


import math


def       _snake_case     (  A				,					A )		->					Union[str, Any]:
							lowerCAmelCase__      =			len(__lowercase )
							lowerCAmelCase__      =			int(math.floor(math.sqrt(__lowercase ) ) )
							lowerCAmelCase__      =			0
							while arr[min(__lowercase				,					__lowercase ) - 1] < x:
														lowerCAmelCase__      =			step
														step += int(math.floor(math.sqrt(__lowercase ) ) )
														if prev >= n:
																					return -1

							while arr[prev] < x:
														lowerCAmelCase__      =			prev + 1
														if prev == min(__lowercase				,					__lowercase ):
																					return -1
							if arr[prev] == x:
														return prev
							return -1


if __name__ == "__main__":
		__UpperCAmelCase					       =							input('''Enter numbers separated by a comma:\n''').strip()
		__UpperCAmelCase					       =							[int(item) for item in user_input.split(''',''')]
		__UpperCAmelCase					       =							int(input('''Enter the number to be searched:\n'''))
		__UpperCAmelCase					       =							jump_search(arr, x)
		if res == -1:
				print('''Number not found!''')
		else:
				print(f"""Number {x} is at index {res}""")



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

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


def       _snake_case     (  A				,					A				,					A				,					A				,					A )		->					Optional[Any]:
							for attribute in key.split('''.''' ):
														lowerCAmelCase__      =			getattr(A				,					A )

							if weight_type is not None:
														lowerCAmelCase__      =			getattr(A				,					A ).shape
							else:
														lowerCAmelCase__      =			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__      =			value
							elif weight_type == "weight_g":
														lowerCAmelCase__      =			value
							elif weight_type == "weight_v":
														lowerCAmelCase__      =			value
							elif weight_type == "bias":
														lowerCAmelCase__      =			value
							elif weight_type == "running_mean":
														lowerCAmelCase__      =			value
							elif weight_type == "running_var":
														lowerCAmelCase__      =			value
							elif weight_type == "num_batches_tracked":
														lowerCAmelCase__      =			value
							elif weight_type == "inv_freq":
														lowerCAmelCase__      =			value
							else:
														lowerCAmelCase__      =			value

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


def       _snake_case     (  A				,					A				,					A )		->					Any:
							lowerCAmelCase__      =			[]
							lowerCAmelCase__      =			fairseq_model.state_dict()

							lowerCAmelCase__      =			hf_model.wavaveca_conformer.feature_extractor

							for name, value in fairseq_dict.items():
														lowerCAmelCase__      =			False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A				,					A				,					A				,					A				,					hf_model.config.feat_extract_norm == '''group'''				,					)
																					lowerCAmelCase__      =			True
														else:
																					for key, mapped_key in MAPPING.items():
																												lowerCAmelCase__      =			'''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__      =			True
																																			if "*" in mapped_key:
																																										lowerCAmelCase__      =			name.split(A )[0].split('''.''' )[-2]
																																										lowerCAmelCase__      =			mapped_key.replace('''*'''				,					A )
																																			if "pos_bias_u" in name:
																																										lowerCAmelCase__      =			None
																																			elif "pos_bias_v" in name:
																																										lowerCAmelCase__      =			None
																																			elif "weight_g" in name:
																																										lowerCAmelCase__      =			'''weight_g'''
																																			elif "weight_v" in name:
																																										lowerCAmelCase__      =			'''weight_v'''
																																			elif "bias" in name:
																																										lowerCAmelCase__      =			'''bias'''
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										lowerCAmelCase__      =			'''weight'''
																																			elif "running_mean" in name:
																																										lowerCAmelCase__      =			'''running_mean'''
																																			elif "inv_freq" in name:
																																										lowerCAmelCase__      =			'''inv_freq'''
																																			elif "running_var" in name:
																																										lowerCAmelCase__      =			'''running_var'''
																																			elif "num_batches_tracked" in name:
																																										lowerCAmelCase__      =			'''num_batches_tracked'''
																																			else:
																																										lowerCAmelCase__      =			None
																																			set_recursively(A				,					A				,					A				,					A				,					A )
																												continue
														if not is_used:
																					unused_weights.append(A )

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


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


@torch.no_grad()
def       _snake_case     (  A				,					A				,					A=None				,					A=None				,					A=True )		->					Optional[int]:
							if config_path is not None:
														lowerCAmelCase__      =			WavaVecaConformerConfig.from_pretrained(A				,					hidden_act='''swish''' )
							else:
														lowerCAmelCase__      =			WavaVecaConformerConfig()

							if "rope" in checkpoint_path:
														lowerCAmelCase__      =			'''rotary'''

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

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					lowerCAmelCase__      =			target_dict.pad_index
																					lowerCAmelCase__      =			target_dict.bos_index
																					lowerCAmelCase__      =			target_dict.eos_index
																					lowerCAmelCase__      =			len(target_dict.symbols )
																					lowerCAmelCase__      =			os.path.join(A				,					'''vocab.json''' )
																					if not os.path.isdir(A ):
																												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(A ) )
																												return
																					os.makedirs(A				,					exist_ok=A )
																					lowerCAmelCase__      =			target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					lowerCAmelCase__      =			0
																					lowerCAmelCase__      =			1
																					with open(A				,					'''w'''				,					encoding='''utf-8''' ) as vocab_handle:
																												json.dump(A				,					A )
																					lowerCAmelCase__      =			WavaVecaCTCTokenizer(
																					    A				,					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=A				,					)
																					lowerCAmelCase__      =			True if config.feat_extract_norm == '''layer''' else False
																					lowerCAmelCase__      =			WavaVecaFeatureExtractor(
																					    feature_size=1				,					sampling_rate=16000				,					padding_value=0				,					do_normalize=A				,					return_attention_mask=A				,					)
																					lowerCAmelCase__      =			WavaVecaProcessor(feature_extractor=A				,					tokenizer=A )
																					processor.save_pretrained(A )

														lowerCAmelCase__      =			WavaVecaConformerForCTC(A )
							else:
														lowerCAmelCase__      =			WavaVecaConformerForPreTraining(A )

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

														lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__      =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,					task=A )

							lowerCAmelCase__      =			model[0].eval()

							recursively_load_weights(A				,					A				,					not is_finetuned )

							hf_wavavec.save_pretrained(A )


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