


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   _UpperCAmelCase       (     UpperCAmelCase      :			int						,	UpperCAmelCase      :			str						,	UpperCAmelCase      :			Tuple=None      ):
      """simple docstring"""




      assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
      __lowerCamelCase     :      Any       						=						nn.Parameter(UpperCAmelCase      )
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
            __lowerCamelCase     :      Tuple       						=						nn.Parameter(UpperCAmelCase      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Any      ):
      """simple docstring"""




      __lowerCamelCase     :      Dict       						=						np.asarray(weights[0]      )
      __lowerCamelCase     :      Tuple       						=						np.asarray(weights[1]      )
      __lowerCamelCase     :      Tuple       						=						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   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Union[str, Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      Any       						=						np.asarray(weights[0]      )
      __lowerCamelCase     :      List[Any]       						=						np.asarray(weights[1]      )
      __lowerCamelCase     :      Optional[int]       						=						np.asarray(weights[2]      )
      __lowerCamelCase     :      Tuple       						=						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   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      str       						=						weights[0][0][0]
      __lowerCamelCase     :      Union[str, Any]       						=						np.asarray(layer_norm_a[0]      )
      __lowerCamelCase     :      Tuple       						=						np.asarray(layer_norm_a[1]      )
      set_param(
          torch_block.attention.layer_norm						,	torch.tensor(UpperCAmelCase      )						,	torch.tensor(UpperCAmelCase      )						,	)

      # lsh weights + output
      __lowerCamelCase     :      str       						=						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
      __lowerCamelCase     :      Optional[Any]       						=						weights[2][0][1][2]

      # Chunked Feed Forward
      if len(UpperCAmelCase      ) == 4:
            __lowerCamelCase     :      List[str]       						=						intermediate_weights[2]

      # layernorm 2
      __lowerCamelCase     :      Optional[Any]       						=						np.asarray(intermediate_weights[0][0]      )
      __lowerCamelCase     :      Optional[int]       						=						np.asarray(intermediate_weights[0][1]      )
      set_param(
          torch_block.feed_forward.layer_norm						,	torch.tensor(UpperCAmelCase      )						,	torch.tensor(UpperCAmelCase      )						,	)

      # intermediate dense
      __lowerCamelCase     :      Optional[Any]       						=						np.asarray(intermediate_weights[1][0]      )
      __lowerCamelCase     :      Optional[Any]       						=						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
      __lowerCamelCase     :      Dict       						=						np.asarray(intermediate_weights[4][0]      )
      __lowerCamelCase     :      List[Any]       						=						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   _UpperCAmelCase       (     UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			Tuple      ):
      """simple docstring"""




      __lowerCamelCase     :      Tuple       						=						torch_model.reformer

      # word embeds
      __lowerCamelCase     :      Optional[Any]       						=						np.asarray(weights[1]      )
      set_param(
          torch_model_reformer.embeddings.word_embeddings						,	torch.tensor(UpperCAmelCase      )						,	)

      if isinstance(weights[3]						,	UpperCAmelCase      ):
            __lowerCamelCase     :      int       						=						torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights      )      ):
                  __lowerCamelCase     :      List[str]       						=						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"""
                  __lowerCamelCase     :      Optional[int]       						=						nn.Parameter(torch.tensor(UpperCAmelCase      )      )

      __lowerCamelCase     :      List[str]       						=						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      ):
            __lowerCamelCase     :      Tuple       						=						trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )

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

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			str						,	UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      Dict       						=						ReformerConfig.from_json_file(UpperCAmelCase      )
      print(f"""Building PyTorch model from configuration: {config}"""      )
      __lowerCamelCase     :      str       						=						ReformerModelWithLMHead(UpperCAmelCase      )

      with open(UpperCAmelCase						,	"""rb"""      ) as f:
            __lowerCamelCase     :      List[str]       						=						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    :						Dict     =							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    :						List[Any]     =							parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)







def   _UpperCAmelCase       (     UpperCAmelCase      :			str      ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						hex_num.strip()
      if not hex_num:
            raise ValueError("""No value was passed to the function"""      )

      __lowerCamelCase     :      Dict       						=						hex_num[0] == """-"""
      if is_negative:
            __lowerCamelCase     :      Optional[Any]       						=						hex_num[1:]

      try:
            __lowerCamelCase     :      Any       						=						int(UpperCAmelCase						,	16      )
      except ValueError:
            raise ValueError("""Invalid value was passed to the function"""      )

      __lowerCamelCase     :      List[str]       						=						""""""
      while int_num > 0:
            __lowerCamelCase     :      Dict       						=						str(int_num % 2      ) + bin_str
            int_num >>= 1

      return int(("""-""" + bin_str) if is_negative else bin_str      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()



