





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase							:      int						=	{
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase							:      int						=	[
     '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''SwitchTransformersEncoderModel''',
     '''SwitchTransformersForConditionalGeneration''',
     '''SwitchTransformersModel''',
     '''SwitchTransformersPreTrainedModel''',
     '''SwitchTransformersTop1Router''',
     '''SwitchTransformersSparseMLP''',
 ]


if TYPE_CHECKING:
 from .configuration_switch_transformers import (
     SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SwitchTransformersConfig,
     SwitchTransformersOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_switch_transformers import (
      SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
      SwitchTransformersEncoderModel,
      SwitchTransformersForConditionalGeneration,
      SwitchTransformersModel,
      SwitchTransformersPreTrainedModel,
      SwitchTransformersSparseMLP,
      SwitchTransformersTopaRouter,
  )


else:
 import sys

 _lowerCAmelCase							:      Optional[Any]						=	_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 ViltImageProcessor


class 					__magic_name__  (							unittest.TestCase							):





     """simple docstring"""



     def __init__(							self       :Dict			,      snake_case       :str			,      snake_case       :Tuple=7			,      snake_case       :Union[str, Any]=3			,      snake_case       :Any=18			,      snake_case       :Tuple=30			,      snake_case       :List[str]=400			,      snake_case       :Optional[Any]=True			,      snake_case       :Optional[int]=None			,      snake_case       :str=2			,      snake_case       :Tuple=True			,      snake_case       :List[str]=[0.5, 0.5, 0.5]			,      snake_case       :int=[0.5, 0.5, 0.5]			,      ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			size if size is not None else {"shortest_edge": 30}
        A_					:				Tuple          =			parent
        A_					:				Tuple          =			batch_size
        A_					:				Any          =			num_channels
        A_					:				Dict          =			image_size
        A_					:				int          =			min_resolution
        A_					:				Optional[Any]          =			max_resolution
        A_					:				Union[str, Any]          =			do_resize
        A_					:				List[Any]          =			size
        A_					:				List[str]          =			size_divisor
        A_					:				str          =			do_normalize
        A_					:				int          =			image_mean
        A_					:				str          =			image_std



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]  ):





        '''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 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :Tuple			,      snake_case       :List[str]=False  ):





        '''simple docstring'''




        if not batched:
           A_					:				List[Any]          =			self.size["shortest_edge"]
           A_					:				Tuple          =			image_inputs[0]
           if isinstance(snake_case			,      Image.Image  ):
              A_      ,						A_					:				str          =			image.size
           else:
              A_      ,						A_					:				Optional[int]          =			image.shape[1], image.shape[2]
           A_					:				int          =			size / min(snake_case			,      snake_case  )
           if h < w:
              A_      ,						A_					:				Dict          =			size, scale * w
           else:
              A_      ,						A_					:				Tuple          =			scale * h, size

           A_					:				List[Any]          =			int((1_333 / 800) * size  )
           if max(snake_case			,      snake_case  ) > max_size:
              A_					:				Union[str, Any]          =			max_size / max(snake_case			,      snake_case  )
              A_					:				Tuple          =			newh * scale
              A_					:				List[str]          =			neww * scale

           A_      ,						A_					:				Optional[int]          =			int(newh + 0.5  ), int(neww + 0.5  )
           A_      ,						A_					:				Tuple          =			(
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

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

        return expected_height, expected_width




@require_torch
@require_vision
class 					__magic_name__  (							lowerCamelCase__    ,				unittest.TestCase							):





     """simple docstring"""



     __UpperCamelCase					    =     ViltImageProcessor if is_vision_available() else None



     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        A_					:				Dict          =			ViltImageProcessingTester(self  )



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]  ):





        '''simple docstring'''




        A_					:				Optional[int]          =			self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(snake_case			,      "image_mean"  )  )
        self.assertTrue(hasattr(snake_case			,      "image_std"  )  )
        self.assertTrue(hasattr(snake_case			,      "do_normalize"  )  )
        self.assertTrue(hasattr(snake_case			,      "do_resize"  )  )
        self.assertTrue(hasattr(snake_case			,      "size"  )  )
        self.assertTrue(hasattr(snake_case			,      "size_divisor"  )  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple  ):





        '''simple docstring'''




        A_					:				Tuple          =			self.image_processing_class.from_dict(self.image_processor_dict  )
        self.assertEqual(image_processor.size			,      {"shortest_edge": 30}  )

        A_					:				Any          =			self.image_processing_class.from_dict(self.image_processor_dict			,      size=42  )
        self.assertEqual(image_processor.size			,      {"shortest_edge": 42}  )



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]  ):





        '''simple docstring'''




        pass



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Dict          =			self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        A_					:				str          =			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
        A_					:				Optional[Any]          =			image_processing(image_inputs[0]			,      return_tensors="pt"  ).pixel_values

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

        # Test batched
        A_					:				int          =			image_processing(snake_case			,      return_tensors="pt"  ).pixel_values

        A_      ,						A_					:				Dict          =			self.image_processor_tester.get_expected_values(snake_case			,      batched=snake_case  )
        self.assertEqual(
            encoded_images.shape			,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )			,      )



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]  ):





        '''simple docstring'''




        A_					:				Any          =			self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        A_					:				List[str]          =			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
        A_					:				List[Any]          =			image_processing(image_inputs[0]			,      return_tensors="pt"  ).pixel_values

        A_      ,						A_					:				int          =			self.image_processor_tester.get_expected_values(snake_case  )
        self.assertEqual(
            encoded_images.shape			,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,      )

        # Test batched
        A_					:				Dict          =			image_processing(snake_case			,      return_tensors="pt"  ).pixel_values

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





     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        A_					:				Any          =			self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        A_					:				Optional[Any]          =			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
        A_					:				int          =			image_processing(image_inputs[0]			,      return_tensors="pt"  ).pixel_values

        A_      ,						A_					:				Union[str, Any]          =			self.image_processor_tester.get_expected_values(snake_case  )
        self.assertEqual(
            encoded_images.shape			,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,      )

        # Test batched
        A_					:				List[str]          =			image_processing(snake_case			,      return_tensors="pt"  ).pixel_values

        A_      ,						A_					:				Optional[int]          =			self.image_processor_tester.get_expected_values(snake_case			,      batched=snake_case  )
        self.assertEqual(
            encoded_images.shape			,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )			,      )
