

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 					lowercase							(			lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     KandinskyVaaControlnetPipeline
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     ['''image_embeds''', '''negative_image_embeds''', '''hint''']
				__SCREAMING_SNAKE_CASE   :					Dict			     =     ['''image_embeds''', '''negative_image_embeds''', '''hint''']
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     [
				    '''generator''',
				    '''height''',
				    '''width''',
				    '''latents''',
				    '''guidance_scale''',
				    '''num_inference_steps''',
				    '''return_dict''',
				    '''guidance_scale''',
				    '''num_images_per_prompt''',
				    '''output_type''',
				    '''return_dict''',
				]
				__SCREAMING_SNAKE_CASE   :					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			)

											snake_case_     							=       {
											    '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,
											}

											snake_case_     							=       UNetaDConditionModel(**snake_case			)
											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			)
											snake_case_     							=       VQModel(**self.dummy_movq_kwargs			)
											return model




				def        a				(   self			):
											snake_case_     							=       self.dummy_unet
											snake_case_     							=       self.dummy_movq

											snake_case_     							=       DDIMScheduler(
											    num_train_timesteps=1000 ,  beta_schedule='linear' ,  beta_start=0.0_00_85 ,  beta_end=0.0_12 ,  clip_sample=snake_case ,  set_alpha_to_one=snake_case ,  steps_offset=1 ,  prediction_type='epsilon' ,  thresholding=snake_case ,  )

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




				def        a				(   self ,  snake_case ,  snake_case=0			):
											snake_case_     							=       floats_tensor((1, self.text_embedder_hidden_size) ,  rng=random.Random(snake_case			)			).to(snake_case			)
											snake_case_     							=       floats_tensor((1, self.text_embedder_hidden_size) ,  rng=random.Random(seed + 1			)			).to(
											    snake_case			)

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

											if str(snake_case			).startswith('mps'			):
																		snake_case_     							=       torch.manual_seed(snake_case			)
											else:
																		snake_case_     							=       torch.Generator(device=snake_case			).manual_seed(snake_case			)
											snake_case_     							=       {
											    '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			):
											snake_case_     							=       'cpu'

											snake_case_     							=       self.get_dummy_components()

											snake_case_     							=       self.pipeline_class(**snake_case			)
											snake_case_     							=       pipe.to(snake_case			)

											pipe.set_progress_bar_config(disable=snake_case			)

											snake_case_     							=       pipe(**self.get_dummy_inputs(snake_case			)			)
											snake_case_     							=       output.images

											snake_case_     							=       pipe(
											    **self.get_dummy_inputs(snake_case			) ,  return_dict=snake_case ,  )[0]

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

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

											snake_case_     							=       np.array(
											    [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]			)

											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 					lowercase							(			unittest.TestCase     ):




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




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

											snake_case_     							=       load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/kandinskyv22/hint_image_cat.png'			)
											snake_case_     							=       torch.from_numpy(np.array(snake_case			)			).float() / 2_55.0
											snake_case_     							=       hint.permute(2 ,  0 ,  1			).unsqueeze(0			)

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

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

											snake_case_     							=       'A robot, 4k photo'

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

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

											snake_case_     							=       output.images[0]

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

											assert_mean_pixel_difference(snake_case ,  snake_case			)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:  int									=			{
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  str									=			[
					    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LiltForQuestionAnswering""",
					    """LiltForSequenceClassification""",
					    """LiltForTokenClassification""",
					    """LiltModel""",
					    """LiltPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_lilt import (
										    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LiltForQuestionAnswering,
										    LiltForSequenceClassification,
										    LiltForTokenClassification,
										    LiltModel,
										    LiltPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase						:  Dict									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




