




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 					__lowercase      (     _A						,    _A						,    _A=None			)     ->						Tuple:
      assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
      SCREAMING_SNAKE_CASE :      List[Any]       =   nn.Parameter(_A			)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
            SCREAMING_SNAKE_CASE :      Any       =   nn.Parameter(_A			)






def 					__lowercase      (     _A						,    _A						,    _A			)     ->						Union[str, Any]:
      SCREAMING_SNAKE_CASE :      List[Any]       =   np.asarray(weights[0]			)
      SCREAMING_SNAKE_CASE :      Dict       =   np.asarray(weights[1]			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   np.asarray(weights[2]			)

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






def 					__lowercase      (     _A						,    _A						,    _A			)     ->						Optional[Any]:
      SCREAMING_SNAKE_CASE :      Tuple       =   np.asarray(weights[0]			)
      SCREAMING_SNAKE_CASE :      List[Any]       =   np.asarray(weights[1]			)
      SCREAMING_SNAKE_CASE :      str       =   np.asarray(weights[2]			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   np.asarray(weights[3]			)

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






def 					__lowercase      (     _A						,    _A						,    _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      List[Any]       =   weights[0][0][0]
      SCREAMING_SNAKE_CASE :      str       =   np.asarray(layer_norm_a[0]			)
      SCREAMING_SNAKE_CASE :      Any       =   np.asarray(layer_norm_a[1]			)
      set_param(
          torch_block.attention.layer_norm						,    torch.tensor(_A			)						,    torch.tensor(_A			)						,    )

      # lsh weights + output
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   weights[0][1]
      if len(_A			) < 4:
            set_layer_weights_in_torch_lsh(_A						,    torch_block.attention						,    _A			)
      else:
            set_layer_weights_in_torch_local(_A						,    torch_block.attention						,    _A			)

      # intermediate weighs
      SCREAMING_SNAKE_CASE :      Dict       =   weights[2][0][1][2]

      # Chunked Feed Forward
      if len(_A			) == 4:
            SCREAMING_SNAKE_CASE :      Optional[int]       =   intermediate_weights[2]

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

      # intermediate dense
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   np.asarray(intermediate_weights[1][0]			)
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   np.asarray(intermediate_weights[1][1]			)
      set_param(
          torch_block.feed_forward.dense.dense						,    torch.tensor(_A			).transpose(0						,    1			).contiguous()						,    torch.tensor(_A			)						,    )

      # intermediate out
      SCREAMING_SNAKE_CASE :      str       =   np.asarray(intermediate_weights[4][0]			)
      SCREAMING_SNAKE_CASE :      Tuple       =   np.asarray(intermediate_weights[4][1]			)
      set_param(
          torch_block.feed_forward.output.dense						,    torch.tensor(_A			).transpose(0						,    1			).contiguous()						,    torch.tensor(_A			)						,    )






def 					__lowercase      (     _A						,    _A						,    _A			)     ->						Dict:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   torch_model.reformer

      # word embeds
      SCREAMING_SNAKE_CASE :      List[Any]       =   np.asarray(weights[1]			)
      set_param(
          torch_model_reformer.embeddings.word_embeddings						,    torch.tensor(_A			)						,    )

      if isinstance(weights[3]						,    _A			):
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights			)			):
                  SCREAMING_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"
                  SCREAMING_SNAKE_CASE :      str       =   nn.Parameter(torch.tensor(_A			)			)

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

      # output layer norm
      SCREAMING_SNAKE_CASE :      Dict       =   np.asarray(weights[7][0]			)
      SCREAMING_SNAKE_CASE :      Any       =   np.asarray(weights[7][1]			)
      set_param(
          torch_model_reformer.encoder.layer_norm						,    torch.tensor(_A			)						,    torch.tensor(_A			)						,    )

      # output embeddings
      SCREAMING_SNAKE_CASE :      List[Any]       =   np.asarray(weights[9][0]			)
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.asarray(weights[9][1]			)
      set_param(
          torch_model.lm_head.decoder						,    torch.tensor(_A			).transpose(0						,    1			).contiguous()						,    torch.tensor(_A			)						,    )






def 					__lowercase      (     _A						,    _A						,    _A			)     ->						Optional[int]:
      SCREAMING_SNAKE_CASE :      Any       =   ReformerConfig.from_json_file(_A			)
      print(F"Building PyTorch model from configuration: {config}"			)
      SCREAMING_SNAKE_CASE :      List[Any]       =   ReformerModelWithLMHead(_A			)

      with open(_A						,    """rb"""			) as f:
            SCREAMING_SNAKE_CASE :      Tuple       =   pickle.load(_A			)["weights"]

      set_model_weights_in_torch(_A						,    _A						,    config.hidden_size			)

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


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







def 					__lowercase      (     _A = 50			)     ->						int:
      SCREAMING_SNAKE_CASE :      Tuple       =   [1] * (length + 1)

      for row_length in range(3						,    length + 1			):
            for block_length in range(3						,    row_length + 1			):
                  for block_start in range(row_length - block_length			):
                        ways_number[row_length] += ways_number[
                            row_length - block_start - block_length - 1
                        ]

                  ways_number[row_length] += 1

      return ways_number[length]


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




