





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
       from ..ta.tokenization_ta import TaTokenizer
else:
       from ...utils.dummy_sentencepiece_objects import TaTokenizer

UpperCamelCase__      =TaTokenizer

if is_tokenizers_available():
       from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
       from ...utils.dummy_tokenizers_objects import TaTokenizerFast

UpperCamelCase__      =TaTokenizerFast

UpperCamelCase__      ={'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'MT5EncoderModel',
           'MT5ForConditionalGeneration',
           'MT5ForQuestionAnswering',
           'MT5Model',
           'MT5PreTrainedModel',
           'MT5Stack',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
       from .configuration_mta import MTaConfig, MTaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mta import (
                  MTaEncoderModel,
                  MTaForConditionalGeneration,
                  MTaForQuestionAnswering,
                  MTaModel,
                  MTaPreTrainedModel,
                  MTaStack,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
       import sys

       UpperCamelCase__      =_LazyModule(
           __name__,
           globals()['__file__'],
           _import_structure,
           extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
           module_spec=__spec__,
       )






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     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     # Construct model
     if gpta_config_file == "":
          _SCREAMING_SNAKE_CASE       :	str						    =							GPTaConfig()
     else:
          _SCREAMING_SNAKE_CASE       :	int						    =							GPTaConfig.from_json_file(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							GPTaModel(__lowerCamelCase	)

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

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


if __name__ == "__main__":
       UpperCamelCase__      =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.'
           ),
       )
       UpperCamelCase__      =parser.parse_args()
       convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)