

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						(__magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Tuple  							=	KandinskyVaaControlnetPipeline
					UpperCAmelCase__:       str  							=	['''image_embeds''', '''negative_image_embeds''', '''hint''']
					UpperCAmelCase__:       List[Any]  							=	['''image_embeds''', '''negative_image_embeds''', '''hint''']
					UpperCAmelCase__:       str  							=	[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''latents''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					UpperCAmelCase__:       Union[str, Any]  							=	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      )

						A__							:   str					       =		{
						    """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,
						}

						A__							:   List[Any]					       =		UNetaDConditionModel(**A__      )
						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      )
						A__							:   Tuple					       =		VQModel(**self.dummy_movq_kwargs      )
						return model






					def   __A      (			self      ):
						A__							:   List[str]					       =		self.dummy_unet
						A__							:   Optional[Any]					       =		self.dummy_movq

						A__							:   Optional[Any]					       =		DDIMScheduler(
						    num_train_timesteps=1000   ,	beta_schedule="""linear"""   ,	beta_start=0.0_0_0_8_5   ,	beta_end=0.0_1_2   ,	clip_sample=A__   ,	set_alpha_to_one=A__   ,	steps_offset=1   ,	prediction_type="""epsilon"""   ,	thresholding=A__   ,	)

						A__							:   Optional[Any]					       =		{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}
						return components






					def   __A      (			self   ,	A__   ,	A__=0      ):
						A__							:   List[str]					       =		floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(A__      )      ).to(A__      )
						A__							:   Optional[Any]					       =		floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(seed + 1      )      ).to(
						    A__      )

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

						if str(A__      ).startswith("""mps"""      ):
							A__							:   List[Any]					       =		torch.manual_seed(A__      )
						else:
							A__							:   str					       =		torch.Generator(device=A__      ).manual_seed(A__      )
						A__							:   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   __A      (			self      ):
						A__							:   Tuple					       =		"""cpu"""

						A__							:   Dict					       =		self.get_dummy_components()

						A__							:   Union[str, Any]					       =		self.pipeline_class(**A__      )
						A__							:   Any					       =		pipe.to(A__      )

						pipe.set_progress_bar_config(disable=A__      )

						A__							:   Dict					       =		pipe(**self.get_dummy_inputs(A__      )      )
						A__							:   List[Any]					       =		output.images

						A__							:   Dict					       =		pipe(
						    **self.get_dummy_inputs(A__      )   ,	return_dict=A__   ,	)[0]

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

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

						A__							:   str					       =		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   __A      (			self      ):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






					def   __A      (			self      ):
						A__							:   Dict					       =		load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""      )

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

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

						A__							:   Tuple					       =		KandinskyVaaControlnetPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-controlnet-depth"""   ,	torch_dtype=torch.floataa      )
						A__							:   List[Any]					       =		pipeline.to(A__      )
						pipeline.set_progress_bar_config(disable=A__      )

						A__							:   List[Any]					       =		"""A robot, 4k photo"""

						A__							:   Union[str, Any]					       =		torch.Generator(device="""cuda"""      ).manual_seed(0      )
						A__       ,       A__							:   Dict					       =		pipe_prior(
						    A__   ,	generator=A__   ,	num_inference_steps=5   ,	negative_prompt=""""""   ,	).to_tuple()

						A__							:   Optional[int]					       =		torch.Generator(device="""cuda"""      ).manual_seed(0      )
						A__							:   Any					       =		pipeline(
						    image_embeds=A__   ,	negative_image_embeds=A__   ,	hint=A__   ,	generator=A__   ,	num_inference_steps=100   ,	output_type="""np"""   ,	)

						A__							:   int					       =		output.images[0]

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

						assert_mean_pixel_difference(A__   ,	A__      )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A_	:			Optional[int]  	=      {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['FNetTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Tuple  	=      ['FNetTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			List[Any]  	=      [
	    'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FNetForMaskedLM',
	    'FNetForMultipleChoice',
	    'FNetForNextSentencePrediction',
	    'FNetForPreTraining',
	    'FNetForQuestionAnswering',
	    'FNetForSequenceClassification',
	    'FNetForTokenClassification',
	    'FNetLayer',
	    'FNetModel',
	    'FNetPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet import FNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet_fast import FNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fnet import (
		    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FNetForMaskedLM,
		    FNetForMultipleChoice,
		    FNetForNextSentencePrediction,
		    FNetForPreTraining,
		    FNetForQuestionAnswering,
		    FNetForSequenceClassification,
		    FNetForTokenClassification,
		    FNetLayer,
		    FNetModel,
		    FNetPreTrainedModel,
		)


else:
	import sys

	A_	:			Optional[Any]  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



