'''simple docstring'''





import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


SCREAMING_SNAKE_CASE_:	Union[str, Any]    ='__DUMMY_TRANSFORMERS_USER__'
SCREAMING_SNAKE_CASE_:	Optional[Any]    ='Dummy User'
SCREAMING_SNAKE_CASE_:	Tuple    ='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'

SCREAMING_SNAKE_CASE_:	str    ='https://hub-ci.huggingface.co'
SCREAMING_SNAKE_CASE_:	Dict    =CI_HUB_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
SCREAMING_SNAKE_CASE_:	List[Any]    =CI_HUB_ENDPOINT + '/{repo_id}/resolve/{revision}/{filename}'
SCREAMING_SNAKE_CASE_:	List[str]    =Path('~/.huggingface/hub_ci_token').expanduser()





@pytest.fixture
def       lowerCAmelCase_							(   snake_case_			:					int     )       -> int:


     '''simple docstring'''




     monkeypatch.setattr(
         "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"   ,       snake_case_     )





@pytest.fixture
def       lowerCAmelCase_							(   snake_case_			:					Optional[int]     )       -> List[str]:


     '''simple docstring'''




     monkeypatch.setattr("datasets.config.HF_ENDPOINT"   ,       snake_case_     )
     monkeypatch.setattr("datasets.config.HUB_DATASETS_URL"   ,       snake_case_     )





@pytest.fixture
def       lowerCAmelCase_							(   snake_case_			:					Dict     )       -> Optional[Any]:


     '''simple docstring'''




     monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token"   ,       snake_case_     )





@pytest.fixture
def       lowerCAmelCase_							(   snake_case_			:					Tuple   ,       snake_case_			:					Optional[int]     )       -> int:


     '''simple docstring'''




     HfFolder.save_token(snake_case_     )
     yield
     HfFolder.delete_token()





@pytest.fixture(scope="session"     )
def       lowerCAmelCase_							(   )       -> Any:


     '''simple docstring'''




     return HfApi(endpoint=snake_case_     )





@pytest.fixture(scope="session"     )
def       lowerCAmelCase_							(   snake_case_			:					HfApi     )       -> List[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  HfFolder.get_token()
     HfFolder.save_token(snake_case_     )
     yield CI_HUB_USER_TOKEN
     if previous_token is not None:
          HfFolder.save_token(snake_case_     )





@pytest.fixture
def       lowerCAmelCase_							(   snake_case_			:					int     )       -> Dict:


     '''simple docstring'''




     def _cleanup_repo(snake_case_			:					str     ):
          hf_api.delete_repo(snake_case_   ,       token=snake_case_   ,       repo_type="dataset"     )

     return _cleanup_repo





@pytest.fixture
def       lowerCAmelCase_							(   snake_case_			:					Dict     )       -> Optional[Any]:


     '''simple docstring'''




     @contextmanager
     def _temporary_repo(snake_case_			:					Optional[Any]     ):
          try:
               yield repo_id
          finally:
               cleanup_repo(snake_case_     )

     return _temporary_repo





@pytest.fixture(scope="session"     )
def       lowerCAmelCase_							(   snake_case_			:					HfApi   ,       snake_case_			:					Optional[Any]   ,       snake_case_			:					Union[str, Any]     )       -> Tuple:


     '''simple docstring'''




     UpperCAmelCase_			       =  f"""repo_txt_data-{int(time.time() * 1_0E3     )}"""
     UpperCAmelCase_			       =  f"""{CI_HUB_USER}/{repo_name}"""
     hf_api.create_repo(snake_case_   ,       token=snake_case_   ,       repo_type="dataset"   ,       private=snake_case_     )
     hf_api.upload_file(
         token=snake_case_   ,       path_or_fileobj=str(snake_case_     )   ,       path_in_repo="data/text_data.txt"   ,       repo_id=snake_case_   ,       repo_type="dataset"   ,       )
     yield repo_id
     try:
          hf_api.delete_repo(snake_case_   ,       token=snake_case_   ,       repo_type="dataset"     )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass





@pytest.fixture()
def       lowerCAmelCase_							(   snake_case_			:					List[Any]   ,       snake_case_			:					Union[str, Any]   ,       snake_case_			:					Union[str, Any]     )       -> Any:


     '''simple docstring'''




     return hf_private_dataset_repo_txt_data_





@pytest.fixture(scope="session"     )
def       lowerCAmelCase_							(   snake_case_			:					HfApi   ,       snake_case_			:					Tuple   ,       snake_case_			:					List[Any]     )       -> Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_			       =  f"""repo_zipped_txt_data-{int(time.time() * 1_0E3     )}"""
     UpperCAmelCase_			       =  f"""{CI_HUB_USER}/{repo_name}"""
     hf_api.create_repo(snake_case_   ,       token=snake_case_   ,       repo_type="dataset"   ,       private=snake_case_     )
     hf_api.upload_file(
         token=snake_case_   ,       path_or_fileobj=str(snake_case_     )   ,       path_in_repo="data.zip"   ,       repo_id=snake_case_   ,       repo_type="dataset"   ,       )
     yield repo_id
     try:
          hf_api.delete_repo(snake_case_   ,       token=snake_case_   ,       repo_type="dataset"     )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass





@pytest.fixture()
def       lowerCAmelCase_							(   snake_case_			:					Any   ,       snake_case_			:					Union[str, Any]   ,       snake_case_			:					List[str]     )       -> int:


     '''simple docstring'''




     return hf_private_dataset_repo_zipped_txt_data_





@pytest.fixture(scope="session"     )
def       lowerCAmelCase_							(   snake_case_			:					HfApi   ,       snake_case_			:					str   ,       snake_case_			:					Dict     )       -> Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  f"""repo_zipped_img_data-{int(time.time() * 1_0E3     )}"""
     UpperCAmelCase_			       =  f"""{CI_HUB_USER}/{repo_name}"""
     hf_api.create_repo(snake_case_   ,       token=snake_case_   ,       repo_type="dataset"   ,       private=snake_case_     )
     hf_api.upload_file(
         token=snake_case_   ,       path_or_fileobj=str(snake_case_     )   ,       path_in_repo="data.zip"   ,       repo_id=snake_case_   ,       repo_type="dataset"   ,       )
     yield repo_id
     try:
          hf_api.delete_repo(snake_case_   ,       token=snake_case_   ,       repo_type="dataset"     )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass






@pytest.fixture()
def       lowerCAmelCase_							(   snake_case_			:					Union[str, Any]   ,       snake_case_			:					Optional[int]   ,       snake_case_			:					Union[str, Any]     )       -> List[str]:


     '''simple docstring'''




     return hf_private_dataset_repo_zipped_img_data_







import gc
import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Union[str, Any]             =       KandinskyInpaintPipeline
							UpperCAmelCase__			:		Optional[int]             =       ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
							UpperCAmelCase__			:		Optional[Any]             =       [
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							    "mask_image",
							]
							UpperCAmelCase__			:		Optional[int]             =       [
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase__			:		Any             =       False


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       int:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												return self.time_input_dim


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												return self.time_input_dim * 4


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												return 1_0_0


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Any   =						XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''			)
												return tokenizer


							@property
							def 							__lowercase							(     self			)       ->       str:
												torch.manual_seed(0			)
												_a      :       List[Any]   =						MCLIPConfig(
												    numDims=self.cross_attention_dim			,				transformerDimensions=self.text_embedder_hidden_size			,				hidden_size=self.text_embedder_hidden_size			,				intermediate_size=3_7			,				num_attention_heads=4			,				num_hidden_layers=5			,				vocab_size=1_0_0_5			,				)

												_a      :       Optional[int]   =						MultilingualCLIP(_a			)
												_a      :       Tuple   =						text_encoder.eval()

												return text_encoder


							@property
							def 							__lowercase							(     self			)       ->       str:
												torch.manual_seed(0			)

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

												_a      :       Dict   =						UNetaDConditionModel(**_a			)
												return model


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return {
												    "block_out_channels": [3_2, 6_4],
												    "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": 1_2,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}


							@property
							def 							__lowercase							(     self			)       ->       Tuple:
												torch.manual_seed(0			)
												_a      :       List[Any]   =						VQModel(**self.dummy_movq_kwargs			)
												return model


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       List[Any]   =						self.dummy_text_encoder
												_a      :       Optional[Any]   =						self.dummy_tokenizer
												_a      :       Optional[Any]   =						self.dummy_unet
												_a      :       Union[str, Any]   =						self.dummy_movq

												_a      :       Tuple   =						DDIMScheduler(
												    num_train_timesteps=1_0_0_0			,				beta_schedule='''linear'''			,				beta_start=0.0_0085			,				beta_end=0.012			,				clip_sample=_a			,				set_alpha_to_one=_a			,				steps_offset=1			,				prediction_type='''epsilon'''			,				thresholding=_a			,				)

												_a      :       str   =						{
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}

												return components


							def 							__lowercase							(     self			,				_a			,				_a=0			)       ->       int:
												_a      :       Union[str, Any]   =						floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       List[str]   =						floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1			)			).to(_a			)
												# create init_image
												_a      :       Tuple   =						floats_tensor((1, 3, 6_4, 6_4)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       Dict   =						image.cpu().permute(0			,				2			,				3			,				1			)[0]
												_a      :       Optional[int]   =						Image.fromarray(np.uinta(_a			)			).convert('''RGB'''			).resize((2_5_6, 2_5_6)			)
												# create mask
												_a      :       Union[str, Any]   =						np.ones((6_4, 6_4)			,				dtype=np.floataa			)
												_a      :       List[str]   =						0

												if str(_a			).startswith('''mps'''			):
																	_a      :       Tuple   =						torch.manual_seed(_a			)
												else:
																	_a      :       Any   =						torch.Generator(device=_a			).manual_seed(_a			)
												_a      :       Any   =						{
												    '''prompt''': '''horse''',
												    '''image''': init_image,
												    '''mask_image''': mask,
												    '''image_embeds''': image_embeds,
												    '''negative_image_embeds''': negative_image_embeds,
												    '''generator''': generator,
												    '''height''': 6_4,
												    '''width''': 6_4,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 4.0,
												    '''output_type''': '''np''',
												}
												return inputs


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Optional[Any]   =						'''cpu'''

												_a      :       List[Any]   =						self.get_dummy_components()

												_a      :       Tuple   =						self.pipeline_class(**_a			)
												_a      :       int   =						pipe.to(_a			)

												pipe.set_progress_bar_config(disable=_a			)

												_a      :       Any   =						pipe(**self.get_dummy_inputs(_a			)			)
												_a      :       str   =						output.images

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

												_a      :       Union[str, Any]   =						image[0, -3:, -3:, -1]
												_a      :       Tuple   =						image_from_tuple[0, -3:, -3:, -1]

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

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

												_a      :       str   =						np.array(
												    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]			)

												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 							__lowercase							(     self			)       ->       Dict:
												super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



@slow
@require_torch_gpu
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







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




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       Tuple   =						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''			)

												_a      :       str   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''			)
												_a      :       Tuple   =						np.ones((7_6_8, 7_6_8)			,				dtype=np.floataa			)
												_a      :       Any   =						0

												_a      :       Optional[Any]   =						'''a hat'''

												_a      :       Optional[Any]   =						KandinskyPriorPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-prior'''			,				torch_dtype=torch.floataa			)
												pipe_prior.to(_a			)

												_a      :       Tuple   =						KandinskyInpaintPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-inpaint'''			,				torch_dtype=torch.floataa			)
												_a      :       Union[str, Any]   =						pipeline.to(_a			)
												pipeline.set_progress_bar_config(disable=_a			)

												_a      :       Union[str, Any]   =						torch.Generator(device='''cpu'''			).manual_seed(0			)
												_a ,   _a      :       Dict   =						pipe_prior(
												    _a			,				generator=_a			,				num_inference_steps=5			,				negative_prompt=''''''			,				).to_tuple()

												_a      :       Optional[int]   =						pipeline(
												    _a			,				image=_a			,				mask_image=_a			,				image_embeds=_a			,				negative_image_embeds=_a			,				generator=_a			,				num_inference_steps=1_0_0			,				height=7_6_8			,				width=7_6_8			,				output_type='''np'''			,				)

												_a      :       Optional[int]   =						output.images[0]

												assert image.shape == (7_6_8, 7_6_8, 3)

												assert_mean_pixel_difference(_a			,				_a			)



