
"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase								=						{"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["YolosFeatureExtractor"]
							lowerCAmelCase								=						["YolosImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "YolosForObjectDetection",
							    "YolosModel",
							    "YolosPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_yolos import YolosFeatureExtractor
														from .image_processing_yolos import YolosImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yolos import (
														    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YolosForObjectDetection,
														    YolosModel,
														    YolosPreTrainedModel,
														)


else:
							import sys

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






'''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    UpperCAmelCase	(       UpperCamelCase__			,       unittest.TestCase				):
 __lowercase   		=				KandinskyVaaPipeline
 __lowercase   		=				[
     """image_embeds""",
     """negative_image_embeds""",
 ]
 __lowercase   		=				["""image_embeds""", """negative_image_embeds"""]
 __lowercase   		=				[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 __lowercase   		=				False
 @property
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							str:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :int							)->							List[Any]:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Dict:
     return self.time_input_dim
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							Union[str, Any]:
     return self.time_input_dim * 4
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							List[Any]:
     return 1_00
 @property
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Union[str, Any]:
     torch.manual_seed(0							)

     A__				=			{
         "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,
     }

     A__				=			UNetaDConditionModel(**lowercase_							)
     return model
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							Optional[Any]:
     return {
         "block_out_channels": [32, 64],
         "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": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }
 @property
 def      UpperCAmelCase_							(				self      :Dict							)->							Union[str, Any]:
     torch.manual_seed(0							)
     A__				=			VQModel(**self.dummy_movq_kwargs							)
     return model
 def      UpperCAmelCase_							(				self      :Dict							)->							Optional[Any]:
     A__				=			self.dummy_unet
     A__				=			self.dummy_movq

     A__				=			DDIMScheduler(
         num_train_timesteps=10_00						,       beta_schedule="linear"						,       beta_start=0.0_0_0_8_5						,       beta_end=0.0_1_2						,       clip_sample=lowercase_						,       set_alpha_to_one=lowercase_						,       steps_offset=1						,       prediction_type="epsilon"						,       thresholding=lowercase_						,       )

     A__				=			{
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }
     return components
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :int						,       lowercase_      :Union[str, Any]=0							)->							Dict:
     A__				=			floats_tensor((1, self.text_embedder_hidden_size)						,       rng=random.Random(lowercase_							)							).to(lowercase_							)
     A__				=			floats_tensor((1, self.text_embedder_hidden_size)						,       rng=random.Random(seed + 1							)							).to(
         lowercase_							)
     if str(lowercase_							).startswith("mps"							):
         A__				=			torch.manual_seed(lowercase_							)
     else:
         A__				=			torch.Generator(device=lowercase_							).manual_seed(lowercase_							)
     A__				=			{
         "image_embeds": image_embeds,
         "negative_image_embeds": negative_image_embeds,
         "generator": generator,
         "height": 64,
         "width": 64,
         "guidance_scale": 4.0,
         "num_inference_steps": 2,
         "output_type": "np",
     }
     return inputs



 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							str:
     A__				=			"cpu"

     A__				=			self.get_dummy_components()

     A__				=			self.pipeline_class(**lowercase_							)
     A__				=			pipe.to(lowercase_							)

     pipe.set_progress_bar_config(disable=lowercase_							)

     A__				=			pipe(**self.get_dummy_inputs(lowercase_							)							)
     A__				=			output.images

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

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

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

     A__				=			np.array(
         [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6]							)

     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    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Dict							)->							str:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



 def      UpperCAmelCase_							(				self      :Any							)->							List[Any]:
     A__				=			load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"							)

     A__				=			KandinskyVaaPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-prior"						,       torch_dtype=torch.floataa							)
     pipe_prior.to(lowercase_							)

     A__				=			KandinskyVaaPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-decoder"						,       torch_dtype=torch.floataa							)
     A__				=			pipeline.to(lowercase_							)
     pipeline.set_progress_bar_config(disable=lowercase_							)

     A__				=			"red cat, 4k photo"

     A__				=			torch.Generator(device="cuda"							).manual_seed(0							)
     A__,	A__				=			pipe_prior(
         lowercase_						,       generator=lowercase_						,       num_inference_steps=5						,       negative_prompt=""						,       ).to_tuple()

     A__				=			torch.Generator(device="cuda"							).manual_seed(0							)
     A__				=			pipeline(
         image_embeds=lowercase_						,       negative_image_embeds=lowercase_						,       generator=lowercase_						,       num_inference_steps=1_00						,       output_type="np"						,       )

     A__				=			output.images[0]

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

     assert_mean_pixel_difference(lowercase_						,       lowercase_							)



