

'''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()
lowerCAmelCase__      				=			logging.get_logger(__name__)

lowerCAmelCase__      				=			{
    "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     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     Optional[int]:
	for attribute in key.split('.'	):
		UpperCAmelCase_		:   List[Any]													=       getattr(__a ,__a	)

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

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







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     Tuple:
	UpperCAmelCase_		:   Optional[Any]													=       []
	UpperCAmelCase_		:   List[Any]													=       fairseq_model.state_dict()

	UpperCAmelCase_		:   Tuple													=       hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		UpperCAmelCase_		:   List[Any]													=       False
		if "conv_layers" in name:
			load_conv_layer(
			    __a ,__a ,__a ,__a ,hf_model.config.feat_extract_norm == 'group' ,)
			UpperCAmelCase_		:   Optional[int]													=       True
		else:
			for key, mapped_key in MAPPING.items():
				UpperCAmelCase_		:   Optional[int]													=       '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]:
					UpperCAmelCase_		:   Tuple													=       True
					if "*" in mapped_key:
						UpperCAmelCase_		:   List[str]													=       name.split(__a	)[0].split('.'	)[-2]
						if not layer_index.isnumeric():
							continue
						UpperCAmelCase_		:   int													=       mapped_key.replace('*' ,__a	)
					if "weight_g" in name:
						UpperCAmelCase_		:   List[str]													=       'weight_g'
					elif "weight_v" in name:
						UpperCAmelCase_		:   List[str]													=       'weight_v'
					elif "weight" in name:
						UpperCAmelCase_		:   Union[str, Any]													=       'weight'
					elif "bias" in name:
						UpperCAmelCase_		:   Tuple													=       'bias'
					else:
						UpperCAmelCase_		:   Tuple													=       None
					set_recursively(__a ,__a ,__a ,__a ,__a	)
				continue
		if not is_used:
			unused_weights.append(__a	)

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







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     Union[str, Any]:
	UpperCAmelCase_		:   str													=       full_name.split('conv_layers.'	)[-1]
	UpperCAmelCase_		:   Optional[Any]													=       name.split('.'	)
	UpperCAmelCase_		:   Dict													=       int(items[0]	)
	UpperCAmelCase_		:   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."""
			)
			UpperCAmelCase_		:   Union[str, Any]													=       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."""
			)
			UpperCAmelCase_		:   Optional[int]													=       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."
			)
			UpperCAmelCase_		:   int													=       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."""
			)
			UpperCAmelCase_		:   int													=       value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
	else:
		unused_weights.append(__a	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     Optional[int]:
	UpperCAmelCase_		:   Any													=       SEWDConfig()
	if is_finetuned:
		UpperCAmelCase_		:   Optional[Any]													=       model.wav_encoder.wav_model.cfg
	else:
		UpperCAmelCase_		:   List[str]													=       model.cfg

	UpperCAmelCase_		:   str													=       fs_config.conv_bias
	UpperCAmelCase_		:   Tuple													=       eval(fs_config.conv_feature_layers	)
	UpperCAmelCase_		:   Optional[Any]													=       [x[0] for x in conv_layers]
	UpperCAmelCase_		:   Union[str, Any]													=       [x[1] for x in conv_layers]
	UpperCAmelCase_		:   Tuple													=       [x[2] for x in conv_layers]
	UpperCAmelCase_		:   str													=       'gelu'
	UpperCAmelCase_		:   Union[str, Any]													=       'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	UpperCAmelCase_		:   Tuple													=       0.0
	UpperCAmelCase_		:   List[Any]													=       fs_config.activation_fn.name
	UpperCAmelCase_		:   Optional[int]													=       fs_config.encoder_embed_dim
	UpperCAmelCase_		:   Tuple													=       0.02
	UpperCAmelCase_		:   Optional[Any]													=       fs_config.encoder_ffn_embed_dim
	UpperCAmelCase_		:   Tuple													=       1e-5
	UpperCAmelCase_		:   Optional[Any]													=       fs_config.encoder_layerdrop
	UpperCAmelCase_		:   Union[str, Any]													=       fs_config.encoder_attention_heads
	UpperCAmelCase_		:   List[Any]													=       fs_config.conv_pos_groups
	UpperCAmelCase_		:   Any													=       fs_config.conv_pos
	UpperCAmelCase_		:   Tuple													=       len(__a	)
	UpperCAmelCase_		:   List[str]													=       fs_config.encoder_layers
	UpperCAmelCase_		:   List[str]													=       fs_config.squeeze_factor
	# DeBERTa-specific parameters:
	UpperCAmelCase_		:   Optional[int]													=       fs_config.max_position_embeddings
	UpperCAmelCase_		:   int													=       fs_config.position_buckets
	UpperCAmelCase_		:   int													=       fs_config.share_att_key
	UpperCAmelCase_		:   Any													=       fs_config.relative_attention
	UpperCAmelCase_		:   Optional[int]													=       fs_config.position_biased_input
	UpperCAmelCase_		:   Tuple													=       tuple(fs_config.pos_att_type.split('|'	)	)
	UpperCAmelCase_		:   Dict													=       fs_config.norm_rel_ebd

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		UpperCAmelCase_		:   Optional[int]													=       model.cfg
		UpperCAmelCase_		:   List[str]													=       fs_config.final_dropout
		UpperCAmelCase_		:   Dict													=       fs_config.layerdrop
	UpperCAmelCase_		:   Any													=       fs_config.activation_dropout
	UpperCAmelCase_		:   Tuple													=       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	UpperCAmelCase_		:   Union[str, Any]													=       fs_config.attention_dropout
	UpperCAmelCase_		:   Any													=       fs_config.dropout_input
	UpperCAmelCase_		:   Any													=       fs_config.dropout
	UpperCAmelCase_		:   Optional[Any]													=       fs_config.mask_channel_length
	UpperCAmelCase_		:   Optional[int]													=       fs_config.mask_channel_prob
	UpperCAmelCase_		:   List[Any]													=       fs_config.mask_length
	UpperCAmelCase_		:   Any													=       fs_config.mask_prob

	UpperCAmelCase_		:   Optional[int]													=       'Wav2Vec2FeatureExtractor'
	UpperCAmelCase_		:   Optional[int]													=       'Wav2Vec2CTCTokenizer'

	return config







@torch.no_grad()
def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase=True	)     ->     List[str]:

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

	if config_path is not None:
		UpperCAmelCase_		:   int													=       SEWDConfig.from_pretrained(__a	)
	else:
		UpperCAmelCase_		:   Dict													=       convert_config(model[0] ,__a	)
	UpperCAmelCase_		:   int													=       model[0].eval()

	UpperCAmelCase_		:   Dict													=       True if config.feat_extract_norm == 'layer' else False
	UpperCAmelCase_		:   Tuple													=       WavaVecaFeatureExtractor(
	    feature_size=1 ,sampling_rate=1_6_0_0_0 ,padding_value=0 ,do_normalize=__a ,return_attention_mask=__a ,)

	if is_finetuned:
		if dict_path:
			UpperCAmelCase_		:   Optional[int]													=       Dictionary.load(__a	)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			UpperCAmelCase_		:   str													=       target_dict.pad_index
			UpperCAmelCase_		:   Tuple													=       target_dict.bos_index
			UpperCAmelCase_		:   Any													=       target_dict.pad_index
			UpperCAmelCase_		:   Optional[int]													=       target_dict.bos_index
			UpperCAmelCase_		:   int													=       target_dict.eos_index
			UpperCAmelCase_		:   Optional[int]													=       len(target_dict.symbols	)
			UpperCAmelCase_		:   Optional[Any]													=       os.path.join(__a ,'vocab.json'	)
			if not os.path.isdir(__a	):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__a	)	)
				return
			os.makedirs(__a ,exist_ok=__a	)
			with open(__a ,'w' ,encoding='utf-8'	) as vocab_handle:
				json.dump(target_dict.indices ,__a	)
			UpperCAmelCase_		:   Optional[Any]													=       WavaVecaCTCTokenizer(
			    __a ,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=__a ,)
			UpperCAmelCase_		:   Any													=       WavaVecaProcessor(feature_extractor=__a ,tokenizer=__a	)
			processor.save_pretrained(__a	)

		UpperCAmelCase_		:   Any													=       SEWDForCTC(__a	)
	else:
		UpperCAmelCase_		:   int													=       SEWDModel(__a	)
		feature_extractor.save_pretrained(__a	)

	recursively_load_weights(__a ,__a ,__a	)

	hf_model.save_pretrained(__a	)


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 math import gcd

def       A_   (						__a  :					int = 12_000		):

				"""simple docstring"""
				a__					   =		0
				for d in range(max_d + 1		):
								for n in range(d // 3 + 1			,		(d + 1) // 2		):
												if gcd(__a			,		__a		) == 1:
																fractions_number += 1
				return fractions_number


if __name__ == "__main__":
	print(f"""{solution() = }""")

