


'''simple docstring'''






import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()





def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :int				,      lowerCamelCase_  :Tuple=None ):







		'''simple docstring'''
		# set parameter of one layer
		assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
		snake_case_		:							Optional[Any]			       =						nn.Parameter(lowerCamelCase_ )
		if bias is not None:
				assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
				snake_case_		:							Tuple			       =						nn.Parameter(lowerCamelCase_ )





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :int				,      lowerCamelCase_  :Dict ):







		'''simple docstring'''
		# set torch weights for 1-to-1 comparison
		snake_case_		:							Optional[int]			       =						np.asarray(weights[0] )
		snake_case_		:							int			       =						np.asarray(weights[1] )
		snake_case_		:							Tuple			       =						np.asarray(weights[2] )

		set_param(
		    torch_layer.self_attention.query_key				,      torch.tensor(lowerCamelCase_ ).transpose(1				,      2 ).contiguous().view(-1				,      lowerCamelCase_ )				,      )
		set_param(
		    torch_layer.self_attention.value				,      torch.tensor(lowerCamelCase_ ).transpose(1				,      2 ).contiguous().view(-1				,      lowerCamelCase_ )				,      )
		set_param(
		    torch_layer.output.dense				,      torch.tensor(lowerCamelCase_ ).view(-1				,      lowerCamelCase_ ).contiguous().transpose(0				,      1 )				,      )





def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		# set torch weights for 1-to-1 comparison
		snake_case_		:							Dict			       =						np.asarray(weights[0] )
		snake_case_		:							Optional[int]			       =						np.asarray(weights[1] )
		snake_case_		:							str			       =						np.asarray(weights[2] )
		snake_case_		:							List[Any]			       =						np.asarray(weights[3] )

		set_param(
		    torch_layer.self_attention.query				,      torch.tensor(lowerCamelCase_ ).transpose(1				,      2 ).contiguous().view(-1				,      lowerCamelCase_ )				,      )
		set_param(
		    torch_layer.self_attention.key				,      torch.tensor(lowerCamelCase_ ).transpose(1				,      2 ).contiguous().view(-1				,      lowerCamelCase_ )				,      )
		set_param(
		    torch_layer.self_attention.value				,      torch.tensor(lowerCamelCase_ ).transpose(1				,      2 ).contiguous().view(-1				,      lowerCamelCase_ )				,      )
		set_param(
		    torch_layer.output.dense				,      torch.tensor(lowerCamelCase_ ).view(-1				,      lowerCamelCase_ ).contiguous().transpose(0				,      1 )				,      )





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		# layernorm 1
		snake_case_		:							int			       =						weights[0][0][0]
		snake_case_		:							Tuple			       =						np.asarray(layer_norm_a[0] )
		snake_case_		:							Tuple			       =						np.asarray(layer_norm_a[1] )
		set_param(
		    torch_block.attention.layer_norm				,      torch.tensor(lowerCamelCase_ )				,      torch.tensor(lowerCamelCase_ )				,      )

		# lsh weights + output
		snake_case_		:							List[Any]			       =						weights[0][1]
		if len(lowerCamelCase_ ) < 4:
				set_layer_weights_in_torch_lsh(lowerCamelCase_				,      torch_block.attention				,      lowerCamelCase_ )
		else:
				set_layer_weights_in_torch_local(lowerCamelCase_				,      torch_block.attention				,      lowerCamelCase_ )

		# intermediate weighs
		snake_case_		:							str			       =						weights[2][0][1][2]

		# Chunked Feed Forward
		if len(lowerCamelCase_ ) == 4:
				snake_case_		:							Tuple			       =						intermediate_weights[2]

		# layernorm 2
		snake_case_		:							List[Any]			       =						np.asarray(intermediate_weights[0][0] )
		snake_case_		:							List[Any]			       =						np.asarray(intermediate_weights[0][1] )
		set_param(
		    torch_block.feed_forward.layer_norm				,      torch.tensor(lowerCamelCase_ )				,      torch.tensor(lowerCamelCase_ )				,      )

		# intermediate dense
		snake_case_		:							List[str]			       =						np.asarray(intermediate_weights[1][0] )
		snake_case_		:							Optional[int]			       =						np.asarray(intermediate_weights[1][1] )
		set_param(
		    torch_block.feed_forward.dense.dense				,      torch.tensor(lowerCamelCase_ ).transpose(0				,      1 ).contiguous()				,      torch.tensor(lowerCamelCase_ )				,      )

		# intermediate out
		snake_case_		:							Union[str, Any]			       =						np.asarray(intermediate_weights[4][0] )
		snake_case_		:							Optional[int]			       =						np.asarray(intermediate_weights[4][1] )
		set_param(
		    torch_block.feed_forward.output.dense				,      torch.tensor(lowerCamelCase_ ).transpose(0				,      1 ).contiguous()				,      torch.tensor(lowerCamelCase_ )				,      )





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Optional[Any] ):







		'''simple docstring'''
		# reformer model
		snake_case_		:							int			       =						torch_model.reformer

		# word embeds
		snake_case_		:							int			       =						np.asarray(weights[1] )
		set_param(
		    torch_model_reformer.embeddings.word_embeddings				,      torch.tensor(lowerCamelCase_ )				,      )

		if isinstance(weights[3]				,      lowerCamelCase_ ):
				snake_case_		:							Tuple			       =						torch_model_reformer.embeddings.position_embeddings
				for emb_idx in range(len(position_embeddings.weights ) ):
						snake_case_		:							Any			       =						np.asarray(weights[3][emb_idx][0] )
						assert (
						    position_embeddings.weights[emb_idx].shape == emb_weights.shape
						), F'''{position_embeddings[emb_idx]} emb does not match'''
						snake_case_		:							List[Any]			       =						nn.Parameter(torch.tensor(lowerCamelCase_ ) )

		snake_case_		:							Dict			       =						weights[5]
		assert len(torch_model_reformer.encoder.layers ) * 4 == len(
		    lowerCamelCase_ ), "HF and trax model do not have the same number of layers"
		for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
				snake_case_		:							int			       =						trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
				set_block_weights_in_torch(lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ )

		# output layer norm
		snake_case_		:							str			       =						np.asarray(weights[7][0] )
		snake_case_		:							str			       =						np.asarray(weights[7][1] )
		set_param(
		    torch_model_reformer.encoder.layer_norm				,      torch.tensor(lowerCamelCase_ )				,      torch.tensor(lowerCamelCase_ )				,      )

		# output embeddings
		snake_case_		:							int			       =						np.asarray(weights[9][0] )
		snake_case_		:							Dict			       =						np.asarray(weights[9][1] )
		set_param(
		    torch_model.lm_head.decoder				,      torch.tensor(lowerCamelCase_ ).transpose(0				,      1 ).contiguous()				,      torch.tensor(lowerCamelCase_ )				,      )





def      UpperCAmelCase      (		lowerCamelCase_  :str				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		# Initialise PyTorch model
		snake_case_		:							Any			       =						ReformerConfig.from_json_file(lowerCamelCase_ )
		print(F'''Building PyTorch model from configuration: {config}''' )
		snake_case_		:							int			       =						ReformerModelWithLMHead(lowerCamelCase_ )

		with open(lowerCamelCase_				,      """rb""" ) as f:
				snake_case_		:							Tuple			       =						pickle.load(lowerCamelCase_ )["""weights"""]

		set_model_weights_in_torch(lowerCamelCase_				,      lowerCamelCase_				,      config.hidden_size )

		# Save pytorch-model
		print(F'''Save PyTorch model to {pytorch_dump_path}''' )
		torch.save(model.state_dict()				,      lowerCamelCase_ )


if __name__ == "__main__":
							__A	:	Dict     =     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained Reformer model. \n'
							        'This specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							__A	:	Tuple     =     parser.parse_args()
							convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



'''simple docstring'''






import math





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						len(lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
		snake_case_		:							str			       =						0
		while arr[min(lowerCamelCase_				,      lowerCamelCase_ ) - 1] < x:
				snake_case_		:							Any			       =						step
				step += int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
				if prev >= n:
						return -1

		while arr[prev] < x:
				snake_case_		:							Optional[Any]			       =						prev + 1
				if prev == min(lowerCamelCase_				,      lowerCamelCase_ ):
						return -1
		if arr[prev] == x:
				return prev
		return -1


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