

'''simple docstring'''

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 							_UpperCamelCase				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					Optional[int]   )      -> Dict:
							if isinstance(lowerCAmelCase__		,torch.Tensor   ):
														return image
							elif isinstance(lowerCAmelCase__		,PIL.Image.Image   ):
														SCREAMING_SNAKE_CASE_				=				[image]

							if isinstance(image[0]		,PIL.Image.Image   ):
														SCREAMING_SNAKE_CASE_				=				[np.array(i.resize((w, h)		,resample=PIL_INTERPOLATION['lanczos']   )   )[None, :] for i in image]
														SCREAMING_SNAKE_CASE_				=				np.concatenate(lowerCAmelCase__		,axis=0   )
														SCREAMING_SNAKE_CASE_				=				np.array(lowerCAmelCase__   ).astype(np.floataa   ) / 255.0
														SCREAMING_SNAKE_CASE_				=				image.transpose(0		,3		,1		,2   )
														SCREAMING_SNAKE_CASE_				=				2.0 * image - 1.0
														SCREAMING_SNAKE_CASE_				=				torch.from_numpy(lowerCAmelCase__   )
							elif isinstance(image[0]		,torch.Tensor   ):
														SCREAMING_SNAKE_CASE_				=				torch.cat(lowerCAmelCase__		,dim=0   )
							return image




def 							_UpperCamelCase				(   lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					Dict		,lowerCAmelCase__:					Dict		,lowerCAmelCase__:					List[str]=0.9_995   )      -> str:
							if not isinstance(lowerCAmelCase__		,np.ndarray   ):
														SCREAMING_SNAKE_CASE_				=				True
														SCREAMING_SNAKE_CASE_				=				va.device
														SCREAMING_SNAKE_CASE_				=				va.cpu().numpy()
														SCREAMING_SNAKE_CASE_				=				va.cpu().numpy()

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

							if inputs_are_torch:
														SCREAMING_SNAKE_CASE_				=				torch.from_numpy(lowerCAmelCase__   ).to(lowerCAmelCase__   )

							return va




def 							_UpperCamelCase				(   lowerCAmelCase__:					Optional[Any]		,lowerCAmelCase__:					str   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				F.normalize(lowerCAmelCase__		,dim=-1   )
							SCREAMING_SNAKE_CASE_				=				F.normalize(lowerCAmelCase__		,dim=-1   )
							return (x - y).norm(dim=-1   ).div(2   ).arcsin().pow(2   ).mul(2   )




def 							_UpperCamelCase				(   lowerCAmelCase__:					Union[str, Any]		,lowerCAmelCase__:					Optional[Any]   )      -> Any:
							for param in model.parameters():
														SCREAMING_SNAKE_CASE_				=				value






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase=None,     _lowercase=None,     _lowercase=None,     )     ->							Union[str, Any]:
											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,     )
											SCREAMING_SNAKE_CASE_				=				(
											    feature_extractor.size
											    if isinstance(feature_extractor.size,     _lowercase       )
											    else feature_extractor.size['shortest_edge']
											)
											SCREAMING_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,     _lowercase = "auto"       )     ->							List[str]:
											if slice_size == "auto":
																		# half the attention head size is usually a good trade-off between
																		# speed and memory
																		SCREAMING_SNAKE_CASE_				=				self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(_lowercase       )



				def   a__					(    self       )     ->							Dict:
											self.enable_attention_slicing(_lowercase       )



				def   a__					(    self       )     ->							Tuple:
											set_requires_grad(self.vae,     _lowercase       )



				def   a__					(    self       )     ->							List[Any]:
											set_requires_grad(self.vae,     _lowercase       )



				def   a__					(    self       )     ->							Optional[int]:
											set_requires_grad(self.unet,     _lowercase       )



				def   a__					(    self       )     ->							int:
											set_requires_grad(self.unet,     _lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							Tuple:
											# get the original timestep using init_timestep
											SCREAMING_SNAKE_CASE_				=				min(int(num_inference_steps * strength       ),     _lowercase       )

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

											return timesteps, num_inference_steps - t_start



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase=None       )     ->							Dict:
											if not isinstance(_lowercase,     torch.Tensor       ):
																		raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(_lowercase       )}"""       )

											SCREAMING_SNAKE_CASE_				=				image.to(device=_lowercase,     dtype=_lowercase       )

											if isinstance(_lowercase,     _lowercase       ):
																		SCREAMING_SNAKE_CASE_				=				[
																		    self.vae.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(_lowercase       )
																		]
																		SCREAMING_SNAKE_CASE_				=				torch.cat(_lowercase,     dim=0       )
											else:
																		SCREAMING_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
											SCREAMING_SNAKE_CASE_				=				0.18_215 * init_latents
											SCREAMING_SNAKE_CASE_				=				init_latents.repeat_interleave(_lowercase,     dim=0       )

											SCREAMING_SNAKE_CASE_				=				randn_tensor(init_latents.shape,     generator=_lowercase,     device=_lowercase,     dtype=_lowercase       )

											# get latents
											SCREAMING_SNAKE_CASE_				=				self.scheduler.add_noise(_lowercase,     _lowercase,     _lowercase       )
											SCREAMING_SNAKE_CASE_				=				init_latents

											return latents



				def   a__					(    self,     _lowercase       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				self.coca_transform(_lowercase       ).unsqueeze(0       )
											with torch.no_grad(), torch.cuda.amp.autocast():
																		SCREAMING_SNAKE_CASE_				=				self.coca_model.generate(transformed_image.to(device=self.device,     dtype=self.coca_model.dtype       )       )
											SCREAMING_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,     _lowercase,     _lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				self.feature_extractor.preprocess(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				torch.from_numpy(clip_image_input['pixel_values'][0]       ).unsqueeze(0       ).to(self.device       ).half()
											SCREAMING_SNAKE_CASE_				=				self.clip_model.get_image_features(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				image_embeddings_clip / image_embeddings_clip.norm(p=2,     dim=-1,     keepdim=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				image_embeddings_clip.repeat_interleave(_lowercase,     dim=0       )
											return image_embeddings_clip



				@torch.enable_grad()
				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				latents.detach().requires_grad_()

											SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )

											# predict the noise residual
											SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase,     encoder_hidden_states=_lowercase       ).sample

											if isinstance(self.scheduler,     (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.alphas_cumprod[timestep]
																		SCREAMING_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
																		SCREAMING_SNAKE_CASE_				=				(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

																		SCREAMING_SNAKE_CASE_				=				torch.sqrt(_lowercase       )
																		SCREAMING_SNAKE_CASE_				=				pred_original_sample * (fac) + latents * (1 - fac)
											elif isinstance(self.scheduler,     _lowercase       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.sigmas[index]
																		SCREAMING_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
											SCREAMING_SNAKE_CASE_				=				1 / 0.18_215 * sample
											SCREAMING_SNAKE_CASE_				=				self.vae.decode(_lowercase       ).sample
											SCREAMING_SNAKE_CASE_				=				(image / 2 + 0.5).clamp(0,     1       )

											SCREAMING_SNAKE_CASE_				=				transforms.Resize(self.feature_extractor_size       )(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				self.normalize(_lowercase       ).to(latents.dtype       )

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

											SCREAMING_SNAKE_CASE_				=				spherical_dist_loss(_lowercase,     _lowercase       ).mean() * clip_guidance_scale

											SCREAMING_SNAKE_CASE_				=				-torch.autograd.grad(_lowercase,     _lowercase       )[0]

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





				@torch.no_grad()
				def __call__(    self,     _lowercase,     _lowercase,     _lowercase = None,     _lowercase = None,     _lowercase = 512,     _lowercase = 512,     _lowercase = 0.6,     _lowercase = 50,     _lowercase = 7.5,     _lowercase = 1,     _lowercase = 0.0,     _lowercase = 100,     _lowercase = None,     _lowercase = "pil",     _lowercase = True,     _lowercase = 0.8,     _lowercase = 0.1,     _lowercase = 0.1,     )     ->							List[str]:
											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:
																		SCREAMING_SNAKE_CASE_				=				[generator] + [None] * (batch_size - 1)

											SCREAMING_SNAKE_CASE_				=				[
											    ('model', self.coca_model is None),
											    ('tokenizer', self.coca_tokenizer is None),
											    ('transform', self.coca_transform is None),
											]
											SCREAMING_SNAKE_CASE_				=				[x[0] for x in coca_is_none if x[1]]
											SCREAMING_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."""       )
																		SCREAMING_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."""       )
																		SCREAMING_SNAKE_CASE_				=				self.get_image_description(_lowercase       )

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

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

											SCREAMING_SNAKE_CASE_				=				slerp(_lowercase,     _lowercase,     _lowercase       )

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

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

											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.get_timesteps(_lowercase,     _lowercase,     self.device       )
											SCREAMING_SNAKE_CASE_				=				timesteps[:1].repeat(_lowercase       )

											# Preprocess image
											SCREAMING_SNAKE_CASE_				=				preprocess(_lowercase,     _lowercase,     _lowercase       )
											SCREAMING_SNAKE_CASE_				=				self.prepare_latents(
											    _lowercase,     _lowercase,     _lowercase,     text_embeddings.dtype,     self.device,     _lowercase       )

											SCREAMING_SNAKE_CASE_				=				preprocess(_lowercase,     _lowercase,     _lowercase       )
											SCREAMING_SNAKE_CASE_				=				self.prepare_latents(
											    _lowercase,     _lowercase,     _lowercase,     text_embeddings.dtype,     self.device,     _lowercase       )

											SCREAMING_SNAKE_CASE_				=				slerp(_lowercase,     _lowercase,     _lowercase       )

											if clip_guidance_scale > 0:
																		SCREAMING_SNAKE_CASE_				=				self.get_clip_image_embeddings(_lowercase,     _lowercase       )
																		SCREAMING_SNAKE_CASE_				=				self.get_clip_image_embeddings(_lowercase,     _lowercase       )
																		SCREAMING_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.
											SCREAMING_SNAKE_CASE_				=				guidance_scale > 1.0
											# get unconditional embeddings for classifier free guidance
											if do_classifier_free_guidance:
																		SCREAMING_SNAKE_CASE_				=				content_text_input.input_ids.shape[-1]
																		SCREAMING_SNAKE_CASE_				=				self.tokenizer([''],     padding='max_length',     max_length=_lowercase,     return_tensors='pt'       )
																		SCREAMING_SNAKE_CASE_				=				self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]
																		# duplicate unconditional embeddings for each generation per prompt
																		SCREAMING_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
																		SCREAMING_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`.
											SCREAMING_SNAKE_CASE_				=				(batch_size, self.unet.config.in_channels, height // 8, width // 8)
											SCREAMING_SNAKE_CASE_				=				text_embeddings.dtype
											if latents is None:
																		if self.device.type == "mps":
																									# randn does not work reproducibly on mps
																									SCREAMING_SNAKE_CASE_				=				torch.randn(_lowercase,     generator=_lowercase,     device='cpu',     dtype=_lowercase       ).to(
																									    self.device       )
																		else:
																									SCREAMING_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}"""       )
																		SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_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]
											SCREAMING_SNAKE_CASE_				=				'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											# check if the scheduler accepts generator
											SCREAMING_SNAKE_CASE_				=				'generator' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
											if accepts_generator:
																		SCREAMING_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
																									SCREAMING_SNAKE_CASE_				=				torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
																									SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )

																									# predict the noise residual
																									SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase,     encoder_hidden_states=_lowercase       ).sample

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

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

																									# compute the previous noisy sample x_t -> x_t-1
																									SCREAMING_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
											SCREAMING_SNAKE_CASE_				=				1 / 0.18_215 * latents
											SCREAMING_SNAKE_CASE_				=				self.vae.decode(_lowercase       ).sample

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

											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (image, None)

											return StableDiffusionPipelineOutput(images=_lowercase,     nsfw_content_detected=_lowercase       )








'''simple docstring'''

import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:






							class    snake_case     :






											"""simple docstring"""



											@staticmethod
											def   a__					(    *_lowercase,     **_lowercase       )     ->							Tuple:
																		pass






@is_pipeline_test
@require_vision
@require_torch
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""
				_a    	=     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				pipeline(
											    'zero-shot-object-detection',     model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				[
											    {
											        'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
											        'candidate_labels': ['cat', 'remote', 'couch'],
											    }
											]
											return object_detector, examples



				def   a__					(    self,     _lowercase,     _lowercase       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				object_detector(examples[0],     threshold=0.0       )

											SCREAMING_SNAKE_CASE_				=				len(_lowercase       )
											self.assertGreater(_lowercase,     0       )
											self.assertEqual(
											    _lowercase,     [
											        {
											            'score': ANY(_lowercase       ),
											            'label': ANY(_lowercase       ),
											            'box': {'xmin': ANY(_lowercase       ), 'ymin': ANY(_lowercase       ), 'xmax': ANY(_lowercase       ), 'ymax': ANY(_lowercase       )},
											        }
											        for i in range(_lowercase       )
											    ],     )



				@require_tf
				@unittest.skip('Zero Shot Object Detection not implemented in TF'       )
				def   a__					(    self       )     ->							Union[str, Any]:
											pass



				@require_torch
				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				pipeline(
											    'zero-shot-object-detection',     model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    './tests/fixtures/tests_samples/COCO/000000039769.png',     candidate_labels=['cat', 'remote', 'couch'],     threshold=0.64,     )

											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											        {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
											        {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											    ],     )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    [
											        {
											            'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
											            'candidate_labels': ['cat', 'remote', 'couch'],
											        }
											    ],     threshold=0.64,     )

											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        [
											            {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											            {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
											            {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											        ]
											    ],     )



				@require_torch
				@slow
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											        {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											        {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											    ],     )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    [
											        {
											            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
											            'candidate_labels': ['cat', 'remote', 'couch'],
											        },
											        {
											            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
											            'candidate_labels': ['cat', 'remote', 'couch'],
											        },
											    ],     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        [
											            {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											        ],
											        [
											            {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											        ],
											    ],     )



				@require_tf
				@unittest.skip('Zero Shot Object Detection not implemented in TF'       )
				def   a__					(    self       )     ->							Optional[Any]:
											pass



				@require_torch
				@slow
				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				0.2
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     threshold=_lowercase,     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											    ],     )





				@require_torch
				@slow
				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     top_k=_lowercase,     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											    ],     )





