
'''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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a__		:   Any            =       logging.get_logger(__name__)

a__		:   List[Any]            =       {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "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        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)->			List[str]:







						"""simple docstring"""
						for attribute in key.split('.'	):
												__A       	=			getattr(UpperCAmelCase      ,  UpperCAmelCase	)

						if weight_type is not None:
												__A       	=			getattr(UpperCAmelCase      ,  UpperCAmelCase	).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        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)->			List[Any]:







						"""simple docstring"""
						__A       	=			[]
						__A       	=			fairseq_model.state_dict()

						__A       	=			hf_model.sew_d.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(
																		    UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  hf_model.config.feat_extract_norm == 'group'      ,  )
																		__A       	=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								__A       	=			'sew_d.' + 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(UpperCAmelCase	)[0].split('.'	)[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				__A       	=			mapped_key.replace('*'      ,  UpperCAmelCase	)
																														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(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)
																								continue
												if not is_used:
																		unused_weights.append(UpperCAmelCase	)

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





def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)->			Dict:







						"""simple docstring"""
						__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(UpperCAmelCase	)





def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase	)->			List[Any]:







						"""simple docstring"""
						__A       	=			SEWDConfig()
						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(UpperCAmelCase	)
						__A       	=			fs_config.encoder_layers
						__A       	=			fs_config.squeeze_factor
						# DeBERTa-specific parameters:
						__A       	=			fs_config.max_position_embeddings
						__A       	=			fs_config.position_buckets
						__A       	=			fs_config.share_att_key
						__A       	=			fs_config.relative_attention
						__A       	=			fs_config.position_biased_input
						__A       	=			tuple(fs_config.pos_att_type.split('|'	)	)
						__A       	=			fs_config.norm_rel_ebd

						# 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        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=None      ,  UpperCAmelCase=None      ,  UpperCAmelCase=True	)->			int:







						"""simple docstring"""

						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       	=			SEWDConfig.from_pretrained(UpperCAmelCase	)
						else:
												__A       	=			convert_config(model[0]      ,  UpperCAmelCase	)
						__A       	=			model[0].eval()

						__A       	=			True if config.feat_extract_norm == 'layer' else False
						__A       	=			WavaVecaFeatureExtractor(
						    feature_size=1      ,  sampling_rate=1_6_0_0_0      ,  padding_value=0      ,  do_normalize=UpperCAmelCase      ,  return_attention_mask=UpperCAmelCase      ,  )

						if is_finetuned:
												if dict_path:
																		__A       	=			Dictionary.load(UpperCAmelCase	)

																		# 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(UpperCAmelCase      ,  'vocab.json'	)
																		if not os.path.isdir(UpperCAmelCase	):
																								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(UpperCAmelCase	)	)
																								return
																		os.makedirs(UpperCAmelCase      ,  exist_ok=UpperCAmelCase	)
																		with open(UpperCAmelCase      ,  'w'      ,  encoding='utf-8'	) as vocab_handle:
																								json.dump(target_dict.indices      ,  UpperCAmelCase	)
																		__A       	=			WavaVecaCTCTokenizer(
																		    UpperCAmelCase      ,  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=UpperCAmelCase      ,  )
																		__A       	=			WavaVecaProcessor(feature_extractor=UpperCAmelCase      ,  tokenizer=UpperCAmelCase	)
																		processor.save_pretrained(UpperCAmelCase	)

												__A       	=			SEWDForCTC(UpperCAmelCase	)
						else:
												__A       	=			SEWDModel(UpperCAmelCase	)
												feature_extractor.save_pretrained(UpperCAmelCase	)

						recursively_load_weights(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)

						hf_model.save_pretrained(UpperCAmelCase	)


if __name__ == "__main__":
	a__		:   Optional[int]            =       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"
	)
	a__		:   Optional[int]            =       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'''





from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime


class  UpperCamelCase__  (    SCREAMING_SNAKE_CASE):






 def __init__(			self  :Union[str, Any]	,				*_A  :int	,				**_A  :Optional[Any]			)				->			Union[str, Any]:







       '''simple docstring'''



       super().__init__(*_A	,				**_A			)






 def 	lowercase_						(			self  :Dict	,				_A  :Union[str, Any]	,				_A  :Dict			)				->			Dict:







       '''simple docstring'''



       __A       	=			(
           next_prime(value % self.size_table			)
           if not is_prime(value % self.size_table			)
           else value % self.size_table
       )  # gt = bigger than
       return next_prime_gt - (data % next_prime_gt)






 def 	lowercase_						(			self  :List[Any]	,				_A  :int	,				_A  :Tuple	,				_A  :Union[str, Any]			)				->			List[Any]:







       '''simple docstring'''



       return (increment * self.__hash_function_a(_A	,				_A			)) % self.size_table






 def 	lowercase_						(			self  :List[Any]	,				_A  :List[str]	,				_A  :str=None			)				->			List[Any]:







       '''simple docstring'''



       __A       	=			1
       __A       	=			self.hash_function(_A			)

       while self.values[new_key] is not None and self.values[new_key] != key:
             __A       	=			(
                 self.__hash_double_function(_A	,				_A	,				_A			)
                 if self.balanced_factor() >= self.lim_charge
                 else None
             )
             if new_key is None:
                   break
             else:
                   i += 1

       return new_key






