
import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)






def 	a_		(	__lowercase       :     str	, __lowercase       :     List[str]	, __lowercase       :     Dict			)			->  List[str]:
			if isinstance(__lowercase	, torch.Tensor			):
						return image
			elif isinstance(__lowercase	, PIL.Image.Image			):
						_snake_case          =      [image]

			if isinstance(image[0]	, PIL.Image.Image			):
						_snake_case          =      [np.array(i.resize((w, h)	, resample=PIL_INTERPOLATION['lanczos']			)			)[None, :] for i in image]
						_snake_case          =      np.concatenate(__lowercase	, axis=0			)
						_snake_case          =      np.array(__lowercase			).astype(np.floataa			) / 2_5_5.0
						_snake_case          =      image.transpose(0	, 3	, 1	, 2			)
						_snake_case          =      2.0 * image - 1.0
						_snake_case          =      torch.from_numpy(__lowercase			)
			elif isinstance(image[0]	, torch.Tensor			):
						_snake_case          =      torch.cat(__lowercase	, dim=0			)
			return image






def 	a_		(	__lowercase       :     int	, __lowercase       :     Any	, __lowercase       :     List[Any]	, __lowercase       :     Tuple=0.9_9_9_5			)			->  List[str]:
			if not isinstance(__lowercase	, np.ndarray			):
						_snake_case          =      True
						_snake_case          =      va.device
						_snake_case          =      va.cpu().numpy()
						_snake_case          =      va.cpu().numpy()

			_snake_case          =      np.sum(va * va / (np.linalg.norm(__lowercase			) * np.linalg.norm(__lowercase			))			)
			if np.abs(__lowercase			) > DOT_THRESHOLD:
						_snake_case          =      (1 - t) * va + t * va
			else:
						_snake_case          =      np.arccos(__lowercase			)
						_snake_case          =      np.sin(__lowercase			)
						_snake_case          =      theta_a * t
						_snake_case          =      np.sin(__lowercase			)
						_snake_case          =      np.sin(theta_a - theta_t			) / sin_theta_a
						_snake_case          =      sin_theta_t / sin_theta_a
						_snake_case          =      sa * va + sa * va

			if inputs_are_torch:
						_snake_case          =      torch.from_numpy(__lowercase			).to(__lowercase			)

			return va






def 	a_		(	__lowercase       :     int	, __lowercase       :     Optional[int]			)			->  List[Any]:
			_snake_case          =      F.normalize(__lowercase	, dim=-1			)
			_snake_case          =      F.normalize(__lowercase	, dim=-1			)
			return (x - y).norm(dim=-1			).div(2			).arcsin().pow(2			).mul(2			)






def 	a_		(	__lowercase       :     int	, __lowercase       :     Any			)			->  Optional[Any]:
			for param in model.parameters():
						_snake_case          =      value




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def __init__(  self     :   List[Any]      ,  lowercase     :   AutoencoderKL      ,  lowercase     :   CLIPTextModel      ,  lowercase     :   CLIPModel      ,  lowercase     :   CLIPTokenizer      ,  lowercase     :   UNetaDConditionModel      ,  lowercase     :   Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]      ,  lowercase     :   CLIPFeatureExtractor      ,  lowercase     :   Any=None      ,  lowercase     :   List[str]=None      ,  lowercase     :   List[str]=None      ,  ):


				'''simple docstring'''


				super().__init__()
				self.register_modules(
				    vae=lowercase      ,  text_encoder=lowercase      ,  clip_model=lowercase      ,  tokenizer=lowercase      ,  unet=lowercase      ,  scheduler=lowercase      ,  feature_extractor=lowercase      ,  coca_model=lowercase      ,  coca_tokenizer=lowercase      ,  coca_transform=lowercase      ,  )
				_snake_case          =      (
				    feature_extractor.size
				    if isinstance(feature_extractor.size      ,  lowercase     )
				    else feature_extractor.size['shortest_edge']
				)
				_snake_case          =      transforms.Normalize(mean=feature_extractor.image_mean      ,  std=feature_extractor.image_std     )
				set_requires_grad(self.text_encoder      ,  lowercase     )
				set_requires_grad(self.clip_model      ,  lowercase     )



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   Optional[Union[str, int]] = "auto"     ):


				'''simple docstring'''


				if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							_snake_case          =      self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(lowercase     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				self.enable_attention_slicing(lowercase     )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				set_requires_grad(self.vae      ,  lowercase     )



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				set_requires_grad(self.vae      ,  lowercase     )



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				set_requires_grad(self.unet      ,  lowercase     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				set_requires_grad(self.unet      ,  lowercase     )



	def 			A							(  self     :   str      ,  lowercase     :   Optional[Any]      ,  lowercase     :   Optional[int]      ,  lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      min(int(num_inference_steps * strength     )      ,  lowercase     )

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

				return timesteps, num_inference_steps - t_start



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   List[str]      ,  lowercase     :   Optional[Any]      ,  lowercase     :   str      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   List[str]      ,  lowercase     :   List[Any]=None     ):


				'''simple docstring'''


				if not isinstance(lowercase      ,  torch.Tensor     ):
							raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(lowercase     )}'''     )

				_snake_case          =      image.to(device=lowercase      ,  dtype=lowercase     )

				if isinstance(lowercase      ,  lowercase     ):
							_snake_case          =      [
							    self.vae.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(lowercase     )
							]
							_snake_case          =      torch.cat(lowercase      ,  dim=0     )
				else:
							_snake_case          =      self.vae.encode(lowercase     ).latent_dist.sample(lowercase     )

				# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				_snake_case          =      0.18215 * init_latents
				_snake_case          =      init_latents.repeat_interleave(lowercase      ,  dim=0     )

				_snake_case          =      randn_tensor(init_latents.shape      ,  generator=lowercase      ,  device=lowercase      ,  dtype=lowercase     )

				# get latents
				_snake_case          =      self.scheduler.add_noise(lowercase      ,  lowercase      ,  lowercase     )
				_snake_case          =      init_latents

				return latents



	def 			A							(  self     :   int      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      self.coca_transform(lowercase     ).unsqueeze(0     )
				with torch.no_grad(), torch.cuda.amp.autocast():
							_snake_case          =      self.coca_model.generate(transformed_image.to(device=self.device      ,  dtype=self.coca_model.dtype     )     )
				_snake_case          =      self.coca_tokenizer.decode(generated[0].cpu().numpy()     )
				return generated.split('<end_of_text>'     )[0].replace('<start_of_text>'      ,  ''     ).rstrip(' .,'     )



	def 			A							(  self     :   List[Any]      ,  lowercase     :   Dict      ,  lowercase     :   Any     ):


				'''simple docstring'''


				_snake_case          =      self.feature_extractor.preprocess(lowercase     )
				_snake_case          =      torch.from_numpy(clip_image_input['pixel_values'][0]     ).unsqueeze(0     ).to(self.device     ).half()
				_snake_case          =      self.clip_model.get_image_features(lowercase     )
				_snake_case          =      image_embeddings_clip / image_embeddings_clip.norm(p=2      ,  dim=-1      ,  keepdim=lowercase     )
				_snake_case          =      image_embeddings_clip.repeat_interleave(lowercase      ,  dim=0     )
				return image_embeddings_clip



	@torch.enable_grad()
	def 			A							(  self     :   int      ,  lowercase     :   Optional[int]      ,  lowercase     :   Optional[int]      ,  lowercase     :   Optional[int]      ,  lowercase     :   int      ,  lowercase     :   Any      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Optional[int]      ,  ):


				'''simple docstring'''


				_snake_case          =      latents.detach().requires_grad_()

				_snake_case          =      self.scheduler.scale_model_input(lowercase      ,  lowercase     )

				# predict the noise residual
				_snake_case          =      self.unet(lowercase      ,  lowercase      ,  encoder_hidden_states=lowercase     ).sample

				if isinstance(self.scheduler      ,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)     ):
							_snake_case          =      self.scheduler.alphas_cumprod[timestep]
							_snake_case          =      1 - alpha_prod_t
							# compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case          =      (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

							_snake_case          =      torch.sqrt(lowercase     )
							_snake_case          =      pred_original_sample * (fac) + latents * (1 - fac)
				elif isinstance(self.scheduler      ,  lowercase     ):
							_snake_case          =      self.scheduler.sigmas[index]
							_snake_case          =      latents - sigma * noise_pred
				else:
							raise ValueError(f'''scheduler type {type(self.scheduler     )} not supported'''     )

				# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				_snake_case          =      1 / 0.18215 * sample
				_snake_case          =      self.vae.decode(lowercase     ).sample
				_snake_case          =      (image / 2 + 0.5).clamp(0      ,  1     )

				_snake_case          =      transforms.Resize(self.feature_extractor_size     )(lowercase     )
				_snake_case          =      self.normalize(lowercase     ).to(latents.dtype     )

				_snake_case          =      self.clip_model.get_image_features(lowercase     )
				_snake_case          =      image_embeddings_clip / image_embeddings_clip.norm(p=2      ,  dim=-1      ,  keepdim=lowercase     )

				_snake_case          =      spherical_dist_loss(lowercase      ,  lowercase     ).mean() * clip_guidance_scale

				_snake_case          =      -torch.autograd.grad(lowercase      ,  lowercase     )[0]

				if isinstance(self.scheduler      ,  lowercase     ):
							_snake_case          =      latents.detach() + grads * (sigma**2)
							_snake_case          =      noise_pred_original
				else:
							_snake_case          =      noise_pred_original - torch.sqrt(lowercase     ) * grads
				return noise_pred, latents





	@torch.no_grad()
	def __call__(  self     :   int      ,  lowercase     :   Union[torch.FloatTensor, PIL.Image.Image]      ,  lowercase     :   Union[torch.FloatTensor, PIL.Image.Image]      ,  lowercase     :   Optional[str] = None      ,  lowercase     :   Optional[str] = None      ,  lowercase     :   Optional[int] = 512      ,  lowercase     :   Optional[int] = 512      ,  lowercase     :   float = 0.6      ,  lowercase     :   Optional[int] = 50      ,  lowercase     :   Optional[float] = 7.5      ,  lowercase     :   Optional[int] = 1      ,  lowercase     :   float = 0.0      ,  lowercase     :   Optional[float] = 100      ,  lowercase     :   Optional[torch.Generator] = None      ,  lowercase     :   Optional[str] = "pil"      ,  lowercase     :   bool = True      ,  lowercase     :   float = 0.8      ,  lowercase     :   float = 0.1      ,  lowercase     :   float = 0.1      ,  ):


				'''simple docstring'''


				if isinstance(lowercase      ,  lowercase     ) and len(lowercase     ) != batch_size:
							raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(lowercase     )} generators.'''     )

				if height % 8 != 0 or width % 8 != 0:
							raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''     )

				if isinstance(lowercase      ,  torch.Generator     ) and batch_size > 1:
							_snake_case          =      [generator] + [None] * (batch_size - 1)

				_snake_case          =      [
				    ('model', self.coca_model is None),
				    ('tokenizer', self.coca_tokenizer is None),
				    ('transform', self.coca_transform is None),
				]
				_snake_case          =      [x[0] for x in coca_is_none if x[1]]
				_snake_case          =      ', '.join(lowercase     )
				# generate prompts with coca model if prompt is None
				if content_prompt is None:
							if len(lowercase     ):
										raise ValueError(
										    f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
										    f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''     )
							_snake_case          =      self.get_image_description(lowercase     )
				if style_prompt is None:
							if len(lowercase     ):
										raise ValueError(
										    f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
										    f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''     )
							_snake_case          =      self.get_image_description(lowercase     )

				# get prompt text embeddings for content and style
				_snake_case          =      self.tokenizer(
				    lowercase      ,  padding='max_length'      ,  max_length=self.tokenizer.model_max_length      ,  truncation=lowercase      ,  return_tensors='pt'      ,  )
				_snake_case          =      self.text_encoder(content_text_input.input_ids.to(self.device     )     )[0]

				_snake_case          =      self.tokenizer(
				    lowercase      ,  padding='max_length'      ,  max_length=self.tokenizer.model_max_length      ,  truncation=lowercase      ,  return_tensors='pt'      ,  )
				_snake_case          =      self.text_encoder(style_text_input.input_ids.to(self.device     )     )[0]

				_snake_case          =      slerp(lowercase      ,  lowercase      ,  lowercase     )

				# duplicate text embeddings for each generation per prompt
				_snake_case          =      text_embeddings.repeat_interleave(lowercase      ,  dim=0     )

				# set timesteps
				_snake_case          =      'offset' in set(inspect.signature(self.scheduler.set_timesteps     ).parameters.keys()     )
				_snake_case          =      {}
				if accepts_offset:
							_snake_case          =      1

				self.scheduler.set_timesteps(lowercase      ,  **lowercase     )
				# Some schedulers like PNDM have timesteps as arrays
				# It's more optimized to move all timesteps to correct device beforehand
				self.scheduler.timesteps.to(self.device     )

				_snake_case     , _snake_case          =      self.get_timesteps(lowercase      ,  lowercase      ,  self.device     )
				_snake_case          =      timesteps[:1].repeat(lowercase     )

				# Preprocess image
				_snake_case          =      preprocess(lowercase      ,  lowercase      ,  lowercase     )
				_snake_case          =      self.prepare_latents(
				    lowercase      ,  lowercase      ,  lowercase      ,  text_embeddings.dtype      ,  self.device      ,  lowercase     )

				_snake_case          =      preprocess(lowercase      ,  lowercase      ,  lowercase     )
				_snake_case          =      self.prepare_latents(
				    lowercase      ,  lowercase      ,  lowercase      ,  text_embeddings.dtype      ,  self.device      ,  lowercase     )

				_snake_case          =      slerp(lowercase      ,  lowercase      ,  lowercase     )

				if clip_guidance_scale > 0:
							_snake_case          =      self.get_clip_image_embeddings(lowercase      ,  lowercase     )
							_snake_case          =      self.get_clip_image_embeddings(lowercase      ,  lowercase     )
							_snake_case          =      slerp(
							    lowercase      ,  lowercase      ,  lowercase     )

				# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
				# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
				# corresponds to doing no classifier free guidance.
				_snake_case          =      guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
							_snake_case          =      content_text_input.input_ids.shape[-1]
							_snake_case          =      self.tokenizer(['']      ,  padding='max_length'      ,  max_length=lowercase      ,  return_tensors='pt'     )
							_snake_case          =      self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]
							# duplicate unconditional embeddings for each generation per prompt
							_snake_case          =      uncond_embeddings.repeat_interleave(lowercase      ,  dim=0     )

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							_snake_case          =      torch.cat([uncond_embeddings, text_embeddings]     )

				# get the initial random noise unless the user supplied it

				# Unlike in other pipelines, latents need to be generated in the target device
				# for 1-to-1 results reproducibility with the CompVis implementation.
				# However this currently doesn't work in `mps`.
				_snake_case          =      (batch_size, self.unet.config.in_channels, height // 8, width // 8)
				_snake_case          =      text_embeddings.dtype
				if latents is None:
							if self.device.type == "mps":
										# randn does not work reproducibly on mps
										_snake_case          =      torch.randn(lowercase      ,  generator=lowercase      ,  device='cpu'      ,  dtype=lowercase     ).to(
										    self.device     )
							else:
										_snake_case          =      torch.randn(lowercase      ,  generator=lowercase      ,  device=self.device      ,  dtype=lowercase     )
				else:
							if latents.shape != latents_shape:
										raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''     )
							_snake_case          =      latents.to(self.device     )

				# scale the initial noise by the standard deviation required by the scheduler
				_snake_case          =      latents * self.scheduler.init_noise_sigma

				# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
				# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
				# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
				# and should be between [0, 1]
				_snake_case          =      'eta' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
				_snake_case          =      {}
				if accepts_eta:
							_snake_case          =      eta

				# check if the scheduler accepts generator
				_snake_case          =      'generator' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
				if accepts_generator:
							_snake_case          =      generator

				with self.progress_bar(total=lowercase     ):
							for i, t in enumerate(lowercase     ):
										# expand the latents if we are doing classifier free guidance
										_snake_case          =      torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents
										_snake_case          =      self.scheduler.scale_model_input(lowercase      ,  lowercase     )

										# predict the noise residual
										_snake_case          =      self.unet(lowercase      ,  lowercase      ,  encoder_hidden_states=lowercase     ).sample

										# perform classifier free guidance
										if do_classifier_free_guidance:
													_snake_case     , _snake_case          =      noise_pred.chunk(2     )
													_snake_case          =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

										# perform clip guidance
										if clip_guidance_scale > 0:
													_snake_case          =      (
													    text_embeddings.chunk(2     )[1] if do_classifier_free_guidance else text_embeddings
													)
													_snake_case     , _snake_case          =      self.cond_fn(
													    lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  )

										# compute the previous noisy sample x_t -> x_t-1
										_snake_case          =      self.scheduler.step(lowercase      ,  lowercase      ,  lowercase      ,  **lowercase     ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				_snake_case          =      1 / 0.18215 * latents
				_snake_case          =      self.vae.decode(lowercase     ).sample

				_snake_case          =      (image / 2 + 0.5).clamp(0      ,  1     )
				_snake_case          =      image.cpu().permute(0      ,  2      ,  3      ,  1     ).numpy()

				if output_type == "pil":
							_snake_case          =      self.numpy_to_pil(lowercase     )

				if not return_dict:
							return (image, None)

				return StableDiffusionPipelineOutput(images=lowercase      ,  nsfw_content_detected=lowercase     )

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 DeformableDetrImageProcessor




class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def __init__(  self     :   int      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   str=7      ,  lowercase     :   Union[str, Any]=3      ,  lowercase     :   Tuple=30      ,  lowercase     :   Optional[Any]=400      ,  lowercase     :   List[Any]=True      ,  lowercase     :   Any=None      ,  lowercase     :   str=True      ,  lowercase     :   Tuple=[0.5, 0.5, 0.5]      ,  lowercase     :   List[Any]=[0.5, 0.5, 0.5]      ,  lowercase     :   Union[str, Any]=True      ,  lowercase     :   List[Any]=1 / 255      ,  lowercase     :   int=True      ,  ):


				'''simple docstring'''


				_snake_case          =      size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
				_snake_case          =      parent
				_snake_case          =      batch_size
				_snake_case          =      num_channels
				_snake_case          =      min_resolution
				_snake_case          =      max_resolution
				_snake_case          =      do_resize
				_snake_case          =      size
				_snake_case          =      do_normalize
				_snake_case          =      image_mean
				_snake_case          =      image_std
				_snake_case          =      do_rescale
				_snake_case          =      rescale_factor
				_snake_case          =      do_pad



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				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     :   Optional[int]      ,  lowercase     :   List[Any]      ,  lowercase     :   Tuple=False     ):


				'''simple docstring'''


				if not batched:
							_snake_case          =      image_inputs[0]
							if isinstance(lowercase      ,  Image.Image     ):
										_snake_case     , _snake_case          =      image.size
							else:
										_snake_case     , _snake_case          =      image.shape[1], image.shape[2]
							if w < h:
										_snake_case          =      int(self.size['shortest_edge'] * h / w     )
										_snake_case          =      self.size['shortest_edge']
							elif w > h:
										_snake_case          =      self.size['shortest_edge']
										_snake_case          =      int(self.size['shortest_edge'] * w / h     )
							else:
										_snake_case          =      self.size['shortest_edge']
										_snake_case          =      self.size['shortest_edge']

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

				return expected_height, expected_width




@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Dict          = DeformableDetrImageProcessor if is_vision_available() else None



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      DeformableDetrImageProcessingTester(self     )



	@property
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				return self.image_processor_tester.prepare_image_processor_dict()



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      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      ,  'do_rescale'     )     )
				self.assertTrue(hasattr(lowercase      ,  'do_pad'     )     )
				self.assertTrue(hasattr(lowercase      ,  'size'     )     )



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class.from_dict(self.image_processor_dict     )
				self.assertEqual(image_processor.size      ,  {'shortest_edge': 18, 'longest_edge': 1_333}     )
				self.assertEqual(image_processor.do_pad      ,  lowercase     )

				_snake_case          =      self.image_processing_class.from_dict(
				    self.image_processor_dict      ,  size=42      ,  max_size=84      ,  pad_and_return_pixel_mask=lowercase     )
				self.assertEqual(image_processor.size      ,  {'shortest_edge': 42, 'longest_edge': 84}     )
				self.assertEqual(image_processor.do_pad      ,  lowercase     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				pass



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


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

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

				_snake_case     , _snake_case          =      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
				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase      ,  batched=lowercase     )

				_snake_case          =      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]     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class(**self.image_processor_dict     )
				# create random numpy tensors
				_snake_case          =      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
				_snake_case          =      image_processing(image_inputs[0]      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      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
				_snake_case          =      image_processing(lowercase      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      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     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class(**self.image_processor_dict     )
				# create random PyTorch tensors
				_snake_case          =      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
				_snake_case          =      image_processing(image_inputs[0]      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      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
				_snake_case          =      image_processing(lowercase      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      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[str]     ):


				'''simple docstring'''


				_snake_case          =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
				with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,  'r'     ) as f:
							_snake_case          =      json.loads(f.read()     )

				_snake_case          =      {'image_id': 39_769, 'annotations': target}

				# encode them
				_snake_case          =      DeformableDetrImageProcessor()
				_snake_case          =      image_processing(images=lowercase      ,  annotations=lowercase      ,  return_tensors='pt'     )

				# verify pixel values
				_snake_case          =      torch.Size([1, 3, 800, 1_066]     )
				self.assertEqual(encoding['pixel_values'].shape      ,  lowercase     )

				_snake_case          =      torch.tensor([0.2796, 0.3138, 0.3481]     )
				self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,  lowercase      ,  atol=1E-4     )     )

				# verify area
				_snake_case          =      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,  lowercase     )     )
				# verify boxes
				_snake_case          =      torch.Size([6, 4]     )
				self.assertEqual(encoding['labels'][0]['boxes'].shape      ,  lowercase     )
				_snake_case          =      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,  lowercase      ,  atol=1E-3     )     )
				# verify image_id
				_snake_case          =      torch.tensor([39_769]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,  lowercase     )     )
				# verify is_crowd
				_snake_case          =      torch.tensor([0, 0, 0, 0, 0, 0]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,  lowercase     )     )
				# verify class_labels
				_snake_case          =      torch.tensor([75, 75, 63, 65, 17, 17]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,  lowercase     )     )
				# verify orig_size
				_snake_case          =      torch.tensor([480, 640]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,  lowercase     )     )
				# verify size
				_snake_case          =      torch.tensor([800, 1_066]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,  lowercase     )     )





	@slow
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
				with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,  'r'     ) as f:
							_snake_case          =      json.loads(f.read()     )

				_snake_case          =      {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

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

				# encode them
				_snake_case          =      DeformableDetrImageProcessor(format='coco_panoptic'     )
				_snake_case          =      image_processing(images=lowercase      ,  annotations=lowercase      ,  masks_path=lowercase      ,  return_tensors='pt'     )

				# verify pixel values
				_snake_case          =      torch.Size([1, 3, 800, 1_066]     )
				self.assertEqual(encoding['pixel_values'].shape      ,  lowercase     )

				_snake_case          =      torch.tensor([0.2796, 0.3138, 0.3481]     )
				self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,  lowercase      ,  atol=1E-4     )     )

				# verify area
				_snake_case          =      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,  lowercase     )     )
				# verify boxes
				_snake_case          =      torch.Size([6, 4]     )
				self.assertEqual(encoding['labels'][0]['boxes'].shape      ,  lowercase     )
				_snake_case          =      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,  lowercase      ,  atol=1E-3     )     )
				# verify image_id
				_snake_case          =      torch.tensor([39_769]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,  lowercase     )     )
				# verify is_crowd
				_snake_case          =      torch.tensor([0, 0, 0, 0, 0, 0]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,  lowercase     )     )
				# verify class_labels
				_snake_case          =      torch.tensor([17, 17, 63, 75, 75, 93]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,  lowercase     )     )
				# verify masks
				_snake_case          =      822_873
				self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,  lowercase     )
				# verify orig_size
				_snake_case          =      torch.tensor([480, 640]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,  lowercase     )     )
				# verify size
				_snake_case          =      torch.tensor([800, 1_066]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,  lowercase     )     )