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

__A        =     {
    "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 			_A		(				lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__						):


						for attribute in key.split("""."""						):
												lowercase__							=							getattr(_lowercase		,		_lowercase						)

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

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





def 			_A		(				lowercase__		,		lowercase__		,		lowercase__						):
						lowercase__							=							[]
						lowercase__							=							fairseq_model.state_dict()

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

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

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





def 			_A		(				lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__						):
						lowercase__							=							full_name.split("""conv_layers."""						)[-1]
						lowercase__							=							name.split("""."""						)
						lowercase__							=							int(items[0]						)
						lowercase__							=							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.'''
																		)
																		lowercase__							=							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.'''
																		)
																		lowercase__							=							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."
																		)
																		lowercase__							=							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.'''
																		)
																		lowercase__							=							value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
						else:
												unused_weights.append(_lowercase						)





def 			_A		(				lowercase__		,		lowercase__						):
						lowercase__							=							SEWConfig()
						if is_finetuned:
												lowercase__							=							model.wav_encoder.wav_model.cfg
						else:
												lowercase__							=							model.cfg

						lowercase__							=							fs_config.conv_bias
						lowercase__							=							eval(fs_config.conv_feature_layers						)
						lowercase__							=							[x[0] for x in conv_layers]
						lowercase__							=							[x[1] for x in conv_layers]
						lowercase__							=							[x[2] for x in conv_layers]
						lowercase__							=							"""gelu"""
						lowercase__							=							"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
						lowercase__							=							0.0
						lowercase__							=							fs_config.activation_fn.name
						lowercase__							=							fs_config.encoder_embed_dim
						lowercase__							=							0.0_2
						lowercase__							=							fs_config.encoder_ffn_embed_dim
						lowercase__							=							1e-5
						lowercase__							=							fs_config.encoder_layerdrop
						lowercase__							=							fs_config.encoder_attention_heads
						lowercase__							=							fs_config.conv_pos_groups
						lowercase__							=							fs_config.conv_pos
						lowercase__							=							len(_lowercase						)
						lowercase__							=							fs_config.encoder_layers
						lowercase__							=							fs_config.squeeze_factor

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

						lowercase__							=							"""Wav2Vec2FeatureExtractor"""
						lowercase__							=							"""Wav2Vec2CTCTokenizer"""

						return config





@torch.no_grad()
def 			_A		(				lowercase__		,		lowercase__		,		lowercase__=None		,		lowercase__=None		,		lowercase__=True						):


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

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

						lowercase__							=							True if config.feat_extract_norm == """layer""" else False
						lowercase__							=							WavaVecaFeatureExtractor(
						    feature_size=1		,		sampling_rate=16000		,		padding_value=0		,		do_normalize=_lowercase		,		return_attention_mask=_lowercase		,		)

						if is_finetuned:
												if dict_path:
																		lowercase__							=							Dictionary.load(_lowercase						)

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

												lowercase__							=							SEWForCTC(_lowercase						)
						else:
												lowercase__							=							SEWModel(_lowercase						)
												feature_extractor.save_pretrained(_lowercase						)

						recursively_load_weights(_lowercase		,		_lowercase		,		_lowercase						)

						hf_model.save_pretrained(_lowercase						)


if __name__ == "__main__":
				__A        =     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        =     parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)










def 		_A		(		_lowercase = 1_00 )					-> int:


     """simple docstring"""


     __UpperCamelCase =			0
     __UpperCamelCase =			0
     for i in range(1					,  n + 1 ):
          sum_of_squares += i**2
          sum_of_ints += i
     return sum_of_ints**2 - sum_of_squares


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


