'''simple docstring'''







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 _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							int     		= KandinskyVaaControlnetPipeline
						A_			:							str     		= ['image_embeds', 'negative_image_embeds', 'hint']
						A_			:							List[str]     		= ['image_embeds', 'negative_image_embeds', 'hint']
						A_			:							Tuple     		= [
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						A_			:							Dict     		= False




						@property
						def      __lowerCamelCase       (    self :       Any      )     ->				Optional[Any]:
													return 32




						@property
						def      __lowerCamelCase       (    self :       Dict      )     ->				str:
													return 32




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




						@property
						def      __lowerCamelCase       (    self :       str      )     ->				Union[str, Any]:
													return self.time_input_dim * 4




						@property
						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				List[str]:
													return 1_00




						@property
						def      __lowerCamelCase       (    self :       int      )     ->				int:
													torch.manual_seed(0      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = {
													    '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,
													}

													SCREAMING_SNAKE_CASE__     :int						    = UNetaDConditionModel(**UpperCamelCase_      )
													return model




						@property
						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				List[str]:
													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      __lowerCamelCase       (    self :       Optional[int]      )     ->				str:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Any						    = VQModel(**self.dummy_movq_kwargs      )
													return model




						def      __lowerCamelCase       (    self :       str      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.dummy_unet
													SCREAMING_SNAKE_CASE__     :Any						    = self.dummy_movq

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = DDIMScheduler(
													    num_train_timesteps=10_00							,							beta_schedule='linear'							,							beta_start=0.0_0085							,							beta_end=0.012							,							clip_sample=UpperCamelCase_							,							set_alpha_to_one=UpperCamelCase_							,							steps_offset=1							,							prediction_type='epsilon'							,							thresholding=UpperCamelCase_							,							)

													SCREAMING_SNAKE_CASE__     :List[str]						    = {
													    'unet': unet,
													    'scheduler': scheduler,
													    'movq': movq,
													}
													return components




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       Optional[Any]=0      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :List[str]						    = floats_tensor((1, self.text_embedder_hidden_size)							,							rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = floats_tensor((1, self.text_embedder_hidden_size)							,							rng=random.Random(seed + 1      )      ).to(
													    UpperCamelCase_      )

													# create hint
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = floats_tensor((1, 3, 64, 64)							,							rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )

													if str(UpperCamelCase_      ).startswith('mps'      ):
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = torch.manual_seed(UpperCamelCase_      )
													else:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = {
													    '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      __lowerCamelCase       (    self :       str      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Tuple						    = 'cpu'

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.get_dummy_components()

													SCREAMING_SNAKE_CASE__     :str						    = self.pipeline_class(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = pipe.to(UpperCamelCase_      )

													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :int						    = pipe(**self.get_dummy_inputs(UpperCamelCase_      )      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = output.images

													SCREAMING_SNAKE_CASE__     :List[Any]						    = pipe(
													    **self.get_dummy_inputs(UpperCamelCase_      )							,							return_dict=UpperCamelCase_							,							)[0]

													SCREAMING_SNAKE_CASE__     :List[Any]						    = image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE__     :List[str]						    = image_from_tuple[0, -3:, -3:, -1]

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

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = np.array(
													    [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]      )

													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 _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




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




						def      __lowerCamelCase       (    self :       Dict      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Tuple						    = load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'      )

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

													SCREAMING_SNAKE_CASE__     :List[Any]						    = KandinskyVaaPriorPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-2-prior'							,							torch_dtype=torch.floataa      )
													pipe_prior.to(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = KandinskyVaaControlnetPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-2-controlnet-depth'							,							torch_dtype=torch.floataa      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = pipeline.to(UpperCamelCase_      )
													pipeline.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 'A robot, 4k photo'

													SCREAMING_SNAKE_CASE__     :str						    = torch.Generator(device='cuda'      ).manual_seed(0      )
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Dict						    = pipe_prior(
													    UpperCamelCase_							,							generator=UpperCamelCase_							,							num_inference_steps=5							,							negative_prompt=''							,							).to_tuple()

													SCREAMING_SNAKE_CASE__     :int						    = torch.Generator(device='cuda'      ).manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Dict						    = pipeline(
													    image_embeds=UpperCamelCase_							,							negative_image_embeds=UpperCamelCase_							,							hint=UpperCamelCase_							,							generator=UpperCamelCase_							,							num_inference_steps=1_00							,							output_type='np'							,							)

													SCREAMING_SNAKE_CASE__     :Tuple						    = output.images[0]

													assert image.shape == (5_12, 5_12, 3)

													assert_mean_pixel_difference(UpperCamelCase_							,							UpperCamelCase_      )







'''simple docstring'''







from typing import TYPE_CHECKING

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


UpperCamelCase_      	=	{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	['''BloomTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BloomForCausalLM''',
		    '''BloomModel''',
		    '''BloomPreTrainedModel''',
		    '''BloomForSequenceClassification''',
		    '''BloomForTokenClassification''',
		    '''BloomForQuestionAnswering''',
		]

if TYPE_CHECKING:
		from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bloom_fast import BloomTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bloom import (
				    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BloomForCausalLM,
				    BloomForQuestionAnswering,
				    BloomForSequenceClassification,
				    BloomForTokenClassification,
				    BloomModel,
				    BloomPreTrainedModel,
				)

else:
		import sys

		UpperCamelCase_      	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






