




'''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        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase=None				):
     # set parameter of one layer
     assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
     snake_case			:      Tuple        =			nn.Parameter(lowerCamelCase__				)
     if bias is not None:
          assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
          snake_case			:      List[str]        =			nn.Parameter(lowerCamelCase__				)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     # set torch weights for 1-to-1 comparison
     snake_case			:      int        =			np.asarray(weights[0]				)
     snake_case			:      Dict        =			np.asarray(weights[1]				)
     snake_case			:      int        =			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        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     # set torch weights for 1-to-1 comparison
     snake_case			:      str        =			np.asarray(weights[0]				)
     snake_case			:      Any        =			np.asarray(weights[1]				)
     snake_case			:      Tuple        =			np.asarray(weights[2]				)
     snake_case			:      int        =			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        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     # layernorm 1
     snake_case			:      Optional[int]        =			weights[0][0][0]
     snake_case			:      Union[str, Any]        =			np.asarray(layer_norm_a[0]				)
     snake_case			:      Optional[int]        =			np.asarray(layer_norm_a[1]				)
     set_param(
         torch_block.attention.layer_norm     ,   torch.tensor(lowerCamelCase__				)     ,   torch.tensor(lowerCamelCase__				)     ,   )

     # lsh weights + output
     snake_case			:      int        =			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
     snake_case			:      Union[str, Any]        =			weights[2][0][1][2]

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

     # layernorm 2
     snake_case			:      Union[str, Any]        =			np.asarray(intermediate_weights[0][0]				)
     snake_case			:      int        =			np.asarray(intermediate_weights[0][1]				)
     set_param(
         torch_block.feed_forward.layer_norm     ,   torch.tensor(lowerCamelCase__				)     ,   torch.tensor(lowerCamelCase__				)     ,   )

     # intermediate dense
     snake_case			:      Optional[Any]        =			np.asarray(intermediate_weights[1][0]				)
     snake_case			:      Any        =			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
     snake_case			:      Dict        =			np.asarray(intermediate_weights[4][0]				)
     snake_case			:      Dict        =			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        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     # reformer model
     snake_case			:      List[str]        =			torch_model.reformer

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

     if isinstance(weights[3]     ,   lowerCamelCase__				):
          snake_case			:      Dict        =			torch_model_reformer.embeddings.position_embeddings
          for emb_idx in range(len(position_embeddings.weights				)				):
               snake_case			:      Optional[int]        =			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'''
               snake_case			:      List[Any]        =			nn.Parameter(torch.tensor(lowerCamelCase__				)				)

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

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

     # output embeddings
     snake_case			:      Tuple        =			np.asarray(weights[9][0]				)
     snake_case			:      List[Any]        =			np.asarray(weights[9][1]				)
     set_param(
         torch_model.lm_head.decoder     ,   torch.tensor(lowerCamelCase__				).transpose(0     ,   1				).contiguous()     ,   torch.tensor(lowerCamelCase__				)     ,   )



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     # Initialise PyTorch model
     snake_case			:      str        =			ReformerConfig.from_json_file(lowerCamelCase__				)
     print(F'''Building PyTorch model from configuration: {config}'''				)
     snake_case			:      List[str]        =			ReformerModelWithLMHead(lowerCamelCase__				)

     with open(lowerCamelCase__     ,   """rb"""				) as f:
          snake_case			:      List[str]        =			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      :   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."""
      )
      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    _lowerCamelCase   (     lowerCamelCase__	:							Optional[Any]  ):
 lowercase__       :       List[str]				=						len(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						sum(lowerCamelCase__  )

 lowercase__       :       Optional[int]				=						[[False for x in range(s + 1  )] for y in range(n + 1  )]

 for i in range(1       ,		n + 1  ):
  lowercase__       :       int				=						True

 for i in range(1       ,		s + 1  ):
  lowercase__       :       int				=						False

 for i in range(1       ,		n + 1  ):
  for j in range(1       ,		s + 1  ):
   lowercase__       :       Optional[Any]				=						dp[i][j - 1]

   if arr[i - 1] <= j:
    lowercase__       :       int				=						dp[i][j] or dp[i - 1][j - arr[i - 1]]

 for j in range(int(s / 2  )       ,		-1       ,		-1  ):
  if dp[n][j] is True:
   lowercase__       :       List[Any]				=						s - 2 * j
   break

 return diff