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 GLPNImageProcessor







class __snake_case       (		unittest.TestCase	):




   def __init__(					self    :       Optional[Any] ,			_lowercase    :       List[Any] ,			_lowercase    :       Dict=7 ,			_lowercase    :       List[Any]=3 ,			_lowercase    :       int=18 ,			_lowercase    :       Dict=30 ,			_lowercase    :       Tuple=4_00 ,			_lowercase    :       List[Any]=True ,			_lowercase    :       List[Any]=32 ,			_lowercase    :       int=True ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   parent
     SCREAMING_SNAKE_CASE__    							=   batch_size
     SCREAMING_SNAKE_CASE__    							=   num_channels
     SCREAMING_SNAKE_CASE__    							=   image_size
     SCREAMING_SNAKE_CASE__    							=   min_resolution
     SCREAMING_SNAKE_CASE__    							=   max_resolution
     SCREAMING_SNAKE_CASE__    							=   do_resize
     SCREAMING_SNAKE_CASE__    							=   size_divisor
     SCREAMING_SNAKE_CASE__    							=   do_rescale




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     return {
         "do_resize": self.do_resize,
         "size_divisor": self.size_divisor,
         "do_rescale": self.do_rescale,
     }







@require_torch
@require_vision
class __snake_case       (		lowerCamelCase_     ,	unittest.TestCase	):
   lowerCAmelCase_														=							GLPNImageProcessor if is_vision_available() else None




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   GLPNImageProcessingTester(self   )




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



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(_lowercase ,			"""do_resize"""   )   )
     self.assertTrue(hasattr(_lowercase ,			"""size_divisor"""   )   )
     self.assertTrue(hasattr(_lowercase ,			"""resample"""   )   )
     self.assertTrue(hasattr(_lowercase ,			"""do_rescale"""   )   )




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



     """simple docstring"""






     pass




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



     """simple docstring"""






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

     # Test not batched input (GLPNImageProcessor doesn't support batching)
     SCREAMING_SNAKE_CASE__    							=   image_processing(image_inputs[0] ,			return_tensors="""pt"""   ).pixel_values
     self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
     self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






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

     # Test not batched input (GLPNImageProcessor doesn't support batching)
     SCREAMING_SNAKE_CASE__    							=   image_processing(image_inputs[0] ,			return_tensors="""pt"""   ).pixel_values
     self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
     self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






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

     # Test not batched input (GLPNImageProcessor doesn't support batching)
     SCREAMING_SNAKE_CASE__    							=   image_processing(image_inputs[0] ,			return_tensors="""pt"""   ).pixel_values
     self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
     self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )

from typing import TYPE_CHECKING

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


__lowerCamelCase    :				List[Any]							     =	{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase    :				Union[str, Any]							     =	[
       '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''InformerForPrediction''',
       '''InformerModel''',
       '''InformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase    :				Dict							     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
