





"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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 __magic_name__   (       _UpperCamelCase    ,unittest.TestCase		):
       UpperCamelCase    :						List[Any]      =	KandinskyVaaPipeline
       UpperCamelCase    :						List[str]      =	[
           "image_embeds",
           "negative_image_embeds",
       ]
       UpperCamelCase    :						Union[str, Any]      =	["image_embeds", "negative_image_embeds"]
       UpperCamelCase    :						Any      =	[
           "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 		_lowerCamelCase   (     self	):





         """simple docstring"""


         return 3_2
       @property
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         return 3_2
       @property
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         return self.time_input_dim
       @property
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         return self.time_input_dim * 4
       @property
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         return 1_0_0
       @property
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         torch.manual_seed(0	)

         _lowerCAmelCase	      =					{
             '''in_channels''': 4,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''image''',
             '''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	      =					UNetaDConditionModel(**__lowerCAmelCase	)
         return model
       @property
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         return {
             "block_out_channels": [3_2, 6_4],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 1_2,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }
       @property
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         torch.manual_seed(0	)
         _lowerCAmelCase	      =					VQModel(**self.dummy_movq_kwargs	)
         return model
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         _lowerCAmelCase	      =					self.dummy_unet
         _lowerCAmelCase	      =					self.dummy_movq

         _lowerCAmelCase	      =					DDIMScheduler(
             num_train_timesteps=1_0_0_0   ,  beta_schedule='linear'   ,  beta_start=0.0_00_85   ,  beta_end=0.0_12   ,  clip_sample=__lowerCAmelCase   ,  set_alpha_to_one=__lowerCAmelCase   ,  steps_offset=1   ,  prediction_type='epsilon'   ,  thresholding=__lowerCAmelCase   ,  )

         _lowerCAmelCase	      =					{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }
         return components
       def 		_lowerCamelCase   (     self   ,  __magic_name__   ,  __magic_name__=0	):





         """simple docstring"""


         _lowerCAmelCase	      =					floats_tensor((1, self.text_embedder_hidden_size)   ,  rng=random.Random(__lowerCAmelCase	)	).to(__lowerCAmelCase	)
         _lowerCAmelCase	      =					floats_tensor((1, self.text_embedder_hidden_size)   ,  rng=random.Random(seed + 1	)	).to(
             __lowerCAmelCase	)
         if str(__lowerCAmelCase	).startswith('mps'	):
           _lowerCAmelCase	      =					torch.manual_seed(__lowerCAmelCase	)
         else:
           _lowerCAmelCase	      =					torch.Generator(device=__lowerCAmelCase	).manual_seed(__lowerCAmelCase	)
         _lowerCAmelCase	      =					{
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 6_4,
             '''width''': 6_4,
             '''guidance_scale''': 4.0,
             '''num_inference_steps''': 2,
             '''output_type''': '''np''',
         }
         return inputs
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         _lowerCAmelCase	      =					'''cpu'''

         _lowerCAmelCase	      =					self.get_dummy_components()

         _lowerCAmelCase	      =					self.pipeline_class(**__lowerCAmelCase	)
         _lowerCAmelCase	      =					pipe.to(__lowerCAmelCase	)

         pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

         _lowerCAmelCase	      =					pipe(**self.get_dummy_inputs(__lowerCAmelCase	)	)
         _lowerCAmelCase	      =					output.images

         _lowerCAmelCase	      =					pipe(
             **self.get_dummy_inputs(__lowerCAmelCase	)   ,  return_dict=__lowerCAmelCase   ,  )[0]

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

         assert image.shape == (1, 6_4, 6_4, 3)

         _lowerCAmelCase	      =					np.array(
             [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]	)

         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 __magic_name__   (       unittest.TestCase		):
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         _lowerCAmelCase	      =					load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'	)

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

         _lowerCAmelCase	      =					KandinskyVaaPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-decoder'   ,  torch_dtype=torch.floataa	)
         _lowerCAmelCase	      =					pipeline.to(__lowerCAmelCase	)
         pipeline.set_progress_bar_config(disable=__lowerCAmelCase	)

         _lowerCAmelCase	      =					'''red cat, 4k photo'''

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

         _lowerCAmelCase	      =					torch.Generator(device='cuda'	).manual_seed(0	)
         _lowerCAmelCase	      =					pipeline(
             image_embeds=__lowerCAmelCase   ,  negative_image_embeds=__lowerCAmelCase   ,  generator=__lowerCAmelCase   ,  num_inference_steps=1_0_0   ,  output_type='np'   ,  )

         _lowerCAmelCase	      =					output.images[0]

         assert image.shape == (5_1_2, 5_1_2, 3)

         assert_mean_pixel_difference(__lowerCAmelCase   ,  __lowerCAmelCase	)











from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   :      Dict				=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      str				=		[
					    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """CanineForMultipleChoice""",
					    """CanineForQuestionAnswering""",
					    """CanineForSequenceClassification""",
					    """CanineForTokenClassification""",
					    """CanineLayer""",
					    """CanineModel""",
					    """CaninePreTrainedModel""",
					    """load_tf_weights_in_canine""",
					]


if TYPE_CHECKING:
					from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
					from .tokenization_canine import CanineTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_canine import (
										    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CanineForMultipleChoice,
										    CanineForQuestionAnswering,
										    CanineForSequenceClassification,
										    CanineForTokenClassification,
										    CanineLayer,
										    CanineModel,
										    CaninePreTrainedModel,
										    load_tf_weights_in_canine,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






