"""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 MobileNetVaImageProcessor



class 			_lowerCamelCase      (unittest.TestCase  ):







				def __init__(			self     :							Optional[Any]      ,       lowerCamelCase_     :							List[str]      ,       lowerCamelCase_     :							Optional[Any]=7      ,       lowerCamelCase_     :							Optional[Any]=3      ,       lowerCamelCase_     :							Optional[Any]=1_8      ,       lowerCamelCase_     :							Tuple=3_0      ,       lowerCamelCase_     :							List[Any]=4_0_0      ,       lowerCamelCase_     :							Tuple=True      ,       lowerCamelCase_     :							Union[str, Any]=None      ,       lowerCamelCase_     :							Any=True      ,       lowerCamelCase_     :							Tuple=None      ,       ):

					"""simple docstring"""






					_lowercase			:     str      					=				size if size is not None else {'shortest_edge': 2_0}
					_lowercase			:     Any      					=				crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
					_lowercase			:     int      					=				parent
					_lowercase			:     Union[str, Any]      					=				batch_size
					_lowercase			:     Optional[Any]      					=				num_channels
					_lowercase			:     List[Any]      					=				image_size
					_lowercase			:     str      					=				min_resolution
					_lowercase			:     Optional[Any]      					=				max_resolution
					_lowercase			:     List[Any]      					=				do_resize
					_lowercase			:     Dict      					=				size
					_lowercase			:     List[str]      					=				do_center_crop
					_lowercase			:     Dict      					=				crop_size







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					}



@require_torch
@require_vision
class 			_lowerCamelCase      (__lowerCamelCase    ,				unittest.TestCase  ):
				_snake_case  =	MobileNetVaImageProcessor if is_vision_available() else None







				def 				__UpperCAmelCase       (			self     :							str    ):

					"""simple docstring"""






					_lowercase			:     Union[str, Any]      					=				MobileNetVaImageProcessingTester(self    )







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

					"""simple docstring"""






					return self.image_processor_tester.prepare_image_processor_dict()







				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






					_lowercase			:     List[str]      					=				self.image_processing_class(**self.image_processor_dict    )
					self.assertTrue(hasattr(lowerCamelCase_      ,       'do_resize'    )    )
					self.assertTrue(hasattr(lowerCamelCase_      ,       'size'    )    )
					self.assertTrue(hasattr(lowerCamelCase_      ,       'do_center_crop'    )    )
					self.assertTrue(hasattr(lowerCamelCase_      ,       'center_crop'    )    )







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

					"""simple docstring"""






					_lowercase			:     Dict      					=				self.image_processing_class.from_dict(self.image_processor_dict    )
					self.assertEqual(image_processor.size      ,       {'shortest_edge': 2_0}    )
					self.assertEqual(image_processor.crop_size      ,       {'height': 1_8, 'width': 1_8}    )

					_lowercase			:     Union[str, Any]      					=				self.image_processing_class.from_dict(self.image_processor_dict      ,       size=4_2      ,       crop_size=8_4    )
					self.assertEqual(image_processor.size      ,       {'shortest_edge': 4_2}    )
					self.assertEqual(image_processor.crop_size      ,       {'height': 8_4, 'width': 8_4}    )







				def 				__UpperCAmelCase       (			self     :							Dict    ):

					"""simple docstring"""






					pass







				def 				__UpperCAmelCase       (			self     :							Dict    ):

					"""simple docstring"""






					_lowercase			:     Optional[Any]      					=				self.image_processing_class(**self.image_processor_dict    )
					# create random PIL images
					_lowercase			:     Tuple      					=				prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=lowerCamelCase_    )
					for image in image_inputs:
						self.assertIsInstance(lowerCamelCase_      ,       Image.Image    )

					# Test not batched input
					_lowercase			:     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
					_lowercase			:     str      					=				image_processing(lowerCamelCase_      ,       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     :							Any    ):

					"""simple docstring"""






					_lowercase			:     Union[str, Any]      					=				self.image_processing_class(**self.image_processor_dict    )
					# create random numpy tensors
					_lowercase			:     List[Any]      					=				prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=lowerCamelCase_      ,       numpify=lowerCamelCase_    )
					for image in image_inputs:
						self.assertIsInstance(lowerCamelCase_      ,       np.ndarray    )

					# Test not batched input
					_lowercase			:     List[str]      					=				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
					_lowercase			:     Optional[int]      					=				image_processing(lowerCamelCase_      ,       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     :							Dict    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				self.image_processing_class(**self.image_processor_dict    )
					# create random PyTorch tensors
					_lowercase			:     Optional[int]      					=				prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=lowerCamelCase_      ,       torchify=lowerCamelCase_    )
					for image in image_inputs:
						self.assertIsInstance(lowerCamelCase_      ,       torch.Tensor    )

					# Test not batched input
					_lowercase			:     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
					_lowercase			:     int      					=				image_processing(lowerCamelCase_      ,       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"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE						=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=    {
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}



class 			_lowerCamelCase      (__lowerCamelCase  ):
				_snake_case  =	"regnet"
				_snake_case  =	["x", "y"]







				def __init__(			self     :							Any      ,       lowerCamelCase_     :							Optional[Any]=3      ,       lowerCamelCase_     :							List[str]=3_2      ,       lowerCamelCase_     :							str=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]      ,       lowerCamelCase_     :							int=[2, 6, 1_2, 2]      ,       lowerCamelCase_     :							Union[str, Any]=6_4      ,       lowerCamelCase_     :							int="y"      ,       lowerCamelCase_     :							Optional[Any]="relu"      ,       **lowerCamelCase_     :							Optional[Any]      ,       ):

					"""simple docstring"""






					super().__init__(**lowerCamelCase_    )
					if layer_type not in self.layer_types:
						raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types    )}'''    )
					_lowercase			:     str      					=				num_channels
					_lowercase			:     Optional[Any]      					=				embedding_size
					_lowercase			:     Union[str, Any]      					=				hidden_sizes
					_lowercase			:     Optional[int]      					=				depths
					_lowercase			:     Optional[int]      					=				groups_width
					_lowercase			:     Tuple      					=				layer_type
					_lowercase			:     Union[str, Any]      					=				hidden_act
					# always downsample in the first stage
					_lowercase			:     Optional[int]      					=				True



