


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 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_=0.999 ,   lowerCAmelCase_="cosine" ,   )-> Optional[Any]:
				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowerCAmelCase_ ):
												return math.exp(t * -12.0 )

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

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







class 				snake_case__(							UpperCAmelCase__,						UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							[e.name for e in KarrasDiffusionSchedulers]
						SCREAMING_SNAKE_CASE__      :  List[str]					=							2




						@register_to_config
						def __init__(				self   ,						__lowercase = 1_0_0_0   ,						__lowercase = 0.0_00_85   ,						__lowercase = 0.0_12   ,						__lowercase = "linear"   ,						__lowercase = None   ,						__lowercase = "epsilon"   ,						__lowercase = False   ,						__lowercase = False   ,						__lowercase = 1.0   ,						__lowercase = "linspace"   ,						__lowercase = 0   ,						)					->    Tuple:
										if trained_betas is not None:
														lowerCAmelCase_ :      int    				=				torch.tensor(__lowercase   ,						dtype=torch.floataa							)
										elif beta_schedule == "linear":
														lowerCAmelCase_ :      Dict    				=				torch.linspace(__lowercase   ,						__lowercase   ,						__lowercase   ,						dtype=torch.floataa							)
										elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														lowerCAmelCase_ :      List[Any]    				=				(
														    torch.linspace(beta_start**0.5   ,						beta_end**0.5   ,						__lowercase   ,						dtype=torch.floataa							) ** 2
														)
										elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														lowerCAmelCase_ :      str    				=				betas_for_alpha_bar(__lowercase   ,						alpha_transform_type='''cosine'''							)
										elif beta_schedule == "exp":
														lowerCAmelCase_ :      List[Any]    				=				betas_for_alpha_bar(__lowercase   ,						alpha_transform_type='''exp'''							)
										else:
														raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""							)

										lowerCAmelCase_ :      List[Any]    				=				1.0 - self.betas
										lowerCAmelCase_ :      List[str]    				=				torch.cumprod(self.alphas   ,						dim=0							)

										#  set all values
										self.set_timesteps(__lowercase   ,						__lowercase   ,						__lowercase							)
										lowerCAmelCase_ :      Optional[int]    				=				use_karras_sigmas




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase=None							)					->    List[str]:
										if schedule_timesteps is None:
														lowerCAmelCase_ :      List[Any]    				=				self.timesteps

										lowerCAmelCase_ :      Dict    				=				(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_ :      Optional[Any]    				=				1 if len(__lowercase							) > 1 else 0
										else:
														lowerCAmelCase_ :      List[str]    				=				timestep.cpu().item() if torch.is_tensor(__lowercase							) else timestep
														lowerCAmelCase_ :      str    				=				self._index_counter[timestep_int]

										return indices[pos].item()




						@property
						def 						lowercase_ (				self							)					->    Union[str, Any]:
										# 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 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						)					->    torch.FloatTensor:
										lowerCAmelCase_ :      Any    				=				self.index_for_timestep(__lowercase							)

										lowerCAmelCase_ :      Union[str, Any]    				=				self.sigmas[step_index]
										lowerCAmelCase_ :      List[Any]    				=				sample / ((sigma**2 + 1) ** 0.5)
										return sample




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = None   ,						__lowercase = None   ,						)					->    Optional[Any]:
										lowerCAmelCase_ :      Tuple    				=				num_inference_steps

										lowerCAmelCase_ :      Tuple    				=				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_ :      Union[str, Any]    				=				np.linspace(0   ,						num_train_timesteps - 1   ,						__lowercase   ,						dtype=__lowercase							)[::-1].copy()
										elif self.config.timestep_spacing == "leading":
														lowerCAmelCase_ :      int    				=				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_ :      Union[str, Any]    				=				(np.arange(0   ,						__lowercase							) * step_ratio).round()[::-1].copy().astype(__lowercase							)
														timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
														lowerCAmelCase_ :      str    				=				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_ :      str    				=				(np.arange(__lowercase   ,						0   ,						-step_ratio							)).round().copy().astype(__lowercase							)
														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_ :      Union[str, Any]    				=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
										lowerCAmelCase_ :      Any    				=				np.log(__lowercase							)
										lowerCAmelCase_ :      Union[str, Any]    				=				np.interp(__lowercase   ,						np.arange(0   ,						len(__lowercase							)							)   ,						__lowercase							)

										if self.config.use_karras_sigmas:
														lowerCAmelCase_ :      int    				=				self._convert_to_karras(in_sigmas=__lowercase   ,						num_inference_steps=self.num_inference_steps							)
														lowerCAmelCase_ :      Tuple    				=				np.array([self._sigma_to_t(__lowercase   ,						__lowercase							) for sigma in sigmas]							)

										lowerCAmelCase_ :      Any    				=				np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
										lowerCAmelCase_ :      List[str]    				=				torch.from_numpy(__lowercase							).to(device=__lowercase							)
										lowerCAmelCase_ :      List[str]    				=				torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2							), sigmas[-1:]]							)

										lowerCAmelCase_ :      Dict    				=				torch.from_numpy(__lowercase							)
										lowerCAmelCase_ :      Dict    				=				torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2							)]							)

										if str(__lowercase							).startswith('''mps'''							):
														# mps does not support float64
														lowerCAmelCase_ :      Union[str, Any]    				=				timesteps.to(__lowercase   ,						dtype=torch.floataa							)
										else:
														lowerCAmelCase_ :      Tuple    				=				timesteps.to(device=__lowercase							)

										# empty dt and derivative
										lowerCAmelCase_ :      str    				=				None
										lowerCAmelCase_ :      List[Any]    				=				None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										lowerCAmelCase_ :      List[str]    				=				defaultdict(__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase							)					->    Optional[int]:
										# get log sigma
										lowerCAmelCase_ :      List[str]    				=				np.log(__lowercase							)

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

										# get sigmas range
										lowerCAmelCase_ :      List[Any]    				=				np.cumsum((dists >= 0)   ,						axis=0							).argmax(axis=0							).clip(max=log_sigmas.shape[0] - 2							)
										lowerCAmelCase_ :      Union[str, Any]    				=				low_idx + 1

										lowerCAmelCase_ :      Optional[int]    				=				log_sigmas[low_idx]
										lowerCAmelCase_ :      str    				=				log_sigmas[high_idx]

										# interpolate sigmas
										lowerCAmelCase_ :      List[str]    				=				(low - log_sigma) / (low - high)
										lowerCAmelCase_ :      int    				=				np.clip(__lowercase   ,						0   ,						1							)

										# transform interpolation to time range
										lowerCAmelCase_ :      str    				=				(1 - w) * low_idx + w * high_idx
										lowerCAmelCase_ :      List[Any]    				=				t.reshape(sigma.shape							)
										return t




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase							)					->    torch.FloatTensor:
										lowerCAmelCase_ :      float    				=				in_sigmas[-1].item()
										lowerCAmelCase_ :      float    				=				in_sigmas[0].item()

										lowerCAmelCase_ :      Dict    				=				7.0  # 7.0 is the value used in the paper
										lowerCAmelCase_ :      str    				=				np.linspace(0   ,						1   ,						__lowercase							)
										lowerCAmelCase_ :      List[Any]    				=				sigma_min ** (1 / rho)
										lowerCAmelCase_ :      Tuple    				=				sigma_max ** (1 / rho)
										lowerCAmelCase_ :      str    				=				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas




						@property
						def 						lowercase_ (				self							)					->    List[str]:
										return self.dt is None




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase = True   ,						)					->    Union[SchedulerOutput, Tuple]:
										lowerCAmelCase_ :      int    				=				self.index_for_timestep(__lowercase							)

										# advance index counter by 1
										lowerCAmelCase_ :      Tuple    				=				timestep.cpu().item() if torch.is_tensor(__lowercase							) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
														lowerCAmelCase_ :      List[str]    				=				self.sigmas[step_index]
														lowerCAmelCase_ :      str    				=				self.sigmas[step_index + 1]
										else:
														# 2nd order / Heun's method
														lowerCAmelCase_ :      Optional[Any]    				=				self.sigmas[step_index - 1]
														lowerCAmelCase_ :      Dict    				=				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
										lowerCAmelCase_ :      str    				=				0
										lowerCAmelCase_ :      Optional[int]    				=				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":
														lowerCAmelCase_ :      Dict    				=				sigma_hat if self.state_in_first_order else sigma_next
														lowerCAmelCase_ :      Optional[int]    				=				sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
														lowerCAmelCase_ :      Union[str, Any]    				=				sigma_hat if self.state_in_first_order else sigma_next
														lowerCAmelCase_ :      Optional[Any]    				=				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
														    sample / (sigma_input**2 + 1)
														)
										elif self.config.prediction_type == "sample":
														lowerCAmelCase_ :      Optional[int]    				=				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:
														lowerCAmelCase_ :      Any    				=				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
														lowerCAmelCase_ :      Dict    				=				(sample - pred_original_sample) / sigma_hat
														# 3. delta timestep
														lowerCAmelCase_ :      Dict    				=				sigma_next - sigma_hat

														# store for 2nd order step
														lowerCAmelCase_ :      Optional[int]    				=				derivative
														lowerCAmelCase_ :      Union[str, Any]    				=				dt
														lowerCAmelCase_ :      int    				=				sample
										else:
														# 2. 2nd order / Heun's method
														lowerCAmelCase_ :      int    				=				(sample - pred_original_sample) / sigma_next
														lowerCAmelCase_ :      Any    				=				(self.prev_derivative + derivative) / 2

														# 3. take prev timestep & sample
														lowerCAmelCase_ :      int    				=				self.dt
														lowerCAmelCase_ :      int    				=				self.sample

														# free dt and derivative
														# Note, this puts the scheduler in "first order mode"
														lowerCAmelCase_ :      str    				=				None
														lowerCAmelCase_ :      str    				=				None
														lowerCAmelCase_ :      Dict    				=				None

										lowerCAmelCase_ :      Union[str, Any]    				=				sample + derivative * dt

										if not return_dict:
														return (prev_sample,)

										return SchedulerOutput(prev_sample=__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						)					->    torch.FloatTensor:
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										lowerCAmelCase_ :      Optional[Any]    				=				self.sigmas.to(device=original_samples.device   ,						dtype=original_samples.dtype							)
										if original_samples.device.type == "mps" and torch.is_floating_point(__lowercase							):
														# mps does not support float64
														lowerCAmelCase_ :      Dict    				=				self.timesteps.to(original_samples.device   ,						dtype=torch.floataa							)
														lowerCAmelCase_ :      str    				=				timesteps.to(original_samples.device   ,						dtype=torch.floataa							)
										else:
														lowerCAmelCase_ :      str    				=				self.timesteps.to(original_samples.device							)
														lowerCAmelCase_ :      Union[str, Any]    				=				timesteps.to(original_samples.device							)

										lowerCAmelCase_ :      int    				=				[self.index_for_timestep(__lowercase   ,						__lowercase							) for t in timesteps]

										lowerCAmelCase_ :      Union[str, Any]    				=				sigmas[step_indices].flatten()
										while len(sigma.shape							) < len(original_samples.shape							):
														lowerCAmelCase_ :      Optional[Any]    				=				sigma.unsqueeze(-1							)

										lowerCAmelCase_ :      Optional[Any]    				=				original_samples + noise * sigma
										return noisy_samples




						def __len__(				self							)					->    Any:
										return self.config.num_train_timesteps



import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase			:  Any     ="""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase			:  Optional[Any]     =direct_transformers_import(PATH_TO_TRANSFORMERS)

_UpperCAmelCase			:  List[str]     =transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_UpperCAmelCase			:  Dict     =re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


_UpperCAmelCase			:  Any     ={
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 			lowerCAmelCase (		lowerCAmelCase_ )-> str:
				lowerCAmelCase_ :      Any    				=				None

				# source code of `config_class`
				lowerCAmelCase_ :      Optional[int]    				=				inspect.getsource(lowerCAmelCase_ )
				lowerCAmelCase_ :      str    				=				_re_checkpoint.findall(lowerCAmelCase_ )

				# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
				# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
				for ckpt_name, ckpt_link in checkpoints:
								# allow the link to end with `/`
								if ckpt_link.endswith('''/''' ):
												lowerCAmelCase_ :      Optional[Any]    				=				ckpt_link[:-1]

								# verify the checkpoint name corresponds to the checkpoint link
								lowerCAmelCase_ :      Tuple    				=				f"""https://huggingface.co/{ckpt_name}"""
								if ckpt_link == ckpt_link_from_name:
												lowerCAmelCase_ :      List[str]    				=				ckpt_name
												break

				return checkpoint



def 			lowerCAmelCase (		)-> Optional[Any]:
				lowerCAmelCase_ :      Tuple    				=				[]

				for config_class in list(CONFIG_MAPPING.values() ):
								# Skip deprecated models
								if "models.deprecated" in config_class.__module__:
												continue
								lowerCAmelCase_ :      int    				=				get_checkpoint_from_config_class(lowerCAmelCase_ )

								lowerCAmelCase_ :      Tuple    				=				config_class.__name__
								if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
												configs_without_checkpoint.append(lowerCAmelCase_ )

				if len(lowerCAmelCase_ ) > 0:
								lowerCAmelCase_ :      List[Any]    				=				'''\n'''.join(sorted(lowerCAmelCase_ ) )
								raise ValueError(f"""The following configurations don't contain any valid checkpoint:\n{message}""" )


if __name__ == "__main__":
						check_config_docstrings_have_checkpoints()