code: 
  
  
  """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 ..utils import randn_tensor
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
  
  
  
  
  
  def 			a_   (  lowercase__   :Optional[Any], lowercase__   :Any=0.999, lowercase__   :List[str]="cosine", ):
  			if alpha_transform_type == "cosine":
  
  						def alpha_bar_fn(lowercase__   :Any    ):
  									return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2
  
  			elif alpha_transform_type == "exp":
  
  						def alpha_bar_fn(lowercase__   :List[str]    ):
  									return math.exp(t * -12.0    )
  
  			else:
  						raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'    )
  
  			__lowerCamelCase        =			[]
  			for i in range(lowercase__    ):
  						__lowerCamelCase        =			i / num_diffusion_timesteps
  						__lowerCamelCase        =			(i + 1) / num_diffusion_timesteps
  						betas.append(min(1 - alpha_bar_fn(lowercase__    ) / alpha_bar_fn(lowercase__    ), lowercase__    )    )
  			return torch.tensor(lowercase__, dtype=torch.floataa    )
  
  
  
  
  class  __snake_case      (lowerCamelCase ,		lowerCamelCase     ):
  							__a				   =					[e.name for e in KarrasDiffusionSchedulers]
  							__a				   =					2
  
  
  
  
  							@register_to_config
  							def __init__(    self:						Optional[int]    ,       A_:						int = 10_00    ,       A_:						float = 0.00_085    ,       A_:						float = 0.012    ,       A_:						str = "linear"    ,       A_:						Optional[Union[np.ndarray, List[float]]] = None    ,       A_:						str = "epsilon"    ,       A_:						str = "linspace"    ,       A_:						int = 0    ,       ):
  										if trained_betas is not None:
  													__lowerCamelCase        =			torch.tensor(A_    ,       dtype=torch.floataa      )
  										elif beta_schedule == "linear":
  													__lowerCamelCase        =			torch.linspace(A_    ,       A_    ,       A_    ,       dtype=torch.floataa      )
  										elif beta_schedule == "scaled_linear":
  													# this schedule is very specific to the latent diffusion model.
  													__lowerCamelCase        =			(
  													    torch.linspace(beta_start**0.5    ,       beta_end**0.5    ,       A_    ,       dtype=torch.floataa      ) ** 2
  													)
  										elif beta_schedule == "squaredcos_cap_v2":
  													# Glide cosine schedule
  													__lowerCamelCase        =			betas_for_alpha_bar(A_      )
  										else:
  													raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'      )
  
  										__lowerCamelCase        =			1.0 - self.betas
  										__lowerCamelCase        =			torch.cumprod(self.alphas    ,       dim=0      )
  
  										#  set all values
  										self.set_timesteps(A_    ,       A_    ,       A_      )
  
  
  
  
  							def      __a      (    self:						Optional[Any]    ,       A_:						Any    ,       A_:						List[str]=None      ):
  										if schedule_timesteps is None:
  													__lowerCamelCase        =			self.timesteps
  
  										__lowerCamelCase        =			(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:
  													__lowerCamelCase        =			1 if len(A_      ) > 1 else 0
  										else:
  													__lowerCamelCase        =			timestep.cpu().item() if torch.is_tensor(A_      ) else timestep
  													__lowerCamelCase        =			self._index_counter[timestep_int]
  
  										return indices[pos].item()
  
  
  
  
  							@property
  							def      __a      (    self:						List[str]      ):
  										# standard deviation of the initial noise distribution
  										if self.config.timestep_spacing in ["linspace", "trailing"]:
  													return self.sigmas.max()
  
  										return (self.sigmas.max() ** 2 + 1) ** 0.5
  
  
  
  
  							def      __a      (    self:						List[str]    ,       A_:						torch.FloatTensor    ,       A_:						Union[float, torch.FloatTensor]    ,       ):
  										__lowerCamelCase        =			self.index_for_timestep(A_      )
  
  										if self.state_in_first_order:
  													__lowerCamelCase        =			self.sigmas[step_index]
  										else:
  													__lowerCamelCase        =			self.sigmas_interpol[step_index - 1]
  
  										__lowerCamelCase        =			sample / ((sigma**2 + 1) ** 0.5)
  										return sample
  
  
  
  
  							def      __a      (    self:						Dict    ,       A_:						int    ,       A_:						Union[str, torch.device] = None    ,       A_:						Optional[int] = None    ,       ):
  										__lowerCamelCase        =			num_inference_steps
  
  										__lowerCamelCase        =			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":
  													__lowerCamelCase        =			np.linspace(0    ,       num_train_timesteps - 1    ,       A_    ,       dtype=A_      )[::-1].copy()
  										elif self.config.timestep_spacing == "leading":
  													__lowerCamelCase        =			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
  													__lowerCamelCase        =			(np.arange(0    ,       A_      ) * step_ratio).round()[::-1].copy().astype(A_      )
  													timesteps += self.config.steps_offset
  										elif self.config.timestep_spacing == "trailing":
  													__lowerCamelCase        =			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
  													__lowerCamelCase        =			(np.arange(A_    ,       0    ,       -step_ratio      )).round().copy().astype(A_      )
  													timesteps -= 1
  										else:
  													raise ValueError(
  													    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'      )
  
  										__lowerCamelCase        =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
  										__lowerCamelCase        =			torch.from_numpy(np.log(A_      )      ).to(A_      )
  
  										__lowerCamelCase        =			np.interp(A_    ,       np.arange(0    ,       len(A_      )      )    ,       A_      )
  										__lowerCamelCase        =			np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
  										__lowerCamelCase        =			torch.from_numpy(A_      ).to(device=A_      )
  
  										# compute up and down sigmas
  										__lowerCamelCase        =			sigmas.roll(-1      )
  										__lowerCamelCase        =			0.0
  										__lowerCamelCase        =			(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
  										__lowerCamelCase        =			(sigmas_next**2 - sigmas_up**2) ** 0.5
  										__lowerCamelCase        =			0.0
  
  										# compute interpolated sigmas
  										__lowerCamelCase        =			sigmas.log().lerp(sigmas_down.log()    ,       0.5      ).exp()
  										__lowerCamelCase        =			0.0
  
  										# set sigmas
  										__lowerCamelCase        =			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
  										__lowerCamelCase        =			torch.cat(
  										    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )
  										__lowerCamelCase        =			torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2      ), sigmas_up[-1:]]      )
  										__lowerCamelCase        =			torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2      ), sigmas_down[-1:]]      )
  
  										if str(A_      ).startswith("""mps"""      ):
  													# mps does not support float64
  													__lowerCamelCase        =			torch.from_numpy(A_      ).to(A_    ,       dtype=torch.floataa      )
  										else:
  													__lowerCamelCase        =			torch.from_numpy(A_      ).to(A_      )
  
  										__lowerCamelCase        =			self.sigma_to_t(A_      ).to(A_    ,       dtype=timesteps.dtype      )
  										__lowerCamelCase        =			torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])    ,       dim=-1      ).flatten()
  
  										__lowerCamelCase        =			torch.cat([timesteps[:1], interleaved_timesteps]      )
  
  										__lowerCamelCase        =			None
  
  										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
  										# we need an index counter
  										__lowerCamelCase        =			defaultdict(A_      )
  
  
  
  
  							def      __a      (    self:						List[str]    ,       A_:						List[str]      ):
  										# get log sigma
  										__lowerCamelCase        =			sigma.log()
  
  										# get distribution
  										__lowerCamelCase        =			log_sigma - self.log_sigmas[:, None]
  
  										# get sigmas range
  										__lowerCamelCase        =			dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
  										__lowerCamelCase        =			low_idx + 1
  
  										__lowerCamelCase        =			self.log_sigmas[low_idx]
  										__lowerCamelCase        =			self.log_sigmas[high_idx]
  
  										# interpolate sigmas
  										__lowerCamelCase        =			(low - log_sigma) / (low - high)
  										__lowerCamelCase        =			w.clamp(0    ,       1      )
  
  										# transform interpolation to time range
  										__lowerCamelCase        =			(1 - w) * low_idx + w * high_idx
  										__lowerCamelCase        =			t.view(sigma.shape      )
  										return t
  
  
  
  
  							@property
  							def      __a      (    self:						List[str]      ):
  										return self.sample is None
  
  
  
  
  							def      __a      (    self:						Dict    ,       A_:						Union[torch.FloatTensor, np.ndarray]    ,       A_:						Union[float, torch.FloatTensor]    ,       A_:						Union[torch.FloatTensor, np.ndarray]    ,       A_:						Optional[torch.Generator] = None    ,       A_:						bool = True    ,       ):
  										__lowerCamelCase        =			self.index_for_timestep(A_      )
  
  										# advance index counter by 1
  										__lowerCamelCase        =			timestep.cpu().item() if torch.is_tensor(A_      ) else timestep
  										self._index_counter[timestep_int] += 1
  
  										if self.state_in_first_order:
  													__lowerCamelCase        =			self.sigmas[step_index]
  													__lowerCamelCase        =			self.sigmas_interpol[step_index]
  													__lowerCamelCase        =			self.sigmas_up[step_index]
  													__lowerCamelCase        =			self.sigmas_down[step_index - 1]
  										else:
  													# 2nd order / KPDM2's method
  													__lowerCamelCase        =			self.sigmas[step_index - 1]
  													__lowerCamelCase        =			self.sigmas_interpol[step_index - 1]
  													__lowerCamelCase        =			self.sigmas_up[step_index - 1]
  													__lowerCamelCase        =			self.sigmas_down[step_index - 1]
  
  										# 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
  										__lowerCamelCase        =			0
  										__lowerCamelCase        =			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now
  
  										__lowerCamelCase        =			model_output.device
  										__lowerCamelCase        =			randn_tensor(model_output.shape    ,       dtype=model_output.dtype    ,       device=A_    ,       generator=A_      )
  
  										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  										if self.config.prediction_type == "epsilon":
  													__lowerCamelCase        =			sigma_hat if self.state_in_first_order else sigma_interpol
  													__lowerCamelCase        =			sample - sigma_input * model_output
  										elif self.config.prediction_type == "v_prediction":
  													__lowerCamelCase        =			sigma_hat if self.state_in_first_order else sigma_interpol
  													__lowerCamelCase        =			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
  													    sample / (sigma_input**2 + 1)
  													)
  										elif self.config.prediction_type == "sample":
  													raise NotImplementedError("""prediction_type not implemented yet: sample"""      )
  										else:
  													raise ValueError(
  													    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'      )
  
  										if self.state_in_first_order:
  													# 2. Convert to an ODE derivative for 1st order
  													__lowerCamelCase        =			(sample - pred_original_sample) / sigma_hat
  													# 3. delta timestep
  													__lowerCamelCase        =			sigma_interpol - sigma_hat
  
  													# store for 2nd order step
  													__lowerCamelCase        =			sample
  													__lowerCamelCase        =			dt
  													__lowerCamelCase        =			sample + derivative * dt
  										else:
  													# DPM-Solver-2
  													# 2. Convert to an ODE derivative for 2nd order
  													__lowerCamelCase        =			(sample - pred_original_sample) / sigma_interpol
  													# 3. delta timestep
  													__lowerCamelCase        =			sigma_down - sigma_hat
  
  													__lowerCamelCase        =			self.sample
  													__lowerCamelCase        =			None
  
  													__lowerCamelCase        =			sample + derivative * dt
  													__lowerCamelCase        =			prev_sample + noise * sigma_up
  
  										if not return_dict:
  													return (prev_sample,)
  
  										return SchedulerOutput(prev_sample=A_      )
  
  
  
  
  							def      __a      (    self:						int    ,       A_:						torch.FloatTensor    ,       A_:						torch.FloatTensor    ,       A_:						torch.FloatTensor    ,       ):
  										# Make sure sigmas and timesteps have the same device and dtype as original_samples
  										__lowerCamelCase        =			self.sigmas.to(device=original_samples.device    ,       dtype=original_samples.dtype      )
  										if original_samples.device.type == "mps" and torch.is_floating_point(A_      ):
  													# mps does not support float64
  													__lowerCamelCase        =			self.timesteps.to(original_samples.device    ,       dtype=torch.floataa      )
  													__lowerCamelCase        =			timesteps.to(original_samples.device    ,       dtype=torch.floataa      )
  										else:
  													__lowerCamelCase        =			self.timesteps.to(original_samples.device      )
  													__lowerCamelCase        =			timesteps.to(original_samples.device      )
  
  										__lowerCamelCase        =			[self.index_for_timestep(A_    ,       A_      ) for t in timesteps]
  
  										__lowerCamelCase        =			sigmas[step_indices].flatten()
  										while len(sigma.shape      ) < len(original_samples.shape      ):
  													__lowerCamelCase        =			sigma.unsqueeze(-1      )
  
  										__lowerCamelCase        =			original_samples + noise * sigma
  										return noisy_samples
  
  
  
  
  							def __len__(    self:						int      ):
  										return self.config.num_train_timesteps
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import unittest
  
  from transformers import load_tool
  from transformers.tools.agent_types import AGENT_TYPE_MAPPING
  
  from .test_tools_common import ToolTesterMixin, output_types
  
  
  
  
  class  __snake_case      (unittest.TestCase ,		lowerCamelCase     ):
  
  
  
  
  							def      __a      (    self:						Optional[Any]      ):
  										__lowerCamelCase        =			load_tool("""translation"""      )
  										self.tool.setup()
  										__lowerCamelCase        =			load_tool("""translation"""    ,       remote=A_      )
  
  
  
  
  							def      __a      (    self:						int      ):
  										__lowerCamelCase        =			self.tool("""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						int      ):
  										__lowerCamelCase        =			self.remote_tool("""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						Union[str, Any]      ):
  										__lowerCamelCase        =			self.tool(text="""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						Any      ):
  										__lowerCamelCase        =			self.remote_tool(text="""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						List[Any]      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										# There is a single output
  										if len(self.tool.outputs      ) == 1:
  													__lowerCamelCase        =			[outputs]
  
  										self.assertListEqual(output_types(A_      )    ,       self.tool.outputs      )
  
  
  
  
  							def      __a      (    self:						Union[str, Any]      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										if not isinstance(A_    ,       A_      ):
  													__lowerCamelCase        =			[outputs]
  
  										self.assertEqual(len(A_      )    ,       len(self.tool.outputs      )      )
  
  										for output, output_type in zip(A_    ,       self.tool.outputs      ):
  													__lowerCamelCase        =			AGENT_TYPE_MAPPING[output_type]
  													self.assertTrue(isinstance(A_    ,       A_      )      )
  
  
  
  
  							def      __a      (    self:						Dict      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  
  										__lowerCamelCase        =			[]
  
  										for _input, input_type in zip(A_    ,       self.tool.inputs      ):
  													if isinstance(A_    ,       A_      ):
  																_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input      ) for _input_type in input_type]      )
  													else:
  																_inputs.append(AGENT_TYPE_MAPPING[input_type](_input      )      )
  
          # Should not raise an error
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										if not isinstance(A_    ,       A_      ):
  													__lowerCamelCase        =			[outputs]
  
  										self.assertEqual(len(A_      )    ,       len(self.tool.outputs      )      )
  
  
  
  
  