






"""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


_UpperCamelCase		:  Optional[Any]						=		logging.get_logger(__name__)

_UpperCamelCase		:  int						=		{
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}

class 	a					(       a_     ):
    UpperCAmelCase_			:					Optional[Any]     					="mobilevitv2"







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

      lowercase							   =	num_channels
      lowercase							   =	image_size
      lowercase							   =	patch_size
      lowercase							   =	expand_ratio
      lowercase							   =	hidden_act
      lowercase							   =	conv_kernel_size
      lowercase							   =	output_stride
      lowercase							   =	initializer_range
      lowercase							   =	layer_norm_eps
      lowercase							   =	n_attn_blocks
      lowercase							   =	base_attn_unit_dims
      lowercase							   =	width_multiplier
      lowercase							   =	ffn_multiplier
      lowercase							   =	ffn_dropout
      lowercase							   =	attn_dropout
      lowercase							   =	classifier_dropout_prob

      # decode head attributes for semantic segmentation
      lowercase							   =	aspp_out_channels
      lowercase							   =	atrous_rates
      lowercase							   =	aspp_dropout_prob
      lowercase							   =	semantic_loss_ignore_index


class 	a					(       a_     ):
    UpperCAmelCase_			:					List[str]     					=version.parse("1.11"     )







    @property
    def       UpperCamelCase_				(      self	):
      return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]	)







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







    @property
    def       UpperCamelCase_				(      self	):
      return 1e-4










"""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 ViTImageProcessor

class 	a					(       unittest.TestCase     ):







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=3							,		_lowerCamelCase=1_8							,		_lowerCamelCase=3_0							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		):
      lowercase							   =	size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase							   =	parent
      lowercase							   =	batch_size
      lowercase							   =	num_channels
      lowercase							   =	image_size
      lowercase							   =	min_resolution
      lowercase							   =	max_resolution
      lowercase							   =	do_resize
      lowercase							   =	size
      lowercase							   =	do_normalize
      lowercase							   =	image_mean
      lowercase							   =	image_std







    def       UpperCamelCase_				(      self	):
      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 	a					(       a_,    unittest.TestCase     ):
    UpperCAmelCase_			:					int     					=ViTImageProcessor if is_vision_available() else None







    def       UpperCamelCase_				(      self	):
      lowercase							   =	ViTImageProcessingTester(self	)







    @property
    def       UpperCamelCase_				(      self	):
      return self.image_processor_tester.prepare_image_processor_dict()







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_mean'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_std'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_normalize'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_resize'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'size'	)	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size							,		{'height': 1_8, 'width': 1_8}	)

      lowercase							   =	self.image_processing_class.from_dict(self.image_processor_dict							,		size=4_2	)
      self.assertEqual(image_processor.size							,		{'height': 4_2, 'width': 4_2}	)







    def       UpperCamelCase_				(      self	):
      pass







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      lowercase							   =	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							   =	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['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      lowercase							   =	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							   =	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['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      lowercase							   =	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							   =	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['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)


