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						):






 '''simple docstring'''






 UpperCAmelCase_	 =					['image_processor', 'tokenizer']
 UpperCAmelCase_	 =					'OneFormerImageProcessor'
 UpperCAmelCase_	 =					('CLIPTokenizer', 'CLIPTokenizerFast')




 def __init__(			self					:  Tuple			,    lowercase__					:  Tuple=None			,    lowercase__					:  Optional[Any]=None			,    lowercase__					:  int = 77			,    lowercase__					:  int = 77			,    **lowercase__					:  Union[str, Any]):







        '''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__(lowercase__			,    lowercase__)




 def 		__snake_case						(			self					:  Dict			,    lowercase__					:  int=None			,    lowercase__					:  List[Any]=77):







        '''simple docstring'''



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

        lowerCAmelCase__     	=					self.tokenizer(lowercase__			,    padding='max_length'			,    max_length=lowercase__			,    truncation=lowercase__)

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

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

        lowerCAmelCase__     	=					torch.cat(lowercase__			,    dim=0)
        return token_inputs




 def __call__(			self					:  List[Any]			,    lowercase__					:  Optional[int]=None			,    lowercase__					:  List[str]=None			,    lowercase__					:  Optional[int]=None			,    **lowercase__					:  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.')

        lowerCAmelCase__     	=					self.image_processor(lowercase__			,    lowercase__			,    lowercase__			,    **lowercase__)

        if isinstance(lowercase__			,    lowercase__):
               lowerCAmelCase__     	=					[task_inputs]

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

        if hasattr(lowercase__			,    'text_inputs'):
               lowerCAmelCase__     	=					encoded_inputs.text_inputs

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

               lowerCAmelCase__     	=					torch.cat(lowercase__			,    dim=0)

        return encoded_inputs




 def 		__snake_case						(			self					:  Any			,    lowercase__					:  Union[str, Any]=None			,    lowercase__					:  Any=None			,    lowercase__					:  Dict=None			,    **lowercase__					:  int):







        '''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(lowercase__			,    lowercase__			,    lowercase__			,    **lowercase__)

        if isinstance(lowercase__			,    lowercase__):
               lowerCAmelCase__     	=					[task_inputs]

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

        if hasattr(lowercase__			,    'text_inputs'):
               lowerCAmelCase__     	=					encoded_inputs.text_inputs

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

               lowerCAmelCase__     	=					torch.cat(lowercase__			,    dim=0)

        return encoded_inputs




 def 		__snake_case						(			self					:  int			,    *lowercase__					:  Dict			,    **lowercase__					:  Dict):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  List[Any]			,    *lowercase__					:  int			,    **lowercase__					:  int):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  Any			,    *lowercase__					:  Union[str, Any]			,    **lowercase__					:  int):







        '''simple docstring'''



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








"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCamelCase     			=  logging.get_logger(__name__)







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			['''pixel_values''']






    def __init__( self	,A = True	,A = None	,A = PILImageResampling.BILINEAR	,A = True	,A = 1 / 255	,A = True	,A = None	,A = None	,**A	,):
          super().__init__(**A					)
          UpperCAmelCase							=       size if size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase							=       get_size_dict(A					)
          UpperCAmelCase							=       do_resize
          UpperCAmelCase							=       do_rescale
          UpperCAmelCase							=       do_normalize
          UpperCAmelCase							=       size
          UpperCAmelCase							=       resample
          UpperCAmelCase							=       rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase							=       image_std if image_std is not None else IMAGENET_STANDARD_STD






    def 				_UpperCamelCase							( self	,A	,A	,A = PILImageResampling.BILINEAR	,A = None	,**A	,):
          UpperCAmelCase							=       get_size_dict(A					)
          if "height" not in size or "width" not in size:
                raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''					)
          return resize(
              A	,size=(size["""height"""], size["""width"""])	,resample=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A = None	,**A					):
          return rescale(A	,scale=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A = None	,**A	,):
          return normalize(A	,mean=A	,std=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = ChannelDimension.FIRST	,**A	,):
          UpperCAmelCase							=       do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase							=       do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase							=       do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase							=       resample if resample is not None else self.resample
          UpperCAmelCase							=       rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase							=       image_std if image_std is not None else self.image_std

          UpperCAmelCase							=       size if size is not None else self.size
          UpperCAmelCase							=       get_size_dict(A					)

          UpperCAmelCase							=       make_list_of_images(A					)

          if not valid_images(A					):
                raise ValueError(
                    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

          if do_resize and size is None:
                raise ValueError("""Size must be specified if do_resize is True."""					)

          if do_rescale and rescale_factor is None:
                raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

          # All transformations expect numpy arrays.
          UpperCAmelCase							=       [to_numpy_array(A					) for image in images]

          if do_resize:
                UpperCAmelCase							=       [self.resize(image=A	,size=A	,resample=A					) for image in images]

          if do_rescale:
                UpperCAmelCase							=       [self.rescale(image=A	,scale=A					) for image in images]

          if do_normalize:
                UpperCAmelCase							=       [self.normalize(image=A	,mean=A	,std=A					) for image in images]

          UpperCAmelCase							=       [to_channel_dimension_format(A	,A					) for image in images]

          UpperCAmelCase							=       {"""pixel_values""": images}
          return BatchFeature(data=A	,tensor_type=A					)



