


"""simple docstring"""



from typing import List

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


if is_torch_available():
		import torch







class       __a	(   __snake_case       ):
				lowerCamelCase    :					str 					=['image_processor', 'tokenizer']
				lowerCamelCase    :					str 					='OneFormerImageProcessor'
				lowerCamelCase    :					Tuple 					=('CLIPTokenizer', 'CLIPTokenizerFast')







				def __init__(   self			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  UpperCAmelCase = 77			,  UpperCAmelCase = 77			,  **UpperCAmelCase      ):

					'''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`.'''      )

					lowerCAmelCase_			       = max_seq_length
					lowerCAmelCase_			       = task_seq_length

					super().__init__(UpperCAmelCase			,  UpperCAmelCase      )







				def     lowerCamelCase_    (   self			,  UpperCAmelCase=None			,  UpperCAmelCase=77      ):

					'''simple docstring'''






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

					lowerCAmelCase_			       = self.tokenizer(UpperCAmelCase			,  padding='''max_length'''			,  max_length=UpperCAmelCase			,  truncation=UpperCAmelCase      )

					lowerCAmelCase_       ,  lowerCAmelCase_			       = tokens['''attention_mask'''], tokens['''input_ids''']

					lowerCAmelCase_			       = []
					for attn_mask, input_id in zip(UpperCAmelCase			,  UpperCAmelCase      ):
						lowerCAmelCase_			       = torch.tensor(UpperCAmelCase      ) * torch.tensor(UpperCAmelCase      )
						token_inputs.append(token.unsqueeze(0      )      )

					lowerCAmelCase_			       = torch.cat(UpperCAmelCase			,  dim=0      )
					return token_inputs







				def __call__(   self			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  **UpperCAmelCase      ):

					'''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.'''      )

					lowerCAmelCase_			       = self.image_processor(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  **UpperCAmelCase      )

					if isinstance(UpperCAmelCase			,  UpperCAmelCase      ):
						lowerCAmelCase_			       = [task_inputs]

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

					if hasattr(UpperCAmelCase			,  '''text_inputs'''      ):
						lowerCAmelCase_			       = encoded_inputs.text_inputs

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

						lowerCAmelCase_			       = torch.cat(UpperCAmelCase			,  dim=0      )

					return encoded_inputs







				def     lowerCamelCase_    (   self			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  **UpperCAmelCase      ):

					'''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.'''      )

					lowerCAmelCase_			       = self.image_processor.encode_inputs(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  **UpperCAmelCase      )

					if isinstance(UpperCAmelCase			,  UpperCAmelCase      ):
						lowerCAmelCase_			       = [task_inputs]

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

					if hasattr(UpperCAmelCase			,  '''text_inputs'''      ):
						lowerCAmelCase_			       = encoded_inputs.text_inputs

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

						lowerCAmelCase_			       = torch.cat(UpperCAmelCase			,  dim=0      )

					return encoded_inputs







				def     lowerCamelCase_    (   self			,  *UpperCAmelCase			,  **UpperCAmelCase      ):

					'''simple docstring'''






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







				def     lowerCamelCase_    (   self			,  *UpperCAmelCase			,  **UpperCAmelCase      ):

					'''simple docstring'''






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







				def     lowerCamelCase_    (   self			,  *UpperCAmelCase			,  **UpperCAmelCase      ):

					'''simple docstring'''






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



"""simple docstring"""



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
class       __a	(   __snake_case       ):
				lowerCamelCase    :					int 					={
				    'sld_warmup_steps': 15,
				    'sld_guidance_scale': 20,
				    'sld_threshold': 0.0,
				    'sld_momentum_scale': 0.0,
				    'sld_mom_beta': 0.0,
				}
				lowerCamelCase    :					Optional[Any] 					={
				    'sld_warmup_steps': 10,
				    'sld_guidance_scale': 1000,
				    'sld_threshold': 0.01,
				    'sld_momentum_scale': 0.3,
				    'sld_mom_beta': 0.4,
				}
				lowerCamelCase    :					List[str] 					={
				    'sld_warmup_steps': 7,
				    'sld_guidance_scale': 2000,
				    'sld_threshold': 0.025,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}
				lowerCamelCase    :					Any 					={
				    'sld_warmup_steps': 0,
				    'sld_guidance_scale': 5000,
				    'sld_threshold': 1.0,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}







@dataclass
class       __a	(   __snake_case       ):
				lowerCamelCase    :					Union[List[PIL.Image.Image], np.ndarray]
				lowerCamelCase    :					Optional[List[bool]]
				lowerCamelCase    :					Optional[Union[List[PIL.Image.Image], np.ndarray]]
				lowerCamelCase    :					Optional[str]


if is_transformers_available() and is_torch_available():
		from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
		from .safety_checker import SafeStableDiffusionSafetyChecker