





"""simple docstring"""




from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


lowerCamelCase_					:						Optional[Any]      = logging.get_logger(__name__)  # pylint: disable=invalid-name

class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			int        =       ["""safety_checker""", """feature_extractor"""]

      def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ = True ,				):

        """simple docstring"""

        super().__init__()
        if hasattr(scheduler.config ,				'steps_offset'  ) and scheduler.config.steps_offset != 1:
          A_  :	Dict    						=  (
              F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
              F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
              'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
              ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
              ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
              ' file'
          )
          deprecate('steps_offset!=1' ,				'1.0.0' ,				snake_case_ ,				standard_warn=snake_case_  )
          A_  :	int    						=  dict(scheduler.config  )
          A_  :	Dict    						=  1
          A_  :	List[str]    						=  FrozenDict(snake_case_  )

        self.register_modules(
            vae=snake_case_ ,				text_encoder=snake_case_ ,				tokenizer=snake_case_ ,				unet=snake_case_ ,				scheduler=snake_case_ ,				safety_checker=snake_case_ ,				feature_extractor=snake_case_ ,				)
        self.register_to_config(requires_safety_checker=snake_case_  )

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return {k: getattr(self ,				snake_case_  ) for k in self.config.keys() if not k.startswith('_'  )}

      def  lowerCamelCase_      (					self ,				snake_case_ = "auto"  ):

        """simple docstring"""

        if slice_size == "auto":
          # half the attention head size is usually a good trade-off between
          # speed and memory
          A_  :	List[str]    						=  self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self.enable_attention_slicing(snake_case_  )

      @torch.no_grad()
      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ = 0.8 ,				snake_case_ = 5_0 ,				snake_case_ = 7.5 ,				snake_case_ = None ,				snake_case_ = 1 ,				snake_case_ = 0.0 ,				snake_case_ = None ,				snake_case_ = "pil" ,				snake_case_ = True ,				snake_case_ = None ,				snake_case_ = 1 ,				):

        """simple docstring"""

        return StableDiffusionInpaintPipelineLegacy(**self.components  )(
            prompt=snake_case_ ,				image=snake_case_ ,				mask_image=snake_case_ ,				strength=snake_case_ ,				num_inference_steps=snake_case_ ,				guidance_scale=snake_case_ ,				negative_prompt=snake_case_ ,				num_images_per_prompt=snake_case_ ,				eta=snake_case_ ,				generator=snake_case_ ,				output_type=snake_case_ ,				return_dict=snake_case_ ,				callback=snake_case_ ,				)

      @torch.no_grad()
      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ = 0.8 ,				snake_case_ = 5_0 ,				snake_case_ = 7.5 ,				snake_case_ = None ,				snake_case_ = 1 ,				snake_case_ = 0.0 ,				snake_case_ = None ,				snake_case_ = "pil" ,				snake_case_ = True ,				snake_case_ = None ,				snake_case_ = 1 ,				**snake_case_ ,				):

        """simple docstring"""

        return StableDiffusionImgaImgPipeline(**self.components  )(
            prompt=snake_case_ ,				image=snake_case_ ,				strength=snake_case_ ,				num_inference_steps=snake_case_ ,				guidance_scale=snake_case_ ,				negative_prompt=snake_case_ ,				num_images_per_prompt=snake_case_ ,				eta=snake_case_ ,				generator=snake_case_ ,				output_type=snake_case_ ,				return_dict=snake_case_ ,				callback=snake_case_ ,				callback_steps=snake_case_ ,				)

      @torch.no_grad()
      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ = 5_1_2 ,				snake_case_ = 5_1_2 ,				snake_case_ = 5_0 ,				snake_case_ = 7.5 ,				snake_case_ = None ,				snake_case_ = 1 ,				snake_case_ = 0.0 ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = "pil" ,				snake_case_ = True ,				snake_case_ = None ,				snake_case_ = 1 ,				):

        """simple docstring"""

        return StableDiffusionPipeline(**self.components  )(
            prompt=snake_case_ ,				height=snake_case_ ,				width=snake_case_ ,				num_inference_steps=snake_case_ ,				guidance_scale=snake_case_ ,				negative_prompt=snake_case_ ,				num_images_per_prompt=snake_case_ ,				eta=snake_case_ ,				generator=snake_case_ ,				latents=snake_case_ ,				output_type=snake_case_ ,				return_dict=snake_case_ ,				callback=snake_case_ ,				callback_steps=snake_case_ ,				)






"""simple docstring"""




def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Any    						=  len(_UpperCAmelCase )
  # Create a stack and push index of fist element to it
  A_  :	List[Any]    						=  []
  st.append(0 )

  # Span value of first element is always 1
  A_  :	List[str]    						=  1

  # Calculate span values for rest of the elements
  for i in range(1			, _UpperCAmelCase ):
    # Pop elements from stack while stack is not
    # empty and top of stack is smaller than price[i]
    while len(_UpperCAmelCase ) > 0 and price[st[0]] <= price[i]:
      st.pop()

    # If stack becomes empty, then price[i] is greater
    # than all elements on left of it, i.e. price[0],
    # price[1], ..price[i-1]. Else the price[i]  is
    # greater than elements after top of stack
    A_  :	Union[str, Any]    						=  i + 1 if len(_UpperCAmelCase ) <= 0 else (i - st[0])

    # Push this element to stack
    st.append(_UpperCAmelCase )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  for i in range(0			, _UpperCAmelCase ):
    print(arr[i]			, end=' ' )


# Driver program to test above function
lowerCamelCase_					:						List[Any]      = [10, 4, 5, 90, 1_20, 80]
lowerCamelCase_					:						Optional[Any]      = [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)





print_array(S, len(price))