




import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    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 a (     __SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       Union[str, Any]       	=		KandinskyVaaControlnetPipeline
						UpperCamelCase_	:       Optional[Any]       	=		['image_embeds', 'negative_image_embeds', 'hint']
						UpperCamelCase_	:       Optional[int]       	=		['image_embeds', 'negative_image_embeds', 'hint']
						UpperCamelCase_	:       Tuple       	=		[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						UpperCamelCase_	:       Optional[Any]       	=		False






						@property
						def     UpperCAmelCase_      ( self :     Any				)    ->						List[Any]:
													"""simple docstring"""


													return 32






						@property
						def     UpperCAmelCase_      ( self :     str				)    ->						Union[str, Any]:
													"""simple docstring"""


													return 32






						@property
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						str:
													"""simple docstring"""


													return self.time_input_dim






						@property
						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						str:
													"""simple docstring"""


													return self.time_input_dim * 4






						@property
						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						List[Any]:
													"""simple docstring"""


													return 100






						@property
						def     UpperCAmelCase_      ( self :     Dict				)    ->						str:
													"""simple docstring"""


													torch.manual_seed(0				)

													__lowercase      =    {
													    '''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,
													}

													__lowercase      =    UNetaDConditionModel(**lowerCamelCase__				)
													return model






						@property
						def     UpperCAmelCase_      ( self :     Dict				)    ->						Tuple:
													"""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     UpperCAmelCase_      ( self :     Optional[int]				)    ->						Any:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.dummy_unet
													__lowercase      =    self.dummy_movq

													__lowercase      =    DDIMScheduler(
													    num_train_timesteps=1_000				,							beta_schedule='''linear'''				,							beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							clip_sample=lowerCamelCase__				,							set_alpha_to_one=lowerCamelCase__				,							steps_offset=1				,							prediction_type='''epsilon'''				,							thresholding=lowerCamelCase__				,							)

													__lowercase      =    {
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''movq''': movq,
													}
													return components






						def     UpperCAmelCase_      ( self :     Union[str, Any]				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     Optional[Any]=0				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    floats_tensor((1, self.text_embedder_hidden_size)				,							rng=random.Random(lowerCamelCase__				)				).to(lowerCamelCase__				)
													__lowercase      =    floats_tensor((1, self.text_embedder_hidden_size)				,							rng=random.Random(seed + 1				)				).to(
													    lowerCamelCase__				)

													# create hint
													__lowercase      =    floats_tensor((1, 3, 64, 64)				,							rng=random.Random(lowerCamelCase__				)				).to(lowerCamelCase__				)

													if str(lowerCamelCase__				).startswith('''mps'''				):
																				__lowercase      =    torch.manual_seed(lowerCamelCase__				)
													else:
																				__lowercase      =    torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)
													__lowercase      =    {
													    '''image_embeds''': image_embeds,
													    '''negative_image_embeds''': negative_image_embeds,
													    '''hint''': hint,
													    '''generator''': generator,
													    '''height''': 64,
													    '''width''': 64,
													    '''guidance_scale''': 4.0,
													    '''num_inference_steps''': 2,
													    '''output_type''': '''np''',
													}
													return inputs






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    '''cpu'''

													__lowercase      =    self.get_dummy_components()

													__lowercase      =    self.pipeline_class(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)

													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    pipe(**self.get_dummy_inputs(lowerCamelCase__				)				)
													__lowercase      =    output.images

													__lowercase      =    pipe(
													    **self.get_dummy_inputs(lowerCamelCase__				)				,							return_dict=lowerCamelCase__				,							)[0]

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

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

													__lowercase      =    np.array(
													    [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]				)

													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 a (     unittest.TestCase  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Optional[int]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''				)

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

													__lowercase      =    KandinskyVaaPriorPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-2-prior'''				,							torch_dtype=torch.floataa				)
													pipe_prior.to(lowerCamelCase__				)

													__lowercase      =    KandinskyVaaControlnetPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''				,							torch_dtype=torch.floataa				)
													__lowercase      =    pipeline.to(lowerCamelCase__				)
													pipeline.set_progress_bar_config(disable=lowerCamelCase__				)

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

													__lowercase      =    torch.Generator(device='''cuda'''				).manual_seed(0				)
													__lowercase     ,     __lowercase      =    pipe_prior(
													    lowerCamelCase__				,							generator=lowerCamelCase__				,							num_inference_steps=5				,							negative_prompt=''''''				,							).to_tuple()

													__lowercase      =    torch.Generator(device='''cuda'''				).manual_seed(0				)
													__lowercase      =    pipeline(
													    image_embeds=lowerCamelCase__				,							negative_image_embeds=lowerCamelCase__				,							hint=lowerCamelCase__				,							generator=lowerCamelCase__				,							num_inference_steps=100				,							output_type='''np'''				,							)

													__lowercase      =    output.images[0]

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

													assert_mean_pixel_difference(lowerCamelCase__				,							lowerCamelCase__				)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase__				     = {
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__				     = [
			    "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "M2M100ForConditionalGeneration",
			    "M2M100Model",
			    "M2M100PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
			from .tokenization_mam_aaa import MaMaaaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mam_aaa import (
						    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MaMaaaForConditionalGeneration,
						    MaMaaaModel,
						    MaMaaaPreTrainedModel,
						)


else:
			import sys

			UpperCAmelCase__				     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






