





"""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 LevitImageProcessor




class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




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







					def 	_lowerCamelCase						(  self       ):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "do_center_crop": self.do_center_crop,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}






@require_torch
@require_vision
class 			__lowercase     (     _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					LevitImageProcessor if is_vision_available() else None







					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					LevitImageProcessingTester(self       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[int]         =					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		,     '''do_center_crop'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''size'''       )       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					self.image_processing_class.from_dict(self.image_processor_dict       )
									self.assertEqual(image_processor.size		,     {'''shortest_edge''': 18}       )
									self.assertEqual(image_processor.crop_size		,     {'''height''': 18, '''width''': 18}       )

									__a	:  Dict         =					self.image_processing_class.from_dict(self.image_processor_dict		,     size=42		,     crop_size=84       )
									self.assertEqual(image_processor.size		,     {'''shortest_edge''': 42}       )
									self.assertEqual(image_processor.crop_size		,     {'''height''': 84, '''width''': 84}       )







					def 	_lowerCamelCase						(  self       ):
									pass







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

									# Test not batched input
									__a	:  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.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )		,     )

									# Test batched
									__a	:  str         =					image_processing(_UpperCAmelCase		,     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.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processing
									__a	:  List[str]         =					self.image_processing_class(**self.image_processor_dict       )
									# create random numpy tensors
									__a	:  Dict         =					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
									__a	:  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.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )		,     )

									# Test batched
									__a	:  Dict         =					image_processing(_UpperCAmelCase		,     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.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processing
									__a	:  Tuple         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PyTorch tensors
									__a	:  Optional[int]         =					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
									__a	:  List[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.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )		,     )

									# Test batched
									__a	:  Dict         =					image_processing(_UpperCAmelCase		,     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.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )		,     )






"""simple docstring"""






import warnings

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




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					['''image_processor''', '''tokenizer''']
					__lowerCAmelCase					=					'''CLIPImageProcessor'''
					__lowerCAmelCase					=					('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')







					def __init__(  self		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase       ):
									__a	:  Optional[Any]         =					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	:  Any         =					kwargs.pop('''feature_extractor'''       )

									__a	:  Optional[Any]         =					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		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase       ):

									if text is None and images is None:
													raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

									if text is not None:
													__a	:  Any         =					self.tokenizer(_UpperCAmelCase		,     return_tensors=_UpperCAmelCase		,     **_UpperCAmelCase       )

									if images is not None:
													__a	:  List[str]         =					self.image_processor(_UpperCAmelCase		,     return_tensors=_UpperCAmelCase		,     **_UpperCAmelCase       )

									if text is not None and images is not None:
													__a	:  Optional[int]         =					image_features.pixel_values
													return encoding
									elif text is not None:
													return encoding
									else:
													return BatchEncoding(data=dict(**_UpperCAmelCase       )		,     tensor_type=_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									return self.tokenizer.batch_decode(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									return self.tokenizer.decode(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					@property
					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					self.tokenizer.model_input_names
									__a	:  Dict         =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )