



import copy

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


UpperCAmelCase								=   logging.get_logger(__name__)

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Optional[Any]             =   """hybrid-clip"""
							UpperCamelCase__			: Optional[int]             =   True


							def __init__(							self					,					a_=512					,					**a_						):
											super().__init__(**a_						)

											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`."""						)

											a__			 =						kwargs.pop("""text_config"""						)
											a__			 =						kwargs.pop("""vision_config"""						)

											a__			 =						text_config.pop("""model_type"""						)
											a__			 =						vision_config.pop("""model_type"""						)

											from transformers import AutoConfig

											a__			 =						AutoConfig.for_model(a_					,					**a_						)

											if vision_model_type == "clip":
															a__			 =						AutoConfig.for_model(a_					,					**a_						).vision_config
											elif vision_model_type == "clip_vision_model":
															from transformers import CLIPVisionConfig

															a__			 =						CLIPVisionConfig(**a_						)
											else:
															a__			 =						AutoConfig.for_model(a_					,					**a_						)

											a__			 =						projection_dim
											a__			 =						1.0


							@classmethod
							def _a (							cls					,					a_					,					a_					,					**a_						):
											return cls(text_config=text_config.to_dict()					,					vision_config=vision_config.to_dict()					,					**a_						)







							def _a (							self						):
											a__			 =						copy.deepcopy(self.__dict__						)
											a__			 =						self.text_config.to_dict()
											a__			 =						self.vision_config.to_dict()
											a__			 =						self.__class__.model_type
											return output






import unittest

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

class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							@cached_property
							def _a (							self						):
											return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""						)


							@cached_property
							def _a (							self						):
											return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""						)


							def _a (							self						):
											a__			 =						self.tokenizer_ab
											a__			 =						""" I am a small frog."""
											a__			 =						tok([src_text]					,					padding=a_					,					truncation=a_						)["""input_ids"""]
											a__			 =						tok.batch_decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)[0]
											assert src_text == decoded


							def _a (							self						):
											a__			 =						self.rust_tokenizer_ab
											a__			 =						""" I am a small frog."""
											a__			 =						tok([src_text]					,					padding=a_					,					truncation=a_						)["""input_ids"""]
											a__			 =						tok.batch_decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)[0]
											assert src_text == decoded


							def _a (							self						):
											assert self.tokenizer_ab.add_prefix_space
											assert self.tokenizer_ab([""" Sam""", """Sam"""]						).input_ids == [[5_502, 2], [5_502, 2]]







							def _a (							self						):
											assert self.rust_tokenizer_ab.add_prefix_space
											assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]						).input_ids == [[5_502, 2], [5_502, 2]]

