



'''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_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase=None       )      ->      int:
  # set parameter of one layer
  assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
  __lowerCamelCase       :       str           = nn.Parameter(_lowerCAmelCase       )
  if bias is not None:
    assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
    __lowerCamelCase       :       Tuple           = nn.Parameter(_lowerCAmelCase       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Tuple:
  # set torch weights for 1-to-1 comparison
  __lowerCamelCase       :       List[str]           = np.asarray(weights[0]       )
  __lowerCamelCase       :       Optional[int]           = np.asarray(weights[1]       )
  __lowerCamelCase       :       Tuple           = 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       )      ->      Optional[int]:
  # set torch weights for 1-to-1 comparison
  __lowerCamelCase       :       Tuple           = np.asarray(weights[0]       )
  __lowerCamelCase       :       Dict           = np.asarray(weights[1]       )
  __lowerCamelCase       :       List[str]           = np.asarray(weights[2]       )
  __lowerCamelCase       :       Union[str, Any]           = 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       )      ->      Union[str, Any]:
  # layernorm 1
  __lowerCamelCase       :       str           = weights[0][0][0]
  __lowerCamelCase       :       int           = np.asarray(layer_norm_a[0]       )
  __lowerCamelCase       :       Tuple           = np.asarray(layer_norm_a[1]       )
  set_param(
      torch_block.attention.layer_norm       ,torch.tensor(_lowerCAmelCase       )       ,torch.tensor(_lowerCAmelCase       )       ,)

  # lsh weights + output
  __lowerCamelCase       :       int           = 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
  __lowerCamelCase       :       List[Any]           = weights[2][0][1][2]

  # Chunked Feed Forward
  if len(_lowerCAmelCase       ) == 4:
    __lowerCamelCase       :       int           = intermediate_weights[2]

  # layernorm 2
  __lowerCamelCase       :       Optional[int]           = np.asarray(intermediate_weights[0][0]       )
  __lowerCamelCase       :       Any           = np.asarray(intermediate_weights[0][1]       )
  set_param(
      torch_block.feed_forward.layer_norm       ,torch.tensor(_lowerCAmelCase       )       ,torch.tensor(_lowerCAmelCase       )       ,)

  # intermediate dense
  __lowerCamelCase       :       List[str]           = np.asarray(intermediate_weights[1][0]       )
  __lowerCamelCase       :       Any           = 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
  __lowerCamelCase       :       List[Any]           = np.asarray(intermediate_weights[4][0]       )
  __lowerCamelCase       :       List[str]           = 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       )      ->      Tuple:
  # reformer model
  __lowerCamelCase       :       Optional[int]           = torch_model.reformer

  # word embeds
  __lowerCamelCase       :       Any           = np.asarray(weights[1]       )
  set_param(
      torch_model_reformer.embeddings.word_embeddings       ,torch.tensor(_lowerCAmelCase       )       ,)

  if isinstance(weights[3]       ,_lowerCAmelCase       ):
    __lowerCamelCase       :       Dict           = torch_model_reformer.embeddings.position_embeddings
    for emb_idx in range(len(position_embeddings.weights       )       ):
      __lowerCamelCase       :       Any           = 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'
      __lowerCamelCase       :       List[Any]           = nn.Parameter(torch.tensor(_lowerCAmelCase       )       )

  __lowerCamelCase       :       List[str]           = 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       ):
    __lowerCamelCase       :       Optional[int]           = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
    set_block_weights_in_torch(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )

  # output layer norm
  __lowerCamelCase       :       Optional[Any]           = np.asarray(weights[7][0]       )
  __lowerCamelCase       :       int           = np.asarray(weights[7][1]       )
  set_param(
      torch_model_reformer.encoder.layer_norm       ,torch.tensor(_lowerCAmelCase       )       ,torch.tensor(_lowerCAmelCase       )       ,)

  # output embeddings
  __lowerCamelCase       :       Tuple           = np.asarray(weights[9][0]       )
  __lowerCamelCase       :       str           = 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       )      ->      Optional[Any]:
  # Initialise PyTorch model
  __lowerCamelCase       :       List[Any]           = ReformerConfig.from_json_file(_lowerCAmelCase       )
  print(F'Building PyTorch model from configuration: {config}'       )
  __lowerCamelCase       :       Tuple           = ReformerModelWithLMHead(_lowerCAmelCase       )

  with open(_lowerCAmelCase       ,'rb'       ) as f:
    __lowerCamelCase       :       List[str]           = 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__":
   _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'''





from typing import TYPE_CHECKING

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


_UpperCamelCase											=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						[
       'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BioGptForCausalLM',
       'BioGptForTokenClassification',
       'BioGptForSequenceClassification',
       'BioGptModel',
       'BioGptPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






