

import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
  )





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Tuple     ,						A_ :	Optional[Any]     ,						A_ :	Dict=1_3     ,						A_ :	str=7     ,						A_ :	Union[str, Any]=True     ,						A_ :	int=True     ,						A_ :	Any=False     ,						A_ :	str=True     ,						A_ :	int=9_9     ,						A_ :	int=3_2     ,						A_ :	Optional[int]=5     ,						A_ :	List[str]=4     ,						A_ :	int=6_4     ,						A_ :	Optional[int]="gelu"     ,						A_ :	List[Any]=0.1     ,						A_ :	int=0.1     ,						A_ :	List[str]=5_1_2     ,						A_ :	Optional[Any]=1_6     ,						A_ :	int=2     ,						A_ :	Optional[int]=0.02     ,						A_ :	Any=3     ,						A_ :	Optional[Any]=4     ,						A_ :	Union[str, Any]=None     ,						A_ :	Union[str, Any]=2     ,						A_ :	Tuple=2     ,						A_ :	Optional[int]=2     ,						A_ :	List[Any]=2     ,						A_ :	List[str]=4     ,						A_ :	Union[str, Any]=1     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    parent
      _lowerCAmelCase   :					List[str]        =    batch_size
      _lowerCAmelCase   :					str        =    seq_length
      _lowerCAmelCase   :					int        =    is_training
      _lowerCAmelCase   :					Union[str, Any]        =    use_input_mask
      _lowerCAmelCase   :					Union[str, Any]        =    use_token_type_ids
      _lowerCAmelCase   :					List[Any]        =    use_labels
      _lowerCAmelCase   :					Optional[int]        =    vocab_size
      _lowerCAmelCase   :					Union[str, Any]        =    hidden_size
      _lowerCAmelCase   :					int        =    num_hidden_layers
      _lowerCAmelCase   :					Optional[int]        =    num_attention_heads
      _lowerCAmelCase   :					Optional[Any]        =    intermediate_size
      _lowerCAmelCase   :					Any        =    hidden_act
      _lowerCAmelCase   :					Tuple        =    hidden_dropout_prob
      _lowerCAmelCase   :					Optional[int]        =    attention_probs_dropout_prob
      _lowerCAmelCase   :					List[Any]        =    max_position_embeddings
      _lowerCAmelCase   :					List[Any]        =    type_vocab_size
      _lowerCAmelCase   :					int        =    type_sequence_label_size
      _lowerCAmelCase   :					List[str]        =    initializer_range
      _lowerCAmelCase   :					Tuple        =    num_labels
      _lowerCAmelCase   :					Dict        =    num_choices
      _lowerCAmelCase   :					Optional[Any]        =    scope
      _lowerCAmelCase   :					Union[str, Any]        =    q_groups
      _lowerCAmelCase   :					Tuple        =    k_groups
      _lowerCAmelCase   :					str        =    v_groups
      _lowerCAmelCase   :					Tuple        =    post_attention_groups
      _lowerCAmelCase   :					Tuple        =    intermediate_groups
      _lowerCAmelCase   :					List[Any]        =    output_groups

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size       )

      _lowerCAmelCase   :					int        =    None
      if self.use_input_mask:
           _lowerCAmelCase   :					Union[str, Any]        =    random_attention_mask([self.batch_size, self.seq_length]       )

      _lowerCAmelCase   :					Dict        =    None
      _lowerCAmelCase   :					Any        =    None
      _lowerCAmelCase   :					List[Any]        =    None
      if self.use_labels:
           _lowerCAmelCase   :					List[Any]        =    ids_tensor([self.batch_size]     ,						self.type_sequence_label_size       )
           _lowerCAmelCase   :					Any        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.num_labels       )
           _lowerCAmelCase   :					Tuple        =    ids_tensor([self.batch_size]     ,						self.num_choices       )

      _lowerCAmelCase   :					Any        =    self.get_config()

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

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return SqueezeBertConfig(
          embedding_size=self.hidden_size     ,						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     ,						attention_probs_dropout_prob=self.hidden_dropout_prob     ,						attention_dropout=self.attention_probs_dropout_prob     ,						max_position_embeddings=self.max_position_embeddings     ,						initializer_range=self.initializer_range     ,						q_groups=self.q_groups     ,						k_groups=self.k_groups     ,						v_groups=self.v_groups     ,						post_attention_groups=self.post_attention_groups     ,						intermediate_groups=self.intermediate_groups     ,						output_groups=self.output_groups     ,						)

 def    __magic_name__       (		self :	List[str]     ,						A_ :	Dict     ,						A_ :	Union[str, Any]     ,						A_ :	List[str]     ,						A_ :	Optional[int]     ,						A_ :	str     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertModel(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[Any]        =    model(A_     ,						A_       )
      _lowerCAmelCase   :					Tuple        =    model(A_       )
      self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	int     ,						A_ :	Dict     ,						A_ :	Any     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertForMaskedLM(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					int        =    model(A_     ,						attention_mask=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size)       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Union[str, Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	List[str]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertForQuestionAnswering(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[Any]        =    model(
          A_     ,						attention_mask=A_     ,						start_positions=A_     ,						end_positions=A_       )
      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    __magic_name__       (		self :	Tuple     ,						A_ :	Optional[int]     ,						A_ :	Dict     ,						A_ :	str     ,						A_ :	Tuple     ,						A_ :	List[Any]     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.num_labels
      _lowerCAmelCase   :					int        =    SqueezeBertForSequenceClassification(A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[Any]        =    model(A_     ,						attention_mask=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels)       )

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	Tuple     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.num_labels
      _lowerCAmelCase   :					Any        =    SqueezeBertForTokenClassification(config=A_       )
      model.to(A_       )
      model.eval()

      _lowerCAmelCase   :					Any        =    model(A_     ,						attention_mask=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.num_labels)       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	Tuple     ,						A_ :	Tuple     ,						A_ :	Union[str, Any]     ,						A_ :	int     ,						A_ :	List[Any]     ,						A_ :	int       ):






      '''simple docstring'''




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


 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




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





@require_torch
class A__    ( A  ,		A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Tuple      =						(
     (
         SqueezeBertModel,
         SqueezeBertForMaskedLM,
         SqueezeBertForMultipleChoice,
         SqueezeBertForQuestionAnswering,
         SqueezeBertForSequenceClassification,
         SqueezeBertForTokenClassification,
     )
     if is_torch_available()
     else None
 )
 _lowercase     :					Optional[Any]      =						(
     {
         '''feature-extraction''': SqueezeBertModel,
         '''fill-mask''': SqueezeBertForMaskedLM,
         '''question-answering''': SqueezeBertForQuestionAnswering,
         '''text-classification''': SqueezeBertForSequenceClassification,
         '''token-classification''': SqueezeBertForTokenClassification,
         '''zero-shot''': SqueezeBertForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _lowercase     :					Union[str, Any]      =						False
 _lowercase     :					int      =						True
 _lowercase     :					List[str]      =						False

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    SqueezeBertModelTester(self       )
      _lowerCAmelCase   :					Tuple        =    ConfigTester(self     ,						config_class=A_     ,						dim=3_7       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      self.config_tester.run_common_tests()

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_model(*A_       )

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_masked_lm(*A_       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_question_answering(*A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_sequence_classification(*A_       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_token_classification(*A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_multiple_choice(*A_       )


 @slow
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowerCAmelCase   :					int        =    SqueezeBertModel.from_pretrained(A_       )
           self.assertIsNotNone(A_       )





@require_sentencepiece
@require_tokenizers
@require_torch
class A__    ( unittest.TestCase ):



 """simple docstring"""







 @slow
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"       )

      _lowerCAmelCase   :					Optional[int]        =    torch.tensor([[1, 2_9_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 1_3, 1_5_8_8, 2]]       )
      _lowerCAmelCase   :					List[str]        =    model(A_       )[0]
      _lowerCAmelCase   :					Any        =    torch.Size((1, 3)       )
      self.assertEqual(output.shape     ,						A_       )
      _lowerCAmelCase   :					Any        =    torch.tensor([[0.6401, -0.0349, -0.6041]]       )
      self.assertTrue(torch.allclose(A_     ,						A_     ,						atol=1E-4       )       )









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					List[str]      =						KandinskyVaaControlnetImgaImgPipeline
 _lowercase     :					Tuple      =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 _lowercase     :					str      =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 _lowercase     :					Optional[Any]      =						[
     '''generator''',
     '''height''',
     '''width''',
     '''strength''',
     '''guidance_scale''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 _lowercase     :					Tuple      =						False

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return 3_2

 @property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      return 3_2

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






      '''simple docstring'''




      return self.time_input_dim

 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      return self.time_input_dim * 4

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return 1_0_0

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      torch.manual_seed(0       )

      _lowerCAmelCase   :					Tuple        =    {
          "in_channels": 8,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "image_hint",
          "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": "image_proj",
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": None,
      }

      _lowerCAmelCase   :					Optional[int]        =    UNetaDConditionModel(**A_       )
      return model

 @property
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      return {
          "block_out_channels": [3_2, 3_2, 6_4, 6_4],
          "down_block_types": [
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "AttnDownEncoderBlock2D",
          ],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 1_2,
          "out_channels": 3,
          "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
          "vq_embed_dim": 4,
      }

 @property
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					List[str]        =    VQModel(**self.dummy_movq_kwargs       )
      return model

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.dummy_unet
      _lowerCAmelCase   :					List[Any]        =    self.dummy_movq

      _lowerCAmelCase   :					Tuple        =    {
          "num_train_timesteps": 1_0_0_0,
          "beta_schedule": "linear",
          "beta_start": 0.00085,
          "beta_end": 0.012,
          "clip_sample": False,
          "set_alpha_to_one": False,
          "steps_offset": 0,
          "prediction_type": "epsilon",
          "thresholding": False,
      }

      _lowerCAmelCase   :					int        =    DDIMScheduler(**A_       )

      _lowerCAmelCase   :					Tuple        =    {
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }

      return components

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Union[str, Any]     ,						A_ :	Optional[int]=0       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    floats_tensor((1, self.text_embedder_hidden_size)     ,						rng=random.Random(A_       )       ).to(A_       )
      _lowerCAmelCase   :					str        =    floats_tensor((1, self.text_embedder_hidden_size)     ,						rng=random.Random(seed + 1       )       ).to(
          A_       )
      # create init_image
      _lowerCAmelCase   :					Dict        =    floats_tensor((1, 3, 6_4, 6_4)     ,						rng=random.Random(A_       )       ).to(A_       )
      _lowerCAmelCase   :					Dict        =    image.cpu().permute(0     ,						2     ,						3     ,						1       )[0]
      _lowerCAmelCase   :					Tuple        =    Image.fromarray(np.uinta(A_       )       ).convert("RGB"       ).resize((2_5_6, 2_5_6)       )
      # create hint
      _lowerCAmelCase   :					Union[str, Any]        =    floats_tensor((1, 3, 6_4, 6_4)     ,						rng=random.Random(A_       )       ).to(A_       )

      if str(A_       ).startswith("mps"       ):
           _lowerCAmelCase   :					Tuple        =    torch.manual_seed(A_       )
      else:
           _lowerCAmelCase   :					Any        =    torch.Generator(device=A_       ).manual_seed(A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    {
          "image": init_image,
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "hint": hint,
          "generator": generator,
          "height": 6_4,
          "width": 6_4,
          "num_inference_steps": 1_0,
          "guidance_scale": 7.0,
          "strength": 0.2,
          "output_type": "np",
      }
      return inputs


 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




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

      _lowerCAmelCase   :					Tuple        =    self.get_dummy_components()

      _lowerCAmelCase   :					Optional[int]        =    self.pipeline_class(**A_       )
      _lowerCAmelCase   :					Optional[Any]        =    pipe.to(A_       )

      pipe.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					List[str]        =    pipe(**self.get_dummy_inputs(A_       )       )
      _lowerCAmelCase   :					List[Any]        =    output.images

      _lowerCAmelCase   :					str        =    pipe(
          **self.get_dummy_inputs(A_       )     ,						return_dict=A_     ,						)[0]

      _lowerCAmelCase   :					int        =    image[0, -3:, -3:, -1]
      _lowerCAmelCase   :					List[str]        =    image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)

      _lowerCAmelCase   :					List[Any]        =    np.array(
          [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]       )
      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()}'''





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



 """simple docstring"""







 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




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


 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"       )

      _lowerCAmelCase   :					str        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"       )
      _lowerCAmelCase   :					Union[str, Any]        =    init_image.resize((5_1_2, 5_1_2)       )

      _lowerCAmelCase   :					Dict        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/hint_image_cat.png"       )
      _lowerCAmelCase   :					Tuple        =    torch.from_numpy(np.array(A_       )       ).float() / 255.0
      _lowerCAmelCase   :					Optional[int]        =    hint.permute(2     ,						0     ,						1       ).unsqueeze(0       )

      _lowerCAmelCase   :					List[str]        =    "A robot, 4k photo"

      _lowerCAmelCase   :					Dict        =    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"     ,						torch_dtype=torch.floataa       )
      pipe_prior.to(A_       )

      _lowerCAmelCase   :					str        =    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-controlnet-depth"     ,						torch_dtype=torch.floataa       )
      _lowerCAmelCase   :					Optional[int]        =    pipeline.to(A_       )

      pipeline.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					Dict        =    torch.Generator(device="cpu"       ).manual_seed(0       )

      _lowerCAmelCase      ,			_lowerCAmelCase   :					Tuple        =    pipe_prior(
          A_     ,						image=A_     ,						strength=0.85     ,						generator=A_     ,						negative_prompt=""     ,						).to_tuple()

      _lowerCAmelCase   :					List[Any]        =    pipeline(
          image=A_     ,						image_embeds=A_     ,						negative_image_embeds=A_     ,						hint=A_     ,						generator=A_     ,						num_inference_steps=1_0_0     ,						height=5_1_2     ,						width=5_1_2     ,						strength=0.5     ,						output_type="np"     ,						)

      _lowerCAmelCase   :					str        =    output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)

      assert_mean_pixel_difference(A_     ,						A_       )






