


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 			_lowercase (							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			KandinskyInpaintPipeline
       A__											=			["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
       A__											=			[
           "prompt",
           "negative_prompt",
           "image_embeds",
           "negative_image_embeds",
           "image",
           "mask_image",
       ]
       A__											=			[
           "generator",
           "height",
           "width",
           "latents",
           "guidance_scale",
           "negative_prompt",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       A__											=			False





       @property
       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             return 32





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


             '''simple docstring'''



             return 32





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


             '''simple docstring'''



             return self.time_input_dim





       @property
       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             return self.time_input_dim * 4





       @property
       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             return 100





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


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"       )
             return tokenizer





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


             '''simple docstring'''



             torch.manual_seed(0       )
             lowerCamelCase__		:				Optional[int]    =  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=1005					,      )

             lowerCamelCase__		:				Union[str, Any]    =  MultilingualCLIP(__lowerCamelCase       )
             lowerCamelCase__		:				Any    =  text_encoder.eval()

             return text_encoder





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


             '''simple docstring'''



             torch.manual_seed(0       )

             lowerCamelCase__		:				Dict    =  {
                 "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,
             }

             lowerCamelCase__		:				str    =  UNetaDConditionModel(**__lowerCamelCase       )
             return model





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


             '''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 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             torch.manual_seed(0       )
             lowerCamelCase__		:				Any    =  VQModel(**self.dummy_movq_kwargs       )
             return model





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  self.dummy_text_encoder
             lowerCamelCase__		:				str    =  self.dummy_tokenizer
             lowerCamelCase__		:				Optional[Any]    =  self.dummy_unet
             lowerCamelCase__		:				Union[str, Any]    =  self.dummy_movq

             lowerCamelCase__		:				List[str]    =  DDIMScheduler(
                 num_train_timesteps=1000					,      beta_schedule="linear"					,      beta_start=0.0_0_0_8_5					,      beta_end=0.0_1_2					,      clip_sample=__lowerCamelCase					,      set_alpha_to_one=__lowerCamelCase					,      steps_offset=1					,      prediction_type="epsilon"					,      thresholding=__lowerCamelCase					,      )

             lowerCamelCase__		:				Any    =  {
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "unet": unet,
                 "scheduler": scheduler,
                 "movq": movq,
             }

             return components





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			List[str]=0       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  floats_tensor((1, self.cross_attention_dim)					,      rng=random.Random(__lowerCamelCase       )       ).to(__lowerCamelCase       )
             lowerCamelCase__		:				Tuple    =  floats_tensor((1, self.cross_attention_dim)					,      rng=random.Random(seed + 1       )       ).to(__lowerCamelCase       )
             # create init_image
             lowerCamelCase__		:				int    =  floats_tensor((1, 3, 64, 64)					,      rng=random.Random(__lowerCamelCase       )       ).to(__lowerCamelCase       )
             lowerCamelCase__		:				Tuple    =  image.cpu().permute(0					,      2					,      3					,      1       )[0]
             lowerCamelCase__		:				str    =  Image.fromarray(np.uinta(__lowerCamelCase       )       ).convert("RGB"       ).resize((256, 256)       )
             # create mask
             lowerCamelCase__		:				Optional[Any]    =  np.ones((64, 64)					,      dtype=np.floataa       )
             lowerCamelCase__		:				str    =  0

             if str(__lowerCamelCase       ).startswith("mps"       ):
                   lowerCamelCase__		:				Any    =  torch.manual_seed(__lowerCamelCase       )
             else:
                   lowerCamelCase__		:				Any    =  torch.Generator(device=__lowerCamelCase       ).manual_seed(__lowerCamelCase       )
             lowerCamelCase__		:				Optional[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 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  "cpu"

             lowerCamelCase__		:				Dict    =  self.get_dummy_components()

             lowerCamelCase__		:				str    =  self.pipeline_class(**__lowerCamelCase       )
             lowerCamelCase__		:				str    =  pipe.to(__lowerCamelCase       )

             pipe.set_progress_bar_config(disable=__lowerCamelCase       )

             lowerCamelCase__		:				List[str]    =  pipe(**self.get_dummy_inputs(__lowerCamelCase       )       )
             lowerCamelCase__		:				str    =  output.images

             lowerCamelCase__		:				str    =  pipe(
                 **self.get_dummy_inputs(__lowerCamelCase       )					,      return_dict=__lowerCamelCase					,      )[0]

             lowerCamelCase__		:				Dict    =  image[0, -3:, -3:, -1]
             lowerCamelCase__		:				Any    =  image_from_tuple[0, -3:, -3:, -1]

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

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

             lowerCamelCase__		:				str    =  np.array(
                 [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_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()}"





       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



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







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


       """simple docstring"""




       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



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

             lowerCamelCase__		:				Any    =  load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"       )
             lowerCamelCase__		:				Optional[int]    =  np.ones((768, 768)					,      dtype=np.floataa       )
             lowerCamelCase__		:				str    =  0

             lowerCamelCase__		:				int    =  "a hat"

             lowerCamelCase__		:				Any    =  KandinskyPriorPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-1-prior"					,      torch_dtype=torch.floataa       )
             pipe_prior.to(__lowerCamelCase       )

             lowerCamelCase__		:				Optional[Any]    =  KandinskyInpaintPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-1-inpaint"					,      torch_dtype=torch.floataa       )
             lowerCamelCase__		:				Tuple    =  pipeline.to(__lowerCamelCase       )
             pipeline.set_progress_bar_config(disable=__lowerCamelCase       )

             lowerCamelCase__		:				str    =  torch.Generator(device="cpu"       ).manual_seed(0       )
             lowerCamelCase__    ,   lowerCamelCase__		:				str    =  pipe_prior(
                 __lowerCamelCase					,      generator=__lowerCamelCase					,      num_inference_steps=5					,      negative_prompt=""					,      ).to_tuple()

             lowerCamelCase__		:				int    =  pipeline(
                 __lowerCamelCase					,      image=__lowerCamelCase					,      mask_image=__lowerCamelCase					,      image_embeds=__lowerCamelCase					,      negative_image_embeds=__lowerCamelCase					,      generator=__lowerCamelCase					,      num_inference_steps=100					,      height=768					,      width=768					,      output_type="np"					,      )

             lowerCamelCase__		:				Any    =  output.images[0]

             assert image.shape == (768, 768, 3)

             assert_mean_pixel_difference(__lowerCamelCase					,      __lowerCamelCase       )






import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


A      :    List[str]		=					logging.get_logger(__name__)







class 			_lowercase (							lowercase__):


       """simple docstring"""




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


             '''simple docstring'''



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


