
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 						__magic_name__    (       lowercase_	,						unittest.TestCase    ):
 UpperCAmelCase							    =						UnCLIPImageVariationPipeline
 UpperCAmelCase							    =						IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
 UpperCAmelCase							    =						IMAGE_VARIATION_BATCH_PARAMS

 UpperCAmelCase							    =						[
     '''generator''',
     '''return_dict''',
     '''decoder_num_inference_steps''',
     '''super_res_num_inference_steps''',
 ]
 UpperCAmelCase							    =						False


 @property
 def _UpperCamelCase	(						self		: List[str]    )    ->      List[str]:
   return 3_2


 @property
 def _UpperCamelCase	(						self		: List[Any]    )    ->      Union[str, Any]:
   return 3_2


 @property
 def _UpperCamelCase	(						self		: List[Any]    )    ->      Dict:
   return self.time_input_dim


 @property
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Union[str, Any]:
   return self.time_input_dim * 4


 @property
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      List[Any]:
   return 1_0_0


 @property
 def _UpperCamelCase	(						self		: Dict    )    ->      Tuple:
   UpperCAmelCase					      =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
   return tokenizer


 @property
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      str:
   torch.manual_seed(0    )
   UpperCAmelCase					      =  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=3_7			,						layer_norm_eps=1e-0_5			,						num_attention_heads=4			,						num_hidden_layers=5			,						pad_token_id=1			,						vocab_size=1_0_0_0			,						)
   return CLIPTextModelWithProjection(UpperCamelCase__    )


 @property
 def _UpperCamelCase	(						self		: Any    )    ->      Tuple:
   torch.manual_seed(0    )
   UpperCAmelCase					      =  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=3_2			,						intermediate_size=3_7			,						patch_size=1			,						)
   return CLIPVisionModelWithProjection(UpperCamelCase__    )


 @property
 def _UpperCamelCase	(						self		: Any    )    ->      int:
   torch.manual_seed(0    )

   UpperCAmelCase					      =  {
       "clip_embeddings_dim": self.text_embedder_hidden_size,
       "time_embed_dim": self.time_embed_dim,
       "cross_attention_dim": self.cross_attention_dim,
   }

   UpperCAmelCase					      =  UnCLIPTextProjModel(**UpperCamelCase__    )
   return model


 @property
 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Any:
   torch.manual_seed(0    )

   UpperCAmelCase					      =  {
       "sample_size": 3_2,
       # 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",
   }

   UpperCAmelCase					      =  UNetaDConditionModel(**UpperCamelCase__    )
   return model


 @property
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      str:
   return {
       "sample_size": 6_4,
       "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 _UpperCamelCase	(						self		: Dict    )    ->      Optional[Any]:
   torch.manual_seed(0    )

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


 @property
 def _UpperCamelCase	(						self		: List[str]    )    ->      Union[str, Any]:
   torch.manual_seed(1    )

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


 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Any:
   UpperCAmelCase					      =  self.dummy_decoder
   UpperCAmelCase					      =  self.dummy_text_proj
   UpperCAmelCase					      =  self.dummy_text_encoder
   UpperCAmelCase					      =  self.dummy_tokenizer
   UpperCAmelCase					      =  self.dummy_super_res_first
   UpperCAmelCase					      =  self.dummy_super_res_last

   UpperCAmelCase					      =  UnCLIPScheduler(
       variance_type="learned_range"			,						prediction_type="epsilon"			,						num_train_timesteps=1_0_0_0			,						)

   UpperCAmelCase					      =  UnCLIPScheduler(
       variance_type="fixed_small_log"			,						prediction_type="epsilon"			,						num_train_timesteps=1_0_0_0			,						)

   UpperCAmelCase					      =  CLIPImageProcessor(crop_size=3_2			,						size=3_2    )

   UpperCAmelCase					      =  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 _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: Dict			,						lowerCAmelCase__		: str=0			,						lowerCAmelCase__		: Dict=True    )    ->      Union[str, Any]:
   UpperCAmelCase					      =  floats_tensor((1, 3, 3_2, 3_2)			,						rng=random.Random(UpperCamelCase__    )    ).to(UpperCamelCase__    )
   if str(UpperCamelCase__    ).startswith("mps"    ):
     UpperCAmelCase					      =  torch.manual_seed(UpperCamelCase__    )
   else:
     UpperCAmelCase					      =  torch.Generator(device=UpperCamelCase__    ).manual_seed(UpperCamelCase__    )

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

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


 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Union[str, Any]:
   UpperCAmelCase					      =  "cpu"

   UpperCAmelCase					      =  self.get_dummy_components()

   UpperCAmelCase					      =  self.pipeline_class(**UpperCamelCase__    )
   UpperCAmelCase					      =  pipe.to(UpperCamelCase__    )

   pipe.set_progress_bar_config(disable=UpperCamelCase__    )

   UpperCAmelCase					      =  self.get_dummy_inputs(UpperCamelCase__			,						pil_image=UpperCamelCase__    )

   UpperCAmelCase					      =  pipe(**UpperCamelCase__    )
   UpperCAmelCase					      =  output.images

   UpperCAmelCase					      =  self.get_dummy_inputs(UpperCamelCase__			,						pil_image=UpperCamelCase__    )

   UpperCAmelCase					      =  pipe(
       **UpperCamelCase__			,						return_dict=UpperCamelCase__			,						)[0]

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

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

   UpperCAmelCase					      =  np.array(
       [
           0.9_997,
           0.0_002,
           0.9_997,
           0.9_997,
           0.9_969,
           0.0_023,
           0.9_997,
           0.9_969,
           0.9_970,
       ]    )

   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 _UpperCamelCase	(						self		: List[str]    )    ->      Optional[Any]:
   UpperCAmelCase					      =  "cpu"

   UpperCAmelCase					      =  self.get_dummy_components()

   UpperCAmelCase					      =  self.pipeline_class(**UpperCamelCase__    )
   UpperCAmelCase					      =  pipe.to(UpperCamelCase__    )

   pipe.set_progress_bar_config(disable=UpperCamelCase__    )

   UpperCAmelCase					      =  self.get_dummy_inputs(UpperCamelCase__			,						pil_image=UpperCamelCase__    )

   UpperCAmelCase					      =  pipe(**UpperCamelCase__    )
   UpperCAmelCase					      =  output.images

   UpperCAmelCase					      =  self.get_dummy_inputs(UpperCamelCase__			,						pil_image=UpperCamelCase__    )

   UpperCAmelCase					      =  pipe(
       **UpperCamelCase__			,						return_dict=UpperCamelCase__			,						)[0]

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

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

   UpperCAmelCase					      =  np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971]    )

   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 _UpperCamelCase	(						self		: str    )    ->      str:
   UpperCAmelCase					      =  "cpu"

   UpperCAmelCase					      =  self.get_dummy_components()

   UpperCAmelCase					      =  self.pipeline_class(**UpperCamelCase__    )
   UpperCAmelCase					      =  pipe.to(UpperCamelCase__    )

   pipe.set_progress_bar_config(disable=UpperCamelCase__    )

   UpperCAmelCase					      =  self.get_dummy_inputs(UpperCamelCase__			,						pil_image=UpperCamelCase__    )
   UpperCAmelCase					      =  [
       pipeline_inputs["image"],
       pipeline_inputs["image"],
   ]

   UpperCAmelCase					      =  pipe(**UpperCamelCase__    )
   UpperCAmelCase					      =  output.images

   UpperCAmelCase					      =  self.get_dummy_inputs(UpperCamelCase__			,						pil_image=UpperCamelCase__    )
   UpperCAmelCase					      =  [
       tuple_pipeline_inputs["image"],
       tuple_pipeline_inputs["image"],
   ]

   UpperCAmelCase					      =  pipe(
       **UpperCamelCase__			,						return_dict=UpperCamelCase__			,						)[0]

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

   assert image.shape == (2, 6_4, 6_4, 3)

   UpperCAmelCase					      =  np.array(
       [
           0.9_997,
           0.9_989,
           0.0_008,
           0.0_021,
           0.9_960,
           0.0_018,
           0.0_014,
           0.0_002,
           0.9_933,
       ]    )

   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 _UpperCamelCase	(						self		: int    )    ->      List[Any]:
   UpperCAmelCase					      =  torch.device("cpu"    )






   class 						__magic_name__    :
    UpperCAmelCase							    =						1

   UpperCAmelCase					      =  self.get_dummy_components()

   UpperCAmelCase					      =  self.pipeline_class(**UpperCamelCase__    )
   UpperCAmelCase					      =  pipe.to(UpperCamelCase__    )

   pipe.set_progress_bar_config(disable=UpperCamelCase__    )

   UpperCAmelCase					      =  torch.Generator(device=UpperCamelCase__    ).manual_seed(0    )
   UpperCAmelCase					      =  pipe.decoder.dtype
   UpperCAmelCase					      =  1

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

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

   UpperCAmelCase					      =  self.get_dummy_inputs(UpperCamelCase__			,						pil_image=UpperCamelCase__    )

   UpperCAmelCase					      =  pipe(
       **UpperCamelCase__			,						decoder_latents=UpperCamelCase__			,						super_res_latents=UpperCamelCase__    ).images

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

   UpperCAmelCase					      =  pipe(
       **UpperCamelCase__			,						decoder_latents=UpperCamelCase__			,						super_res_latents=UpperCamelCase__			,						image_embeddings=UpperCamelCase__			,						).images

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


 @skip_mps
 def _UpperCamelCase	(						self		: List[str]    )    ->      str:
   UpperCAmelCase					      =  torch_device == "cpu"

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

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


 @skip_mps
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Dict:
   UpperCAmelCase					      =  torch_device == "cpu"
   UpperCAmelCase					      =  True
   UpperCAmelCase					      =  [
       "decoder_num_inference_steps",
       "super_res_num_inference_steps",
   ]

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


 def _UpperCamelCase	(						self		: List[str]    )    ->      Any:
   UpperCAmelCase					      =  [
       "decoder_num_inference_steps",
       "super_res_num_inference_steps",
   ]

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


 @skip_mps
 def _UpperCamelCase	(						self		: Tuple    )    ->      List[Any]:
   return super().test_dict_tuple_outputs_equivalent()


 @skip_mps
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Tuple:
   return super().test_save_load_local()


 @skip_mps
 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Union[str, Any]:
   return super().test_save_load_optional_components()






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


 def _UpperCamelCase	(						self		: str    )    ->      List[Any]:
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()


 def _UpperCamelCase	(						self		: List[str]    )    ->      int:
   UpperCAmelCase					      =  load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"    )
   UpperCAmelCase					      =  load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"    )

   UpperCAmelCase					      =  UnCLIPImageVariationPipeline.from_pretrained(
       "kakaobrain/karlo-v1-alpha-image-variations"			,						torch_dtype=torch.floataa    )
   UpperCAmelCase					      =  pipeline.to(UpperCamelCase__    )
   pipeline.set_progress_bar_config(disable=UpperCamelCase__    )

   UpperCAmelCase					      =  torch.Generator(device="cpu"    ).manual_seed(0    )
   UpperCAmelCase					      =  pipeline(
       UpperCamelCase__			,						generator=UpperCamelCase__			,						output_type="np"			,						)

   UpperCAmelCase					      =  output.images[0]

   assert image.shape == (2_5_6, 2_5_6, 3)

   assert_mean_pixel_difference(UpperCamelCase__			,						UpperCamelCase__			,						1_5    )






import unittest

import numpy as np
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A = None   ,       ):
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)

  if shape_a[0] != shape_b[0]:
    UpperCAmelCase					      =  (
        "Expected the same number of rows for A and B. "
        F"Instead found A of size {shape_a} and B of size {shape_b}"
    )
    raise ValueError(__A			)

  if shape_b[1] != shape_c[1]:
    UpperCAmelCase					      =  (
        "Expected the same number of columns for B and C. "
        F"Instead found B of size {shape_b} and C of size {shape_c}"
    )
    raise ValueError(__A			)

  UpperCAmelCase					      =  pseudo_inv
  if a_inv is None:
    try:
      UpperCAmelCase					      =  np.linalg.inv(__A			)
    except np.linalg.LinAlgError:
      raise ValueError(
          "Input matrix A is not invertible. Cannot compute Schur complement."			)

  return mat_c - mat_b.T @ a_inv @ mat_b






class 						__magic_name__    (       unittest.TestCase    ):


 def _UpperCamelCase	(						self		: List[str]    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   UpperCAmelCase					      =  schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )

   UpperCAmelCase					      =  np.block([[a, b], [b.T, c]]    )

   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )

   self.assertAlmostEqual(lowerCAmelCase__			,						det_a * det_s    )


 def _UpperCamelCase	(						self		: str    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Dict    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1, 3], [6, 3, 5]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      unittest.main()



