




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      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: Union[str, Any]					,	UpperCamelCase__							: Union[str, Any]=None )	->			Tuple:





				"""simple docstring"""


				assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
				__lowerCamelCase									=       nn.Parameter(UpperCamelCase__ )
				if bias is not None:
								assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
								__lowerCamelCase									=       nn.Parameter(UpperCamelCase__ )







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[int]					,	UpperCamelCase__							: Tuple					,	UpperCamelCase__							: List[Any] )	->			Optional[int]:





				"""simple docstring"""


				__lowerCamelCase									=       np.asarray(weights[0] )
				__lowerCamelCase									=       np.asarray(weights[1] )
				__lowerCamelCase									=       np.asarray(weights[2] )

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







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: str					,	UpperCamelCase__							: str )	->			Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase									=       np.asarray(weights[0] )
				__lowerCamelCase									=       np.asarray(weights[1] )
				__lowerCamelCase									=       np.asarray(weights[2] )
				__lowerCamelCase									=       np.asarray(weights[3] )

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







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[int]					,	UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: Any )	->			Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase									=       weights[0][0][0]
				__lowerCamelCase									=       np.asarray(layer_norm_a[0] )
				__lowerCamelCase									=       np.asarray(layer_norm_a[1] )
				set_param(
				    torch_block.attention.layer_norm					,	torch.tensor(UpperCamelCase__ )					,	torch.tensor(UpperCamelCase__ )					,	)

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

				# intermediate weighs
				__lowerCamelCase									=       weights[2][0][1][2]

				# Chunked Feed Forward
				if len(UpperCamelCase__ ) == 4:
								__lowerCamelCase									=       intermediate_weights[2]

				# layernorm 2
				__lowerCamelCase									=       np.asarray(intermediate_weights[0][0] )
				__lowerCamelCase									=       np.asarray(intermediate_weights[0][1] )
				set_param(
				    torch_block.feed_forward.layer_norm					,	torch.tensor(UpperCamelCase__ )					,	torch.tensor(UpperCamelCase__ )					,	)

				# intermediate dense
				__lowerCamelCase									=       np.asarray(intermediate_weights[1][0] )
				__lowerCamelCase									=       np.asarray(intermediate_weights[1][1] )
				set_param(
				    torch_block.feed_forward.dense.dense					,	torch.tensor(UpperCamelCase__ ).transpose(0					,	1 ).contiguous()					,	torch.tensor(UpperCamelCase__ )					,	)

				# intermediate out
				__lowerCamelCase									=       np.asarray(intermediate_weights[4][0] )
				__lowerCamelCase									=       np.asarray(intermediate_weights[4][1] )
				set_param(
				    torch_block.feed_forward.output.dense					,	torch.tensor(UpperCamelCase__ ).transpose(0					,	1 ).contiguous()					,	torch.tensor(UpperCamelCase__ )					,	)







def      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: Tuple					,	UpperCamelCase__							: Optional[int] )	->			int:





				"""simple docstring"""


				__lowerCamelCase									=       torch_model.reformer

				# word embeds
				__lowerCamelCase									=       np.asarray(weights[1] )
				set_param(
				    torch_model_reformer.embeddings.word_embeddings					,	torch.tensor(UpperCamelCase__ )					,	)

				if isinstance(weights[3]					,	UpperCamelCase__ ):
								__lowerCamelCase									=       torch_model_reformer.embeddings.position_embeddings
								for emb_idx in range(len(position_embeddings.weights ) ):
												__lowerCamelCase									=       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"""
												__lowerCamelCase									=       nn.Parameter(torch.tensor(UpperCamelCase__ ) )

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

				# output layer norm
				__lowerCamelCase									=       np.asarray(weights[7][0] )
				__lowerCamelCase									=       np.asarray(weights[7][1] )
				set_param(
				    torch_model_reformer.encoder.layer_norm					,	torch.tensor(UpperCamelCase__ )					,	torch.tensor(UpperCamelCase__ )					,	)

				# output embeddings
				__lowerCamelCase									=       np.asarray(weights[9][0] )
				__lowerCamelCase									=       np.asarray(weights[9][1] )
				set_param(
				    torch_model.lm_head.decoder					,	torch.tensor(UpperCamelCase__ ).transpose(0					,	1 ).contiguous()					,	torch.tensor(UpperCamelCase__ )					,	)







def      lowerCamelCase_						(			UpperCamelCase__							: Union[str, Any]					,	UpperCamelCase__							: List[str]					,	UpperCamelCase__							: int )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       ReformerConfig.from_json_file(UpperCamelCase__ )
				print(F"""Building PyTorch model from configuration: {config}""" )
				__lowerCamelCase									=       ReformerModelWithLMHead(UpperCamelCase__ )

				with open(UpperCamelCase__					,	'rb' ) as f:
								__lowerCamelCase									=       pickle.load(UpperCamelCase__ )['weights']

				set_model_weights_in_torch(UpperCamelCase__					,	UpperCamelCase__					,	config.hidden_size )

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


if __name__ == "__main__":
					__A								=       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								=       parser.parse_args()
					convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)








def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			list:





				"""simple docstring"""


				if n_term == "":
								return []
				__lowerCamelCase									=       []
				for temp in range(int(UpperCamelCase__ ) ):
								series.append(F"""1/{temp + 1}""" if series else '1' )
				return series


if __name__ == "__main__":
					__A								=       input("Enter the last number (nth term) of the Harmonic Series")
					print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
					print(harmonic_series(nth_term))


