


import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()







def 						_lowercase       (     __lowerCamelCase			:					Optional[Any]		,__lowerCamelCase			:					int		,__lowerCamelCase			:					str			)  ->   Optional[Any]:





     '''simple docstring'''





     UpperCamelCase__							:  Union[str, Any]								=     RoFormerConfig.from_json_file(_A			)
     print(F'Building PyTorch model from configuration: {config}'			)
     UpperCamelCase__							:  Optional[int]								=     RoFormerForMaskedLM(_A			)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_A		,_A		,_A			)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'			)
     torch.save(model.state_dict()		,_A		,_use_new_zipfile_serialization=_A			)


if __name__ == "__main__":
      _SCREAMING_SNAKE_CASE :					List[str]			=	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--bert_config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained BERT 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."""
      )
      _SCREAMING_SNAKE_CASE :					Dict			=	parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class  UpperCamelCase__ ( unittest.TestCase			):






      @cached_property
      def 	__lowercase(     self					:   List[Any]   )  -> int:
           return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''   )






      @cached_property
      def 	__lowercase(     self					:   List[Any]   )  -> Tuple:
           return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''   )






      def 	__lowercase(     self					:   List[str]   )  -> Optional[int]:
           UpperCamelCase__							:  Optional[Any]								=     self.tokenizer_ab
           UpperCamelCase__							:  str								=     ''' I am a small frog.'''
           UpperCamelCase__							:  Dict								=     tok([src_text],		padding=__lowerCamelCase,		truncation=__lowerCamelCase   )['''input_ids''']
           UpperCamelCase__							:  List[str]								=     tok.batch_decode(__lowerCamelCase,		skip_special_tokens=__lowerCamelCase,		clean_up_tokenization_spaces=__lowerCamelCase   )[0]
           assert src_text == decoded






      def 	__lowercase(     self					:   int   )  -> Any:
           UpperCamelCase__							:  str								=     self.rust_tokenizer_ab
           UpperCamelCase__							:  Optional[int]								=     ''' I am a small frog.'''
           UpperCamelCase__							:  List[str]								=     tok([src_text],		padding=__lowerCamelCase,		truncation=__lowerCamelCase   )['''input_ids''']
           UpperCamelCase__							:  Optional[int]								=     tok.batch_decode(__lowerCamelCase,		skip_special_tokens=__lowerCamelCase,		clean_up_tokenization_spaces=__lowerCamelCase   )[0]
           assert src_text == decoded






      def 	__lowercase(     self					:   Optional[int]   )  -> Optional[Any]:
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([''' Sam''', '''Sam''']   ).input_ids == [[55_02, 2], [55_02, 2]]






      def 	__lowercase(     self					:   Union[str, Any]   )  -> Optional[int]:
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']   ).input_ids == [[55_02, 2], [55_02, 2]]




