

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 				snake_case_(_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  _snake_case						=							None

  # source code of `config_class`
  _snake_case						=							inspect.getsource(_UpperCamelCase					)
  _snake_case						=							_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('''/'''					):
      _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 				snake_case_()							->     Tuple:



  """simple docstring"""






  _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(_UpperCamelCase					)

    _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(_UpperCamelCase					)

  if len(_UpperCamelCase					) > 0:
    _snake_case						=							'''\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()




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import VivitImageProcessor



class   lowercase_   (  unittest.TestCase					):



      def __init__(      self				:    Union[str, Any]      , A__				:    int      , A__				:    List[str]=7      , A__				:    Tuple=3      , A__				:    List[str]=10      , A__				:    Optional[int]=18      , A__				:    int=30      , A__				:    Tuple=400      , A__				:    Dict=True      , A__				:    str=None      , A__				:    str=True      , A__				:    List[str]=[0.5, 0.5, 0.5]      , A__				:    int=[0.5, 0.5, 0.5]      , A__				:    List[Any]=None      , )   ->					int:
        _snake_case						=							size if size is not None else {'''shortest_edge''': 18}
        _snake_case						=							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

        _snake_case						=							parent
        _snake_case						=							batch_size
        _snake_case						=							num_channels
        _snake_case						=							num_frames
        _snake_case						=							image_size
        _snake_case						=							min_resolution
        _snake_case						=							max_resolution
        _snake_case						=							do_resize
        _snake_case						=							size
        _snake_case						=							do_normalize
        _snake_case						=							image_mean
        _snake_case						=							image_std
        _snake_case						=							crop_size







      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Union[str, Any]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Tuple										=    VivitImageProcessor if is_vision_available() else None



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[str]:
        _snake_case						=							VivitImageProcessingTester(self		)



      @property
      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Optional[Any]:
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(A__      , '''image_mean'''		)		)
        self.assertTrue(hasattr(A__      , '''image_std'''		)		)
        self.assertTrue(hasattr(A__      , '''do_normalize'''		)		)
        self.assertTrue(hasattr(A__      , '''do_resize'''		)		)
        self.assertTrue(hasattr(A__      , '''do_center_crop'''		)		)
        self.assertTrue(hasattr(A__      , '''size'''		)		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					List[Any]:
        _snake_case						=							self.image_processing_class.from_dict(self.image_processor_dict		)
        self.assertEqual(image_processor.size      , {'''shortest_edge''': 18}		)
        self.assertEqual(image_processor.crop_size      , {'''height''': 18, '''width''': 18}		)

        _snake_case						=							self.image_processing_class.from_dict(self.image_processor_dict      , size=42      , crop_size=84		)
        self.assertEqual(image_processor.size      , {'''shortest_edge''': 42}		)
        self.assertEqual(image_processor.crop_size      , {'''height''': 84, '''width''': 84}		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[Any]:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PIL videos
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , Image.Image		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )



      def 						UpperCamelCase_		(      self				:    Any		)   ->					List[str]:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__      , numpify=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , np.ndarray		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )







      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					int:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__      , torchify=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , torch.Tensor		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

