
'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''







 def __init__(					self		:  Optional[int]			,				*snake_case__		:  str			,				**snake_case__		:  Optional[int] ):



   '''simple docstring'''




   warnings.warn(
       "The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
       " Please use VideoMAEImageProcessor instead."			,				snake_case__			,				)
   super().__init__(*snake_case__			,				**snake_case__ )





'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Union[str, Any]				=							KandinskyInpaintPipeline
 lowercase_					:    Tuple				=							["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
 lowercase_					:    Dict				=							[
     "prompt",
     "negative_prompt",
     "image_embeds",
     "negative_image_embeds",
     "image",
     "mask_image",
 ]
 lowercase_					:    Optional[Any]				=							[
     "generator",
     "height",
     "width",
     "latents",
     "guidance_scale",
     "negative_prompt",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 lowercase_					:    Optional[Any]				=							False







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return self.time_input_dim







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



   '''simple docstring'''




   return self.time_input_dim * 4







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



   '''simple docstring'''




   return 1_00







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
   return tokenizer







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   torch.manual_seed(0 )
   UpperCAmelCase__      :					Dict				       =							MCLIPConfig(
       numDims=self.cross_attention_dim			,				transformerDimensions=self.text_embedder_hidden_size			,				hidden_size=self.text_embedder_hidden_size			,				intermediate_size=37			,				num_attention_heads=4			,				num_hidden_layers=5			,				vocab_size=10_05			,				)

   UpperCAmelCase__      :					Any				       =							MultilingualCLIP(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							text_encoder.eval()

   return text_encoder







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



   '''simple docstring'''




   torch.manual_seed(0 )

   UpperCAmelCase__      :					str				       =							{
       "in_channels": 9,
       # Out channels is double in channels because predicts mean and variance
       "out_channels": 8,
       "addition_embed_type": "text_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": "text_image_proj",
       "cross_attention_dim": self.cross_attention_dim,
       "attention_head_dim": 4,
       "resnet_time_scale_shift": "scale_shift",
       "class_embed_type": None,
   }

   UpperCAmelCase__      :					List[str]				       =							UNetaDConditionModel(**snake_case__ )
   return model







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''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 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   torch.manual_seed(0 )
   UpperCAmelCase__      :					str				       =							VQModel(**self.dummy_movq_kwargs )
   return model







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.dummy_text_encoder
   UpperCAmelCase__      :					Any				       =							self.dummy_tokenizer
   UpperCAmelCase__      :					Tuple				       =							self.dummy_unet
   UpperCAmelCase__      :					str				       =							self.dummy_movq

   UpperCAmelCase__      :					str				       =							DDIMScheduler(
       num_train_timesteps=10_00			,				beta_schedule="linear"			,				beta_start=0.00085			,				beta_end=0.012			,				clip_sample=snake_case__			,				set_alpha_to_one=snake_case__			,				steps_offset=1			,				prediction_type="epsilon"			,				thresholding=snake_case__			,				)

   UpperCAmelCase__      :					List[Any]				       =							{
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  Any			,				snake_case__		:  Tuple=0 ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					Optional[Any]				       =							floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1 ) ).to(snake_case__ )
   # create init_image
   UpperCAmelCase__      :					Any				       =							floats_tensor((1, 3, 64, 64)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							image.cpu().permute(0			,				2			,				3			,				1 )[0]
   UpperCAmelCase__      :					Optional[int]				       =							Image.fromarray(np.uinta(snake_case__ ) ).convert("RGB" ).resize((2_56, 2_56) )
   # create mask
   UpperCAmelCase__      :					Any				       =							np.ones((64, 64)			,				dtype=np.floataa )
   UpperCAmelCase__      :					Any				       =							0

   if str(snake_case__ ).startswith("mps" ):
     UpperCAmelCase__      :					Tuple				       =							torch.manual_seed(snake_case__ )
   else:
     UpperCAmelCase__      :					str				       =							torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							{
       "prompt": "horse",
       "image": init_image,
       "mask_image": mask,
       "image_embeds": image_embeds,
       "negative_image_embeds": negative_image_embeds,
       "generator": generator,
       "height": 64,
       "width": 64,
       "num_inference_steps": 2,
       "guidance_scale": 4.0,
       "output_type": "np",
   }
   return inputs







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							"cpu"

   UpperCAmelCase__      :					Optional[Any]				       =							self.get_dummy_components()

   UpperCAmelCase__      :					int				       =							self.pipeline_class(**snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							pipe.to(snake_case__ )

   pipe.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							pipe(**self.get_dummy_inputs(snake_case__ ) )
   UpperCAmelCase__      :					int				       =							output.images

   UpperCAmelCase__      :					Tuple				       =							pipe(
       **self.get_dummy_inputs(snake_case__ )			,				return_dict=snake_case__			,				)[0]

   UpperCAmelCase__      :					Any				       =							image[0, -3:, -3:, -1]
   UpperCAmelCase__      :					Optional[int]				       =							image_from_tuple[0, -3:, -3:, -1]

   print(F"""image.shape {image.shape}""" )

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

   UpperCAmelCase__      :					Any				       =							np.array(
       [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )

   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()}"""







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






@slow
@require_torch_gpu
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy" )

   UpperCAmelCase__      :					List[str]				       =							load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
   UpperCAmelCase__      :					Union[str, Any]				       =							np.ones((7_68, 7_68)			,				dtype=np.floataa )
   UpperCAmelCase__      :					List[Any]				       =							0

   UpperCAmelCase__      :					Tuple				       =							"a hat"

   UpperCAmelCase__      :					Union[str, Any]				       =							KandinskyPriorPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-prior"			,				torch_dtype=torch.floataa )
   pipe_prior.to(snake_case__ )

   UpperCAmelCase__      :					int				       =							KandinskyInpaintPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-inpaint"			,				torch_dtype=torch.floataa )
   UpperCAmelCase__      :					Optional[int]				       =							pipeline.to(snake_case__ )
   pipeline.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							torch.Generator(device="cpu" ).manual_seed(0 )
   UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							pipe_prior(
       snake_case__			,				generator=snake_case__			,				num_inference_steps=5			,				negative_prompt=""			,				).to_tuple()

   UpperCAmelCase__      :					Optional[int]				       =							pipeline(
       snake_case__			,				image=snake_case__			,				mask_image=snake_case__			,				image_embeds=snake_case__			,				negative_image_embeds=snake_case__			,				generator=snake_case__			,				num_inference_steps=1_00			,				height=7_68			,				width=7_68			,				output_type="np"			,				)

   UpperCAmelCase__      :					int				       =							output.images[0]

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

   assert_mean_pixel_difference(snake_case__			,				snake_case__ )



