
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_SCREAMING_SNAKE_CASE    :      List[str]          =			logging.get_logger(__name__)  # pylint: disable=invalid-name







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       ):
					super().__init__()
					self.register_modules(
					    vae=UpperCamelCase__				,       text_encoder=UpperCamelCase__				,       tokenizer=UpperCamelCase__				,       unet=UpperCamelCase__				,       scheduler=UpperCamelCase__				,       safety_checker=UpperCamelCase__				,       feature_extractor=UpperCamelCase__				,       )





	def   __snake_case				(			self				,       UpperCamelCase__ = "auto"		):
					if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									A__  :	Union[str, Any]         =							self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(UpperCamelCase__		)





	def   __snake_case				(			self		):
					self.enable_attention_slicing(UpperCamelCase__		)





	@torch.no_grad()
	def __call__(			self				,       UpperCamelCase__				,       UpperCamelCase__ = 512				,       UpperCamelCase__ = 512				,       UpperCamelCase__ = 50				,       UpperCamelCase__ = 7.5				,       UpperCamelCase__ = None				,       UpperCamelCase__ = 1				,       UpperCamelCase__ = 0.0				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = "pil"				,       UpperCamelCase__ = True				,       UpperCamelCase__ = None				,       UpperCamelCase__ = 1				,       UpperCamelCase__ = None				,       **UpperCamelCase__				,       ):

					if isinstance(UpperCamelCase__				,       UpperCamelCase__		):
									A__  :	Tuple         =							1
					elif isinstance(UpperCamelCase__				,       UpperCamelCase__		):
									A__  :	Dict         =							len(UpperCamelCase__		)
					else:
									raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase__		)}"		)

					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 (callback_steps is None) or (
					    callback_steps is not None and (not isinstance(UpperCamelCase__				,       UpperCamelCase__		) or callback_steps <= 0)
					):
									raise ValueError(
									    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
									    F" {type(UpperCamelCase__		)}."		)

					# get prompt text embeddings
					A__  :	Any         =							self.tokenizer(
					    UpperCamelCase__				,       padding='''max_length'''				,       max_length=self.tokenizer.model_max_length				,       return_tensors='''pt'''				,       )
					A__  :	Union[str, Any]         =							text_inputs.input_ids

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									A__  :	Union[str, Any]         =							self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
									logger.warning(
									    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
									    F" {self.tokenizer.model_max_length} tokens: {removed_text}"		)
									A__  :	Any         =							text_input_ids[:, : self.tokenizer.model_max_length]

					if text_embeddings is None:
									A__  :	str         =							self.text_encoder(text_input_ids.to(self.device		)		)[0]

					# duplicate text embeddings for each generation per prompt, using mps friendly method
					A__       ,							A__       ,							A__  :	Dict         =							text_embeddings.shape
					A__  :	Any         =							text_embeddings.repeat(1				,       UpperCamelCase__				,       1		)
					A__  :	Any         =							text_embeddings.view(bs_embed * num_images_per_prompt				,       UpperCamelCase__				,       -1		)

					# 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.
					A__  :	int         =							guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
									A__  :	List[str]
									if negative_prompt is None:
													A__  :	Dict         =							['''''']
									elif type(UpperCamelCase__		) is not type(UpperCamelCase__		):
													raise TypeError(
													    F"`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase__		)} !="
													    F" {type(UpperCamelCase__		)}."		)
									elif isinstance(UpperCamelCase__				,       UpperCamelCase__		):
													A__  :	Tuple         =							[negative_prompt]
									elif batch_size != len(UpperCamelCase__		):
													raise ValueError(
													    F"`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase__		)}, but `prompt`:"
													    F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
													    ''' the batch size of `prompt`.'''		)
									else:
													A__  :	Optional[Any]         =							negative_prompt

									A__  :	int         =							text_input_ids.shape[-1]
									A__  :	Dict         =							self.tokenizer(
									    UpperCamelCase__				,       padding='''max_length'''				,       max_length=UpperCamelCase__				,       truncation=UpperCamelCase__				,       return_tensors='''pt'''				,       )
									A__  :	Optional[Any]         =							self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									A__  :	int         =							uncond_embeddings.shape[1]
									A__  :	List[str]         =							uncond_embeddings.repeat(UpperCamelCase__				,       UpperCamelCase__				,       1		)
									A__  :	Optional[Any]         =							uncond_embeddings.view(batch_size * num_images_per_prompt				,       UpperCamelCase__				,       -1		)

									# 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
									A__  :	List[Any]         =							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`.
					A__  :	List[Any]         =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
					A__  :	Any         =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
					A__  :	List[str]         =							text_embeddings.dtype
					if latents is None:
									if self.device.type == "mps":
													# randn does not exist on mps
													A__  :	Any         =							torch.randn(
													    UpperCamelCase__				,       generator=UpperCamelCase__				,       device='''cpu'''				,       dtype=UpperCamelCase__		).to(self.device		)
													A__  :	Dict         =							torch.randn(UpperCamelCase__				,       generator=UpperCamelCase__				,       device='''cpu'''				,       dtype=UpperCamelCase__		).to(
													    self.device		)
									else:
													A__  :	Any         =							torch.randn(
													    UpperCamelCase__				,       generator=UpperCamelCase__				,       device=self.device				,       dtype=UpperCamelCase__		)
													A__  :	Optional[Any]         =							torch.randn(UpperCamelCase__				,       generator=UpperCamelCase__				,       device=self.device				,       dtype=UpperCamelCase__		)
					else:
									if latents_reference.shape != latents_shape:
													raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"		)
									A__  :	Union[str, Any]         =							latents_reference.to(self.device		)
									A__  :	List[Any]         =							latents.to(self.device		)

					# This is the key part of the pipeline where we
					# try to ensure that the generated images w/ the same seed
					# but different sizes actually result in similar images
					A__  :	Union[str, Any]         =							(latents_shape[3] - latents_shape_reference[3]) // 2
					A__  :	Dict         =							(latents_shape[2] - latents_shape_reference[2]) // 2
					A__  :	List[Any]         =							latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
					A__  :	Optional[Any]         =							latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
					A__  :	Dict         =							0 if dx < 0 else dx
					A__  :	Optional[Any]         =							0 if dy < 0 else dy
					A__  :	Union[str, Any]         =							max(-dx				,       0		)
					A__  :	str         =							max(-dy				,       0		)
					# import pdb
					# pdb.set_trace()
					A__  :	Union[str, Any]         =							latents_reference[:, :, dy : dy + h, dx : dx + w]

					# set timesteps
					self.scheduler.set_timesteps(UpperCamelCase__		)

					# Some schedulers like PNDM have timesteps as arrays
					# It's more optimized to move all timesteps to correct device beforehand
					A__  :	Union[str, Any]         =							self.scheduler.timesteps.to(self.device		)

					# scale the initial noise by the standard deviation required by the scheduler
					A__  :	Any         =							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]
					A__  :	Dict         =							'''eta''' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
					A__  :	int         =							{}
					if accepts_eta:
									A__  :	Optional[Any]         =							eta

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

									# predict the noise residual
									A__  :	List[Any]         =							self.unet(UpperCamelCase__				,       UpperCamelCase__				,       encoder_hidden_states=UpperCamelCase__		).sample

									# perform guidance
									if do_classifier_free_guidance:
													A__       ,							A__  :	List[Any]         =							noise_pred.chunk(2		)
													A__  :	Any         =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# compute the previous noisy sample x_t -> x_t-1
									A__  :	List[Any]         =							self.scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       **UpperCamelCase__		).prev_sample

									# call the callback, if provided
									if callback is not None and i % callback_steps == 0:
													callback(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)

					A__  :	Optional[Any]         =							1 / 0.1_8_2_1_5 * latents
					A__  :	List[Any]         =							self.vae.decode(UpperCamelCase__		).sample

					A__  :	Any         =							(image / 2 + 0.5).clamp(0				,       1		)

					# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
					A__  :	List[Any]         =							image.cpu().permute(0				,       2				,       3				,       1		).float().numpy()

					if self.safety_checker is not None:
									A__  :	Optional[int]         =							self.feature_extractor(self.numpy_to_pil(UpperCamelCase__		)				,       return_tensors='''pt'''		).to(
									    self.device		)
									A__       ,							A__  :	List[Any]         =							self.safety_checker(
									    images=UpperCamelCase__				,       clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype		)		)
					else:
									A__  :	List[Any]         =							None

					if output_type == "pil":
									A__  :	Any         =							self.numpy_to_pil(UpperCamelCase__		)

					if not return_dict:
									return (image, has_nsfw_concept)

					return StableDiffusionPipelineOutput(images=UpperCamelCase__				,       nsfw_content_detected=UpperCamelCase__		)

import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import YolosForObjectDetection, YolosModel
					from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__=13				,       UpperCamelCase__=[30, 30]				,       UpperCamelCase__=2				,       UpperCamelCase__=3				,       UpperCamelCase__=True				,       UpperCamelCase__=True				,       UpperCamelCase__=32				,       UpperCamelCase__=5				,       UpperCamelCase__=4				,       UpperCamelCase__=37				,       UpperCamelCase__="gelu"				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.1				,       UpperCamelCase__=10				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=3				,       UpperCamelCase__=None				,       UpperCamelCase__=8				,       UpperCamelCase__=10				,       ):
					A__  :	Optional[int]         =							parent
					A__  :	List[Any]         =							batch_size
					A__  :	Dict         =							image_size
					A__  :	Any         =							patch_size
					A__  :	Dict         =							num_channels
					A__  :	List[Any]         =							is_training
					A__  :	int         =							use_labels
					A__  :	Any         =							hidden_size
					A__  :	List[str]         =							num_hidden_layers
					A__  :	Optional[int]         =							num_attention_heads
					A__  :	Optional[Any]         =							intermediate_size
					A__  :	str         =							hidden_act
					A__  :	str         =							hidden_dropout_prob
					A__  :	Optional[int]         =							attention_probs_dropout_prob
					A__  :	Optional[int]         =							type_sequence_label_size
					A__  :	Any         =							initializer_range
					A__  :	Optional[int]         =							num_labels
					A__  :	Union[str, Any]         =							scope
					A__  :	Union[str, Any]         =							n_targets
					A__  :	Dict         =							num_detection_tokens
					# we set the expected sequence length (which is used in several tests)
					# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
					A__  :	int         =							(image_size[1] // patch_size) * (image_size[0] // patch_size)
					A__  :	List[str]         =							num_patches + 1 + self.num_detection_tokens





	def   __snake_case				(			self		):
					A__  :	Dict         =							floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]		)

					A__  :	int         =							None
					if self.use_labels:
									# labels is a list of Dict (each Dict being the labels for a given example in the batch)
									A__  :	Tuple         =							[]
									for i in range(self.batch_size		):
													A__  :	List[Any]         =							{}
													A__  :	Tuple         =							torch.randint(
													    high=self.num_labels				,       size=(self.n_targets,)				,       device=UpperCamelCase__		)
													A__  :	Any         =							torch.rand(self.n_targets				,       4				,       device=UpperCamelCase__		)
													labels.append(UpperCamelCase__		)

					A__  :	Union[str, Any]         =							self.get_config()

					return config, pixel_values, labels





	def   __snake_case				(			self		):
					return YolosConfig(
					    image_size=self.image_size				,       patch_size=self.patch_size				,       num_channels=self.num_channels				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       intermediate_size=self.intermediate_size				,       hidden_act=self.hidden_act				,       hidden_dropout_prob=self.hidden_dropout_prob				,       attention_probs_dropout_prob=self.attention_probs_dropout_prob				,       is_decoder=UpperCamelCase__				,       initializer_range=self.initializer_range				,       num_detection_tokens=self.num_detection_tokens				,       num_labels=self.num_labels				,       )





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Tuple         =							YolosModel(config=UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()
					A__  :	Optional[Any]         =							model(UpperCamelCase__		)
					self.parent.assertEqual(
					    result.last_hidden_state.shape				,       (self.batch_size, self.expected_seq_len, self.hidden_size)		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Any         =							YolosForObjectDetection(UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()

					A__  :	Union[str, Any]         =							model(pixel_values=UpperCamelCase__		)
					A__  :	Optional[int]         =							model(UpperCamelCase__		)

					self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
					self.parent.assertEqual(result.pred_boxes.shape				,       (self.batch_size, self.num_detection_tokens, 4)		)

					A__  :	Union[str, Any]         =							model(pixel_values=UpperCamelCase__				,       labels=UpperCamelCase__		)

					self.parent.assertEqual(result.loss.shape				,       ()		)
					self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
					self.parent.assertEqual(result.pred_boxes.shape				,       (self.batch_size, self.num_detection_tokens, 4)		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.prepare_config_and_inputs()
					A__       ,							A__       ,							A__  :	Optional[Any]         =							config_and_inputs
					A__  :	Optional[int]         =							{'''pixel_values''': pixel_values}
					return config, inputs_dict







@require_torch
class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
	_lowerCAmelCase							       =				(
	    {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
	)

	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=False		):
					A__  :	Optional[int]         =							super()._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__				,       return_labels=UpperCamelCase__		)

					if return_labels:
									if model_class.__name__ == "YolosForObjectDetection":
													A__  :	str         =							[]
													for i in range(self.model_tester.batch_size		):
																	A__  :	int         =							{}
																	A__  :	Dict         =							torch.ones(
																	    size=(self.model_tester.n_targets,)				,       device=UpperCamelCase__				,       dtype=torch.long		)
																	A__  :	Dict         =							torch.ones(
																	    self.model_tester.n_targets				,       4				,       device=UpperCamelCase__				,       dtype=torch.float		)
																	labels.append(UpperCamelCase__		)
													A__  :	Dict         =							labels

					return inputs_dict





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							YolosModelTester(self		)
					A__  :	List[str]         =							ConfigTester(self				,       config_class=UpperCamelCase__				,       has_text_modality=UpperCamelCase__				,       hidden_size=37		)





	def   __snake_case				(			self		):
					self.config_tester.run_common_tests()





	def   __snake_case				(			self		):
					# YOLOS does not use inputs_embeds
					pass





	def   __snake_case				(			self		):
					A__       ,							A__  :	Dict         =							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									A__  :	Any         =							model_class(UpperCamelCase__		)
									self.assertIsInstance(model.get_input_embeddings()				,       (nn.Module)		)
									A__  :	str         =							model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(UpperCamelCase__				,       nn.Linear		)		)





	def   __snake_case				(			self		):
					A__       ,							A__  :	int         =							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									A__  :	List[str]         =							model_class(UpperCamelCase__		)
									A__  :	str         =							inspect.signature(model.forward		)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									A__  :	Optional[int]         =							[*signature.parameters.keys()]

									A__  :	Optional[Any]         =							['''pixel_values''']
									self.assertListEqual(arg_names[:1]				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__       ,							A__  :	List[Any]         =							self.model_tester.prepare_config_and_inputs_for_common()
					A__  :	Tuple         =							True

					# in YOLOS, the seq_len is different
					A__  :	List[Any]         =							self.model_tester.expected_seq_len
					for model_class in self.all_model_classes:
									A__  :	Any         =							True
									A__  :	Optional[int]         =							False
									A__  :	Optional[Any]         =							True
									A__  :	int         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()
									with torch.no_grad():
													A__  :	List[str]         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)
									A__  :	Optional[int]         =							outputs.attentions
									self.assertEqual(len(UpperCamelCase__		)				,       self.model_tester.num_hidden_layers		)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									A__  :	Tuple         =							True
									A__  :	Optional[Any]         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()
									with torch.no_grad():
													A__  :	Tuple         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)
									A__  :	Tuple         =							outputs.attentions
									self.assertEqual(len(UpperCamelCase__		)				,       self.model_tester.num_hidden_layers		)

									self.assertListEqual(
									    list(attentions[0].shape[-3:]		)				,       [self.model_tester.num_attention_heads, seq_len, seq_len]				,       )
									A__  :	List[Any]         =							len(UpperCamelCase__		)

									# Check attention is always last and order is fine
									A__  :	List[str]         =							True
									A__  :	List[Any]         =							True
									A__  :	int         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()
									with torch.no_grad():
													A__  :	Tuple         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)

									A__  :	Tuple         =							1
									self.assertEqual(out_len + added_hidden_states				,       len(UpperCamelCase__		)		)

									A__  :	List[str]         =							outputs.attentions

									self.assertEqual(len(UpperCamelCase__		)				,       self.model_tester.num_hidden_layers		)
									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]		)				,       [self.model_tester.num_attention_heads, seq_len, seq_len]				,       )





	def   __snake_case				(			self		):
					def check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
									A__  :	str         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()

									with torch.no_grad():
													A__  :	int         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)

									A__  :	Optional[Any]         =							outputs.hidden_states

									A__  :	int         =							getattr(
									    self.model_tester				,       '''expected_num_hidden_layers'''				,       self.model_tester.num_hidden_layers + 1		)
									self.assertEqual(len(UpperCamelCase__		)				,       UpperCamelCase__		)

									# YOLOS has a different seq_length
									A__  :	Union[str, Any]         =							self.model_tester.expected_seq_len

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]		)				,       [seq_length, self.model_tester.hidden_size]				,       )

					A__       ,							A__  :	Optional[Any]         =							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									A__  :	int         =							True
									check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									A__  :	Optional[int]         =							True

									check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Tuple         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_object_detection(*UpperCamelCase__		)





	@slow
	def   __snake_case				(			self		):
					for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									A__  :	Union[str, Any]         =							YolosModel.from_pretrained(UpperCamelCase__		)
									self.assertIsNotNone(UpperCamelCase__		)




def 				SCREAMING_SNAKE_CASE     (      ) ->       List[str]:

				"""simple docstring"""







				A__  :	int         =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
				return image







@require_torch
@require_vision
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	@cached_property
	def   __snake_case				(			self		):
					return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''		) if is_vision_available() else None





	@slow
	def   __snake_case				(			self		):
					A__  :	Tuple         =							YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''		).to(UpperCamelCase__		)

					A__  :	str         =							self.default_image_processor
					A__  :	Tuple         =							prepare_img()
					A__  :	Tuple         =							image_processor(images=UpperCamelCase__				,       return_tensors='''pt'''		).to(UpperCamelCase__		)

					# forward pass
					with torch.no_grad():
									A__  :	Any         =							model(inputs.pixel_values		)

					# verify outputs
					A__  :	List[Any]         =							torch.Size((1, 100, 92)		)
					self.assertEqual(outputs.logits.shape				,       UpperCamelCase__		)

					A__  :	Optional[int]         =							torch.tensor(
					    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]				,       device=UpperCamelCase__				,       )
					A__  :	Optional[int]         =							torch.tensor(
					    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]				,       device=UpperCamelCase__		)
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]				,       UpperCamelCase__				,       atol=1e-4		)		)
					self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]				,       UpperCamelCase__				,       atol=1e-4		)		)

					# verify postprocessing
					A__  :	Dict         =							image_processor.post_process_object_detection(
					    UpperCamelCase__				,       threshold=0.3				,       target_sizes=[image.size[::-1]]		)[0]
					A__  :	int         =							torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]		).to(UpperCamelCase__		)
					A__  :	str         =							[75, 75, 17, 63, 17]
					A__  :	Tuple         =							torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]		).to(UpperCamelCase__		)

					self.assertEqual(len(results['''scores''']		)				,       5		)
					self.assertTrue(torch.allclose(results['''scores''']				,       UpperCamelCase__				,       atol=1e-4		)		)
					self.assertSequenceEqual(results['''labels'''].tolist()				,       UpperCamelCase__		)
					self.assertTrue(torch.allclose(results['''boxes'''][0, :]				,       UpperCamelCase__		)		)