

def      A__					(			__A  :						int     ,				__A  :						List[str]     ,				__A  :						Tuple     ,				__A  :						Dict     ,				__A  :						int     ,				__A  :						Any			)					->Optional[int]:
      if index == r:
            for j in range(__A			):
                  print(data[j]     ,				end=''' '''			)
            print(''' '''			)
            return
      #  When no more elements are there to put in data[]
      if i >= n:
            return
      # current is included, put next at next location
      __A    =arr[i]
      combination_util(__A     ,				__A     ,				__A     ,				index + 1     ,				__A     ,				i + 1			)
      # current is excluded, replace it with
      # next (Note that i+1 is passed, but
      # index is not changed)
      combination_util(__A     ,				__A     ,				__A     ,				__A     ,				__A     ,				i + 1			)
      # The main function that prints all combinations
      # of size r in arr[] of size n. This function
      # mainly uses combinationUtil()





def      A__					(			__A  :						List[str]     ,				__A  :						Dict     ,				__A  :						Tuple			)					->Dict:
      # A temporary array to store all combination one by one
      __A    =[0] * r
      # Print all combination using temporary array 'data[]'
      combination_util(__A     ,				__A     ,				__A     ,				0     ,				__A     ,				0			)


if __name__ == "__main__":
   # Driver code to check the function above
   _lowerCamelCase      :					str					    =      [10, 20, 30, 40, 50]
   print_combination(arr, len(arr), 3)
   # This code is contributed by Ambuj sahu





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__					(			__A  :						Any     ,				__A  :						Dict     ,				__A  :						Optional[int]=None			)					->Tuple:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
      __A    =nn.Parameter(__A			)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
            __A    =nn.Parameter(__A			)





def      A__					(			__A  :						List[Any]     ,				__A  :						Tuple     ,				__A  :						List[Any]			)					->Dict:
      # 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(__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      A__					(			__A  :						Optional[Any]     ,				__A  :						Tuple     ,				__A  :						Optional[Any]			)					->int:
      # 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(__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      A__					(			__A  :						int     ,				__A  :						List[str]     ,				__A  :						Optional[Any]			)					->Any:
      # 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(__A			)     ,				torch.tensor(__A			)     ,				)

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

      # Chunked Feed Forward
      if len(__A			) == 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(__A			)     ,				torch.tensor(__A			)     ,				)

      # 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(__A			).transpose(0     ,				1			).contiguous()     ,				torch.tensor(__A			)     ,				)

      # 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(__A			).transpose(0     ,				1			).contiguous()     ,				torch.tensor(__A			)     ,				)





def      A__					(			__A  :						Tuple     ,				__A  :						List[Any]     ,				__A  :						Optional[int]			)					->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(__A			)     ,				)

      if isinstance(weights[3]     ,				__A			):
            __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(__A			)			)

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

      # 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(__A			)     ,				torch.tensor(__A			)     ,				)

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





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Tuple			)					->Union[str, Any]:
      # Initialise PyTorch model
      __A    =ReformerConfig.from_json_file(__A			)
      print(F'''Building PyTorch model from configuration: {config}'''			)
      __A    =ReformerModelWithLMHead(__A			)

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


