
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__				,    lowerCAmelCase__				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					# set parameter of one layer
					assert torch_layer.weight.shape == weight.shape, f'{torch_layer} layer.weight does not match'
					lowercase					=		nn.Parameter(lowerCamelCase__	)
					if bias is not None:
										assert torch_layer.bias.shape == bias.shape, f'{torch_layer} layer.bias does not match'
										lowercase					=		nn.Parameter(lowerCamelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					# set torch weights for 1-to-1 comparison
					lowercase					=		np.asarray(weights[0]	)
					lowercase					=		np.asarray(weights[1]	)
					lowercase					=		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__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					# set torch weights for 1-to-1 comparison
					lowercase					=		np.asarray(weights[0]	)
					lowercase					=		np.asarray(weights[1]	)
					lowercase					=		np.asarray(weights[2]	)
					lowercase					=		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__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					# layernorm 1
					lowercase					=		weights[0][0][0]
					lowercase					=		np.asarray(layer_norm_a[0]	)
					lowercase					=		np.asarray(layer_norm_a[1]	)
					set_param(
					    torch_block.attention.layer_norm				,    torch.tensor(lowerCamelCase__	)				,    torch.tensor(lowerCamelCase__	)				,    )

					# lsh weights + output
					lowercase					=		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
					lowercase					=		weights[2][0][1][2]

					# Chunked Feed Forward
					if len(lowerCamelCase__	) == 4:
										lowercase					=		intermediate_weights[2]

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

					# intermediate dense
					lowercase					=		np.asarray(intermediate_weights[1][0]	)
					lowercase					=		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
					lowercase					=		np.asarray(intermediate_weights[4][0]	)
					lowercase					=		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__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					# reformer model
					lowercase					=		torch_model.reformer

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

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

					lowercase					=		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	):
										lowercase					=		trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
										set_block_weights_in_torch(lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__	)

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

					# output embeddings
					lowercase					=		np.asarray(weights[9][0]	)
					lowercase					=		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__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					# Initialise PyTorch model
					lowercase					=		ReformerConfig.from_json_file(lowerCamelCase__	)
					print(f'Building PyTorch model from configuration: {config}'	)
					lowercase					=		ReformerModelWithLMHead(lowerCamelCase__	)

					with open(lowerCamelCase__				,    '''rb'''	) as f:
										lowercase					=		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__":
							lowercase__	:Tuple										=					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."
							)
							lowercase__	:Optional[int]										=					parser.parse_args()
							convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






from __future__ import annotations



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if len(lowerCAmelCase__	) < 2:
										raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''	)
					if any(i <= 0 for i in nums	):
										raise ValueError('''All values must be greater than 0'''	)
					lowercase					=		nums.copy()
					copy_nums.sort()
					return copy_nums[-1] < sum(copy_nums[:-1]	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




