from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


A__           =							logging.get_logger(__name__)  # pylint: disable=invalid-name

A__           =							"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""





def  _lowerCamelCase					(   a_				: int						,			a_				: Optional[int]						,			a_				: int=8):
			lowerCamelCase						:Union[str, Any]						  =     height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			lowerCamelCase						:Dict						  =     width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor





def  _lowerCamelCase					(   a_				: Dict						,			a_				: Union[str, Any]=5_12						,			a_				: Optional[int]=5_12):
			lowerCamelCase						:str						  =     pil_image.resize((w, h)						,			resample=Image.BICUBIC						,			reducing_gap=1)
			lowerCamelCase						:Optional[int]						  =     np.array(pil_image.convert('''RGB'''))
			lowerCamelCase						:Any						  =     arr.astype(np.floataa) / 127.5 - 1
			lowerCamelCase						:Optional[Any]						  =     np.transpose(UpperCAmelCase__						,			[2, 0, 1])
			lowerCamelCase						:Optional[int]						  =     torch.from_numpy(UpperCAmelCase__).unsqueeze(0)
			return image


class   _lowerCAmelCase		(     _a				):







							def __init__(				self     :    Dict	,					__snake_case     :    int	,					__snake_case     :    str	,					__snake_case     :    str	,					):
										super().__init__()

										self.register_modules(
										    unet=_A	,					scheduler=_A	,					movq=_A	,					)
										lowerCamelCase						:Optional[Any]						  =     2 ** (len(self.movq.config.block_out_channels		) - 1)







							def 				snake_case  (				self     :    List[Any]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Optional[int]	,					__snake_case     :    Dict		):
										# get the original timestep using init_timestep
										lowerCamelCase						:Dict						  =     min(int(num_inference_steps * strength		)	,					_A		)

										lowerCamelCase						:Union[str, Any]						  =     max(num_inference_steps - init_timestep	,					0		)
										lowerCamelCase						:Any						  =     self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start







							def 				snake_case  (				self     :    Dict	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Tuple	,					__snake_case     :    str	,					__snake_case     :    Optional[int]	,					__snake_case     :    Any	,					__snake_case     :    List[Any]	,					__snake_case     :    List[str]=None		):
										if not isinstance(_A	,					(torch.Tensor, PIL.Image.Image, list)		):
													raise ValueError(
													    F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A		)}"		)

										lowerCamelCase						:Tuple						  =     image.to(device=_A	,					dtype=_A		)

										lowerCamelCase						:Any						  =     batch_size * num_images_per_prompt

										if image.shape[1] == 4:
													lowerCamelCase						:str						  =     image

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

													elif isinstance(_A	,					_A		):
																lowerCamelCase						:List[str]						  =     [
																    self.movq.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(_A		)
																]
																lowerCamelCase						:Tuple						  =     torch.cat(_A	,					dim=0		)
													else:
																lowerCamelCase						:Optional[int]						  =     self.movq.encode(_A		).latent_dist.sample(_A		)

													lowerCamelCase						:int						  =     self.movq.config.scaling_factor * init_latents

										lowerCamelCase						:Union[str, Any]						  =     torch.cat([init_latents]	,					dim=0		)

										lowerCamelCase						:List[str]						  =     init_latents.shape
										lowerCamelCase						:str						  =     randn_tensor(_A	,					generator=_A	,					device=_A	,					dtype=_A		)

										# get latents
										lowerCamelCase						:int						  =     self.scheduler.add_noise(_A	,					_A	,					_A		)

										lowerCamelCase						:Dict						  =     init_latents

										return latents







							def 				snake_case  (				self     :    str	,					__snake_case     :    List[Any]=0		):
										if is_accelerate_available():
													from accelerate import cpu_offload
										else:
													raise ImportError('''Please install accelerate via `pip install accelerate`'''		)

										lowerCamelCase						:Optional[int]						  =     torch.device(F"cuda:{gpu_id}"		)

										lowerCamelCase						:Optional[Any]						  =     [
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																cpu_offload(_A	,					_A		)







							def 				snake_case  (				self     :    List[Any]	,					__snake_case     :    Union[str, Any]=0		):
										if is_accelerate_available() and is_accelerate_version('''>='''	,					'''0.17.0.dev0'''		):
													from accelerate import cpu_offload_with_hook
										else:
													raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''		)

										lowerCamelCase						:Optional[Any]						  =     torch.device(F"cuda:{gpu_id}"		)

										if self.device.type != "cpu":
													self.to('''cpu'''	,					silence_dtype_warnings=_A		)
													torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										lowerCamelCase						:Any						  =     None
										for cpu_offloaded_model in [self.unet, self.movq]:
													lowerCamelCase  ,    lowerCamelCase						:Optional[Any]						  =     cpu_offload_with_hook(_A	,					_A	,					prev_module_hook=_A		)

										# We'll offload the last model manually.
										lowerCamelCase						:int						  =     hook







							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def 				snake_case  (				self     :    List[str]		):
										if not hasattr(self.unet	,					'''_hf_hook'''		):
													return self.device
										for module in self.unet.modules():
													if (
													    hasattr(_A	,					'''_hf_hook'''		)
													    and hasattr(module._hf_hook	,					'''execution_device'''		)
													    and module._hf_hook.execution_device is not None
													):
																return torch.device(module._hf_hook.execution_device		)
										return self.device







							@torch.no_grad()
							@replace_example_docstring(_A		)
							def __call__(				self     :    Optional[int]	,					__snake_case     :    Any	,					__snake_case     :    Optional[int]	,					__snake_case     :    Tuple	,					__snake_case     :    Union[str, Any] = 512	,					__snake_case     :    Optional[int] = 512	,					__snake_case     :    List[str] = 100	,					__snake_case     :    Optional[int] = 4.0	,					__snake_case     :    Any = 0.3	,					__snake_case     :    Union[str, Any] = 1	,					__snake_case     :    Any = None	,					__snake_case     :    str = "pil"	,					__snake_case     :    Optional[int] = True	,					):
										lowerCamelCase						:Dict						  =     self._execution_device

										lowerCamelCase						:int						  =     guidance_scale > 1.0

										if isinstance(_A	,					_A		):
													lowerCamelCase						:Union[str, Any]						  =     torch.cat(_A	,					dim=0		)
										lowerCamelCase						:str						  =     image_embeds.shape[0]
										if isinstance(_A	,					_A		):
													lowerCamelCase						:Dict						  =     torch.cat(_A	,					dim=0		)

										if do_classifier_free_guidance:
													lowerCamelCase						:List[Any]						  =     image_embeds.repeat_interleave(_A	,					dim=0		)
													lowerCamelCase						:Dict						  =     negative_image_embeds.repeat_interleave(_A	,					dim=0		)

										lowerCamelCase						:Optional[Any]						  =     torch.cat([negative_image_embeds, image_embeds]	,					dim=0		).to(dtype=self.unet.dtype	,					device=_A		)

										if not isinstance(_A	,					_A		):
													lowerCamelCase						:Tuple						  =     [image]
										if not all(isinstance(_A	,					(PIL.Image.Image, torch.Tensor)		) for i in image		):
													raise ValueError(
													    F"Input is in incorrect format: {[type(_A		) for i in image]}. Currently, we only support  PIL image and pytorch tensor"		)

										lowerCamelCase						:List[str]						  =     torch.cat([prepare_image(_A	,					_A	,					_A		) for i in image]	,					dim=0		)
										lowerCamelCase						:Tuple						  =     image.to(dtype=image_embeds.dtype	,					device=_A		)

										lowerCamelCase						:Dict						  =     self.movq.encode(_A		)['''latents''']
										lowerCamelCase						:Dict						  =     latents.repeat_interleave(_A	,					dim=0		)
										self.scheduler.set_timesteps(_A	,					device=_A		)
										lowerCamelCase  ,    lowerCamelCase						:Tuple						  =     self.get_timesteps(_A	,					_A	,					_A		)
										lowerCamelCase						:List[str]						  =     timesteps[:1].repeat(batch_size * num_images_per_prompt		)
										lowerCamelCase  ,    lowerCamelCase						:Tuple						  =     downscale_height_and_width(_A	,					_A	,					self.movq_scale_factor		)
										lowerCamelCase						:int						  =     self.prepare_latents(
										    _A	,					_A	,					_A	,					_A	,					image_embeds.dtype	,					_A	,					_A		)
										for i, t in enumerate(self.progress_bar(_A		)		):
													# expand the latents if we are doing classifier free guidance
													lowerCamelCase						:List[str]						  =     torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

													lowerCamelCase						:Any						  =     {'''image_embeds''': image_embeds}
													lowerCamelCase						:List[Any]						  =     self.unet(
													    sample=_A	,					timestep=_A	,					encoder_hidden_states=_A	,					added_cond_kwargs=_A	,					return_dict=_A	,					)[0]

													if do_classifier_free_guidance:
																lowerCamelCase  ,    lowerCamelCase						:Union[str, Any]						  =     noise_pred.split(latents.shape[1]	,					dim=1		)
																lowerCamelCase  ,    lowerCamelCase						:str						  =     noise_pred.chunk(2		)
																lowerCamelCase  ,    lowerCamelCase						:List[str]						  =     variance_pred.chunk(2		)
																lowerCamelCase						:str						  =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																lowerCamelCase						:Tuple						  =     torch.cat([noise_pred, variance_pred_text]	,					dim=1		)

													if not (
													    hasattr(self.scheduler.config	,					'''variance_type'''		)
													    and self.scheduler.config.variance_type in ["learned", "learned_range"]
													):
																lowerCamelCase  ,    lowerCamelCase						:Optional[int]						  =     noise_pred.split(latents.shape[1]	,					dim=1		)

													# compute the previous noisy sample x_t -> x_t-1
													lowerCamelCase						:Any						  =     self.scheduler.step(
													    _A	,					_A	,					_A	,					generator=_A	,					)[0]

										# post-processing
										lowerCamelCase						:Dict						  =     self.movq.decode(_A	,					force_not_quantize=_A		)['''sample''']

										if output_type not in ["pt", "np", "pil"]:
													raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"		)

										if output_type in ["np", "pil"]:
													lowerCamelCase						:int						  =     image * 0.5 + 0.5
													lowerCamelCase						:Dict						  =     image.clamp(0	,					1		)
													lowerCamelCase						:int						  =     image.cpu().permute(0	,					2	,					3	,					1		).float().numpy()

										if output_type == "pil":
													lowerCamelCase						:Union[str, Any]						  =     self.numpy_to_pil(_A		)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=_A		)





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   _lowerCAmelCase		:







							def __init__(				self     :    Any	,					__snake_case     :    Optional[int]	,					__snake_case     :    int=13	,					__snake_case     :    str=[30, 30]	,					__snake_case     :    Tuple=2	,					__snake_case     :    Optional[Any]=3	,					__snake_case     :    int=True	,					__snake_case     :    Tuple=True	,					__snake_case     :    List[Any]=32	,					__snake_case     :    int=5	,					__snake_case     :    Optional[Any]=4	,					__snake_case     :    Union[str, Any]=37	,					__snake_case     :    str="gelu"	,					__snake_case     :    Tuple=0.1	,					__snake_case     :    List[Any]=0.1	,					__snake_case     :    Union[str, Any]=10	,					__snake_case     :    str=0.0_2	,					__snake_case     :    Union[str, Any]=3	,					__snake_case     :    Union[str, Any]=None	,					__snake_case     :    List[str]=8	,					__snake_case     :    Any=10	,					):
										lowerCamelCase						:Optional[Any]						  =     parent
										lowerCamelCase						:List[Any]						  =     batch_size
										lowerCamelCase						:Any						  =     image_size
										lowerCamelCase						:Union[str, Any]						  =     patch_size
										lowerCamelCase						:Any						  =     num_channels
										lowerCamelCase						:List[Any]						  =     is_training
										lowerCamelCase						:Optional[Any]						  =     use_labels
										lowerCamelCase						:Any						  =     hidden_size
										lowerCamelCase						:List[Any]						  =     num_hidden_layers
										lowerCamelCase						:List[str]						  =     num_attention_heads
										lowerCamelCase						:Tuple						  =     intermediate_size
										lowerCamelCase						:List[str]						  =     hidden_act
										lowerCamelCase						:List[str]						  =     hidden_dropout_prob
										lowerCamelCase						:Any						  =     attention_probs_dropout_prob
										lowerCamelCase						:List[Any]						  =     type_sequence_label_size
										lowerCamelCase						:Optional[int]						  =     initializer_range
										lowerCamelCase						:List[Any]						  =     num_labels
										lowerCamelCase						:Any						  =     scope
										lowerCamelCase						:Union[str, Any]						  =     n_targets
										lowerCamelCase						:Optional[Any]						  =     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
										lowerCamelCase						:Tuple						  =     (image_size[1] // patch_size) * (image_size[0] // patch_size)
										lowerCamelCase						:str						  =     num_patches + 1 + self.num_detection_tokens







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:str						  =     floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]		)

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

										lowerCamelCase						:str						  =     self.get_config()

										return config, pixel_values, labels







							def 				snake_case  (				self     :    Union[str, Any]		):
										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=__snake_case	,					initializer_range=self.initializer_range	,					num_detection_tokens=self.num_detection_tokens	,					num_labels=self.num_labels	,					)







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







							def 				snake_case  (				self     :    Dict	,					__snake_case     :    str	,					__snake_case     :    Optional[int]	,					__snake_case     :    Optional[Any]		):
										lowerCamelCase						:int						  =     YolosForObjectDetection(__snake_case		)
										model.to(__snake_case		)
										model.eval()

										lowerCamelCase						:str						  =     model(pixel_values=__snake_case		)
										lowerCamelCase						:Any						  =     model(__snake_case		)

										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)		)

										lowerCamelCase						:int						  =     model(pixel_values=__snake_case	,					labels=__snake_case		)

										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     :    int		):
										lowerCamelCase						:List[Any]						  =     self.prepare_config_and_inputs()
										lowerCamelCase  ,    lowerCamelCase  ,    lowerCamelCase						:str						  =     config_and_inputs
										lowerCamelCase						:Union[str, Any]						  =     {'''pixel_values''': pixel_values}
										return config, inputs_dict


@require_torch
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
							_UpperCAmelCase						    =     (
							    {'feature-extraction': YolosModel, 'object-detection': YolosForObjectDetection} if is_torch_available() else {}
							)

							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False







							def 				snake_case  (				self     :    Any	,					__snake_case     :    List[Any]	,					__snake_case     :    Tuple	,					__snake_case     :    Dict=False		):
										lowerCamelCase						:Optional[int]						  =     super()._prepare_for_class(__snake_case	,					__snake_case	,					return_labels=__snake_case		)

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

										return inputs_dict







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Union[str, Any]						  =     YolosModelTester(self		)
										lowerCamelCase						:Dict						  =     ConfigTester(self	,					config_class=__snake_case	,					has_text_modality=__snake_case	,					hidden_size=37		)







							def 				snake_case  (				self     :    Union[str, Any]		):
										self.config_tester.run_common_tests()







							def 				snake_case  (				self     :    Optional[Any]		):
										# YOLOS does not use inputs_embeds
										pass







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase  ,    lowerCamelCase						:str						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:Optional[int]						  =     model_class(__snake_case		)
													self.assertIsInstance(model.get_input_embeddings()	,					(nn.Module)		)
													lowerCamelCase						:str						  =     model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__snake_case	,					nn.Linear		)		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase  ,    lowerCamelCase						:str						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:str						  =     model_class(__snake_case		)
													lowerCamelCase						:Any						  =     inspect.signature(model.forward		)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase						:Tuple						  =     [*signature.parameters.keys()]

													lowerCamelCase						:Tuple						  =     ['''pixel_values''']
													self.assertListEqual(arg_names[:1]	,					__snake_case		)







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:List[str]						  =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase  ,    lowerCamelCase						:List[Any]						  =     self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase						:int						  =     True

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

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

													self.assertListEqual(
													    list(attentions[0].shape[-3:]		)	,					[self.model_tester.num_attention_heads, seq_len, seq_len]	,					)
													lowerCamelCase						:Optional[int]						  =     len(__snake_case		)

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

													lowerCamelCase						:Dict						  =     1
													self.assertEqual(out_len + added_hidden_states	,					len(__snake_case		)		)

													lowerCamelCase						:Dict						  =     outputs.attentions

													self.assertEqual(len(__snake_case		)	,					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     :    List[str]		):
										def check_hidden_states_output(__snake_case     :    Union[str, Any]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Tuple		):
													lowerCamelCase						:Union[str, Any]						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()

													with torch.no_grad():
																lowerCamelCase						:Any						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)

													lowerCamelCase						:Optional[Any]						  =     outputs.hidden_states

													lowerCamelCase						:Any						  =     getattr(
													    self.model_tester	,					'''expected_num_hidden_layers'''	,					self.model_tester.num_hidden_layers + 1		)
													self.assertEqual(len(__snake_case		)	,					__snake_case		)

													# YOLOS has a different seq_length
													lowerCamelCase						:List[str]						  =     self.model_tester.expected_seq_len

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

										lowerCamelCase  ,    lowerCamelCase						:List[Any]						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:Union[str, Any]						  =     True
													check_hidden_states_output(__snake_case	,					__snake_case	,					__snake_case		)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowerCamelCase						:Any						  =     True

													check_hidden_states_output(__snake_case	,					__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Optional[Any]						  =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_object_detection(*__snake_case		)







							@slow
							def 				snake_case  (				self     :    Dict		):
										for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase						:Tuple						  =     YolosModel.from_pretrained(__snake_case		)
													self.assertIsNotNone(__snake_case		)





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


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







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







							@slow
							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Union[str, Any]						  =     YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''		).to(__snake_case		)

										lowerCamelCase						:Optional[Any]						  =     self.default_image_processor
										lowerCamelCase						:str						  =     prepare_img()
										lowerCamelCase						:Dict						  =     image_processor(images=__snake_case	,					return_tensors='''pt'''		).to(__snake_case		)

										# forward pass
										with torch.no_grad():
													lowerCamelCase						:Optional[Any]						  =     model(inputs.pixel_values		)

										# verify outputs
										lowerCamelCase						:int						  =     torch.Size((1, 100, 92)		)
										self.assertEqual(outputs.logits.shape	,					__snake_case		)

										lowerCamelCase						:Any						  =     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=__snake_case	,					)
										lowerCamelCase						:Any						  =     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=__snake_case		)
										self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]	,					__snake_case	,					atol=1e-4		)		)
										self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]	,					__snake_case	,					atol=1e-4		)		)

										# verify postprocessing
										lowerCamelCase						:List[str]						  =     image_processor.post_process_object_detection(
										    __snake_case	,					threshold=0.3	,					target_sizes=[image.size[::-1]]		)[0]
										lowerCamelCase						:List[str]						  =     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(__snake_case		)
										lowerCamelCase						:str						  =     [75, 75, 17, 63, 17]
										lowerCamelCase						: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(__snake_case		)

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



