
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__	(			__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase       :					Dict					 =    {
          """sld_warmup_steps""": 15,
          """sld_guidance_scale""": 20,
          """sld_threshold""": 0.0,
          """sld_momentum_scale""": 0.0,
          """sld_mom_beta""": 0.0,
      }
      _UpperCAmelCase       :					Optional[Any]					 =    {
          """sld_warmup_steps""": 10,
          """sld_guidance_scale""": 1000,
          """sld_threshold""": 0.01,
          """sld_momentum_scale""": 0.3,
          """sld_mom_beta""": 0.4,
      }
      _UpperCAmelCase       :					List[Any]					 =    {
          """sld_warmup_steps""": 7,
          """sld_guidance_scale""": 2000,
          """sld_threshold""": 0.025,
          """sld_momentum_scale""": 0.5,
          """sld_mom_beta""": 0.7,
      }
      _UpperCAmelCase       :					Dict					 =    {
          """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__	(			__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase       :					Tuple					 =    42
      _UpperCAmelCase       :					Optional[Any]					 =    42
      _UpperCAmelCase       :					Optional[int]					 =    42
      _UpperCAmelCase       :					Optional[Any]					 =    42


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






from typing import List

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


if is_torch_available():
      import torch





class 							A__	(			__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase       :					Optional[int]					 =    ["""image_processor""", """tokenizer"""]
      _UpperCAmelCase       :					List[Any]					 =    """OneFormerImageProcessor"""
      _UpperCAmelCase       :					Optional[int]					 =    ("""CLIPTokenizer""", """CLIPTokenizerFast""")
      def __init__(   self					,						__magic_name__=None					,						__magic_name__=None					,						__magic_name__ = 7_7					,						__magic_name__ = 7_7					,						**__magic_name__   ):
       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	:			List[Any]           =   max_seq_length
       lowerCamelCase	:			Dict           =   task_seq_length

       super().__init__(__magic_name__					,						__magic_name__   )
      def 		UpperCamelCase__							(   self					,						__magic_name__=None					,						__magic_name__=7_7   ):
       if text_list is None:
        raise ValueError("""tokens cannot be None."""   )

       lowerCamelCase	:			Tuple           =   self.tokenizer(__magic_name__					,						padding="""max_length"""					,						max_length=__magic_name__					,						truncation=__magic_name__   )

       lowerCamelCase    ,					lowerCamelCase	:			int           =   tokens["""attention_mask"""], tokens["""input_ids"""]

       lowerCamelCase	:			List[str]           =   []
       for attn_mask, input_id in zip(__magic_name__					,						__magic_name__   ):
        lowerCamelCase	:			Optional[int]           =   torch.tensor(__magic_name__   ) * torch.tensor(__magic_name__   )
        token_inputs.append(token.unsqueeze(0   )   )

       lowerCamelCase	:			Optional[Any]           =   torch.cat(__magic_name__					,						dim=0   )
       return token_inputs
      def __call__(   self					,						__magic_name__=None					,						__magic_name__=None					,						__magic_name__=None					,						**__magic_name__   ):

       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	:			Any           =   self.image_processor(__magic_name__					,						__magic_name__					,						__magic_name__					,						**__magic_name__   )

       if isinstance(__magic_name__					,						__magic_name__   ):
        lowerCamelCase	:			List[str]           =   [task_inputs]

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

       if hasattr(__magic_name__					,						"""text_inputs"""   ):
        lowerCamelCase	:			List[str]           =   encoded_inputs.text_inputs

        lowerCamelCase	:			str           =   []
        for texts in texts_list:
         lowerCamelCase	:			int           =   self._preprocess_text(__magic_name__					,						max_length=self.max_seq_length   )
         text_inputs.append(text_input_list.unsqueeze(0   )   )

        lowerCamelCase	:			int           =   torch.cat(__magic_name__					,						dim=0   )

       return encoded_inputs
      def 		UpperCamelCase__							(   self					,						__magic_name__=None					,						__magic_name__=None					,						__magic_name__=None					,						**__magic_name__   ):

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

       if isinstance(__magic_name__					,						__magic_name__   ):
        lowerCamelCase	:			int           =   [task_inputs]

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

       if hasattr(__magic_name__					,						"""text_inputs"""   ):
        lowerCamelCase	:			int           =   encoded_inputs.text_inputs

        lowerCamelCase	:			Dict           =   []
        for texts in texts_list:
         lowerCamelCase	:			Union[str, Any]           =   self._preprocess_text(__magic_name__					,						max_length=self.max_seq_length   )
         text_inputs.append(text_input_list.unsqueeze(0   )   )

        lowerCamelCase	:			Optional[int]           =   torch.cat(__magic_name__					,						dim=0   )

       return encoded_inputs
      def 		UpperCamelCase__							(   self					,						*__magic_name__					,						**__magic_name__   ):
       return self.image_processor.post_process_semantic_segmentation(*__magic_name__					,						**__magic_name__   )
      def 		UpperCamelCase__							(   self					,						*__magic_name__					,						**__magic_name__   ):
       return self.image_processor.post_process_instance_segmentation(*__magic_name__					,						**__magic_name__   )






      def 		UpperCamelCase__							(   self					,						*__magic_name__					,						**__magic_name__   ):
       return self.image_processor.post_process_panoptic_segmentation(*__magic_name__					,						**__magic_name__   )




