import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class        snake_case__							(					UpperCamelCase__       ,    unittest.TestCase				):
   _SCREAMING_SNAKE_CASE						:			Optional[int]         =   KandinskyInpaintPipeline
   _SCREAMING_SNAKE_CASE						:			List[str]         =   ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
   _SCREAMING_SNAKE_CASE						:			Dict         =   [
       "prompt",
       "negative_prompt",
       "image_embeds",
       "negative_image_embeds",
       "image",
       "mask_image",
   ]
   _SCREAMING_SNAKE_CASE						:			Tuple         =   [
       "generator",
       "height",
       "width",
       "latents",
       "guidance_scale",
       "negative_prompt",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   _SCREAMING_SNAKE_CASE						:			Tuple         =   False



   @property
   def        UpperCAmelCase__							(					self : Union[str, Any] )				->  Any:




         '''simple docstring'''




         return 32



   @property
   def        UpperCAmelCase__							(					self : Optional[Any] )				->  List[Any]:




         '''simple docstring'''




         return 32



   @property
   def        UpperCAmelCase__							(					self : List[Any] )				->  List[Any]:




         '''simple docstring'''




         return self.time_input_dim



   @property
   def        UpperCAmelCase__							(					self : Dict )				->  str:




         '''simple docstring'''




         return self.time_input_dim * 4



   @property
   def        UpperCAmelCase__							(					self : List[Any] )				->  str:




         '''simple docstring'''




         return 1_00



   @property
   def        UpperCAmelCase__							(					self : Dict )				->  Any:




         '''simple docstring'''




         snake_case_  :      Dict  	=							XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
         return tokenizer



   @property
   def        UpperCAmelCase__							(					self : str )				->  str:




         '''simple docstring'''




         torch.manual_seed(0 )
         snake_case_  :      str  	=							MCLIPConfig(
             numDims=self.cross_attention_dim      ,						transformerDimensions=self.text_embedder_hidden_size      ,						hidden_size=self.text_embedder_hidden_size      ,						intermediate_size=37      ,						num_attention_heads=4      ,						num_hidden_layers=5      ,						vocab_size=10_05      ,						)

         snake_case_  :      Tuple  	=							MultilingualCLIP(__snake_case )
         snake_case_  :      Optional[int]  	=							text_encoder.eval()

         return text_encoder



   @property
   def        UpperCAmelCase__							(					self : Dict )				->  Optional[int]:




         '''simple docstring'''




         torch.manual_seed(0 )

         snake_case_  :      Optional[Any]  	=							{
             """in_channels""": 9,
             # Out channels is double in channels because predicts mean and variance
             """out_channels""": 8,
             """addition_embed_type""": """text_image""",
             """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,
             """encoder_hid_dim""": self.text_embedder_hidden_size,
             """encoder_hid_dim_type""": """text_image_proj""",
             """cross_attention_dim""": self.cross_attention_dim,
             """attention_head_dim""": 4,
             """resnet_time_scale_shift""": """scale_shift""",
             """class_embed_type""": None,
         }

         snake_case_  :      Union[str, Any]  	=							UNetaDConditionModel(**__snake_case )
         return model



   @property
   def        UpperCAmelCase__							(					self : List[str] )				->  Dict:




         '''simple docstring'''




         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }



   @property
   def        UpperCAmelCase__							(					self : Tuple )				->  int:




         '''simple docstring'''




         torch.manual_seed(0 )
         snake_case_  :      Dict  	=							VQModel(**self.dummy_movq_kwargs )
         return model



   def        UpperCAmelCase__							(					self : Optional[Any] )				->  List[Any]:




         '''simple docstring'''




         snake_case_  :      List[str]  	=							self.dummy_text_encoder
         snake_case_  :      Optional[Any]  	=							self.dummy_tokenizer
         snake_case_  :      Optional[Any]  	=							self.dummy_unet
         snake_case_  :      Tuple  	=							self.dummy_movq

         snake_case_  :      List[str]  	=							DDIMScheduler(
             num_train_timesteps=10_00      ,						beta_schedule="linear"      ,						beta_start=0.0_0085      ,						beta_end=0.012      ,						clip_sample=__snake_case      ,						set_alpha_to_one=__snake_case      ,						steps_offset=1      ,						prediction_type="epsilon"      ,						thresholding=__snake_case      ,						)

         snake_case_  :      str  	=							{
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """unet""": unet,
             """scheduler""": scheduler,
             """movq""": movq,
         }

         return components



   def        UpperCAmelCase__							(					self : str      ,						A__ : Optional[Any]      ,						A__ : int=0 )				->  int:




         '''simple docstring'''




         snake_case_  :      Union[str, Any]  	=							floats_tensor((1, self.cross_attention_dim)      ,						rng=random.Random(__snake_case ) ).to(__snake_case )
         snake_case_  :      Dict  	=							floats_tensor((1, self.cross_attention_dim)      ,						rng=random.Random(seed + 1 ) ).to(__snake_case )
         # create init_image
         snake_case_  :      str  	=							floats_tensor((1, 3, 64, 64)      ,						rng=random.Random(__snake_case ) ).to(__snake_case )
         snake_case_  :      int  	=							image.cpu().permute(0      ,						2      ,						3      ,						1 )[0]
         snake_case_  :      str  	=							Image.fromarray(np.uinta(__snake_case ) ).convert("RGB" ).resize((2_56, 2_56) )
         # create mask
         snake_case_  :      Dict  	=							np.ones((64, 64)      ,						dtype=np.floataa )
         snake_case_  :      Any  	=							0

         if str(__snake_case ).startswith("mps" ):
               snake_case_  :      Dict  	=							torch.manual_seed(__snake_case )
         else:
               snake_case_  :      Tuple  	=							torch.Generator(device=__snake_case ).manual_seed(__snake_case )
         snake_case_  :      List[Any]  	=							{
             """prompt""": """horse""",
             """image""": init_image,
             """mask_image""": mask,
             """image_embeds""": image_embeds,
             """negative_image_embeds""": negative_image_embeds,
             """generator""": generator,
             """height""": 64,
             """width""": 64,
             """num_inference_steps""": 2,
             """guidance_scale""": 4.0,
             """output_type""": """np""",
         }
         return inputs



   def        UpperCAmelCase__							(					self : List[str] )				->  int:




         '''simple docstring'''




         snake_case_  :      Tuple  	=							"""cpu"""

         snake_case_  :      List[Any]  	=							self.get_dummy_components()

         snake_case_  :      Union[str, Any]  	=							self.pipeline_class(**__snake_case )
         snake_case_  :      Tuple  	=							pipe.to(__snake_case )

         pipe.set_progress_bar_config(disable=__snake_case )

         snake_case_  :      Tuple  	=							pipe(**self.get_dummy_inputs(__snake_case ) )
         snake_case_  :      List[Any]  	=							output.images

         snake_case_  :      Any  	=							pipe(
             **self.get_dummy_inputs(__snake_case )      ,						return_dict=__snake_case      ,						)[0]

         snake_case_  :      int  	=							image[0, -3:, -3:, -1]
         snake_case_  :      str  	=							image_from_tuple[0, -3:, -3:, -1]

         print(f"image.shape {image.shape}" )

         assert image.shape == (1, 64, 64, 3)

         snake_case_  :      Optional[Any]  	=							np.array(
             [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )

         assert (
             np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
         ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
         ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"



   def        UpperCAmelCase__							(					self : Dict )				->  List[str]:




         '''simple docstring'''




         super().test_inference_batch_single_identical(expected_max_diff=3E-3 )



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



   def        UpperCAmelCase__							(					self : List[Any] )				->  Optional[int]:




         '''simple docstring'''




         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



   def        UpperCAmelCase__							(					self : Union[str, Any] )				->  Optional[int]:




         '''simple docstring'''




         snake_case_  :      List[str]  	=							load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy" )

         snake_case_  :      int  	=							load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
         snake_case_  :      List[Any]  	=							np.ones((7_68, 7_68)      ,						dtype=np.floataa )
         snake_case_  :      Any  	=							0

         snake_case_  :      int  	=							"""a hat"""

         snake_case_  :      int  	=							KandinskyPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1-prior"      ,						torch_dtype=torch.floataa )
         pipe_prior.to(__snake_case )

         snake_case_  :      Dict  	=							KandinskyInpaintPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1-inpaint"      ,						torch_dtype=torch.floataa )
         snake_case_  :      int  	=							pipeline.to(__snake_case )
         pipeline.set_progress_bar_config(disable=__snake_case )

         snake_case_  :      Union[str, Any]  	=							torch.Generator(device="cpu" ).manual_seed(0 )
         snake_case_  :      Dict  	=							pipe_prior(
             __snake_case      ,						generator=__snake_case      ,						num_inference_steps=5      ,						negative_prompt=""      ,						).to_tuple()

         snake_case_  :      Optional[Any]  	=							pipeline(
             __snake_case      ,						image=__snake_case      ,						mask_image=__snake_case      ,						image_embeds=__snake_case      ,						negative_image_embeds=__snake_case      ,						generator=__snake_case      ,						num_inference_steps=1_00      ,						height=7_68      ,						width=7_68      ,						output_type="np"      ,						)

         snake_case_  :      Optional[int]  	=							output.images[0]

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

         assert_mean_pixel_difference(__snake_case      ,						__snake_case )









from collections.abc import Sequence




def 		lowerCAmelCase_ (						lowerCamelCase = None ):
				if nums is None or not nums:
								raise ValueError("""Input sequence should not be empty""" )

				__magic_name__						:						str										=nums[0]
				for i in range(1						,  len(lowerCamelCase ) ):
								__magic_name__						:						Any										=nums[i]
								__magic_name__						:						Dict										=max(lowerCamelCase						,  ans + num						,  lowerCamelCase )

				return ans


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Try on a sample input from the user
							UpperCAmelCase_		:					List[str]          =  int(input("Enter number of elements : ").strip())
							UpperCAmelCase_		:					Tuple          =  list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
							print(max_subsequence_sum(array))




