





import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Tuple				      = UnCLIPImageVariationPipeline
   _UpperCamelCase					:	List[Any]				      = IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
   _UpperCamelCase					:	Any				      = IMAGE_VARIATION_BATCH_PARAMS

   _UpperCamelCase					:	Dict				      = [
       'generator',
       'return_dict',
       'decoder_num_inference_steps',
       'super_res_num_inference_steps',
   ]
   _UpperCamelCase					:	List[str]				      = False


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





     """simple docstring"""







     return 32


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





     """simple docstring"""







     return 32


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





     """simple docstring"""







     return self.time_input_dim


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





     """simple docstring"""







     return self.time_input_dim * 4


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





     """simple docstring"""







     return 100


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





     """simple docstring"""







     lowercase__			    =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
     return tokenizer


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





     """simple docstring"""







     torch.manual_seed(0 )
     lowercase__			    =						CLIPTextConfig(
         bos_token_id=0    ,  eos_token_id=2    ,  hidden_size=self.text_embedder_hidden_size    ,  projection_dim=self.text_embedder_hidden_size    ,  intermediate_size=37    ,  layer_norm_eps=1E-0_5    ,  num_attention_heads=4    ,  num_hidden_layers=5    ,  pad_token_id=1    ,  vocab_size=1_000    ,  )
     return CLIPTextModelWithProjection(a )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Dict:





     """simple docstring"""







     torch.manual_seed(0 )
     lowercase__			    =						CLIPVisionConfig(
         hidden_size=self.text_embedder_hidden_size    ,  projection_dim=self.text_embedder_hidden_size    ,  num_hidden_layers=5    ,  num_attention_heads=4    ,  image_size=32    ,  intermediate_size=37    ,  patch_size=1    ,  )
     return CLIPVisionModelWithProjection(a )


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





     """simple docstring"""







     torch.manual_seed(0 )

     lowercase__			    =						{
         'clip_embeddings_dim': self.text_embedder_hidden_size,
         'time_embed_dim': self.time_embed_dim,
         'cross_attention_dim': self.cross_attention_dim,
     }

     lowercase__			    =						UnCLIPTextProjModel(**a )
     return model


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





     """simple docstring"""







     torch.manual_seed(0 )

     lowercase__			    =						{
         'sample_size': 32,
         # RGB in channels
         'in_channels': 3,
         # Out channels is double in channels because predicts mean and variance
         'out_channels': 6,
         '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,
         'cross_attention_dim': self.cross_attention_dim,
         'attention_head_dim': 4,
         'resnet_time_scale_shift': 'scale_shift',
         'class_embed_type': 'identity',
     }

     lowercase__			    =						UNetaDConditionModel(**a )
     return model


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





     """simple docstring"""







     return {
         "sample_size": 64,
         "layers_per_block": 1,
         "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
         "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "in_channels": 6,
         "out_channels": 3,
     }


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





     """simple docstring"""







     torch.manual_seed(0 )

     lowercase__			    =						UNetaDModel(**self.dummy_super_res_kwargs )
     return model


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





     """simple docstring"""







     torch.manual_seed(1 )

     lowercase__			    =						UNetaDModel(**self.dummy_super_res_kwargs )
     return model


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Tuple:





     """simple docstring"""







     lowercase__			    =						self.dummy_decoder
     lowercase__			    =						self.dummy_text_proj
     lowercase__			    =						self.dummy_text_encoder
     lowercase__			    =						self.dummy_tokenizer
     lowercase__			    =						self.dummy_super_res_first
     lowercase__			    =						self.dummy_super_res_last

     lowercase__			    =						UnCLIPScheduler(
         variance_type='learned_range'    ,  prediction_type='epsilon'    ,  num_train_timesteps=1_000    ,  )

     lowercase__			    =						UnCLIPScheduler(
         variance_type='fixed_small_log'    ,  prediction_type='epsilon'    ,  num_train_timesteps=1_000    ,  )

     lowercase__			    =						CLIPImageProcessor(crop_size=32    ,  size=32 )

     lowercase__			    =						self.dummy_image_encoder

     return {
         "decoder": decoder,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "text_proj": text_proj,
         "feature_extractor": feature_extractor,
         "image_encoder": image_encoder,
         "super_res_first": super_res_first,
         "super_res_last": super_res_last,
         "decoder_scheduler": decoder_scheduler,
         "super_res_scheduler": super_res_scheduler,
     }


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





     """simple docstring"""







     lowercase__			    =						floats_tensor((1, 3, 32, 32)    ,  rng=random.Random(a ) ).to(a )
     if str(a ).startswith('mps' ):
       lowercase__			    =						torch.manual_seed(a )
     else:
       lowercase__			    =						torch.Generator(device=a ).manual_seed(a )

     if pil_image:
       lowercase__			    =						input_image * 0.5 + 0.5
       lowercase__			    =						input_image.clamp(0    ,  1 )
       lowercase__			    =						input_image.cpu().permute(0    ,  2    ,  3    ,  1 ).float().numpy()
       lowercase__			    =						DiffusionPipeline.numpy_to_pil(a )[0]

     return {
         "image": input_image,
         "generator": generator,
         "decoder_num_inference_steps": 2,
         "super_res_num_inference_steps": 2,
         "output_type": "np",
     }


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





     """simple docstring"""







     lowercase__			    =						'cpu'

     lowercase__			    =						self.get_dummy_components()

     lowercase__			    =						self.pipeline_class(**a )
     lowercase__			    =						pipe.to(a )

     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						self.get_dummy_inputs(a    ,  pil_image=a )

     lowercase__			    =						pipe(**a )
     lowercase__			    =						output.images

     lowercase__			    =						self.get_dummy_inputs(a    ,  pil_image=a )

     lowercase__			    =						pipe(
         **a    ,  return_dict=a    ,  )[0]

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

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

     lowercase__			    =						np.array(
         [
             0.9997,
             0.0002,
             0.9997,
             0.9997,
             0.9969,
             0.0023,
             0.9997,
             0.9969,
             0.9970,
         ] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Any:





     """simple docstring"""







     lowercase__			    =						'cpu'

     lowercase__			    =						self.get_dummy_components()

     lowercase__			    =						self.pipeline_class(**a )
     lowercase__			    =						pipe.to(a )

     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						self.get_dummy_inputs(a    ,  pil_image=a )

     lowercase__			    =						pipe(**a )
     lowercase__			    =						output.images

     lowercase__			    =						self.get_dummy_inputs(a    ,  pil_image=a )

     lowercase__			    =						pipe(
         **a    ,  return_dict=a    ,  )[0]

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

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

     lowercase__			    =						np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2


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





     """simple docstring"""







     lowercase__			    =						'cpu'

     lowercase__			    =						self.get_dummy_components()

     lowercase__			    =						self.pipeline_class(**a )
     lowercase__			    =						pipe.to(a )

     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						self.get_dummy_inputs(a    ,  pil_image=a )
     lowercase__			    =						[
         pipeline_inputs['image'],
         pipeline_inputs['image'],
     ]

     lowercase__			    =						pipe(**a )
     lowercase__			    =						output.images

     lowercase__			    =						self.get_dummy_inputs(a    ,  pil_image=a )
     lowercase__			    =						[
         tuple_pipeline_inputs['image'],
         tuple_pipeline_inputs['image'],
     ]

     lowercase__			    =						pipe(
         **a    ,  return_dict=a    ,  )[0]

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

     assert image.shape == (2, 64, 64, 3)

     lowercase__			    =						np.array(
         [
             0.9997,
             0.9989,
             0.0008,
             0.0021,
             0.9960,
             0.0018,
             0.0014,
             0.0002,
             0.9933,
         ] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2


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





     """simple docstring"""







     lowercase__			    =						torch.device('cpu' )
     class        SCREAMING_SNAKE_CASE     :
        _UpperCamelCase					:	Tuple				      = 1






     lowercase__			    =						self.get_dummy_components()

     lowercase__			    =						self.pipeline_class(**a )
     lowercase__			    =						pipe.to(a )

     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						torch.Generator(device=a ).manual_seed(0 )
     lowercase__			    =						pipe.decoder.dtype
     lowercase__			    =						1

     lowercase__			    =						(
         batch_size,
         pipe.decoder.config.in_channels,
         pipe.decoder.config.sample_size,
         pipe.decoder.config.sample_size,
     )
     lowercase__			    =						pipe.prepare_latents(
         a    ,  dtype=a    ,  device=a    ,  generator=a    ,  latents=a    ,  scheduler=DummyScheduler() )

     lowercase__			    =						(
         batch_size,
         pipe.super_res_first.config.in_channels // 2,
         pipe.super_res_first.config.sample_size,
         pipe.super_res_first.config.sample_size,
     )
     lowercase__			    =						pipe.prepare_latents(
         a    ,  dtype=a    ,  device=a    ,  generator=a    ,  latents=a    ,  scheduler=DummyScheduler() )

     lowercase__			    =						self.get_dummy_inputs(a    ,  pil_image=a )

     lowercase__			    =						pipe(
         **a    ,  decoder_latents=a    ,  super_res_latents=a ).images

     lowercase__			    =						self.get_dummy_inputs(a    ,  pil_image=a )
     # Don't pass image, instead pass embedding
     lowercase__			    =						pipeline_inputs.pop('image' )
     lowercase__			    =						pipe.image_encoder(a ).image_embeds

     lowercase__			    =						pipe(
         **a    ,  decoder_latents=a    ,  super_res_latents=a    ,  image_embeddings=a    ,  ).images

     # make sure passing text embeddings manually is identical
     assert np.abs(img_out_a - img_out_a ).max() < 1E-4


   @skip_mps
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Dict:





     """simple docstring"""







     lowercase__			    =						torch_device == 'cpu'

     # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
     lowercase__			    =						1E-2

     self._test_attention_slicing_forward_pass(
         test_max_difference=a    ,  expected_max_diff=a )


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





     """simple docstring"""







     lowercase__			    =						torch_device == 'cpu'
     lowercase__			    =						True
     lowercase__			    =						[
         'decoder_num_inference_steps',
         'super_res_num_inference_steps',
     ]

     self._test_inference_batch_single_identical(
         test_max_difference=a    ,  relax_max_difference=a    ,  additional_params_copy_to_batched_inputs=a    ,  )


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





     """simple docstring"""







     lowercase__			    =						[
         'decoder_num_inference_steps',
         'super_res_num_inference_steps',
     ]

     if torch_device == "mps":
       # TODO: MPS errors with larger batch sizes
       lowercase__			    =						[2, 3]
       self._test_inference_batch_consistent(
           batch_sizes=a    ,  additional_params_copy_to_batched_inputs=a    ,  )
     else:
       self._test_inference_batch_consistent(
           additional_params_copy_to_batched_inputs=a )


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





     """simple docstring"""







     return super().test_dict_tuple_outputs_equivalent()


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





     """simple docstring"""







     return super().test_save_load_local()


   @skip_mps
   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     str:





     """simple docstring"""







     return super().test_save_load_optional_components()






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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Dict:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Dict:





     """simple docstring"""







     lowercase__			    =						load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png' )
     lowercase__			    =						load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/unclip/karlo_v1_alpha_cat_variation_fp16.npy' )

     lowercase__			    =						UnCLIPImageVariationPipeline.from_pretrained(
         'kakaobrain/karlo-v1-alpha-image-variations'    ,  torch_dtype=torch.floataa )
     lowercase__			    =						pipeline.to(a )
     pipeline.set_progress_bar_config(disable=a )

     lowercase__			    =						torch.Generator(device='cpu' ).manual_seed(0 )
     lowercase__			    =						pipeline(
         a    ,  generator=a    ,  output_type='np'    ,  )

     lowercase__			    =						output.images[0]

     assert image.shape == (256, 256, 3)

     assert_mean_pixel_difference(a    ,  a    ,  15 )









from PIL import Image




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Image:

  def brightness(_SCREAMING_SNAKE_CASE	) -> float:
    return 128 + level + (c - 128)

  if not -2_5_5.0 <= level <= 2_5_5.0:
    raise ValueError('level must be between -255.0 (black) and 255.0 (white)'	)
  return img.point(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      # Load image
      with Image.open("""image_data/lena.jpg""") as img:
            # Change brightness to 100
            lowercase_					     =	change_brightness(img, 100)
            brigt_img.save("""image_data/lena_brightness.png""", format="""png""")


