





'''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  a__    (    lowercase :					Optional[Any], lowercase :					Any, lowercase :					List[str]=None				)		->		List[Any]:





     """simple docstring"""

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







def  a__    (    lowercase :					Union[str, Any], lowercase :					List[Any], lowercase :					Dict				)		->		Dict:





     """simple docstring"""

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

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







def  a__    (    lowercase :					str, lowercase :					Tuple, lowercase :					Optional[Any]				)		->		Optional[Any]:





     """simple docstring"""

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

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







def  a__    (    lowercase :					Tuple, lowercase :					Union[str, Any], lowercase :					Optional[int]				)		->		str:





     """simple docstring"""

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

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

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

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

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

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

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







def  a__    (    lowercase :					Any, lowercase :					Optional[Any], lowercase :					Optional[int]				)		->		Tuple:





     """simple docstring"""

     _UpperCamelCase       =	torch_model.reformer

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

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

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

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

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







def  a__    (    lowercase :					Dict, lowercase :					int, lowercase :					Dict				)		->		int:





     """simple docstring"""

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

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

     set_model_weights_in_torch(lowercase, lowercase, config.hidden_size				)

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


if __name__ == "__main__":
   lowercase__						:      Optional[Any]	=    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__						:      Union[str, Any]	=    parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''

def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

     def update_area_of_max_square(lowercase :					int, lowercase :					int				) -> int:
          # BASE CASE
          if row >= rows or col >= cols:
               return 0

          _UpperCamelCase       =	update_area_of_max_square(lowercase, col + 1				)
          _UpperCamelCase       =	update_area_of_max_square(row + 1, col + 1				)
          _UpperCamelCase       =	update_area_of_max_square(row + 1, lowercase				)

          if mat[row][col]:
               _UpperCamelCase       =	1 + min([right, diagonal, down]				)
               _UpperCamelCase       =	max(largest_square_area[0], lowercase				)
               return sub_problem_sol
          else:
               return 0

     _UpperCamelCase       =	[0]
     update_area_of_max_square(0, 0				)
     return largest_square_area[0]







def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

     def update_area_of_max_square_using_dp_array(
         lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				) -> int:
          if row >= rows or col >= cols:
               return 0
          if dp_array[row][col] != -1:
               return dp_array[row][col]

          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(lowercase, col + 1, lowercase				)
          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(row + 1, col + 1, lowercase				)
          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(row + 1, lowercase, lowercase				)

          if mat[row][col]:
               _UpperCamelCase       =	1 + min([right, diagonal, down]				)
               _UpperCamelCase       =	max(largest_square_area[0], lowercase				)
               _UpperCamelCase       =	sub_problem_sol
               return sub_problem_sol
          else:
               return 0

     _UpperCamelCase       =	[0]
     _UpperCamelCase       =	[[-1] * cols for _ in range(lowercase				)]
     update_area_of_max_square_using_dp_array(0, 0, lowercase				)

     return largest_square_area[0]







def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

     _UpperCamelCase       =	[[0] * (cols + 1) for _ in range(rows + 1				)]
     _UpperCamelCase       =	0
     for row in range(rows - 1, -1, -1				):
          for col in range(cols - 1, -1, -1				):
               _UpperCamelCase       =	dp_array[row][col + 1]
               _UpperCamelCase       =	dp_array[row + 1][col + 1]
               _UpperCamelCase       =	dp_array[row + 1][col]

               if mat[row][col] == 1:
                    _UpperCamelCase       =	1 + min(lowercase, lowercase, lowercase				)
                    _UpperCamelCase       =	max(dp_array[row][col], lowercase				)
               else:
                    _UpperCamelCase       =	0

     return largest_square_area







def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

     _UpperCamelCase       =	[0] * (cols + 1)
     _UpperCamelCase       =	[0] * (cols + 1)
     _UpperCamelCase       =	0
     for row in range(rows - 1, -1, -1				):
          for col in range(cols - 1, -1, -1				):
               _UpperCamelCase       =	current_row[col + 1]
               _UpperCamelCase       =	next_row[col + 1]
               _UpperCamelCase       =	next_row[col]

               if mat[row][col] == 1:
                    _UpperCamelCase       =	1 + min(lowercase, lowercase, lowercase				)
                    _UpperCamelCase       =	max(current_row[col], lowercase				)
               else:
                    _UpperCamelCase       =	0
          _UpperCamelCase       =	current_row

     return largest_square_area


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))



