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 ViltImageProcessor




class       __lowerCAmelCase     (				unittest.TestCase  ):



 '''simple docstring'''

 def __init__(	self				:						Any				,	lowerCamelCase				:						List[str]				,	lowerCamelCase				:						List[Any]=7				,	lowerCamelCase				:						List[Any]=3				,	lowerCamelCase				:						Dict=1_8				,	lowerCamelCase				:						Optional[int]=3_0				,	lowerCamelCase				:						Optional[int]=4_0_0				,	lowerCamelCase				:						List[str]=True				,	lowerCamelCase				:						Optional[Any]=None				,	lowerCamelCase				:						Any=2				,	lowerCamelCase				:						List[Any]=True				,	lowerCamelCase				:						Union[str, Any]=[0.5, 0.5, 0.5]				,	lowerCamelCase				:						Any=[0.5, 0.5, 0.5]				,	)       ->							int:
        _UpperCamelCase   :				int       =     size if size is not None else {'shortest_edge': 3_0}
        _UpperCamelCase   :				int       =     parent
        _UpperCamelCase   :				List[str]       =     batch_size
        _UpperCamelCase   :				Dict       =     num_channels
        _UpperCamelCase   :				Dict       =     image_size
        _UpperCamelCase   :				List[Any]       =     min_resolution
        _UpperCamelCase   :				Optional[int]       =     max_resolution
        _UpperCamelCase   :				Union[str, Any]       =     do_resize
        _UpperCamelCase   :				int       =     size
        _UpperCamelCase   :				int       =     size_divisor
        _UpperCamelCase   :				Optional[Any]       =     do_normalize
        _UpperCamelCase   :				List[str]       =     image_mean
        _UpperCamelCase   :				Tuple       =     image_std

 def     __snake_case    (	self				:						str     )       ->							Dict:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }

 def     __snake_case    (	self				:						Optional[Any]				,	lowerCamelCase				:						Dict				,	lowerCamelCase				:						Any=False     )       ->							Optional[Any]:
        if not batched:
               _UpperCamelCase   :				List[str]       =     self.size['shortest_edge']
               _UpperCamelCase   :				int       =     image_inputs[0]
               if isinstance(lowerCamelCase				,	Image.Image     ):
                      _UpperCamelCase  ,					_UpperCamelCase   :				int       =     image.size
               else:
                      _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     image.shape[1], image.shape[2]
               _UpperCamelCase   :				Union[str, Any]       =     size / min(lowerCamelCase				,	lowerCamelCase     )
               if h < w:
                      _UpperCamelCase  ,					_UpperCamelCase   :				Any       =     size, scale * w
               else:
                      _UpperCamelCase  ,					_UpperCamelCase   :				int       =     scale * h, size

               _UpperCamelCase   :				str       =     int((1_3_3_3 / 8_0_0) * size     )
               if max(lowerCamelCase				,	lowerCamelCase     ) > max_size:
                      _UpperCamelCase   :				List[Any]       =     max_size / max(lowerCamelCase				,	lowerCamelCase     )
                      _UpperCamelCase   :				List[str]       =     newh * scale
                      _UpperCamelCase   :				Tuple       =     neww * scale

               _UpperCamelCase  ,					_UpperCamelCase   :				Optional[Any]       =     int(newh + 0.5     ), int(neww + 0.5     )
               _UpperCamelCase  ,					_UpperCamelCase   :				Dict       =     (
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

        else:
               _UpperCamelCase   :				Tuple       =     []
               for image in image_inputs:
                      _UpperCamelCase  ,					_UpperCamelCase   :				List[str]       =     self.get_expected_values([image]     )
                      expected_values.append((expected_height, expected_width)     )
               _UpperCamelCase   :				str       =     max(lowerCamelCase				,	key=lambda lowerCamelCase     : item[0]     )[0]
               _UpperCamelCase   :				int       =     max(lowerCamelCase				,	key=lambda lowerCamelCase     : item[1]     )[1]

        return expected_height, expected_width







@require_torch
@require_vision
class       __lowerCAmelCase     (				a_,		unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		Tuple 			=       ViltImageProcessor if is_vision_available() else None

 def     __snake_case    (	self				:						Optional[int]     )       ->							Any:
        _UpperCamelCase   :				List[Any]       =     ViltImageProcessingTester(self     )

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

 def     __snake_case    (	self				:						Optional[int]     )       ->							int:
        _UpperCamelCase   :				str       =     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'     )     )
        self.assertTrue(hasattr(lowerCamelCase				,	'size_divisor'     )     )

 def     __snake_case    (	self				:						List[Any]     )       ->							Optional[int]:
        _UpperCamelCase   :				Tuple       =     self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size				,	{'shortest_edge': 3_0}     )

        _UpperCamelCase   :				Union[str, Any]       =     self.image_processing_class.from_dict(self.image_processor_dict				,	size=4_2     )
        self.assertEqual(image_processor.size				,	{'shortest_edge': 4_2}     )

 def     __snake_case    (	self				:						Tuple     )       ->							int:
        pass

 def     __snake_case    (	self				:						List[Any]     )       ->							Union[str, Any]:
        # Initialize image_processing
        _UpperCamelCase   :				Union[str, Any]       =     self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        _UpperCamelCase   :				str       =     prepare_image_inputs(self.image_processor_tester				,	equal_resolution=lowerCamelCase     )
        for image in image_inputs:
               self.assertIsInstance(lowerCamelCase				,	Image.Image     )

        # Test not batched input
        _UpperCamelCase   :				Tuple       =     image_processing(image_inputs[0]				,	return_tensors='pt'     ).pixel_values

        _UpperCamelCase  ,					_UpperCamelCase   :				str       =     self.image_processor_tester.get_expected_values(lowerCamelCase     )
        self.assertEqual(
            encoded_images.shape				,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,	)

        # Test batched
        _UpperCamelCase   :				Optional[int]       =     image_processing(lowerCamelCase				,	return_tensors='pt'     ).pixel_values

        _UpperCamelCase  ,					_UpperCamelCase   :				str       =     self.image_processor_tester.get_expected_values(lowerCamelCase				,	batched=lowerCamelCase     )
        self.assertEqual(
            encoded_images.shape				,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )				,	)

 def     __snake_case    (	self				:						Tuple     )       ->							Tuple:
        # Initialize image_processing
        _UpperCamelCase   :				Any       =     self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        _UpperCamelCase   :				Optional[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
        _UpperCamelCase   :				Union[str, Any]       =     image_processing(image_inputs[0]				,	return_tensors='pt'     ).pixel_values

        _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     self.image_processor_tester.get_expected_values(lowerCamelCase     )
        self.assertEqual(
            encoded_images.shape				,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,	)

        # Test batched
        _UpperCamelCase   :				Tuple       =     image_processing(lowerCamelCase				,	return_tensors='pt'     ).pixel_values

        _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     self.image_processor_tester.get_expected_values(lowerCamelCase				,	batched=lowerCamelCase     )
        self.assertEqual(
            encoded_images.shape				,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )				,	)

 def     __snake_case    (	self				:						Any     )       ->							List[str]:
        # Initialize image_processing
        _UpperCamelCase   :				Union[str, Any]       =     self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        _UpperCamelCase   :				List[Any]       =     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
        _UpperCamelCase   :				str       =     image_processing(image_inputs[0]				,	return_tensors='pt'     ).pixel_values

        _UpperCamelCase  ,					_UpperCamelCase   :				Dict       =     self.image_processor_tester.get_expected_values(lowerCamelCase     )
        self.assertEqual(
            encoded_images.shape				,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,	)

        # Test batched
        _UpperCamelCase   :				List[str]       =     image_processing(lowerCamelCase				,	return_tensors='pt'     ).pixel_values

        _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     self.image_processor_tester.get_expected_values(lowerCamelCase				,	batched=lowerCamelCase     )
        self.assertEqual(
            encoded_images.shape				,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )				,	)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase							= {'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase							= [
     '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''NatForImageClassification''',
     '''NatModel''',
     '''NatPreTrainedModel''',
     '''NatBackbone''',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase							= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



