






'''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			:		Optional[int]     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Optional[Any]=7     ,      UpperCAmelCase			:		Dict=3     ,      UpperCAmelCase			:		Optional[int]=18     ,      UpperCAmelCase			:		str=30     ,      UpperCAmelCase			:		Dict=400     ,      UpperCAmelCase			:		List[Any]=True     ,      UpperCAmelCase			:		Union[str, Any]=32     ,      UpperCAmelCase			:		Optional[Any]=True     ,      )  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Tuple				  =parent
       lowercase					:   List[Any]				  =batch_size
       lowercase					:   Tuple				  =num_channels
       lowercase					:   Any				  =image_size
       lowercase					:   Dict				  =min_resolution
       lowercase					:   Optional[int]				  =max_resolution
       lowercase					:   List[str]				  =do_resize
       lowercase					:   Optional[int]				  =size_divisor
       lowercase					:   List[Any]				  =do_rescale






      def 				A__      ( self			:		List[Any]		)  ->  Optional[int]:


       '''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"""




      UpperCamelCase_								=			GLPNImageProcessor if is_vision_available() else None






      def 				A__      ( self			:		Optional[Any]		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   List[str]				  =GLPNImageProcessingTester(self		)






      @property
      def 				A__      ( self			:		Optional[int]		)  ->  Optional[int]:


       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()






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


       '''simple docstring'''

       lowercase					:   Any				  =self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''do_resize'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''size_divisor'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''resample'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''do_rescale'''		)		)






      def 				A__      ( self			:		int		)  ->  str:


       '''simple docstring'''

       pass






      def 				A__      ( self			:		str		)  ->  Tuple:


       '''simple docstring'''

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

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


       '''simple docstring'''

       lowercase					:   int				  =self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       lowercase					:   Optional[Any]				  =prepare_image_inputs(self.image_processor_tester     ,      equal_resolution=UpperCAmelCase     ,      numpify=UpperCAmelCase		)
       for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase     ,      np.ndarray		)

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


       '''simple docstring'''

       lowercase					:   Optional[int]				  =self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       lowercase					:   int				  =prepare_image_inputs(self.image_processor_tester     ,      equal_resolution=UpperCAmelCase     ,      torchify=UpperCAmelCase		)
       for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase     ,      torch.Tensor		)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       lowercase					:   Dict				  =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_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE						=	{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
          'M2M100ForConditionalGeneration',
          'M2M100Model',
          'M2M100PreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

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



