import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


A   :     str       =				logging.get_logger(__name__)  # pylint: disable=invalid-name


class      lowerCamelCase    (UpperCAmelCase__    ):
  """simple docstring"""







  def __init__(							self       :				Optional[Any]     ,   __magic_name__       :				List[str]     ,   __magic_name__       :				Union[str, Any]     ,   __magic_name__       :				Optional[int]     ,   __magic_name__       :				List[str]     ,   __magic_name__       :				Optional[int]     ,   __magic_name__       :				Dict     ,   __magic_name__       :				str     ,   __magic_name__       :				List[str]     ,   __magic_name__       :				Any     ,   )     ->		int:
        super().__init__()

        if safety_checker is None:
              logger.warning(
                  F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                  " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                  " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                  " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                  " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                  " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."      )

        self.register_modules(
            speech_model=_SCREAMING_SNAKE_CASE     ,   speech_processor=_SCREAMING_SNAKE_CASE     ,   vae=_SCREAMING_SNAKE_CASE     ,   text_encoder=_SCREAMING_SNAKE_CASE     ,   tokenizer=_SCREAMING_SNAKE_CASE     ,   unet=_SCREAMING_SNAKE_CASE     ,   scheduler=_SCREAMING_SNAKE_CASE     ,   feature_extractor=_SCREAMING_SNAKE_CASE     ,   )







  def       __A   (							self       :				Dict     ,   __magic_name__       :				Union[str, Any] = "auto"      )     ->		Optional[Any]:




        if slice_size == "auto":
              SCREAMING_SNAKE_CASE_      =     self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE      )







  def       __A   (							self       :				List[Any]      )     ->		Optional[Any]:
        self.enable_attention_slicing(_SCREAMING_SNAKE_CASE      )







  @torch.no_grad()
  def __call__(							self       :				Union[str, Any]     ,   __magic_name__       :				Dict     ,   __magic_name__       :				Optional[int]=16_000     ,   __magic_name__       :				List[str] = 512     ,   __magic_name__       :				Dict = 512     ,   __magic_name__       :				Dict = 50     ,   __magic_name__       :				str = 7.5     ,   __magic_name__       :				List[str] = None     ,   __magic_name__       :				int = 1     ,   __magic_name__       :				int = 0.0     ,   __magic_name__       :				Any = None     ,   __magic_name__       :				List[str] = None     ,   __magic_name__       :				Union[str, Any] = "pil"     ,   __magic_name__       :				List[Any] = True     ,   __magic_name__       :				List[Any] = None     ,   __magic_name__       :				Union[str, Any] = 1     ,   **__magic_name__       :				Tuple     ,   )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     self.speech_processor.feature_extractor(
            _SCREAMING_SNAKE_CASE     ,   return_tensors="pt"     ,   sampling_rate=_SCREAMING_SNAKE_CASE      ).input_features.to(self.device      )
        SCREAMING_SNAKE_CASE_      =     self.speech_model.generate(_SCREAMING_SNAKE_CASE     ,   max_length=480_000      )

        SCREAMING_SNAKE_CASE_      =     self.speech_processor.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE     ,   skip_special_tokens=_SCREAMING_SNAKE_CASE     ,   normalize=_SCREAMING_SNAKE_CASE      )[
            0
        ]

        if isinstance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE      ):
              SCREAMING_SNAKE_CASE_      =     1
        elif isinstance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE      ):
              SCREAMING_SNAKE_CASE_      =     len(_SCREAMING_SNAKE_CASE      )
        else:
              raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(_SCREAMING_SNAKE_CASE      )}'''      )

        if height % 8 != 0 or width % 8 != 0:
              raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''      )

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE      ) or callback_steps <= 0)
        ):
              raise ValueError(
                  F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                  F''' {type(_SCREAMING_SNAKE_CASE      )}.'''      )

        # get prompt text embeddings
        SCREAMING_SNAKE_CASE_      =     self.tokenizer(
            _SCREAMING_SNAKE_CASE     ,   padding="max_length"     ,   max_length=self.tokenizer.model_max_length     ,   return_tensors="pt"     ,   )
        SCREAMING_SNAKE_CASE_      =     text_inputs.input_ids

        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
              SCREAMING_SNAKE_CASE_      =     self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]      )
              logger.warning(
                  "The following part of your input was truncated because CLIP can only handle sequences up to"
                  F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''      )
              SCREAMING_SNAKE_CASE_      =     text_input_ids[:, : self.tokenizer.model_max_length]
        SCREAMING_SNAKE_CASE_      =     self.text_encoder(text_input_ids.to(self.device      )      )[0]

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        SCREAMING_SNAKE_CASE_      =     text_embeddings.shape
        SCREAMING_SNAKE_CASE_      =     text_embeddings.repeat(1     ,   _SCREAMING_SNAKE_CASE     ,   1      )
        SCREAMING_SNAKE_CASE_      =     text_embeddings.view(bs_embed * num_images_per_prompt     ,   _SCREAMING_SNAKE_CASE     ,   -1      )

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        SCREAMING_SNAKE_CASE_      =     guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
              SCREAMING_SNAKE_CASE_      =     42
              if negative_prompt is None:
                    SCREAMING_SNAKE_CASE_      =     [""""""] * batch_size
              elif type(_SCREAMING_SNAKE_CASE      ) is not type(_SCREAMING_SNAKE_CASE      ):
                    raise TypeError(
                        F'''`negative_prompt` should be the same type to `prompt`, but got {type(_SCREAMING_SNAKE_CASE      )} !='''
                        F''' {type(_SCREAMING_SNAKE_CASE      )}.'''      )
              elif isinstance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE      ):
                    SCREAMING_SNAKE_CASE_      =     [negative_prompt]
              elif batch_size != len(_SCREAMING_SNAKE_CASE      ):
                    raise ValueError(
                        F'''`negative_prompt`: {negative_prompt} has batch size {len(_SCREAMING_SNAKE_CASE      )}, but `prompt`:'''
                        F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                        " the batch size of `prompt`."      )
              else:
                    SCREAMING_SNAKE_CASE_      =     negative_prompt

              SCREAMING_SNAKE_CASE_      =     text_input_ids.shape[-1]
              SCREAMING_SNAKE_CASE_      =     self.tokenizer(
                  _SCREAMING_SNAKE_CASE     ,   padding="max_length"     ,   max_length=_SCREAMING_SNAKE_CASE     ,   truncation=_SCREAMING_SNAKE_CASE     ,   return_tensors="pt"     ,   )
              SCREAMING_SNAKE_CASE_      =     self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

              # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
              SCREAMING_SNAKE_CASE_      =     uncond_embeddings.shape[1]
              SCREAMING_SNAKE_CASE_      =     uncond_embeddings.repeat(1     ,   _SCREAMING_SNAKE_CASE     ,   1      )
              SCREAMING_SNAKE_CASE_      =     uncond_embeddings.view(batch_size * num_images_per_prompt     ,   _SCREAMING_SNAKE_CASE     ,   -1      )

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              SCREAMING_SNAKE_CASE_      =     torch.cat([uncond_embeddings, text_embeddings]      )

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        SCREAMING_SNAKE_CASE_      =     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        SCREAMING_SNAKE_CASE_      =     text_embeddings.dtype
        if latents is None:
              if self.device.type == "mps":
                    # randn does not exist on mps
                    SCREAMING_SNAKE_CASE_      =     torch.randn(_SCREAMING_SNAKE_CASE     ,   generator=_SCREAMING_SNAKE_CASE     ,   device="cpu"     ,   dtype=_SCREAMING_SNAKE_CASE      ).to(
                        self.device      )
              else:
                    SCREAMING_SNAKE_CASE_      =     torch.randn(_SCREAMING_SNAKE_CASE     ,   generator=_SCREAMING_SNAKE_CASE     ,   device=self.device     ,   dtype=_SCREAMING_SNAKE_CASE      )
        else:
              if latents.shape != latents_shape:
                    raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''      )
              SCREAMING_SNAKE_CASE_      =     latents.to(self.device      )

        # set timesteps
        self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE      )

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        SCREAMING_SNAKE_CASE_      =     self.scheduler.timesteps.to(self.device      )

        # scale the initial noise by the standard deviation required by the scheduler
        SCREAMING_SNAKE_CASE_      =     latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        SCREAMING_SNAKE_CASE_      =     """eta""" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
        SCREAMING_SNAKE_CASE_      =     {}
        if accepts_eta:
              SCREAMING_SNAKE_CASE_      =     eta

        for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE      )      ):
              # expand the latents if we are doing classifier free guidance
              SCREAMING_SNAKE_CASE_      =     torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
              SCREAMING_SNAKE_CASE_      =     self.scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE      )

              # predict the noise residual
              SCREAMING_SNAKE_CASE_      =     self.unet(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   encoder_hidden_states=_SCREAMING_SNAKE_CASE      ).sample

              # perform guidance
              if do_classifier_free_guidance:
                    SCREAMING_SNAKE_CASE_      =     noise_pred.chunk(2      )
                    SCREAMING_SNAKE_CASE_      =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

              # compute the previous noisy sample x_t -> x_t-1
              SCREAMING_SNAKE_CASE_      =     self.scheduler.step(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   **_SCREAMING_SNAKE_CASE      ).prev_sample

              # call the callback, if provided
              if callback is not None and i % callback_steps == 0:
                    callback(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE      )

        SCREAMING_SNAKE_CASE_      =     1 / 0.1_8215 * latents
        SCREAMING_SNAKE_CASE_      =     self.vae.decode(_SCREAMING_SNAKE_CASE      ).sample

        SCREAMING_SNAKE_CASE_      =     (image / 2 + 0.5).clamp(0     ,   1      )

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        SCREAMING_SNAKE_CASE_      =     image.cpu().permute(0     ,   2     ,   3     ,   1      ).float().numpy()

        if output_type == "pil":
              SCREAMING_SNAKE_CASE_      =     self.numpy_to_pil(_SCREAMING_SNAKE_CASE      )

        if not return_dict:
              return image

        return StableDiffusionPipelineOutput(images=_SCREAMING_SNAKE_CASE     ,   nsfw_content_detected=_SCREAMING_SNAKE_CASE      )







"""simple docstring"""
from math import pi, sqrt, tan

def       _snake_case						(						UpperCamelCase						:		float						):
 if side_length < 0:
  raise ValueError("""surface_area_cube() only accepts non-negative values"""						)
 return 6 * side_length**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if length < 0 or breadth < 0 or height < 0:
  raise ValueError("""surface_area_cuboid() only accepts non-negative values"""						)
 return 2 * ((length * breadth) + (breadth * height) + (length * height))

def       _snake_case						(						UpperCamelCase						:		float						):
 if radius < 0:
  raise ValueError("""surface_area_sphere() only accepts non-negative values"""						)
 return 4 * pi * radius**2

def       _snake_case						(						UpperCamelCase						:		float						):
 if radius < 0:
  raise ValueError("""surface_area_hemisphere() only accepts non-negative values"""						)
 return 3 * pi * radius**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius < 0 or height < 0:
  raise ValueError("""surface_area_cone() only accepts non-negative values"""						)
 return pi * radius * (radius + (height**2 + radius**2) ** 0.5)

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError(
      """surface_area_conical_frustum() only accepts non-negative values"""						)
 UpperCAmelCase	:					Tuple	=	(height**2 + (radius_a - radius_a) ** 2) ** 0.5
 return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius < 0 or height < 0:
  raise ValueError("""surface_area_cylinder() only accepts non-negative values"""						)
 return 2 * pi * radius * (height + radius)

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if torus_radius < 0 or tube_radius < 0:
  raise ValueError("""surface_area_torus() only accepts non-negative values"""						)
 if torus_radius < tube_radius:
  raise ValueError(
      """surface_area_torus() does not support spindle or self intersecting tori"""						)
 return 4 * pow(UpperCamelCase			,		2						) * torus_radius * tube_radius

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if length < 0 or width < 0:
  raise ValueError("""area_rectangle() only accepts non-negative values"""						)
 return length * width

def       _snake_case						(						UpperCamelCase						:		float						):
 if side_length < 0:
  raise ValueError("""area_square() only accepts non-negative values"""						)
 return side_length**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if base < 0 or height < 0:
  raise ValueError("""area_triangle() only accepts non-negative values"""						)
 return (base * height) / 2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if sidea < 0 or sidea < 0 or sidea < 0:
  raise ValueError("""area_triangle_three_sides() only accepts non-negative values"""						)
 elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
  raise ValueError("""Given three sides do not form a triangle"""						)
 UpperCAmelCase	:					Union[str, Any]	=	(sidea + sidea + sidea) / 2
 UpperCAmelCase	:					Union[str, Any]	=	sqrt(
     semi_perimeter
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)						)
 return area

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if base < 0 or height < 0:
  raise ValueError("""area_parallelogram() only accepts non-negative values"""						)
 return base * height

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if basea < 0 or basea < 0 or height < 0:
  raise ValueError("""area_trapezium() only accepts non-negative values"""						)
 return 1 / 2 * (basea + basea) * height

def       _snake_case						(						UpperCamelCase						:		float						):
 if radius < 0:
  raise ValueError("""area_circle() only accepts non-negative values"""						)
 return pi * radius**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius_x < 0 or radius_y < 0:
  raise ValueError("""area_ellipse() only accepts non-negative values"""						)
 return pi * radius_x * radius_y

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if diagonal_a < 0 or diagonal_a < 0:
  raise ValueError("""area_rhombus() only accepts non-negative values"""						)
 return 1 / 2 * diagonal_a * diagonal_a

def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		float						):
 if not isinstance(UpperCamelCase			,		UpperCamelCase						) or sides < 3:
  raise ValueError(
      """area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"""						)
 elif length < 0:
  raise ValueError(
      """area_reg_polygon() only accepts non-negative values as \
length of a side"""						)
 return (sides * length**2) / (4 * tan(pi / sides						))
 return (sides * length**2) / (4 * tan(pi / sides						))


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

      print("[DEMO] Areas of various geometric shapes: \n")
      print(f"""Rectangle: {area_rectangle(1_0, 2_0) = }""")
      print(f"""Square: {area_square(1_0) = }""")
      print(f"""Triangle: {area_triangle(1_0, 1_0) = }""")
      print(f"""Triangle: {area_triangle_three_sides(5, 1_2, 1_3) = }""")
      print(f"""Parallelogram: {area_parallelogram(1_0, 2_0) = }""")
      print(f"""Rhombus: {area_rhombus(1_0, 2_0) = }""")
      print(f"""Trapezium: {area_trapezium(1_0, 2_0, 3_0) = }""")
      print(f"""Circle: {area_circle(2_0) = }""")
      print(f"""Ellipse: {area_ellipse(1_0, 2_0) = }""")
      print("\nSurface Areas of various geometric shapes: \n")
      print(f"""Cube: {surface_area_cube(2_0) = }""")
      print(f"""Cuboid: {surface_area_cuboid(1_0, 2_0, 3_0) = }""")
      print(f"""Sphere: {surface_area_sphere(2_0) = }""")
      print(f"""Hemisphere: {surface_area_hemisphere(2_0) = }""")
      print(f"""Cone: {surface_area_cone(1_0, 2_0) = }""")
      print(f"""Conical Frustum: {surface_area_conical_frustum(1_0, 2_0, 3_0) = }""")
      print(f"""Cylinder: {surface_area_cylinder(1_0, 2_0) = }""")
      print(f"""Torus: {surface_area_torus(2_0, 1_0) = }""")
      print(f"""Equilateral Triangle: {area_reg_polygon(3, 1_0) = }""")
      print(f"""Square: {area_reg_polygon(4, 1_0) = }""")
      print(f"""Reqular Pentagon: {area_reg_polygon(5, 1_0) = }""")
