

"""simple docstring"""
import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        NystromformerForMaskedLM,
        NystromformerForMultipleChoice,
        NystromformerForQuestionAnswering,
        NystromformerForSequenceClassification,
        NystromformerForTokenClassification,
        NystromformerModel,
    )
    from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST

class       lowerCamelCase	:


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase							,       _lowerCamelCase=13							,       _lowerCamelCase=7							,       _lowerCamelCase=True							,       _lowerCamelCase=True							,       _lowerCamelCase=True							,       _lowerCamelCase=True							,       _lowerCamelCase=99							,       _lowerCamelCase=32							,       _lowerCamelCase=5							,       _lowerCamelCase=4							,       _lowerCamelCase=37							,       _lowerCamelCase="gelu"							,       _lowerCamelCase=0.1							,       _lowerCamelCase=0.1							,       _lowerCamelCase=512							,       _lowerCamelCase=16							,       _lowerCamelCase=2							,       _lowerCamelCase=0.02							,       _lowerCamelCase=3							,       _lowerCamelCase=4							,       _lowerCamelCase=None							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    parent
        UpperCAmelCase__			:							Tuple							=    batch_size
        UpperCAmelCase__			:							Tuple							=    seq_length
        UpperCAmelCase__			:							Optional[Any]							=    is_training
        UpperCAmelCase__			:							Union[str, Any]							=    use_input_mask
        UpperCAmelCase__			:							List[str]							=    use_token_type_ids
        UpperCAmelCase__			:							int							=    use_labels
        UpperCAmelCase__			:							Optional[Any]							=    vocab_size
        UpperCAmelCase__			:							Optional[int]							=    hidden_size
        UpperCAmelCase__			:							Tuple							=    num_hidden_layers
        UpperCAmelCase__			:							Any							=    num_attention_heads
        UpperCAmelCase__			:							List[str]							=    intermediate_size
        UpperCAmelCase__			:							Optional[Any]							=    hidden_act
        UpperCAmelCase__			:							Optional[int]							=    hidden_dropout_prob
        UpperCAmelCase__			:							List[str]							=    attention_probs_dropout_prob
        UpperCAmelCase__			:							Optional[Any]							=    max_position_embeddings
        UpperCAmelCase__			:							Optional[int]							=    type_vocab_size
        UpperCAmelCase__			:							Any							=    type_sequence_label_size
        UpperCAmelCase__			:							Union[str, Any]							=    initializer_range
        UpperCAmelCase__			:							Optional[Any]							=    num_labels
        UpperCAmelCase__			:							Optional[Any]							=    num_choices
        UpperCAmelCase__			:							Tuple							=    scope



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    ids_tensor([self.batch_size, self.seq_length]							,       self.vocab_size	)

        UpperCAmelCase__			:							Any							=    None
        if self.use_input_mask:
              UpperCAmelCase__			:							Optional[Any]							=    random_attention_mask([self.batch_size, self.seq_length]	)

        UpperCAmelCase__			:							Union[str, Any]							=    None
        if self.use_token_type_ids:
              UpperCAmelCase__			:							Dict							=    ids_tensor([self.batch_size, self.seq_length]							,       self.type_vocab_size	)

        UpperCAmelCase__			:							str							=    None
        UpperCAmelCase__			:							Union[str, Any]							=    None
        UpperCAmelCase__			:							List[str]							=    None
        if self.use_labels:
              UpperCAmelCase__			:							Any							=    ids_tensor([self.batch_size]							,       self.type_sequence_label_size	)
              UpperCAmelCase__			:							Dict							=    ids_tensor([self.batch_size, self.seq_length]							,       self.num_labels	)
              UpperCAmelCase__			:							int							=    ids_tensor([self.batch_size]							,       self.num_choices	)

        UpperCAmelCase__			:							List[str]							=    self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



  def  _a      (self	):


        """simple docstring"""

        return NystromformerConfig(
            vocab_size=self.vocab_size							,       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							,       max_position_embeddings=self.max_position_embeddings							,       type_vocab_size=self.type_vocab_size							,       is_decoder=_lowerCamelCase							,       initializer_range=self.initializer_range							,       )



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    NystromformerModel(config=_lowerCamelCase	)
        model.to(_lowerCamelCase	)
        model.eval()
        UpperCAmelCase__			:							Union[str, Any]							=    model(_lowerCamelCase							,       attention_mask=_lowerCamelCase							,       token_type_ids=_lowerCamelCase	)
        UpperCAmelCase__			:							Tuple							=    model(_lowerCamelCase							,       token_type_ids=_lowerCamelCase	)
        UpperCAmelCase__			:							int							=    model(_lowerCamelCase	)
        self.parent.assertEqual(result.last_hidden_state.shape							,       (self.batch_size, self.seq_length, self.hidden_size)	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    NystromformerForMaskedLM(config=_lowerCamelCase	)
        model.to(_lowerCamelCase	)
        model.eval()
        UpperCAmelCase__			:							Union[str, Any]							=    model(_lowerCamelCase							,       attention_mask=_lowerCamelCase							,       token_type_ids=_lowerCamelCase							,       labels=_lowerCamelCase	)
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.vocab_size)	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    NystromformerForQuestionAnswering(config=_lowerCamelCase	)
        model.to(_lowerCamelCase	)
        model.eval()
        UpperCAmelCase__			:							int							=    model(
            _lowerCamelCase							,       attention_mask=_lowerCamelCase							,       token_type_ids=_lowerCamelCase							,       start_positions=_lowerCamelCase							,       end_positions=_lowerCamelCase							,       )
        self.parent.assertEqual(result.start_logits.shape							,       (self.batch_size, self.seq_length)	)
        self.parent.assertEqual(result.end_logits.shape							,       (self.batch_size, self.seq_length)	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    self.num_labels
        UpperCAmelCase__			:							str							=    NystromformerForSequenceClassification(_lowerCamelCase	)
        model.to(_lowerCamelCase	)
        model.eval()
        UpperCAmelCase__			:							Tuple							=    model(_lowerCamelCase							,       attention_mask=_lowerCamelCase							,       token_type_ids=_lowerCamelCase							,       labels=_lowerCamelCase	)
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels)	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    self.num_labels
        UpperCAmelCase__			:							Optional[Any]							=    NystromformerForTokenClassification(config=_lowerCamelCase	)
        model.to(_lowerCamelCase	)
        model.eval()
        UpperCAmelCase__			:							Union[str, Any]							=    model(_lowerCamelCase							,       attention_mask=_lowerCamelCase							,       token_type_ids=_lowerCamelCase							,       labels=_lowerCamelCase	)
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.num_labels)	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    self.num_choices
        UpperCAmelCase__			:							int							=    NystromformerForMultipleChoice(config=_lowerCamelCase	)
        model.to(_lowerCamelCase	)
        model.eval()
        UpperCAmelCase__			:							Dict							=    input_ids.unsqueeze(1	).expand(-1							,       self.num_choices							,       -1	).contiguous()
        UpperCAmelCase__			:							List[Any]							=    token_type_ids.unsqueeze(1	).expand(-1							,       self.num_choices							,       -1	).contiguous()
        UpperCAmelCase__			:							List[Any]							=    input_mask.unsqueeze(1	).expand(-1							,       self.num_choices							,       -1	).contiguous()
        UpperCAmelCase__			:							List[str]							=    model(
            _lowerCamelCase							,       attention_mask=_lowerCamelCase							,       token_type_ids=_lowerCamelCase							,       labels=_lowerCamelCase							,       )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_choices)	)




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    self.prepare_config_and_inputs()
        (
            (
            UpperCAmelCase__
        )					,		(
            UpperCAmelCase__
        )					,		(
            UpperCAmelCase__
        )					,		(
            UpperCAmelCase__
        )					,		(
            UpperCAmelCase__
        )					,		(
            UpperCAmelCase__
        )					,		(
            UpperCAmelCase__
        )					,		
        )			:							Optional[int]							=    config_and_inputs
        UpperCAmelCase__			:							Optional[int]							=    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
        return config, inputs_dict




@require_torch
class       lowerCamelCase	(       lowerCAmelCase__					,					lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  (
      (
          NystromformerModel,
          NystromformerForMaskedLM,
          NystromformerForMultipleChoice,
          NystromformerForQuestionAnswering,
          NystromformerForSequenceClassification,
          NystromformerForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE					      =  (
      {
          'feature-extraction': NystromformerModel,
          'fill-mask': NystromformerForMaskedLM,
          'question-answering': NystromformerForQuestionAnswering,
          'text-classification': NystromformerForSequenceClassification,
          'token-classification': NystromformerForTokenClassification,
          'zero-shot': NystromformerForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE					      =  False
  SCREAMING_SNAKE_CASE					      =  False



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    NystromformerModelTester(self	)
        UpperCAmelCase__			:							List[Any]							=    ConfigTester(self							,       config_class=_lowerCamelCase							,       hidden_size=37	)



  def  _a      (self	):


        """simple docstring"""

        self.config_tester.run_common_tests()



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
              UpperCAmelCase__			:							Dict							=    type
              self.model_tester.create_and_check_model(*_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase	)




  @slow
  def  _a      (self	):


        """simple docstring"""

        for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase__			:							List[Any]							=    NystromformerModel.from_pretrained(_lowerCamelCase	)
              self.assertIsNotNone(_lowerCamelCase	)




@require_torch
class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    NystromformerModel.from_pretrained("""uw-madison/nystromformer-512"""	)
        UpperCAmelCase__			:							Dict							=    torch.tensor([[0, 1, 2, 3, 4, 5]]	)

        with torch.no_grad():
              UpperCAmelCase__			:							Dict							=    model(_lowerCamelCase	)[0]

        UpperCAmelCase__			:							int							=    torch.Size((1, 6, 768)	)
        self.assertEqual(output.shape							,       _lowerCamelCase	)

        UpperCAmelCase__			:							Tuple							=    torch.tensor(
            [[[-0.4_532, -0.0_936, 0.5_137], [-0.2_676, 0.0_628, 0.6_186], [-0.3_629, -0.1_726, 0.4_716]]]	)

        self.assertTrue(torch.allclose(output[:, :3, :3]							,       _lowerCamelCase							,       atol=1e-4	)	)




  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    """the [MASK] of Belgium is Brussels"""

        UpperCAmelCase__			:							str							=    AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512"""	)
        UpperCAmelCase__			:							Dict							=    NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512"""	)

        UpperCAmelCase__			:							List[str]							=    tokenizer(_lowerCamelCase							,       return_tensors="""pt"""	)

        with torch.no_grad():
              UpperCAmelCase__			:							List[Any]							=    model(encoding.input_ids	).logits

        UpperCAmelCase__			:							Tuple							=    token_logits[:, 2, :].argmax(-1	)[0]

        self.assertEqual(tokenizer.decode(_lowerCamelCase	)							,       """capital"""	)




"""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       lowerCamelCase	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  KandinskyInpaintPipeline
  SCREAMING_SNAKE_CASE					      =  ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
  SCREAMING_SNAKE_CASE					      =  [
      'prompt',
      'negative_prompt',
      'image_embeds',
      'negative_image_embeds',
      'image',
      'mask_image',
  ]
  SCREAMING_SNAKE_CASE					      =  [
      'generator',
      'height',
      'width',
      'latents',
      'guidance_scale',
      'negative_prompt',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  SCREAMING_SNAKE_CASE					      =  False



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim * 4



  @property
  def  _a      (self	):


        """simple docstring"""

        return 100



  @property
  def  _a      (self	):


        """simple docstring"""

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



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Optional[int]							=    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=1005							,       )

        UpperCAmelCase__			:							List[Any]							=    MultilingualCLIP(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    text_encoder.eval()

        return text_encoder



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)

        UpperCAmelCase__			:							Dict							=    {
            """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[Any]							=    UNetaDConditionModel(**_lowerCamelCase	)
        return model



  @property
  def  _a      (self	):


        """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  _a      (self	):


        """simple docstring"""

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



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    self.dummy_text_encoder
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_tokenizer
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_unet
        UpperCAmelCase__			:							List[Any]							=    self.dummy_movq

        UpperCAmelCase__			:							Union[str, Any]							=    DDIMScheduler(
            num_train_timesteps=1000							,       beta_schedule="""linear"""							,       beta_start=0.00_085							,       beta_end=0.012							,       clip_sample=_lowerCamelCase							,       set_alpha_to_one=_lowerCamelCase							,       steps_offset=1							,       prediction_type="""epsilon"""							,       thresholding=_lowerCamelCase							,       )

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

        return components



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase=0	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(_lowerCamelCase	)	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[int]							=    floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(seed + 1	)	).to(_lowerCamelCase	)
        # create init_image
        UpperCAmelCase__			:							Optional[int]							=    floats_tensor((1, 3, 64, 64)							,       rng=random.Random(_lowerCamelCase	)	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[int]							=    image.cpu().permute(0							,       2							,       3							,       1	)[0]
        UpperCAmelCase__			:							Optional[Any]							=    Image.fromarray(np.uinta(_lowerCamelCase	)	).convert("""RGB"""	).resize((256, 256)	)
        # create mask
        UpperCAmelCase__			:							List[Any]							=    np.ones((64, 64)							,       dtype=np.floataa	)
        UpperCAmelCase__			:							Optional[int]							=    0

        if str(_lowerCamelCase	).startswith("""mps"""	):
              UpperCAmelCase__			:							Tuple							=    torch.manual_seed(_lowerCamelCase	)
        else:
              UpperCAmelCase__			:							Optional[int]							=    torch.Generator(device=_lowerCamelCase	).manual_seed(_lowerCamelCase	)
        UpperCAmelCase__			:							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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    """cpu"""

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

        UpperCAmelCase__			:							Optional[Any]							=    self.pipeline_class(**_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    pipe.to(_lowerCamelCase	)

        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

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

        UpperCAmelCase__			:							Union[str, Any]							=    pipe(
            **self.get_dummy_inputs(_lowerCamelCase	)							,       return_dict=_lowerCamelCase							,       )[0]

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

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

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

        UpperCAmelCase__			:							Any							=    np.array(
            [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]	)

        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  _a      (self	):


        """simple docstring"""

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




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


  '''simple docstring'''







  def  _a      (self	):


        """simple docstring"""

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




  def  _a      (self	):


        """simple docstring"""

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

        UpperCAmelCase__			:							Union[str, Any]							=    load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""	)
        UpperCAmelCase__			:							List[str]							=    np.ones((768, 768)							,       dtype=np.floataa	)
        UpperCAmelCase__			:							Optional[int]							=    0

        UpperCAmelCase__			:							int							=    """a hat"""

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

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

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

        UpperCAmelCase__			:							Union[str, Any]							=    pipeline(
            _lowerCamelCase							,       image=_lowerCamelCase							,       mask_image=_lowerCamelCase							,       image_embeds=_lowerCamelCase							,       negative_image_embeds=_lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=100							,       height=768							,       width=768							,       output_type="""np"""							,       )

        UpperCAmelCase__			:							Any							=    output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(_lowerCamelCase							,       _lowerCamelCase	)

