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 ViTImageProcessor





class 					lowercase__						(   unittest.TestCase):


						def __init__(       self	:				Optional[Any]    ,		UpperCamelCase__	:				Dict    ,		UpperCamelCase__	:				List[Any]=13    ,		UpperCamelCase__	:				Any=3    ,		UpperCamelCase__	:				Union[str, Any]=224    ,		UpperCamelCase__	:				List[Any]=30    ,		UpperCamelCase__	:				int=400    ,		UpperCamelCase__	:				Optional[Any]=True    ,		UpperCamelCase__	:				str=None    ,		UpperCamelCase__	:				Union[str, Any]=True    ,		UpperCamelCase__	:				Any=[0.5, 0.5, 0.5]    ,		UpperCamelCase__	:				Any=[0.5, 0.5, 0.5]    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							size if size is not None else {'''height''': 18, '''width''': 18}
												SCREAMING_SNAKE_CASE							:    List[str]							=							parent
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							batch_size
												SCREAMING_SNAKE_CASE							:    str							=							num_channels
												SCREAMING_SNAKE_CASE							:    Tuple							=							image_size
												SCREAMING_SNAKE_CASE							:    Dict							=							min_resolution
												SCREAMING_SNAKE_CASE							:    Dict							=							max_resolution
												SCREAMING_SNAKE_CASE							:    str							=							do_resize
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							size
												SCREAMING_SNAKE_CASE							:    int							=							do_normalize
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							image_mean
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							image_std



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





												'''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,
												}






@require_torch
@require_vision
class 					lowercase__						(   UpperCamelCase_	,							unittest.TestCase):
						UpperCamelCase_     =		ViTImageProcessor if is_vision_available() else None


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							EfficientFormerImageProcessorTester(self						)


						@property
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												return self.image_proc_tester.prepare_image_processor_dict()


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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'''						)						)


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												pass


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							self.image_processing_class(**self.image_processor_dict						)
												# create random PIL images
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							prepare_image_inputs(self.image_proc_tester    ,		equal_resolution=UpperCamelCase__						)
												for image in image_inputs:
																		self.assertIsInstance(UpperCamelCase__    ,		Image.Image						)

												# Test not batched input
												SCREAMING_SNAKE_CASE							:    Dict							=							image_processor(image_inputs[0]    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['''height'''],
												        self.image_proc_tester.size['''width'''],
												    )    ,		)

												# Test batched
												SCREAMING_SNAKE_CASE							:    int							=							image_processor(UpperCamelCase__    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['''height'''],
												        self.image_proc_tester.size['''width'''],
												    )    ,		)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.image_processing_class(**self.image_processor_dict						)
												# create random numpy tensors
												SCREAMING_SNAKE_CASE							:    List[Any]							=							prepare_image_inputs(self.image_proc_tester    ,		equal_resolution=UpperCamelCase__    ,		numpify=UpperCamelCase__						)
												for image in image_inputs:
																		self.assertIsInstance(UpperCamelCase__    ,		np.ndarray						)

												# Test not batched input
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							image_processor(image_inputs[0]    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['''height'''],
												        self.image_proc_tester.size['''width'''],
												    )    ,		)

												# Test batched
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							image_processor(UpperCamelCase__    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['''height'''],
												        self.image_proc_tester.size['''width'''],
												    )    ,		)



						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.image_processing_class(**self.image_processor_dict						)
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE							:    Any							=							prepare_image_inputs(self.image_proc_tester    ,		equal_resolution=UpperCamelCase__    ,		torchify=UpperCamelCase__						)
												for image in image_inputs:
																		self.assertIsInstance(UpperCamelCase__    ,		torch.Tensor						)

												# Test not batched input
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							image_processor(image_inputs[0]    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['''height'''],
												        self.image_proc_tester.size['''width'''],
												    )    ,		)

												# Test batched
												SCREAMING_SNAKE_CASE							:    int							=							image_processor(UpperCamelCase__    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['''height'''],
												        self.image_proc_tester.size['''width'''],
												    )    ,		)






from typing import TYPE_CHECKING

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


__UpperCamelCase		:       int									=     {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       int									=     ['YolosFeatureExtractor']
					__UpperCamelCase		:       Union[str, Any]									=     ['YolosImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'YolosForObjectDetection',
					    'YolosModel',
					    'YolosPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_yolos import YolosFeatureExtractor
										from .image_processing_yolos import YolosImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_yolos import (
										    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    YolosForObjectDetection,
										    YolosModel,
										    YolosPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase		:       int									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





