



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

SCREAMING_SNAKE_CASE     				=						{
    "post_extract_proj": "feature_projection.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.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							int:
		for attribute in key.split("." ):
				UpperCAmelCase_						=				getattr(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

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

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







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[int]:
		UpperCAmelCase_						=				[]
		UpperCAmelCase_						=				fairseq_model.state_dict()

		UpperCAmelCase_						=				hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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







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







@torch.no_grad()
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=True )		->							Dict:
		if config_path is not None:
				UpperCAmelCase_						=				HubertConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
		else:
				UpperCAmelCase_						=				HubertConfig()

		if is_finetuned:
				if dict_path:
						UpperCAmelCase_						=				Dictionary.load(__SCREAMING_SNAKE_CASE )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						UpperCAmelCase_						=				target_dict.pad_index
						UpperCAmelCase_						=				target_dict.bos_index
						UpperCAmelCase_						=				target_dict.eos_index
						UpperCAmelCase_						=				len(target_dict.symbols )
						UpperCAmelCase_						=				os.path.join(__SCREAMING_SNAKE_CASE			,     "vocab.json" )
						if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
								logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__SCREAMING_SNAKE_CASE ) )
								return
						os.makedirs(__SCREAMING_SNAKE_CASE			,     exist_ok=__SCREAMING_SNAKE_CASE )
						with open(__SCREAMING_SNAKE_CASE			,     "w"			,     encoding="utf-8" ) as vocab_handle:
								json.dump(target_dict.indices			,     __SCREAMING_SNAKE_CASE )
						UpperCAmelCase_						=				WavaVecaCTCTokenizer(
						    __SCREAMING_SNAKE_CASE			,     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=__SCREAMING_SNAKE_CASE			,     )
						UpperCAmelCase_						=				True if config.feat_extract_norm == "layer" else False
						UpperCAmelCase_						=				WavaVecaFeatureExtractor(
						    feature_size=1			,     sampling_rate=1_6000			,     padding_value=0			,     do_normalize=__SCREAMING_SNAKE_CASE			,     return_attention_mask=__SCREAMING_SNAKE_CASE			,     )
						UpperCAmelCase_						=				WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE			,     tokenizer=__SCREAMING_SNAKE_CASE )
						processor.save_pretrained(__SCREAMING_SNAKE_CASE )

				UpperCAmelCase_						=				HubertForCTC(__SCREAMING_SNAKE_CASE )
		else:
				UpperCAmelCase_						=				HubertModel(__SCREAMING_SNAKE_CASE )

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

		UpperCAmelCase_						=				model[0].eval()

		recursively_load_weights(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

		hf_wavavec.save_pretrained(__SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						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(
			    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
			)
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()
			convert_hubert_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)






import math







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							list[int]:
		UpperCAmelCase_						=				[]
		UpperCAmelCase_						=				2
		UpperCAmelCase_						=				int(math.sqrt(__SCREAMING_SNAKE_CASE ) )  # Size of every segment
		UpperCAmelCase_						=				[True] * (end + 1)
		UpperCAmelCase_						=				[]

		while start <= end:
				if temp[start] is True:
						in_prime.append(__SCREAMING_SNAKE_CASE )
						for i in range(start * start			,     end + 1			,     __SCREAMING_SNAKE_CASE ):
								UpperCAmelCase_						=				False
				start += 1
		prime += in_prime

		UpperCAmelCase_						=				end + 1
		UpperCAmelCase_						=				min(2 * end			,     __SCREAMING_SNAKE_CASE )

		while low <= n:
				UpperCAmelCase_						=				[True] * (high - low + 1)
				for each in in_prime:
						UpperCAmelCase_						=				math.floor(low / each ) * each
						if t < low:
								t += each

						for j in range(__SCREAMING_SNAKE_CASE			,     high + 1			,     __SCREAMING_SNAKE_CASE ):
								UpperCAmelCase_						=				False

				for j in range(len(__SCREAMING_SNAKE_CASE ) ):
						if temp[j] is True:
								prime.append(j + low )

				UpperCAmelCase_						=				high + 1
				UpperCAmelCase_						=				min(high + end			,     __SCREAMING_SNAKE_CASE )

		return prime

print(sieve(10**6))

