




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 PoolFormerImageProcessor



class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def __init__(     self       :      Any ,   __snake_case       :      str ,   __snake_case       :      Optional[int]=7 ,   __snake_case       :      int=3 ,   __snake_case       :      Tuple=30 ,   __snake_case       :      Optional[Any]=4_00 ,   __snake_case       :      str=True ,   __snake_case       :      Union[str, Any]=None ,   __snake_case       :      Union[str, Any]=0.9 ,   __snake_case       :      Any=None ,   __snake_case       :      int=True ,   __snake_case       :      Union[str, Any]=[0.5, 0.5, 0.5] ,   __snake_case       :      Dict=[0.5, 0.5, 0.5] ,   ):
         UpperCAmelCase_										=					size if size is not None else {'''shortest_edge''': 30}
         UpperCAmelCase_										=					crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					num_channels
         UpperCAmelCase_										=					min_resolution
         UpperCAmelCase_										=					max_resolution
         UpperCAmelCase_										=					do_resize_and_center_crop
         UpperCAmelCase_										=					size
         UpperCAmelCase_										=					crop_pct
         UpperCAmelCase_										=					crop_size
         UpperCAmelCase_										=					do_normalize
         UpperCAmelCase_										=					image_mean
         UpperCAmelCase_										=					image_std




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         return {
             "size": self.size,
             "do_resize_and_center_crop": self.do_resize_and_center_crop,
             "crop_pct": self.crop_pct,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }



@require_torch
@require_vision
class       a					(       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  PoolFormerImageProcessor if is_vision_available() else None
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					PoolFormerImageProcessingTester(self    )
     @property
     def   lowerCamelCase_       (     self       :      int    ):
         return self.image_processor_tester.prepare_image_processor_dict()
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(__snake_case ,   '''do_resize_and_center_crop'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''size'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''crop_pct'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''do_normalize'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''image_mean'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''image_std'''    )    )
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size ,   {'''shortest_edge''': 30}    )
         self.assertEqual(image_processor.crop_size ,   {'''height''': 30, '''width''': 30}    )

         UpperCAmelCase_										=					self.image_processing_class.from_dict(self.image_processor_dict ,   size=42 ,   crop_size=84    )
         self.assertEqual(image_processor.size ,   {'''shortest_edge''': 42}    )
         self.assertEqual(image_processor.crop_size ,   {'''height''': 84, '''width''': 84}    )
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         pass
     def   lowerCamelCase_       (     self       :      List[str]    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					image_processing(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values

         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					image_processing(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )




     def   lowerCamelCase_       (     self       :      int    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					image_processing(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )











from typing import TYPE_CHECKING

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


_lowerCamelCase      			=						{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						['ConditionalDetrFeatureExtractor']
 _lowerCamelCase      			=						['ConditionalDetrImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
     'ConditionalDetrForObjectDetection',
     'ConditionalDetrForSegmentation',
     'ConditionalDetrModel',
     'ConditionalDetrPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

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





