


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A							:					List[str]     						=     logging.get_logger(__name__)

A							:					Optional[int]     						=     {
    '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',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	Any:


						'''simple docstring'''






						for attribute in key.split("." ):
												__snake_case				=		getattr(_lowerCAmelCase      ,	_lowerCAmelCase )

						if weight_type is not None:
												__snake_case				=		getattr(_lowerCAmelCase      ,	_lowerCAmelCase ).shape
						else:
												__snake_case				=		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":
												__snake_case				=		value
						elif weight_type == "weight_g":
												__snake_case				=		value
						elif weight_type == "weight_v":
												__snake_case				=		value
						elif weight_type == "bias":
												__snake_case				=		value
						else:
												__snake_case				=		value

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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	List[Any]:


						'''simple docstring'''






						__snake_case				=		[]
						__snake_case				=		fairseq_model.state_dict()

						__snake_case				=		hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	Dict:


						'''simple docstring'''






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





@torch.no_grad()
def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase=None      ,	_lowerCAmelCase=None      ,	_lowerCAmelCase=True )						->	List[Any]:


						'''simple docstring'''






						if config_path is not None:
												__snake_case				=		HubertConfig.from_pretrained(_lowerCAmelCase )
						else:
												__snake_case				=		HubertConfig()

						if is_finetuned:
												if dict_path:
																		__snake_case				=		Dictionary.load(_lowerCAmelCase )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		__snake_case				=		target_dict.pad_index
																		__snake_case				=		target_dict.bos_index
																		__snake_case				=		target_dict.eos_index
																		__snake_case				=		len(target_dict.symbols )
																		__snake_case				=		os.path.join(_lowerCAmelCase      ,	"vocab.json" )
																		if not os.path.isdir(_lowerCAmelCase ):
																								logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowerCAmelCase ) )
																								return
																		os.makedirs(_lowerCAmelCase      ,	exist_ok=_lowerCAmelCase )
																		with open(_lowerCAmelCase      ,	"w"      ,	encoding="utf-8" ) as vocab_handle:
																								json.dump(target_dict.indices      ,	_lowerCAmelCase )
																		__snake_case				=		WavaVecaCTCTokenizer(
																		    _lowerCAmelCase      ,	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=_lowerCAmelCase      ,	)
																		__snake_case				=		True if config.feat_extract_norm == "layer" else False
																		__snake_case				=		WavaVecaFeatureExtractor(
																		    feature_size=1      ,	sampling_rate=1_6000      ,	padding_value=0      ,	do_normalize=_lowerCAmelCase      ,	return_attention_mask=_lowerCAmelCase      ,	)
																		__snake_case				=		WavaVecaProcessor(feature_extractor=_lowerCAmelCase      ,	tokenizer=_lowerCAmelCase )
																		processor.save_pretrained(_lowerCAmelCase )

												__snake_case				=		HubertForCTC(_lowerCAmelCase )
						else:
												__snake_case				=		HubertModel(_lowerCAmelCase )

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

						__snake_case				=		model[0].eval()

						recursively_load_weights(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )

						hf_wavavec.save_pretrained(_lowerCAmelCase )


if __name__ == "__main__":
	A							:					Union[str, Any]     						=     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'
	)
	A							:					Optional[Any]     						=     parser.parse_args()
	convert_hubert_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)









def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		set()

						# Replace all the whitespace in our sentence
						__snake_case				=		input_str.replace(" "      ,	"" )
						for alpha in input_str:
												if "a" <= alpha.lower() <= "z":
																		frequency.add(alpha.lower() )
						return len(_lowerCAmelCase ) == 26





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		[False] * 26
						for char in input_str:
												if char.islower():
																		__snake_case				=		True
												elif char.isupper():
																		__snake_case				=		True
						return all(_lowerCAmelCase )





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						return len({char for char in input_str.lower() if char.isalpha()} ) == 26





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from timeit import timeit

						__snake_case				=		"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
						print(timeit("is_pangram()"      ,	setup=_lowerCAmelCase ) )
						print(timeit("is_pangram_faster()"      ,	setup=_lowerCAmelCase ) )
						print(timeit("is_pangram_fastest()"      ,	setup=_lowerCAmelCase ) )
						# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
						# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





