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_ (     __A,  __A,  __A=None							)     ->				List[str]:







      '''simple docstring'''


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




def    lowerCAmelCase_ (     __A,  __A,  __A							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  np.asarray(weights[0]							)
      UpperCAmelCase__								=  np.asarray(weights[1]							)
      UpperCAmelCase__								=  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    lowerCAmelCase_ (     __A,  __A,  __A							)     ->				Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  np.asarray(weights[0]							)
      UpperCAmelCase__								=  np.asarray(weights[1]							)
      UpperCAmelCase__								=  np.asarray(weights[2]							)
      UpperCAmelCase__								=  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    lowerCAmelCase_ (     __A,  __A,  __A							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  weights[0][0][0]
      UpperCAmelCase__								=  np.asarray(layer_norm_a[0]							)
      UpperCAmelCase__								=  np.asarray(layer_norm_a[1]							)
      set_param(
          torch_block.attention.layer_norm,  torch.tensor(__A							),  torch.tensor(__A							),  )

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

      # Chunked Feed Forward
      if len(__A							) == 4:
            UpperCAmelCase__								=  intermediate_weights[2]

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

      # intermediate dense
      UpperCAmelCase__								=  np.asarray(intermediate_weights[1][0]							)
      UpperCAmelCase__								=  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
      UpperCAmelCase__								=  np.asarray(intermediate_weights[4][0]							)
      UpperCAmelCase__								=  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    lowerCAmelCase_ (     __A,  __A,  __A							)     ->				str:







      '''simple docstring'''


      UpperCAmelCase__								=  torch_model.reformer

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

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

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

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

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




def    lowerCAmelCase_ (     __A,  __A,  __A							)     ->				str:







      '''simple docstring'''


      UpperCAmelCase__								=  ReformerConfig.from_json_file(__A							)
      print(f"""Building PyTorch model from configuration: {config}"""							)
      UpperCAmelCase__								=  ReformerModelWithLMHead(__A							)

      with open(__A,  "rb"							) as f:
            UpperCAmelCase__								=  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__":
      UpperCamelCase__											=					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__											=					parser.parse_args()
      convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




'''simple docstring'''




def 							lowerCamelCase	(      UpperCAmelCase__       :					list )			->      list:
							if any(not isinstance(UpperCAmelCase__      ,      UpperCAmelCase__ ) or x < 0 for x in sequence ):
														raise TypeError("""Sequence must be list of non-negative integers""" )
							for _ in range(len(UpperCAmelCase__ ) ):
														for i, (rod_upper, rod_lower) in enumerate(zip(UpperCAmelCase__      ,      sequence[1:] ) ):
																					if rod_upper > rod_lower:
																												sequence[i] -= rod_upper - rod_lower
																												sequence[i + 1] += rod_upper - rod_lower
							return sequence


if __name__ == "__main__":
						assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]





