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 		__lowerCamelCase							(UpperCAmelCase__   :							Union[str, Any]					,       UpperCAmelCase__   :							List[str]=0.999					,       UpperCAmelCase__   :							Dict="cosine"					,       ):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(UpperCAmelCase__   :							Dict     ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(UpperCAmelCase__   :							Optional[int]     ):
															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(snake_case__     ):
										SCREAMING_SNAKE_CASE				=				i / num_diffusion_timesteps
										SCREAMING_SNAKE_CASE				=				(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(snake_case__     ) / alpha_bar_fn(snake_case__     )					,       snake_case__     )     )
					return torch.tensor(snake_case__					,       dtype=torch.floataa     )


class   lowercase			(   _UpperCamelCase ,				_UpperCamelCase   ):
							lowercase__  :				List[Any]									=							[e.name for e in KarrasDiffusionSchedulers]
							lowercase__  :				List[str]									=							2




							@register_to_config
							def __init__(  self      :    Any		,							_UpperCamelCase      :    Union[str, Any] = 1_000		,							_UpperCamelCase      :    Dict = 0.0_0_0_8_5		,							_UpperCamelCase      :    Dict = 0.0_1_2		,							_UpperCamelCase      :    List[str] = "linear"		,							_UpperCamelCase      :    Dict = None		,							_UpperCamelCase      :    str = "epsilon"		,							_UpperCamelCase      :    Tuple = "linspace"		,							_UpperCamelCase      :    List[Any] = 0		,							)    -> Optional[int]:



												'''simple docstring'''

												if trained_betas is not None:
																	SCREAMING_SNAKE_CASE				=				torch.tensor(__a		,							dtype=torch.floataa					)
												elif beta_schedule == "linear":
																	SCREAMING_SNAKE_CASE				=				torch.linspace(__a		,							__a		,							__a		,							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		,							__a		,							dtype=torch.floataa					) ** 2
																	)
												elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	SCREAMING_SNAKE_CASE				=				betas_for_alpha_bar(__a					)
												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(__a		,							__a		,							__a					)




							def 		__snake_case(  self      :    Union[str, Any]		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    Any=None					)    -> List[str]:



												'''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(__a					) > 1 else 0
												else:
																	SCREAMING_SNAKE_CASE				=				timestep.cpu().item() if torch.is_tensor(__a					) else timestep
																	SCREAMING_SNAKE_CASE				=				self._index_counter[timestep_int]

												return indices[pos].item()




							@property
							def 		__snake_case(  self      :    Any					)    -> Any:



												'''simple docstring'''

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

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




							def 		__snake_case(  self      :    Tuple		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    str		,							)    -> torch.FloatTensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.index_for_timestep(__a					)

												if self.state_in_first_order:
																	SCREAMING_SNAKE_CASE				=				self.sigmas[step_index]
												else:
																	SCREAMING_SNAKE_CASE				=				self.sigmas_interpol[step_index - 1]

												SCREAMING_SNAKE_CASE				=				sample / ((sigma**2 + 1) ** 0.5)
												return sample




							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    Optional[int] = None		,							_UpperCamelCase      :    List[Any] = None		,							)    -> List[str]:



												'''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		,							__a		,							dtype=__a					)[::-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		,							__a					) * step_ratio).round()[::-1].copy().astype(__a					)
																	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(__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'."					)

												SCREAMING_SNAKE_CASE				=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
												SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.log(__a					)					).to(__a					)

												SCREAMING_SNAKE_CASE				=				np.interp(__a		,							np.arange(0		,							len(__a					)					)		,							__a					)
												SCREAMING_SNAKE_CASE				=				np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
												SCREAMING_SNAKE_CASE				=				torch.from_numpy(__a					).to(device=__a					)

												# compute up and down sigmas
												SCREAMING_SNAKE_CASE				=				sigmas.roll(-1					)
												SCREAMING_SNAKE_CASE				=				0.0
												SCREAMING_SNAKE_CASE				=				(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
												SCREAMING_SNAKE_CASE				=				(sigmas_next**2 - sigmas_up**2) ** 0.5
												SCREAMING_SNAKE_CASE				=				0.0

												# compute interpolated sigmas
												SCREAMING_SNAKE_CASE				=				sigmas.log().lerp(sigmas_down.log()		,							0.5					).exp()
												SCREAMING_SNAKE_CASE				=				0.0

												# set sigmas
												SCREAMING_SNAKE_CASE				=				torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
												SCREAMING_SNAKE_CASE				=				torch.cat(
												    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)
												SCREAMING_SNAKE_CASE				=				torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2					), sigmas_up[-1:]]					)
												SCREAMING_SNAKE_CASE				=				torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2					), sigmas_down[-1:]]					)

												if str(__a					).startswith("mps"					):
																	# mps does not support float64
																	SCREAMING_SNAKE_CASE				=				torch.from_numpy(__a					).to(__a		,							dtype=torch.floataa					)
												else:
																	SCREAMING_SNAKE_CASE				=				torch.from_numpy(__a					).to(__a					)

												SCREAMING_SNAKE_CASE				=				self.sigma_to_t(__a					).to(__a		,							dtype=timesteps.dtype					)
												SCREAMING_SNAKE_CASE				=				torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])		,							dim=-1					).flatten()

												SCREAMING_SNAKE_CASE				=				torch.cat([timesteps[:1], interleaved_timesteps]					)

												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(__a					)




							def 		__snake_case(  self      :    Union[str, Any]		,							_UpperCamelCase      :    Dict					)    -> int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				sigma.log()

												# get distribution
												SCREAMING_SNAKE_CASE				=				log_sigma - self.log_sigmas[:, None]

												# get sigmas range
												SCREAMING_SNAKE_CASE				=				dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
												SCREAMING_SNAKE_CASE				=				low_idx + 1

												SCREAMING_SNAKE_CASE				=				self.log_sigmas[low_idx]
												SCREAMING_SNAKE_CASE				=				self.log_sigmas[high_idx]

												# interpolate sigmas
												SCREAMING_SNAKE_CASE				=				(low - log_sigma) / (low - high)
												SCREAMING_SNAKE_CASE				=				w.clamp(0		,							1					)

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




							@property
							def 		__snake_case(  self      :    Optional[Any]					)    -> List[Any]:



												'''simple docstring'''

												return self.sample is None




							def 		__snake_case(  self      :    List[str]		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    Tuple = None		,							_UpperCamelCase      :    Optional[int] = True		,							)    -> Union[SchedulerOutput, Tuple]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.index_for_timestep(__a					)

												# advance index counter by 1
												SCREAMING_SNAKE_CASE				=				timestep.cpu().item() if torch.is_tensor(__a					) 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_interpol[step_index]
																	SCREAMING_SNAKE_CASE				=				self.sigmas_up[step_index]
																	SCREAMING_SNAKE_CASE				=				self.sigmas_down[step_index - 1]
												else:
																	# 2nd order / KPDM2's method
																	SCREAMING_SNAKE_CASE				=				self.sigmas[step_index - 1]
																	SCREAMING_SNAKE_CASE				=				self.sigmas_interpol[step_index - 1]
																	SCREAMING_SNAKE_CASE				=				self.sigmas_up[step_index - 1]
																	SCREAMING_SNAKE_CASE				=				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
												SCREAMING_SNAKE_CASE				=				0
												SCREAMING_SNAKE_CASE				=				sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

												SCREAMING_SNAKE_CASE				=				model_output.device
												SCREAMING_SNAKE_CASE				=				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":
																	SCREAMING_SNAKE_CASE				=				sigma_hat if self.state_in_first_order else sigma_interpol
																	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_interpol
																	SCREAMING_SNAKE_CASE				=				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
																	SCREAMING_SNAKE_CASE				=				(sample - pred_original_sample) / sigma_hat
																	# 3. delta timestep
																	SCREAMING_SNAKE_CASE				=				sigma_interpol - sigma_hat

																	# store for 2nd order step
																	SCREAMING_SNAKE_CASE				=				sample
																	SCREAMING_SNAKE_CASE				=				dt
																	SCREAMING_SNAKE_CASE				=				sample + derivative * dt
												else:
																	# DPM-Solver-2
																	# 2. Convert to an ODE derivative for 2nd order
																	SCREAMING_SNAKE_CASE				=				(sample - pred_original_sample) / sigma_interpol
																	# 3. delta timestep
																	SCREAMING_SNAKE_CASE				=				sigma_down - sigma_hat

																	SCREAMING_SNAKE_CASE				=				self.sample
																	SCREAMING_SNAKE_CASE				=				None

																	SCREAMING_SNAKE_CASE				=				sample + derivative * dt
																	SCREAMING_SNAKE_CASE				=				prev_sample + noise * sigma_up

												if not return_dict:
																	return (prev_sample,)

												return SchedulerOutput(prev_sample=__a					)




							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    Any		,							_UpperCamelCase      :    List[str]		,							)    -> 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(__a					):
																	# 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(__a		,							__a					) 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      :    int					)    -> Optional[int]:



												'''simple docstring'''

												return self.config.num_train_timesteps







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     lowercase						(       unittest.TestCase ,				_UpperCamelCase					):



 '''simple docstring'''


 def   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				load_tool('translation' )
  self.tool.setup()
  UpperCAmelCase__							=				load_tool('translation'       ,       remote=__a )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				self.tool('Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				self.remote_tool('Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				self.tool(text='Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				self.remote_tool(text='Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']
  UpperCAmelCase__							=				self.tool(*__a )

  # There is a single output
  if len(self.tool.outputs ) == 1:
   UpperCAmelCase__							=				[outputs]

  self.assertListEqual(output_types(__a )       ,       self.tool.outputs )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']
  UpperCAmelCase__							=				self.tool(*__a )

  if not isinstance(__a       ,       __a ):
   UpperCAmelCase__							=				[outputs]

  self.assertEqual(len(__a )       ,       len(self.tool.outputs ) )

  for output, output_type in zip(__a       ,       self.tool.outputs ):
   UpperCAmelCase__							=				AGENT_TYPE_MAPPING[output_type]
   self.assertTrue(isinstance(__a       ,       __a ) )




 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']

  UpperCAmelCase__							=				[]

  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
  UpperCAmelCase__							=				self.tool(*__a )

  if not isinstance(__a       ,       __a ):
   UpperCAmelCase__							=				[outputs]

  self.assertEqual(len(__a )       ,       len(self.tool.outputs ) )



