
"""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 ConditionalDetrImageProcessor
class      _UpperCAmelCase							(		unittest.TestCase							):




						def __init__(							self   ,       lowercase_   ,       lowercase_=7   ,       lowercase_=3   ,       lowercase_=3_0   ,       lowercase_=4_0_0   ,       lowercase_=True   ,       lowercase_=None   ,       lowercase_=True   ,       lowercase_=[0.5, 0.5, 0.5]   ,       lowercase_=[0.5, 0.5, 0.5]   ,       lowercase_=True   ,       lowercase_=1 / 2_5_5   ,       lowercase_=True   ,       )						->					int:
										# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
										UpperCAmelCase									=					size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
										UpperCAmelCase									=					parent
										UpperCAmelCase									=					batch_size
										UpperCAmelCase									=					num_channels
										UpperCAmelCase									=					min_resolution
										UpperCAmelCase									=					max_resolution
										UpperCAmelCase									=					do_resize
										UpperCAmelCase									=					size
										UpperCAmelCase									=					do_normalize
										UpperCAmelCase									=					image_mean
										UpperCAmelCase									=					image_std
										UpperCAmelCase									=					do_rescale
										UpperCAmelCase									=					rescale_factor
										UpperCAmelCase									=					do_pad




						def     a_      (							self							)						->					Optional[int]:
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										}




						def     a_      (							self   ,       lowercase_   ,       lowercase_=False							)						->					Dict:
										if not batched:
														UpperCAmelCase									=					image_inputs[0]
														if isinstance(lowercase_   ,       Image.Image							):
																		UpperCAmelCase			,							UpperCAmelCase									=					image.size
														else:
																		UpperCAmelCase			,							UpperCAmelCase									=					image.shape[1], image.shape[2]
														if w < h:
																		UpperCAmelCase									=					int(self.size['shortest_edge'] * h / w							)
																		UpperCAmelCase									=					self.size['shortest_edge']
														elif w > h:
																		UpperCAmelCase									=					self.size['shortest_edge']
																		UpperCAmelCase									=					int(self.size['shortest_edge'] * w / h							)
														else:
																		UpperCAmelCase									=					self.size['shortest_edge']
																		UpperCAmelCase									=					self.size['shortest_edge']

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

										return expected_height, expected_width

@require_torch
@require_vision
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
						__SCREAMING_SNAKE_CASE   :							Optional[int]							      =	ConditionalDetrImageProcessor if is_vision_available() else None




						def     a_      (							self							)						->					Dict:
										UpperCAmelCase									=					ConditionalDetrImageProcessingTester(self							)




						@property
						def     a_      (							self							)						->					Dict:
										return self.image_processor_tester.prepare_image_processor_dict()




						def     a_      (							self							)						->					Union[str, Any]:
										UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(lowercase_   ,       'image_mean'							)							)
										self.assertTrue(hasattr(lowercase_   ,       'image_std'							)							)
										self.assertTrue(hasattr(lowercase_   ,       'do_normalize'							)							)
										self.assertTrue(hasattr(lowercase_   ,       'do_resize'							)							)
										self.assertTrue(hasattr(lowercase_   ,       'size'							)							)




						def     a_      (							self							)						->					Union[str, Any]:
										UpperCAmelCase									=					self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size   ,       {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}							)
										self.assertEqual(image_processor.do_pad   ,       lowercase_							)

										UpperCAmelCase									=					self.image_processing_class.from_dict(
										    self.image_processor_dict   ,       size=4_2   ,       max_size=8_4   ,       pad_and_return_pixel_mask=lowercase_							)
										self.assertEqual(image_processor.size   ,       {'shortest_edge': 4_2, 'longest_edge': 8_4}							)
										self.assertEqual(image_processor.do_pad   ,       lowercase_							)




						def     a_      (							self							)						->					Any:
										pass




						def     a_      (							self							)						->					Any:
										# 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=lowercase_							)
										for image in image_inputs:
														self.assertIsInstance(lowercase_   ,       Image.Image							)

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

										UpperCAmelCase			,							UpperCAmelCase									=					self.image_processor_tester.get_expected_values(lowercase_							)

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

										# Test batched
										UpperCAmelCase			,							UpperCAmelCase									=					self.image_processor_tester.get_expected_values(lowercase_   ,       batched=lowercase_							)

										UpperCAmelCase									=					image_processing(lowercase_   ,       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     a_      (							self							)						->					List[str]:
										# 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=lowercase_   ,       numpify=lowercase_							)
										for image in image_inputs:
														self.assertIsInstance(lowercase_   ,       np.ndarray							)

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

										UpperCAmelCase			,							UpperCAmelCase									=					self.image_processor_tester.get_expected_values(lowercase_							)

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

										# Test batched
										UpperCAmelCase									=					image_processing(lowercase_   ,       return_tensors='pt'							).pixel_values

										UpperCAmelCase			,							UpperCAmelCase									=					self.image_processor_tester.get_expected_values(lowercase_   ,       batched=lowercase_							)

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




						def     a_      (							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=lowercase_   ,       torchify=lowercase_							)
										for image in image_inputs:
														self.assertIsInstance(lowercase_   ,       torch.Tensor							)

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

										UpperCAmelCase			,							UpperCAmelCase									=					self.image_processor_tester.get_expected_values(lowercase_							)

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

										# Test batched
										UpperCAmelCase									=					image_processing(lowercase_   ,       return_tensors='pt'							).pixel_values

										UpperCAmelCase			,							UpperCAmelCase									=					self.image_processor_tester.get_expected_values(lowercase_   ,       batched=lowercase_							)

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




						@slow
						def     a_      (							self							)						->					List[Any]:
										# prepare image and target
										UpperCAmelCase									=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
										with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'   ,       'r'							) as f:
														UpperCAmelCase									=					json.loads(f.read()							)

										UpperCAmelCase									=					{'image_id': 3_9_7_6_9, 'annotations': target}

										# encode them
										UpperCAmelCase									=					ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'							)
										UpperCAmelCase									=					image_processing(images=lowercase_   ,       annotations=lowercase_   ,       return_tensors='pt'							)

										# verify pixel values
										UpperCAmelCase									=					torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
										self.assertEqual(encoding['pixel_values'].shape   ,       lowercase_							)

										UpperCAmelCase									=					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]							)
										self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,       lowercase_   ,       atol=1E-4							)							)

										# verify area
										UpperCAmelCase									=					torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,       lowercase_							)							)
										# verify boxes
										UpperCAmelCase									=					torch.Size([6, 4]							)
										self.assertEqual(encoding['labels'][0]['boxes'].shape   ,       lowercase_							)
										UpperCAmelCase									=					torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,       lowercase_   ,       atol=1E-3							)							)
										# verify image_id
										UpperCAmelCase									=					torch.tensor([3_9_7_6_9]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,       lowercase_							)							)
										# verify is_crowd
										UpperCAmelCase									=					torch.tensor([0, 0, 0, 0, 0, 0]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,       lowercase_							)							)
										# verify class_labels
										UpperCAmelCase									=					torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,       lowercase_							)							)
										# verify orig_size
										UpperCAmelCase									=					torch.tensor([4_8_0, 6_4_0]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,       lowercase_							)							)
										# verify size
										UpperCAmelCase									=					torch.tensor([8_0_0, 1_0_6_6]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,       lowercase_							)							)




						@slow
						def     a_      (							self							)						->					List[str]:
										# prepare image, target and masks_path
										UpperCAmelCase									=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
										with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'   ,       'r'							) as f:
														UpperCAmelCase									=					json.loads(f.read()							)

										UpperCAmelCase									=					{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

										UpperCAmelCase									=					pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'							)

										# encode them
										UpperCAmelCase									=					ConditionalDetrImageProcessor(format='coco_panoptic'							)
										UpperCAmelCase									=					image_processing(images=lowercase_   ,       annotations=lowercase_   ,       masks_path=lowercase_   ,       return_tensors='pt'							)

										# verify pixel values
										UpperCAmelCase									=					torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
										self.assertEqual(encoding['pixel_values'].shape   ,       lowercase_							)

										UpperCAmelCase									=					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]							)
										self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,       lowercase_   ,       atol=1E-4							)							)

										# verify area
										UpperCAmelCase									=					torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,       lowercase_							)							)
										# verify boxes
										UpperCAmelCase									=					torch.Size([6, 4]							)
										self.assertEqual(encoding['labels'][0]['boxes'].shape   ,       lowercase_							)
										UpperCAmelCase									=					torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,       lowercase_   ,       atol=1E-3							)							)
										# verify image_id
										UpperCAmelCase									=					torch.tensor([3_9_7_6_9]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,       lowercase_							)							)
										# verify is_crowd
										UpperCAmelCase									=					torch.tensor([0, 0, 0, 0, 0, 0]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,       lowercase_							)							)
										# verify class_labels
										UpperCAmelCase									=					torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,       lowercase_							)							)
										# verify masks
										UpperCAmelCase									=					8_2_2_8_7_3
										self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,       lowercase_							)
										# verify orig_size
										UpperCAmelCase									=					torch.tensor([4_8_0, 6_4_0]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,       lowercase_							)							)
										# verify size
										UpperCAmelCase									=					torch.tensor([8_0_0, 1_0_6_6]							)
										self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,       lowercase_							)							)





"""simple docstring"""

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


SCREAMING_SNAKE_CASE_    					=						transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      List[str]:






    """simple docstring"""




    if isinstance(lowerCAmelCase ,				torch.Tensor ):
        return image
    elif isinstance(lowerCAmelCase ,				PIL.Image.Image ):
        UpperCAmelCase									=					[image]

    UpperCAmelCase									=					[trans(img.convert('RGB' ) ) for img in image]
    UpperCAmelCase									=					torch.stack(lowerCAmelCase )
    return image







class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):




      def __init__(							self   ,       lowercase_   ,       lowercase_							)						->					List[str]:
          super().__init__()

          # make sure scheduler can always be converted to DDIM
          UpperCAmelCase									=					DDIMScheduler.from_config(scheduler.config							)

          self.register_modules(unet=lowercase_   ,       scheduler=lowercase_							)




      def     a_      (							self   ,       lowercase_							)						->					Tuple:
          if strength < 0 or strength > 1:
              raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}"							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					Any:
          # get the original timestep using init_timestep
          UpperCAmelCase									=					min(int(num_inference_steps * strength							)   ,       lowercase_							)

          UpperCAmelCase									=					max(num_inference_steps - init_timestep   ,       0							)
          UpperCAmelCase									=					self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_=None							)						->					Optional[Any]:
          if not isinstance(lowercase_   ,       (torch.Tensor, PIL.Image.Image, list)							):
              raise ValueError(
                  F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase_							)}"							)

          UpperCAmelCase									=					image.to(device=lowercase_   ,       dtype=lowercase_							)

          if isinstance(lowercase_   ,       lowercase_							) and len(lowercase_							) != batch_size:
              raise ValueError(
                  F"You have passed a list of generators of length {len(lowercase_							)}, but requested an effective batch"
                  F" size of {batch_size}. Make sure the batch size matches the length of the generators."							)

          UpperCAmelCase									=					init_latents.shape
          UpperCAmelCase									=					randn_tensor(lowercase_   ,       generator=lowercase_   ,       device=lowercase_   ,       dtype=lowercase_							)

          # get latents
          print('add noise to latents at timestep'   ,       lowercase_							)
          UpperCAmelCase									=					self.scheduler.add_noise(lowercase_   ,       lowercase_   ,       lowercase_							)
          UpperCAmelCase									=					init_latents

          return latents




      @torch.no_grad()
      def __call__(							self   ,       lowercase_ = None   ,       lowercase_ = 0.8   ,       lowercase_ = 1   ,       lowercase_ = None   ,       lowercase_ = 0.0   ,       lowercase_ = 5_0   ,       lowercase_ = None   ,       lowercase_ = "pil"   ,       lowercase_ = True   ,       )						->					Union[ImagePipelineOutput, Tuple]:
          self.check_inputs(lowercase_							)

          # 2. Preprocess image
          UpperCAmelCase									=					preprocess(lowercase_							)

          # 3. set timesteps
          self.scheduler.set_timesteps(lowercase_   ,       device=self.device							)
          UpperCAmelCase			,							UpperCAmelCase									=					self.get_timesteps(lowercase_   ,       lowercase_   ,       self.device							)
          UpperCAmelCase									=					timesteps[:1].repeat(lowercase_							)

          # 4. Prepare latent variables
          UpperCAmelCase									=					self.prepare_latents(lowercase_   ,       lowercase_   ,       lowercase_   ,       self.unet.dtype   ,       self.device   ,       lowercase_							)
          UpperCAmelCase									=					latents

          # 5. Denoising loop
          for t in self.progress_bar(lowercase_							):
              # 1. predict noise model_output
              UpperCAmelCase									=					self.unet(lowercase_   ,       lowercase_							).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              UpperCAmelCase									=					self.scheduler.step(
                  lowercase_   ,       lowercase_   ,       lowercase_   ,       eta=lowercase_   ,       use_clipped_model_output=lowercase_   ,       generator=lowercase_   ,       ).prev_sample

          UpperCAmelCase									=					(image / 2 + 0.5).clamp(0   ,       1							)
          UpperCAmelCase									=					image.cpu().permute(0   ,       2   ,       3   ,       1							).numpy()
          if output_type == "pil":
              UpperCAmelCase									=					self.numpy_to_pil(lowercase_							)

          if not return_dict:
              return (image, latent_timestep.item())

          return ImagePipelineOutput(images=lowercase_							)



