import logging

from transformers.configuration_utils import PretrainedConfig


__lowerCamelCase			: List[str]							   =		logging.getLogger(__name__)







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							"masked_bert"




   def __init__(    self			:				int       ,	_lowercase			:				Tuple=3_05_22       ,	_lowercase			:				int=7_68       ,	_lowercase			:				Optional[int]=12       ,	_lowercase			:				int=12       ,	_lowercase			:				Tuple=30_72       ,	_lowercase			:				int="gelu"       ,	_lowercase			:				List[str]=0.1       ,	_lowercase			:				Any=0.1       ,	_lowercase			:				List[str]=5_12       ,	_lowercase			:				List[str]=2       ,	_lowercase			:				Optional[int]=0.02       ,	_lowercase			:				List[Any]=1E-12       ,	_lowercase			:				Optional[Any]=0       ,	_lowercase			:				Optional[int]="topK"       ,	_lowercase			:				Dict="constant"       ,	_lowercase			:				Any=0.0       ,	**_lowercase			:				List[Any]       ,	):



     """simple docstring"""






     super().__init__(pad_token_id=_lowercase       ,	**_lowercase      )

     SCREAMING_SNAKE_CASE__										=      vocab_size
     SCREAMING_SNAKE_CASE__										=      hidden_size
     SCREAMING_SNAKE_CASE__										=      num_hidden_layers
     SCREAMING_SNAKE_CASE__										=      num_attention_heads
     SCREAMING_SNAKE_CASE__										=      hidden_act
     SCREAMING_SNAKE_CASE__										=      intermediate_size
     SCREAMING_SNAKE_CASE__										=      hidden_dropout_prob
     SCREAMING_SNAKE_CASE__										=      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__										=      max_position_embeddings
     SCREAMING_SNAKE_CASE__										=      type_vocab_size
     SCREAMING_SNAKE_CASE__										=      initializer_range
     SCREAMING_SNAKE_CASE__										=      layer_norm_eps
     SCREAMING_SNAKE_CASE__										=      pruning_method
     SCREAMING_SNAKE_CASE__										=      mask_init
     SCREAMING_SNAKE_CASE__										=      mask_scale

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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 __snake_case				(						lowerCamelCase_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							KandinskyVaaImgaImgPipeline
   lowerCAmelCase_														=							["image_embeds", "negative_image_embeds", "image"]
   lowerCAmelCase_														=							[
       "image_embeds",
       "negative_image_embeds",
       "image",
   ]
   lowerCAmelCase_														=							[
       "generator",
       "height",
       "width",
       "strength",
       "guidance_scale",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   lowerCAmelCase_														=							False




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     return 32




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     return 32




   @property
   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     return self.time_input_dim




   @property
   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     return self.time_input_dim * 4




   @property
   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     return 1_00




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     torch.manual_seed(0      )

     SCREAMING_SNAKE_CASE__										=      {
         """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,
     }

     SCREAMING_SNAKE_CASE__										=      UNetaDConditionModel(**_lowercase      )
     return model




   @property
   def 	__a						(    self			:				str      ):



     """simple docstring"""






     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 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     torch.manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      VQModel(**self.dummy_movq_kwargs      )
     return model




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.dummy_unet
     SCREAMING_SNAKE_CASE__										=      self.dummy_movq

     SCREAMING_SNAKE_CASE__										=      {
         """num_train_timesteps""": 10_00,
         """beta_schedule""": """linear""",
         """beta_start""": 0.0_00_85,
         """beta_end""": 0.0_12,
         """clip_sample""": False,
         """set_alpha_to_one""": False,
         """steps_offset""": 0,
         """prediction_type""": """epsilon""",
         """thresholding""": False,
     }

     SCREAMING_SNAKE_CASE__										=      DDIMScheduler(**_lowercase      )

     SCREAMING_SNAKE_CASE__										=      {
         """unet""": unet,
         """scheduler""": scheduler,
         """movq""": movq,
     }

     return components




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				Any       ,	_lowercase			:				Tuple=0      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(_lowercase      )      ).to(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(seed + 1      )      ).to(
         _lowercase      )
     # create init_image
     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, 3, 64, 64)       ,	rng=random.Random(_lowercase      )      ).to(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      image.cpu().permute(0       ,	2       ,	3       ,	1      )[0]
     SCREAMING_SNAKE_CASE__										=      Image.fromarray(np.uinta(_lowercase      )      ).convert("""RGB"""      ).resize((2_56, 2_56)      )

     if str(_lowercase      ).startswith("""mps"""      ):
       SCREAMING_SNAKE_CASE__										=      torch.manual_seed(_lowercase      )
     else:
       SCREAMING_SNAKE_CASE__										=      torch.Generator(device=_lowercase      ).manual_seed(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      {
         """image""": init_image,
         """image_embeds""": image_embeds,
         """negative_image_embeds""": negative_image_embeds,
         """generator""": generator,
         """height""": 64,
         """width""": 64,
         """num_inference_steps""": 10,
         """guidance_scale""": 7.0,
         """strength""": 0.2,
         """output_type""": """np""",
     }
     return inputs




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """cpu"""

     SCREAMING_SNAKE_CASE__										=      self.get_dummy_components()

     SCREAMING_SNAKE_CASE__										=      self.pipeline_class(**_lowercase      )
     SCREAMING_SNAKE_CASE__										=      pipe.to(_lowercase      )

     pipe.set_progress_bar_config(disable=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      pipe(**self.get_dummy_inputs(_lowercase      )      )
     SCREAMING_SNAKE_CASE__										=      output.images

     SCREAMING_SNAKE_CASE__										=      pipe(
         **self.get_dummy_inputs(_lowercase      )       ,	return_dict=_lowercase       ,	)[0]

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

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

     SCREAMING_SNAKE_CASE__										=      np.array(
         [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]      )
     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 __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/kandinskyv22/kandinskyv22_img2img_frog.npy"""      )

     SCREAMING_SNAKE_CASE__										=      load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""      )
     SCREAMING_SNAKE_CASE__										=      """A red cartoon frog, 4k"""

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

     SCREAMING_SNAKE_CASE__										=      KandinskyVaaImgaImgPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-decoder"""       ,	torch_dtype=torch.floataa      )
     SCREAMING_SNAKE_CASE__										=      pipeline.to(_lowercase      )

     pipeline.set_progress_bar_config(disable=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      torch.Generator(device="""cpu"""      ).manual_seed(0      )
     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      pipe_prior(
         _lowercase       ,	generator=_lowercase       ,	num_inference_steps=5       ,	negative_prompt=""""""       ,	).to_tuple()

     SCREAMING_SNAKE_CASE__										=      pipeline(
         image=_lowercase       ,	image_embeds=_lowercase       ,	negative_image_embeds=_lowercase       ,	generator=_lowercase       ,	num_inference_steps=1_00       ,	height=7_68       ,	width=7_68       ,	strength=0.2       ,	output_type="""np"""       ,	)

     SCREAMING_SNAKE_CASE__										=      output.images[0]

     assert image.shape == (7_68, 7_68, 3)

     assert_mean_pixel_difference(_lowercase       ,	_lowercase      )
