
'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 	__lowerCAmelCase    (  __a       ,					__a       ,					unittest.TestCase				):
     snake_case   :    Optional[Any]							      =      StableDiffusionParadigmsPipeline
     snake_case   :    Any							      =      TEXT_TO_IMAGE_PARAMS
     snake_case   :    Any							      =      TEXT_TO_IMAGE_BATCH_PARAMS
     snake_case   :    Dict							      =      TEXT_TO_IMAGE_IMAGE_PARAMS
     snake_case   :    Any							      =      TEXT_TO_IMAGE_IMAGE_PARAMS



     def     snake_case_ (self	):
          torch.manual_seed(0	)
          _UpperCAmelCase					:  Tuple         =    UNetaDConditionModel(
              block_out_channels=(3_2, 6_4)						,	layers_per_block=2						,	sample_size=3_2						,	in_channels=4						,	out_channels=4						,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,	cross_attention_dim=3_2						,	attention_head_dim=(2, 4)						,	use_linear_projection=lowerCAmelCase__						,	)
          _UpperCAmelCase					:  Union[str, Any]         =    DDIMParallelScheduler(
              beta_start=0.0_0_0_8_5						,	beta_end=0.0_1_2						,	beta_schedule="""scaled_linear"""						,	clip_sample=lowerCAmelCase__						,	set_alpha_to_one=lowerCAmelCase__						,	)
          torch.manual_seed(0	)
          _UpperCAmelCase					:  str         =    AutoencoderKL(
              block_out_channels=[3_2, 6_4]						,	in_channels=3						,	out_channels=3						,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,	latent_channels=4						,	sample_size=1_2_8						,	)
          torch.manual_seed(0	)
          _UpperCAmelCase					:  Any         =    CLIPTextConfig(
              bos_token_id=0						,	eos_token_id=2						,	hidden_size=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						,	hidden_act="""gelu"""						,	projection_dim=5_1_2						,	)
          _UpperCAmelCase					:  List[Any]         =    CLIPTextModel(lowerCAmelCase__	)
          _UpperCAmelCase					:  Tuple         =    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

          _UpperCAmelCase					:  Optional[int]         =    {
              """unet""": unet,
              """scheduler""": scheduler,
              """vae""": vae,
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """safety_checker""": None,
              """feature_extractor""": None,
          }
          return components



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__=0	):
          if str(lowerCAmelCase__	).startswith("""mps"""	):
               _UpperCAmelCase					:  Optional[int]         =    torch.manual_seed(lowerCAmelCase__	)
          else:
               _UpperCAmelCase					:  int         =    torch.Generator(device=lowerCAmelCase__	).manual_seed(lowerCAmelCase__	)
          _UpperCAmelCase					:  str         =    {
              """prompt""": """a photograph of an astronaut riding a horse""",
              """generator""": generator,
              """num_inference_steps""": 1_0,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
              """parallel""": 3,
              """debug""": True,
          }
          return inputs



     def     snake_case_ (self	):
          _UpperCAmelCase					:  List[str]         =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _UpperCAmelCase					:  Optional[int]         =    self.get_dummy_components()
          _UpperCAmelCase					:  List[str]         =    StableDiffusionParadigmsPipeline(**lowerCAmelCase__	)
          _UpperCAmelCase					:  Optional[Any]         =    sd_pipe.to(lowerCAmelCase__	)
          sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[Any]         =    self.get_dummy_inputs(lowerCAmelCase__	)
          _UpperCAmelCase					:  Tuple         =    sd_pipe(**lowerCAmelCase__	).images
          _UpperCAmelCase					:  Tuple         =    image[0, -3:, -3:, -1]
          assert image.shape == (1, 6_4, 6_4, 3)

          _UpperCAmelCase					:  Dict         =    np.array([0.4_7_7_3, 0.5_4_1_7, 0.4_7_2_3, 0.4_9_2_5, 0.5_6_3_1, 0.4_7_5_2, 0.5_2_4_0, 0.4_9_3_5, 0.5_0_2_3]	)

          assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Dict         =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _UpperCAmelCase					:  List[str]         =    self.get_dummy_components()
          torch.manual_seed(0	)
          _UpperCAmelCase					:  Tuple         =    DDPMParallelScheduler()
          torch.manual_seed(0	)
          _UpperCAmelCase					:  Optional[Any]         =    StableDiffusionParadigmsPipeline(**lowerCAmelCase__	)
          _UpperCAmelCase					:  Union[str, Any]         =    sd_pipe.to(lowerCAmelCase__	)
          sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

          _UpperCAmelCase					:  Union[str, Any]         =    self.get_dummy_inputs(lowerCAmelCase__	)
          _UpperCAmelCase					:  int         =    sd_pipe(**lowerCAmelCase__	).images
          _UpperCAmelCase					:  int         =    image[0, -3:, -3:, -1]
          assert image.shape == (1, 6_4, 6_4, 3)

          _UpperCAmelCase					:  Tuple         =    np.array([0.3_5_7_3, 0.4_4_2_0, 0.4_9_6_0, 0.4_7_9_9, 0.3_7_9_6, 0.3_8_7_9, 0.4_8_1_9, 0.4_3_6_5, 0.4_4_6_8]	)

          assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2



     def     snake_case_ (self	):
          super().test_inference_batch_consistent(batch_sizes=[1, 2]	)



     def     snake_case_ (self	):
          super().test_inference_batch_single_identical(batch_size=2						,	expected_max_diff=3e-3	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  str         =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _UpperCAmelCase					:  Optional[Any]         =    self.get_dummy_components()
          _UpperCAmelCase					:  Optional[Any]         =    StableDiffusionParadigmsPipeline(**lowerCAmelCase__	)
          _UpperCAmelCase					:  Optional[int]         =    sd_pipe.to(lowerCAmelCase__	)
          sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

          _UpperCAmelCase					:  Dict         =    self.get_dummy_inputs(lowerCAmelCase__	)
          _UpperCAmelCase					:  int         =    """french fries"""
          _UpperCAmelCase					:  Optional[Any]         =    sd_pipe(**lowerCAmelCase__						,	negative_prompt=lowerCAmelCase__	)
          _UpperCAmelCase					:  int         =    output.images
          _UpperCAmelCase					:  Tuple         =    image[0, -3:, -3:, -1]

          assert image.shape == (1, 6_4, 6_4, 3)

          _UpperCAmelCase					:  int         =    np.array([0.4_7_7_1, 0.5_4_2_0, 0.4_6_8_3, 0.4_9_1_8, 0.5_6_3_6, 0.4_7_2_5, 0.5_2_3_0, 0.4_9_2_3, 0.5_0_1_5]	)

          assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







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



     def     snake_case_ (self	):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



     def     snake_case_ (self						,	lowerCAmelCase__=0	):
          _UpperCAmelCase					:  Any         =    torch.Generator(device=lowerCAmelCase__	).manual_seed(lowerCAmelCase__	)
          _UpperCAmelCase					:  int         =    {
              """prompt""": """a photograph of an astronaut riding a horse""",
              """generator""": generator,
              """num_inference_steps""": 1_0,
              """guidance_scale""": 7.5,
              """output_type""": """numpy""",
              """parallel""": 3,
              """debug""": True,
          }
          return inputs



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Any         =    """stabilityai/stable-diffusion-2-base"""
          _UpperCAmelCase					:  int         =    DDIMParallelScheduler.from_pretrained(lowerCAmelCase__						,	subfolder="""scheduler"""	)
          _UpperCAmelCase					:  str         =    StableDiffusionParadigmsPipeline.from_pretrained(lowerCAmelCase__						,	scheduler=lowerCAmelCase__						,	safety_checker=lowerCAmelCase__	)
          pipe.to(lowerCAmelCase__	)
          pipe.set_progress_bar_config(disable=lowerCAmelCase__	)
          pipe.enable_attention_slicing()

          _UpperCAmelCase					:  List[Any]         =    self.get_inputs()
          _UpperCAmelCase					:  Union[str, Any]         =    pipe(**lowerCAmelCase__	).images
          _UpperCAmelCase					:  int         =    image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 5_1_2, 5_1_2, 3)

          _UpperCAmelCase					:  Union[str, Any]         =    np.array([0.9_6_2_2, 0.9_6_0_2, 0.9_7_4_8, 0.9_5_9_1, 0.9_6_3_0, 0.9_6_9_1, 0.9_6_6_1, 0.9_6_3_1, 0.9_7_4_1]	)

          assert np.abs(expected_slice - image_slice	).max() < 1e-2









'''simple docstring'''







def    _lowerCAmelCase						(						lowercase	)					->					int:
			__lowerCAmelCase											=				len(lowercase	)
			__lowerCAmelCase											=				[0] * length
			__lowerCAmelCase											=				[[False for i in range(lowercase	)] for j in range(lowercase	)]
			for i, c in enumerate(lowercase	):
						__lowerCAmelCase											=				i
						for j in range(i + 1	):
									if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
												__lowerCAmelCase											=				True
												__lowerCAmelCase											=				min(lowercase					,	0 if j == 0 else (cut[j - 1] + 1)	)
						__lowerCAmelCase											=				mincut
			return cut[length - 1]


if __name__ == "__main__":
	_a					: int      =    input("""Enter the string: """).strip()
	_a					: Dict      =    find_minimum_partitions(s)
	print(f'Minimum number of partitions required for the \'{s}\' is {ans}')

