

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     lowercase						(       unittest.TestCase					):



 '''simple docstring'''


 def __init__(self       ,       __a       ,       __a=7       ,       __a=3       ,       __a=18       ,       __a=30       ,       __a=400       ,       __a=True       ,       __a=None       ,       __a=2       ,       __a=True       ,       __a=[0.5, 0.5, 0.5]       ,       __a=[0.5, 0.5, 0.5]       ,       ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				size if size is not None else {'shortest_edge': 30}
  UpperCAmelCase__							=				parent
  UpperCAmelCase__							=				batch_size
  UpperCAmelCase__							=				num_channels
  UpperCAmelCase__							=				image_size
  UpperCAmelCase__							=				min_resolution
  UpperCAmelCase__							=				max_resolution
  UpperCAmelCase__							=				do_resize
  UpperCAmelCase__							=				size
  UpperCAmelCase__							=				size_divisor
  UpperCAmelCase__							=				do_normalize
  UpperCAmelCase__							=				image_mean
  UpperCAmelCase__							=				image_std




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  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   UpperCamelCase__							(self       ,       __a       ,       __a=False ) ->					List[Any]:






  """simple docstring"""





  if not batched:
   UpperCAmelCase__							=				self.size['shortest_edge']
   UpperCAmelCase__							=				image_inputs[0]
   if isinstance(__a       ,       Image.Image ):
    UpperCAmelCase__				,    UpperCAmelCase__							=				image.size
   else:
    UpperCAmelCase__				,    UpperCAmelCase__							=				image.shape[1], image.shape[2]
   UpperCAmelCase__							=				size / min(__a       ,       __a )
   if h < w:
    UpperCAmelCase__				,    UpperCAmelCase__							=				size, scale * w
   else:
    UpperCAmelCase__				,    UpperCAmelCase__							=				scale * h, size

   UpperCAmelCase__							=				int((1333 / 800) * size )
   if max(__a       ,       __a ) > max_size:
    UpperCAmelCase__							=				max_size / max(__a       ,       __a )
    UpperCAmelCase__							=				newh * scale
    UpperCAmelCase__							=				neww * scale

   UpperCAmelCase__				,    UpperCAmelCase__							=				int(newh + 0.5 ), int(neww + 0.5 )
   UpperCAmelCase__				,    UpperCAmelCase__							=				(
       newh // self.size_divisor * self.size_divisor,
       neww // self.size_divisor * self.size_divisor,
   )

  else:
   UpperCAmelCase__							=				[]
   for image in image_inputs:
    UpperCAmelCase__				,    UpperCAmelCase__							=				self.get_expected_values([image] )
    expected_values.append((expected_height, expected_width) )
   UpperCAmelCase__							=				max(__a       ,       key=lambda __a : item[0] )[0]
   UpperCAmelCase__							=				max(__a       ,       key=lambda __a : item[1] )[1]

  return expected_height, expected_width







@require_torch
@require_vision
class     lowercase						(       _UpperCamelCase ,				unittest.TestCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				ViltImageProcessor if is_vision_available() else None




 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				ViltImageProcessingTester(self )




 @property
 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  return self.image_processor_tester.prepare_image_processor_dict()




 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				self.image_processing_class(**self.image_processor_dict )
  self.assertTrue(hasattr(__a       ,       'image_mean' ) )
  self.assertTrue(hasattr(__a       ,       'image_std' ) )
  self.assertTrue(hasattr(__a       ,       'do_normalize' ) )
  self.assertTrue(hasattr(__a       ,       'do_resize' ) )
  self.assertTrue(hasattr(__a       ,       'size' ) )
  self.assertTrue(hasattr(__a       ,       'size_divisor' ) )




 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				self.image_processing_class.from_dict(self.image_processor_dict )
  self.assertEqual(image_processor.size       ,       {'shortest_edge': 30} )

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




 def   UpperCamelCase__							(self ) ->					int:






  """simple docstring"""





  pass




 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				self.image_processing_class(**self.image_processor_dict )
  # create random PIL images
  UpperCAmelCase__							=				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=__a )
  for image in image_inputs:
   self.assertIsInstance(__a       ,       Image.Image )

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

  UpperCAmelCase__				,    UpperCAmelCase__							=				self.image_processor_tester.get_expected_values(__a )
  self.assertEqual(
      encoded_images.shape       ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,       )

  # Test batched
  UpperCAmelCase__							=				image_processing(__a       ,       return_tensors='pt' ).pixel_values

  UpperCAmelCase__				,    UpperCAmelCase__							=				self.image_processor_tester.get_expected_values(__a       ,       batched=__a )
  self.assertEqual(
      encoded_images.shape       ,       (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      )       ,       )




 def   UpperCamelCase__							(self ) ->					Tuple:






  """simple docstring"""





  UpperCAmelCase__							=				self.image_processing_class(**self.image_processor_dict )
  # create random numpy tensors
  UpperCAmelCase__							=				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=__a       ,       numpify=__a )
  for image in image_inputs:
   self.assertIsInstance(__a       ,       np.ndarray )

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

  UpperCAmelCase__				,    UpperCAmelCase__							=				self.image_processor_tester.get_expected_values(__a )
  self.assertEqual(
      encoded_images.shape       ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,       )

  # Test batched
  UpperCAmelCase__							=				image_processing(__a       ,       return_tensors='pt' ).pixel_values

  UpperCAmelCase__				,    UpperCAmelCase__							=				self.image_processor_tester.get_expected_values(__a       ,       batched=__a )
  self.assertEqual(
      encoded_images.shape       ,       (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      )       ,       )




 def   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				self.image_processing_class(**self.image_processor_dict )
  # create random PyTorch tensors
  UpperCAmelCase__							=				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=__a       ,       torchify=__a )
  for image in image_inputs:
   self.assertIsInstance(__a       ,       torch.Tensor )

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

  UpperCAmelCase__				,    UpperCAmelCase__							=				self.image_processor_tester.get_expected_values(__a )
  self.assertEqual(
      encoded_images.shape       ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,       )

  # Test batched
  UpperCAmelCase__							=				image_processing(__a       ,       return_tensors='pt' ).pixel_values

  UpperCAmelCase__				,    UpperCAmelCase__							=				self.image_processor_tester.get_expected_values(__a       ,       batched=__a )
  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_flax_available,
    is_tf_available,
    is_torch_available,
)


_snake_case				= {"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RegNetForImageClassification""",
      """RegNetModel""",
      """RegNetPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRegNetForImageClassification""",
      """TFRegNetModel""",
      """TFRegNetPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """FlaxRegNetForImageClassification""",
      """FlaxRegNetModel""",
      """FlaxRegNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_regnet import (
        REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        RegNetForImageClassification,
        RegNetModel,
        RegNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_regnet import (
        TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRegNetForImageClassification,
        TFRegNetModel,
        TFRegNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_regnet import (
        FlaxRegNetForImageClassification,
        FlaxRegNetModel,
        FlaxRegNetPreTrainedModel,
    )


else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure)






