




'''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()
_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{
    "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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		int:
			for attribute in key.split('.'     ):
						A_									= getattr(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )

			if weight_type is not None:
						A_									= getattr(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     ).shape
			else:
						A_									= 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":
						A_									= value
			elif weight_type == "weight_g":
						A_									= value
			elif weight_type == "weight_v":
						A_									= value
			elif weight_type == "bias":
						A_									= value
			else:
						A_									= 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_       ,SCREAMING_SNAKE_CASE_     )		->		str:
			A_									= []
			A_									= fairseq_model.state_dict()

			A_									= hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Any:
			A_									= full_name.split('conv_layers.'     )[-1]
			A_									= name.split('.'     )
			A_									= int(items[0]     )
			A_									= 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.'
									)
									A_									= 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.'
									)
									A_									= 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."
									)
									A_									= 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.'
									)
									A_									= value
									logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
			else:
						unused_weights.append(SCREAMING_SNAKE_CASE_     )





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Tuple:
			A_									= SEWConfig()
			if is_finetuned:
						A_									= model.wav_encoder.wav_model.cfg
			else:
						A_									= model.cfg

			A_									= fs_config.conv_bias
			A_									= eval(fs_config.conv_feature_layers     )
			A_									= [x[0] for x in conv_layers]
			A_									= [x[1] for x in conv_layers]
			A_									= [x[2] for x in conv_layers]
			A_									= 'gelu'
			A_									= 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
			A_									= 0.0
			A_									= fs_config.activation_fn.name
			A_									= fs_config.encoder_embed_dim
			A_									= 0.02
			A_									= fs_config.encoder_ffn_embed_dim
			A_									= 1e-5
			A_									= fs_config.encoder_layerdrop
			A_									= fs_config.encoder_attention_heads
			A_									= fs_config.conv_pos_groups
			A_									= fs_config.conv_pos
			A_									= len(SCREAMING_SNAKE_CASE_     )
			A_									= fs_config.encoder_layers
			A_									= fs_config.squeeze_factor

			# take care of any params that are overridden by the Wav2VecCtc model
			if is_finetuned:
						A_									= model.cfg
						A_									= fs_config.final_dropout
						A_									= fs_config.layerdrop
			A_									= fs_config.activation_dropout
			A_									= fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
			A_									= fs_config.attention_dropout
			A_									= fs_config.dropout_input
			A_									= fs_config.dropout
			A_									= fs_config.mask_channel_length
			A_									= fs_config.mask_channel_prob
			A_									= fs_config.mask_length
			A_									= fs_config.mask_prob

			A_									= 'Wav2Vec2FeatureExtractor'
			A_									= 'Wav2Vec2CTCTokenizer'

			return config





@torch.no_grad()
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_=None       ,SCREAMING_SNAKE_CASE_=None       ,SCREAMING_SNAKE_CASE_=True     )		->		Dict:

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

			if config_path is not None:
						A_									= SEWConfig.from_pretrained(SCREAMING_SNAKE_CASE_     )
			else:
						A_									= convert_config(model[0]       ,SCREAMING_SNAKE_CASE_     )
			A_									= model[0].eval()

			A_									= True if config.feat_extract_norm == 'layer' else False
			A_									= WavaVecaFeatureExtractor(
			    feature_size=1       ,sampling_rate=1_6000       ,padding_value=0       ,do_normalize=SCREAMING_SNAKE_CASE_       ,return_attention_mask=SCREAMING_SNAKE_CASE_       ,)

			if is_finetuned:
						if dict_path:
									A_									= Dictionary.load(SCREAMING_SNAKE_CASE_     )

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

						A_									= SEWForCTC(SCREAMING_SNAKE_CASE_     )
			else:
						A_									= SEWModel(SCREAMING_SNAKE_CASE_     )
						feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_     )

			recursively_load_weights(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )

			hf_model.save_pretrained(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
							_SCREAMING_SNAKE_CASE			=						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"
							)
							_SCREAMING_SNAKE_CASE			=						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'''
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		bool:
			A_									= len(SCREAMING_SNAKE_CASE_     ) + 1
			A_									= len(SCREAMING_SNAKE_CASE_     ) + 1

			# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
			# length i of input_string matches with prefix string of length j of
			# given pattern.
			# "dp" stands for dynamic programming.
			A_									= [[0 for i in range(SCREAMING_SNAKE_CASE_     )] for j in range(SCREAMING_SNAKE_CASE_     )]

			# since string of zero length match pattern of zero length
			A_									= 1

			# since pattern of zero length will never match with string of non-zero length
			for i in range(1       ,SCREAMING_SNAKE_CASE_     ):
						A_									= 0

			# since string of zero length will match with pattern where there
			# is at least one * alternatively
			for j in range(1       ,SCREAMING_SNAKE_CASE_     ):
						A_									= dp[0][j - 2] if pattern[j - 1] == '*' else 0

			# now using bottom-up approach to find for all remaining lengths
			for i in range(1       ,SCREAMING_SNAKE_CASE_     ):
						for j in range(1       ,SCREAMING_SNAKE_CASE_     ):
									if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
												A_									= dp[i - 1][j - 1]

									elif pattern[j - 1] == "*":
												if dp[i][j - 2] == 1:
															A_									= 1
												elif pattern[j - 2] in (input_string[i - 1], "."):
															A_									= dp[i - 1][j]
												else:
															A_									= 0
									else:
												A_									= 0

			return bool(dp[-1][-1]     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							# inputing the strings
							# input_string = input("input a string :")
							# pattern = input("input a pattern :")

							_SCREAMING_SNAKE_CASE			=						"aab"
							_SCREAMING_SNAKE_CASE			=						"c*a*b"

							# using function to check whether given string matches the given pattern
							if match_pattern(input_string, pattern):
														print(f"""{input_string} matches the given pattern {pattern}""")
							else:
														print(f"""{input_string} does not match with the given pattern {pattern}""")




