

"""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 lowerCAmelCase__			(  lowercase,			unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__   =    KandinskyVaaControlnetImgaImgPipeline
			lowerCamelCase__   =    ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
			lowerCamelCase__   =    ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
			lowerCamelCase__   =    [
			    """generator""",
			    """height""",
			    """width""",
			    """strength""",
			    """guidance_scale""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			lowerCamelCase__   =    False
			@property
			def       A_					(						self						):
							return 32
			@property
			def       A_					(						self						):
							return 32
			@property
			def       A_					(						self						):
							return self.time_input_dim
			@property
			def       A_					(						self						):
							return self.time_input_dim * 4
			@property
			def       A_					(						self						):
							return 100
			@property
			def       A_					(						self						):
							torch.manual_seed(0						)

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

							_lowerCamelCase  :    Optional[int]					=					UNetaDConditionModel(**lowercase						)
							return model
			@property
			def       A_					(						self						):
							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						):
							torch.manual_seed(0						)
							_lowerCamelCase  :    Union[str, Any]					=					VQModel(**self.dummy_movq_kwargs						)
							return model
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					self.dummy_unet
							_lowerCamelCase  :    int					=					self.dummy_movq

							_lowerCamelCase  :    Any					=					{
							    '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  :    List[str]					=					DDIMScheduler(**lowercase						)

							_lowerCamelCase  :    Dict					=					{
							    'unet': unet,
							    'scheduler': scheduler,
							    'movq': movq,
							}

							return components
			def       A_					(						self						,   lowercase						,   lowercase=0						):
							_lowerCamelCase  :    Any					=					floats_tensor((1, self.text_embedder_hidden_size)						,   rng=random.Random(lowercase						)						).to(lowercase						)
							_lowerCamelCase  :    List[Any]					=					floats_tensor((1, self.text_embedder_hidden_size)						,   rng=random.Random(seed + 1						)						).to(
							    lowercase						)
							# create init_image
							_lowerCamelCase  :    Dict					=					floats_tensor((1, 3, 64, 64)						,   rng=random.Random(lowercase						)						).to(lowercase						)
							_lowerCamelCase  :    List[str]					=					image.cpu().permute(0						,   2						,   3						,   1						)[0]
							_lowerCamelCase  :    int					=					Image.fromarray(np.uinta(lowercase						)						).convert('RGB'						).resize((256, 256)						)
							# create hint
							_lowerCamelCase  :    Union[str, Any]					=					floats_tensor((1, 3, 64, 64)						,   rng=random.Random(lowercase						)						).to(lowercase						)

							if str(lowercase						).startswith('mps'						):
											_lowerCamelCase  :    Optional[Any]					=					torch.manual_seed(lowercase						)
							else:
											_lowerCamelCase  :    Dict					=					torch.Generator(device=lowercase						).manual_seed(lowercase						)
							_lowerCamelCase  :    str					=					{
							    '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						):
							_lowerCamelCase  :    Optional[Any]					=					'cpu'

							_lowerCamelCase  :    Optional[int]					=					self.get_dummy_components()

							_lowerCamelCase  :    List[Any]					=					self.pipeline_class(**lowercase						)
							_lowerCamelCase  :    int					=					pipe.to(lowercase						)

							pipe.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    Dict					=					pipe(**self.get_dummy_inputs(lowercase						)						)
							_lowerCamelCase  :    Optional[Any]					=					output.images

							_lowerCamelCase  :    Any					=					pipe(
							    **self.get_dummy_inputs(lowercase						)						,   return_dict=lowercase						,   )[0]

							_lowerCamelCase  :    Optional[Any]					=					image[0, -3:, -3:, -1]
							_lowerCamelCase  :    str					=					image_from_tuple[0, -3:, -3:, -1]

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

							_lowerCamelCase  :    List[Any]					=					np.array(
							    [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]						)
							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 lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



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






			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'						)

							_lowerCamelCase  :    Union[str, Any]					=					load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'						)
							_lowerCamelCase  :    List[str]					=					init_image.resize((512, 512)						)

							_lowerCamelCase  :    Union[str, Any]					=					load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinskyv22/hint_image_cat.png'						)
							_lowerCamelCase  :    List[Any]					=					torch.from_numpy(np.array(lowercase						)						).float() / 255.0
							_lowerCamelCase  :    Tuple					=					hint.permute(2						,   0						,   1						).unsqueeze(0						)

							_lowerCamelCase  :    str					=					'A robot, 4k photo'

							_lowerCamelCase  :    str					=					KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-prior'						,   torch_dtype=torch.floataa						)
							pipe_prior.to(lowercase						)

							_lowerCamelCase  :    Optional[int]					=					KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-controlnet-depth'						,   torch_dtype=torch.floataa						)
							_lowerCamelCase  :    List[str]					=					pipeline.to(lowercase						)

							pipeline.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    Optional[Any]					=					torch.Generator(device='cpu'						).manual_seed(0						)

							_lowerCamelCase  :    Optional[int]					=					pipe_prior(
							    lowercase						,   image=lowercase						,   strength=0.85						,   generator=lowercase						,   negative_prompt=''						,   ).to_tuple()

							_lowerCamelCase  :    Union[str, Any]					=					pipeline(
							    image=lowercase						,   image_embeds=lowercase						,   negative_image_embeds=lowercase						,   hint=lowercase						,   generator=lowercase						,   num_inference_steps=100						,   height=512						,   width=512						,   strength=0.5						,   output_type='np'						,   )

							_lowerCamelCase  :    Any					=					output.images[0]

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

							assert_mean_pixel_difference(lowercase						,   lowercase						)





"""simple docstring"""






# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{}
lowercase__  		=		{}
lowercase__  		=		{}







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__ = None							,					):
				_lowerCamelCase  :    str					=					aliases if aliases is not None else []
				if format_type in _FORMAT_TYPES:
								logger.warning(
								    f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''	)
				_lowerCamelCase  :    str					=					formatter_cls
				for alias in set(aliases + [format_type]	):
								if alias in _FORMAT_TYPES_ALIASES:
												logger.warning(
												    f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''	)
								_lowerCamelCase  :    Dict					=					format_type







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__ = None	):
				_lowerCamelCase  :    Dict					=					aliases if aliases is not None else []
				for alias in set(aliases + [format_type]	):
								_lowerCamelCase  :    Optional[Any]					=					unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
			from .torch_formatter import TorchFormatter

			_register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
			lowercase__  		=		ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
			_register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
			from .tf_formatter import TFFormatter

			_register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
			lowercase__  		=		ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
			_register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
			from .jax_formatter import JaxFormatter

			_register_formatter(JaxFormatter, """jax""", aliases=[])
else:
			lowercase__  		=		ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
			_register_unavailable_formatter(_jax_error, """jax""", aliases=[])







def   _snake_case    (       lowercase__	):
				if format_type in _FORMAT_TYPES_ALIASES:
								return _FORMAT_TYPES_ALIASES[format_type]
				else:
								return format_type







def   _snake_case    (       lowercase__							,					**lowercase__	):
				_lowerCamelCase  :    List[str]					=					get_format_type_from_alias(lowercase__	)
				if format_type in _FORMAT_TYPES:
								return _FORMAT_TYPES[format_type](**lowercase__	)
				if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
								raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
				else:
								raise ValueError(
								    f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None	)}, but got \'{format_type}\''''	)