


'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class   snake_case					(					unittest.TestCase    ):





		"""simple docstring"""




		def 						snake_case	(					self  ):







			"""simple docstring"""
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()






		@property
		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						1
			lowerCamelCase_  				=						3
			lowerCamelCase_  				=						(32, 32)

			lowerCamelCase_  				=						floats_tensor((batch_size, num_channels) + sizes    ,						rng=random.Random(0  )  ).to(_UpperCAmelCase  )
			return image






		@property
		def 						snake_case	(					self  ):







			"""simple docstring"""
			torch.manual_seed(0  )
			lowerCamelCase_  				=						UNetaDConditionModel(
			    block_out_channels=(32, 32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=7    ,						out_channels=4    ,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")    ,						up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")    ,						cross_attention_dim=32    ,						attention_head_dim=8    ,						use_linear_projection=_UpperCAmelCase    ,						only_cross_attention=(True, True, False)    ,						num_class_embeds=100    ,						)
			return model






		@property
		def 						snake_case	(					self  ):







			"""simple docstring"""
			torch.manual_seed(0  )
			lowerCamelCase_  				=						AutoencoderKL(
			    block_out_channels=[32, 32, 64]    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]    ,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]    ,						latent_channels=4    ,						)
			return model






		@property
		def 						snake_case	(					self  ):







			"""simple docstring"""
			torch.manual_seed(0  )
			lowerCamelCase_  				=						CLIPTextConfig(
			    bos_token_id=0    ,						eos_token_id=2    ,						hidden_size=32    ,						intermediate_size=37    ,						layer_norm_eps=1e-05    ,						num_attention_heads=4    ,						num_hidden_layers=5    ,						pad_token_id=1    ,						vocab_size=1000    ,						hidden_act="gelu"    ,						projection_dim=512    ,						)
			return CLIPTextModel(_UpperCAmelCase  )






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						"cpu"  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase_  				=						self.dummy_cond_unet_upscale
			lowerCamelCase_  				=						DDPMScheduler()
			lowerCamelCase_  				=						DDIMScheduler(prediction_type="v_prediction"  )
			lowerCamelCase_  				=						self.dummy_vae
			lowerCamelCase_  				=						self.dummy_text_encoder
			lowerCamelCase_  				=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

			lowerCamelCase_  				=						self.dummy_image.cpu().permute(0    ,						2    ,						3    ,						1  )[0]
			lowerCamelCase_  				=						Image.fromarray(np.uinta(_UpperCAmelCase  )  ).convert("RGB"  ).resize((64, 64)  )

			# make sure here that pndm scheduler skips prk
			lowerCamelCase_  				=						StableDiffusionUpscalePipeline(
			    unet=_UpperCAmelCase    ,						low_res_scheduler=_UpperCAmelCase    ,						scheduler=_UpperCAmelCase    ,						vae=_UpperCAmelCase    ,						text_encoder=_UpperCAmelCase    ,						tokenizer=_UpperCAmelCase    ,						max_noise_level=350    ,						)
			lowerCamelCase_  				=						sd_pipe.to(_UpperCAmelCase  )
			sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase  )

			lowerCamelCase_  				=						"A painting of a squirrel eating a burger"
			lowerCamelCase_  				=						torch.Generator(device=_UpperCAmelCase  ).manual_seed(0  )
			lowerCamelCase_  				=						sd_pipe(
			    [prompt]    ,						image=_UpperCAmelCase    ,						generator=_UpperCAmelCase    ,						guidance_scale=6.0    ,						noise_level=20    ,						num_inference_steps=2    ,						output_type="np"    ,						)

			lowerCamelCase_  				=						output.images

			lowerCamelCase_  				=						torch.Generator(device=_UpperCAmelCase  ).manual_seed(0  )
			lowerCamelCase_  				=						sd_pipe(
			    [prompt]    ,						image=_UpperCAmelCase    ,						generator=_UpperCAmelCase    ,						guidance_scale=6.0    ,						noise_level=20    ,						num_inference_steps=2    ,						output_type="np"    ,						return_dict=_UpperCAmelCase    ,						)[0]

			lowerCamelCase_  				=						image[0, -3:, -3:, -1]
			lowerCamelCase_  				=						image_from_tuple[0, -3:, -3:, -1]

			lowerCamelCase_  				=						low_res_image.size[0] * 4
			assert image.shape == (1, expected_height_width, expected_height_width, 3)
			lowerCamelCase_  				=						np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]  )

			assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						"cpu"  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase_  				=						self.dummy_cond_unet_upscale
			lowerCamelCase_  				=						DDPMScheduler()
			lowerCamelCase_  				=						DDIMScheduler(prediction_type="v_prediction"  )
			lowerCamelCase_  				=						self.dummy_vae
			lowerCamelCase_  				=						self.dummy_text_encoder
			lowerCamelCase_  				=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

			lowerCamelCase_  				=						self.dummy_image.cpu().permute(0    ,						2    ,						3    ,						1  )[0]
			lowerCamelCase_  				=						Image.fromarray(np.uinta(_UpperCAmelCase  )  ).convert("RGB"  ).resize((64, 64)  )

			# make sure here that pndm scheduler skips prk
			lowerCamelCase_  				=						StableDiffusionUpscalePipeline(
			    unet=_UpperCAmelCase    ,						low_res_scheduler=_UpperCAmelCase    ,						scheduler=_UpperCAmelCase    ,						vae=_UpperCAmelCase    ,						text_encoder=_UpperCAmelCase    ,						tokenizer=_UpperCAmelCase    ,						max_noise_level=350    ,						)
			lowerCamelCase_  				=						sd_pipe.to(_UpperCAmelCase  )
			sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase  )

			lowerCamelCase_  				=						"A painting of a squirrel eating a burger"
			lowerCamelCase_  				=						sd_pipe(
			    2 * [prompt]    ,						image=2 * [low_res_image]    ,						guidance_scale=6.0    ,						noise_level=20    ,						num_inference_steps=2    ,						output_type="np"    ,						)
			lowerCamelCase_  				=						output.images
			assert image.shape[0] == 2

			lowerCamelCase_  				=						torch.Generator(device=_UpperCAmelCase  ).manual_seed(0  )
			lowerCamelCase_  				=						sd_pipe(
			    [prompt]    ,						image=_UpperCAmelCase    ,						generator=_UpperCAmelCase    ,						num_images_per_prompt=2    ,						guidance_scale=6.0    ,						noise_level=20    ,						num_inference_steps=2    ,						output_type="np"    ,						)
			lowerCamelCase_  				=						output.images
			assert image.shape[0] == 2







		@unittest.skipIf(torch_device != "cuda"    ,						"This test requires a GPU"  )
		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						self.dummy_cond_unet_upscale
			lowerCamelCase_  				=						DDPMScheduler()
			lowerCamelCase_  				=						DDIMScheduler(prediction_type="v_prediction"  )
			lowerCamelCase_  				=						self.dummy_vae
			lowerCamelCase_  				=						self.dummy_text_encoder
			lowerCamelCase_  				=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

			lowerCamelCase_  				=						self.dummy_image.cpu().permute(0    ,						2    ,						3    ,						1  )[0]
			lowerCamelCase_  				=						Image.fromarray(np.uinta(_UpperCAmelCase  )  ).convert("RGB"  ).resize((64, 64)  )

			# put models in fp16, except vae as it overflows in fp16
			lowerCamelCase_  				=						unet.half()
			lowerCamelCase_  				=						text_encoder.half()

			# make sure here that pndm scheduler skips prk
			lowerCamelCase_  				=						StableDiffusionUpscalePipeline(
			    unet=_UpperCAmelCase    ,						low_res_scheduler=_UpperCAmelCase    ,						scheduler=_UpperCAmelCase    ,						vae=_UpperCAmelCase    ,						text_encoder=_UpperCAmelCase    ,						tokenizer=_UpperCAmelCase    ,						max_noise_level=350    ,						)
			lowerCamelCase_  				=						sd_pipe.to(_UpperCAmelCase  )
			sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase  )

			lowerCamelCase_  				=						"A painting of a squirrel eating a burger"
			lowerCamelCase_  				=						torch.manual_seed(0  )
			lowerCamelCase_  				=						sd_pipe(
			    [prompt]    ,						image=_UpperCAmelCase    ,						generator=_UpperCAmelCase    ,						num_inference_steps=2    ,						output_type="np"    ,						).images

			lowerCamelCase_  				=						low_res_image.size[0] * 4
			assert image.shape == (1, expected_height_width, expected_height_width, 3)





@slow
@require_torch_gpu
class   snake_case					(					unittest.TestCase    ):





		"""simple docstring"""




		def 						snake_case	(					self  ):







			"""simple docstring"""
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						load_image(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
			    "/sd2-upscale/low_res_cat.png"  )
			lowerCamelCase_  				=						load_numpy(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
			    "/upsampled_cat.npy"  )

			lowerCamelCase_  				=						"stabilityai/stable-diffusion-x4-upscaler"
			lowerCamelCase_  				=						StableDiffusionUpscalePipeline.from_pretrained(_UpperCAmelCase  )
			pipe.to(_UpperCAmelCase  )
			pipe.set_progress_bar_config(disable=_UpperCAmelCase  )
			pipe.enable_attention_slicing()

			lowerCamelCase_  				=						"a cat sitting on a park bench"

			lowerCamelCase_  				=						torch.manual_seed(0  )
			lowerCamelCase_  				=						pipe(
			    prompt=_UpperCAmelCase    ,						image=_UpperCAmelCase    ,						generator=_UpperCAmelCase    ,						output_type="np"    ,						)
			lowerCamelCase_  				=						output.images[0]

			assert image.shape == (512, 512, 3)
			assert np.abs(expected_image - image  ).max() < 1e-3






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						load_image(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
			    "/sd2-upscale/low_res_cat.png"  )
			lowerCamelCase_  				=						load_numpy(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
			    "/upsampled_cat_fp16.npy"  )

			lowerCamelCase_  				=						"stabilityai/stable-diffusion-x4-upscaler"
			lowerCamelCase_  				=						StableDiffusionUpscalePipeline.from_pretrained(
			    _UpperCAmelCase    ,						torch_dtype=torch.floataa    ,						)
			pipe.to(_UpperCAmelCase  )
			pipe.set_progress_bar_config(disable=_UpperCAmelCase  )
			pipe.enable_attention_slicing()

			lowerCamelCase_  				=						"a cat sitting on a park bench"

			lowerCamelCase_  				=						torch.manual_seed(0  )
			lowerCamelCase_  				=						pipe(
			    prompt=_UpperCAmelCase    ,						image=_UpperCAmelCase    ,						generator=_UpperCAmelCase    ,						output_type="np"    ,						)
			lowerCamelCase_  				=						output.images[0]

			assert image.shape == (512, 512, 3)
			assert np.abs(expected_image - image  ).max() < 5e-1







		def 						snake_case	(					self  ):







			"""simple docstring"""
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()

			lowerCamelCase_  				=						load_image(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
			    "/sd2-upscale/low_res_cat.png"  )

			lowerCamelCase_  				=						"stabilityai/stable-diffusion-x4-upscaler"
			lowerCamelCase_  				=						StableDiffusionUpscalePipeline.from_pretrained(
			    _UpperCAmelCase    ,						torch_dtype=torch.floataa    ,						)
			pipe.to(_UpperCAmelCase  )
			pipe.set_progress_bar_config(disable=_UpperCAmelCase  )
			pipe.enable_attention_slicing(1  )
			pipe.enable_sequential_cpu_offload()

			lowerCamelCase_  				=						"a cat sitting on a park bench"

			lowerCamelCase_  				=						torch.manual_seed(0  )
			lowerCamelCase_  				=						pipe(
			    prompt=_UpperCAmelCase    ,						image=_UpperCAmelCase    ,						generator=_UpperCAmelCase    ,						num_inference_steps=5    ,						output_type="np"    ,						)

			lowerCamelCase_  				=						torch.cuda.max_memory_allocated()
			# make sure that less than 2.9 GB is allocated
			assert mem_bytes < 2.9 * 10**9







'''simple docstring'''

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetrImageProcessor





class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=7    ,						UpperCamelCase=3    ,						UpperCamelCase=30    ,						UpperCamelCase=400    ,						UpperCamelCase=True    ,						UpperCamelCase=None    ,						UpperCamelCase=True    ,						UpperCamelCase=1 / 255    ,						UpperCamelCase=True    ,						UpperCamelCase=[0.5, 0.5, 0.5]    ,						UpperCamelCase=[0.5, 0.5, 0.5]    ,						UpperCamelCase=True    ,						):







   """simple docstring"""
   # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
   lowerCamelCase_  				=						size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						num_channels
   lowerCamelCase_  				=						min_resolution
   lowerCamelCase_  				=						max_resolution
   lowerCamelCase_  				=						do_resize
   lowerCamelCase_  				=						size
   lowerCamelCase_  				=						do_rescale
   lowerCamelCase_  				=						rescale_factor
   lowerCamelCase_  				=						do_normalize
   lowerCamelCase_  				=						image_mean
   lowerCamelCase_  				=						image_std
   lowerCamelCase_  				=						do_pad






  def 						snake_case	(					self  ):







   """simple docstring"""
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_rescale": self.do_rescale,
       "rescale_factor": self.rescale_factor,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_pad": self.do_pad,
   }







  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=False  ):







   """simple docstring"""
   if not batched:
    lowerCamelCase_  				=						image_inputs[0]
    if isinstance(UpperCamelCase    ,						Image.Image  ):
     lowerCamelCase_				,lowerCamelCase_  				=						image.size
    else:
     lowerCamelCase_				,lowerCamelCase_  				=						image.shape[1], image.shape[2]
    if w < h:
     lowerCamelCase_  				=						int(self.size["shortest_edge"] * h / w  )
     lowerCamelCase_  				=						self.size["shortest_edge"]
    elif w > h:
     lowerCamelCase_  				=						self.size["shortest_edge"]
     lowerCamelCase_  				=						int(self.size["shortest_edge"] * w / h  )
    else:
     lowerCamelCase_  				=						self.size["shortest_edge"]
     lowerCamelCase_  				=						self.size["shortest_edge"]

   else:
    lowerCamelCase_  				=						[]
    for image in image_inputs:
     lowerCamelCase_				,lowerCamelCase_  				=						self.get_expected_values([image]  )
     expected_values.append((expected_height, expected_width)  )
    lowerCamelCase_  				=						max(UpperCamelCase    ,						key=lambda UpperCamelCase  : item[0]  )[0]
    lowerCamelCase_  				=						max(UpperCamelCase    ,						key=lambda UpperCamelCase  : item[1]  )[1]

   return expected_height, expected_width





@require_torch
@require_vision
class   snake_case					(					lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						DetrImageProcessor if is_vision_available() else None






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						DetrImageProcessingTester(self  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return self.image_processor_tester.prepare_image_processor_dict()






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"image_mean"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"image_std"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_normalize"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_rescale"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"rescale_factor"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_resize"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"size"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_pad"  )  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.image_processing_class.from_dict(self.image_processor_dict  )
   self.assertEqual(image_processor.size    ,						{"shortest_edge": 18, "longest_edge": 1333}  )
   self.assertEqual(image_processor.do_pad    ,						UpperCamelCase  )

   lowerCamelCase_  				=						self.image_processing_class.from_dict(
       self.image_processor_dict    ,						size=42    ,						max_size=84    ,						pad_and_return_pixel_mask=UpperCamelCase  )
   self.assertEqual(image_processor.size    ,						{"shortest_edge": 42, "longest_edge": 84}  )
   self.assertEqual(image_processor.do_pad    ,						UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   # create random PIL images
   lowerCamelCase_  				=						prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase  )
   for image in image_inputs:
    self.assertIsInstance(UpperCamelCase    ,						Image.Image  )

   # Test not batched input
   lowerCamelCase_  				=						image_processing(image_inputs[0]    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,						)

   # Test batched
   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase    ,						batched=UpperCamelCase  )

   lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values
   self.assertEqual(
       encoded_images.shape    ,						(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )    ,						)






  def 						snake_case	(					self  ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   # create random numpy tensors
   lowerCamelCase_  				=						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
   lowerCamelCase_  				=						image_processing(image_inputs[0]    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,						)

   # Test batched
   lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase    ,						batched=UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )    ,						)






  def 						snake_case	(					self  ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   # create random PyTorch tensors
   lowerCamelCase_  				=						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
   lowerCamelCase_  				=						image_processing(image_inputs[0]    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,						)

   # Test batched
   lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase    ,						batched=UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )    ,						)






  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   # prepare image and target
   lowerCamelCase_  				=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
   with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"    ,						"r"  ) as f:
    lowerCamelCase_  				=						json.loads(f.read()  )

   lowerCamelCase_  				=						{"image_id": 3_9769, "annotations": target}

   # encode them
   lowerCamelCase_  				=						DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"  )
   lowerCamelCase_  				=						image_processing(images=UpperCamelCase    ,						annotations=UpperCamelCase    ,						return_tensors="pt"  )

   # verify pixel values
   lowerCamelCase_  				=						torch.Size([1, 3, 800, 1066]  )
   self.assertEqual(encoding["pixel_values"].shape    ,						UpperCamelCase  )

   lowerCamelCase_  				=						torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
   self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,						UpperCamelCase    ,						atol=1e-4  )  )

   # verify area
   lowerCamelCase_  				=						torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,						UpperCamelCase  )  )
   # verify boxes
   lowerCamelCase_  				=						torch.Size([6, 4]  )
   self.assertEqual(encoding["labels"][0]["boxes"].shape    ,						UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,						UpperCamelCase    ,						atol=1e-3  )  )
   # verify image_id
   lowerCamelCase_  				=						torch.tensor([3_9769]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,						UpperCamelCase  )  )
   # verify is_crowd
   lowerCamelCase_  				=						torch.tensor([0, 0, 0, 0, 0, 0]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,						UpperCamelCase  )  )
   # verify class_labels
   lowerCamelCase_  				=						torch.tensor([75, 75, 63, 65, 17, 17]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,						UpperCamelCase  )  )
   # verify orig_size
   lowerCamelCase_  				=						torch.tensor([480, 640]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,						UpperCamelCase  )  )
   # verify size
   lowerCamelCase_  				=						torch.tensor([800, 1066]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,						UpperCamelCase  )  )







  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   # prepare image, target and masks_path
   lowerCamelCase_  				=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
   with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"    ,						"r"  ) as f:
    lowerCamelCase_  				=						json.loads(f.read()  )

   lowerCamelCase_  				=						{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

   lowerCamelCase_  				=						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"  )

   # encode them
   lowerCamelCase_  				=						DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"  )
   lowerCamelCase_  				=						image_processing(images=UpperCamelCase    ,						annotations=UpperCamelCase    ,						masks_path=UpperCamelCase    ,						return_tensors="pt"  )

   # verify pixel values
   lowerCamelCase_  				=						torch.Size([1, 3, 800, 1066]  )
   self.assertEqual(encoding["pixel_values"].shape    ,						UpperCamelCase  )

   lowerCamelCase_  				=						torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
   self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,						UpperCamelCase    ,						atol=1e-4  )  )

   # verify area
   lowerCamelCase_  				=						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,						UpperCamelCase  )  )
   # verify boxes
   lowerCamelCase_  				=						torch.Size([6, 4]  )
   self.assertEqual(encoding["labels"][0]["boxes"].shape    ,						UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,						UpperCamelCase    ,						atol=1e-3  )  )
   # verify image_id
   lowerCamelCase_  				=						torch.tensor([3_9769]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,						UpperCamelCase  )  )
   # verify is_crowd
   lowerCamelCase_  				=						torch.tensor([0, 0, 0, 0, 0, 0]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,						UpperCamelCase  )  )
   # verify class_labels
   lowerCamelCase_  				=						torch.tensor([17, 17, 63, 75, 75, 93]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,						UpperCamelCase  )  )
   # verify masks
   lowerCamelCase_  				=						82_2873
   self.assertEqual(encoding["labels"][0]["masks"].sum().item()    ,						UpperCamelCase  )
   # verify orig_size
   lowerCamelCase_  				=						torch.tensor([480, 640]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,						UpperCamelCase  )  )
   # verify size
   lowerCamelCase_  				=						torch.tensor([800, 1066]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,						UpperCamelCase  )  )



