"""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 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


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

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

        torch.manual_seed(0			)
        lowercase_	:				List[str]													=	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			)
        lowercase_	:				List[str]													=	DDPMScheduler(
            num_train_timesteps=1000     ,beta_schedule='squaredcos_cap_v2'     ,beta_start=0.0001     ,beta_end=0.02     ,thresholding=SCREAMING_SNAKE_CASE__     ,dynamic_thresholding_ratio=0.95     ,sample_max_value=1.0     ,prediction_type='epsilon'     ,variance_type='learned_range'     ,)

        torch.manual_seed(0			)
        lowercase_	:				List[str]													=	IFWatermarker()

        return {
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "unet": unet,
            "scheduler": scheduler,
            "watermarker": watermarker,
            "safety_checker": None,
            "feature_extractor": None,
        }
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        torch.manual_seed(0			)
        lowercase_	:				List[Any]													=	TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'			)

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

        torch.manual_seed(0			)
        lowercase_	:				Optional[int]													=	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=1.414     ,time_embedding_act_fn='gelu'     ,time_embedding_dim=32     ,)
        unet.set_attn_processor(AttnAddedKVProcessor()			)  # For reproducibility tests

        torch.manual_seed(0			)
        lowercase_	:				Optional[Any]													=	DDPMScheduler(
            num_train_timesteps=1000     ,beta_schedule='squaredcos_cap_v2'     ,beta_start=0.0001     ,beta_end=0.02     ,thresholding=SCREAMING_SNAKE_CASE__     ,dynamic_thresholding_ratio=0.95     ,sample_max_value=1.0     ,prediction_type='epsilon'     ,variance_type='learned_range'     ,)

        torch.manual_seed(0			)
        lowercase_	:				Optional[Any]													=	DDPMScheduler(
            num_train_timesteps=1000     ,beta_schedule='squaredcos_cap_v2'     ,beta_start=0.0001     ,beta_end=0.02     ,)

        torch.manual_seed(0			)
        lowercase_	:				List[str]													=	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 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Any													=	self.get_dummy_components()
        lowercase_	:				List[str]													=	self.pipeline_class(**SCREAMING_SNAKE_CASE__			)
        pipe.to(SCREAMING_SNAKE_CASE__			)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__			)

        lowercase_	:				Optional[int]													=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__			)

        lowercase_	:				int													=	inputs["""prompt"""]
        lowercase_	:				Union[str, Any]													=	inputs["""generator"""]
        lowercase_	:				Optional[Any]													=	inputs["""num_inference_steps"""]
        lowercase_	:				List[Any]													=	inputs["""output_type"""]

        if "image" in inputs:
             lowercase_	:				Any													=	inputs["""image"""]
        else:
             lowercase_	:				Optional[Any]													=	None

        if "mask_image" in inputs:
             lowercase_	:				str													=	inputs["""mask_image"""]
        else:
             lowercase_	:				str													=	None

        if "original_image" in inputs:
             lowercase_	:				str													=	inputs["""original_image"""]
        else:
             lowercase_	:				Union[str, Any]													=	None

        lowercase_	:				int													=	pipe.encode_prompt(SCREAMING_SNAKE_CASE__			)

        # inputs with prompt converted to embeddings
        lowercase_	:				Optional[Any]													=	{
            """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:
             lowercase_	:				Optional[Any]													=	image

        if mask_image is not None:
             lowercase_	:				Optional[int]													=	mask_image

        if original_image is not None:
             lowercase_	:				str													=	original_image

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

        lowercase_	:				Dict													=	pipe(**SCREAMING_SNAKE_CASE__			)[0]

        with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(SCREAMING_SNAKE_CASE__			)
             lowercase_	:				int													=	self.pipeline_class.from_pretrained(SCREAMING_SNAKE_CASE__			)
             pipe_loaded.to(SCREAMING_SNAKE_CASE__			)
             pipe_loaded.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__			)

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

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

        lowercase_	:				Optional[Any]													=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__			)

        lowercase_	:				int													=	inputs["""generator"""]
        lowercase_	:				Any													=	inputs["""num_inference_steps"""]
        lowercase_	:				Union[str, Any]													=	inputs["""output_type"""]

        # inputs with prompt converted to embeddings
        lowercase_	:				Optional[int]													=	{
            """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:
             lowercase_	:				Any													=	image

        if mask_image is not None:
             lowercase_	:				int													=	mask_image

        if original_image is not None:
             lowercase_	:				str													=	original_image

        lowercase_	:				List[str]													=	pipe_loaded(**SCREAMING_SNAKE_CASE__			)[0]

        lowercase_	:				Dict													=	np.abs(to_np(SCREAMING_SNAKE_CASE__			) - to_np(SCREAMING_SNAKE_CASE__			)			).max()
        self.assertLess(SCREAMING_SNAKE_CASE__     ,1e-4			)







   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	self.get_dummy_components()
        lowercase_	:				Optional[int]													=	self.pipeline_class(**SCREAMING_SNAKE_CASE__			)
        pipe.to(SCREAMING_SNAKE_CASE__			)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__			)

        lowercase_	:				List[Any]													=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__			)
        lowercase_	:				Tuple													=	pipe(**SCREAMING_SNAKE_CASE__			)[0]

        with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(SCREAMING_SNAKE_CASE__			)
             lowercase_	:				Union[str, Any]													=	self.pipeline_class.from_pretrained(SCREAMING_SNAKE_CASE__			)
             pipe_loaded.to(SCREAMING_SNAKE_CASE__			)
             pipe_loaded.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__			)

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

        lowercase_	:				List[str]													=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__			)
        lowercase_	:				str													=	pipe_loaded(**SCREAMING_SNAKE_CASE__			)[0]

        lowercase_	:				Tuple													=	np.abs(to_np(SCREAMING_SNAKE_CASE__			) - to_np(SCREAMING_SNAKE_CASE__			)			).max()
        self.assertLess(SCREAMING_SNAKE_CASE__     ,1e-4			)



"""simple docstring"""




def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   [1]

							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   0, 0, 0
							SCREAMING_SNAKE_CASE__						:       List[str]           =   ugly_nums[ia] * 2
							SCREAMING_SNAKE_CASE__						:       int           =   ugly_nums[ia] * 3
							SCREAMING_SNAKE_CASE__						:       Any           =   ugly_nums[ia] * 5

							for _ in range(1   ,_snake_case						):
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   min(_snake_case   ,_snake_case   ,_snake_case						)
														ugly_nums.append(_snake_case						)
														if next_num == next_a:
																					ia += 1
																					SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ugly_nums[ia] * 2
														if next_num == next_a:
																					ia += 1
																					SCREAMING_SNAKE_CASE__						:       List[str]           =   ugly_nums[ia] * 3
														if next_num == next_a:
																					ia += 1
																					SCREAMING_SNAKE_CASE__						:       Tuple           =   ugly_nums[ia] * 5
							return ugly_nums[-1]


if __name__ == "__main__":
					from doctest import testmod

					testmod(verbose=True)
					print(f"""{ugly_numbers(2_0_0) = }""")




