



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    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def __init__(   self		:		Optional[Any]      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		Optional[int]=7      ,				__lowercase		:		List[Any]=3      ,				__lowercase		:		Tuple=18      ,				__lowercase		:		Dict=30      ,				__lowercase		:		List[Any]=400      ,				__lowercase		:		int=True      ,				__lowercase		:		str=None      ,				__lowercase		:		List[str]=True      ,				__lowercase		:		Dict=None      ,				__lowercase		:		Optional[int]=True      ,				__lowercase		:		Optional[int]=[0.5, 0.5, 0.5]      ,				__lowercase		:		str=[0.5, 0.5, 0.5]      ,				):

											'''simple docstring'''
											__a       =		size if size is not None else {"""shortest_edge""": 18}
											__a       =		crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											__a       =		parent
											__a       =		batch_size
											__a       =		num_channels
											__a       =		image_size
											__a       =		min_resolution
											__a       =		max_resolution
											__a       =		do_resize
											__a       =		size
											__a       =		do_center_crop
											__a       =		crop_size
											__a       =		do_normalize
											__a       =		image_mean
											__a       =		image_std







							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											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    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Optional[int]    =LevitImageProcessor if is_vision_available() else None





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		LevitImageProcessingTester(self	)





							@property
							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											return self.image_processor_tester.prepare_image_processor_dict()





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		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      ,				"""do_center_crop"""	)	)
											self.assertTrue(hasattr(__A      ,				"""size"""	)	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		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       =		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 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											pass





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											# Initialize image_processing
											__a       =		self.image_processing_class(**self.image_processor_dict	)
											# create random PIL images
											__a       =		prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=__A	)
											for image in image_inputs:
															self.assertIsInstance(__A      ,				Image.Image	)

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





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											# Initialize image_processing
											__a       =		self.image_processing_class(**self.image_processor_dict	)
											# create random numpy tensors
											__a       =		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
											__a       =		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       =		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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )      ,				)







							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											# Initialize image_processing
											__a       =		self.image_processing_class(**self.image_processor_dict	)
											# create random PyTorch tensors
											__a       =		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
											__a       =		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       =		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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )      ,				)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase__			= {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NezhaForNextSentencePrediction""",
						    """NezhaForMaskedLM""",
						    """NezhaForPreTraining""",
						    """NezhaForMultipleChoice""",
						    """NezhaForQuestionAnswering""",
						    """NezhaForSequenceClassification""",
						    """NezhaForTokenClassification""",
						    """NezhaModel""",
						    """NezhaPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
