

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

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







			__lowerCamelCase        =       42
			__lowerCamelCase        =       42


			def __init__(    self      ,      lowercase      ,      lowercase       )  ->						List[str]:
							'''simple docstring'''






							super().__init__()
							self.register_modules(unet=lowercase      ,      scheduler=lowercase       )


			@torch.no_grad()
			def __call__(    self      ,      lowercase = 1      ,      lowercase = 50      ,      lowercase = None      ,      lowercase = "pil"      ,      lowercase = True      ,      **lowercase      ,      )  ->						Union[Tuple, ImagePipelineOutput]:
							'''simple docstring'''






							A__        =			self.unet.config.sample_size
							A__        =			(batch_size, 3, img_size, img_size)

							A__        =			self.unet

							# sample x_0 ~ N(0, sigma_0^2 * I)
							A__        =			randn_tensor(lowercase      ,      generator=lowercase      ,      device=self.device       ) * self.scheduler.init_noise_sigma

							self.scheduler.set_timesteps(lowercase       )

							for t in self.progress_bar(self.scheduler.timesteps       ):
											# here sigma_t == t_i from the paper
											A__        =			self.scheduler.schedule[t]
											A__        =			self.scheduler.schedule[t - 1] if t > 0 else 0

											# 1. Select temporarily increased noise level sigma_hat
											# 2. Add new noise to move from sample_i to sample_hat
											A__    ,				A__        =			self.scheduler.add_noise_to_input(lowercase      ,      lowercase      ,      generator=lowercase       )

											# 3. Predict the noise residual given the noise magnitude `sigma_hat`
											# The model inputs and output are adjusted by following eq. (213) in [1].
											A__        =			(sigma_hat / 2) * model((sample_hat + 1) / 2      ,      sigma_hat / 2       ).sample

											# 4. Evaluate dx/dt at sigma_hat
											# 5. Take Euler step from sigma to sigma_prev
											A__        =			self.scheduler.step(lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )

											if sigma_prev != 0:
															# 6. Apply 2nd order correction
															# The model inputs and output are adjusted by following eq. (213) in [1].
															A__        =			(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2      ,      sigma_prev / 2       ).sample
															A__        =			self.scheduler.step_correct(
															    lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      step_output.prev_sample      ,      step_output["derivative"]      ,      )
											A__        =			step_output.prev_sample

							A__        =			(sample / 2 + 0.5).clamp(0      ,      1       )
							A__        =			sample.cpu().permute(0      ,      2      ,      3      ,      1       ).numpy()
							if output_type == "pil":
											A__        =			self.numpy_to_pil(lowercase       )

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=lowercase       )




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()

