





'''simple docstring'''


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 	UpperCAmelCase	(		unittest.TestCase):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_=7							,  lowerCAmelCase_=3							,  lowerCAmelCase_=1_8							,  lowerCAmelCase_=3_0							,  lowerCAmelCase_=4_0_0							,  lowerCAmelCase_=True							,  lowerCAmelCase_=3_2							,  lowerCAmelCase_=True							,  ) ->      Tuple:



      """simple docstring"""







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




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



      """simple docstring"""







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







@require_torch
@require_vision
class 	UpperCAmelCase	(		__a					,	unittest.TestCase):


 '''simple docstring'''




 __magic_name__					: Dict  =      GLPNImageProcessor if is_vision_available() else None




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



      """simple docstring"""







      a_       		=GLPNImageProcessingTester(self)




 @property
 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      return self.image_processor_tester.prepare_image_processor_dict()




 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







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




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



      """simple docstring"""







      pass




 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







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

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      a_       		=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       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      a_       		=self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      a_       		=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 (GLPNImageProcessor doesn't support batching)
      a_       		=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       lowercase_  (   self) ->      int:



      """simple docstring"""







      a_       		=self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      a_       		=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 (GLPNImageProcessor doesn't support batching)
      a_       		=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)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase							       =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			[
      '''RagModel''',
      '''RagPreTrainedModel''',
      '''RagSequenceForGeneration''',
      '''RagTokenForGeneration''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			[
      '''TFRagModel''',
      '''TFRagPreTrainedModel''',
      '''TFRagSequenceForGeneration''',
      '''TFRagTokenForGeneration''',
  ]


if TYPE_CHECKING:
  from .configuration_rag import RagConfig
  from .retrieval_rag import RagRetriever
  from .tokenization_rag import RagTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_rag import (
        TFRagModel,
        TFRagPreTrainedModel,
        TFRagSequenceForGeneration,
        TFRagTokenForGeneration,
    )

else:
  import sys

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


