



'''simple docstring'''
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()
snake_case_ :	str         =  logging.get_logger(__name__)

snake_case_ :	Union[str, Any]         =  {
    "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 A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						for attribute in key.split('.'							):
												_UpperCamelCase					:		Any 				=   getattr(__A  ,  __A							)

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

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






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		Tuple 				=   []
						_UpperCamelCase					:		List[str] 				=   fairseq_model.state_dict()

						_UpperCamelCase					:		Dict 				=   hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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






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






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		List[Any] 				=   SEWConfig()
						if is_finetuned:
												_UpperCamelCase					:		str 				=   model.wav_encoder.wav_model.cfg
						else:
												_UpperCamelCase					:		int 				=   model.cfg

						_UpperCamelCase					:		List[Any] 				=   fs_config.conv_bias
						_UpperCamelCase					:		Optional[Any] 				=   eval(fs_config.conv_feature_layers							)
						_UpperCamelCase					:		Optional[Any] 				=   [x[0] for x in conv_layers]
						_UpperCamelCase					:		Any 				=   [x[1] for x in conv_layers]
						_UpperCamelCase					:		Tuple 				=   [x[2] for x in conv_layers]
						_UpperCamelCase					:		Optional[int] 				=   'gelu'
						_UpperCamelCase					:		Union[str, Any] 				=   'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
						_UpperCamelCase					:		Dict 				=   0.0
						_UpperCamelCase					:		Any 				=   fs_config.activation_fn.name
						_UpperCamelCase					:		Any 				=   fs_config.encoder_embed_dim
						_UpperCamelCase					:		Optional[int] 				=   0.02
						_UpperCamelCase					:		Optional[int] 				=   fs_config.encoder_ffn_embed_dim
						_UpperCamelCase					:		str 				=   1E-5
						_UpperCamelCase					:		str 				=   fs_config.encoder_layerdrop
						_UpperCamelCase					:		Tuple 				=   fs_config.encoder_attention_heads
						_UpperCamelCase					:		List[Any] 				=   fs_config.conv_pos_groups
						_UpperCamelCase					:		int 				=   fs_config.conv_pos
						_UpperCamelCase					:		Optional[Any] 				=   len(__A							)
						_UpperCamelCase					:		Dict 				=   fs_config.encoder_layers
						_UpperCamelCase					:		Any 				=   fs_config.squeeze_factor

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

						_UpperCamelCase					:		Dict 				=   'Wav2Vec2FeatureExtractor'
						_UpperCamelCase					:		Tuple 				=   'Wav2Vec2CTCTokenizer'

						return config






@torch.no_grad()
def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=True							):

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

						if config_path is not None:
												_UpperCamelCase					:		Dict 				=   SEWConfig.from_pretrained(__A							)
						else:
												_UpperCamelCase					:		str 				=   convert_config(model[0]  ,  __A							)
						_UpperCamelCase					:		Union[str, Any] 				=   model[0].eval()

						_UpperCamelCase					:		Any 				=   True if config.feat_extract_norm == 'layer' else False
						_UpperCamelCase					:		Optional[int] 				=   WavaVecaFeatureExtractor(
						    feature_size=1  ,  sampling_rate=1_6_0_0_0  ,  padding_value=0  ,  do_normalize=__A  ,  return_attention_mask=__A  ,  )

						if is_finetuned:
												if dict_path:
																		_UpperCamelCase					:		Optional[Any] 				=   Dictionary.load(__A							)

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

												_UpperCamelCase					:		Tuple 				=   SEWForCTC(__A							)
						else:
												_UpperCamelCase					:		Any 				=   SEWModel(__A							)
												feature_extractor.save_pretrained(__A							)

						recursively_load_weights(__A  ,  __A  ,  __A							)

						hf_model.save_pretrained(__A							)


if __name__ == "__main__":
					snake_case_ :	int         =  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'
					)
					snake_case_ :	Union[str, Any]         =  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'''







from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowercase    :							str      =					(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase    :							list[int]      =					[ord(letter) for letter in string.ascii_lowercase]
lowercase    :							set[int]      =					{ord(char) for char in VALID_CHARS}

lowercase    :							list[str]      =					["the", "be", "to", "of", "and", "in", "that", "have"]







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str | None:
	_snake_case   						=		""
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42

	for keychar, cipherchar in zip(cycle(__A	)			,				__A	):
		_snake_case   						=		cipherchar ^ keychar
		if decodedchar not in VALID_INTS:
			return None
		decoded += chr(__A	)

	return decoded







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	list[str]:
	_snake_case   						=		[]
	for key in product(__A			,				repeat=3	):
		_snake_case   						=		try_key(__A			,				__A	)
		if encoded is not None:
			possibles.append(__A	)
	return possibles







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	list[str]:
	return [possible for possible in possibles if common_word in possible.lower()]







def 				SCREAMING_SNAKE_CASE__	(   __A = "p059_cipher.txt"	)       ->	int:
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		Path(__A	).parent.joinpath(__A	).read_text(encoding='utf-8'	)

	_snake_case   						=		[int(__A	) for number in data.strip().split(','	)]

	_snake_case   						=		filter_valid_chars(__A	)
	for common_word in COMMON_WORDS:
		_snake_case   						=		filter_common_word(__A			,				__A	)
		if len(__A	) == 1:
			break

	_snake_case   						=		possibles[0]
	return sum(ord(__A	) for char in decoded_text	)


if __name__ == "__main__":
					print(F'''{solution() = }''')

