

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase							:  str     =				{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase							:  str     =				[
         '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''QDQBertForMaskedLM''',
         '''QDQBertForMultipleChoice''',
         '''QDQBertForNextSentencePrediction''',
         '''QDQBertForQuestionAnswering''',
         '''QDQBertForSequenceClassification''',
         '''QDQBertForTokenClassification''',
         '''QDQBertLayer''',
         '''QDQBertLMHeadModel''',
         '''QDQBertModel''',
         '''QDQBertPreTrainedModel''',
         '''load_tf_weights_in_qdqbert''',
     ]


if TYPE_CHECKING:
     from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_qdqbert import (
              QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              QDQBertForMaskedLM,
              QDQBertForMultipleChoice,
              QDQBertForNextSentencePrediction,
              QDQBertForQuestionAnswering,
              QDQBertForSequenceClassification,
              QDQBertForTokenClassification,
              QDQBertLayer,
              QDQBertLMHeadModel,
              QDQBertModel,
              QDQBertPreTrainedModel,
              load_tf_weights_in_qdqbert,
          )


else:
     import sys

     _lowerCamelCase							:  int     =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Initialize PyTorch model
   __lowerCamelCase:					str	 =						CanineConfig()
   __lowerCamelCase:					Union[str, Any]	 =						CanineModel(snake_case			)
   model.eval()

   print(f'Building PyTorch model from configuration: {config}'			)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(snake_case    ,   snake_case    ,   snake_case			)

   # Save pytorch-model (weights and configuration)
   print(f'Save PyTorch model to {pytorch_dump_path}'			)
   model.save_pretrained(snake_case			)

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


if __name__ == "__main__":
   _A		:					int          =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to a folder where the PyTorch model will be placed.''',
   )
   _A		:					int          =							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


