     (					_UpperCAmelCase    :       int							,       _UpperCAmelCase    :       int   )    ->			int:







			'''simple docstring'''
			return b if a == 0 else greatest_common_divisor(b % a							,       _UpperCAmelCase   )







class       __lowerCAmelCase   :
			UpperCamelCase        =					string.ascii_uppercase + string.digits
			# This cipher takes alphanumerics into account
			# i.e. a total of 36 characters

			# take x and return x % len(key_string)
			UpperCamelCase        =					numpy.vectorize(lambda A						: x % 3_6						)

			UpperCamelCase        =					numpy.vectorize(A						)



			def __init__(						self       :  Tuple			,		A       :  numpy.ndarray)       ->   None:



						"""simple docstring"""






						_UpperCAmelCase			=       self.modulus(A)  # mod36 calc's on the encrypt key
						self.check_determinant()  # validate the determinant of the encryption key
						_UpperCAmelCase			=       encrypt_key.shape[0]



			def    _lowerCamelCase	(						self       :  str			,		A       :  str)       ->   int:



						"""simple docstring"""






						return self.key_string.index(A)



			def    _lowerCamelCase	(						self       :  Any			,		A       :  int)       ->   str:



						"""simple docstring"""






						return self.key_string[round(A)]



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   None:



						"""simple docstring"""






						_UpperCAmelCase			=       round(numpy.linalg.det(self.encrypt_key))

						if det < 0:
									_UpperCAmelCase			=       det % len(self.key_string)

						_UpperCAmelCase			=       len(self.key_string)
						if greatest_common_divisor(A			,		len(self.key_string)) != 1:
									_UpperCAmelCase			=       (
									    F"determinant modular {req_l} of encryption key({det}) "
									    F"is not co prime w.r.t {req_l}.\nTry another key."
									)
									raise ValueError(A)



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       [char for char in text.upper() if char in self.key_string]

						_UpperCAmelCase			=       chars[-1]
						while len(A) % self.break_key != 0:
									chars.append(A)

						return "".join(A)



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.process_text(text.upper())
						_UpperCAmelCase			=       ''

						for i in range(0			,		len(A) - self.break_key + 1			,		self.break_key):
									_UpperCAmelCase			=       text[i : i + self.break_key]
									_UpperCAmelCase			=       [self.replace_letters(A) for char in batch]
									_UpperCAmelCase			=       numpy.array([vec]).T
									_UpperCAmelCase			=       self.modulus(self.encrypt_key.dot(A)).T.tolist()[
									    0
									]
									_UpperCAmelCase			=       ''.join(
									    self.replace_digits(A) for num in batch_encrypted)
									encrypted += encrypted_batch

						return encrypted



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   numpy.ndarray:



						"""simple docstring"""






						_UpperCAmelCase			=       round(numpy.linalg.det(self.encrypt_key))

						if det < 0:
									_UpperCAmelCase			=       det % len(self.key_string)
						_UpperCAmelCase			=       None
						for i in range(len(self.key_string)):
									if (det * i) % len(self.key_string) == 1:
												_UpperCAmelCase			=       i
												break

						_UpperCAmelCase			=       (
						    det_inv
						    * numpy.linalg.det(self.encrypt_key)
						    * numpy.linalg.inv(self.encrypt_key)
						)

						return self.to_int(self.modulus(A))



			def    _lowerCamelCase	(						self       :  Tuple			,		A       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.make_decrypt_key()
						_UpperCAmelCase			=       self.process_text(text.upper())
						_UpperCAmelCase			=       ''

						for i in range(0			,		len(A) - self.break_key + 1			,		self.break_key):
									_UpperCAmelCase			=       text[i : i + self.break_key]
									_UpperCAmelCase			=       [self.replace_letters(A) for char in batch]
									_UpperCAmelCase			=       numpy.array([vec]).T
									_UpperCAmelCase			=       self.modulus(decrypt_key.dot(A)).T.tolist()[0]
									_UpperCAmelCase			=       ''.join(
									    self.replace_digits(A) for num in batch_decrypted)
									decrypted += decrypted_batch

						return decrypted





def 							A     (					)    ->			None:







			'''simple docstring'''
			_UpperCAmelCase			=       int(input('Enter the order of the encryption key: '   )   )
			_UpperCAmelCase			=       []

			print('Enter each row of the encryption key with space separated integers'   )
			for _ in range(_UpperCAmelCase   ):
						_UpperCAmelCase			=       [int(_UpperCAmelCase   ) for x in input().split()]
						hill_matrix.append(_UpperCAmelCase   )

			_UpperCAmelCase			=       HillCipher(numpy.array(_UpperCAmelCase   )   )

			print('Would you like to encrypt or decrypt some text? (1 or 2)'   )
			_UpperCAmelCase			=       input('\n1. Encrypt\n2. Decrypt\n'   )
			if option == "1":
						_UpperCAmelCase			=       input('What text would you like to encrypt?: '   )
						print('Your encrypted text is:'   )
						print(hc.encrypt(_UpperCAmelCase   )   )
			elif option == "2":
						_UpperCAmelCase			=       input('What text would you like to decrypt?: '   )
						print('Your decrypted text is:'   )
						print(hc.decrypt(_UpperCAmelCase   )   )


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							main()
