

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin

class 			a					(		UpperCAmelCase							,	UpperCAmelCase			):
						_lowercase        =     1


						@register_to_config
						def __init__(    self  ,   A_=2000  ,   A_=0.1  ,   A_=20  ,   A_=1e-3		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      None
										_UpperCAmelCase    :						Any							=      None
										_UpperCAmelCase    :						List[str]							=      None


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      torch.linspace(1  ,   self.config.sampling_eps  ,   A_  ,   device=A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_=None		):







										'''simple docstring'''


										if self.timesteps is None:
														raise ValueError(
														    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										_UpperCAmelCase    :						Tuple							=      (
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										_UpperCAmelCase    :						Any							=      torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff		)		)
										_UpperCAmelCase    :						Optional[int]							=      std.flatten()
										while len(std.shape		) < len(score.shape		):
														_UpperCAmelCase    :						Tuple							=      std.unsqueeze(-1		)
										_UpperCAmelCase    :						str							=      -score / std

										# compute
										_UpperCAmelCase    :						Optional[int]							=      -1.0 / len(self.timesteps		)

										_UpperCAmelCase    :						Dict							=      self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										_UpperCAmelCase    :						Any							=      beta_t.flatten()
										while len(beta_t.shape		) < len(x.shape		):
														_UpperCAmelCase    :						Dict							=      beta_t.unsqueeze(-1		)
										_UpperCAmelCase    :						Tuple							=      -0.5 * beta_t * x

										_UpperCAmelCase    :						int							=      torch.sqrt(A_		)
										_UpperCAmelCase    :						int							=      drift - diffusion**2 * score
										_UpperCAmelCase    :						Dict							=      x + drift * dt

										# add noise
										_UpperCAmelCase    :						Dict							=      randn_tensor(x.shape  ,   layout=x.layout  ,   generator=A_  ,   device=x.device  ,   dtype=x.dtype		)
										_UpperCAmelCase    :						Optional[int]							=      x_mean + diffusion * math.sqrt(-dt		) * noise

										return x, x_mean





						def __len__(    self		):







										'''simple docstring'''


										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
SCREAMING_SNAKE_CASE_            = 'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE_            = direct_transformers_import(PATH_TO_TRANSFORMERS)

SCREAMING_SNAKE_CASE_            = 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)`
SCREAMING_SNAKE_CASE_            = re.compile(R'\[(.+?)\]\((https://huggingface\.co/.+?)\)')


SCREAMING_SNAKE_CASE_            = {
    'DecisionTransformerConfig',
    'EncoderDecoderConfig',
    'MusicgenConfig',
    'RagConfig',
    'SpeechEncoderDecoderConfig',
    'TimmBackboneConfig',
    'VisionEncoderDecoderConfig',
    'VisionTextDualEncoderConfig',
    'LlamaConfig',
}

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Dict	)				->    Union[str, Any]:
				_UpperCAmelCase    :						List[Any]							=      None

				# source code of `config_class`
				_UpperCAmelCase    :						int							=      inspect.getsource(lowerCAmelCase	)
				_UpperCAmelCase    :						Tuple							=      _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("/"	):
												_UpperCAmelCase    :						Dict							=      ckpt_link[:-1]

								# verify the checkpoint name corresponds to the checkpoint link
								_UpperCAmelCase    :						Any							=      F'https://huggingface.co/{ckpt_name}'
								if ckpt_link == ckpt_link_from_name:
												_UpperCAmelCase    :						Union[str, Any]							=      ckpt_name
												break

				return checkpoint

def 							__SCREAMING_SNAKE_CASE							(			)				->    Dict:
				_UpperCAmelCase    :						Any							=      []

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

								_UpperCAmelCase    :						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:
								_UpperCAmelCase    :						int							=      "\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()


