




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 GLPNImageProcessor




class 			UpperCAmelCase_      (   unittest.TestCase       ):




       '''simple docstring'''



       def __init__(					self       :	Tuple       ,     a       :	Union[str, Any]       ,     a       :	Tuple=7       ,     a       :	int=3       ,     a       :	List[str]=18       ,     a       :	int=30       ,     a       :	Optional[int]=400       ,     a       :	Tuple=True       ,     a       :	Optional[int]=32       ,     a       :	str=True       ,     )			->					Optional[int]:
          SCREAMING_SNAKE_CASE           =					parent
          SCREAMING_SNAKE_CASE           =					batch_size
          SCREAMING_SNAKE_CASE           =					num_channels
          SCREAMING_SNAKE_CASE           =					image_size
          SCREAMING_SNAKE_CASE           =					min_resolution
          SCREAMING_SNAKE_CASE           =					max_resolution
          SCREAMING_SNAKE_CASE           =					do_resize
          SCREAMING_SNAKE_CASE           =					size_divisor
          SCREAMING_SNAKE_CASE           =					do_rescale







       def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[int]:
          return {
              "do_resize": self.do_resize,
              "size_divisor": self.size_divisor,
              "do_rescale": self.do_rescale,
          }





@require_torch
@require_vision
class 			UpperCAmelCase_      (   A		,      unittest.TestCase       ):




       '''simple docstring'''



       a__						  =     GLPNImageProcessor if is_vision_available() else None





       def    _UpperCAmelCase     (					self       :	Dict			)			->					int:
          SCREAMING_SNAKE_CASE           =					GLPNImageProcessingTester(self			)





       @property
       def    _UpperCAmelCase     (					self       :	List[Any]			)			->					List[str]:
          return self.image_processor_tester.prepare_image_processor_dict()





       def    _UpperCAmelCase     (					self       :	Any			)			->					Tuple:
          SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(A__       ,     """do_resize"""			)			)
          self.assertTrue(hasattr(A__       ,     """size_divisor"""			)			)
          self.assertTrue(hasattr(A__       ,     """resample"""			)			)
          self.assertTrue(hasattr(A__       ,     """do_rescale"""			)			)





       def    _UpperCAmelCase     (					self       :	Any			)			->					Dict:
          pass





       def    _UpperCAmelCase     (					self       :	Dict			)			->					Optional[int]:
          # Initialize image_processing
          SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          SCREAMING_SNAKE_CASE           =					prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=A__			)
          for image in image_inputs:
             self.assertIsInstance(A__       ,     Image.Image			)

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          SCREAMING_SNAKE_CASE           =					image_processing(image_inputs[0]       ,     return_tensors="""pt"""			).pixel_values
          self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0			)
          self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0			)





       def    _UpperCAmelCase     (					self       :	str			)			->					Optional[int]:
          # Initialize image_processing
          SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          SCREAMING_SNAKE_CASE           =					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 (GLPNImageProcessor doesn't support batching)
          SCREAMING_SNAKE_CASE           =					image_processing(image_inputs[0]       ,     return_tensors="""pt"""			).pixel_values
          self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0			)
          self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0			)







       def    _UpperCAmelCase     (					self       :	Tuple			)			->					Optional[int]:
          # Initialize image_processing
          SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          SCREAMING_SNAKE_CASE           =					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 (GLPNImageProcessor doesn't support batching)
          SCREAMING_SNAKE_CASE           =					image_processing(image_inputs[0]       ,     return_tensors="""pt"""			).pixel_values
          self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0			)
          self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0			)












import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     ['''image_processor''', '''tokenizer''']
							a__						  =     '''ViTImageProcessor'''
							a__						  =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')





							def __init__(					self       :	List[str]       ,     a       :	Dict=None       ,     a       :	List[str]=None       ,     **a       :	List[str]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
													    """ instead."""       ,     a       ,     )
													SCREAMING_SNAKE_CASE           =					kwargs.pop("""feature_extractor"""			)

										SCREAMING_SNAKE_CASE           =					image_processor if image_processor is not None else feature_extractor
										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`."""			)

										super().__init__(a       ,     a			)





							def __call__(					self       :	List[Any]       ,     a       :	int=None       ,     a       :	Dict=None       ,     a       :	int=None       ,     a       :	Tuple=None       ,     **a       :	Any			)			->					Any:
										if text is None and visual_prompt is None and images is None:
													raise ValueError("""You have to specify either text, visual prompt or images."""			)

										if text is not None and visual_prompt is not None:
													raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""			)

										if text is not None:
													SCREAMING_SNAKE_CASE           =					self.tokenizer(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if images is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """pixel_values""": image_features.pixel_values,
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										elif text is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										elif visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										else:
													return BatchEncoding(data=dict(**a			)       ,     tensor_type=a			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     *a       :	Dict       ,     **a       :	Union[str, Any]			)			->					Any:
										return self.tokenizer.batch_decode(*a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Tuple       ,     *a       :	List[Any]       ,     **a       :	Union[str, Any]			)			->					Dict:
										return self.tokenizer.decode(*a       ,     **a			)





							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[Any]:
										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,     a       ,     )
										return self.image_processor_class







							@property
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					str:
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,     a       ,     )
										return self.image_processor






