





'''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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None       ):







  '''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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''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__":
 __magic_name__   :						str    =			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."""
 )
 __magic_name__   :						Tuple    =			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 ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__magic_name__   :						Any    =			{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __magic_name__   :						List[Any]    =			[
     """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPTNeoXJapaneseForCausalLM""",
     """GPTNeoXJapaneseLayer""",
     """GPTNeoXJapaneseModel""",
     """GPTNeoXJapanesePreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 __magic_name__   :						Union[str, Any]    =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


