



from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Any      =			["""image_processor""", """tokenizer"""]
			lowercase__      :  int      =			"""BlipImageProcessor"""
			lowercase__      :  Optional[Any]      =			"""AutoTokenizer"""





			def __init__(				self	:       Tuple			,     lowercase	:       Any			,     lowercase	:       Any   )						->					Any:
									"""simple docstring"""







									__lowercase					=				False
									super().__init__(lowercase			,     lowercase   )
									__lowercase					=				self.image_processor





			def __call__(				self	:       int			,     lowercase	:       ImageInput = None			,     lowercase	:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,     lowercase	:       bool = True			,     lowercase	:       Union[bool, str, PaddingStrategy] = False			,     lowercase	:       Union[bool, str, TruncationStrategy] = None			,     lowercase	:       Optional[int] = None			,     lowercase	:       int = 0			,     lowercase	:       Optional[int] = None			,     lowercase	:       Optional[bool] = None			,     lowercase	:       bool = False			,     lowercase	:       bool = False			,     lowercase	:       bool = False			,     lowercase	:       bool = False			,     lowercase	:       bool = False			,     lowercase	:       bool = True			,     lowercase	:       Optional[Union[str, TensorType]] = None			,     **lowercase	:       Dict			,     )						->					BatchEncoding:
									"""simple docstring"""







									if images is None and text is None:
															raise ValueError("""You have to specify either images or text."""   )

									# Get only text
									if images is None:
															__lowercase					=				self.tokenizer
															__lowercase					=				self.tokenizer(
															    text=lowercase			,     add_special_tokens=lowercase			,     padding=lowercase			,     truncation=lowercase			,     max_length=lowercase			,     stride=lowercase			,     pad_to_multiple_of=lowercase			,     return_attention_mask=lowercase			,     return_overflowing_tokens=lowercase			,     return_special_tokens_mask=lowercase			,     return_offsets_mapping=lowercase			,     return_token_type_ids=lowercase			,     return_length=lowercase			,     verbose=lowercase			,     return_tensors=lowercase			,     **lowercase			,     )
															return text_encoding

									# add pixel_values
									__lowercase					=				self.image_processor(lowercase			,     return_tensors=lowercase   )

									if text is not None:
															__lowercase					=				self.tokenizer(
															    text=lowercase			,     add_special_tokens=lowercase			,     padding=lowercase			,     truncation=lowercase			,     max_length=lowercase			,     stride=lowercase			,     pad_to_multiple_of=lowercase			,     return_attention_mask=lowercase			,     return_overflowing_tokens=lowercase			,     return_special_tokens_mask=lowercase			,     return_offsets_mapping=lowercase			,     return_token_type_ids=lowercase			,     return_length=lowercase			,     verbose=lowercase			,     return_tensors=lowercase			,     **lowercase			,     )
									else:
															__lowercase					=				None

									if text_encoding is not None:
															encoding_image_processor.update(lowercase   )

									return encoding_image_processor





			def 		snake_case__			(				self	:       List[Any]			,     *lowercase	:       Optional[int]			,     **lowercase	:       List[Any]   )						->					str:
									"""simple docstring"""







									return self.tokenizer.batch_decode(*lowercase			,     **lowercase   )





			def 		snake_case__			(				self	:       Optional[int]			,     *lowercase	:       List[Any]			,     **lowercase	:       str   )						->					str:
									"""simple docstring"""







									return self.tokenizer.decode(*lowercase			,     **lowercase   )







			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def 		snake_case__			(				self	:       Optional[int]   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				self.tokenizer.model_input_names
									__lowercase					=				self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )










import random






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__ = False   ) ->      dict:
						__lowercase					=				{i: [] for i in range(lowercase__   )}

						# if probability is greater or equal than 1, then generate a complete graph
						if probability >= 1:
												return complete_graph(lowercase__   )
						# if probability is lower or equal than 0, then return a graph without edges
						if probability <= 0:
												return graph

						# for each couple of nodes, add an edge from u to v
						# if the number randomly generated is greater than probability probability
						for i in range(lowercase__   ):
												for j in range(i + 1						,       lowercase__   ):
																		if random.random() < probability:
																								graph[i].append(lowercase__   )
																								if not directed:
																														# if the graph is undirected, add an edge in from j to i, either
																														graph[j].append(lowercase__   )
						return graph






def  UpperCAmelCase__    (    lowercase__   ) ->      dict:
						return {
						    i: [j for j in range(lowercase__   ) if i != j] for i in range(lowercase__   )
						}


if __name__ == "__main__":
			import doctest

			doctest.testmod()





