

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()
lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{
    '''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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						for attribute in key.split('''.'''       ):
												snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )

						if weight_type is not None:
												snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ).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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[]
						snake_case_   =	fairseq_model.state_dict()

						snake_case_   =	hf_model.sew.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(
																		    SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		hf_model.config.feat_extract_norm == '''group'''  ,		)
																		snake_case_   =	True
												else:
																		for key, mapped_key in MAPPING.items():
																								snake_case_   =	'''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]:
																														snake_case_   =	True
																														if "*" in mapped_key:
																																				snake_case_   =	name.split(SCREAMING_SNAKE_CASE__       )[0].split('''.'''       )[-2]
																																				snake_case_   =	mapped_key.replace('''*'''  ,		SCREAMING_SNAKE_CASE__       )
																														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(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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						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(SCREAMING_SNAKE_CASE__       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	SEWConfig()
						if is_finetuned:
												snake_case_   =	model.wav_encoder.wav_model.cfg
						else:
												snake_case_   =	model.cfg

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

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

						snake_case_   =	'''Wav2Vec2FeatureExtractor'''
						snake_case_   =	'''Wav2Vec2CTCTokenizer'''

						return config


@torch.no_grad()
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None  ,		SCREAMING_SNAKE_CASE__=None  ,		SCREAMING_SNAKE_CASE__=True       ):

						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]       )

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

						snake_case_   =	True if config.feat_extract_norm == '''layer''' else False
						snake_case_   =	WavaVecaFeatureExtractor(
						    feature_size=1  ,		sampling_rate=16000  ,		padding_value=0  ,		do_normalize=SCREAMING_SNAKE_CASE__  ,		return_attention_mask=SCREAMING_SNAKE_CASE__  ,		)

						if is_finetuned:
												if dict_path:
																		snake_case_   =	Dictionary.load(SCREAMING_SNAKE_CASE__       )

																		# 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.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(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__       )
																		snake_case_   =	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__  ,		)
																		snake_case_   =	WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__  ,		tokenizer=SCREAMING_SNAKE_CASE__       )
																		processor.save_pretrained(SCREAMING_SNAKE_CASE__       )

												snake_case_   =	SEWForCTC(SCREAMING_SNAKE_CASE__       )
						else:
												snake_case_   =	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__":
					lowerCAmelCase_       	=	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'''
					)
					lowerCAmelCase_       	=	parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)


from collections import deque

from .hash_table import HashTable





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   *_UpperCamelCase :				int							,   **_UpperCamelCase :				Tuple							)      ->Tuple:
									super().__init__(*_UpperCamelCase							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Dict							)      ->Tuple:
									snake_case_   =	deque([]							) if self.values[key] is None else self.values[key]
									self.values[key].appendleft(_UpperCamelCase							)
									snake_case_   =	self.values[key]







			def 				snake_case__(     self :				List[Any]							)      ->str:
									return (
									    sum(self.charge_factor - len(_UpperCamelCase							) for slot in self.values							)
									    / self.size_table
									    * self.charge_factor
									)







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Optional[int]=None							)      ->str:
									if not (
									    len(self.values[key]							) == self.charge_factor and self.values.count(_UpperCamelCase							) == 0
									):
															return key
									return super()._collision_resolution(_UpperCamelCase							,   _UpperCamelCase							)