
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  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__=None						):







				'''simple docstring'''


				assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
				lowercase         =      nn.Parameter(__magic_name__						)
				if bias is not None:
								assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
								lowercase         =      nn.Parameter(__magic_name__						)

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


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

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


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

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				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(__magic_name__						)		,    torch.tensor(__magic_name__						)		,    )

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

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

				# Chunked Feed Forward
				if len(__magic_name__						) == 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(__magic_name__						)		,    torch.tensor(__magic_name__						)		,    )

				# 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(__magic_name__						).transpose(0		,    1						).contiguous()		,    torch.tensor(__magic_name__						)		,    )

				# 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(__magic_name__						).transpose(0		,    1						).contiguous()		,    torch.tensor(__magic_name__						)		,    )

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      torch_model.reformer

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

				if isinstance(weights[3]		,    __magic_name__						):
								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(__magic_name__						)						)

				lowercase         =      weights[5]
				assert len(torch_model_reformer.encoder.layers						) * 4 == len(
				    __magic_name__						), "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(__magic_name__		,    __magic_name__		,    __magic_name__						)

				# 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(__magic_name__						)		,    torch.tensor(__magic_name__						)		,    )

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

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      ReformerConfig.from_json_file(__magic_name__						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				lowercase         =      ReformerModelWithLMHead(__magic_name__						)

				with open(__magic_name__		,    "rb"						) as f:
								lowercase         =      pickle.load(__magic_name__						)["weights"]

				set_model_weights_in_torch(__magic_name__		,    __magic_name__		,    config.hidden_size						)

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


if __name__ == "__main__":
						_snake_case      :							List[str]     	=    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."
						)
						_snake_case      :							List[Any]     	=    parser.parse_args()
						convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)


def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      set()
				# To detect a back edge, keep track of vertices currently in the recursion stack
				lowercase         =      set()
				return any(
				    node not in visited and depth_first_search(__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						)
				    for node in graph						)

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				visited.add(__magic_name__						)
				rec_stk.add(__magic_name__						)

				for node in graph[vertex]:
								if node not in visited:
												if depth_first_search(__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):
																return True
								elif node in rec_stk:
												return True

    # The node needs to be removed from recursion stack before function ends
				rec_stk.remove(__magic_name__						)
				return False


if __name__ == "__main__":
						from doctest import testmod

						testmod()
