
"""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 			_SCREAMING_SNAKE_CASE					:






			def  __lowerCAmelCase (					self       )			->     Tuple:
						torch.manual_seed(0       )
						lowerCAmelCase_    :int			     =  TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""       )

						torch.manual_seed(0       )
						lowerCAmelCase_    :Optional[Any]			     =  AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""       )

						torch.manual_seed(0       )
						lowerCAmelCase_    :List[Any]			     =  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_    :str			     =  DDPMScheduler(
						    num_train_timesteps=1000       ,   beta_schedule="""squaredcos_cap_v2"""       ,   beta_start=0.0_0_0_1       ,   beta_end=0.0_2       ,   thresholding=__A       ,   dynamic_thresholding_ratio=0.9_5       ,   sample_max_value=1.0       ,   prediction_type="""epsilon"""       ,   variance_type="""learned_range"""       ,   )

						torch.manual_seed(0       )
						lowerCAmelCase_    :int			     =  IFWatermarker()

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






			def  __lowerCAmelCase (					self       )			->     List[str]:
						torch.manual_seed(0       )
						lowerCAmelCase_    :Dict			     =  TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""       )

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

						torch.manual_seed(0       )
						lowerCAmelCase_    :Optional[Any]			     =  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_    :str			     =  DDPMScheduler(
						    num_train_timesteps=1000       ,   beta_schedule="""squaredcos_cap_v2"""       ,   beta_start=0.0_0_0_1       ,   beta_end=0.0_2       ,   thresholding=__A       ,   dynamic_thresholding_ratio=0.9_5       ,   sample_max_value=1.0       ,   prediction_type="""epsilon"""       ,   variance_type="""learned_range"""       ,   )

						torch.manual_seed(0       )
						lowerCAmelCase_    :Optional[int]			     =  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       )
						lowerCAmelCase_    :Dict			     =  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       )			->     Dict:
						lowerCAmelCase_    :Dict			     =  self.get_dummy_components()
						lowerCAmelCase_    :Tuple			     =  self.pipeline_class(**__A       )
						pipe.to(__A       )
						pipe.set_progress_bar_config(disable=__A       )

						lowerCAmelCase_    :Any			     =  self.get_dummy_inputs(__A       )

						lowerCAmelCase_    :Optional[int]			     =  inputs["""prompt"""]
						lowerCAmelCase_    :Optional[int]			     =  inputs["""generator"""]
						lowerCAmelCase_    :Any			     =  inputs["""num_inference_steps"""]
						lowerCAmelCase_    :Optional[int]			     =  inputs["""output_type"""]

						if "image" in inputs:
									lowerCAmelCase_    :List[Any]			     =  inputs["""image"""]
						else:
									lowerCAmelCase_    :int			     =  None

						if "mask_image" in inputs:
									lowerCAmelCase_    :List[Any]			     =  inputs["""mask_image"""]
						else:
									lowerCAmelCase_    :int			     =  None

						if "original_image" in inputs:
									lowerCAmelCase_    :List[Any]			     =  inputs["""original_image"""]
						else:
									lowerCAmelCase_    :List[Any]			     =  None

						lowerCAmelCase_     ,       lowerCAmelCase_    :int			     =  pipe.encode_prompt(__A       )

						# inputs with prompt converted to embeddings
						lowerCAmelCase_    :List[str]			     =  {
						    """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_    :int			     =  image

						if mask_image is not None:
									lowerCAmelCase_    :Tuple			     =  mask_image

						if original_image is not None:
									lowerCAmelCase_    :Optional[Any]			     =  original_image

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

						lowerCAmelCase_    :Optional[int]			     =  pipe(**__A       )[0]

						with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(__A       )
									lowerCAmelCase_    :Optional[int]			     =  self.pipeline_class.from_pretrained(__A       )
									pipe_loaded.to(__A       )
									pipe_loaded.set_progress_bar_config(disable=__A       )

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

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

						lowerCAmelCase_    :Dict			     =  self.get_dummy_inputs(__A       )

						lowerCAmelCase_    :Union[str, Any]			     =  inputs["""generator"""]
						lowerCAmelCase_    :Any			     =  inputs["""num_inference_steps"""]
						lowerCAmelCase_    :Tuple			     =  inputs["""output_type"""]

						# inputs with prompt converted to embeddings
						lowerCAmelCase_    :Tuple			     =  {
						    """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_    :Optional[int]			     =  image

						if mask_image is not None:
									lowerCAmelCase_    :str			     =  mask_image

						if original_image is not None:
									lowerCAmelCase_    :Tuple			     =  original_image

						lowerCAmelCase_    :Union[str, Any]			     =  pipe_loaded(**__A       )[0]

						lowerCAmelCase_    :Dict			     =  np.abs(to_np(__A       ) - to_np(__A       )       ).max()
						self.assertLess(__A       ,   1E-4       )






			def  __lowerCAmelCase (					self       )			->     List[str]:
						lowerCAmelCase_    :Any			     =  self.get_dummy_components()
						lowerCAmelCase_    :Optional[int]			     =  self.pipeline_class(**__A       )
						pipe.to(__A       )
						pipe.set_progress_bar_config(disable=__A       )

						lowerCAmelCase_    :Optional[int]			     =  self.get_dummy_inputs(__A       )
						lowerCAmelCase_    :Dict			     =  pipe(**__A       )[0]

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

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

						lowerCAmelCase_    :List[Any]			     =  self.get_dummy_inputs(__A       )
						lowerCAmelCase_    :str			     =  pipe_loaded(**__A       )[0]

						lowerCAmelCase_    :Dict			     =  np.abs(to_np(__A       ) - to_np(__A       )       ).max()
						self.assertLess(__A       ,   1E-4       )









def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Tuple:





    '''simple docstring'''






    A__          =   0
    A__          =   len(SCREAMING_SNAKE_CASE__      ) - 1

    while left <= right:
        # avoid divided by 0 during interpolation
        if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                return left
            else:
                return None

        A__          =   left + ((item - sorted_collection[left]) * (right - left)) // (
            sorted_collection[right] - sorted_collection[left]
        )

        # out of range check
        if point < 0 or point >= len(SCREAMING_SNAKE_CASE__      ):
            return None

        A__          =   sorted_collection[point]
        if current_item == item:
            return point
        else:
            if point < left:
                A__          =   left
                A__          =   point
            elif point > right:
                A__          =   right
                A__          =   point
            else:
                if item < current_item:
                    A__          =   point - 1
                else:
                    A__          =   point + 1
    return None
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      str:





    '''simple docstring'''






    if sorted_collection[left] == sorted_collection[right]:
        if sorted_collection[left] == item:
            return left
        else:
            return None

    A__          =   left + ((item - sorted_collection[left]) * (right - left)) // (
        sorted_collection[right] - sorted_collection[left]
    )

    # out of range check
    if point < 0 or point >= len(SCREAMING_SNAKE_CASE__      ):
        return None

    if sorted_collection[point] == item:
        return point
    elif point < left:
        return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    elif point > right:
        return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    else:
        if sorted_collection[point] > item:
            return interpolation_search_by_recursion(
                SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			point - 1      )
        else:
            return interpolation_search_by_recursion(
                SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			point + 1    ,			SCREAMING_SNAKE_CASE__      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple      )							->      Tuple:





    '''simple docstring'''






    if collection != sorted(SCREAMING_SNAKE_CASE__      ):
        raise ValueError('Collection must be ascending sorted'      )
    return True


if __name__ == "__main__":
     import sys
     lowercase_       		=  0
     if debug == 1:
          lowercase_       		=  [10, 30, 40, 45, 50, 66, 77, 93]
          try:
               __assert_sorted(collection)
          except ValueError:
               sys.exit("Sequence must be ascending sorted to apply interpolation search")
          lowercase_       		=  67

     lowercase_       		=  interpolation_search(collection, target)
     if result is not None:
          print(f"""{target} found at positions: {result}""")
     else:
          print("Not found")

