




'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

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





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			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_     )		->		Tuple:
			A_									= []
			A_									= fairseq_model.state_dict()

			A_									= hf_model.feature_extractor

			# if encoder has different dim to decoder -> use proj_weight
			A_									= None

			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
						elif name.split('.'     )[0] == "proj":
									A_									= fairseq_model.proj
									A_									= True
						else:
									for key, mapped_key in MAPPING.items():
												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 "bias" in name:
																		A_									= 'bias'
															elif "weight" in name:
																		A_									= 'weight'
															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}'     )

			return proj_weight





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Optional[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_     )		->		str:
			A_		, A_									= emb.weight.shape
			A_									= nn.Linear(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,bias=SCREAMING_SNAKE_CASE_     )
			A_									= emb.weight.data
			return lin_layer





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		List[Any]:
			with open(SCREAMING_SNAKE_CASE_       ,'r'       ,encoding='utf-8'     ) as f:
						A_									= f.readlines()
						A_									= [line.split(' '     )[0] for line in lines]

			A_									= len(SCREAMING_SNAKE_CASE_     )

			A_									= {
			    '<s>': 0,
			    '<pad>': 1,
			    '</s>': 2,
			    '<unk>': 3,
			}

			vocab_dict.update(dict(zip(SCREAMING_SNAKE_CASE_       ,range(4       ,num_words + 4     )     )     )     )
			return vocab_dict





@torch.no_grad()
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,)		->		str:
			A_									= WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_     )
			A_									= SpeechaTextaConfig.from_pretrained(
			    SCREAMING_SNAKE_CASE_       ,vocab_size=SCREAMING_SNAKE_CASE_       ,decoder_layers=SCREAMING_SNAKE_CASE_       ,do_stable_layer_norm=SCREAMING_SNAKE_CASE_     )

			A_									= WavaVecaFeatureExtractor(
			    feature_size=1       ,sampling_rate=1_6000       ,padding_value=0       ,do_normalize=SCREAMING_SNAKE_CASE_       ,return_attention_mask=SCREAMING_SNAKE_CASE_       ,)

			A_		, A_		, A_									= fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]       ,arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
			A_									= model[0].eval()

			# set weights for wav2vec2 encoder
			A_									= WavaVecaModel(SCREAMING_SNAKE_CASE_     )
			A_									= recursively_load_weights_wavaveca(model.encoder       ,SCREAMING_SNAKE_CASE_     )

			A_									= SpeechaTextaForCausalLM(SCREAMING_SNAKE_CASE_     )
			A_		, A_									= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()       ,strict=SCREAMING_SNAKE_CASE_     )

			# set output linear layer
			unexpected_keys.remove('embed_out'     )
			A_									= nn.Parameter(model.decoder.embed_out.detach()     )

			# layer norm is init to identity matrix so leaving it is fine
			logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}'     )
			logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'     )

			A_									= SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE_       ,decoder=SCREAMING_SNAKE_CASE_     )
			A_									= False

			# add projection layer
			A_									= nn.Parameter(projection_layer.weight     )
			A_									= nn.Parameter(projection_layer.bias     )

			A_									= create_vocab_dict(SCREAMING_SNAKE_CASE_     )

			with open(os.path.join(SCREAMING_SNAKE_CASE_       ,'vocab.json'     )       ,'w'     ) as fp:
						json.dump(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )

			A_									= SpeechaTextaTokenizer(os.path.join(SCREAMING_SNAKE_CASE_       ,'vocab.json'     )     )
			tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_     )

			A_									= hf_wavavec.config.to_dict()
			A_									= tokenizer.pad_token_id
			A_									= tokenizer.bos_token_id
			A_									= tokenizer.eos_token_id
			A_									= 'speech_to_text_2'
			A_									= 'wav2vec2'

			A_									= SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE_     )

			hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_     )
			feature_extractor.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(
							    "--encoder_config_path",
							    default="facebook/wav2vec2-large-lv60",
							    type=str,
							    help="Path to hf encoder wav2vec2 checkpoint config",
							)
							parser.add_argument(
							    "--decoder_config_path",
							    default="facebook/s2t-small-mustc-en-fr-st",
							    type=str,
							    help="Path to hf decoder s2t checkpoint config",
							)
							parser.add_argument("--vocab_size", default=1_02_24, type=int, help="Vocab size of decoder")
							parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

							_SCREAMING_SNAKE_CASE			=						parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    vocab_size=args.vocab_size,
							    num_decoder_layers=args.num_decoder_layers,
							)










'''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}""")




