


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   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__=None   ):
   # set parameter of one layer
   assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
   __a					=  nn.Parameter(lowerCAmelCase__   )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
      __a					=  nn.Parameter(lowerCAmelCase__   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   # 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(lowerCAmelCase__   ).transpose(1		,				2   ).contiguous().view(-1		,				lowerCAmelCase__   )		,				)
   set_param(
       torch_layer.self_attention.value		,				torch.tensor(lowerCAmelCase__   ).transpose(1		,				2   ).contiguous().view(-1		,				lowerCAmelCase__   )		,				)
   set_param(
       torch_layer.output.dense		,				torch.tensor(lowerCAmelCase__   ).view(-1		,				lowerCAmelCase__   ).contiguous().transpose(0		,				1   )		,				)




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   # 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(lowerCAmelCase__   ).transpose(1		,				2   ).contiguous().view(-1		,				lowerCAmelCase__   )		,				)
   set_param(
       torch_layer.self_attention.key		,				torch.tensor(lowerCAmelCase__   ).transpose(1		,				2   ).contiguous().view(-1		,				lowerCAmelCase__   )		,				)
   set_param(
       torch_layer.self_attention.value		,				torch.tensor(lowerCAmelCase__   ).transpose(1		,				2   ).contiguous().view(-1		,				lowerCAmelCase__   )		,				)
   set_param(
       torch_layer.output.dense		,				torch.tensor(lowerCAmelCase__   ).view(-1		,				lowerCAmelCase__   ).contiguous().transpose(0		,				1   )		,				)




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   # 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(lowerCAmelCase__   )		,				torch.tensor(lowerCAmelCase__   )		,				)

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

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

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

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

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




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   # reformer model
   __a					=  torch_model.reformer

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

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

   __a					=  weights[5]
   assert len(torch_model_reformer.encoder.layers   ) * 4 == len(
       lowerCAmelCase__   ), "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(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   )

   # 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(lowerCAmelCase__   )		,				torch.tensor(lowerCAmelCase__   )		,				)

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




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   # Initialise PyTorch model
   __a					=  ReformerConfig.from_json_file(lowerCAmelCase__   )
   print(f'''Building PyTorch model from configuration: {config}'''   )
   __a					=  ReformerModelWithLMHead(lowerCAmelCase__   )

   with open(lowerCAmelCase__		,				"""rb"""   ) as f:
      __a					=  pickle.load(lowerCAmelCase__   )["""weights"""]

   set_model_weights_in_torch(lowerCAmelCase__		,				lowerCAmelCase__		,				config.hidden_size   )

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE							    =							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.'
  )
  SCREAMING_SNAKE_CASE							    =							parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """luke"""


     def __init__(	self  ,       __A=50267  ,       __A=500000  ,       __A=768  ,       __A=256  ,       __A=12  ,       __A=12  ,       __A=3072  ,       __A="gelu"  ,       __A=0.1  ,       __A=0.1  ,       __A=512  ,       __A=2  ,       __A=0.02  ,       __A=1E-12  ,       __A=True  ,       __A=None  ,       __A=1  ,       __A=0  ,       __A=2  ,       **__A  ,       ):
        super().__init__(pad_token_id=__A  ,       bos_token_id=__A  ,       eos_token_id=__A  ,       **__A    )

        __a					=  vocab_size
        __a					=  entity_vocab_size
        __a					=  hidden_size
        __a					=  entity_emb_size
        __a					=  num_hidden_layers
        __a					=  num_attention_heads
        __a					=  hidden_act
        __a					=  intermediate_size
        __a					=  hidden_dropout_prob
        __a					=  attention_probs_dropout_prob
        __a					=  max_position_embeddings
        __a					=  type_vocab_size
        __a					=  initializer_range
        __a					=  layer_norm_eps
        __a					=  use_entity_aware_attention
        __a					=  classifier_dropout



