"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    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.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
SCREAMING_SNAKE_CASE									=						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	for attribute in key.split('.'			):
		_lowercase :    List[str]							=							getattr(__UpperCAmelCase     ,__UpperCAmelCase			)

	if weight_type is not None:
		_lowercase :    List[str]							=							getattr(__UpperCAmelCase     ,__UpperCAmelCase			).shape
	else:
		_lowercase :    Tuple							=							hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    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 :    Dict							=							value
	elif weight_type == "weight_g":
		_lowercase :    str							=							value
	elif weight_type == "weight_v":
		_lowercase :    Optional[int]							=							value
	elif weight_type == "bias":
		_lowercase :    Optional[int]							=							value
	elif weight_type == "running_mean":
		_lowercase :    Dict							=							value
	elif weight_type == "running_var":
		_lowercase :    Tuple							=							value
	elif weight_type == "num_batches_tracked":
		_lowercase :    Tuple							=							value
	elif weight_type == "inv_freq":
		_lowercase :    Optional[int]							=							value
	else:
		_lowercase :    Dict							=							value

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    List[Any]							=							[]
	_lowercase :    Dict							=							fairseq_model.state_dict()

	_lowercase :    Any							=							hf_model.wavaveca_conformer.feature_extractor

	for name, value in fairseq_dict.items():
		_lowercase :    Any							=							False
		if "conv_layers" in name:
			load_conv_layer(
			    __UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,hf_model.config.feat_extract_norm == 'group'     ,)
			_lowercase :    Union[str, Any]							=							True
		else:
			for key, mapped_key in MAPPING.items():
				_lowercase :    Any							=							'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
				if key in name or key.split('w2v_model.'			)[-1] == name.split('.'			)[0]:
					_lowercase :    Optional[Any]							=							True
					if "*" in mapped_key:
						_lowercase :    Union[str, Any]							=							name.split(__UpperCAmelCase			)[0].split('.'			)[-2]
						_lowercase :    List[Any]							=							mapped_key.replace('*'     ,__UpperCAmelCase			)
					if "pos_bias_u" in name:
						_lowercase :    int							=							None
					elif "pos_bias_v" in name:
						_lowercase :    Tuple							=							None
					elif "weight_g" in name:
						_lowercase :    Dict							=							'weight_g'
					elif "weight_v" in name:
						_lowercase :    Union[str, Any]							=							'weight_v'
					elif "bias" in name:
						_lowercase :    Dict							=							'bias'
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						_lowercase :    Any							=							'weight'
					elif "running_mean" in name:
						_lowercase :    int							=							'running_mean'
					elif "inv_freq" in name:
						_lowercase :    Tuple							=							'inv_freq'
					elif "running_var" in name:
						_lowercase :    Optional[int]							=							'running_var'
					elif "num_batches_tracked" in name:
						_lowercase :    Optional[int]							=							'num_batches_tracked'
					else:
						_lowercase :    Tuple							=							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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							full_name.split('conv_layers.'			)[-1]
	_lowercase :    List[str]							=							name.split('.'			)
	_lowercase :    int							=							int(items[0]			)
	_lowercase :    List[Any]							=							int(items[1]			)

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''			)
			_lowercase :    Optional[int]							=							value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''			)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''			)
			_lowercase :    Tuple							=							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:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''			)
			_lowercase :    List[Any]							=							value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''			)
			_lowercase :    Dict							=							value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
	else:
		unused_weights.append(__UpperCAmelCase			)


@torch.no_grad()
def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=None     ,__UpperCAmelCase=None     ,__UpperCAmelCase=True			):






	"""simple docstring"""





	if config_path is not None:
		_lowercase :    Tuple							=							WavaVecaConformerConfig.from_pretrained(__UpperCAmelCase     ,hidden_act='swish'			)
	else:
		_lowercase :    Optional[int]							=							WavaVecaConformerConfig()

	if "rope" in checkpoint_path:
		_lowercase :    Optional[Any]							=							'rotary'

	if is_finetuned:
		if dict_path:
			_lowercase :    Tuple							=							Dictionary.load(__UpperCAmelCase			)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			_lowercase :    Dict							=							target_dict.pad_index
			_lowercase :    Any							=							target_dict.bos_index
			_lowercase :    Tuple							=							target_dict.eos_index
			_lowercase :    List[Any]							=							len(target_dict.symbols			)
			_lowercase :    Dict							=							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			)
			_lowercase :    Tuple							=							target_dict.indices

			# fairseq has the <pad> and <s> switched
			_lowercase :    Optional[Any]							=							0
			_lowercase :    Union[str, Any]							=							1
			with open(__UpperCAmelCase     ,'w'     ,encoding='utf-8'			) as vocab_handle:
				json.dump(__UpperCAmelCase     ,__UpperCAmelCase			)
			_lowercase :    Optional[int]							=							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     ,)
			_lowercase :    Any							=							True if config.feat_extract_norm == 'layer' else False
			_lowercase :    Union[str, Any]							=							WavaVecaFeatureExtractor(
			    feature_size=1     ,sampling_rate=16_000     ,padding_value=0     ,do_normalize=__UpperCAmelCase     ,return_attention_mask=__UpperCAmelCase     ,)
			_lowercase :    Tuple							=							WavaVecaProcessor(feature_extractor=__UpperCAmelCase     ,tokenizer=__UpperCAmelCase			)
			processor.save_pretrained(__UpperCAmelCase			)

		_lowercase :    Union[str, Any]							=							WavaVecaConformerForCTC(__UpperCAmelCase			)
	else:
		_lowercase :    Tuple							=							WavaVecaConformerForPreTraining(__UpperCAmelCase			)

	if is_finetuned:
		_lowercase :    int							=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]     ,arg_overrides={'data': '/'.join(dict_path.split('/'			)[:-1]			)}			)
	else:
		_lowercase :    List[str]							=							argparse.Namespace(task='audio_pretraining'			)
		_lowercase :    List[Any]							=							fairseq.tasks.setup_task(__UpperCAmelCase			)

		_lowercase :    Union[str, Any]							=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,task=__UpperCAmelCase			)

	_lowercase :    str							=							model[0].eval()

	recursively_load_weights(__UpperCAmelCase     ,__UpperCAmelCase     ,not is_finetuned			)

	hf_wavavec.save_pretrained(__UpperCAmelCase			)


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_wavaveca_conformer_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)


"""simple docstring"""







from __future__ import annotations


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							[True] * limit
	_lowercase :    Union[str, Any]							=							False
	_lowercase :    Any							=							False
	_lowercase :    List[Any]							=							True

	for i in range(3     ,int(limit**0.5 + 1			)     ,2			):
		_lowercase :    Optional[Any]							=							i * 2
		while index < limit:
			_lowercase :    int							=							False
			_lowercase :    Tuple							=							index + i

	_lowercase :    Optional[Any]							=							[2]

	for i in range(3     ,__UpperCAmelCase     ,2			):
		if is_prime[i]:
			primes.append(__UpperCAmelCase			)

	return primes


def      __lowerCAmelCase(		__UpperCAmelCase = 1_000_000			):






	"""simple docstring"""





	_lowercase :    Optional[Any]							=							prime_sieve(__UpperCAmelCase			)
	_lowercase :    int							=							0
	_lowercase :    List[Any]							=							0

	for i in range(len(__UpperCAmelCase			)			):
		for j in range(i + length     ,len(__UpperCAmelCase			)			):
			_lowercase :    List[Any]							=							sum(primes[i:j]			)
			if sol >= ceiling:
				break

			if sol in primes:
				_lowercase :    Optional[int]							=							j - i
				_lowercase :    Tuple							=							sol

	return largest


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



