




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 		_A(       UpperCamelCase__							:			Dict				,       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Optional[int]=None				)    -> Optional[Any]:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			List[str]				,       UpperCamelCase__							:			List[str]				,       UpperCamelCase__							:			int				)    -> Optional[int]:
							'''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(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 		_A(       UpperCamelCase__							:			Dict				,       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			str				)    -> Optional[Any]:
							'''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(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 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			List[Any]				)    -> Tuple:
							'''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(UpperCamelCase__				)				,       torch.tensor(UpperCamelCase__				)				,       )

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

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

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

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







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			str				,       UpperCamelCase__							:			List[str]				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    torch_model.reformer

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

							if isinstance(weights[3]				,       UpperCamelCase__				):
														__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(UpperCamelCase__				)				)

							__lowercase      =    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				):
														__lowercase      =    trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				)

							# 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(UpperCamelCase__				)				,       torch.tensor(UpperCamelCase__				)				,       )

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







def 		_A(       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			str				,       UpperCamelCase__							:			str				)    -> List[str]:
							'''simple docstring'''


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

							with open(UpperCamelCase__				,       '''rb'''				) as f:
														__lowercase      =    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__":
			UpperCAmelCase__				     = 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."
			)
			UpperCAmelCase__				     = parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)












import os







def 		_A(       UpperCamelCase__							:			str = "matrix.txt"				)    -> int:
							'''simple docstring'''


							with open(os.path.join(os.path.dirname(UpperCamelCase__				)				,       UpperCamelCase__				)				) as in_file:
														__lowercase      =    in_file.read()

							__lowercase      =    [[int(UpperCamelCase__				) for cell in row.split(''','''				)] for row in data.strip().splitlines()]
							__lowercase      =    [[0 for cell in row] for row in grid]
							__lowercase      =    len(grid[0]				)

							__lowercase      =    [[0 for i in range(UpperCamelCase__				)] for j in range(UpperCamelCase__				)]
							__lowercase      =    grid[0][0]
							for i in range(1				,       UpperCamelCase__				):
														__lowercase      =    grid[0][i] + dp[0][i - 1]
							for i in range(1				,       UpperCamelCase__				):
														__lowercase      =    grid[i][0] + dp[i - 1][0]

							for i in range(1				,       UpperCamelCase__				):
														for j in range(1				,       UpperCamelCase__				):
																					__lowercase      =    grid[i][j] + min(dp[i - 1][j]				,       dp[i][j - 1]				)

							return dp[-1][-1]


if __name__ == "__main__":
			print(F"""{solution() = }""")






