





'''simple docstring'''

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=0.9_9_9				,						__UpperCAmelCase="cosine"				,						)    ->       Union[str, Any]:

   '''simple docstring'''







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__UpperCAmelCase   ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__UpperCAmelCase   ):
         return math.exp(t * -1_2.0   )

   else:
      raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""   )

   __SCREAMING_SNAKE_CASE   				=							[]
   for i in range(__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							i / num_diffusion_timesteps
      __SCREAMING_SNAKE_CASE   				=							(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase   ) / alpha_bar_fn(__UpperCAmelCase   )				,						__UpperCAmelCase   )   )
   return torch.tensor(__UpperCAmelCase				,						dtype=torch.floataa   )







class __a			( _snake_case, _snake_case      ):
   __UpperCamelCase :			Union[str, Any]						   =						[e.name for e in KarrasDiffusionSchedulers]
   __UpperCamelCase :			Union[str, Any]						   =						2





   @register_to_config
   def __init__(       self					:      Any					,lowerCamelCase					:      int = 1000					,lowerCamelCase					:      float = 0.00_085					,lowerCamelCase					:      float = 0.012					,lowerCamelCase					:      str = "linear"					,lowerCamelCase					:      Optional[Union[np.ndarray, List[float]]] = None					,lowerCamelCase					:      str = "epsilon"					,lowerCamelCase					:      Optional[bool] = False					,lowerCamelCase					:      Optional[bool] = False					,lowerCamelCase					:      float = 1.0					,lowerCamelCase					:      str = "linspace"					,lowerCamelCase					:      int = 0					,):

      '''simple docstring'''

      if trained_betas is not None:
         __SCREAMING_SNAKE_CASE   				=							torch.tensor(lowerCamelCase					,dtype=torch.floataa     )
      elif beta_schedule == "linear":
         __SCREAMING_SNAKE_CASE   				=							torch.linspace(lowerCamelCase					,lowerCamelCase					,lowerCamelCase					,dtype=torch.floataa     )
      elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         __SCREAMING_SNAKE_CASE   				=							(
             torch.linspace(beta_start**0.5					,beta_end**0.5					,lowerCamelCase					,dtype=torch.floataa     ) ** 2
         )
      elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         __SCREAMING_SNAKE_CASE   				=							betas_for_alpha_bar(lowerCamelCase					,alpha_transform_type="""cosine"""     )
      elif beta_schedule == "exp":
         __SCREAMING_SNAKE_CASE   				=							betas_for_alpha_bar(lowerCamelCase					,alpha_transform_type="""exp"""     )
      else:
         raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""     )

      __SCREAMING_SNAKE_CASE   				=							1.0 - self.betas
      __SCREAMING_SNAKE_CASE   				=							torch.cumprod(self.alphas					,dim=0     )

      #  set all values
      self.set_timesteps(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							use_karras_sigmas





   def   UpperCAmelCase__  (       self					:      Any					,lowerCamelCase					:      int					,lowerCamelCase					:      List[Any]=None     ):

      '''simple docstring'''

      if schedule_timesteps is None:
         __SCREAMING_SNAKE_CASE   				=							self.timesteps

      __SCREAMING_SNAKE_CASE   				=							(schedule_timesteps == timestep).nonzero()

      # The sigma index that is taken for the **very** first `step`
      # is always the second index (or the last index if there is only 1)
      # This way we can ensure we don't accidentally skip a sigma in
      # case we start in the middle of the denoising schedule (e.g. for image-to-image)
      if len(self._index_counter     ) == 0:
         __SCREAMING_SNAKE_CASE   				=							1 if len(lowerCamelCase     ) > 1 else 0
      else:
         __SCREAMING_SNAKE_CASE   				=							timestep.cpu().item() if torch.is_tensor(lowerCamelCase     ) else timestep
         __SCREAMING_SNAKE_CASE   				=							self._index_counter[timestep_int]

      return indices[pos].item()





   @property
   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      if self.config.timestep_spacing in ["linspace", "trailing"]:
         return self.sigmas.max()

      return (self.sigmas.max() ** 2 + 1) ** 0.5





   def   UpperCAmelCase__  (       self					:      Any					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      Union[float, torch.FloatTensor]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.index_for_timestep(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.sigmas[step_index]
      __SCREAMING_SNAKE_CASE   				=							sample / ((sigma**2 + 1) ** 0.5)
      return sample





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      int					,lowerCamelCase					:      Union[str, torch.device] = None					,lowerCamelCase					:      Optional[int] = None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							num_inference_steps

      __SCREAMING_SNAKE_CASE   				=							num_train_timesteps or self.config.num_train_timesteps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
         __SCREAMING_SNAKE_CASE   				=							np.linspace(0					,num_train_timesteps - 1					,lowerCamelCase					,dtype=lowerCamelCase     )[::-1].copy()
      elif self.config.timestep_spacing == "leading":
         __SCREAMING_SNAKE_CASE   				=							num_train_timesteps // self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         __SCREAMING_SNAKE_CASE   				=							(np.arange(0					,lowerCamelCase     ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase     )
         timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
         __SCREAMING_SNAKE_CASE   				=							num_train_timesteps / self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         __SCREAMING_SNAKE_CASE   				=							(np.arange(lowerCamelCase					,0					,-step_ratio     )).round().copy().astype(lowerCamelCase     )
         timesteps -= 1
      else:
         raise ValueError(
             f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""     )

      __SCREAMING_SNAKE_CASE   				=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
      __SCREAMING_SNAKE_CASE   				=							np.log(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							np.interp(lowerCamelCase					,np.arange(0					,len(lowerCamelCase     )     )					,lowerCamelCase     )

      if self.config.use_karras_sigmas:
         __SCREAMING_SNAKE_CASE   				=							self._convert_to_karras(in_sigmas=lowerCamelCase					,num_inference_steps=self.num_inference_steps     )
         __SCREAMING_SNAKE_CASE   				=							np.array([self._sigma_to_t(lowerCamelCase					,lowerCamelCase     ) for sigma in sigmas]     )

      __SCREAMING_SNAKE_CASE   				=							np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
      __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(lowerCamelCase     ).to(device=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2     ), sigmas[-1:]]     )

      __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2     )]     )

      if str(lowerCamelCase     ).startswith("""mps"""     ):
         # mps does not support float64
         __SCREAMING_SNAKE_CASE   				=							timesteps.to(lowerCamelCase					,dtype=torch.floataa     )
      else:
         __SCREAMING_SNAKE_CASE   				=							timesteps.to(device=lowerCamelCase     )

      # empty dt and derivative
      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      __SCREAMING_SNAKE_CASE   				=							defaultdict(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							np.log(lowerCamelCase     )

      # get distribution
      __SCREAMING_SNAKE_CASE   				=							log_sigma - log_sigmas[:, np.newaxis]

      # get sigmas range
      __SCREAMING_SNAKE_CASE   				=							np.cumsum((dists >= 0)					,axis=0     ).argmax(axis=0     ).clip(max=log_sigmas.shape[0] - 2     )
      __SCREAMING_SNAKE_CASE   				=							low_idx + 1

      __SCREAMING_SNAKE_CASE   				=							log_sigmas[low_idx]
      __SCREAMING_SNAKE_CASE   				=							log_sigmas[high_idx]

      # interpolate sigmas
      __SCREAMING_SNAKE_CASE   				=							(low - log_sigma) / (low - high)
      __SCREAMING_SNAKE_CASE   				=							np.clip(lowerCamelCase					,0					,1     )

      # transform interpolation to time range
      __SCREAMING_SNAKE_CASE   				=							(1 - w) * low_idx + w * high_idx
      __SCREAMING_SNAKE_CASE   				=							t.reshape(sigma.shape     )
      return t





   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							in_sigmas[-1].item()
      __SCREAMING_SNAKE_CASE   				=							in_sigmas[0].item()

      __SCREAMING_SNAKE_CASE   				=							7.0  # 7.0 is the value used in the paper
      __SCREAMING_SNAKE_CASE   				=							np.linspace(0					,1					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							sigma_min ** (1 / rho)
      __SCREAMING_SNAKE_CASE   				=							sigma_max ** (1 / rho)
      __SCREAMING_SNAKE_CASE   				=							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas





   @property
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      return self.dt is None





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      Union[torch.FloatTensor, np.ndarray]					,lowerCamelCase					:      Union[float, torch.FloatTensor]					,lowerCamelCase					:      Union[torch.FloatTensor, np.ndarray]					,lowerCamelCase					:      bool = True					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.index_for_timestep(lowerCamelCase     )

      # advance index counter by 1
      __SCREAMING_SNAKE_CASE   				=							timestep.cpu().item() if torch.is_tensor(lowerCamelCase     ) else timestep
      self._index_counter[timestep_int] += 1

      if self.state_in_first_order:
         __SCREAMING_SNAKE_CASE   				=							self.sigmas[step_index]
         __SCREAMING_SNAKE_CASE   				=							self.sigmas[step_index + 1]
      else:
         # 2nd order / Heun's method
         __SCREAMING_SNAKE_CASE   				=							self.sigmas[step_index - 1]
         __SCREAMING_SNAKE_CASE   				=							self.sigmas[step_index]

      # currently only gamma=0 is supported. This usually works best anyways.
      # We can support gamma in the future but then need to scale the timestep before
      # passing it to the model which requires a change in API
      __SCREAMING_SNAKE_CASE   				=							0
      __SCREAMING_SNAKE_CASE   				=							sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
         __SCREAMING_SNAKE_CASE   				=							sigma_hat if self.state_in_first_order else sigma_next
         __SCREAMING_SNAKE_CASE   				=							sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
         __SCREAMING_SNAKE_CASE   				=							sigma_hat if self.state_in_first_order else sigma_next
         __SCREAMING_SNAKE_CASE   				=							model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**2 + 1)
         )
      elif self.config.prediction_type == "sample":
         __SCREAMING_SNAKE_CASE   				=							model_output
      else:
         raise ValueError(
             f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""     )

      if self.config.clip_sample:
         __SCREAMING_SNAKE_CASE   				=							pred_original_sample.clamp(
             -self.config.clip_sample_range					,self.config.clip_sample_range     )

      if self.state_in_first_order:
         # 2. Convert to an ODE derivative for 1st order
         __SCREAMING_SNAKE_CASE   				=							(sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         __SCREAMING_SNAKE_CASE   				=							sigma_next - sigma_hat

         # store for 2nd order step
         __SCREAMING_SNAKE_CASE   				=							derivative
         __SCREAMING_SNAKE_CASE   				=							dt
         __SCREAMING_SNAKE_CASE   				=							sample
      else:
         # 2. 2nd order / Heun's method
         __SCREAMING_SNAKE_CASE   				=							(sample - pred_original_sample) / sigma_next
         __SCREAMING_SNAKE_CASE   				=							(self.prev_derivative + derivative) / 2

         # 3. take prev timestep & sample
         __SCREAMING_SNAKE_CASE   				=							self.dt
         __SCREAMING_SNAKE_CASE   				=							self.sample

         # free dt and derivative
         # Note, this puts the scheduler in "first order mode"
         __SCREAMING_SNAKE_CASE   				=							None
         __SCREAMING_SNAKE_CASE   				=							None
         __SCREAMING_SNAKE_CASE   				=							None

      __SCREAMING_SNAKE_CASE   				=							sample + derivative * dt

      if not return_dict:
         return (prev_sample,)

      return SchedulerOutput(prev_sample=lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      torch.FloatTensor					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.sigmas.to(device=original_samples.device					,dtype=original_samples.dtype     )
      if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase     ):
         # mps does not support float64
         __SCREAMING_SNAKE_CASE   				=							self.timesteps.to(original_samples.device					,dtype=torch.floataa     )
         __SCREAMING_SNAKE_CASE   				=							timesteps.to(original_samples.device					,dtype=torch.floataa     )
      else:
         __SCREAMING_SNAKE_CASE   				=							self.timesteps.to(original_samples.device     )
         __SCREAMING_SNAKE_CASE   				=							timesteps.to(original_samples.device     )

      __SCREAMING_SNAKE_CASE   				=							[self.index_for_timestep(lowerCamelCase					,lowerCamelCase     ) for t in timesteps]

      __SCREAMING_SNAKE_CASE   				=							sigmas[step_indices].flatten()
      while len(sigma.shape     ) < len(original_samples.shape     ):
         __SCREAMING_SNAKE_CASE   				=							sigma.unsqueeze(-1     )

      __SCREAMING_SNAKE_CASE   				=							original_samples + noise * sigma
      return noisy_samples





   def __len__(       self					:      Any     ):

      '''simple docstring'''

      return self.config.num_train_timesteps








"""simple docstring"""

from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

_UpperCamelCase		     =    logging.get_logger(__name__)






@add_end_docstrings(__magic_name__       )
class 			__a					(						__magic_name__       ):





       """simple docstring"""


       def __init__(   self			,					*snake_case			,					**snake_case   ):





        """simple docstring"""

        super().__init__(*snake_case			,					**snake_case   )
        self.check_model_type(snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case=None			,					snake_case=None			,					snake_case=None			,					**snake_case   ):





        """simple docstring"""

        lowerCAmelCase__			,						lowerCAmelCase__   :				List[Any] 			=				{}, {}
        if padding is not None:
         lowerCAmelCase__   :				Dict 			=				padding
        if truncation is not None:
         lowerCAmelCase__   :				Tuple 			=				truncation
        if top_k is not None:
         lowerCAmelCase__   :				str 			=				top_k
        return preprocess_params, {}, postprocess_params






       def __call__(   self			,					snake_case			,					snake_case = None			,					**snake_case   ):





        """simple docstring"""

        if isinstance(snake_case			,					(Image.Image, str)   ) and isinstance(snake_case			,					snake_case   ):
         lowerCAmelCase__   :				int 			=				{"image": image, "question": question}
        else:
         lowerCAmelCase__   :				Optional[int] 			=				image
        lowerCAmelCase__   :				Optional[Any] 			=				super().__call__(snake_case			,					**snake_case   )
        return results






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case=False			,					snake_case=False   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				load_image(inputs["image"]   )
        lowerCAmelCase__   :				str 			=				self.tokenizer(
            inputs["question"]			,					return_tensors=self.framework			,					padding=snake_case			,					truncation=snake_case   )
        lowerCAmelCase__   :				int 			=				self.image_processor(images=snake_case			,					return_tensors=self.framework   )
        model_inputs.update(snake_case   )
        return model_inputs






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				self.model(**snake_case   )
        return model_outputs






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case=5   ):





        """simple docstring"""

        if top_k > self.model.config.num_labels:
         lowerCAmelCase__   :				List[Any] 			=				self.model.config.num_labels

        if self.framework == "pt":
         lowerCAmelCase__   :				Optional[int] 			=				model_outputs.logits.sigmoid()[0]
         lowerCAmelCase__			,						lowerCAmelCase__   :				Optional[int] 			=				probs.topk(snake_case   )
        else:
         raise ValueError(F"""Unsupported framework: {self.framework}"""   )

        lowerCAmelCase__   :				int 			=				scores.tolist()
        lowerCAmelCase__   :				str 			=				ids.tolist()
        return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(snake_case			,					snake_case   )]






