


'''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()
__SCREAMING_SNAKE_CASE				:							List[str]		       =		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		{
    '''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 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							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 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							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 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							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 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							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 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_=None	,     UpperCamelCase_=None	,     UpperCamelCase_=True   ):

							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__":
							__SCREAMING_SNAKE_CASE				:							Tuple		       =		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'''
							)
							__SCREAMING_SNAKE_CASE				:							Tuple		       =		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'''
def 			a_							(  UpperCamelCase_	,     UpperCamelCase_ = 1_0_0_0   ):
							if (not isinstance(UpperCamelCase_	,     UpperCamelCase_   )) or (digit_position <= 0):
														raise ValueError("Digit position must be a positive integer"   )
							elif (not isinstance(UpperCamelCase_	,     UpperCamelCase_   )) or (precision < 0):
														raise ValueError("Precision must be a nonnegative integer"   )

							# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
							# accurate
							A_ 						=		(
							    4 * _subsum(UpperCamelCase_	,     1	,     UpperCamelCase_   )
							    - 2 * _subsum(UpperCamelCase_	,     4	,     UpperCamelCase_   )
							    - _subsum(UpperCamelCase_	,     5	,     UpperCamelCase_   )
							    - _subsum(UpperCamelCase_	,     6	,     UpperCamelCase_   )
							)

							# return the first hex digit of the fractional part of the result
							return hex(int((sum_result % 1) * 1_6   )   )[2:]







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		0.0
							for sum_index in range(digit_pos_to_extract + precision   ):
														A_ 						=		8 * sum_index + denominator_addend
														if sum_index < digit_pos_to_extract:
																					# if the exponential term is an integer and we mod it by the denominator
																					# before dividing, only the integer part of the sum will change;
																					# the fractional part will not
																					A_ 						=		pow(
																					    1_6	,     digit_pos_to_extract - 1 - sum_index	,     UpperCamelCase_   )
														else:
																					A_ 						=		pow(1_6	,     digit_pos_to_extract - 1 - sum_index   )
														total += exponential_term / denominator
							return total


if __name__ == "__main__":
							import doctest

							doctest.testmod()





