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()
lowerCAmelCase__     :   int		=   logging.get_logger(__name__)

lowerCAmelCase__     :   Tuple		=   {
    '''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''',
}
lowerCAmelCase__     :   List[Any]		=   [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def  UpperCamelCase__						(				A__			, A__			, A__			, A__			, A__			, A__    )   ->					int:
				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
																snake_case__					:	List[Any]												=      'lm_head'

								snake_case__					:	Tuple												=      getattr(lowerCAmelCase_			, lowerCAmelCase_    )

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

				logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""    )
def  UpperCamelCase__						(				A__			, A__			, A__    )   ->					Any:
				snake_case__					:	List[Any]												=      []
				snake_case__					:	Dict												=      fairseq_model.state_dict()

				snake_case__					:	Union[str, Any]												=      hf_model.unispeech.feature_extractor

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

				logger.warning(F"""Unused weights: {unused_weights}"""    )
def  UpperCamelCase__						(				A__			, A__			, A__			, A__			, A__    )   ->					Any:
				snake_case__					:	int												=      full_name.split('conv_layers.'    )[-1]
				snake_case__					:	Tuple												=      name.split('.'    )
				snake_case__					:	Optional[Any]												=      int(items[0]    )
				snake_case__					:	List[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."""
												)
												snake_case__					:	int												=      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__					:	Optional[Any]												=      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__					:	List[Any]												=      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__					:	Optional[Any]												=      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  UpperCamelCase__						(				A__			, A__			, A__=None			, A__=None			, A__=True    )   ->					Dict:
				if config_path is not None:
								snake_case__					:	List[str]												=      UniSpeechConfig.from_pretrained(lowerCAmelCase_    )
				else:
								snake_case__					:	Tuple												=      UniSpeechConfig()

				if is_finetuned:
								if dict_path:
												snake_case__					:	Optional[Any]												=      Dictionary.load_from_json(lowerCAmelCase_    )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												snake_case__					:	Any												=      target_dict.pad_index
												snake_case__					:	Optional[Any]												=      target_dict.bos_index
												snake_case__					:	Optional[int]												=      target_dict.eos_index
												snake_case__					:	List[Any]												=      len(target_dict.symbols    )
												snake_case__					:	Tuple												=      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_    )
												snake_case__					:	List[str]												=      target_dict.indices

												# fairseq has the <pad> and <s> switched
												snake_case__					:	List[Any]												=      42
												snake_case__					:	Any												=      43
												with open(lowerCAmelCase_			, 'w'			, encoding='utf-8'    ) as vocab_handle:
																json.dump(lowerCAmelCase_			, lowerCAmelCase_    )
												snake_case__					:	Optional[Any]												=      WavaVecaPhonemeCTCTokenizer(
												    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__					:	List[Any]												=      True if config.feat_extract_norm == 'layer' else False
												snake_case__					:	List[str]												=      WavaVecaFeatureExtractor(
												    feature_size=1			, sampling_rate=1_6000			, padding_value=0			, do_normalize=lowerCAmelCase_			, return_attention_mask=lowerCAmelCase_			, )
												snake_case__					:	Dict												=      WavaVecaProcessor(feature_extractor=lowerCAmelCase_			, tokenizer=lowerCAmelCase_    )
												processor.save_pretrained(lowerCAmelCase_    )

								snake_case__					:	List[str]												=      UniSpeechForCTC(lowerCAmelCase_    )
				else:
								snake_case__					:	int												=      UniSpeechForPreTraining(lowerCAmelCase_    )

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

				snake_case__					:	Optional[Any]												=      model[0].eval()

				recursively_load_weights(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_    )

				hf_unispeech.save_pretrained(lowerCAmelCase_    )


if __name__ == "__main__":
				lowerCAmelCase__     :   Dict		=   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'''
				)
				lowerCAmelCase__     :   List[Any]		=   parser.parse_args()
				convert_unispeech_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)







'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

		def get_lens(lowerCAmelCase_  ):
				_a     :      Dict        =							tqdm(
				    DataLoader(lowerCAmelCase_						,			batch_size=512						,			num_workers=8						,			shuffle=lowerCAmelCase_						,			collate_fn=ds.collate_fn  )						,			desc=str(ds.len_file  )						,			)
				_a     :      Union[str, Any]        =							[]
				for batch in dl:
						_a     :      Optional[Any]        =							batch['input_ids'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						_a     :      Optional[Any]        =							batch['labels'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						if consider_target:
								for src, tgt in zip(lowerCAmelCase_						,			lowerCAmelCase_  ):
										max_lens.append(max(lowerCAmelCase_						,			lowerCAmelCase_  )  )
						else:
								max_lens.extend(lowerCAmelCase_  )
				return max_lens

		_a     :      str        =							get_lens(lowerCAmelCase_  )
		_a     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							get_lens(lowerCAmelCase_  )
		pickle_save(lowerCAmelCase_						,			train_ds.len_file  )
		pickle_save(lowerCAmelCase_						,			val_ds.len_file  )


if __name__ == "__main__":
							fire.Fire(save_len_file)




