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 		_lowerCamelCase		( unittest.TestCase     ):
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase=7			,			lowerCAmelCase=3			,			lowerCAmelCase=30			,			lowerCAmelCase=400			,			lowerCAmelCase=True			,			lowerCAmelCase=None			,			lowerCAmelCase=0.9			,			lowerCAmelCase=None			,			lowerCAmelCase=True			,			lowerCAmelCase=[0.5, 0.5, 0.5]			,			lowerCAmelCase=[0.5, 0.5, 0.5]			,			)							->       str:
     SCREAMING_SNAKE_CASE__: List[str]=   size if size is not None else {'''shortest_edge''': 30}
     SCREAMING_SNAKE_CASE__: Any=   crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
     SCREAMING_SNAKE_CASE__: Dict=   parent
     SCREAMING_SNAKE_CASE__: List[str]=   batch_size
     SCREAMING_SNAKE_CASE__: int=   num_channels
     SCREAMING_SNAKE_CASE__: int=   min_resolution
     SCREAMING_SNAKE_CASE__: List[Any]=   max_resolution
     SCREAMING_SNAKE_CASE__: List[str]=   do_resize_and_center_crop
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   size
     SCREAMING_SNAKE_CASE__: Dict=   crop_pct
     SCREAMING_SNAKE_CASE__: Optional[int]=   crop_size
     SCREAMING_SNAKE_CASE__: Dict=   do_normalize
     SCREAMING_SNAKE_CASE__: List[str]=   image_mean
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   image_std



    def     UpperCamelCase_					(	self )							->       Tuple:
     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 		_lowerCamelCase		( UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   PoolFormerImageProcessor if is_vision_available() else None
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: Any=   PoolFormerImageProcessingTester(self )
    @property
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     return self.image_processor_tester.prepare_image_processor_dict()
    def     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''do_resize_and_center_crop''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''size''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''crop_pct''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''do_normalize''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''image_mean''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''image_std''' ) )
    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: Any=   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} )

     SCREAMING_SNAKE_CASE__: Dict=   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     UpperCamelCase_					(	self )							->       Tuple:
     pass
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__: Optional[int]=   self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     SCREAMING_SNAKE_CASE__: Optional[Any]=   prepare_image_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase )
     for image in image_inputs:
      self.assertIsInstance(lowerCAmelCase			,			Image.Image )

     # Test not batched input
     SCREAMING_SNAKE_CASE__: Optional[int]=   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
     SCREAMING_SNAKE_CASE__: Dict=   image_processing(lowerCAmelCase			,			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     UpperCamelCase_					(	self )							->       Dict:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__: Dict=   self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     SCREAMING_SNAKE_CASE__: Optional[Any]=   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
     SCREAMING_SNAKE_CASE__: List[Any]=   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
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   image_processing(lowerCAmelCase			,			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     UpperCamelCase_					(	self )							->       int:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__: List[Any]=   self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE__: Any=   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
     SCREAMING_SNAKE_CASE__: Optional[int]=   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
     SCREAMING_SNAKE_CASE__: Any=   image_processing(lowerCAmelCase			,			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'''],
         )			,			)






'''simple docstring'''
import os
from pathlib import Path






def A__       (	):
						from torch.utils.cpp_extension import load

						_UpperCamelCase   :						Optional[Any]												=  Path(UpperCAmelCase_				).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
						_UpperCamelCase   :						Tuple												=  [
						    root / filename
						    for filename in [
						        'vision.cpp',
						        os.path.join('cpu'    ,    'ms_deform_attn_cpu.cpp'				),
						        os.path.join('cuda'    ,    'ms_deform_attn_cuda.cu'				),
						    ]
						]

						load(
						    'MultiScaleDeformableAttention'    ,    UpperCAmelCase_    ,    with_cuda=UpperCAmelCase_    ,    extra_include_paths=[str(UpperCAmelCase_				)]    ,    extra_cflags=['-DWITH_CUDA=1']    ,    extra_cuda_cflags=[
						        '-DCUDA_HAS_FP16=1',
						        '-D__CUDA_NO_HALF_OPERATORS__',
						        '-D__CUDA_NO_HALF_CONVERSIONS__',
						        '-D__CUDA_NO_HALF2_OPERATORS__',
						    ]    ,    )

						import MultiScaleDeformableAttention as MSDA

						return MSDA


