



"""simple docstring"""

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()




class  _UpperCAmelCase (    lowerCAmelCase__					,  lowerCAmelCase__					,  lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    Dict	      =	StableUnCLIPPipeline
 _lowerCAmelCase     :    Optional[Any]	      =	TEXT_TO_IMAGE_PARAMS
 _lowerCAmelCase     :    int	      =	TEXT_TO_IMAGE_BATCH_PARAMS
 _lowerCAmelCase     :    Tuple	      =	TEXT_TO_IMAGE_IMAGE_PARAMS
 _lowerCAmelCase     :    List[Any]	      =	TEXT_TO_IMAGE_IMAGE_PARAMS

 # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
 _lowerCAmelCase     :    Union[str, Any]	      =	False

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Tuple       =			32
     snake_case_				:  Dict       =			embedder_hidden_size

     # prior components

     torch.manual_seed(0       )
     snake_case_				:  List[str]       =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

     torch.manual_seed(0       )
     snake_case_				:  List[Any]       =			CLIPTextModelWithProjection(
         CLIPTextConfig(
             bos_token_id=0						,  eos_token_id=2						,  hidden_size=lowercase_						,  projection_dim=lowercase_						,  intermediate_size=37						,  layer_norm_eps=1E-05						,  num_attention_heads=4						,  num_hidden_layers=5						,  pad_token_id=1						,  vocab_size=1000						,  )       )

     torch.manual_seed(0       )
     snake_case_				:  List[str]       =			PriorTransformer(
         num_attention_heads=2						,  attention_head_dim=12						,  embedding_dim=lowercase_						,  num_layers=1						,  )

     torch.manual_seed(0       )
     snake_case_				:  Tuple       =			DDPMScheduler(
         variance_type='''fixed_small_log'''						,  prediction_type='''sample'''						,  num_train_timesteps=1000						,  clip_sample=lowercase_						,  clip_sample_range=5.0						,  beta_schedule='''squaredcos_cap_v2'''						,  )

     # regular denoising components

     torch.manual_seed(0       )
     snake_case_				:  Optional[Any]       =			StableUnCLIPImageNormalizer(embedding_dim=lowercase_       )
     snake_case_				:  List[str]       =			DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''       )

     torch.manual_seed(0       )
     snake_case_				:  Optional[int]       =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

     torch.manual_seed(0       )
     snake_case_				:  Tuple       =			CLIPTextModel(
         CLIPTextConfig(
             bos_token_id=0						,  eos_token_id=2						,  hidden_size=lowercase_						,  projection_dim=32						,  intermediate_size=37						,  layer_norm_eps=1E-05						,  num_attention_heads=4						,  num_hidden_layers=5						,  pad_token_id=1						,  vocab_size=1000						,  )       )

     torch.manual_seed(0       )
     snake_case_				:  Optional[int]       =			UNetaDConditionModel(
         sample_size=32						,  in_channels=4						,  out_channels=4						,  down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')						,  up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')						,  block_out_channels=(32, 64)						,  attention_head_dim=(2, 4)						,  class_embed_type='''projection'''						,  projection_class_embeddings_input_dim=embedder_projection_dim * 2						,  cross_attention_dim=lowercase_						,  layers_per_block=1						,  upcast_attention=lowercase_						,  use_linear_projection=lowercase_						,  )

     torch.manual_seed(0       )
     snake_case_				:  Union[str, Any]       =			DDIMScheduler(
         beta_schedule='''scaled_linear'''						,  beta_start=0.0_00_85						,  beta_end=0.0_12						,  prediction_type='''v_prediction'''						,  set_alpha_to_one=lowercase_						,  steps_offset=1						,  )

     torch.manual_seed(0       )
     snake_case_				:  Optional[int]       =			AutoencoderKL()

     snake_case_				:  int       =			{
         # prior components
         '''prior_tokenizer''': prior_tokenizer,
         '''prior_text_encoder''': prior_text_encoder,
         '''prior''': prior,
         '''prior_scheduler''': prior_scheduler,
         # image noising components
         '''image_normalizer''': image_normalizer,
         '''image_noising_scheduler''': image_noising_scheduler,
         # regular denoising components
         '''tokenizer''': tokenizer,
         '''text_encoder''': text_encoder,
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''vae''': vae,
     }

     return components

 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: List[Any]						,  lowercase_	: Optional[int]=0       ):
     if str(lowercase_       ).startswith('''mps'''       ):
         snake_case_				:  Optional[Any]       =			torch.manual_seed(lowercase_       )
     else:
         snake_case_				:  List[str]       =			torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )
     snake_case_				:  Tuple       =			{
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''prior_num_inference_steps''': 2,
         '''output_type''': '''numpy''',
     }
     return inputs

 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Union[str, Any]       =			torch_device == '''cpu'''

     self._test_attention_slicing_forward_pass(test_max_difference=lowercase_       )






 def 		_snake_case      (  self	: Any       ):
     snake_case_				:  Any       =			torch_device in ['''cpu''', '''mps''']

     self._test_inference_batch_single_identical(test_max_difference=lowercase_       )





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

 def 		_snake_case      (  self	: List[str]       ):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  int       =			load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''       )

     snake_case_				:  List[Any]       =			StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''						,  torch_dtype=torch.floataa       )
     pipe.to(lowercase_       )
     pipe.set_progress_bar_config(disable=lowercase_       )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     snake_case_				:  Optional[int]       =			torch.Generator(device='''cpu'''       ).manual_seed(0       )
     snake_case_				:  Optional[Any]       =			pipe('''anime turle'''						,  generator=lowercase_						,  output_type='''np'''       )

     snake_case_				:  Dict       =			output.images[0]

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

     assert_mean_pixel_difference(lowercase_						,  lowercase_       )






 def 		_snake_case      (  self	: Tuple       ):
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     snake_case_				:  List[str]       =			StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''						,  torch_dtype=torch.floataa       )
     snake_case_				:  Any       =			pipe.to(lowercase_       )
     pipe.set_progress_bar_config(disable=lowercase_       )
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     snake_case_				:  Dict       =			pipe(
         '''anime turtle'''						,  prior_num_inference_steps=2						,  num_inference_steps=2						,  output_type='''np'''						,  )

     snake_case_				:  Tuple       =			torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9










"""simple docstring"""

import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize




def __lowercase    (							_a					):
    return np.dot(_a	,    _a					)




class  _UpperCAmelCase :

 def __init__(  self	: int						,  *,
     lowercase_	: float = np.inf						,  lowercase_	: str = "linear"						,  lowercase_	: float = 0.0						,  ):
     snake_case_				:  Optional[Any]       =			regularization
     snake_case_				:  Tuple       =			gamma
     if kernel == "linear":
         snake_case_				:  int       =			self.__linear
     elif kernel == "rbf":
         if self.gamma == 0:
             raise ValueError('''rbf kernel requires gamma'''       )
         if not isinstance(self.gamma						,  (float, int)       ):
             raise ValueError('''gamma must be float or int'''       )
         if not self.gamma > 0:
             raise ValueError('''gamma must be > 0'''       )
         snake_case_				:  int       =			self.__rbf
         # in the future, there could be a default value like in sklearn
         # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
         # previously it was 1/(n_features)
     else:
         snake_case_				:  List[Any]       =			f"Unknown kernel: {kernel}"
         raise ValueError(lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: ndarray						,  lowercase_	: ndarray       ):
     return np.dot(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: ndarray						,  lowercase_	: ndarray       ):
     return np.exp(-(self.gamma * norm_squared(vectora - vectora       ))       )

 def 		_snake_case      (  self	: Any						,  lowercase_	: list[ndarray]						,  lowercase_	: ndarray       ):
     snake_case_				:  Union[str, Any]       =			observations
     snake_case_				:  int       =			classes

     # using Wolfe's Dual to calculate w.
     # Primal problem: minimize 1/2*norm_squared(w)
     #   constraint: yn(w . xn + b) >= 1
     #
     # With l a vector
     # Dual problem: maximize sum_n(ln) -
     #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
     #   constraint: self.C >= ln >= 0
     #           and sum_n(ln*yn) = 0
     # Then we get w using w = sum_n(ln*yn*xn)
     # At the end we can get b ~= mean(yn - w . xn)
     #
     # Since we use kernels, we only need l_star to calculate b
     # and to classify observations

     ((snake_case_),			)				:  List[str]       =			np.shape(lowercase_       )

     def to_minimize(lowercase_	: ndarray       ) -> float:
         snake_case_				:  Tuple       =			0
         ((snake_case_),			)				:  Optional[Any]       =			np.shape(lowercase_       )
         for i in range(lowercase_       ):
             for j in range(lowercase_       ):
                 s += (
                     candidate[i]
                     * candidate[j]
                     * classes[i]
                     * classes[j]
                     * self.kernel(observations[i]						,  observations[j]       )
                 )
         return 1 / 2 * s - sum(lowercase_       )

     snake_case_				:  Optional[Any]       =			LinearConstraint(lowercase_						,  0						,  0       )
     snake_case_				:  str       =			Bounds(0						,  self.regularization       )

     snake_case_				:  int       =			minimize(
         lowercase_						,  np.ones(lowercase_       )						,  bounds=lowercase_						,  constraints=[ly_contraint]       ).x
     snake_case_				:  Optional[Any]       =			l_star

     # calculating mean offset of separation plane to points
     snake_case_				:  List[Any]       =			0
     for i in range(lowercase_       ):
         for j in range(lowercase_       ):
             s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                 observations[i]						,  observations[j]       )
     snake_case_				:  Union[str, Any]       =			s / n






 def 		_snake_case      (  self	: List[str]						,  lowercase_	: ndarray       ):
     snake_case_				:  int       =			sum(
         self.optimum[n]
         * self.classes[n]
         * self.kernel(self.observations[n]						,  lowercase_       )
         for n in range(len(self.classes       )       )       )
     return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





