


import logging
import os

from .state import PartialState







class lowerCAmelCase_		(				logging.LoggerAdapter ):

 '''simple docstring'''

 @staticmethod
 def 		UpperCamelCase__  (			_UpperCAmelCase      ):
       snake_case_ =   PartialState()
       return not main_process_only or (main_process_only and state.is_main_process)





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):

       if PartialState._shared_state == {}:
             raise RuntimeError(
                 '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''      )
       snake_case_ =   kwargs.pop('''main_process_only'''	,   _A      )
       snake_case_ =   kwargs.pop('''in_order'''	,   _A      )

       if self.isEnabledFor(_A      ):
             if self._should_log(_A      ):
                   snake_case_ =   self.process(_A	,   _A      )
                   self.logger.log(_A	,   _A	,   *_A	,   **_A      )

             elif in_order:
                   snake_case_ =   PartialState()
                   for i in range(state.num_processes      ):
                         if i == state.process_index:
                               snake_case_ =   self.process(_A	,   _A      )
                               self.logger.log(_A	,   _A	,   *_A	,   **_A      )
                         state.wait_for_everyone()






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = None      )->						List[Any]:




      """simple docstring"""

      if log_level is None:
            snake_case_ =   os.environ.get('''ACCELERATE_LOG_LEVEL'''      ,      _lowerCamelCase      )
      snake_case_ =   logging.getLogger(_lowerCamelCase      )
      if log_level is not None:
            logger.setLevel(log_level.upper()      )
            logger.root.setLevel(log_level.upper()      )
      return MultiProcessAdapter(_lowerCamelCase      ,      {}      )



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					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      )->						Any:




      """simple docstring"""

      assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
      snake_case_ =   nn.Parameter(SCREAMING_SNAKE_CASE      )
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
            snake_case_ =   nn.Parameter(SCREAMING_SNAKE_CASE      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   np.asarray(weights[0]      )
      snake_case_ =   np.asarray(weights[1]      )
      snake_case_ =   np.asarray(weights[2]      )

      set_param(
          torch_layer.self_attention.query_key      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.self_attention.value      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.output.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).view(-1      ,      SCREAMING_SNAKE_CASE      ).contiguous().transpose(0      ,      1      )      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   np.asarray(weights[0]      )
      snake_case_ =   np.asarray(weights[1]      )
      snake_case_ =   np.asarray(weights[2]      )
      snake_case_ =   np.asarray(weights[3]      )

      set_param(
          torch_layer.self_attention.query      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.self_attention.key      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.self_attention.value      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.output.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).view(-1      ,      SCREAMING_SNAKE_CASE      ).contiguous().transpose(0      ,      1      )      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

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

      # lsh weights + output
      snake_case_ =   weights[0][1]
      if len(SCREAMING_SNAKE_CASE      ) < 4:
            set_layer_weights_in_torch_lsh(SCREAMING_SNAKE_CASE      ,      torch_block.attention      ,      SCREAMING_SNAKE_CASE      )
      else:
            set_layer_weights_in_torch_local(SCREAMING_SNAKE_CASE      ,      torch_block.attention      ,      SCREAMING_SNAKE_CASE      )

      # intermediate weighs
      snake_case_ =   weights[2][0][1][2]

      # Chunked Feed Forward
      if len(SCREAMING_SNAKE_CASE      ) == 4:
            snake_case_ =   intermediate_weights[2]

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

      # intermediate dense
      snake_case_ =   np.asarray(intermediate_weights[1][0]      )
      snake_case_ =   np.asarray(intermediate_weights[1][1]      )
      set_param(
          torch_block.feed_forward.dense.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(0      ,      1      ).contiguous()      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      # intermediate out
      snake_case_ =   np.asarray(intermediate_weights[4][0]      )
      snake_case_ =   np.asarray(intermediate_weights[4][1]      )
      set_param(
          torch_block.feed_forward.output.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(0      ,      1      ).contiguous()      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      snake_case_ =   torch_model.reformer

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

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

      snake_case_ =   weights[5]
      assert len(torch_model_reformer.encoder.layers      ) * 4 == len(
          SCREAMING_SNAKE_CASE      ), "HF and trax model do not have the same number of layers"
      for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers      ):
            snake_case_ =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

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

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   ReformerConfig.from_json_file(SCREAMING_SNAKE_CASE      )
      print(f'''Building PyTorch model from configuration: {config}'''      )
      snake_case_ =   ReformerModelWithLMHead(SCREAMING_SNAKE_CASE      )

      with open(SCREAMING_SNAKE_CASE      ,      '''rb'''      ) as f:
            snake_case_ =   pickle.load(SCREAMING_SNAKE_CASE      )['''weights''']

      set_model_weights_in_torch(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      config.hidden_size      )

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}'''      )
      torch.save(model.state_dict()      ,      SCREAMING_SNAKE_CASE      )


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)