





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      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					,							unittest.TestCase					):







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

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->Union[str, Any]:



   '''simple docstring'''



   return 32

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->str:



   '''simple docstring'''



   return 32

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->Optional[int]:



   '''simple docstring'''



   return self.time_input_dim

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int]) ->Optional[int]:



   '''simple docstring'''



   return self.time_input_dim * 4

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->Optional[int]:



   '''simple docstring'''



   return 100

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->List[str]:



   '''simple docstring'''



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

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->List[Any]:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:   List[str]    						=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=1_005 ,				)

   lowerCamelCase__:   Dict    						=MultilingualCLIP(_UpperCAmelCase)
   lowerCamelCase__:   List[Any]    						=text_encoder.eval()

   return text_encoder

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->Tuple:



   '''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__:   Any    						=UNetaDConditionModel(**_UpperCAmelCase)
   return model

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->Union[str, 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 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int]) ->Union[str, Any]:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:   Optional[int]    						=VQModel(**self.dummy_movq_kwargs)
   return model

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:   Tuple    						=self.dummy_text_encoder
   lowerCamelCase__:   int    						=self.dummy_tokenizer
   lowerCamelCase__:   Any    						=self.dummy_unet
   lowerCamelCase__:   Optional[int]    						=self.dummy_movq

   lowerCamelCase__:   List[Any]    						=DDIMScheduler(
       num_train_timesteps=1_000 ,				beta_schedule="linear" ,				beta_start=0.0_0085 ,				beta_end=0.012 ,				clip_sample=_UpperCAmelCase ,				set_alpha_to_one=_UpperCAmelCase ,				steps_offset=1 ,				prediction_type="epsilon" ,				thresholding=_UpperCAmelCase ,				)

   lowerCamelCase__:   Union[str, Any]    						={
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[str] ,				UpperCAmelCase_       :     Any ,				UpperCAmelCase_       :     int=0) ->str:



   '''simple docstring'''



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

   if str(_UpperCAmelCase).startswith("mps"):
     lowerCamelCase__:   Tuple    						=torch.manual_seed(_UpperCAmelCase)
   else:
     lowerCamelCase__:   Tuple    						=torch.Generator(device=_UpperCAmelCase).manual_seed(_UpperCAmelCase)
   lowerCamelCase__:   List[str]    						={
       "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 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:   Dict    						="cpu"

   lowerCamelCase__:   Any    						=self.get_dummy_components()

   lowerCamelCase__:   Union[str, Any]    						=self.pipeline_class(**_UpperCAmelCase)
   lowerCamelCase__:   Optional[int]    						=pipe.to(_UpperCAmelCase)

   pipe.set_progress_bar_config(disable=_UpperCAmelCase)

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

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

   lowerCamelCase__:   Optional[int]    						=image[0, -3:, -3:, -1]
   lowerCamelCase__:   Tuple    						=image_from_tuple[0, -3:, -3:, -1]

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

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

   lowerCamelCase__:   Optional[Any]    						=np.array(
       [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786])

   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 							SCREAMING_SNAKE_CASE_      (self       :     List[Any]) ->int:



   '''simple docstring'''



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



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







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->Dict:



   '''simple docstring'''



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







 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->List[str]:



   '''simple docstring'''



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

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

   lowerCamelCase__:   Any    						="a hat"

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

   lowerCamelCase__:   str    						=KandinskyInpaintPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-inpaint" ,				torch_dtype=torch.floataa)
   lowerCamelCase__:   Tuple    						=pipeline.to(_UpperCAmelCase)
   pipeline.set_progress_bar_config(disable=_UpperCAmelCase)

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

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

   lowerCamelCase__:   str    						=output.images[0]

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

   assert_mean_pixel_difference(_UpperCAmelCase ,				_UpperCAmelCase)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A             =       {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A             =       [
         "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
         "JukeboxModel",
         "JukeboxPreTrainedModel",
         "JukeboxVQVAE",
         "JukeboxPrior",
     ]

if TYPE_CHECKING:
     from .configuration_jukebox import (
         JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
         JukeboxConfig,
         JukeboxPriorConfig,
         JukeboxVQVAEConfig,
     )
     from .tokenization_jukebox import JukeboxTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_jukebox import (
              JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
              JukeboxModel,
              JukeboxPreTrainedModel,
              JukeboxPrior,
              JukeboxVQVAE,
          )

else:
     import sys

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

