





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


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




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



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



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



 def 				UpperCamelCase			(							self ):
        self.enable_attention_slicing(__lowerCamelCase )




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

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

        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(__lowerCamelCase,__lowerCamelCase ) or callback_steps <= 0)
        ):
               raise ValueError(
                   f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                   f" {type(__lowerCamelCase )}." )

        # get prompt text embeddings
        A__											=						self.tokenizer(
            __lowerCamelCase,padding='''max_length''',max_length=self.tokenizer.model_max_length,return_tensors='''pt''',)
        A__											=						text_inputs.input_ids

        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
               A__											=						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__											=						text_input_ids[:, : self.tokenizer.model_max_length]

        if text_embeddings is None:
               A__											=						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__											=						text_embeddings.shape
        A__											=						text_embeddings.repeat(1,__lowerCamelCase,1 )
        A__											=						text_embeddings.view(bs_embed * num_images_per_prompt,__lowerCamelCase,-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__											=						guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
               A__											=						42
               if negative_prompt is None:
                      A__											=						['''''']
               elif type(__lowerCamelCase ) is not type(__lowerCamelCase ):
                      raise TypeError(
                          f"`negative_prompt` should be the same type to `prompt`, but got {type(__lowerCamelCase )} !="
                          f" {type(__lowerCamelCase )}." )
               elif isinstance(__lowerCamelCase,__lowerCamelCase ):
                      A__											=						[negative_prompt]
               elif batch_size != len(__lowerCamelCase ):
                      raise ValueError(
                          f"`negative_prompt`: {negative_prompt} has batch size {len(__lowerCamelCase )}, but `prompt`:"
                          f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                          ''' the batch size of `prompt`.''' )
               else:
                      A__											=						negative_prompt

               A__											=						text_input_ids.shape[-1]
               A__											=						self.tokenizer(
                   __lowerCamelCase,padding='''max_length''',max_length=__lowerCamelCase,truncation=__lowerCamelCase,return_tensors='''pt''',)
               A__											=						self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

               # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
               A__											=						uncond_embeddings.shape[1]
               A__											=						uncond_embeddings.repeat(__lowerCamelCase,__lowerCamelCase,1 )
               A__											=						uncond_embeddings.view(batch_size * num_images_per_prompt,__lowerCamelCase,-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__											=						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__											=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        A__											=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
        A__											=						text_embeddings.dtype
        if latents is None:
               if self.device.type == "mps":
                      # randn does not exist on mps
                      A__											=						torch.randn(
                          __lowerCamelCase,generator=__lowerCamelCase,device='''cpu''',dtype=__lowerCamelCase ).to(self.device )
                      A__											=						torch.randn(__lowerCamelCase,generator=__lowerCamelCase,device='''cpu''',dtype=__lowerCamelCase ).to(
                          self.device )
               else:
                      A__											=						torch.randn(
                          __lowerCamelCase,generator=__lowerCamelCase,device=self.device,dtype=__lowerCamelCase )
                      A__											=						torch.randn(__lowerCamelCase,generator=__lowerCamelCase,device=self.device,dtype=__lowerCamelCase )
        else:
               if latents_reference.shape != latents_shape:
                      raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
               A__											=						latents_reference.to(self.device )
               A__											=						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__											=						(latents_shape[3] - latents_shape_reference[3]) // 2
        A__											=						(latents_shape[2] - latents_shape_reference[2]) // 2
        A__											=						latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
        A__											=						latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
        A__											=						0 if dx < 0 else dx
        A__											=						0 if dy < 0 else dy
        A__											=						max(-dx,0 )
        A__											=						max(-dy,0 )
        # import pdb
        # pdb.set_trace()
        A__											=						latents_reference[:, :, dy : dy + h, dx : dx + w]

        # set timesteps
        self.scheduler.set_timesteps(__lowerCamelCase )

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

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

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

               # predict the noise residual
               A__											=						self.unet(__lowerCamelCase,__lowerCamelCase,encoder_hidden_states=__lowerCamelCase ).sample

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

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

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

        A__											=						1 / 0.18215 * latents
        A__											=						self.vae.decode(__lowerCamelCase ).sample

        A__											=						(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__											=						image.cpu().permute(0,2,3,1 ).float().numpy()

        if self.safety_checker is not None:
               A__											=						self.feature_extractor(self.numpy_to_pil(__lowerCamelCase ),return_tensors='''pt''' ).to(
                   self.device )
               A__					,	A__											=						self.safety_checker(
                   images=__lowerCamelCase,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
        else:
               A__											=						None

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

        if not return_dict:
               return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=__lowerCamelCase,nsfw_content_detected=__lowerCamelCase )












import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
       import torch

       from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

       if is_vision_available():
              from transformers import MaskaFormerImageProcessor

if is_vision_available():
       from PIL import Image




class     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=2,__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=10,__lowerCamelCase=3,__lowerCamelCase=32 * 8,__lowerCamelCase=32 * 8,__lowerCamelCase=4,__lowerCamelCase=64,):
        A__											=						parent
        A__											=						batch_size
        A__											=						is_training
        A__											=						use_auxiliary_loss
        A__											=						num_queries
        A__											=						num_channels
        A__											=						min_size
        A__											=						max_size
        A__											=						num_labels
        A__											=						hidden_dim
        A__											=						hidden_dim



 def 				UpperCamelCase			(							self ):
        A__											=						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
            __lowerCamelCase )

        A__											=						torch.ones([self.batch_size, self.min_size, self.max_size],device=__lowerCamelCase )

        A__											=						(
            torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size],device=__lowerCamelCase ) > 0.5
        ).float()
        A__											=						(torch.rand((self.batch_size, self.num_labels),device=__lowerCamelCase ) > 0.5).long()

        A__											=						self.get_config()
        return config, pixel_values, pixel_mask, mask_labels, class_labels



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerConfig(
            hidden_size=self.hidden_dim,)
        A__											=						self.num_queries
        A__											=						self.num_labels

        A__											=						[1, 1, 1, 1]
        A__											=						self.num_channels

        A__											=						64
        A__											=						128
        A__											=						self.hidden_dim
        A__											=						self.hidden_dim
        A__											=						self.hidden_dim
        return config



 def 				UpperCamelCase			(							self ):
        A__					,	A__					,	A__					,	A__					,	A__											=						self.prepare_config_and_inputs()
        A__											=						{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
        return config, inputs_dict



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						output.encoder_hidden_states
        A__											=						output.pixel_decoder_hidden_states
        A__											=						output.transformer_decoder_hidden_states

        self.parent.assertTrue(len(__lowerCamelCase ),len(config.backbone_config.depths ) )
        self.parent.assertTrue(len(__lowerCamelCase ),len(config.backbone_config.depths ) )
        self.parent.assertTrue(len(__lowerCamelCase ),config.decoder_layers )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=False ):
        with torch.no_grad():
               A__											=						MaskaFormerModel(config=__lowerCamelCase )
               model.to(__lowerCamelCase )
               model.eval()

               A__											=						model(pixel_values=__lowerCamelCase,pixel_mask=__lowerCamelCase )
               A__											=						model(__lowerCamelCase,output_hidden_states=__lowerCamelCase )

        self.parent.assertEqual(
            output.transformer_decoder_last_hidden_state.shape,(self.batch_size, self.num_queries, self.hidden_dim),)
        # let's ensure the other two hidden state exists
        self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
        self.parent.assertTrue(output.encoder_last_hidden_state is not None )

        if output_hidden_states:
               self.check_output_hidden_state(__lowerCamelCase,__lowerCamelCase )




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						MaskaFormerForUniversalSegmentation(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()

        def comm_check_on_output(__lowerCamelCase ):
               # let's still check that all the required stuff is there
               self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
               self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
               self.parent.assertTrue(result.encoder_last_hidden_state is not None )
               # okay, now we need to check the logits shape
               # due to the encoder compression, masks have a //4 spatial size
               self.parent.assertEqual(
                   result.masks_queries_logits.shape,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4),)
               # + 1 for null class
               self.parent.assertEqual(
                   result.class_queries_logits.shape,(self.batch_size, self.num_queries, self.num_labels + 1) )

        with torch.no_grad():
               A__											=						model(pixel_values=__lowerCamelCase,pixel_mask=__lowerCamelCase )
               A__											=						model(__lowerCamelCase )

               comm_check_on_output(__lowerCamelCase )

               A__											=						model(
                   pixel_values=__lowerCamelCase,pixel_mask=__lowerCamelCase,mask_labels=__lowerCamelCase,class_labels=__lowerCamelCase )

        comm_check_on_output(__lowerCamelCase )

        self.parent.assertTrue(result.loss is not None )
        self.parent.assertEqual(result.loss.shape,torch.Size([1] ) )




@require_torch
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE							       =	{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerModelTester(self )
        A__											=						ConfigTester(self,config_class=__lowerCamelCase,has_text_modality=__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(__lowerCamelCase,**__lowerCamelCase,output_hidden_states=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__lowerCamelCase )



 @unittest.skip(reason='''Mask2Former does not use inputs_embeds''' )
 def 				UpperCamelCase			(							self ):
        pass



 @unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method''' )
 def 				UpperCamelCase			(							self ):
        pass



 @unittest.skip(reason='''Mask2Former is not a generative model''' )
 def 				UpperCamelCase			(							self ):
        pass



 @unittest.skip(reason='''Mask2Former does not use token embeddings''' )
 def 				UpperCamelCase			(							self ):
        pass



 @require_torch_multi_gpu
 @unittest.skip(
     reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
 def 				UpperCamelCase			(							self ):
        pass



 @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
 def 				UpperCamelCase			(							self ):
        pass



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

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

               A__											=						['''pixel_values''']
               self.assertListEqual(arg_names[:1],__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
               A__											=						MaskaFormerModel.from_pretrained(__lowerCamelCase )
               self.assertIsNotNone(__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						(self.model_tester.min_size,) * 2
        A__											=						{
            '''pixel_values''': torch.randn((2, 3, *size),device=__lowerCamelCase ),
            '''mask_labels''': torch.randn((2, 10, *size),device=__lowerCamelCase ),
            '''class_labels''': torch.zeros(2,10,device=__lowerCamelCase ).long(),
        }
        A__											=						self.model_tester.get_config()

        A__											=						MaskaFormerForUniversalSegmentation(__lowerCamelCase ).to(__lowerCamelCase )
        A__											=						model(**__lowerCamelCase )
        self.assertTrue(outputs.loss is not None )



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(__lowerCamelCase,**__lowerCamelCase,output_hidden_states=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               A__											=						model_class(__lowerCamelCase ).to(__lowerCamelCase )
               A__											=						model(**__lowerCamelCase,output_attentions=__lowerCamelCase )
               self.assertTrue(outputs.attentions is not None )



 def 				UpperCamelCase			(							self ):
        if not self.model_tester.is_training:
               return

        A__											=						self.all_model_classes[1]
        A__					,	A__					,	A__					,	A__					,	A__											=						self.model_tester.prepare_config_and_inputs()

        A__											=						model_class(__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.train()

        A__											=						model(__lowerCamelCase,mask_labels=__lowerCamelCase,class_labels=__lowerCamelCase ).loss
        loss.backward()




 def 				UpperCamelCase			(							self ):
        A__											=						self.all_model_classes[1]
        A__					,	A__					,	A__					,	A__					,	A__											=						self.model_tester.prepare_config_and_inputs()
        A__											=						True
        A__											=						True

        A__											=						model_class(__lowerCamelCase ).to(__lowerCamelCase )
        model.train()

        A__											=						model(__lowerCamelCase,mask_labels=__lowerCamelCase,class_labels=__lowerCamelCase )

        A__											=						outputs.encoder_hidden_states[0]
        encoder_hidden_states.retain_grad()

        A__											=						outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()

        A__											=						outputs.transformer_decoder_hidden_states[0]
        transformer_decoder_hidden_states.retain_grad()

        A__											=						outputs.attentions[0]
        attentions.retain_grad()

        outputs.loss.backward(retain_graph=__lowerCamelCase )

        self.assertIsNotNone(encoder_hidden_states.grad )
        self.assertIsNotNone(pixel_decoder_hidden_states.grad )
        self.assertIsNotNone(transformer_decoder_hidden_states.grad )
        self.assertIsNotNone(attentions.grad )
a__:      str									=		1e-4


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
       return image




@require_vision
@slow
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 @cached_property
 def 				UpperCamelCase			(							self ):
        return "facebook/mask2former-swin-small-coco-instance"



 @cached_property
 def 				UpperCamelCase			(							self ):
        return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase )
        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(__lowerCamelCase,return_tensors='''pt''' ).to(__lowerCamelCase )
        A__											=						inputs['''pixel_values'''].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
        # check size
        self.assertEqual(__lowerCamelCase,(1, 3, 384, 384) )

        with torch.no_grad():
               A__											=						model(**__lowerCamelCase )

        A__											=						torch.tensor(
            [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__lowerCamelCase )
        self.assertTrue(
            torch.allclose(
                outputs.encoder_last_hidden_state[0, 0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )

        A__											=						torch.tensor(
            [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__lowerCamelCase )
        self.assertTrue(
            torch.allclose(
                outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )

        A__											=						torch.tensor(
            [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__lowerCamelCase )
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase ).eval()
        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(__lowerCamelCase,return_tensors='''pt''' ).to(__lowerCamelCase )
        A__											=						inputs['''pixel_values'''].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
        # check size
        self.assertEqual(__lowerCamelCase,(1, 3, 384, 384) )

        with torch.no_grad():
               A__											=						model(**__lowerCamelCase )
        # masks_queries_logits
        A__											=						outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
        A__											=						[
            [-8.7839, -9.0056, -8.8121],
            [-7.4104, -7.0313, -6.5401],
            [-6.6105, -6.3427, -6.4675],
        ]
        A__											=						torch.tensor(__lowerCamelCase ).to(__lowerCamelCase )
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )
        # class_queries_logits
        A__											=						outputs.class_queries_logits
        self.assertEqual(class_queries_logits.shape,(1, model.config.num_queries, model.config.num_labels + 1) )
        A__											=						torch.tensor(
            [
                [1.8324, -8.0835, -4.1922],
                [0.8450, -9.0050, -3.6053],
                [0.3045, -7.7293, -3.0275],
            ] ).to(__lowerCamelCase )
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )




 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase ).eval()
        A__											=						self.default_image_processor

        A__											=						image_processor(
            [np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )],segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )],return_tensors='''pt''',)

        A__											=						inputs['''pixel_values'''].to(__lowerCamelCase )
        A__											=						[el.to(__lowerCamelCase ) for el in inputs['''mask_labels''']]
        A__											=						[el.to(__lowerCamelCase ) for el in inputs['''class_labels''']]

        with torch.no_grad():
               A__											=						model(**__lowerCamelCase )

        self.assertTrue(outputs.loss is not None )





