


'''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__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							int=None							):







   '''simple docstring'''




   assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
   _UpperCAmelCase       :       Union[str, Any]							   =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 lowerCamelCase__      (						__lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							List[Any]							):







   '''simple docstring'''




   _UpperCAmelCase       :       Any							   =np.asarray(weights[0]							)
   _UpperCAmelCase       :       Optional[Any]							   =np.asarray(weights[1]							)
   _UpperCAmelCase       :       Optional[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__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       Any							   =np.asarray(weights[0]							)
   _UpperCAmelCase       :       Dict							   =np.asarray(weights[1]							)
   _UpperCAmelCase       :       List[str]							   =np.asarray(weights[2]							)
   _UpperCAmelCase       :       Optional[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__      (						__lowerCamelCase	:							Optional[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							Union[str, Any]							):







   '''simple docstring'''




   _UpperCAmelCase       :       Tuple							   =weights[0][0][0]
   _UpperCAmelCase       :       Dict							   =np.asarray(layer_norm_a[0]							)
   _UpperCAmelCase       :       List[str]							   =np.asarray(layer_norm_a[1]							)
   set_param(
       torch_block.attention.layer_norm	,   torch.tensor(__lowerCamelCase							)	,   torch.tensor(__lowerCamelCase							)	,   )

   # lsh weights + output
   _UpperCAmelCase       :       Tuple							   =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       :       str							   =weights[2][0][1][2]

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

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

   # intermediate dense
   _UpperCAmelCase       :       Dict							   =np.asarray(intermediate_weights[1][0]							)
   _UpperCAmelCase       :       Dict							   =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       :       int							   =np.asarray(intermediate_weights[4][0]							)
   _UpperCAmelCase       :       List[str]							   =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__      (						__lowerCamelCase	:							str	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							Optional[Any]							):







   '''simple docstring'''




   _UpperCAmelCase       :       Tuple							   =torch_model.reformer

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

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

   _UpperCAmelCase       :       List[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							):
      _UpperCAmelCase       :       List[str]							   =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       :       str							   =np.asarray(weights[7][1]							)
   set_param(
       torch_model_reformer.encoder.layer_norm	,   torch.tensor(__lowerCamelCase							)	,   torch.tensor(__lowerCamelCase							)	,   )

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





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							Dict	,   __lowerCamelCase	:							Dict							):







   '''simple docstring'''




   _UpperCAmelCase       :       Dict							   =ReformerConfig.from_json_file(__lowerCamelCase							)
   print(f"Building PyTorch model from configuration: {config}"							)
   _UpperCAmelCase       :       Optional[Any]							   =ReformerModelWithLMHead(__lowerCamelCase							)

   with open(__lowerCamelCase	,   'rb'							) as f:
      _UpperCAmelCase       :       Optional[int]							   =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							=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							=parser.parse_args()
 convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





'''simple docstring'''






from string import ascii_uppercase

lowercase							={str(ord(c) - 55): c for c in ascii_uppercase}





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   if isinstance(__lowerCamelCase	,   __lowerCamelCase							):
      raise TypeError('int() can\'t convert non-string with explicit base'							)
   if num < 0:
      raise ValueError('parameter must be positive int'							)
   if isinstance(__lowerCamelCase	,   __lowerCamelCase							):
      raise TypeError('\'str\' object cannot be interpreted as an integer'							)
   if isinstance(__lowerCamelCase	,   __lowerCamelCase							):
      raise TypeError('\'float\' object cannot be interpreted as an integer'							)
   if base in (0, 1):
      raise ValueError('base must be >= 2'							)
   if base > 3_6:
      raise ValueError('base must be <= 36'							)
   _UpperCAmelCase       :       Union[str, Any]							   =''
   _UpperCAmelCase       :       Optional[int]							   =0
   _UpperCAmelCase       :       str							   =0
   while div != 1:
      _UpperCAmelCase   ,	_UpperCAmelCase       :       int							   =divmod(__lowerCamelCase	,   __lowerCamelCase							)
      if base >= 1_1 and 9 < mod < 3_6:
         _UpperCAmelCase       :       str							   =ALPHABET_VALUES[str(__lowerCamelCase							)]
      else:
         _UpperCAmelCase       :       Any							   =str(__lowerCamelCase							)
      new_value += actual_value
      _UpperCAmelCase       :       Union[str, Any]							   =num // base
      _UpperCAmelCase       :       Dict							   =div
      if div == 0:
         return str(new_value[::-1]							)
      elif div == 1:
         new_value += str(__lowerCamelCase							)
         return str(new_value[::-1]							)

   return new_value[::-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 for base in range(2, 37):
  for num in range(1000):
   assert int(decimal_to_any(num, base), base) == num, (
       num,
       base,
       decimal_to_any(num, base),
       int(decimal_to_any(num, base), base),
   )

