


import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor


class 			_a				(     unittest.TestCase     ):

							def __init__(				self:      List[str]       ,		UpperCamelCase_:      Tuple       ,		UpperCamelCase_:      bool = True       ,		UpperCamelCase_:      Dict[str, int] = None       ,		UpperCamelCase_:      int = 32       ,		UpperCamelCase_:      bool = True       ,		UpperCamelCase_:      Union[int, float] = 1 / 255       ,		UpperCamelCase_:      bool = True       ,		UpperCamelCase_:      bool = True       ,		UpperCamelCase_:      Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]       ,		UpperCamelCase_:      Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]       ,		UpperCamelCase_:      bool = True       ,		UpperCamelCase_:      Tuple=7       ,		UpperCamelCase_:      Union[str, Any]=30       ,		UpperCamelCase_:      Optional[int]=400       ,		UpperCamelCase_:      List[str]=3       ,		)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= parent
									lowercase__				= do_resize
									lowercase__				= size if size is not None else {'''shortest_edge''': 288}
									lowercase__				= size_divisor
									lowercase__				= do_rescale
									lowercase__				= rescale_factor
									lowercase__				= do_normalize
									lowercase__				= do_center_crop
									lowercase__				= image_mean
									lowercase__				= image_std
									lowercase__				= do_pad
									lowercase__				= batch_size
									lowercase__				= num_channels
									lowercase__				= min_resolution
									lowercase__				= max_resolution

							def  lowerCamelCase_					(				self:      Tuple				)     ->  str:





									"""simple docstring"""







									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "size_divisor": self.size_divisor,
									}


							def  lowerCamelCase_					(				self:      Tuple       ,		UpperCamelCase_:      Optional[Any]       ,		UpperCamelCase_:      Union[str, Any]=False				)     ->  str:





									"""simple docstring"""







									if not batched:
											lowercase__				= self.size['''shortest_edge''']
											lowercase__				= image_inputs[0]
											if isinstance(UpperCamelCase_       ,		Image.Image				):
													lowercase__	,						lowercase__				= image.size
											else:
													lowercase__	,						lowercase__				= image.shape[1], image.shape[2]
											lowercase__				= size / min(UpperCamelCase_       ,		UpperCamelCase_				)
											if h < w:
													lowercase__	,						lowercase__				= size, scale * w
											else:
													lowercase__	,						lowercase__				= scale * h, size

											lowercase__				= int((1_333 / 800) * size				)
											if max(UpperCamelCase_       ,		UpperCamelCase_				) > max_size:
													lowercase__				= max_size / max(UpperCamelCase_       ,		UpperCamelCase_				)
													lowercase__				= newh * scale
													lowercase__				= neww * scale

											lowercase__	,						lowercase__				= int(newh + 0.5				), int(neww + 0.5				)
											lowercase__	,						lowercase__				= (
											    newh // self.size_divisor * self.size_divisor,
											    neww // self.size_divisor * self.size_divisor,
											)

									else:
											lowercase__				= []
											for image in image_inputs:
													lowercase__	,						lowercase__				= self.get_expected_values([image]				)
													expected_values.append((expected_height, expected_width)				)
											lowercase__				= max(UpperCamelCase_       ,		key=lambda UpperCamelCase_				: item[0]				)[0]
											lowercase__				= max(UpperCamelCase_       ,		key=lambda UpperCamelCase_				: item[1]				)[1]

									return expected_height, expected_width


@require_torch
@require_vision
class 			_a				(     UpperCamelCase__	,      unittest.TestCase     ):
							_lowercase      :     List[Any]     =					BridgeTowerImageProcessor if is_vision_available() else None

							def  lowerCamelCase_					(				self:      int				)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= BridgeTowerImageProcessingTester(self				)

							@property
							def  lowerCamelCase_					(				self:      Union[str, Any]				)     ->  Optional[Any]:





									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()

							def  lowerCamelCase_					(				self:      Optional[Any]				)     ->  Union[str, Any]:





									"""simple docstring"""







									lowercase__				= self.image_processing_class(**self.image_processor_dict				)
									self.assertTrue(hasattr(UpperCamelCase_       ,		'''image_mean'''				)				)
									self.assertTrue(hasattr(UpperCamelCase_       ,		'''image_std'''				)				)
									self.assertTrue(hasattr(UpperCamelCase_       ,		'''do_normalize'''				)				)
									self.assertTrue(hasattr(UpperCamelCase_       ,		'''do_resize'''				)				)
									self.assertTrue(hasattr(UpperCamelCase_       ,		'''size'''				)				)
									self.assertTrue(hasattr(UpperCamelCase_       ,		'''size_divisor'''				)				)

							def  lowerCamelCase_					(				self:      Union[str, Any]				)     ->  Optional[int]:





									"""simple docstring"""







									pass

							def  lowerCamelCase_					(				self:      Union[str, Any]				)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= self.image_processing_class(**self.image_processor_dict				)
									# create random PIL images
									lowercase__				= prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=UpperCamelCase_				)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_       ,		Image.Image				)

									# Test not batched input
									lowercase__				= image_processing(image_inputs[0]       ,		return_tensors='''pt'''				).pixel_values

									lowercase__	,						lowercase__				= self.image_processor_tester.get_expected_values(UpperCamelCase_				)
									self.assertEqual(
									    encoded_images.shape       ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,		)

									# Test batched
									lowercase__				= image_processing(UpperCamelCase_       ,		return_tensors='''pt'''				).pixel_values

									lowercase__	,						lowercase__				= self.image_processor_tester.get_expected_values(UpperCamelCase_       ,		batched=UpperCamelCase_				)
									self.assertEqual(
									    encoded_images.shape       ,		(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,		)

							def  lowerCamelCase_					(				self:      Union[str, Any]				)     ->  Dict:





									"""simple docstring"""







									lowercase__				= self.image_processing_class(**self.image_processor_dict				)
									# create random numpy tensors
									lowercase__				= prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=UpperCamelCase_       ,		numpify=UpperCamelCase_				)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_       ,		np.ndarray				)

									# Test not batched input
									lowercase__				= image_processing(image_inputs[0]       ,		return_tensors='''pt'''				).pixel_values

									lowercase__	,						lowercase__				= self.image_processor_tester.get_expected_values(UpperCamelCase_				)
									self.assertEqual(
									    encoded_images.shape       ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,		)

									# Test batched
									lowercase__				= image_processing(UpperCamelCase_       ,		return_tensors='''pt'''				).pixel_values

									lowercase__	,						lowercase__				= self.image_processor_tester.get_expected_values(UpperCamelCase_       ,		batched=UpperCamelCase_				)
									self.assertEqual(
									    encoded_images.shape       ,		(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,		)


							def  lowerCamelCase_					(				self:      int				)     ->  List[Any]:





									"""simple docstring"""







									lowercase__				= self.image_processing_class(**self.image_processor_dict				)
									# create random PyTorch tensors
									lowercase__				= prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=UpperCamelCase_       ,		torchify=UpperCamelCase_				)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_       ,		torch.Tensor				)

									# Test not batched input
									lowercase__				= image_processing(image_inputs[0]       ,		return_tensors='''pt'''				).pixel_values

									lowercase__	,						lowercase__				= self.image_processor_tester.get_expected_values(UpperCamelCase_				)
									self.assertEqual(
									    encoded_images.shape       ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,		)

									# Test batched
									lowercase__				= image_processing(UpperCamelCase_       ,		return_tensors='''pt'''				).pixel_values

									lowercase__	,						lowercase__				= self.image_processor_tester.get_expected_values(UpperCamelCase_       ,		batched=UpperCamelCase_				)
									self.assertEqual(
									    encoded_images.shape       ,		(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,		)








"""simple docstring"""

import warnings

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





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Any     =      ['''image_processor''', '''tokenizer''']
 UpperCAmelCase       :	Optional[int]     =      '''ViTImageProcessor'''
 UpperCAmelCase       :	int     =      ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(					self			:			Tuple       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Tuple=None       ,				**_UpperCAmelCase			:			Dict						):
       _A     =  None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'       ,				_UpperCAmelCase       ,				)
             _A     =  kwargs.pop('feature_extractor'						)

       _A     =  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__(_UpperCAmelCase       ,				_UpperCAmelCase						)

 def __call__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Optional[Any]=None       ,				_UpperCAmelCase			:			Optional[int]=None       ,				**_UpperCAmelCase			:			Union[str, 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:
             _A     =  self.tokenizer(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if visual_prompt is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if images is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

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

 def      lowerCAmelCase_				(					self			:			str       ,				*_UpperCAmelCase			:			List[Any]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.batch_decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				*_UpperCAmelCase			:			List[str]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 @property
 def      lowerCAmelCase_				(					self			:			Dict						):
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor_class



 @property
 def      lowerCAmelCase_				(					self			:			Tuple						):
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor

