

'''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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:				int     	=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:				Tuple     	=			{
    "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 							_UpperCamelCase				(   lowerCAmelCase__:					Dict		,lowerCAmelCase__:					Optional[Any]		,lowerCAmelCase__:					Optional[Any]		,lowerCAmelCase__:					Any		,lowerCAmelCase__:					List[Any]   )      -> Tuple:
							for attribute in key.split('.'   ):
														SCREAMING_SNAKE_CASE_				=				getattr(lowerCAmelCase__		,lowerCAmelCase__   )

							if weight_type is not None:
														SCREAMING_SNAKE_CASE_				=				getattr(lowerCAmelCase__		,lowerCAmelCase__   ).shape
							else:
														SCREAMING_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":
														SCREAMING_SNAKE_CASE_				=				value
							elif weight_type == "weight_g":
														SCREAMING_SNAKE_CASE_				=				value
							elif weight_type == "weight_v":
														SCREAMING_SNAKE_CASE_				=				value
							elif weight_type == "bias":
														SCREAMING_SNAKE_CASE_				=				value
							else:
														SCREAMING_SNAKE_CASE_				=				value

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




def 							_UpperCamelCase				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					Optional[Any]		,lowerCAmelCase__:					Optional[Any]   )      -> Dict:
							SCREAMING_SNAKE_CASE_				=				[]
							SCREAMING_SNAKE_CASE_				=				fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE_				=				hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														SCREAMING_SNAKE_CASE_				=				False
														if "conv_layers" in name:
																					load_conv_layer(
																					    lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,hf_model.config.feat_extract_norm == 'group'		,)
																					SCREAMING_SNAKE_CASE_				=				True
														else:
																					for key, mapped_key in MAPPING.items():
																												SCREAMING_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]:
																																			SCREAMING_SNAKE_CASE_				=				True
																																			if "*" in mapped_key:
																																										SCREAMING_SNAKE_CASE_				=				name.split(lowerCAmelCase__   )[0].split('.'   )[-2]
																																										SCREAMING_SNAKE_CASE_				=				mapped_key.replace('*'		,lowerCAmelCase__   )
																																			if "weight_g" in name:
																																										SCREAMING_SNAKE_CASE_				=				'weight_g'
																																			elif "weight_v" in name:
																																										SCREAMING_SNAKE_CASE_				=				'weight_v'
																																			elif "weight" in name:
																																										SCREAMING_SNAKE_CASE_				=				'weight'
																																			elif "bias" in name:
																																										SCREAMING_SNAKE_CASE_				=				'bias'
																																			else:
																																										SCREAMING_SNAKE_CASE_				=				None
																																			set_recursively(lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__   )
																												continue
														if not is_used:
																					unused_weights.append(lowerCAmelCase__   )

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




def 							_UpperCamelCase				(   lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					List[str]		,lowerCAmelCase__:					Optional[int]		,lowerCAmelCase__:					Union[str, Any]		,lowerCAmelCase__:					Union[str, Any]   )      -> List[Any]:
							SCREAMING_SNAKE_CASE_				=				full_name.split('conv_layers.'   )[-1]
							SCREAMING_SNAKE_CASE_				=				name.split('.'   )
							SCREAMING_SNAKE_CASE_				=				int(items[0]   )
							SCREAMING_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."""
																					)
																					SCREAMING_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."""
																					)
																					SCREAMING_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."
																					)
																					SCREAMING_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."""
																					)
																					SCREAMING_SNAKE_CASE_				=				value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
							else:
														unused_weights.append(lowerCAmelCase__   )




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[Any]		,lowerCAmelCase__:					Tuple   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				SEWConfig()
							if is_finetuned:
														SCREAMING_SNAKE_CASE_				=				model.wav_encoder.wav_model.cfg
							else:
														SCREAMING_SNAKE_CASE_				=				model.cfg

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

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

							SCREAMING_SNAKE_CASE_				=				'Wav2Vec2FeatureExtractor'
							SCREAMING_SNAKE_CASE_				=				'Wav2Vec2CTCTokenizer'

							return config




@torch.no_grad()
def 							_UpperCamelCase				(   lowerCAmelCase__:					Any		,lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					Optional[Any]=None		,lowerCAmelCase__:					List[Any]=None		,lowerCAmelCase__:					List[Any]=True   )      -> Tuple:

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

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

							SCREAMING_SNAKE_CASE_				=				True if config.feat_extract_norm == 'layer' else False
							SCREAMING_SNAKE_CASE_				=				WavaVecaFeatureExtractor(
							    feature_size=1		,sampling_rate=1_6000		,padding_value=0		,do_normalize=lowerCAmelCase__		,return_attention_mask=lowerCAmelCase__		,)

							if is_finetuned:
														if dict_path:
																					SCREAMING_SNAKE_CASE_				=				Dictionary.load(lowerCAmelCase__   )

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					SCREAMING_SNAKE_CASE_				=				target_dict.pad_index
																					SCREAMING_SNAKE_CASE_				=				target_dict.bos_index
																					SCREAMING_SNAKE_CASE_				=				target_dict.pad_index
																					SCREAMING_SNAKE_CASE_				=				target_dict.bos_index
																					SCREAMING_SNAKE_CASE_				=				target_dict.eos_index
																					SCREAMING_SNAKE_CASE_				=				len(target_dict.symbols   )
																					SCREAMING_SNAKE_CASE_				=				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__   )
																					with open(lowerCAmelCase__		,'w'		,encoding='utf-8'   ) as vocab_handle:
																												json.dump(target_dict.indices		,lowerCAmelCase__   )
																					SCREAMING_SNAKE_CASE_				=				WavaVecaCTCTokenizer(
																					    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__		,)
																					SCREAMING_SNAKE_CASE_				=				WavaVecaProcessor(feature_extractor=lowerCAmelCase__		,tokenizer=lowerCAmelCase__   )
																					processor.save_pretrained(lowerCAmelCase__   )

														SCREAMING_SNAKE_CASE_				=				SEWForCTC(lowerCAmelCase__   )
							else:
														SCREAMING_SNAKE_CASE_				=				SEWModel(lowerCAmelCase__   )
														feature_extractor.save_pretrained(lowerCAmelCase__   )

							recursively_load_weights(lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__   )

							hf_model.save_pretrained(lowerCAmelCase__   )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				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"
							)
							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'''

import os

# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE							:				List[Any]     	=			[int(0.5 * n * (n + 1)) for n in range(1, 101)]




def 							_UpperCamelCase				(   )      -> List[Any]:
							SCREAMING_SNAKE_CASE_				=				os.path.dirname(os.path.realpath(lowerCAmelCase__   )   )
							SCREAMING_SNAKE_CASE_				=				os.path.join(lowerCAmelCase__		,'words.txt'   )

							SCREAMING_SNAKE_CASE_				=				''
							with open(lowerCAmelCase__   ) as f:
														SCREAMING_SNAKE_CASE_				=				f.readline()

							SCREAMING_SNAKE_CASE_				=				[word.strip('"'   ) for word in words.strip('\r\n'   ).split(','   )]
							SCREAMING_SNAKE_CASE_				=				[
							    word
							    for word in [sum(ord(lowerCAmelCase__   ) - 64 for x in word   ) for word in words]
							    if word in TRIANGULAR_NUMBERS
							]
							return len(lowerCAmelCase__   )


if __name__ == "__main__":
							print(solution())





