from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest



@require_tokenizers
class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	SqueezeBertTokenizer
     A_					    =	SqueezeBertTokenizerFast
     A_					    =	True



     def 		UpperCamelCase_    (       self					,					**A_    )			->					Union[str, Any]:






           """simple docstring"""
           return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname					,					**A_    )





     @slow
     def 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''    )

           _lowerCamelCase           =					tokenizer.encode('''sequence builders'''					,					add_special_tokens=A_    )
           _lowerCamelCase           =					tokenizer.encode('''multi-sequence build'''					,					add_special_tokens=A_    )

           _lowerCamelCase           =					tokenizer.build_inputs_with_special_tokens(A_    )
           _lowerCamelCase           =					tokenizer.build_inputs_with_special_tokens(A_					,					A_    )

           assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
           assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
               tokenizer.sep_token_id
           ]
import argparse

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


logging.set_verbosity_info()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					CanineConfig()
      _lowerCamelCase           =					CanineModel(__UpperCAmelCase		)
      model.eval()

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

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

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

      # Save tokenizer files
      _lowerCamelCase           =					CanineTokenizer()
      print(F'Save tokenizer files to {pytorch_dump_path}'		)
      tokenizer.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__						      =			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.',
     )
     snake_case__						      =			parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)