

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

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

@dataclass
class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       42
			__lowerCamelCase        =       42
			__lowerCamelCase        =       None

class 					a__						(					snake_case							,	snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       2


			@register_to_config
			def __init__(    self      ,      lowercase = 0.02      ,      lowercase = 100      ,      lowercase = 1.007      ,      lowercase = 80      ,      lowercase = 0.05      ,      lowercase = 50      ,      )  ->						Any:
							'''simple docstring'''






							A__        =			sigma_max

							# setable values
							A__        =			None
							A__        =			None
							A__        =			None  # sigma(t_i)


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None       )  ->						torch.FloatTensor:
							'''simple docstring'''






							return sample


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None       )  ->						Dict:
							'''simple docstring'''






							A__        =			num_inference_steps
							A__        =			np.arange(0      ,      self.num_inference_steps       )[::-1].copy()
							A__        =			torch.from_numpy(lowercase       ).to(lowercase       )
							A__        =			[
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in self.timesteps
							]
							A__        =			torch.tensor(lowercase      ,      dtype=torch.floataa      ,      device=lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = None       )  ->						Tuple[torch.FloatTensor, float]:
							'''simple docstring'''






							if self.config.s_min <= sigma <= self.config.s_max:
											A__        =			min(self.config.s_churn / self.num_inference_steps      ,      2**0.5 - 1       )
							else:
											A__        =			0

							# sample eps ~ N(0, S_noise^2 * I)
							A__        =			self.config.s_noise * randn_tensor(sample.shape      ,      generator=lowercase       ).to(sample.device       )
							A__        =			sigma + gamma * sigma
							A__        =			sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = True      ,      )  ->						Union[KarrasVeOutput, Tuple]:
							'''simple docstring'''






							A__        =			sample_hat + sigma_hat * model_output
							A__        =			(sample_hat - pred_original_sample) / sigma_hat
							A__        =			sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
											return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase      ,      derivative=lowercase      ,      pred_original_sample=lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = True      ,      )  ->						Union[KarrasVeOutput, Tuple]:
							'''simple docstring'''






							A__        =			sample_prev + sigma_prev * model_output
							A__        =			(sample_prev - pred_original_sample) / sigma_prev
							A__        =			sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
											return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase      ,      derivative=lowercase      ,      pred_original_sample=lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						int:
							'''simple docstring'''






							raise NotImplementedError()




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
lowerCAmelCase__	   = """src/transformers"""


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

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


lowerCAmelCase__	   = {
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]      )			->      Dict:




				'''simple docstring'''





				A__        =			None

				# source code of `config_class`
				A__        =			inspect.getsource(SCREAMING_SNAKE_CASE_      )
				A__        =			_re_checkpoint.findall(SCREAMING_SNAKE_CASE_      )

				# 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("/"      ):
												A__        =			ckpt_link[:-1]

								# verify the checkpoint name corresponds to the checkpoint link
								A__        =			F'https://huggingface.co/{ckpt_name}'
								if ckpt_link == ckpt_link_from_name:
												A__        =			ckpt_name
												break

				return checkpoint



def 					lowerCAmelCase__							(	)			->      List[str]:




				'''simple docstring'''





				A__        =			[]

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

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

				if len(SCREAMING_SNAKE_CASE_      ) > 0:
								A__        =			"\n".join(sorted(SCREAMING_SNAKE_CASE_      )      )
								raise ValueError(F'The following configurations don\'t contain any valid checkpoint:\n{message}'      )


if __name__ == "__main__":
	check_config_docstrings_have_checkpoints()

