
"""simple docstring"""




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def     __magic_name__      ( __snake_case							:			List[str]	,					__snake_case							:			Dict	,					__snake_case							:			Optional[Any]    )					->							Optional[Any]:
       # Construct model
       if gpta_config_file == "":
              lowercase       :							Any									=				GPTaConfig()
       else:
              lowercase       :							Optional[int]									=				GPTaConfig.from_json_file(__snake_case    )
       lowercase       :							Optional[int]									=				GPTaModel(__snake_case    )

       # Load weights from numpy
       load_tf_weights_in_gpta(__snake_case	,					__snake_case	,					__snake_case    )

       # Save pytorch-model
       lowercase       :							Optional[Any]									=				pytorch_dump_folder_path + "/" + WEIGHTS_NAME
       lowercase       :							List[Any]									=				pytorch_dump_folder_path + "/" + CONFIG_NAME
       print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""    )
       torch.save(model.state_dict()	,					__snake_case    )
       print(f"""Save configuration file to {pytorch_config_dump_path}"""    )
       with open(__snake_case	,					"w"	,					encoding="utf-8"    ) as f:
              f.write(config.to_json_string()    )


if __name__ == "__main__":
   _A     :			Any  						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   parser.add_argument(
       """--gpt2_config_file""",
       default="""""",
       type=str,
       help=(
           """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
           """This specifies the model architecture."""
       ),
   )
   _A     :			Any  						=			parser.parse_args()
   convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 						a__		( unittest.TestCase		):
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"      ,       return_dict=_a							).to(_a							)
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("google/mt5-small"							)

          lowercase       :							Optional[Any]									=				tokenizer("Hello there"      ,       return_tensors="pt"							).input_ids
          lowercase       :							Union[str, Any]									=				tokenizer("Hi I am"      ,       return_tensors="pt"							).input_ids

          lowercase       :							int									=				model(input_ids.to(_a							)      ,       labels=labels.to(_a							)							).loss
          lowercase       :							str									=				-(labels.shape[-1] * loss.item())

          lowercase       :							List[str]									=				-8_4.9_1_2_7
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1E-4							)


