




'''simple docstring'''




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class   __UpperCamelCase       :






       def      lowercase__     (							self  ):



              """simple docstring"""







              torch.manual_seed(0  )
              lowerCamelCase_							=TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

              torch.manual_seed(0  )
              lowerCamelCase_							=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

              torch.manual_seed(0  )
              lowerCamelCase_							=UNetaDConditionModel(
                  sample_size=32,		layers_per_block=1,		block_out_channels=[32, 64],		down_block_types=[
                      '''ResnetDownsampleBlock2D''',
                      '''SimpleCrossAttnDownBlock2D''',
                  ],		mid_block_type='''UNetMidBlock2DSimpleCrossAttn''',		up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''],		in_channels=3,		out_channels=6,		cross_attention_dim=32,		encoder_hid_dim=32,		attention_head_dim=8,		addition_embed_type='''text''',		addition_embed_type_num_heads=2,		cross_attention_norm='''group_norm''',		resnet_time_scale_shift='''scale_shift''',		act_fn='''gelu''',		)
              unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

              torch.manual_seed(0  )
              lowerCamelCase_							=DDPMScheduler(
                  num_train_timesteps=1_000,		beta_schedule='''squaredcos_cap_v2''',		beta_start=0.0_0_0_1,		beta_end=0.0_2,		thresholding=lowerCAmelCase,		dynamic_thresholding_ratio=0.9_5,		sample_max_value=1.0,		prediction_type='''epsilon''',		variance_type='''learned_range''',		)

              torch.manual_seed(0  )
              lowerCamelCase_							=IFWatermarker()

              return {
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
                  "unet": unet,
                  "scheduler": scheduler,
                  "watermarker": watermarker,
                  "safety_checker": None,
                  "feature_extractor": None,
              }






       def      lowercase__     (							self  ):



              """simple docstring"""







              torch.manual_seed(0  )
              lowerCamelCase_							=TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

              torch.manual_seed(0  )
              lowerCamelCase_							=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

              torch.manual_seed(0  )
              lowerCamelCase_							=UNetaDConditionModel(
                  sample_size=32,		layers_per_block=[1, 2],		block_out_channels=[32, 64],		down_block_types=[
                      '''ResnetDownsampleBlock2D''',
                      '''SimpleCrossAttnDownBlock2D''',
                  ],		mid_block_type='''UNetMidBlock2DSimpleCrossAttn''',		up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''],		in_channels=6,		out_channels=6,		cross_attention_dim=32,		encoder_hid_dim=32,		attention_head_dim=8,		addition_embed_type='''text''',		addition_embed_type_num_heads=2,		cross_attention_norm='''group_norm''',		resnet_time_scale_shift='''scale_shift''',		act_fn='''gelu''',		class_embed_type='''timestep''',		mid_block_scale_factor=1.4_1_4,		time_embedding_act_fn='''gelu''',		time_embedding_dim=32,		)
              unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

              torch.manual_seed(0  )
              lowerCamelCase_							=DDPMScheduler(
                  num_train_timesteps=1_000,		beta_schedule='''squaredcos_cap_v2''',		beta_start=0.0_0_0_1,		beta_end=0.0_2,		thresholding=lowerCAmelCase,		dynamic_thresholding_ratio=0.9_5,		sample_max_value=1.0,		prediction_type='''epsilon''',		variance_type='''learned_range''',		)

              torch.manual_seed(0  )
              lowerCamelCase_							=DDPMScheduler(
                  num_train_timesteps=1_000,		beta_schedule='''squaredcos_cap_v2''',		beta_start=0.0_0_0_1,		beta_end=0.0_2,		)

              torch.manual_seed(0  )
              lowerCamelCase_							=IFWatermarker()

              return {
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
                  "unet": unet,
                  "scheduler": scheduler,
                  "image_noising_scheduler": image_noising_scheduler,
                  "watermarker": watermarker,
                  "safety_checker": None,
                  "feature_extractor": None,
              }






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.get_dummy_components()
              lowerCamelCase_							=self.pipeline_class(**lowerCAmelCase  )
              pipe.to(lowerCAmelCase  )
              pipe.set_progress_bar_config(disable=lowerCAmelCase  )

              lowerCamelCase_							=self.get_dummy_inputs(lowerCAmelCase  )

              lowerCamelCase_							=inputs['''prompt''']
              lowerCamelCase_							=inputs['''generator''']
              lowerCamelCase_							=inputs['''num_inference_steps''']
              lowerCamelCase_							=inputs['''output_type''']

              if "image" in inputs:
                     lowerCamelCase_							=inputs['''image''']
              else:
                     lowerCamelCase_							=None

              if "mask_image" in inputs:
                     lowerCamelCase_							=inputs['''mask_image''']
              else:
                     lowerCamelCase_							=None

              if "original_image" in inputs:
                     lowerCamelCase_							=inputs['''original_image''']
              else:
                     lowerCamelCase_							=None

              lowerCamelCase_,      lowerCamelCase_							=pipe.encode_prompt(lowerCAmelCase  )

              # inputs with prompt converted to embeddings
              lowerCamelCase_							={
                  '''prompt_embeds''': prompt_embeds,
                  '''negative_prompt_embeds''': negative_prompt_embeds,
                  '''generator''': generator,
                  '''num_inference_steps''': num_inference_steps,
                  '''output_type''': output_type,
              }

              if image is not None:
                     lowerCamelCase_							=image

              if mask_image is not None:
                     lowerCamelCase_							=mask_image

              if original_image is not None:
                     lowerCamelCase_							=original_image

              # set all optional components to None
              for optional_component in pipe._optional_components:
                     setattr(lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  )

              lowerCamelCase_							=pipe(**lowerCAmelCase  )[0]

              with tempfile.TemporaryDirectory() as tmpdir:
                     pipe.save_pretrained(lowerCAmelCase  )
                     lowerCamelCase_							=self.pipeline_class.from_pretrained(lowerCAmelCase  )
                     pipe_loaded.to(lowerCAmelCase  )
                     pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase  )

              pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

              for optional_component in pipe._optional_components:
                     self.assertTrue(
                         getattr(lowerCAmelCase,		lowerCAmelCase  ) is None,		f'''`{optional_component}` did not stay set to None after loading.''',		)

              lowerCamelCase_							=self.get_dummy_inputs(lowerCAmelCase  )

              lowerCamelCase_							=inputs['''generator''']
              lowerCamelCase_							=inputs['''num_inference_steps''']
              lowerCamelCase_							=inputs['''output_type''']

              # inputs with prompt converted to embeddings
              lowerCamelCase_							={
                  '''prompt_embeds''': prompt_embeds,
                  '''negative_prompt_embeds''': negative_prompt_embeds,
                  '''generator''': generator,
                  '''num_inference_steps''': num_inference_steps,
                  '''output_type''': output_type,
              }

              if image is not None:
                     lowerCamelCase_							=image

              if mask_image is not None:
                     lowerCamelCase_							=mask_image

              if original_image is not None:
                     lowerCamelCase_							=original_image

              lowerCamelCase_							=pipe_loaded(**lowerCAmelCase  )[0]

              lowerCamelCase_							=np.abs(to_np(lowerCAmelCase  ) - to_np(lowerCAmelCase  )  ).max()
              self.assertLess(lowerCAmelCase,		1e-4  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.get_dummy_components()
              lowerCamelCase_							=self.pipeline_class(**lowerCAmelCase  )
              pipe.to(lowerCAmelCase  )
              pipe.set_progress_bar_config(disable=lowerCAmelCase  )

              lowerCamelCase_							=self.get_dummy_inputs(lowerCAmelCase  )
              lowerCamelCase_							=pipe(**lowerCAmelCase  )[0]

              with tempfile.TemporaryDirectory() as tmpdir:
                     pipe.save_pretrained(lowerCAmelCase  )
                     lowerCamelCase_							=self.pipeline_class.from_pretrained(lowerCAmelCase  )
                     pipe_loaded.to(lowerCAmelCase  )
                     pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase  )

              pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

              lowerCamelCase_							=self.get_dummy_inputs(lowerCAmelCase  )
              lowerCamelCase_							=pipe_loaded(**lowerCAmelCase  )[0]

              lowerCamelCase_							=np.abs(to_np(lowerCAmelCase  ) - to_np(lowerCAmelCase  )  ).max()
              self.assertLess(lowerCAmelCase,		1e-4  )











def 		lowerCAmelCase_    (  __UpperCAmelCase:			int      )      ->	bool:
    return str(__UpperCAmelCase      ) == str(__UpperCAmelCase      )[::-1]




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int      )      ->	int:
    return int(__UpperCAmelCase      ) + int(str(__UpperCAmelCase      )[::-1]      )




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int = 1_0000      )      ->	int:
    UpperCamelCase__							:				Optional[Any]          =      []
    for num in range(1		,   __UpperCAmelCase      ):
        UpperCamelCase__							:				str          =      0
        UpperCamelCase__							:				Any          =      num
        while iterations < 50:
            UpperCamelCase__							:				List[Any]          =      sum_reverse(__UpperCAmelCase      )
            iterations += 1
            if is_palindrome(__UpperCAmelCase      ):
                break
        else:
            lychrel_nums.append(__UpperCAmelCase      )
    return len(__UpperCAmelCase      )


if __name__ == "__main__":
  print(F'''{solution() = }''')

