





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
snake_case_				=			'src/transformers'


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

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


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






def lowerCamelCase__    (							snake_case_	:  Optional[Any]       )      ->				str:
   __snake_case							       =   None

   # source code of `config_class`
   __snake_case							       =   inspect.getsource(snake_case_       )
   __snake_case							       =   _re_checkpoint.findall(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('''/'''       ):
         __snake_case							       =   ckpt_link[:-1]

      # verify the checkpoint name corresponds to the checkpoint link
      __snake_case							       =   f"""https://huggingface.co/{ckpt_name}"""
      if ckpt_link == ckpt_link_from_name:
         __snake_case							       =   ckpt_name
         break

   return checkpoint






def lowerCamelCase__    (							)      ->				List[Any]:
   __snake_case							       =   []

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

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

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


if __name__ == "__main__":
    check_config_docstrings_have_checkpoints()








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_				=			logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Optional[int]				,			*a__       :      Any				,			**a__       :      Dict      ):





         """simple docstring"""



         super().__init__(*a__				,			**a__      )
         requires_backends(self				,			'''vision'''      )
         self.check_model_type(a__      )


      def __call__(self       :      Optional[int]				,			a__       :      Union[str, List[str], "Image.Image", List["Image.Image"]]				,			**a__       :      Tuple      ):





         """simple docstring"""



         return super().__call__(a__				,			**a__      )


      def    a (self       :      Dict				,			**a__       :      Any      ):





         """simple docstring"""



         return {}, {}, {}


      def    a (self       :      List[str]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   load_image(a__      )
         __snake_case							       =   image.size
         __snake_case							       =   self.image_processor(images=a__				,			return_tensors=self.framework      )
         return model_inputs


      def    a (self       :      int				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model(**a__      )
         return model_outputs


      def    a (self       :      int				,			a__       :      str      ):





         """simple docstring"""



         __snake_case							       =   model_outputs.predicted_depth
         __snake_case							       =   torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1      )				,			size=self.image_size[::-1]				,			mode='''bicubic'''				,			align_corners=a__      )
         __snake_case							       =   prediction.squeeze().cpu().numpy()
         __snake_case							       =   (output * 255 / np.max(a__      )).astype('''uint8'''      )
         __snake_case							       =   Image.fromarray(a__      )
         __snake_case							       =   {}
         __snake_case							       =   predicted_depth
         __snake_case							       =   depth
         return output_dict

