


import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


_SCREAMING_SNAKE_CASE :					Any			=	logging.get_logger(__name__)

class  UpperCamelCase__ ( __lowerCamelCase			):
      a__   :     List[Any]								=						'hybrid-clip'
      a__   :     int								=						True






      def __init__(     self					:   Dict,		__lowerCamelCase					:   Optional[int]=5_12,		**__lowerCamelCase					:   Any   )  -> Optional[int]:
           super().__init__(**__lowerCamelCase   )

           if "text_config" not in kwargs:
                raise ValueError('''`text_config` can not be `None`.'''   )

           if "vision_config" not in kwargs:
                raise ValueError('''`vision_config` can not be `None`.'''   )

           UpperCamelCase__							:  List[Any]								=     kwargs.pop('''text_config'''   )
           UpperCamelCase__							:  Tuple								=     kwargs.pop('''vision_config'''   )

           UpperCamelCase__							:  Union[str, Any]								=     text_config.pop('''model_type'''   )
           UpperCamelCase__							:  Optional[int]								=     vision_config.pop('''model_type'''   )

           from transformers import AutoConfig

           UpperCamelCase__							:  Union[str, Any]								=     AutoConfig.for_model(__lowerCamelCase,		**__lowerCamelCase   )

           if vision_model_type == "clip":
                UpperCamelCase__							:  List[str]								=     AutoConfig.for_model(__lowerCamelCase,		**__lowerCamelCase   ).vision_config
           elif vision_model_type == "clip_vision_model":
                from transformers import CLIPVisionConfig

                UpperCamelCase__							:  List[Any]								=     CLIPVisionConfig(**__lowerCamelCase   )
           else:
                UpperCamelCase__							:  Optional[Any]								=     AutoConfig.for_model(__lowerCamelCase,		**__lowerCamelCase   )

           UpperCamelCase__							:  int								=     projection_dim
           UpperCamelCase__							:  Optional[Any]								=     1.0






      @classmethod
      def 	__lowercase(     cls					:   Dict,		__lowerCamelCase					:   PretrainedConfig,		__lowerCamelCase					:   PretrainedConfig,		**__lowerCamelCase					:   Optional[Any]   )  -> Tuple:
           return cls(text_config=text_config.to_dict(),		vision_config=vision_config.to_dict(),		**__lowerCamelCase   )






      def 	__lowercase(     self					:   Dict   )  -> Tuple:
           UpperCamelCase__							:  Any								=     copy.deepcopy(self.__dict__   )
           UpperCamelCase__							:  Any								=     self.text_config.to_dict()
           UpperCamelCase__							:  List[Any]								=     self.vision_config.to_dict()
           UpperCamelCase__							:  Union[str, Any]								=     self.__class__.model_type
           return output








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]]




