



from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase	       =	{'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase	       =	[
        '''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''FocalNetForImageClassification''',
        '''FocalNetForMaskedImageModeling''',
        '''FocalNetBackbone''',
        '''FocalNetModel''',
        '''FocalNetPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_focalnet import (
            FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            FocalNetBackbone,
            FocalNetForImageClassification,
            FocalNetForMaskedImageModeling,
            FocalNetModel,
            FocalNetPreTrainedModel,
        )

else:
    import sys

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








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     lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=224,      SCREAMING_SNAKE_CASE_=30,      SCREAMING_SNAKE_CASE_=400,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      )					->		List[str]:
          UpperCamelCase   :  Optional[int]						     =     size if size is not None else {'height': 18, 'width': 18}
          UpperCamelCase   :  List[Any]						     =     parent
          UpperCamelCase   :  List[Any]						     =     batch_size
          UpperCamelCase   :  int						     =     num_channels
          UpperCamelCase   :  int						     =     image_size
          UpperCamelCase   :  List[Any]						     =     min_resolution
          UpperCamelCase   :  int						     =     max_resolution
          UpperCamelCase   :  Any						     =     do_resize
          UpperCamelCase   :  Optional[int]						     =     size
          UpperCamelCase   :  List[str]						     =     do_normalize
          UpperCamelCase   :  Optional[Any]						     =     image_mean
          UpperCamelCase   :  Tuple						     =     image_std






       def  snake_case_	(      self	)					->		List[Any]:
          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     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Optional[Any]       					=  ViTImageProcessor if is_vision_available() else None
       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  Dict						     =     EfficientFormerImageProcessorTester(self	)
       @property
       def  snake_case_	(      self	)					->		List[Any]:
          return self.image_proc_tester.prepare_image_processor_dict()
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Optional[int]						     =     self.image_processing_class(**self.image_processor_dict	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_mean'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_std'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_normalize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_resize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'size'	)	)
       def  snake_case_	(      self	)					->		Any:
          pass
       def  snake_case_	(      self	)					->		int:
          # Initialize image_processor
          UpperCamelCase   :  Union[str, Any]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          UpperCamelCase   :  List[str]						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      Image.Image	)

          # Test not batched input
          UpperCamelCase   :  str						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  Optional[Any]						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )
       def  snake_case_	(      self	)					->		str:
          # Initialize image_processor
          UpperCamelCase   :  List[str]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random numpy tensors
          UpperCamelCase   :  Union[str, Any]						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      numpify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      np.ndarray	)

          # Test not batched input
          UpperCamelCase   :  Dict						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  Dict						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )






       def  snake_case_	(      self	)					->		Tuple:
          # Initialize image_processor
          UpperCamelCase   :  Any						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PyTorch tensors
          UpperCamelCase   :  int						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      torchify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      torch.Tensor	)

          # Test not batched input
          UpperCamelCase   :  Optional[int]						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  int						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )



