
"""simple docstring"""
from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
			import torch




class  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    ['''image_processor''', '''tokenizer''']
							SCREAMING_SNAKE_CASE		  =    '''OneFormerImageProcessor'''
							SCREAMING_SNAKE_CASE		  =    ('''CLIPTokenizer''', '''CLIPTokenizerFast''')






							def __init__(       self    :      List[Any]    ,			snake_case__    :      List[str]=None    ,			snake_case__    :      Optional[Any]=None    ,			snake_case__    :      int = 7_7    ,			snake_case__    :      int = 7_7    ,			**snake_case__    :      int     ):






												"""simple docstring"""



												if image_processor is None:
																	raise ValueError("""You need to specify an `image_processor`."""     )
												if tokenizer is None:
																	raise ValueError("""You need to specify a `tokenizer`."""     )

												__a:    Any		     =       max_seq_length
												__a:    Union[str, Any]		     =       task_seq_length

												super().__init__(snake_case__    ,			snake_case__     )






							def 				__lowercase	(       self    :      Optional[Any]    ,			snake_case__    :      Optional[Any]=None    ,			snake_case__    :      Optional[int]=7_7     ):






												"""simple docstring"""



												if text_list is None:
																	raise ValueError("""tokens cannot be None."""     )

												__a:    str		     =       self.tokenizer(snake_case__    ,			padding="""max_length"""    ,			max_length=snake_case__    ,			truncation=snake_case__     )

												__a     ,					__a:    List[Any]		     =       tokens["""attention_mask"""], tokens["""input_ids"""]

												__a:    int		     =       []
												for attn_mask, input_id in zip(snake_case__    ,			snake_case__     ):
																	__a:    Optional[Any]		     =       torch.tensor(snake_case__     ) * torch.tensor(snake_case__     )
																	token_inputs.append(token.unsqueeze(0     )     )

												__a:    Dict		     =       torch.cat(snake_case__    ,			dim=0     )
												return token_inputs






							def __call__(       self    :      List[str]    ,			snake_case__    :      Union[str, Any]=None    ,			snake_case__    :      List[str]=None    ,			snake_case__    :      Any=None    ,			**snake_case__    :      Tuple     ):






												"""simple docstring"""



												if task_inputs is None:
																	raise ValueError("""You have to specify the task_input. Found None."""     )
												elif images is None:
																	raise ValueError("""You have to specify the image. Found None."""     )

												if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs     ):
																	raise ValueError("""task_inputs must be semantic, instance, or panoptic."""     )

												__a:    Optional[int]		     =       self.image_processor(snake_case__    ,			snake_case__    ,			snake_case__    ,			**snake_case__     )

												if isinstance(snake_case__    ,			snake_case__     ):
																	__a:    List[Any]		     =       [task_inputs]

												if isinstance(snake_case__    ,			snake_case__     ) and all(isinstance(snake_case__    ,			snake_case__     ) for task_input in task_inputs     ):
																	__a:    Any		     =       []
																	for task in task_inputs:
																						__a:    Optional[int]		     =       f"""the task is {task}"""
																						task_token_inputs.append(snake_case__     )
																	__a:    Optional[int]		     =       self._preprocess_text(snake_case__    ,			max_length=self.task_seq_length     )
												else:
																	raise TypeError("""Task Inputs should be a string or a list of strings."""     )

												if hasattr(snake_case__    ,			"""text_inputs"""     ):
																	__a:    str		     =       encoded_inputs.text_inputs

																	__a:    Optional[Any]		     =       []
																	for texts in texts_list:
																						__a:    Dict		     =       self._preprocess_text(snake_case__    ,			max_length=self.max_seq_length     )
																						text_inputs.append(text_input_list.unsqueeze(0     )     )

																	__a:    Optional[int]		     =       torch.cat(snake_case__    ,			dim=0     )

												return encoded_inputs






							def 				__lowercase	(       self    :      Optional[int]    ,			snake_case__    :      Optional[Any]=None    ,			snake_case__    :      str=None    ,			snake_case__    :      List[str]=None    ,			**snake_case__    :      List[Any]     ):






												"""simple docstring"""



												if task_inputs is None:
																	raise ValueError("""You have to specify the task_input. Found None."""     )
												elif images is None:
																	raise ValueError("""You have to specify the image. Found None."""     )

												if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs     ):
																	raise ValueError("""task_inputs must be semantic, instance, or panoptic."""     )

												__a:    List[Any]		     =       self.image_processor.encode_inputs(snake_case__    ,			snake_case__    ,			snake_case__    ,			**snake_case__     )

												if isinstance(snake_case__    ,			snake_case__     ):
																	__a:    str		     =       [task_inputs]

												if isinstance(snake_case__    ,			snake_case__     ) and all(isinstance(snake_case__    ,			snake_case__     ) for task_input in task_inputs     ):
																	__a:    Dict		     =       []
																	for task in task_inputs:
																						__a:    str		     =       f"""the task is {task}"""
																						task_token_inputs.append(snake_case__     )
																	__a:    Dict		     =       self._preprocess_text(snake_case__    ,			max_length=self.task_seq_length     )
												else:
																	raise TypeError("""Task Inputs should be a string or a list of strings."""     )

												if hasattr(snake_case__    ,			"""text_inputs"""     ):
																	__a:    Dict		     =       encoded_inputs.text_inputs

																	__a:    Any		     =       []
																	for texts in texts_list:
																						__a:    Any		     =       self._preprocess_text(snake_case__    ,			max_length=self.max_seq_length     )
																						text_inputs.append(text_input_list.unsqueeze(0     )     )

																	__a:    Tuple		     =       torch.cat(snake_case__    ,			dim=0     )

												return encoded_inputs






							def 				__lowercase	(       self    :      Tuple    ,			*snake_case__    :      Optional[int]    ,			**snake_case__    :      Tuple     ):






												"""simple docstring"""



												return self.image_processor.post_process_semantic_segmentation(*snake_case__    ,			**snake_case__     )






							def 				__lowercase	(       self    :      List[str]    ,			*snake_case__    :      Optional[int]    ,			**snake_case__    :      List[str]     ):






												"""simple docstring"""



												return self.image_processor.post_process_instance_segmentation(*snake_case__    ,			**snake_case__     )






							def 				__lowercase	(       self    :      Dict    ,			*snake_case__    :      Optional[int]    ,			**snake_case__    :      Dict     ):






												"""simple docstring"""



												return self.image_processor.post_process_panoptic_segmentation(*snake_case__    ,			**snake_case__     )


"""simple docstring"""
import unittest

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




class  _snake_case	(    unittest.TestCase							):






							@cached_property
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""     )






							@cached_property
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""     )






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												__a:    Optional[int]		     =       self.tokenizer_ab
												__a:    Union[str, Any]		     =       """ I am a small frog."""
												__a:    str		     =       tok([src_text]    ,			padding=snake_case__    ,			truncation=snake_case__     )["""input_ids"""]
												__a:    Optional[Any]		     =       tok.batch_decode(snake_case__    ,			skip_special_tokens=snake_case__    ,			clean_up_tokenization_spaces=snake_case__     )[0]
												assert src_text == decoded






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    str		     =       self.rust_tokenizer_ab
												__a:    Optional[Any]		     =       """ I am a small frog."""
												__a:    str		     =       tok([src_text]    ,			padding=snake_case__    ,			truncation=snake_case__     )["""input_ids"""]
												__a:    Union[str, Any]		     =       tok.batch_decode(snake_case__    ,			skip_special_tokens=snake_case__    ,			clean_up_tokenization_spaces=snake_case__     )[0]
												assert src_text == decoded






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												assert self.tokenizer_ab.add_prefix_space
												assert self.tokenizer_ab([""" Sam""", """Sam"""]     ).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												assert self.rust_tokenizer_ab.add_prefix_space
												assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]     ).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]
