

'''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   UpperCamelCase__   (       unittest.TestCase       ):


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case=7   ,      snake_case=3   ,      snake_case=1_8   ,      snake_case=3_0   ,      snake_case=4_0_0   ,      snake_case=True   ,      snake_case=None   ,      snake_case=True   ,      snake_case=None   ,      ):


							'''simple docstring'''

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







		def 				A_	(			self      ):


							'''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   UpperCamelCase__   (       lowerCamelCase_							,							unittest.TestCase       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Optional[Any]       =							MobileNetVaImageProcessor if is_vision_available() else None







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							int        =       MobileNetVaImageProcessingTester(self      )







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return self.image_processor_tester.prepare_image_processor_dict()







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Union[str, Any]        =       self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(snake_case   ,      "do_resize"      )      )
							self.assertTrue(hasattr(snake_case   ,      "size"      )      )
							self.assertTrue(hasattr(snake_case   ,      "do_center_crop"      )      )
							self.assertTrue(hasattr(snake_case   ,      "center_crop"      )      )







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Union[str, Any]        =       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}      )

							UpperCAmelCase		:							List[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 				A_	(			self      ):


							'''simple docstring'''

							pass







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							int        =       self.image_processing_class(**self.image_processor_dict      )
							# create random PIL images
							UpperCAmelCase		:							Optional[int]        =       prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=snake_case      )
							for image in image_inputs:
												self.assertIsInstance(snake_case   ,      Image.Image      )

							# Test not batched input
							UpperCAmelCase		:							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
							UpperCAmelCase		:							str        =       image_processing(snake_case   ,      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 				A_	(			self      ):


							'''simple docstring'''

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

							# Test not batched input
							UpperCAmelCase		:							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
							UpperCAmelCase		:							Tuple        =       image_processing(snake_case   ,      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 				A_	(			self      ):


							'''simple docstring'''

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

							# Test not batched input
							UpperCAmelCase		:							Tuple        =       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
							UpperCAmelCase		:							Any        =       image_processing(snake_case   ,      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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a       :      List[Any]  	=			logging.get_logger(__name__)

a       :      Dict  	=			{
    "apple/mobilevitv2-1.0": "https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json",
}







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     List[Any]       =							"mobilevitv2"







		def __init__(			self   ,      snake_case=3   ,      snake_case=2_5_6   ,      snake_case=2   ,      snake_case=2.0   ,      snake_case="swish"   ,      snake_case=3   ,      snake_case=3_2   ,      snake_case=0.1   ,      snake_case=0.02   ,      snake_case=1e-5   ,      snake_case=5_1_2   ,      snake_case=[6, 1_2, 1_8]   ,      snake_case=0.1   ,      snake_case=2_5_5   ,      snake_case=[2, 4, 3]   ,      snake_case=[1_2_8, 1_9_2, 2_5_6]   ,      snake_case=1.0   ,      snake_case=2   ,      snake_case=0.0   ,      snake_case=0.0   ,      **snake_case   ,      ):


							'''simple docstring'''

							super().__init__(**snake_case      )

							UpperCAmelCase		:							Tuple        =       num_channels
							UpperCAmelCase		:							Optional[int]        =       image_size
							UpperCAmelCase		:							Optional[int]        =       patch_size
							UpperCAmelCase		:							Optional[int]        =       expand_ratio
							UpperCAmelCase		:							List[Any]        =       hidden_act
							UpperCAmelCase		:							Optional[Any]        =       conv_kernel_size
							UpperCAmelCase		:							str        =       output_stride
							UpperCAmelCase		:							Optional[Any]        =       initializer_range
							UpperCAmelCase		:							Optional[Any]        =       layer_norm_eps
							UpperCAmelCase		:							List[str]        =       n_attn_blocks
							UpperCAmelCase		:							Any        =       base_attn_unit_dims
							UpperCAmelCase		:							str        =       width_multiplier
							UpperCAmelCase		:							Optional[int]        =       ffn_multiplier
							UpperCAmelCase		:							List[Any]        =       ffn_dropout
							UpperCAmelCase		:							List[str]        =       attn_dropout
							UpperCAmelCase		:							Optional[Any]        =       classifier_dropout_prob

							# decode head attributes for semantic segmentation
							UpperCAmelCase		:							Optional[Any]        =       aspp_out_channels
							UpperCAmelCase		:							Dict        =       atrous_rates
							UpperCAmelCase		:							int        =       aspp_dropout_prob
							UpperCAmelCase		:							List[str]        =       semantic_loss_ignore_index







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Optional[int]       =							version.parse("1.11"       )







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]      )







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							if self.task == "image-classification":
												return OrderedDict([("logits", {0: "batch"})]      )
							else:
												return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]      )







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return 1e-4
