





'''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     a__ (					lowercase__  ,    lowercase__  ,    lowercase__=None  ):






   '''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     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''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     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''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     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''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     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''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     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''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__":
     __lowercase  :   Tuple						      =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  :   Union[str, Any]						      =parser.parse_args()
     convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)











__lowercase  :   Dict						      ="""0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
     from .utils import rich




