



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class  __lowerCAmelCase     :
 lowerCAmelCase__       					=  4_2  # [batch_size x 3]
 lowerCAmelCase__       					=  4_2  # [batch_size x 3]
 lowerCAmelCase__       					=  4_2  # [batch_size x 3]
 lowerCAmelCase__       					=  4_2  # [batch_size x 3]
 lowerCAmelCase__       					=  4_2
 lowerCAmelCase__       					=  4_2
 lowerCAmelCase__       					=  4_2
 lowerCAmelCase__       					=  4_2
 lowerCAmelCase__       					=  4_2




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
    assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
    assert len(self.x.shape				) == len(self.y.shape				) == len(self.z.shape				) == len(self.origin.shape				) == 2




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return torch.from_numpy(np.array([self.width, self.height]	,				dtype=np.floataa				)				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return torch.from_numpy(np.array([self.x_fov, self.y_fov]	,				dtype=np.floataa				)				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    torch.arange(self.height * self.width				)
    __lowerCamelCase													=    torch.stack(
        [
            pixel_indices % self.width,
            torch.div(__UpperCAmelCase	,				self.width	,				rounding_mode='''trunc'''				),
        ]	,				axis=1	,				)
    return coords




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,*__lowerCamelCase													=    self.shape
    __lowerCamelCase													=    int(np.prod(__UpperCAmelCase				)				)

    __lowerCamelCase													=    self.get_image_coords()
    __lowerCamelCase													=    torch.broadcast_to(coords.unsqueeze(0				)	,				[batch_size * inner_batch_size, *coords.shape]				)
    __lowerCamelCase													=    self.get_camera_rays(__UpperCAmelCase				)

    __lowerCamelCase													=    rays.view(__UpperCAmelCase	,				inner_batch_size * self.height * self.width	,				2	,				3				)

    return rays




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase							,*__lowerCamelCase							,__lowerCamelCase													=    coords.shape
    assert n_coords == 2
    assert batch_size == self.origin.shape[0]

    __lowerCamelCase													=    coords.view(__UpperCAmelCase	,				-1	,				2				)

    __lowerCamelCase													=    self.resolution()
    __lowerCamelCase													=    self.fov()

    __lowerCamelCase													=    (flat.float() / (res - 1)) * 2 - 1
    __lowerCamelCase													=    fracs * torch.tan(fov / 2				)

    __lowerCamelCase													=    fracs.view(__UpperCAmelCase	,				-1	,				2				)
    __lowerCamelCase													=    (
        self.z.view(__UpperCAmelCase	,				1	,				3				)
        + self.x.view(__UpperCAmelCase	,				1	,				3				) * fracs[:, :, :1]
        + self.y.view(__UpperCAmelCase	,				1	,				3				) * fracs[:, :, 1:]
    )
    __lowerCamelCase													=    directions / directions.norm(dim=-1	,				keepdim=__UpperCAmelCase				)
    __lowerCamelCase													=    torch.stack(
        [
            torch.broadcast_to(self.origin.view(__UpperCAmelCase	,				1	,				3				)	,				[batch_size, directions.shape[1], 3]				),
            directions,
        ]	,				dim=2	,				)
    return rays.view(__UpperCAmelCase	,				*__UpperCAmelCase	,				2	,				3				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    assert width * self.height == height * self.width, "The aspect ratio should not change."
    return DifferentiableProjectiveCamera(
        origin=self.origin	,				x=self.x	,				y=self.y	,				z=self.z	,				width=__UpperCAmelCase	,				height=__UpperCAmelCase	,				x_fov=self.x_fov	,				y_fov=self.y_fov	,				)





def 		a__			(		_UpperCamelCase			: int							):
   __lowerCamelCase													=    []
   __lowerCamelCase													=    []
   __lowerCamelCase													=    []
   __lowerCamelCase													=    []
   for theta in np.linspace(0 ,2 * np.pi ,num=20							):
      __lowerCamelCase													=    np.array([np.sin(_UpperCamelCase							), np.cos(_UpperCamelCase							), -0.5]							)
      z /= np.sqrt(np.sum(z**2							)							)
      __lowerCamelCase													=    -z * 4
      __lowerCamelCase													=    np.array([np.cos(_UpperCamelCase							), -np.sin(_UpperCamelCase							), 0.0]							)
      __lowerCamelCase													=    np.cross(_UpperCamelCase ,_UpperCamelCase							)
      origins.append(_UpperCamelCase							)
      xs.append(_UpperCamelCase							)
      ys.append(_UpperCamelCase							)
      zs.append(_UpperCamelCase							)
   return DifferentiableProjectiveCamera(
       origin=torch.from_numpy(np.stack(_UpperCamelCase ,axis=0							)							).float() ,x=torch.from_numpy(np.stack(_UpperCamelCase ,axis=0							)							).float() ,y=torch.from_numpy(np.stack(_UpperCamelCase ,axis=0							)							).float() ,z=torch.from_numpy(np.stack(_UpperCamelCase ,axis=0							)							).float() ,width=_UpperCamelCase ,height=_UpperCamelCase ,x_fov=0.7 ,y_fov=0.7 ,shape=(1, len(_UpperCamelCase							)) ,)











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
a_			=					"""src/transformers"""


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

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


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





def 		a__			(		_UpperCamelCase			: Union[str, Any]							):
   __lowerCamelCase													=    None

   # source code of `config_class`
   __lowerCamelCase													=    inspect.getsource(_UpperCamelCase							)
   __lowerCamelCase													=    _re_checkpoint.findall(_UpperCamelCase							)

   # 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													=    ckpt_link[:-1]

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

   return checkpoint





def 		a__			(		):
   __lowerCamelCase													=    []

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

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

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


if __name__ == "__main__":
  check_config_docstrings_have_checkpoints()






