'''simple docstring'''

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       :





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=13    ,     _lowerCamelCase=[30, 30]    ,     _lowerCamelCase=2    ,     _lowerCamelCase=3    ,     _lowerCamelCase=True    ,     _lowerCamelCase=True    ,     _lowerCamelCase=32    ,     _lowerCamelCase=5    ,     _lowerCamelCase=4    ,     _lowerCamelCase=37    ,     _lowerCamelCase="gelu"    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=10    ,     _lowerCamelCase=0.02    ,     _lowerCamelCase=3    ,     _lowerCamelCase=None    ,     _lowerCamelCase=8    ,     _lowerCamelCase=10    ,     ):
     lowerCAmelCase_			=      parent
     lowerCAmelCase_			=      batch_size
     lowerCAmelCase_			=      image_size
     lowerCAmelCase_			=      patch_size
     lowerCAmelCase_			=      num_channels
     lowerCAmelCase_			=      is_training
     lowerCAmelCase_			=      use_labels
     lowerCAmelCase_			=      hidden_size
     lowerCAmelCase_			=      num_hidden_layers
     lowerCAmelCase_			=      num_attention_heads
     lowerCAmelCase_			=      intermediate_size
     lowerCAmelCase_			=      hidden_act
     lowerCAmelCase_			=      hidden_dropout_prob
     lowerCAmelCase_			=      attention_probs_dropout_prob
     lowerCAmelCase_			=      type_sequence_label_size
     lowerCAmelCase_			=      initializer_range
     lowerCAmelCase_			=      num_labels
     lowerCAmelCase_			=      scope
     lowerCAmelCase_			=      n_targets
     lowerCAmelCase_			=      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_			=      (image_size[1] // patch_size) * (image_size[0] // patch_size)
     lowerCAmelCase_			=      num_patches + 1 + self.num_detection_tokens





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )

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

     lowerCAmelCase_			=      self.get_config()

     return config, pixel_values, labels





  def 						UpperCAmelCase_	(	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=snake_case__    ,     initializer_range=self.initializer_range    ,     num_detection_tokens=self.num_detection_tokens    ,     num_labels=self.num_labels    ,     )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      YolosModel(config=snake_case__ )
     model.to(snake_case__ )
     model.eval()
     lowerCAmelCase_			=      model(snake_case__ )
     self.parent.assertEqual(
         result.last_hidden_state.shape    ,     (self.batch_size, self.expected_seq_len, self.hidden_size) )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      YolosForObjectDetection(snake_case__ )
     model.to(snake_case__ )
     model.eval()

     lowerCAmelCase_			=      model(pixel_values=snake_case__ )
     lowerCAmelCase_			=      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_			=      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 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.prepare_config_and_inputs()
     lowerCAmelCase_			=      config_and_inputs
     lowerCAmelCase_			=      {"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class     __UpperCAmelCase       ( __a						,       __a						,       unittest.TestCase		):
  __A :				List[str]											= (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
  __A :				Tuple											= (
      {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
  )

  __A :				Any											= False
  __A :				Any											= False
  __A :				List[Any]											= False
  __A :				List[Any]											= False





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=False ):
     lowerCAmelCase_			=      super()._prepare_for_class(snake_case__    ,     snake_case__    ,     return_labels=snake_case__ )

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

     return inputs_dict





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      YolosModelTester(self )
     lowerCAmelCase_			=      ConfigTester(self    ,     config_class=snake_case__    ,     has_text_modality=snake_case__    ,     hidden_size=37 )





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





  def 						UpperCAmelCase_	(	self ):
     pass





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

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





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

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

        lowerCAmelCase_			=      ["pixel_values"]
        self.assertListEqual(arg_names[:1]    ,     snake_case__ )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__ )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()
     lowerCAmelCase_			=      True

     # in YOLOS, the seq_len is different
     lowerCAmelCase_			=      self.model_tester.expected_seq_len
     for model_class in self.all_model_classes:
        lowerCAmelCase_			=      True
        lowerCAmelCase_			=      False
        lowerCAmelCase_			=      True
        lowerCAmelCase_			=      model_class(snake_case__ )
        model.to(snake_case__ )
        model.eval()
        with torch.no_grad():
           lowerCAmelCase_			=      model(**self._prepare_for_class(snake_case__    ,     snake_case__ ) )
        lowerCAmelCase_			=      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_			=      True
        lowerCAmelCase_			=      model_class(snake_case__ )
        model.to(snake_case__ )
        model.eval()
        with torch.no_grad():
           lowerCAmelCase_			=      model(**self._prepare_for_class(snake_case__    ,     snake_case__ ) )
        lowerCAmelCase_			=      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_			=      len(snake_case__ )

        # Check attention is always last and order is fine
        lowerCAmelCase_			=      True
        lowerCAmelCase_			=      True
        lowerCAmelCase_			=      model_class(snake_case__ )
        model.to(snake_case__ )
        model.eval()
        with torch.no_grad():
           lowerCAmelCase_			=      model(**self._prepare_for_class(snake_case__    ,     snake_case__ ) )

        lowerCAmelCase_			=      1
        self.assertEqual(out_len + added_hidden_states    ,     len(snake_case__ ) )

        lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self ):




     def check_hidden_states_output(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
        lowerCAmelCase_			=      model_class(snake_case__ )
        model.to(snake_case__ )
        model.eval()

        with torch.no_grad():
           lowerCAmelCase_			=      model(**self._prepare_for_class(snake_case__    ,     snake_case__ ) )

        lowerCAmelCase_			=      outputs.hidden_states

        lowerCAmelCase_			=      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_			=      self.model_tester.expected_seq_len

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

     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        lowerCAmelCase_			=      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_			=      True

        check_hidden_states_output(snake_case__    ,     snake_case__    ,     snake_case__ )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_object_detection(*snake_case__ )







  @slow
  def 						UpperCAmelCase_	(	self ):




     for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCAmelCase_			=      YolosModel.from_pretrained(snake_case__ )
        self.assertIsNotNone(snake_case__ )






def snake_case_      (					)			->					Tuple:
   lowerCAmelCase_			=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
   return image






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





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







  @slow
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      YolosForObjectDetection.from_pretrained('''hustvl/yolos-small''' ).to(snake_case__ )

     lowerCAmelCase_			=      self.default_image_processor
     lowerCAmelCase_			=      prepare_img()
     lowerCAmelCase_			=      image_processor(images=snake_case__    ,     return_tensors='''pt''' ).to(snake_case__ )

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

     # verify outputs
     lowerCAmelCase_			=      torch.Size((1, 100, 92) )
     self.assertEqual(outputs.logits.shape    ,     snake_case__ )

     lowerCAmelCase_			=      torch.tensor(
         [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]    ,     device=snake_case__    ,     )
     lowerCAmelCase_			=      torch.tensor(
         [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]    ,     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_			=      image_processor.post_process_object_detection(
         snake_case__    ,     threshold=0.3    ,     target_sizes=[image.size[::-1]] )[0]
     lowerCAmelCase_			=      torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61] ).to(snake_case__ )
     lowerCAmelCase_			=      [75, 75, 17, 63, 17]
     lowerCAmelCase_			=      torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95] ).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__ ) )







'''simple docstring'''





import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Union[str, Any]				=							KandinskyInpaintPipeline
 lowercase_					:    Tuple				=							["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
 lowercase_					:    Dict				=							[
     "prompt",
     "negative_prompt",
     "image_embeds",
     "negative_image_embeds",
     "image",
     "mask_image",
 ]
 lowercase_					:    Optional[Any]				=							[
     "generator",
     "height",
     "width",
     "latents",
     "guidance_scale",
     "negative_prompt",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 lowercase_					:    Optional[Any]				=							False







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return self.time_input_dim







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return self.time_input_dim * 4







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return 1_00







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
   return tokenizer







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   torch.manual_seed(0 )
   UpperCAmelCase__      :					Dict				       =							MCLIPConfig(
       numDims=self.cross_attention_dim			,				transformerDimensions=self.text_embedder_hidden_size			,				hidden_size=self.text_embedder_hidden_size			,				intermediate_size=37			,				num_attention_heads=4			,				num_hidden_layers=5			,				vocab_size=10_05			,				)

   UpperCAmelCase__      :					Any				       =							MultilingualCLIP(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							text_encoder.eval()

   return text_encoder







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   torch.manual_seed(0 )

   UpperCAmelCase__      :					str				       =							{
       "in_channels": 9,
       # Out channels is double in channels because predicts mean and variance
       "out_channels": 8,
       "addition_embed_type": "text_image",
       "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
       "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
       "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
       "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
       "layers_per_block": 1,
       "encoder_hid_dim": self.text_embedder_hidden_size,
       "encoder_hid_dim_type": "text_image_proj",
       "cross_attention_dim": self.cross_attention_dim,
       "attention_head_dim": 4,
       "resnet_time_scale_shift": "scale_shift",
       "class_embed_type": None,
   }

   UpperCAmelCase__      :					List[str]				       =							UNetaDConditionModel(**snake_case__ )
   return model







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   return {
       "block_out_channels": [32, 64],
       "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
       "in_channels": 3,
       "latent_channels": 4,
       "layers_per_block": 1,
       "norm_num_groups": 8,
       "norm_type": "spatial",
       "num_vq_embeddings": 12,
       "out_channels": 3,
       "up_block_types": [
           "AttnUpDecoderBlock2D",
           "UpDecoderBlock2D",
       ],
       "vq_embed_dim": 4,
   }







 @property
 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   torch.manual_seed(0 )
   UpperCAmelCase__      :					str				       =							VQModel(**self.dummy_movq_kwargs )
   return model







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.dummy_text_encoder
   UpperCAmelCase__      :					Any				       =							self.dummy_tokenizer
   UpperCAmelCase__      :					Tuple				       =							self.dummy_unet
   UpperCAmelCase__      :					str				       =							self.dummy_movq

   UpperCAmelCase__      :					str				       =							DDIMScheduler(
       num_train_timesteps=10_00			,				beta_schedule="linear"			,				beta_start=0.00085			,				beta_end=0.012			,				clip_sample=snake_case__			,				set_alpha_to_one=snake_case__			,				steps_offset=1			,				prediction_type="epsilon"			,				thresholding=snake_case__			,				)

   UpperCAmelCase__      :					List[Any]				       =							{
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  Any			,				snake_case__		:  Tuple=0 ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					Optional[Any]				       =							floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1 ) ).to(snake_case__ )
   # create init_image
   UpperCAmelCase__      :					Any				       =							floats_tensor((1, 3, 64, 64)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							image.cpu().permute(0			,				2			,				3			,				1 )[0]
   UpperCAmelCase__      :					Optional[int]				       =							Image.fromarray(np.uinta(snake_case__ ) ).convert("RGB" ).resize((2_56, 2_56) )
   # create mask
   UpperCAmelCase__      :					Any				       =							np.ones((64, 64)			,				dtype=np.floataa )
   UpperCAmelCase__      :					Any				       =							0

   if str(snake_case__ ).startswith("mps" ):
     UpperCAmelCase__      :					Tuple				       =							torch.manual_seed(snake_case__ )
   else:
     UpperCAmelCase__      :					str				       =							torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							{
       "prompt": "horse",
       "image": init_image,
       "mask_image": mask,
       "image_embeds": image_embeds,
       "negative_image_embeds": negative_image_embeds,
       "generator": generator,
       "height": 64,
       "width": 64,
       "num_inference_steps": 2,
       "guidance_scale": 4.0,
       "output_type": "np",
   }
   return inputs







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							"cpu"

   UpperCAmelCase__      :					Optional[Any]				       =							self.get_dummy_components()

   UpperCAmelCase__      :					int				       =							self.pipeline_class(**snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							pipe.to(snake_case__ )

   pipe.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							pipe(**self.get_dummy_inputs(snake_case__ ) )
   UpperCAmelCase__      :					int				       =							output.images

   UpperCAmelCase__      :					Tuple				       =							pipe(
       **self.get_dummy_inputs(snake_case__ )			,				return_dict=snake_case__			,				)[0]

   UpperCAmelCase__      :					Any				       =							image[0, -3:, -3:, -1]
   UpperCAmelCase__      :					Optional[int]				       =							image_from_tuple[0, -3:, -3:, -1]

   print(F"""image.shape {image.shape}""" )

   assert image.shape == (1, 64, 64, 3)

   UpperCAmelCase__      :					Any				       =							np.array(
       [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )

   assert (
       np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
   ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
   assert (
       np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
   ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






@slow
@require_torch_gpu
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy" )

   UpperCAmelCase__      :					List[str]				       =							load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
   UpperCAmelCase__      :					Union[str, Any]				       =							np.ones((7_68, 7_68)			,				dtype=np.floataa )
   UpperCAmelCase__      :					List[Any]				       =							0

   UpperCAmelCase__      :					Tuple				       =							"a hat"

   UpperCAmelCase__      :					Union[str, Any]				       =							KandinskyPriorPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-prior"			,				torch_dtype=torch.floataa )
   pipe_prior.to(snake_case__ )

   UpperCAmelCase__      :					int				       =							KandinskyInpaintPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-inpaint"			,				torch_dtype=torch.floataa )
   UpperCAmelCase__      :					Optional[int]				       =							pipeline.to(snake_case__ )
   pipeline.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							torch.Generator(device="cpu" ).manual_seed(0 )
   UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							pipe_prior(
       snake_case__			,				generator=snake_case__			,				num_inference_steps=5			,				negative_prompt=""			,				).to_tuple()

   UpperCAmelCase__      :					Optional[int]				       =							pipeline(
       snake_case__			,				image=snake_case__			,				mask_image=snake_case__			,				image_embeds=snake_case__			,				negative_image_embeds=snake_case__			,				generator=snake_case__			,				num_inference_steps=1_00			,				height=7_68			,				width=7_68			,				output_type="np"			,				)

   UpperCAmelCase__      :					int				       =							output.images[0]

   assert image.shape == (7_68, 7_68, 3)

   assert_mean_pixel_difference(snake_case__			,				snake_case__ )



