






"""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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=None		):







       '''simple docstring'''
       # set parameter of one layer
       assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
       UpperCAmelCase	    =			nn.Parameter(lowerCAmelCase		)
       if bias is not None:
              assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
              UpperCAmelCase	    =			nn.Parameter(lowerCAmelCase		)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       # set torch weights for 1-to-1 comparison
       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(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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       # set torch weights for 1-to-1 comparison
       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(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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       # layernorm 1
       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(lowerCAmelCase		)	,      torch.tensor(lowerCAmelCase		)	,      )

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

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

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

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






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       # reformer model
       UpperCAmelCase	    =			torch_model.reformer

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

       if isinstance(weights[3]	,      lowerCAmelCase		):
              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(lowerCAmelCase		)		)

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

       # 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(lowerCAmelCase		)	,      torch.tensor(lowerCAmelCase		)	,      )

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






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       # Initialise PyTorch model
       UpperCAmelCase	    =			ReformerConfig.from_json_file(lowerCAmelCase		)
       print(F'''Building PyTorch model from configuration: {config}'''		)
       UpperCAmelCase	    =			ReformerModelWithLMHead(lowerCAmelCase		)

       with open(lowerCAmelCase	,      """rb"""		) as f:
              UpperCAmelCase	    =			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__":
 lowerCAmelCase_	:    Optional[int]            =      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_	:    Union[str, Any]            =      parser.parse_args()
 convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_	:    Optional[int]            =      {
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_	:    str            =      [
     '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GraphormerForGraphClassification''',
     '''GraphormerModel''',
     '''GraphormerPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_	:    Optional[Any]            =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
