





'''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    lowerCAmelCase_		(		lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__						,unittest.TestCase		):

 '''simple docstring'''





 __lowerCamelCase						:							Optional[Any]         =					StableUnCLIPPipeline
 __lowerCamelCase						:							Any         =					TEXT_TO_IMAGE_PARAMS
 __lowerCamelCase						:							Optional[int]         =					TEXT_TO_IMAGE_BATCH_PARAMS
 __lowerCamelCase						:							str         =					TEXT_TO_IMAGE_IMAGE_PARAMS
 __lowerCamelCase						:							int         =					TEXT_TO_IMAGE_IMAGE_PARAMS

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




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      32
     _lowerCAmelCase				=      embedder_hidden_size

     # prior components

     torch.manual_seed(0		)
     _lowerCAmelCase				=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

     torch.manual_seed(0		)
     _lowerCAmelCase				=      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=1000			,     )		)

     torch.manual_seed(0		)
     _lowerCAmelCase				=      PriorTransformer(
         num_attention_heads=2			,     attention_head_dim=12			,     embedding_dim=_lowerCamelCase			,     num_layers=1			,     )

     torch.manual_seed(0		)
     _lowerCAmelCase				=      DDPMScheduler(
         variance_type="fixed_small_log"			,     prediction_type="sample"			,     num_train_timesteps=1000			,     clip_sample=_lowerCamelCase			,     clip_sample_range=5.0			,     beta_schedule="squaredcos_cap_v2"			,     )

     # regular denoising components

     torch.manual_seed(0		)
     _lowerCAmelCase				=      StableUnCLIPImageNormalizer(embedding_dim=_lowerCamelCase		)
     _lowerCAmelCase				=      DDPMScheduler(beta_schedule="squaredcos_cap_v2"		)

     torch.manual_seed(0		)
     _lowerCAmelCase				=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

     torch.manual_seed(0		)
     _lowerCAmelCase				=      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=1000			,     )		)

     torch.manual_seed(0		)
     _lowerCAmelCase				=      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		)
     _lowerCAmelCase				=      DDIMScheduler(
         beta_schedule="scaled_linear"			,     beta_start=0.00085			,     beta_end=0.012			,     prediction_type="v_prediction"			,     set_alpha_to_one=_lowerCamelCase			,     steps_offset=1			,     )

     torch.manual_seed(0		)
     _lowerCAmelCase				=      AutoencoderKL()

     _lowerCAmelCase				=      {
         # 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			,     _lowerCAmelCase			,     _lowerCAmelCase=0		)    ->							str:





     if str(_lowerCamelCase		).startswith("mps"		):
         _lowerCAmelCase				=      torch.manual_seed(_lowerCamelCase		)
     else:
         _lowerCAmelCase				=      torch.Generator(device=_lowerCamelCase		).manual_seed(_lowerCamelCase		)
     _lowerCAmelCase				=      {
         "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		)    ->							int:
     _lowerCAmelCase				=      torch_device == "cpu"

     self._test_attention_slicing_forward_pass(test_max_difference=_lowerCamelCase		)





 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      torch_device in ["cpu", "mps"]

     self._test_inference_batch_single_identical(test_max_difference=_lowerCamelCase		)







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

 '''simple docstring'''





 def 							_snake_case   (	self		)    ->							Dict:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"		)

     _lowerCAmelCase				=      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()

     _lowerCAmelCase				=      torch.Generator(device="cpu"		).manual_seed(0		)
     _lowerCAmelCase				=      pipe("anime turle"			,     generator=_lowerCamelCase			,     output_type="np"		)

     _lowerCAmelCase				=      output.images[0]

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

     assert_mean_pixel_difference(_lowerCamelCase			,     _lowerCamelCase		)





 def 							_snake_case   (	self		)    ->							str:
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     _lowerCAmelCase				=      StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"			,     torch_dtype=torch.floataa		)
     _lowerCAmelCase				=      pipe.to(_lowerCamelCase		)
     pipe.set_progress_bar_config(disable=_lowerCamelCase		)
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     _lowerCAmelCase				=      pipe(
         "anime turtle"			,     prior_num_inference_steps=2			,     num_inference_steps=2			,     output_type="np"			,     )

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









'''simple docstring'''


from __future__ import annotations


def 	__a(SCREAMING_SNAKE_CASE_  :					list[float]		,       SCREAMING_SNAKE_CASE_  :					list[float]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sorted(numsa + numsa    )
    _lowerCAmelCase			,			_lowerCAmelCase				=      divmod(len(SCREAMING_SNAKE_CASE_    )		,       2    )
    if mod == 1:
        return all_numbers[div]
    else:
        return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       _SCREAMING_SNAKE_CASE	 =							[float(x) for x in input("Enter the elements of first array: ").split()]
       _SCREAMING_SNAKE_CASE	 =							[float(x) for x in input("Enter the elements of second array: ").split()]
       print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')


