




'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor







class 				_a   (unittest.TestCase  ):





     '''simple docstring'''



     def __init__(							self       ,__a       ,__a=7       ,__a=3       ,__a=18       ,__a=30       ,__a=400       ,__a=True       ,__a=None       ,__a=True       ,__a=[0.5, 0.5, 0.5]       ,__a=[0.5, 0.5, 0.5]       ,)			->							Optional[int]:
          snake_case :			List[Any]					   = size if size is not None else {"""height""": 18, """width""": 18}
          snake_case :			Dict					   = parent
          snake_case :			Optional[int]					   = batch_size
          snake_case :			Tuple					   = num_channels
          snake_case :			str					   = image_size
          snake_case :			Optional[Any]					   = min_resolution
          snake_case :			Union[str, Any]					   = max_resolution
          snake_case :			Any					   = do_resize
          snake_case :			Dict					   = size
          snake_case :			Tuple					   = do_normalize
          snake_case :			List[Any]					   = image_mean
          snake_case :			Dict					   = image_std







     def  snake_case_			(							self       )			->							List[str]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }







@require_torch
@require_vision
class 				_a   (a__,		unittest.TestCase  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Union[str, Any]									=				ViTImageProcessor if is_vision_available() else None







     def  snake_case_			(							self       )			->							List[Any]:
          snake_case :			List[Any]					   = ViTImageProcessingTester(self       )







     @property
     def  snake_case_			(							self       )			->							Tuple:
          return self.image_processor_tester.prepare_image_processor_dict()







     def  snake_case_			(							self       )			->							Optional[int]:
          snake_case :			Any					   = self.image_processing_class(**self.image_processor_dict       )
          self.assertTrue(hasattr(__a       ,"""image_mean"""       )       )
          self.assertTrue(hasattr(__a       ,"""image_std"""       )       )
          self.assertTrue(hasattr(__a       ,"""do_normalize"""       )       )
          self.assertTrue(hasattr(__a       ,"""do_resize"""       )       )
          self.assertTrue(hasattr(__a       ,"""size"""       )       )







     def  snake_case_			(							self       )			->							int:
          snake_case :			Any					   = self.image_processing_class.from_dict(self.image_processor_dict       )
          self.assertEqual(image_processor.size       ,{"""height""": 18, """width""": 18}       )

          snake_case :			Optional[Any]					   = self.image_processing_class.from_dict(self.image_processor_dict       ,size=42       )
          self.assertEqual(image_processor.size       ,{"""height""": 42, """width""": 42}       )







     def  snake_case_			(							self       )			->							List[str]:
          pass







     def  snake_case_			(							self       )			->							Tuple:
          # Initialize image_processing
          snake_case :			Union[str, Any]					   = self.image_processing_class(**self.image_processor_dict       )
          # create random PIL images
          snake_case :			str					   = prepare_image_inputs(self.image_processor_tester       ,equal_resolution=__a       )
          for image in image_inputs:
               self.assertIsInstance(__a       ,Image.Image       )

          # Test not batched input
          snake_case :			Any					   = image_processing(image_inputs[0]       ,return_tensors="""pt"""       ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )       ,)

          # Test batched
          snake_case :			Dict					   = image_processing(__a       ,return_tensors="""pt"""       ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )       ,)







     def  snake_case_			(							self       )			->							List[Any]:
          # Initialize image_processing
          snake_case :			str					   = self.image_processing_class(**self.image_processor_dict       )
          # create random numpy tensors
          snake_case :			Dict					   = prepare_image_inputs(self.image_processor_tester       ,equal_resolution=__a       ,numpify=__a       )
          for image in image_inputs:
               self.assertIsInstance(__a       ,np.ndarray       )

          # Test not batched input
          snake_case :			Union[str, Any]					   = image_processing(image_inputs[0]       ,return_tensors="""pt"""       ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )       ,)

          # Test batched
          snake_case :			Any					   = image_processing(__a       ,return_tensors="""pt"""       ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )       ,)







     def  snake_case_			(							self       )			->							str:
          # Initialize image_processing
          snake_case :			Optional[Any]					   = self.image_processing_class(**self.image_processor_dict       )
          # create random PyTorch tensors
          snake_case :			List[Any]					   = prepare_image_inputs(self.image_processor_tester       ,equal_resolution=__a       ,torchify=__a       )
          for image in image_inputs:
               self.assertIsInstance(__a       ,torch.Tensor       )

          # Test not batched input
          snake_case :			Dict					   = image_processing(image_inputs[0]       ,return_tensors="""pt"""       ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )       ,)

          # Test batched
          snake_case :			List[Any]					   = image_processing(__a       ,return_tensors="""pt"""       ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )       ,)









'''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   (a__  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Tuple									=				["""image_processor""", """tokenizer"""]
     lowerCAmelCase_     :    Optional[Any]									=				"""OneFormerImageProcessor"""
     lowerCAmelCase_     :    int									=				("""CLIPTokenizer""", """CLIPTokenizerFast""")







     def __init__(							self       ,__a=None       ,__a=None       ,__a = 77       ,__a = 77       ,**__a       )			->							Optional[Any]:
          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`."""       )

          snake_case :			str					   = max_seq_length
          snake_case :			Any					   = task_seq_length

          super().__init__(__a       ,__a       )







     def  snake_case_			(							self       ,__a=None       ,__a=77       )			->							List[str]:
          if text_list is None:
               raise ValueError("""tokens cannot be None."""       )

          snake_case :			int					   = self.tokenizer(__a       ,padding="""max_length"""       ,max_length=__a       ,truncation=__a       )

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

          snake_case :			Tuple					   = []
          for attn_mask, input_id in zip(__a       ,__a       ):
               snake_case :			Dict					   = torch.tensor(__a       ) * torch.tensor(__a       )
               token_inputs.append(token.unsqueeze(0       )       )

          snake_case :			Optional[int]					   = torch.cat(__a       ,dim=0       )
          return token_inputs







     def __call__(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							Optional[int]:

          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."""       )

          snake_case :			Union[str, Any]					   = self.image_processor(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Tuple					   = [task_inputs]

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

          if hasattr(__a       ,"""text_inputs"""       ):
               snake_case :			Optional[int]					   = encoded_inputs.text_inputs

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

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

          return encoded_inputs







     def  snake_case_			(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							List[str]:

          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."""       )

          snake_case :			Any					   = self.image_processor.encode_inputs(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Optional[int]					   = [task_inputs]

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

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

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

               snake_case :			Union[str, Any]					   = torch.cat(__a       ,dim=0       )

          return encoded_inputs







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[str]:
          return self.image_processor.post_process_semantic_segmentation(*__a       ,**__a       )







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[Any]:
          return self.image_processor.post_process_instance_segmentation(*__a       ,**__a       )







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							Optional[Any]:
          return self.image_processor.post_process_panoptic_segmentation(*__a       ,**__a       )



