



'''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 GLPNImageProcessor

class 	__magic_name__						(    unittest.TestCase   ):







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







							def 							__lowercase			(							self					:       Any		):
									return {
									    "do_resize": self.do_resize,
									    "size_divisor": self.size_divisor,
									    "do_rescale": self.do_rescale,
									}

@require_torch
@require_vision
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    str   				=					GLPNImageProcessor if is_vision_available() else None







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      List[Any]        =							GLPNImageProcessingTester(self		)







							@property
							def 							__lowercase			(							self					:       List[Any]		):
									return self.image_processor_tester.prepare_image_processor_dict()







							def 							__lowercase			(							self					:       int		):
									_a     :      Dict        =							self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(_UpperCAmelCase	,'do_resize'		)		)
									self.assertTrue(hasattr(_UpperCAmelCase	,'size_divisor'		)		)
									self.assertTrue(hasattr(_UpperCAmelCase	,'resample'		)		)
									self.assertTrue(hasattr(_UpperCAmelCase	,'do_rescale'		)		)







							def 							__lowercase			(							self					:       Any		):
									pass







							def 							__lowercase			(							self					:       Union[str, Any]		):
									# Initialize image_processing
									_a     :      Tuple        =							self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									_a     :      Union[str, 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 (GLPNImageProcessor doesn't support batching)
									_a     :      List[Any]        =							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 							__lowercase			(							self					:       Optional[Any]		):
									# Initialize image_processing
									_a     :      Optional[Any]        =							self.image_processing_class(**self.image_processor_dict		)
									# create random numpy tensors
									_a     :      Any        =							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 (GLPNImageProcessor doesn't support batching)
									_a     :      str        =							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 							__lowercase			(							self					:       Dict		):
									# Initialize image_processing
									_a     :      Optional[Any]        =							self.image_processing_class(**self.image_processor_dict		)
									# create random PyTorch tensors
									_a     :      Any        =							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 (GLPNImageProcessor doesn't support batching)
									_a     :      str        =							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		)









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {
    '''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LiltForQuestionAnswering''',
							    '''LiltForSequenceClassification''',
							    '''LiltForTokenClassification''',
							    '''LiltModel''',
							    '''LiltPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_lilt import (
														    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LiltForQuestionAnswering,
														    LiltForSequenceClassification,
														    LiltForTokenClassification,
														    LiltModel,
														    LiltPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




