



from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
	from transformers import (
	    TFBertModel,
	    TFCLIPVisionModel,
	    TFDeiTModel,
	    TFRobertaModel,
	    TFVisionTextDualEncoderModel,
	    TFViTModel,
	    VisionTextDualEncoderConfig,
	)

if is_vision_available():
	from PIL import Image

	from transformers import VisionTextDualEncoderProcessor


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					if isinstance(snake_case	,						collections.abc.Iterable			):
										return x
					return (x, x)





@require_tf
class 					__lowerCAmelCase :




				def 							snake_case   (							self						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									pass




				def 							snake_case   (							self		):



									"""simple docstring"""





									pass




				def 							snake_case   (							self		):



									"""simple docstring"""





									pass




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case=None						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    VisionTextDualEncoderConfig.from_vision_text_configs(_snake_case						,				_snake_case		)

									_lowerCAmelCase						=    TFVisionTextDualEncoderModel(_snake_case		)

									_lowerCAmelCase						=    model(input_ids=_snake_case						,				pixel_values=_snake_case						,				attention_mask=_snake_case		)

									self.assertEqual(output["""text_embeds"""].shape						,				(input_ids.shape[0], config.projection_dim)		)
									self.assertEqual(output["""image_embeds"""].shape						,				(pixel_values.shape[0], config.projection_dim)		)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case=None						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    self.get_vision_text_model(_snake_case						,				_snake_case		)
									_lowerCAmelCase						=    TFVisionTextDualEncoderModel(vision_model=_snake_case						,				text_model=_snake_case		)

									_lowerCAmelCase						=    model(input_ids=_snake_case						,				pixel_values=_snake_case						,				attention_mask=_snake_case		)

									self.assertEqual(output["""text_embeds"""].shape						,				(input_ids.shape[0], model.config.projection_dim)		)
									self.assertEqual(output["""image_embeds"""].shape						,				(pixel_values.shape[0], model.config.projection_dim)		)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case=None						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    self.get_vision_text_model(_snake_case						,				_snake_case		)
									_lowerCAmelCase						=    {"""vision_model""": vision_model, """text_model""": text_model}
									_lowerCAmelCase						=    TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_snake_case		)

									_lowerCAmelCase						=    model(input_ids=_snake_case						,				pixel_values=_snake_case						,				attention_mask=_snake_case		)

									self.assertEqual(output["""text_embeds"""].shape						,				(input_ids.shape[0], model.config.projection_dim)		)
									self.assertEqual(output["""image_embeds"""].shape						,				(pixel_values.shape[0], model.config.projection_dim)		)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case=None						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    self.get_vision_text_model(_snake_case						,				_snake_case		)
									_lowerCAmelCase						=    TFVisionTextDualEncoderModel(vision_model=_snake_case						,				text_model=_snake_case		)

									_lowerCAmelCase						=    model(input_ids=_snake_case						,				pixel_values=_snake_case						,				attention_mask=_snake_case		)
									_lowerCAmelCase						=    output[0].numpy()

									with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(_snake_case		)
														_lowerCAmelCase						=    TFVisionTextDualEncoderModel.from_pretrained(_snake_case		)

														_lowerCAmelCase						=    model(input_ids=_snake_case						,				pixel_values=_snake_case						,				attention_mask=_snake_case		)
														_lowerCAmelCase						=    after_output[0].numpy()
														_lowerCAmelCase						=    np.amax(np.abs(out_a - out_a		)		)
														self.assertLessEqual(_snake_case						,				1e-5		)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case=None						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    self.get_vision_text_model(_snake_case						,				_snake_case		)
									_lowerCAmelCase						=    TFVisionTextDualEncoderModel(vision_model=_snake_case						,				text_model=_snake_case		)

									_lowerCAmelCase						=    model(
									    input_ids=_snake_case						,				pixel_values=_snake_case						,				attention_mask=_snake_case						,				output_attentions=_snake_case		)

									_lowerCAmelCase						=    output.vision_model_output.attentions
									self.assertEqual(len(_snake_case		)						,				vision_config.num_hidden_layers		)

									# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
									_lowerCAmelCase						=    to_atuple(vision_model.config.image_size		)
									_lowerCAmelCase						=    to_atuple(vision_model.config.patch_size		)
									_lowerCAmelCase						=    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									_lowerCAmelCase						=    num_patches + 1
									self.assertEqual(vision_attentions[0].shape[-3:]						,				(vision_config.num_attention_heads, seq_len, seq_len)		)

									_lowerCAmelCase						=    output.text_model_output.attentions
									self.assertEqual(len(_snake_case		)						,				text_config.num_hidden_layers		)

									self.assertEqual(
									    text_attentions[0].shape[-3:]						,				(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])						,				)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    np.abs((a - b)		).max()
									self.assertLessEqual(_snake_case						,				_snake_case						,				F'Difference between torch and flax is {diff} (>= {tol}).'		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.prepare_config_and_inputs()
									self.check_vision_text_dual_encoder_model(**_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.prepare_config_and_inputs()
									self.check_model_from_pretrained_configs(**_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.prepare_config_and_inputs()
									self.check_vision_text_dual_encoder_from_pretrained(**_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.prepare_config_and_inputs()
									self.check_save_load(**_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.prepare_config_and_inputs()
									self.check_vision_text_output_attention(**_snake_case		)




				@slow
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    self.get_pretrained_model_and_inputs()

									_lowerCAmelCase						=    model_a(**_snake_case		)
									_lowerCAmelCase						=    outputs[0].numpy()

									with tempfile.TemporaryDirectory() as tmp_dirname:
														model_a.save_pretrained(_snake_case		)
														_lowerCAmelCase						=    TFVisionTextDualEncoderModel.from_pretrained(_snake_case		)

														_lowerCAmelCase						=    model_a(**_snake_case		)
														_lowerCAmelCase						=    after_outputs[0].numpy()
														_lowerCAmelCase						=    np.amax(np.abs(out_a - out_a		)		)
														self.assertLessEqual(_snake_case						,				1e-5		)





@require_tf
class 					__lowerCAmelCase (     lowerCamelCase__       ,						unittest.TestCase       ):




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFVisionTextDualEncoderModel.from_vision_text_pretrained(
									    """hf-internal-testing/tiny-random-vit"""						,				"""hf-internal-testing/tiny-random-bert"""		)
									_lowerCAmelCase						=    13
									_lowerCAmelCase						=    floats_tensor(
									    [
									        batch_size,
									        model.vision_model.config.num_channels,
									        model.vision_model.config.image_size,
									        model.vision_model.config.image_size,
									    ]		)
									_lowerCAmelCase						=    ids_tensor([batch_size, 4]						,				model.text_model.config.vocab_size		)
									_lowerCAmelCase						=    random_attention_mask([batch_size, 4]		)
									_lowerCAmelCase						=    {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

									return model, inputs




				def 							snake_case   (							self						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFViTModel(_snake_case						,				name="""vision_model"""		)
									_lowerCAmelCase						=    TFBertModel(_snake_case						,				name="""text_model"""		)
									return vision_model, text_model




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFViTModelTester(self		)
									_lowerCAmelCase						=    TFBertModelTester(self		)
									_lowerCAmelCase						=    vit_model_tester.prepare_config_and_inputs()
									_lowerCAmelCase						=    bert_model_tester.prepare_config_and_inputs()

									_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase						=    vision_config_and_inputs

									(
									    (
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	
									)						=    text_config_and_inputs

									return {
									    "text_config": text_config,
									    "vision_config": vision_config,
									    "pixel_values": pixel_values,
									    "attention_mask": input_mask,
									    "input_ids": input_ids,
									    "text_token_type_ids": token_type_ids,
									    "text_sequence_labels": sequence_labels,
									    "text_token_labels": token_labels,
									    "text_choice_labels": choice_labels,
									}





@require_tf
class 					__lowerCAmelCase (     lowerCamelCase__       ,						unittest.TestCase       ):




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFVisionTextDualEncoderModel.from_vision_text_pretrained(
									    """Rocketknight1/tiny-random-deit-tf"""						,				"""hf-internal-testing/tiny-random-roberta"""		)
									_lowerCAmelCase						=    13
									_lowerCAmelCase						=    floats_tensor(
									    [
									        batch_size,
									        model.vision_model.config.num_channels,
									        model.vision_model.config.image_size,
									        model.vision_model.config.image_size,
									    ]		)
									_lowerCAmelCase						=    ids_tensor([batch_size, 4]						,				model.text_model.config.vocab_size		)
									_lowerCAmelCase						=    random_attention_mask([batch_size, 4]		)
									_lowerCAmelCase						=    {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

									return model, inputs




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case=None						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    self.get_vision_text_model(_snake_case						,				_snake_case		)
									_lowerCAmelCase						=    TFVisionTextDualEncoderModel(vision_model=_snake_case						,				text_model=_snake_case		)

									_lowerCAmelCase						=    model(
									    input_ids=_snake_case						,				pixel_values=_snake_case						,				attention_mask=_snake_case						,				output_attentions=_snake_case		)

									_lowerCAmelCase						=    output.vision_model_output.attentions
									self.assertEqual(len(_snake_case		)						,				vision_config.num_hidden_layers		)

									# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
									_lowerCAmelCase						=    to_atuple(vision_model.config.image_size		)
									_lowerCAmelCase						=    to_atuple(vision_model.config.patch_size		)
									_lowerCAmelCase						=    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									_lowerCAmelCase						=    num_patches + 2
									self.assertEqual(vision_attentions[0].shape[-3:]						,				(vision_config.num_attention_heads, seq_len, seq_len)		)

									_lowerCAmelCase						=    output.text_model_output.attentions
									self.assertEqual(len(_snake_case		)						,				text_config.num_hidden_layers		)

									self.assertEqual(
									    text_attentions[0].shape[-3:]						,				(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])						,				)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFDeiTModel(_snake_case						,				name="""vision_model"""		)
									_lowerCAmelCase						=    TFRobertaModel(_snake_case						,				name="""text_model"""		)
									return vision_model, text_model




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFDeiTModelTester(self		)
									_lowerCAmelCase						=    TFRobertaModelTester(self		)
									_lowerCAmelCase						=    vit_model_tester.prepare_config_and_inputs()
									_lowerCAmelCase						=    bert_model_tester.prepare_config_and_inputs()

									_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase						=    vision_config_and_inputs

									(
									    (
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	
									)						=    text_config_and_inputs

									return {
									    "text_config": text_config,
									    "vision_config": vision_config,
									    "pixel_values": pixel_values,
									    "attention_mask": input_mask,
									    "input_ids": input_ids,
									    "text_token_type_ids": token_type_ids,
									    "text_sequence_labels": sequence_labels,
									    "text_token_labels": token_labels,
									    "text_choice_labels": choice_labels,
									}





@require_tf
class 					__lowerCAmelCase (     lowerCamelCase__       ,						unittest.TestCase       ):




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFVisionTextDualEncoderModel.from_vision_text_pretrained(
									    """Rocketknight1/tiny-random-clip-tf"""						,				"""hf-internal-testing/tiny-random-bert"""		)
									_lowerCAmelCase						=    13
									_lowerCAmelCase						=    floats_tensor(
									    [
									        batch_size,
									        model.vision_model.config.num_channels,
									        model.vision_model.config.image_size,
									        model.vision_model.config.image_size,
									    ]		)
									_lowerCAmelCase						=    ids_tensor([batch_size, 4]						,				model.text_model.config.vocab_size		)
									_lowerCAmelCase						=    random_attention_mask([batch_size, 4]		)
									_lowerCAmelCase						=    {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

									return model, inputs




				def 							snake_case   (							self						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFCLIPVisionModel(_snake_case						,				name="""vision_model"""		)
									_lowerCAmelCase						=    TFBertModel(_snake_case						,				name="""text_model"""		)
									return vision_model, text_model




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFCLIPVisionModelTester(self		)
									_lowerCAmelCase						=    TFBertModelTester(self		)
									_lowerCAmelCase						=    clip_model_tester.prepare_config_and_inputs()
									_lowerCAmelCase						=    bert_model_tester.prepare_config_and_inputs()

									_lowerCAmelCase	,	_lowerCAmelCase						=    vision_config_and_inputs

									(
									    (
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	
									)						=    text_config_and_inputs

									return {
									    "text_config": text_config,
									    "vision_config": vision_config,
									    "pixel_values": pixel_values,
									    "attention_mask": input_mask,
									    "input_ids": input_ids,
									    "text_token_type_ids": token_type_ids,
									    "text_sequence_labels": sequence_labels,
									    "text_token_labels": token_labels,
									    "text_choice_labels": choice_labels,
									}





@require_vision
@require_tf
class 					__lowerCAmelCase (     unittest.TestCase       ):




				@slow
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFVisionTextDualEncoderModel.from_pretrained(
									    """clip-italian/clip-italian"""						,				logit_scale_init_value=1.0						,				from_pt=_snake_case		)
									_lowerCAmelCase						=    VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""		)

									_lowerCAmelCase						=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
									_lowerCAmelCase						=    processor(
									    text=["""una foto di un gatto""", """una foto di un cane"""]						,				images=_snake_case						,				padding=_snake_case						,				return_tensors="""np"""		)

									_lowerCAmelCase						=    model(**_snake_case		)

									# verify the logits
									self.assertEqual(outputs.logits_per_image.shape						,				(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])		)
									self.assertEqual(
									    outputs.logits_per_text.shape						,				(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])						,				)

									_lowerCAmelCase						=    np.array([[1.228_4727, 0.310_4122]]		)

									self.assertTrue(np.allclose(outputs.logits_per_image.numpy()						,				_snake_case						,				atol=1e-3		)		)




'''simple docstring'''

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 				_lowercase     (				UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					KandinskyVaaControlnetImgaImgPipeline
 _SCREAMING_SNAKE_CASE     :       Dict      	=					["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
 _SCREAMING_SNAKE_CASE     :       Dict      	=					[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					False
 @property
 def a					(      self   :	int							)  ->    Optional[Any]:
  return 32
 @property
 def a					(      self   :	Union[str, Any]							)  ->    Dict:
  return 32
 @property
 def a					(      self   :	str							)  ->    Union[str, Any]:
  return self.time_input_dim
 @property
 def a					(      self   :	Tuple							)  ->    Optional[Any]:
  return self.time_input_dim * 4
 @property
 def a					(      self   :	Union[str, Any]							)  ->    List[Any]:
  return 1_00
 @property
 def a					(      self   :	Optional[int]							)  ->    Any:
  torch.manual_seed(0							)

  __lowerCAmelCase											=						{
      """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											=						UNetaDConditionModel(**SCREAMING_SNAKE_CASE__							)
  return model
 @property
 def a					(      self   :	Tuple							)  ->    Optional[Any]:
  return {
      "block_out_channels": [32, 32, 64, 64],
      "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": 12,
      "out_channels": 3,
      "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
      "vq_embed_dim": 4,
  }
 @property
 def a					(      self   :	Optional[Any]							)  ->    Union[str, Any]:
  torch.manual_seed(0							)
  __lowerCAmelCase											=						VQModel(**self.dummy_movq_kwargs							)
  return model
 def a					(      self   :	Any							)  ->    Dict:
  __lowerCAmelCase											=						self.dummy_unet
  __lowerCAmelCase											=						self.dummy_movq

  __lowerCAmelCase											=						{
      """num_train_timesteps""": 10_00,
      """beta_schedule""": """linear""",
      """beta_start""": 0.0_0_0_8_5,
      """beta_end""": 0.0_1_2,
      """clip_sample""": False,
      """set_alpha_to_one""": False,
      """steps_offset""": 0,
      """prediction_type""": """epsilon""",
      """thresholding""": False,
  }

  __lowerCAmelCase											=						DDIMScheduler(**SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						{
      """unet""": unet,
      """scheduler""": scheduler,
      """movq""": movq,
  }

  return components
 def a					(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	List[str]=0							)  ->    Dict:
  __lowerCAmelCase											=						floats_tensor((1, self.text_embedder_hidden_size)      ,       rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						floats_tensor((1, self.text_embedder_hidden_size)      ,       rng=random.Random(seed + 1							)							).to(
      SCREAMING_SNAKE_CASE__							)
  # create init_image
  __lowerCAmelCase											=						floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						image.cpu().permute(0      ,       2      ,       3      ,       1							)[0]
  __lowerCAmelCase											=						Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__							)							).convert("""RGB"""							).resize((2_56, 2_56)							)
  # create hint
  __lowerCAmelCase											=						floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)

  if str(SCREAMING_SNAKE_CASE__							).startswith("""mps"""							):
   __lowerCAmelCase											=						torch.manual_seed(SCREAMING_SNAKE_CASE__							)
  else:
   __lowerCAmelCase											=						torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						{
      """image""": init_image,
      """image_embeds""": image_embeds,
      """negative_image_embeds""": negative_image_embeds,
      """hint""": hint,
      """generator""": generator,
      """height""": 64,
      """width""": 64,
      """num_inference_steps""": 10,
      """guidance_scale""": 7.0,
      """strength""": 0.2,
      """output_type""": """np""",
  }
  return inputs





 def a					(      self   :	List[Any]							)  ->    int:
  __lowerCAmelCase											=						"""cpu"""

  __lowerCAmelCase											=						self.get_dummy_components()

  __lowerCAmelCase											=						self.pipeline_class(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						pipe.to(SCREAMING_SNAKE_CASE__							)

  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)							)
  __lowerCAmelCase											=						output.images

  __lowerCAmelCase											=						pipe(
      **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)      ,       return_dict=SCREAMING_SNAKE_CASE__      ,       )[0]

  __lowerCAmelCase											=						image[0, -3:, -3:, -1]
  __lowerCAmelCase											=						image_from_tuple[0, -3:, -3:, -1]

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

  __lowerCAmelCase											=						np.array(
      [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]							)
  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 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def a					(      self   :	Any							)  ->    Any:
  # clean up the VRAM after each test
  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()





 def a					(      self   :	int							)  ->    Optional[Any]:
  __lowerCAmelCase											=						load_numpy(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
      """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""							)

  __lowerCAmelCase											=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
  __lowerCAmelCase											=						init_image.resize((5_12, 5_12)							)

  __lowerCAmelCase											=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
      """/kandinskyv22/hint_image_cat.png"""							)
  __lowerCAmelCase											=						torch.from_numpy(np.array(SCREAMING_SNAKE_CASE__							)							).float() / 2_5_5.0
  __lowerCAmelCase											=						hint.permute(2      ,       0      ,       1							).unsqueeze(0							)

  __lowerCAmelCase											=						"""A robot, 4k photo"""

  __lowerCAmelCase											=						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
      """kandinsky-community/kandinsky-2-2-prior"""      ,       torch_dtype=torch.floataa							)
  pipe_prior.to(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
      """kandinsky-community/kandinsky-2-2-controlnet-depth"""      ,       torch_dtype=torch.floataa							)
  __lowerCAmelCase											=						pipeline.to(SCREAMING_SNAKE_CASE__							)

  pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.Generator(device="""cpu"""							).manual_seed(0							)

  __lowerCAmelCase							,       __lowerCAmelCase											=						pipe_prior(
      SCREAMING_SNAKE_CASE__      ,       image=SCREAMING_SNAKE_CASE__      ,       strength=0.8_5      ,       generator=SCREAMING_SNAKE_CASE__      ,       negative_prompt=""""""      ,       ).to_tuple()

  __lowerCAmelCase											=						pipeline(
      image=SCREAMING_SNAKE_CASE__      ,       image_embeds=SCREAMING_SNAKE_CASE__      ,       negative_image_embeds=SCREAMING_SNAKE_CASE__      ,       hint=SCREAMING_SNAKE_CASE__      ,       generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=1_00      ,       height=5_12      ,       width=5_12      ,       strength=0.5      ,       output_type="""np"""      ,       )

  __lowerCAmelCase											=						output.images[0]

  assert image.shape == (5_12, 5_12, 3)

  assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)






