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  SCREAMING_SNAKE_CASE_ (					__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     Tuple            =				StableUnCLIPPipeline
  lowercase		:     Tuple            =				TEXT_TO_IMAGE_PARAMS
  lowercase		:     Any            =				TEXT_TO_IMAGE_BATCH_PARAMS
  lowercase		:     Dict            =				TEXT_TO_IMAGE_IMAGE_PARAMS
  lowercase		:     Union[str, Any]            =				TEXT_TO_IMAGE_IMAGE_PARAMS

  # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
  lowercase		:     Dict            =				False







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				str:
         A						:     int         =32
         A						:     str         =embedder_hidden_size

         # prior components

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

         torch.manual_seed(0     )
         A						:     List[Any]         =CLIPTextModelWithProjection(
             CLIPTextConfig(
                 bos_token_id=0      ,	eos_token_id=2      ,	hidden_size=lowerCAmelCase_      ,	projection_dim=lowerCAmelCase_      ,	intermediate_size=37      ,	layer_norm_eps=1e-05      ,	num_attention_heads=4      ,	num_hidden_layers=5      ,	pad_token_id=1      ,	vocab_size=10_00      ,	)     )

         torch.manual_seed(0     )
         A						:     Any         =PriorTransformer(
             num_attention_heads=2      ,	attention_head_dim=12      ,	embedding_dim=lowerCAmelCase_      ,	num_layers=1      ,	)

         torch.manual_seed(0     )
         A						:     Any         =DDPMScheduler(
             variance_type='fixed_small_log'      ,	prediction_type='sample'      ,	num_train_timesteps=10_00      ,	clip_sample=lowerCAmelCase_      ,	clip_sample_range=5.0      ,	beta_schedule='squaredcos_cap_v2'      ,	)

         # regular denoising components

         torch.manual_seed(0     )
         A						:     List[str]         =StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase_     )
         A						:     Tuple         =DDPMScheduler(beta_schedule='squaredcos_cap_v2'     )

         torch.manual_seed(0     )
         A						:     Tuple         =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

         torch.manual_seed(0     )
         A						:     int         =CLIPTextModel(
             CLIPTextConfig(
                 bos_token_id=0      ,	eos_token_id=2      ,	hidden_size=lowerCAmelCase_      ,	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=10_00      ,	)     )

         torch.manual_seed(0     )
         A						:     Optional[Any]         =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=lowerCAmelCase_      ,	layers_per_block=1      ,	upcast_attention=lowerCAmelCase_      ,	use_linear_projection=lowerCAmelCase_      ,	)

         torch.manual_seed(0     )
         A						:     Tuple         =DDIMScheduler(
             beta_schedule='scaled_linear'      ,	beta_start=0.0_0_0_8_5      ,	beta_end=0.0_1_2      ,	prediction_type='v_prediction'      ,	set_alpha_to_one=lowerCAmelCase_      ,	steps_offset=1      ,	)

         torch.manual_seed(0     )
         A						:     Optional[Any]         =AutoencoderKL()

         A						:     Optional[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 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					int=0     )    ->				int:
         if str(lowerCAmelCase_     ).startswith('mps'     ):
                A						:     str         =torch.manual_seed(lowerCAmelCase_     )
         else:
                A						:     Tuple         =torch.Generator(device=lowerCAmelCase_     ).manual_seed(lowerCAmelCase_     )
         A						:     Any         ={
             '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 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Tuple:
         A						:     int         =torch_device == 'cpu'

         self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase_     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Dict:
         A						:     Optional[int]         =torch_device in ['cpu', 'mps']

         self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase_     )







@slow
@require_torch_gpu
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Union[str, Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				Optional[Any]:
         A						:     List[str]         =load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'     )

         A						:     int         =StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'      ,	torch_dtype=torch.floataa     )
         pipe.to(lowerCAmelCase_     )
         pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
         # 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()

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

         A						:     List[Any]         =output.images[0]

         assert image.shape == (7_68, 7_68, 3)

         assert_mean_pixel_difference(lowerCAmelCase_      ,	lowerCAmelCase_     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Union[str, Any]:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         A						:     Dict         =StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'      ,	torch_dtype=torch.floataa     )
         A						:     Optional[Any]         =pipe.to(lowerCAmelCase_     )
         pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

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

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







import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version



def A__  (			*lowercase:     Tuple,		lowercase:     Optional[Union[Dict, Any]] = None,		lowercase:     Dict=True,		lowercase:     Any=2					)				->		List[Any]:
       from .. import __version__

       A						:     Optional[Any]         =take_from
       A						:     Union[str, Any]         =()
       if not isinstance(args[0],		lowercase					):
              A						:     List[str]         =(args,)

       for attribute, version_name, message in args:
              if version.parse(version.parse(lowercase					).base_version					) >= version.parse(lowercase					):
                     raise ValueError(
                         F'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
                         F' version {__version__} is >= {version_name}'					)

              A						:     Tuple         =None
              if isinstance(lowercase,		lowercase					) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(lowercase					),)
                     A						:     Union[str, Any]         =F'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
              elif hasattr(lowercase,		lowercase					):
                     values += (getattr(lowercase,		lowercase					),)
                     A						:     Optional[Any]         =F'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
              elif deprecated_kwargs is None:
                     A						:     List[Any]         =F'`{attribute}` is deprecated and will be removed in version {version_name}.'

              if warning is not None:
                     A						:     List[Any]         =warning + ' ' if standard_warn else ''
                     warnings.warn(warning + message,		lowercase,		stacklevel=lowercase					)

       if isinstance(lowercase,		lowercase					) and len(lowercase					) > 0:
              A						:     Any         =inspect.getouterframes(inspect.currentframe()					)[1]
              A						:     int         =call_frame.filename
              A						:     int         =call_frame.lineno
              A						:     Optional[int]         =call_frame.function
              A  ,  A						:     int         =next(iter(deprecated_kwargs.items()					)					)
              raise TypeError(F'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'					)

       if len(lowercase					) == 0:
              return
       elif len(lowercase					) == 1:
              return values[0]
       return values






