




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 				UpperCamelCase_    ( UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      KandinskyVaaControlnetImgaImgPipeline
    UpperCAmelCase__							  =      ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
    UpperCAmelCase__							  =      ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
    UpperCAmelCase__							  =      [
        '''generator''',
        '''height''',
        '''width''',
        '''strength''',
        '''guidance_scale''',
        '''num_inference_steps''',
        '''return_dict''',
        '''guidance_scale''',
        '''num_images_per_prompt''',
        '''output_type''',
        '''return_dict''',
    ]
    UpperCAmelCase__							  =      False


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Optional[int]:





           '''simple docstring'''

           return 32


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Dict:





           '''simple docstring'''

           return 32


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[Any]:





           '''simple docstring'''

           return self.time_input_dim


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->Any:





           '''simple docstring'''

           return self.time_input_dim * 4


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Any:





           '''simple docstring'''

           return 100


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->str:





           '''simple docstring'''

           torch.manual_seed(0)

           A__       =    {
               '''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,
           }

           A__       =    UNetaDConditionModel(**UpperCAmelCase__)
           return model


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->List[str]:





           '''simple docstring'''

           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        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Optional[int]:





           '''simple docstring'''

           torch.manual_seed(0)
           A__       =    VQModel(**self.dummy_movq_kwargs)
           return model


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Optional[int]:





           '''simple docstring'''

           A__       =    self.dummy_unet
           A__       =    self.dummy_movq

           A__       =    {
               '''num_train_timesteps''': 1_000,
               '''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,
           }

           A__       =    DDIMScheduler(**UpperCAmelCase__)

           A__       =    {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''movq''': movq,
           }

           return components


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Tuple=0)						->Any:





           '''simple docstring'''

           A__       =    floats_tensor((1, self.text_embedder_hidden_size)					, rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           A__       =    floats_tensor((1, self.text_embedder_hidden_size)					, rng=random.Random(seed + 1)).to(
               UpperCAmelCase__)
           # create init_image
           A__       =    floats_tensor((1, 3, 64, 64)					, rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           A__       =    image.cpu().permute(0					, 2					, 3					, 1)[0]
           A__       =    Image.fromarray(np.uinta(UpperCAmelCase__)).convert('''RGB''').resize((256, 256))
           # create hint
           A__       =    floats_tensor((1, 3, 64, 64)					, rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)

           if str(UpperCAmelCase__).startswith('''mps'''):
                  A__       =    torch.manual_seed(UpperCAmelCase__)
           else:
                  A__       =    torch.Generator(device=UpperCAmelCase__).manual_seed(UpperCAmelCase__)
           A__       =    {
               '''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        SCREAMING_SNAKE_CASE							(			self      :	str)						->Any:





           '''simple docstring'''

           A__       =    '''cpu'''

           A__       =    self.get_dummy_components()

           A__       =    self.pipeline_class(**UpperCAmelCase__)
           A__       =    pipe.to(UpperCAmelCase__)

           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__       =    pipe(**self.get_dummy_inputs(UpperCAmelCase__))
           A__       =    output.images

           A__       =    pipe(
               **self.get_dummy_inputs(UpperCAmelCase__)					, return_dict=UpperCAmelCase__					, )[0]

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

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

           A__       =    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 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Optional[Any]:





           '''simple docstring'''

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





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Optional[Any]:





           '''simple docstring'''

           A__       =    load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''')

           A__       =    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''')
           A__       =    init_image.resize((512, 512))

           A__       =    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/kandinskyv22/hint_image_cat.png''')
           A__       =    torch.from_numpy(np.array(UpperCAmelCase__)).float() / 255.0
           A__       =    hint.permute(2					, 0					, 1).unsqueeze(0)

           A__       =    '''A robot, 4k photo'''

           A__       =    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-prior'''					, torch_dtype=torch.floataa)
           pipe_prior.to(UpperCAmelCase__)

           A__       =    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-controlnet-depth'''					, torch_dtype=torch.floataa)
           A__       =    pipeline.to(UpperCAmelCase__)

           pipeline.set_progress_bar_config(disable=UpperCAmelCase__)

           A__       =    torch.Generator(device='''cpu''').manual_seed(0)

           A__ ,							A__       =    pipe_prior(
               UpperCAmelCase__					, image=UpperCAmelCase__					, strength=0.85					, generator=UpperCAmelCase__					, negative_prompt=''''''					, ).to_tuple()

           A__       =    pipeline(
               image=UpperCAmelCase__					, image_embeds=UpperCAmelCase__					, negative_image_embeds=UpperCAmelCase__					, hint=UpperCAmelCase__					, generator=UpperCAmelCase__					, num_inference_steps=100					, height=512					, width=512					, strength=0.5					, output_type='''np'''					, )

           A__       =    output.images[0]

           assert image.shape == (512, 512, 3)

           assert_mean_pixel_difference(UpperCAmelCase__					, UpperCAmelCase__)








import os
import sys
import unittest


_lowerCamelCase	:		Optional[Any]							       =   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


_lowerCamelCase	:		Any							       =   os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
_lowerCamelCase	:		str							       =   os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")


class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Tuple:





           '''simple docstring'''

           A__       =    get_test_to_tester_mapping(UpperCAmelCase__)
           A__       =    get_test_to_tester_mapping(UpperCAmelCase__)

           A__       =    {'''BertModelTest''': '''BertModelTester'''}

           A__       =    {
               '''BlipModelTest''': '''BlipModelTester''',
               '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
               '''BlipTextModelTest''': '''BlipTextModelTester''',
               '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
               '''BlipVQAModelTest''': '''BlipVQAModelTester''',
               '''BlipVisionModelTest''': '''BlipVisionModelTester''',
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->List[Any]:





           '''simple docstring'''

           A__       =    get_model_to_test_mapping(UpperCAmelCase__)
           A__       =    get_model_to_test_mapping(UpperCAmelCase__)

           A__       =    {
               '''BertForMaskedLM''': ['''BertModelTest'''],
               '''BertForMultipleChoice''': ['''BertModelTest'''],
               '''BertForNextSentencePrediction''': ['''BertModelTest'''],
               '''BertForPreTraining''': ['''BertModelTest'''],
               '''BertForQuestionAnswering''': ['''BertModelTest'''],
               '''BertForSequenceClassification''': ['''BertModelTest'''],
               '''BertForTokenClassification''': ['''BertModelTest'''],
               '''BertLMHeadModel''': ['''BertModelTest'''],
               '''BertModel''': ['''BertModelTest'''],
           }

           A__       =    {
               '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
               '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
               '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
               '''BlipModel''': ['''BlipModelTest'''],
               '''BlipTextModel''': ['''BlipTextModelTest'''],
               '''BlipVisionModel''': ['''BlipVisionModelTest'''],
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->str:





           '''simple docstring'''

           A__       =    get_model_to_tester_mapping(UpperCAmelCase__)
           A__       =    get_model_to_tester_mapping(UpperCAmelCase__)

           A__       =    {
               '''BertForMaskedLM''': ['''BertModelTester'''],
               '''BertForMultipleChoice''': ['''BertModelTester'''],
               '''BertForNextSentencePrediction''': ['''BertModelTester'''],
               '''BertForPreTraining''': ['''BertModelTester'''],
               '''BertForQuestionAnswering''': ['''BertModelTester'''],
               '''BertForSequenceClassification''': ['''BertModelTester'''],
               '''BertForTokenClassification''': ['''BertModelTester'''],
               '''BertLMHeadModel''': ['''BertModelTester'''],
               '''BertModel''': ['''BertModelTester'''],
           }

           A__       =    {
               '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
               '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
               '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
               '''BlipModel''': ['''BlipModelTester'''],
               '''BlipTextModel''': ['''BlipTextModelTester'''],
               '''BlipVisionModel''': ['''BlipVisionModelTester'''],
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)


