





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 A_					:




   def _lowerCAmelCase      (self	:Union[str, Any] )->					int:
         torch.manual_seed(0 )
         __A          =				TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )

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

         torch.manual_seed(0 )
         __A          =				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 )
         __A          =				DDPMScheduler(
             num_train_timesteps=1000    ,   beta_schedule='''squaredcos_cap_v2'''    ,   beta_start=0.0_0_0_1    ,   beta_end=0.0_2    ,   thresholding=_UpperCamelCase    ,   dynamic_thresholding_ratio=0.9_5    ,   sample_max_value=1.0    ,   prediction_type='''epsilon'''    ,   variance_type='''learned_range'''    ,   )

         torch.manual_seed(0 )
         __A          =				IFWatermarker()

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




   def _lowerCAmelCase      (self	:int )->					int:
         torch.manual_seed(0 )
         __A          =				TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )

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

         torch.manual_seed(0 )
         __A          =				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 )
         __A          =				DDPMScheduler(
             num_train_timesteps=1000    ,   beta_schedule='''squaredcos_cap_v2'''    ,   beta_start=0.0_0_0_1    ,   beta_end=0.0_2    ,   thresholding=_UpperCamelCase    ,   dynamic_thresholding_ratio=0.9_5    ,   sample_max_value=1.0    ,   prediction_type='''epsilon'''    ,   variance_type='''learned_range'''    ,   )

         torch.manual_seed(0 )
         __A          =				DDPMScheduler(
             num_train_timesteps=1000    ,   beta_schedule='''squaredcos_cap_v2'''    ,   beta_start=0.0_0_0_1    ,   beta_end=0.0_2    ,   )

         torch.manual_seed(0 )
         __A          =				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 _lowerCAmelCase      (self	:List[Any] )->					Any:
         __A          =				self.get_dummy_components()
         __A          =				self.pipeline_class(**_UpperCamelCase )
         pipe.to(_UpperCamelCase )
         pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				self.get_dummy_inputs(_UpperCamelCase )

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

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

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

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

         __A   ,						__A          =				pipe.encode_prompt(_UpperCamelCase )

         # inputs with prompt converted to embeddings
         __A          =				{
             '''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:
               __A          =				image

         if mask_image is not None:
               __A          =				mask_image

         if original_image is not None:
               __A          =				original_image

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

         __A          =				pipe(**_UpperCamelCase )[0]

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

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

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

         __A          =				self.get_dummy_inputs(_UpperCamelCase )

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

         # inputs with prompt converted to embeddings
         __A          =				{
             '''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:
               __A          =				image

         if mask_image is not None:
               __A          =				mask_image

         if original_image is not None:
               __A          =				original_image

         __A          =				pipe_loaded(**_UpperCamelCase )[0]

         __A          =				np.abs(to_np(_UpperCamelCase ) - to_np(_UpperCamelCase ) ).max()
         self.assertLess(_UpperCamelCase    ,   1e-4 )






   def _lowerCAmelCase      (self	:Optional[Any] )->					Union[str, Any]:
         __A          =				self.get_dummy_components()
         __A          =				self.pipeline_class(**_UpperCamelCase )
         pipe.to(_UpperCamelCase )
         pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				self.get_dummy_inputs(_UpperCamelCase )
         __A          =				pipe(**_UpperCamelCase )[0]

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

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

         __A          =				self.get_dummy_inputs(_UpperCamelCase )
         __A          =				pipe_loaded(**_UpperCamelCase )[0]

         __A          =				np.abs(to_np(_UpperCamelCase ) - to_np(_UpperCamelCase ) ).max()
         self.assertLess(_UpperCamelCase    ,   1e-4 )












from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

snake_case__					:							Union[str, Any]													=     (3, 9, -11, 0, 7, 5, 1, -1)
snake_case__					:							int													=     (4, 6, 2, 0, 8, 10, 3, -2)





@dataclass
class A_					:
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42





class A_					:




   def __init__(self	:Dict    ,   _UpperCamelCase	:Iterable[int] )->					None:
         __A          =				None
         for i in sorted(_UpperCamelCase    ,   reverse=_UpperCamelCase ):
               __A          =				Node(_UpperCamelCase    ,   self.head )




   def __iter__(self	:List[str] )->					Iterator[int]:
         __A          =				self.head
         while node:
               yield node.data
               __A          =				node.next_node




   def __len__(self	:Union[str, Any] )->					int:
         return sum(1 for _ in self )






   def __str__(self	:List[Any] )->					str:
         return " -> ".join([str(_UpperCamelCase ) for node in self] )





def _a				(	lowerCamelCase:   SortedLinkedList					,				lowerCamelCase:   SortedLinkedList )	->						SortedLinkedList:







      '''simple docstring'''






      return SortedLinkedList(list(lowerCamelCase ) + list(lowerCamelCase ) )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       snake_case__					:							Any													=     SortedLinkedList
       print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))





