



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 ConvNextImageProcessor







class 				a_    (						unittest.TestCase							):

						def __init__(       self      :str  ,		_lowercase      :Optional[int]  ,		_lowercase      :int=7  ,		_lowercase      :Optional[Any]=3  ,		_lowercase      :Dict=18  ,		_lowercase      :List[Any]=30  ,		_lowercase      :str=400  ,		_lowercase      :List[Any]=True  ,		_lowercase      :int=None  ,		_lowercase      :Tuple=0.875  ,		_lowercase      :Optional[Any]=True  ,		_lowercase      :Union[str, Any]=[0.5, 0.5, 0.5]  ,		_lowercase      :List[str]=[0.5, 0.5, 0.5]  ,		)						->   str:
												UpperCAmelCase_					=	size if size is not None else {'''shortest_edge''': 20}
												UpperCAmelCase_					=	parent
												UpperCAmelCase_					=	batch_size
												UpperCAmelCase_					=	num_channels
												UpperCAmelCase_					=	image_size
												UpperCAmelCase_					=	min_resolution
												UpperCAmelCase_					=	max_resolution
												UpperCAmelCase_					=	do_resize
												UpperCAmelCase_					=	size
												UpperCAmelCase_					=	crop_pct
												UpperCAmelCase_					=	do_normalize
												UpperCAmelCase_					=	image_mean
												UpperCAmelCase_					=	image_std




						def       __a			(       self      :Dict)						->   int:
												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "crop_pct": self.crop_pct,
												}







@require_torch
@require_vision
class 				a_    (						_snake_case   ,					unittest.TestCase							):
						UpperCamelCase__ :		Dict										=ConvNextImageProcessor if is_vision_available() else None

						def       __a			(       self      :Optional[int])						->   Tuple:
												UpperCAmelCase_					=	ConvNextImageProcessingTester(self)

						@property
						def       __a			(       self      :List[str])						->   Dict:
												return self.image_processor_tester.prepare_image_processor_dict()

						def       __a			(       self      :Tuple)						->   Tuple:
												UpperCAmelCase_					=	self.image_processing_class(**self.image_processor_dict)
												self.assertTrue(hasattr(_lowercase  ,		'''do_resize'''))
												self.assertTrue(hasattr(_lowercase  ,		'''size'''))
												self.assertTrue(hasattr(_lowercase  ,		'''crop_pct'''))
												self.assertTrue(hasattr(_lowercase  ,		'''do_normalize'''))
												self.assertTrue(hasattr(_lowercase  ,		'''image_mean'''))
												self.assertTrue(hasattr(_lowercase  ,		'''image_std'''))

						def       __a			(       self      :Optional[Any])						->   str:
												UpperCAmelCase_					=	self.image_processing_class.from_dict(self.image_processor_dict)
												self.assertEqual(image_processor.size  ,		{'''shortest_edge''': 20})

												UpperCAmelCase_					=	self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42)
												self.assertEqual(image_processor.size  ,		{'''shortest_edge''': 42})

						def       __a			(       self      :int)						->   str:
												pass

						def       __a			(       self      :List[str])						->   Dict:
												# Initialize image_processing
												UpperCAmelCase_					=	self.image_processing_class(**self.image_processor_dict)
												# create random PIL images
												UpperCAmelCase_					=	prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=_lowercase)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase  ,		Image.Image)

												# Test not batched input
												UpperCAmelCase_					=	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.size['''shortest_edge'''],
												        self.image_processor_tester.size['''shortest_edge'''],
												    )  ,		)

												# Test batched
												UpperCAmelCase_					=	image_processing(_lowercase  ,		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.size['''shortest_edge'''],
												        self.image_processor_tester.size['''shortest_edge'''],
												    )  ,		)

						def       __a			(       self      :Optional[Any])						->   Union[str, Any]:
												# Initialize image_processing
												UpperCAmelCase_					=	self.image_processing_class(**self.image_processor_dict)
												# create random numpy tensors
												UpperCAmelCase_					=	prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=_lowercase  ,		numpify=_lowercase)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase  ,		np.ndarray)

												# Test not batched input
												UpperCAmelCase_					=	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.size['''shortest_edge'''],
												        self.image_processor_tester.size['''shortest_edge'''],
												    )  ,		)

												# Test batched
												UpperCAmelCase_					=	image_processing(_lowercase  ,		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.size['''shortest_edge'''],
												        self.image_processor_tester.size['''shortest_edge'''],
												    )  ,		)




						def       __a			(       self      :Tuple)						->   str:
												# Initialize image_processing
												UpperCAmelCase_					=	self.image_processing_class(**self.image_processor_dict)
												# create random PyTorch tensors
												UpperCAmelCase_					=	prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=_lowercase  ,		torchify=_lowercase)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase  ,		torch.Tensor)

												# Test not batched input
												UpperCAmelCase_					=	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.size['''shortest_edge'''],
												        self.image_processor_tester.size['''shortest_edge'''],
												    )  ,		)

												# Test batched
												UpperCAmelCase_					=	image_processing(_lowercase  ,		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.size['''shortest_edge'''],
												        self.image_processor_tester.size['''shortest_edge'''],
												    )  ,		)










from typing import TYPE_CHECKING

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


UpperCamelCase_      			=  {
    "configuration_mobilenet_v1": [
        "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV1Config",
        "MobileNetV1OnnxConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  ["MobileNetV1FeatureExtractor"]
							UpperCamelCase_      			=  ["MobileNetV1ImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  [
							    "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MobileNetV1ForImageClassification",
							    "MobileNetV1Model",
							    "MobileNetV1PreTrainedModel",
							    "load_tf_weights_in_mobilenet_v1",
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

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





