




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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=None)      ->					List[Any]:
     # set parameter of one layer
     assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
     a     =				nn.Parameter(__UpperCamelCase)
     if bias is not None:
          assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
          a     =				nn.Parameter(__UpperCamelCase)

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     # set torch weights for 1-to-1 comparison
     a     =				np.asarray(weights[0])
     a     =				np.asarray(weights[1])
     a     =				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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Dict:
     # set torch weights for 1-to-1 comparison
     a     =				np.asarray(weights[0])
     a     =				np.asarray(weights[1])
     a     =				np.asarray(weights[2])
     a     =				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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Tuple:
     # layernorm 1
     a     =				weights[0][0][0]
     a     =				np.asarray(layer_norm_a[0])
     a     =				np.asarray(layer_norm_a[1])
     set_param(
         torch_block.attention.layer_norm		, torch.tensor(__UpperCamelCase)		, torch.tensor(__UpperCamelCase)		, )

     # lsh weights + output
     a     =				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
     a     =				weights[2][0][1][2]

     # Chunked Feed Forward
     if len(__UpperCamelCase) == 4:
          a     =				intermediate_weights[2]

     # layernorm 2
     a     =				np.asarray(intermediate_weights[0][0])
     a     =				np.asarray(intermediate_weights[0][1])
     set_param(
         torch_block.feed_forward.layer_norm		, torch.tensor(__UpperCamelCase)		, torch.tensor(__UpperCamelCase)		, )

     # intermediate dense
     a     =				np.asarray(intermediate_weights[1][0])
     a     =				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
     a     =				np.asarray(intermediate_weights[4][0])
     a     =				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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     # reformer model
     a     =				torch_model.reformer

     # word embeds
     a     =				np.asarray(weights[1])
     set_param(
         torch_model_reformer.embeddings.word_embeddings		, torch.tensor(__UpperCamelCase)		, )

     if isinstance(weights[3]		, __UpperCamelCase):
          a     =				torch_model_reformer.embeddings.position_embeddings
          for emb_idx in range(len(position_embeddings.weights)):
               a     =				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'''
               a     =				nn.Parameter(torch.tensor(__UpperCamelCase))

     a     =				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):
          a     =				trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
          set_block_weights_in_torch(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)

     # output layer norm
     a     =				np.asarray(weights[7][0])
     a     =				np.asarray(weights[7][1])
     set_param(
         torch_model_reformer.encoder.layer_norm		, torch.tensor(__UpperCamelCase)		, torch.tensor(__UpperCamelCase)		, )

     # output embeddings
     a     =				np.asarray(weights[9][0])
     a     =				np.asarray(weights[9][1])
     set_param(
         torch_model.lm_head.decoder		, torch.tensor(__UpperCamelCase).transpose(0		, 1).contiguous()		, torch.tensor(__UpperCamelCase)		, )

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Union[str, Any]:
     # Initialise PyTorch model
     a     =				ReformerConfig.from_json_file(__UpperCamelCase)
     print(f'''Building PyTorch model from configuration: {config}''')
     a     =				ReformerModelWithLMHead(__UpperCamelCase)

     with open(__UpperCamelCase		, "rb") as f:
          a     =				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__":
   lowercase__				:      int	   = 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__				:      Dict	   = parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)







from __future__ import annotations

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					list[int]:
     a     =				2
     a     =				[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__UpperCamelCase)
     if n > 1:
          factors.append(__UpperCamelCase)
     return factors


if __name__ == "__main__":
   import doctest

   doctest.testmod()

