

import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class     lowercase_       (    __lowercase  ,				unittest.TestCase	):
      UpperCamelCase_     :						Optional[int]		 =							UnCLIPPipeline
      UpperCamelCase_     :						Any		 =							TEXT_TO_IMAGE_PARAMS - {
          "negative_prompt",
          "height",
          "width",
          "negative_prompt_embeds",
          "guidance_scale",
          "prompt_embeds",
          "cross_attention_kwargs",
      }
      UpperCamelCase_     :						str		 =							TEXT_TO_IMAGE_BATCH_PARAMS
      UpperCamelCase_     :						Optional[Any]		 =							[
          "generator",
          "return_dict",
          "prior_num_inference_steps",
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]
      UpperCamelCase_     :						Any		 =							False



      @property
      def 		UpperCamelCase_						(     self :							int						)       ->	int:
        return 32



      @property
      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	Optional[Any]:
        return 32



      @property
      def 		UpperCamelCase_						(     self :							int						)       ->	int:
        return self.time_input_dim



      @property
      def 		UpperCamelCase_						(     self :							Tuple						)       ->	Union[str, Any]:
        return self.time_input_dim * 4



      @property
      def 		UpperCamelCase_						(     self :							Any						)       ->	Optional[Any]:
        return 100



      @property
      def 		UpperCamelCase_						(     self :							Any						)       ->	Optional[Any]:
        _snake_case		=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)
        return tokenizer



      @property
      def 		UpperCamelCase_						(     self :							List[str]						)       ->	int:
        torch.manual_seed(0						)
        _snake_case		=				CLIPTextConfig(
            bos_token_id=0     ,   eos_token_id=2     ,   hidden_size=self.text_embedder_hidden_size     ,   projection_dim=self.text_embedder_hidden_size     ,   intermediate_size=37     ,   layer_norm_eps=1e-05     ,   num_attention_heads=4     ,   num_hidden_layers=5     ,   pad_token_id=1     ,   vocab_size=1000     ,   )
        return CLIPTextModelWithProjection(A__						)



      @property
      def 		UpperCamelCase_						(     self :							int						)       ->	Optional[Any]:
        torch.manual_seed(0						)

        _snake_case		=				{
            '''num_attention_heads''': 2,
            '''attention_head_dim''': 12,
            '''embedding_dim''': self.text_embedder_hidden_size,
            '''num_layers''': 1,
        }

        _snake_case		=				PriorTransformer(**A__						)
        return model



      @property
      def 		UpperCamelCase_						(     self :							str						)       ->	int:
        torch.manual_seed(0						)

        _snake_case		=				{
            '''clip_embeddings_dim''': self.text_embedder_hidden_size,
            '''time_embed_dim''': self.time_embed_dim,
            '''cross_attention_dim''': self.cross_attention_dim,
        }

        _snake_case		=				UnCLIPTextProjModel(**A__						)
        return model



      @property
      def 		UpperCamelCase_						(     self :							Dict						)       ->	int:
        torch.manual_seed(0						)

        _snake_case		=				{
            '''sample_size''': 32,
            # RGB in channels
            '''in_channels''': 3,
            # Out channels is double in channels because predicts mean and variance
            '''out_channels''': 6,
            '''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,
            '''cross_attention_dim''': self.cross_attention_dim,
            '''attention_head_dim''': 4,
            '''resnet_time_scale_shift''': '''scale_shift''',
            '''class_embed_type''': '''identity''',
        }

        _snake_case		=				UNetaDConditionModel(**A__						)
        return model



      @property
      def 		UpperCamelCase_						(     self :							Any						)       ->	str:
        return {
            "sample_size": 64,
            "layers_per_block": 1,
            "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
            "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "in_channels": 6,
            "out_channels": 3,
        }



      @property
      def 		UpperCamelCase_						(     self :							Dict						)       ->	Dict:
        torch.manual_seed(0						)

        _snake_case		=				UNetaDModel(**self.dummy_super_res_kwargs						)
        return model



      @property
      def 		UpperCamelCase_						(     self :							Any						)       ->	List[str]:
        # seeded differently to get different unet than `self.dummy_super_res_first`
        torch.manual_seed(1						)

        _snake_case		=				UNetaDModel(**self.dummy_super_res_kwargs						)
        return model



      def 		UpperCamelCase_						(     self :							Any						)       ->	Union[str, Any]:
        _snake_case		=				self.dummy_prior
        _snake_case		=				self.dummy_decoder
        _snake_case		=				self.dummy_text_proj
        _snake_case		=				self.dummy_text_encoder
        _snake_case		=				self.dummy_tokenizer
        _snake_case		=				self.dummy_super_res_first
        _snake_case		=				self.dummy_super_res_last

        _snake_case		=				UnCLIPScheduler(
            variance_type='''fixed_small_log'''     ,   prediction_type='''sample'''     ,   num_train_timesteps=1000     ,   clip_sample_range=5.0     ,   )

        _snake_case		=				UnCLIPScheduler(
            variance_type='''learned_range'''     ,   prediction_type='''epsilon'''     ,   num_train_timesteps=1000     ,   )

        _snake_case		=				UnCLIPScheduler(
            variance_type='''fixed_small_log'''     ,   prediction_type='''epsilon'''     ,   num_train_timesteps=1000     ,   )

        _snake_case		=				{
            '''prior''': prior,
            '''decoder''': decoder,
            '''text_proj''': text_proj,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''super_res_first''': super_res_first,
            '''super_res_last''': super_res_last,
            '''prior_scheduler''': prior_scheduler,
            '''decoder_scheduler''': decoder_scheduler,
            '''super_res_scheduler''': super_res_scheduler,
        }

        return components



      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							str     ,   A__ :							str=0						)       ->	List[Any]:
        if str(A__						).startswith('''mps'''						):
          _snake_case		=				torch.manual_seed(A__						)
        else:
          _snake_case		=				torch.Generator(device=A__						).manual_seed(A__						)
        _snake_case		=				{
            '''prompt''': '''horse''',
            '''generator''': generator,
            '''prior_num_inference_steps''': 2,
            '''decoder_num_inference_steps''': 2,
            '''super_res_num_inference_steps''': 2,
            '''output_type''': '''numpy''',
        }
        return inputs



      def 		UpperCamelCase_						(     self :							List[Any]						)       ->	List[Any]:
        _snake_case		=				'''cpu'''

        _snake_case		=				self.get_dummy_components()

        _snake_case		=				self.pipeline_class(**A__						)
        _snake_case		=				pipe.to(A__						)

        pipe.set_progress_bar_config(disable=A__						)

        _snake_case		=				pipe(**self.get_dummy_inputs(A__						)						)
        _snake_case		=				output.images

        _snake_case		=				pipe(
            **self.get_dummy_inputs(A__						)     ,   return_dict=A__     ,   )[0]

        _snake_case		=				image[0, -3:, -3:, -1]
        _snake_case		=				image_from_tuple[0, -3:, -3:, -1]

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

        _snake_case		=				np.array(
            [
                0.9997,
                0.9988,
                0.0028,
                0.9997,
                0.9984,
                0.9965,
                0.0029,
                0.9986,
                0.0025,
            ]						)

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



      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	List[Any]:
        _snake_case		=				torch.device('''cpu'''						)



        class     lowercase_       :
              UpperCamelCase_     :						Optional[int]		 =							1

        _snake_case		=				self.get_dummy_components()

        _snake_case		=				self.pipeline_class(**A__						)
        _snake_case		=				pipe.to(A__						)

        _snake_case		=				components['''prior''']
        _snake_case		=				components['''decoder''']
        _snake_case		=				components['''super_res_first''']
        _snake_case		=				components['''tokenizer''']
        _snake_case		=				components['''text_encoder''']

        _snake_case		=				torch.Generator(device=A__						).manual_seed(0						)
        _snake_case		=				prior.dtype
        _snake_case		=				1

        _snake_case		=				(batch_size, prior.config.embedding_dim)
        _snake_case		=				pipe.prepare_latents(
            A__     ,   dtype=A__     ,   device=A__     ,   generator=A__     ,   latents=A__     ,   scheduler=DummyScheduler()						)
        _snake_case		=				(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
        _snake_case		=				pipe.prepare_latents(
            A__     ,   dtype=A__     ,   device=A__     ,   generator=A__     ,   latents=A__     ,   scheduler=DummyScheduler()						)

        _snake_case		=				(
            batch_size,
            super_res_first.config.in_channels // 2,
            super_res_first.config.sample_size,
            super_res_first.config.sample_size,
        )
        _snake_case		=				pipe.prepare_latents(
            A__     ,   dtype=A__     ,   device=A__     ,   generator=A__     ,   latents=A__     ,   scheduler=DummyScheduler()						)

        pipe.set_progress_bar_config(disable=A__						)

        _snake_case		=				'''this is a prompt example'''

        _snake_case		=				torch.Generator(device=A__						).manual_seed(0						)
        _snake_case		=				pipe(
            [prompt]     ,   generator=A__     ,   prior_num_inference_steps=2     ,   decoder_num_inference_steps=2     ,   super_res_num_inference_steps=2     ,   prior_latents=A__     ,   decoder_latents=A__     ,   super_res_latents=A__     ,   output_type='''np'''     ,   )
        _snake_case		=				output.images

        _snake_case		=				tokenizer(
            A__     ,   padding='''max_length'''     ,   max_length=tokenizer.model_max_length     ,   return_tensors='''pt'''     ,   )
        _snake_case		=				text_encoder(text_inputs.input_ids						)
        _snake_case		=				text_inputs.attention_mask

        _snake_case		=				torch.Generator(device=A__						).manual_seed(0						)
        _snake_case		=				pipe(
            generator=A__     ,   prior_num_inference_steps=2     ,   decoder_num_inference_steps=2     ,   super_res_num_inference_steps=2     ,   prior_latents=A__     ,   decoder_latents=A__     ,   super_res_latents=A__     ,   text_model_output=A__     ,   text_attention_mask=A__     ,   output_type='''np'''     ,   )[0]

        # make sure passing text embeddings manually is identical
        assert np.abs(image - image_from_text						).max() < 1e-4



      @skip_mps
      def 		UpperCamelCase_						(     self :							Any						)       ->	Optional[int]:
        _snake_case		=				torch_device == '''cpu'''

        self._test_attention_slicing_forward_pass(test_max_difference=A__     ,   expected_max_diff=0.01						)



      @skip_mps
      def 		UpperCamelCase_						(     self :							int						)       ->	Union[str, Any]:
        _snake_case		=				torch_device == '''cpu'''
        _snake_case		=				True
        _snake_case		=				[
            '''prior_num_inference_steps''',
            '''decoder_num_inference_steps''',
            '''super_res_num_inference_steps''',
        ]

        self._test_inference_batch_single_identical(
            test_max_difference=A__     ,   relax_max_difference=A__     ,   additional_params_copy_to_batched_inputs=A__     ,   )



      def 		UpperCamelCase_						(     self :							List[Any]						)       ->	Tuple:
        _snake_case		=				[
            '''prior_num_inference_steps''',
            '''decoder_num_inference_steps''',
            '''super_res_num_inference_steps''',
        ]

        if torch_device == "mps":
          # TODO: MPS errors with larger batch sizes
          _snake_case		=				[2, 3]
          self._test_inference_batch_consistent(
              batch_sizes=A__     ,   additional_params_copy_to_batched_inputs=A__     ,   )
        else:
          self._test_inference_batch_consistent(
              additional_params_copy_to_batched_inputs=A__						)



      @skip_mps
      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	str:
        return super().test_dict_tuple_outputs_equivalent()



      @skip_mps
      def 		UpperCamelCase_						(     self :							int						)       ->	Any:
        return super().test_save_load_local()







      @skip_mps
      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	List[Any]:
        return super().test_save_load_optional_components()



@nightly
class     lowercase_       (    unittest.TestCase	):



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







      def 		UpperCamelCase_						(     self :							Any						)       ->	Any:
        _snake_case		=				load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/unclip/karlo_v1_alpha_horse_cpu.npy'''						)

        _snake_case		=				UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''						)
        pipeline.set_progress_bar_config(disable=A__						)

        _snake_case		=				torch.manual_seed(0						)
        _snake_case		=				pipeline(
            '''horse'''     ,   num_images_per_prompt=1     ,   generator=A__     ,   output_type='''np'''     ,   )

        _snake_case		=				output.images[0]

        assert image.shape == (256, 256, 3)
        assert np.abs(expected_image - image						).max() < 1e-1



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



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



      def 		UpperCamelCase_						(     self :							str						)       ->	int:
        _snake_case		=				load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/unclip/karlo_v1_alpha_horse_fp16.npy'''						)

        _snake_case		=				UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''     ,   torch_dtype=torch.floataa						)
        _snake_case		=				pipeline.to(A__						)
        pipeline.set_progress_bar_config(disable=A__						)

        _snake_case		=				torch.Generator(device='''cpu'''						).manual_seed(0						)
        _snake_case		=				pipeline(
            '''horse'''     ,   generator=A__     ,   output_type='''np'''     ,   )

        _snake_case		=				output.images[0]

        assert image.shape == (256, 256, 3)

        assert_mean_pixel_difference(A__     ,   A__						)







      def 		UpperCamelCase_						(     self :							str						)       ->	List[str]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _snake_case		=				UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''     ,   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(
            '''horse'''     ,   num_images_per_prompt=1     ,   prior_num_inference_steps=2     ,   decoder_num_inference_steps=2     ,   super_res_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 * 10**9




def  snake_case_(_UpperCamelCase	)				->      int:



  """simple docstring"""






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


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

