
import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()







class 					lowerCamelCase		(  A_							,			A_							,			A_							,			unittest.TestCase  ):
     UpperCAmelCase__    :							List[str]									=		StableUnCLIPImgaImgPipeline
     UpperCAmelCase__    :							int									=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS
     UpperCAmelCase__    :							List[Any]									=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCAmelCase__    :							Tuple									=		frozenset(
         []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     UpperCAmelCase__    :							Tuple									=		frozenset([]  )





     def 		UpperCAmelCase(self				:   Tuple       )						->						Optional[Any]:
         snake_case								= 3_2
         snake_case								= embedder_hidden_size

         # image encoding components

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

         torch.manual_seed(0       )
         snake_case								= CLIPVisionModelWithProjection(
             CLIPVisionConfig(
                 hidden_size=_A    ,   projection_dim=_A    ,   num_hidden_layers=5    ,   num_attention_heads=4    ,   image_size=3_2    ,   intermediate_size=3_7    ,   patch_size=1    ,   )       )

         # regular denoising components

         torch.manual_seed(0       )
         snake_case								= StableUnCLIPImageNormalizer(embedding_dim=_A       )
         snake_case								= DDPMScheduler(beta_schedule="squaredcos_cap_v2"       )

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

         torch.manual_seed(0       )
         snake_case								= CLIPTextModel(
             CLIPTextConfig(
                 bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=_A    ,   projection_dim=3_2    ,   intermediate_size=3_7    ,   layer_norm_eps=1E-05    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=1_0_0_0    ,   )       )

         torch.manual_seed(0       )
         snake_case								= UNetaDConditionModel(
             sample_size=3_2    ,   in_channels=4    ,   out_channels=4    ,   down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")    ,   up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")    ,   block_out_channels=(3_2, 6_4)    ,   attention_head_dim=(2, 4)    ,   class_embed_type="projection"    ,   projection_class_embeddings_input_dim=embedder_projection_dim * 2    ,   cross_attention_dim=_A    ,   layers_per_block=1    ,   upcast_attention=_A    ,   use_linear_projection=_A    ,   )

         torch.manual_seed(0       )
         snake_case								= DDIMScheduler(
             beta_schedule="scaled_linear"    ,   beta_start=0.0_00_85    ,   beta_end=0.0_12    ,   prediction_type="v_prediction"    ,   set_alpha_to_one=_A    ,   steps_offset=1    ,   )

         torch.manual_seed(0       )
         snake_case								= AutoencoderKL()

         snake_case								= {
             # image encoding components
             "feature_extractor": feature_extractor,
             "image_encoder": image_encoder.eval(),
             # image noising components
             "image_normalizer": image_normalizer.eval(),
             "image_noising_scheduler": image_noising_scheduler,
             # regular denoising components
             "tokenizer": tokenizer,
             "text_encoder": text_encoder.eval(),
             "unet": unet.eval(),
             "scheduler": scheduler,
             "vae": vae.eval(),
         }

         return components





     def 		UpperCAmelCase(self				:   List[Any]    ,   _A				:   Any    ,   _A				:   Optional[Any]=0    ,   _A				:   List[str]=True       )						->						Dict:
         if str(_A       ).startswith("mps"       ):
             snake_case								= torch.manual_seed(_A       )
         else:
             snake_case								= torch.Generator(device=_A       ).manual_seed(_A       )

         snake_case								= floats_tensor((1, 3, 3_2, 3_2)    ,   rng=random.Random(_A       )       ).to(_A       )

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

         return {
             "prompt": "An anime racoon running a marathon",
             "image": input_image,
             "generator": generator,
             "num_inference_steps": 2,
             "output_type": "np",
         }





     @skip_mps
     def 		UpperCAmelCase(self				:   str       )						->						Optional[Any]:
         snake_case								= "cpu"  # ensure determinism for the device-dependent torch.Generator
         snake_case								= self.get_dummy_components()
         snake_case								= StableUnCLIPImgaImgPipeline(**_A       )
         snake_case								= sd_pipe.to(_A       )
         sd_pipe.set_progress_bar_config(disable=_A       )

         snake_case								= self.get_dummy_inputs(_A       )
         inputs.update({"image_embeds": None}       )
         snake_case								= sd_pipe(**_A       ).images
         snake_case								= image[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         snake_case								= np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3





     def 		UpperCAmelCase(self				:   int       )						->						List[str]:
         snake_case								= torch_device in ["cpu", "mps"]

         self._test_attention_slicing_forward_pass(test_max_difference=_A       )





     def 		UpperCAmelCase(self				:   Dict       )						->						Optional[Any]:
         snake_case								= torch_device in ["cpu", "mps"]

         self._test_inference_batch_single_identical(test_max_difference=_A       )





     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()    ,   reason="XFormers attention is only available with CUDA and `xformers` installed"    ,   )
     def 		UpperCAmelCase(self				:   Any       )						->						List[Any]:
         self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_A       )







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





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





     def 		UpperCAmelCase(self				:   str       )						->						Tuple:
         snake_case								= load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"       )

         snake_case								= load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"       )

         snake_case								= StableUnCLIPImgaImgPipeline.from_pretrained(
             "fusing/stable-unclip-2-1-l-img2img"    ,   torch_dtype=torch.floataa       )
         pipe.to(_A       )
         pipe.set_progress_bar_config(disable=_A       )
         # 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								= torch.Generator(device="cpu"       ).manual_seed(0       )
         snake_case								= pipe(_A    ,   "anime turle"    ,   generator=_A    ,   output_type="np"       )

         snake_case								= output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(_A    ,   _A       )





     def 		UpperCAmelCase(self				:   Tuple       )						->						str:
         snake_case								= load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"       )

         snake_case								= load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"       )

         snake_case								= StableUnCLIPImgaImgPipeline.from_pretrained(
             "fusing/stable-unclip-2-1-h-img2img"    ,   torch_dtype=torch.floataa       )
         pipe.to(_A       )
         pipe.set_progress_bar_config(disable=_A       )
         # 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								= torch.Generator(device="cpu"       ).manual_seed(0       )
         snake_case								= pipe(_A    ,   "anime turle"    ,   generator=_A    ,   output_type="np"       )

         snake_case								= output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(_A    ,   _A       )





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Dict:
         snake_case								= load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"       )

         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         snake_case								= StableUnCLIPImgaImgPipeline.from_pretrained(
             "fusing/stable-unclip-2-1-h-img2img"    ,   torch_dtype=torch.floataa       )
         snake_case								= pipe.to(_A       )
         pipe.set_progress_bar_config(disable=_A       )
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         snake_case								= pipe(
             _A    ,   "anime turtle"    ,   num_inference_steps=2    ,   output_type="np"    ,   )

         snake_case								= torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 1_0**9



class 					lowerCamelCase		:





     def __init__(self				:   List[Any]    ,   _A				:   str       )						->						Any:
         # we need a list not a string, so do something to change the type
         snake_case								= arr.split(","       )





     def 		UpperCAmelCase(self				:   str       )						->						str:
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         for i in range(1    ,   len(self.array       )       ):
             snake_case								= max(
                 int(self.array[i]       ) + sum_value[i - 1]    ,   int(self.array[i]       )       )
             snake_case								= max(sum_value[i]    ,   rear[i - 1]       )
         return rear[len(self.array       ) - 1]


if __name__ == "__main__":
   _A   =			input("please input some numbers:")
   _A   =			SubArray(whole_array)
   _A   =			array.solve_sub_array()
   print(("the results is:", re))

