

"""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        _lowercase					:







     def 	lowerCAmelCase__    (      self       ):
      torch.manual_seed(0       )
      __magic_name__     =      TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''       )

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

      torch.manual_seed(0       )
      __magic_name__     =      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       )
      __magic_name__     =      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       )
      __magic_name__     =      IFWatermarker()

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







     def 	lowerCAmelCase__    (      self       ):
      torch.manual_seed(0       )
      __magic_name__     =      TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''       )

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

      torch.manual_seed(0       )
      __magic_name__     =      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       )
      __magic_name__     =      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       )
      __magic_name__     =      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       )
      __magic_name__     =      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       ):
      __magic_name__     =      self.get_dummy_components()
      __magic_name__     =      self.pipeline_class(**UpperCAmelCase_       )
      pipe.to(UpperCAmelCase_       )
      pipe.set_progress_bar_config(disable=UpperCAmelCase_       )

      __magic_name__     =      self.get_dummy_inputs(UpperCAmelCase_       )

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

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

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

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

      __magic_name__     =      pipe.encode_prompt(UpperCAmelCase_       )

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

      if mask_image is not None:
       __magic_name__     =      mask_image

      if original_image is not None:
       __magic_name__     =      original_image

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

      __magic_name__     =      pipe(**UpperCAmelCase_       )[0]

      with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCAmelCase_       )
       __magic_name__     =      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.''' ,						)

      __magic_name__     =      self.get_dummy_inputs(UpperCAmelCase_       )

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

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

      if mask_image is not None:
       __magic_name__     =      mask_image

      if original_image is not None:
       __magic_name__     =      original_image

      __magic_name__     =      pipe_loaded(**UpperCAmelCase_       )[0]

      __magic_name__     =      np.abs(to_np(UpperCAmelCase_       ) - to_np(UpperCAmelCase_       )       ).max()
      self.assertLess(UpperCAmelCase_ ,						1E-4       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_dummy_components()
      __magic_name__     =      self.pipeline_class(**UpperCAmelCase_       )
      pipe.to(UpperCAmelCase_       )
      pipe.set_progress_bar_config(disable=UpperCAmelCase_       )

      __magic_name__     =      self.get_dummy_inputs(UpperCAmelCase_       )
      __magic_name__     =      pipe(**UpperCAmelCase_       )[0]

      with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCAmelCase_       )
       __magic_name__     =      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

      __magic_name__     =      self.get_dummy_inputs(UpperCAmelCase_       )
      __magic_name__     =      pipe_loaded(**UpperCAmelCase_       )[0]

      __magic_name__     =      np.abs(to_np(UpperCAmelCase_       ) - to_np(UpperCAmelCase_       )       ).max()
      self.assertLess(UpperCAmelCase_ ,						1E-4       )




"""simple docstring"""






from __future__ import annotations

from random import choice





def 	lowercase      (   __UpperCamelCase						) ->      Any:
 return choice(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      int:
 __magic_name__     =      random_pivot(__UpperCamelCase						)

 # partition based on pivot
 # linear time
 __magic_name__     =      [e for e in lst if e < pivot]
 __magic_name__     =      [e for e in lst if e > pivot]

 # if we get lucky, pivot might be the element we want.
 # we can easily see this:
 # small (elements smaller than k)
 # + pivot (kth element)
 # + big (elements larger than k)
 if len(__UpperCamelCase						) == k - 1:
  return pivot
 # pivot is in elements bigger than k
 elif len(__UpperCamelCase						) < k - 1:
  return kth_number(__UpperCamelCase      ,   k - len(__UpperCamelCase						) - 1						)
 # pivot is in elements smaller than k
 else:
  return kth_number(__UpperCamelCase      ,   __UpperCamelCase						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

