





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()
snake_case_							=			logging.get_logger(__name__)

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',
}
snake_case_							=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     List[str]:

		for attribute in key.split('''.'''		):
				lowerCamelCase   : Optional[int]								=getattr(lowerCamelCase_							,       lowerCamelCase_		)

		if weight_type is not None:
				lowerCamelCase   : int								=getattr(lowerCamelCase_							,       lowerCamelCase_		).shape
		else:
				lowerCamelCase   : Optional[Any]								=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":
				lowerCamelCase   : List[str]								=value
		elif weight_type == "weight_g":
				lowerCamelCase   : List[Any]								=value
		elif weight_type == "weight_v":
				lowerCamelCase   : Tuple								=value
		elif weight_type == "bias":
				lowerCamelCase   : Optional[Any]								=value
		elif weight_type == "running_mean":
				lowerCamelCase   : List[Any]								=value
		elif weight_type == "running_var":
				lowerCamelCase   : Union[str, Any]								=value
		elif weight_type == "num_batches_tracked":
				lowerCamelCase   : List[Any]								=value
		elif weight_type == "inv_freq":
				lowerCamelCase   : Any								=value
		else:
				lowerCamelCase   : str								=value

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


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Optional[int]:
		lowerCamelCase   : Tuple								=[]
		lowerCamelCase   : Optional[int]								=fairseq_model.state_dict()

		lowerCamelCase   : Tuple								=hf_model.wavaveca_conformer.feature_extractor

		for name, value in fairseq_dict.items():
				lowerCamelCase   : Any								=False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCamelCase_							,       lowerCamelCase_							,       lowerCamelCase_							,       lowerCamelCase_							,       hf_model.config.feat_extract_norm == '''group'''							,       )
						lowerCamelCase   : Dict								=True
				else:
						for key, mapped_key in MAPPING.items():
								lowerCamelCase   : Dict								='''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]:
										lowerCamelCase   : Optional[int]								=True
										if "*" in mapped_key:
												lowerCamelCase   : List[Any]								=name.split(lowerCamelCase_		)[0].split('''.'''		)[-2]
												lowerCamelCase   : List[str]								=mapped_key.replace('''*'''							,       lowerCamelCase_		)
										if "pos_bias_u" in name:
												lowerCamelCase   : Union[str, Any]								=None
										elif "pos_bias_v" in name:
												lowerCamelCase   : str								=None
										elif "weight_g" in name:
												lowerCamelCase   : Dict								='''weight_g'''
										elif "weight_v" in name:
												lowerCamelCase   : str								='''weight_v'''
										elif "bias" in name:
												lowerCamelCase   : List[str]								='''bias'''
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowerCamelCase   : List[str]								='''weight'''
										elif "running_mean" in name:
												lowerCamelCase   : Any								='''running_mean'''
										elif "inv_freq" in name:
												lowerCamelCase   : Union[str, Any]								='''inv_freq'''
										elif "running_var" in name:
												lowerCamelCase   : Optional[int]								='''running_var'''
										elif "num_batches_tracked" in name:
												lowerCamelCase   : Tuple								='''num_batches_tracked'''
										else:
												lowerCamelCase   : str								=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 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     List[Any]:
		lowerCamelCase   : Optional[int]								=full_name.split('''conv_layers.'''		)[-1]
		lowerCamelCase   : Optional[int]								=name.split('''.'''		)
		lowerCamelCase   : Optional[int]								=int(items[0]		)
		lowerCamelCase   : Optional[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."		)
						lowerCamelCase   : Any								=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."		)
						lowerCamelCase   : Optional[Any]								=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."		)
						lowerCamelCase   : Dict								=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."		)
						lowerCamelCase   : Tuple								=value
						logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
		else:
				unused_weights.append(lowerCamelCase_		)


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

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

		if "rope" in checkpoint_path:
				lowerCamelCase   : Tuple								='''rotary'''

		if is_finetuned:
				if dict_path:
						lowerCamelCase   : Union[str, Any]								=Dictionary.load(lowerCamelCase_		)

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

						# fairseq has the <pad> and <s> switched
						lowerCamelCase   : str								=0
						lowerCamelCase   : Tuple								=1
						with open(lowerCamelCase_							,       '''w'''							,       encoding='''utf-8'''		) as vocab_handle:
								json.dump(lowerCamelCase_							,       lowerCamelCase_		)
						lowerCamelCase   : List[Any]								=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_							,       )
						lowerCamelCase   : Optional[int]								=True if config.feat_extract_norm == '''layer''' else False
						lowerCamelCase   : Union[str, Any]								=WavaVecaFeatureExtractor(
						    feature_size=1							,       sampling_rate=1_6_0_0_0							,       padding_value=0							,       do_normalize=lowerCamelCase_							,       return_attention_mask=lowerCamelCase_							,       )
						lowerCamelCase   : str								=WavaVecaProcessor(feature_extractor=lowerCamelCase_							,       tokenizer=lowerCamelCase_		)
						processor.save_pretrained(lowerCamelCase_		)

				lowerCamelCase   : Union[str, Any]								=WavaVecaConformerForCTC(lowerCamelCase_		)
		else:
				lowerCamelCase   : str								=WavaVecaConformerForPreTraining(lowerCamelCase_		)

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

				lowerCamelCase   : List[Any]								=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,       task=lowerCamelCase_		)

		lowerCamelCase   : List[str]								=model[0].eval()

		recursively_load_weights(lowerCamelCase_							,       lowerCamelCase_							,       not is_finetuned		)

		hf_wavavec.save_pretrained(lowerCamelCase_		)


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








def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = False		)   ->     bool:
		if n == 2:
				return True
		if not n % 2 or n < 2:
				return False
		if n > 5 and n % 1_0 not in (1, 3, 7, 9):  # can quickly check last digit
				return False
		if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
				raise ValueError(
				    '''Warning: upper bound of deterministic test is exceeded. '''
				    '''Pass allow_probable=True to allow probabilistic test. '''
				    '''A return value of True indicates a probable prime.'''		)
		# array bounds provided by analysis
		lowerCamelCase   : Optional[Any]								=[
		    2_0_4_7,
		    1_3_7_3_6_5_3,
		    2_5_3_2_6_0_0_1,
		    3_2_1_5_0_3_1_7_5_1,
		    2_1_5_2_3_0_2_8_9_8_7_4_7,
		    3_4_7_4_7_4_9_6_6_0_3_8_3,
		    3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
		    1,
		    3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
		    1,
		    1,
		    3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
		    3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
		]

		lowerCamelCase   : Dict								=[2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
		for idx, _p in enumerate(SCREAMING_SNAKE_CASE_							,       1		):
				if n < _p:
						# then we have our last prime to check
						lowerCamelCase   : Any								=primes[:idx]
						break
		lowerCamelCase		,   lowerCamelCase   : Any								=n - 1, 0
		# break up n -1 into a power of 2 (s) and
		# remaining odd component
		# essentially, solve for d * 2 ** s == n - 1
		while d % 2 == 0:
				d //= 2
				s += 1
		for prime in plist:
				lowerCamelCase   : Union[str, Any]								=False
				for r in range(SCREAMING_SNAKE_CASE_		):
						lowerCamelCase   : List[str]								=pow(SCREAMING_SNAKE_CASE_							,       d * 2**r							,       SCREAMING_SNAKE_CASE_		)
						# see article for analysis explanation for m
						if (r == 0 and m == 1) or ((m + 1) % n == 0):
								lowerCamelCase   : List[str]								=True
								# this loop will not determine compositeness
								break
				if pr:
						continue
				# if pr is False, then the above loop never evaluated to true,
				# and the n MUST be composite
				return False
		return True


def 					A__	(       )   ->     None:
		assert not miller_rabin(5_6_1		)
		assert miller_rabin(5_6_3		)
		# 2047

		assert not miller_rabin(8_3_8_2_0_1		)
		assert miller_rabin(8_3_8_2_0_7		)
		# 1_373_653

		assert not miller_rabin(1_7_3_1_6_0_0_1		)
		assert miller_rabin(1_7_3_1_6_0_1_7		)
		# 25_326_001

		assert not miller_rabin(3_0_7_8_3_8_6_6_4_1		)
		assert miller_rabin(3_0_7_8_3_8_6_6_5_3		)
		# 3_215_031_751

		assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1		)
		assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9		)
		# 2_152_302_898_747

		assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7		)
		assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7		)
		# 3_474_749_660_383

		assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1		)
		assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7		)
		# 341_550_071_728_321

		assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1		)
		assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1		)
		# 3_825_123_056_546_413_051

		assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7		)
		assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1		)
		# 318_665_857_834_031_151_167_461

		assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3		)
		assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9		)
		# 3_317_044_064_679_887_385_961_981
		# upper limit for probabilistic test


if __name__ == "__main__":
				test_miller_rabin()

