



'''simple docstring'''



import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


lowerCAmelCase	:		List[str]				=   get_logger(__name__)


lowerCAmelCase	:		Dict				=   Path(__file__).parent / '''model_card_template.md'''
lowerCAmelCase	:		int				=   uuida().hex
lowerCAmelCase	:		List[str]				=   os.getenv('''HF_HUB_OFFLINE''', '''''').upper() in ENV_VARS_TRUE_VALUES
lowerCAmelCase	:		str				=   os.getenv('''DISABLE_TELEMETRY''', '''''').upper() in ENV_VARS_TRUE_VALUES
lowerCAmelCase	:		int				=   HUGGINGFACE_CO_RESOLVE_ENDPOINT + '''/api/telemetry/'''



def   __lowerCAmelCase					(							lowerCamelCase   :						Union[Dict, str, None] = None					):






	'''simple docstring'''




	__lowerCAmelCase												=  f'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
	if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
		return ua + "; telemetry/off"
	if is_torch_available():
		ua += f'''; torch/{_torch_version}'''
	if is_flax_available():
		ua += f'''; jax/{_jax_version}'''
		ua += f'''; flax/{_flax_version}'''
	if is_onnx_available():
		ua += f'''; onnxruntime/{_onnxruntime_version}'''
	# CI will set this value to True
	if os.environ.get("DIFFUSERS_IS_CI"				,		""					).upper() in ENV_VARS_TRUE_VALUES:
		ua += "; is_ci/true"
	if isinstance(lowerCamelCase				,		lowerCamelCase					):
		ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items()					)
	elif isinstance(lowerCamelCase				,		lowerCamelCase					):
		ua += "; " + user_agent
	return ua



def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						Optional[str] = None				,		lowerCamelCase   :						Optional[str] = None					):






	'''simple docstring'''




	if token is None:
		__lowerCAmelCase												=  HfFolder.get_token()
	if organization is None:
		__lowerCAmelCase												=  whoami(lowerCamelCase					)["name"]
		return f'''{username}/{model_id}'''
	else:
		return f'''{organization}/{model_id}'''



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						int					):






	'''simple docstring'''




	if not is_jinja_available():
		raise ValueError(
		    "Modelcard rendering is based on Jinja templates."
		    " Please make sure to have `jinja` installed before using `create_model_card`."
		    " To install it, please run `pip install Jinja2`."					)

	if hasattr(lowerCamelCase				,		"local_rank"					) and args.local_rank not in [-1, 0]:
		return

	__lowerCAmelCase												=  args.hub_token if hasattr(lowerCamelCase				,		"hub_token"					) else None
	__lowerCAmelCase												=  get_full_repo_name(lowerCamelCase				,		token=lowerCamelCase					)

	__lowerCAmelCase												=  ModelCard.from_template(
	    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
	        language="en"				,		license="apache-2.0"				,		library_name="diffusers"				,		tags=[]				,		datasets=args.dataset_name				,		metrics=[]				,		)				,		template_path=lowerCamelCase				,		model_name=lowerCamelCase				,		repo_name=lowerCamelCase				,		dataset_name=args.dataset_name if hasattr(lowerCamelCase				,		"dataset_name"					) else None				,		learning_rate=args.learning_rate				,		train_batch_size=args.train_batch_size				,		eval_batch_size=args.eval_batch_size				,		gradient_accumulation_steps=(
	        args.gradient_accumulation_steps if hasattr(lowerCamelCase				,		"gradient_accumulation_steps"					) else None
	    )				,		adam_betaa=args.adam_betaa if hasattr(lowerCamelCase				,		"adam_beta1"					) else None				,		adam_betaa=args.adam_betaa if hasattr(lowerCamelCase				,		"adam_beta2"					) else None				,		adam_weight_decay=args.adam_weight_decay if hasattr(lowerCamelCase				,		"adam_weight_decay"					) else None				,		adam_epsilon=args.adam_epsilon if hasattr(lowerCamelCase				,		"adam_epsilon"					) else None				,		lr_scheduler=args.lr_scheduler if hasattr(lowerCamelCase				,		"lr_scheduler"					) else None				,		lr_warmup_steps=args.lr_warmup_steps if hasattr(lowerCamelCase				,		"lr_warmup_steps"					) else None				,		ema_inv_gamma=args.ema_inv_gamma if hasattr(lowerCamelCase				,		"ema_inv_gamma"					) else None				,		ema_power=args.ema_power if hasattr(lowerCamelCase				,		"ema_power"					) else None				,		ema_max_decay=args.ema_max_decay if hasattr(lowerCamelCase				,		"ema_max_decay"					) else None				,		mixed_precision=args.mixed_precision				,		)

	__lowerCAmelCase												=  os.path.join(args.output_dir				,		"README.md"					)
	model_card.save(lowerCamelCase					)



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[str] = None					):






	'''simple docstring'''




	if resolved_file is None or commit_hash is not None:
		return commit_hash
	__lowerCAmelCase												=  str(Path(lowerCamelCase					).as_posix()					)
	__lowerCAmelCase												=  re.search(r"snapshots/([^/]+)/"				,		lowerCamelCase					)
	if search is None:
		return None
	__lowerCAmelCase												=  search.groups()[0]
	return commit_hash if REGEX_COMMIT_HASH.match(lowerCamelCase					) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
lowerCAmelCase	:		Union[str, Any]				=   os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
lowerCAmelCase	:		List[str]				=   os.path.join(hf_cache_home, '''diffusers''')



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str] = None				,		lowerCamelCase   :						Optional[str] = None					):






	'''simple docstring'''




	if new_cache_dir is None:
		__lowerCAmelCase												=  DIFFUSERS_CACHE
	if old_cache_dir is None:
		__lowerCAmelCase												=  old_diffusers_cache

	__lowerCAmelCase												=  Path(lowerCamelCase					).expanduser()
	__lowerCAmelCase												=  Path(lowerCamelCase					).expanduser()
	for old_blob_path in old_cache_dir.glob("**/blobs/*"					):
		if old_blob_path.is_file() and not old_blob_path.is_symlink():
			__lowerCAmelCase												=  new_cache_dir / old_blob_path.relative_to(lowerCamelCase					)
			new_blob_path.parent.mkdir(parents=lowerCamelCase				,		exist_ok=lowerCamelCase					)
			os.replace(lowerCamelCase				,		lowerCamelCase					)
			try:
				os.symlink(lowerCamelCase				,		lowerCamelCase					)
			except OSError:
				logger.warning(
				    "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."					)
	# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


lowerCAmelCase	:		str				=   os.path.join(DIFFUSERS_CACHE, '''version_diffusers_cache.txt''')
if not os.path.isfile(cache_version_file):
	lowerCAmelCase	:		Any				=   0
else:
	with open(cache_version_file) as f:
		try:
			lowerCAmelCase	:		Union[str, Any]				=   int(f.read())
		except ValueError:
			lowerCAmelCase	:		Optional[Any]				=   0

if cache_version < 1:
	lowerCAmelCase	:		Tuple				=   os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
	if old_cache_is_not_empty:
		logger.warning(
		    '''The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '''
		    '''existing cached models. This is a one-time operation, you can interrupt it or run it '''
		    '''later by calling `diffusers.utils.hub_utils.move_cache()`.'''
		)
		try:
			move_cache()
		except Exception as e:
			lowerCAmelCase	:		Union[str, Any]				=   '''\n'''.join(traceback.format_tb(e.__traceback__))
			logger.error(
			    f'There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '
			    '''file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '''
			    '''message and we will do our best to help.'''
			)

if cache_version < 1:
	try:
		os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
		with open(cache_version_file, '''w''') as f:
			f.write('''1''')
	except Exception:
		logger.warning(
		    f'There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '
		    '''the directory exists and can be written to.'''
		)



def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						Optional[str] = None					):






	'''simple docstring'''




	if variant is not None:
		__lowerCAmelCase												=  weights_name.split("."					)
		__lowerCAmelCase												=  splits[:-1] + [variant] + splits[-1:]
		__lowerCAmelCase												=  ".".join(lowerCamelCase					)

	return weights_name




def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		*,
    lowerCamelCase   :						Dict				,		lowerCamelCase   :						str				,		lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						Any				,		lowerCamelCase   :						str				,		lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						str				,		lowerCamelCase   :						List[Any]				,		lowerCamelCase   :						Tuple=None				,		):






	'''simple docstring'''




	__lowerCAmelCase												=  str(lowerCamelCase					)
	if os.path.isfile(lowerCamelCase					):
		return pretrained_model_name_or_path
	elif os.path.isdir(lowerCamelCase					):
		if os.path.isfile(os.path.join(lowerCamelCase				,		lowerCamelCase					)					):
			# Load from a PyTorch checkpoint
			__lowerCAmelCase												=  os.path.join(lowerCamelCase				,		lowerCamelCase					)
			return model_file
		elif subfolder is not None and os.path.isfile(
		    os.path.join(lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase					)					):
			__lowerCAmelCase												=  os.path.join(lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase					)
			return model_file
		else:
			raise EnvironmentError(
			    f'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'''					)
	else:
		# 1. First check if deprecated way of loading from branches is used
		if (
		    revision in DEPRECATED_REVISION_ARGS
		    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
		    and version.parse(version.parse(lowerCamelCase					).base_version					) >= version.parse("0.20.0"					)
		):
			try:
				__lowerCAmelCase												=  hf_hub_download(
				    lowerCamelCase				,		filename=_add_variant(lowerCamelCase				,		lowerCamelCase					)				,		cache_dir=lowerCamelCase				,		force_download=lowerCamelCase				,		proxies=lowerCamelCase				,		resume_download=lowerCamelCase				,		local_files_only=lowerCamelCase				,		use_auth_token=lowerCamelCase				,		user_agent=lowerCamelCase				,		subfolder=lowerCamelCase				,		revision=revision or commit_hash				,		)
				warnings.warn(
				    f'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.'''				,		lowerCamelCase				,		)
				return model_file
			except:  # noqa: E722
				warnings.warn(
				    f'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(lowerCamelCase				,		lowerCamelCase					)} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(lowerCamelCase				,		lowerCamelCase					)}\' so that the correct variant file can be added.'''				,		lowerCamelCase				,		)
		try:
			# 2. Load model file as usual
			__lowerCAmelCase												=  hf_hub_download(
			    lowerCamelCase				,		filename=lowerCamelCase				,		cache_dir=lowerCamelCase				,		force_download=lowerCamelCase				,		proxies=lowerCamelCase				,		resume_download=lowerCamelCase				,		local_files_only=lowerCamelCase				,		use_auth_token=lowerCamelCase				,		user_agent=lowerCamelCase				,		subfolder=lowerCamelCase				,		revision=revision or commit_hash				,		)
			return model_file

		except RepositoryNotFoundError:
			raise EnvironmentError(
			    f'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
			    "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
			    "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
			    "login`."					)
		except RevisionNotFoundError:
			raise EnvironmentError(
			    f'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
			    "this model name. Check the model page at "
			    f'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'''					)
		except EntryNotFoundError:
			raise EnvironmentError(
			    f'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'''					)
		except HTTPError as err:
			raise EnvironmentError(
			    f'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'''					)
		except ValueError:
			raise EnvironmentError(
			    f'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
			    f''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
			    f''' directory containing a file named {weights_name} or'''
			    " \nCheckout your internet connection or see how to run the library in"
			    " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."					)
		except EnvironmentError:
			raise EnvironmentError(
			    f'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
			    "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
			    f'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
			    f'''containing a file named {weights_name}'''					)







'''simple docstring'''



import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, 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__	(		UpperCamelCase__							,       unittest.TestCase ):
					a      :     List[Any]        =							KandinskyImgaImgPipeline
					a      :     Union[str, Any]        =							["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
					a      :     List[Any]        =							[
					    """prompt""",
					    """negative_prompt""",
					    """image_embeds""",
					    """negative_image_embeds""",
					    """image""",
					]
					a      :     Any        =							[
					    """generator""",
					    """height""",
					    """width""",
					    """strength""",
					    """guidance_scale""",
					    """negative_prompt""",
					    """num_inference_steps""",
					    """return_dict""",
					    """guidance_scale""",
					    """num_images_per_prompt""",
					    """output_type""",
					    """return_dict""",
					]
					a      :     Union[str, Any]        =							False



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return 32



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						return 32



					@property
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						return self.time_input_dim



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return self.time_input_dim * 4



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return 100



					@property
					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"     )
						return tokenizer



					@property
					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						torch.manual_seed(0     )
						__lowerCAmelCase												=  MCLIPConfig(
						    numDims=self.cross_attention_dim			,				transformerDimensions=self.text_embedder_hidden_size			,				hidden_size=self.text_embedder_hidden_size			,				intermediate_size=37			,				num_attention_heads=4			,				num_hidden_layers=5			,				vocab_size=1005			,				)

						__lowerCAmelCase												=  MultilingualCLIP(UpperCamelCase     )
						__lowerCAmelCase												=  text_encoder.eval()

						return text_encoder



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						torch.manual_seed(0     )

						__lowerCAmelCase												=  {
						    "in_channels": 4,
						    # 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,
						}

						__lowerCAmelCase												=  UNetaDConditionModel(**UpperCamelCase     )
						return model



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 12,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}



					@property
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						torch.manual_seed(0     )
						__lowerCAmelCase												=  VQModel(**self.dummy_movq_kwargs     )
						return model



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.dummy_text_encoder
						__lowerCAmelCase												=  self.dummy_tokenizer
						__lowerCAmelCase												=  self.dummy_unet
						__lowerCAmelCase												=  self.dummy_movq

						__lowerCAmelCase												=  {
						    "num_train_timesteps": 1000,
						    "beta_schedule": "linear",
						    "beta_start": 0.0_00_85,
						    "beta_end": 0.0_12,
						    "clip_sample": False,
						    "set_alpha_to_one": False,
						    "steps_offset": 0,
						    "prediction_type": "epsilon",
						    "thresholding": False,
						}

						__lowerCAmelCase												=  DDIMScheduler(**UpperCamelCase     )

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

						return components



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=0     )     ->			Optional[Any]:
						__lowerCAmelCase												=  floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )
						__lowerCAmelCase												=  floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1     )     ).to(UpperCamelCase     )
						# create init_image
						__lowerCAmelCase												=  floats_tensor((1, 3, 64, 64)			,				rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )
						__lowerCAmelCase												=  image.cpu().permute(0			,				2			,				3			,				1     )[0]
						__lowerCAmelCase												=  Image.fromarray(np.uinta(UpperCamelCase     )     ).convert("RGB"     ).resize((256, 256)     )

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



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  "cpu"

						__lowerCAmelCase												=  self.get_dummy_components()

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

						pipe.set_progress_bar_config(disable=UpperCamelCase     )

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

						__lowerCAmelCase												=  pipe(
						    **self.get_dummy_inputs(UpperCamelCase     )			,				return_dict=UpperCamelCase			,				)[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(
						    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]     )
						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 ):



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



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinsky/kandinsky_img2img_frog.npy"     )

						__lowerCAmelCase												=  load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"     )
						__lowerCAmelCase												=  "A red cartoon frog, 4k"

						__lowerCAmelCase												=  KandinskyPriorPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1-prior"			,				torch_dtype=torch.floataa     )
						pipe_prior.to(UpperCamelCase     )

						__lowerCAmelCase												=  KandinskyImgaImgPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1"			,				torch_dtype=torch.floataa     )
						__lowerCAmelCase												=  pipeline.to(UpperCamelCase     )

						pipeline.set_progress_bar_config(disable=UpperCamelCase     )

						__lowerCAmelCase												=  torch.Generator(device="cpu"     ).manual_seed(0     )
						__lowerCAmelCase							,       __lowerCAmelCase												=  pipe_prior(
						    UpperCamelCase			,				generator=UpperCamelCase			,				num_inference_steps=5			,				negative_prompt=""			,				).to_tuple()

						__lowerCAmelCase												=  pipeline(
						    UpperCamelCase			,				image=UpperCamelCase			,				image_embeds=UpperCamelCase			,				negative_image_embeds=UpperCamelCase			,				generator=UpperCamelCase			,				num_inference_steps=100			,				height=768			,				width=768			,				strength=0.2			,				output_type="np"			,				)

						__lowerCAmelCase												=  output.images[0]

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

						assert_mean_pixel_difference(UpperCamelCase			,				UpperCamelCase     )