
"""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     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase=None  ):
   # set parameter of one layer
   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   UpperCAmelCase_					:					Optional[int]			     =    nn.Parameter(__lowerCamelCase  )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
      UpperCAmelCase_					:					Optional[int]			     =    nn.Parameter(__lowerCamelCase  )







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

   # lsh weights + output
   UpperCAmelCase_					:					List[str]			     =    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
   UpperCAmelCase_					:					List[Any]			     =    weights[2][0][1][2]

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

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

   # intermediate dense
   UpperCAmelCase_					:					List[str]			     =    np.asarray(intermediate_weights[1][0]  )
   UpperCAmelCase_					:					List[str]			     =    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
   UpperCAmelCase_					:					str			     =    np.asarray(intermediate_weights[4][0]  )
   UpperCAmelCase_					:					Optional[int]			     =    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 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   # reformer model
   UpperCAmelCase_					:					str			     =    torch_model.reformer

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

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

   UpperCAmelCase_					:					str			     =    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  ):
      UpperCAmelCase_					:					Union[str, Any]			     =    trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )

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

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







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   # Initialise PyTorch model
   UpperCAmelCase_					:					str			     =    ReformerConfig.from_json_file(__lowerCamelCase  )
   print(f"""Building PyTorch model from configuration: {config}"""  )
   UpperCAmelCase_					:					Any			     =    ReformerModelWithLMHead(__lowerCamelCase  )

   with open(__lowerCamelCase,		"rb"  ) as f:
      UpperCAmelCase_					:					Union[str, Any]			     =    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__":
    _a		       =							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.'
    )
    _a		       =							parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




"""simple docstring"""





from __future__ import annotations







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   if (voltage, current, resistance).count(0  ) != 1:
      raise ValueError("One and only one argument must be 0"  )
   if resistance < 0:
      raise ValueError("Resistance cannot be negative"  )
   if voltage == 0:
      return {"voltage": float(current * resistance  )}
   elif current == 0:
      return {"current": voltage / resistance}
   elif resistance == 0:
      return {"resistance": voltage / current}
   else:
      raise ValueError("Exactly one argument must be 0"  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


