




'''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 		lowercase_   (			lowerCamelCase__					,  unittest.TestCase       ):


   """simple docstring"""

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



   @property
   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      return 3_2



   @property
   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      return 3_2



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

      """simple docstring"""
      return self.time_input_dim



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

      """simple docstring"""
      return self.time_input_dim * 4



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

      """simple docstring"""
      return 1_0_0



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"   )
      return tokenizer



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

      """simple docstring"""
      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				MCLIPConfig(
          numDims=self.cross_attention_dim  ,				transformerDimensions=self.text_embedder_hidden_size  ,				hidden_size=self.text_embedder_hidden_size  ,				intermediate_size=3_7  ,				num_attention_heads=4  ,				num_hidden_layers=5  ,				vocab_size=1_0_0_5  ,				)

      _SCREAMING_SNAKE_CASE	=				MultilingualCLIP(lowercase__   )
      _SCREAMING_SNAKE_CASE	=				text_encoder.eval()

      return text_encoder



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

      """simple docstring"""
      torch.manual_seed(0   )

      _SCREAMING_SNAKE_CASE	=				{
          "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,
      }

      _SCREAMING_SNAKE_CASE	=				UNetaDConditionModel(**lowercase__   )
      return model



   @property
   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      return {
          "block_out_channels": [3_2, 6_4],
          "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": 1_2,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }



   @property
   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				VQModel(**self.dummy_movq_kwargs   )
      return model



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.dummy_text_encoder
      _SCREAMING_SNAKE_CASE	=				self.dummy_tokenizer
      _SCREAMING_SNAKE_CASE	=				self.dummy_unet
      _SCREAMING_SNAKE_CASE	=				self.dummy_movq

      _SCREAMING_SNAKE_CASE	=				DDIMScheduler(
          num_train_timesteps=1_0_0_0  ,				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__  ,				)

      _SCREAMING_SNAKE_CASE	=				{
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }

      return components



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     List[Any]=0   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				floats_tensor((1, self.cross_attention_dim)  ,				rng=random.Random(lowercase__   )   ).to(lowercase__   )
      _SCREAMING_SNAKE_CASE	=				floats_tensor((1, self.cross_attention_dim)  ,				rng=random.Random(seed + 1   )   ).to(lowercase__   )
      # create init_image
      _SCREAMING_SNAKE_CASE	=				floats_tensor((1, 3, 6_4, 6_4)  ,				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_5_6, 2_5_6)   )
      # create mask
      _SCREAMING_SNAKE_CASE	=				np.ones((6_4, 6_4)  ,				dtype=np.floataa   )
      _SCREAMING_SNAKE_CASE	=				0

      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	=				{
          "prompt": "horse",
          "image": init_image,
          "mask_image": mask,
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "generator": generator,
          "height": 6_4,
          "width": 6_4,
          "num_inference_steps": 2,
          "guidance_scale": 4.0,
          "output_type": "np",
      }
      return inputs



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """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]

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

      assert image.shape == (1, 6_4, 6_4, 3)

      _SCREAMING_SNAKE_CASE	=				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 :     Optional[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 :     str   ):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"   )

      _SCREAMING_SNAKE_CASE	=				load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"   )
      _SCREAMING_SNAKE_CASE	=				np.ones((7_6_8, 7_6_8)  ,				dtype=np.floataa   )
      _SCREAMING_SNAKE_CASE	=				0

      _SCREAMING_SNAKE_CASE	=				"a hat"

      _SCREAMING_SNAKE_CASE	=				KandinskyPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-1-prior"  ,				torch_dtype=torch.floataa   )
      pipe_prior.to(lowercase__   )

      _SCREAMING_SNAKE_CASE	=				KandinskyInpaintPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-1-inpaint"  ,				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(
          lowercase__  ,				image=lowercase__  ,				mask_image=lowercase__  ,				image_embeds=lowercase__  ,				negative_image_embeds=lowercase__  ,				generator=lowercase__  ,				num_inference_steps=1_0_0  ,				height=7_6_8  ,				width=7_6_8  ,				output_type="np"  ,				)

      _SCREAMING_SNAKE_CASE	=				output.images[0]

      assert image.shape == (7_6_8, 7_6_8, 3)

      assert_mean_pixel_difference(lowercase__  ,				lowercase__   )







'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCamelCase_			=						{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						['BridgeTowerImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BridgeTowerForContrastiveLearning',
     'BridgeTowerForImageAndTextRetrieval',
     'BridgeTowerForMaskedLM',
     'BridgeTowerModel',
     'BridgeTowerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure)

