


"""simple docstring"""





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_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import GLPNImageProcessor



class 						snake_case_(				unittest.TestCase	):






     def __init__(		self     :    Optional[int]    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    Union[str, Any]=7    , UpperCamelCase_     :    Optional[Any]=3    , UpperCamelCase_     :    Union[str, Any]=1_8    , UpperCamelCase_     :    List[Any]=3_0    , UpperCamelCase_     :    Optional[int]=4_0_0    , UpperCamelCase_     :    Optional[int]=True    , UpperCamelCase_     :    Any=3_2    , UpperCamelCase_     :    Tuple=True    , ):
           lowerCAmelCase    :		Tuple				=      parent
           lowerCAmelCase    :		int				=      batch_size
           lowerCAmelCase    :		str				=      num_channels
           lowerCAmelCase    :		Optional[Any]				=      image_size
           lowerCAmelCase    :		str				=      min_resolution
           lowerCAmelCase    :		List[str]				=      max_resolution
           lowerCAmelCase    :		List[str]				=      do_resize
           lowerCAmelCase    :		Dict				=      size_divisor
           lowerCAmelCase    :		Tuple				=      do_rescale






     def    lowerCamelCase__   (		self     :    int ):
           return {
               "do_resize": self.do_resize,
               "size_divisor": self.size_divisor,
               "do_rescale": self.do_rescale,
           }



@require_torch
@require_vision
class 						snake_case_(				a__							,    unittest.TestCase	):
     __UpperCamelCase     =    GLPNImageProcessor if is_vision_available() else None






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		Any				=      GLPNImageProcessingTester(self )






     @property
     def    lowerCamelCase__   (		self     :    Tuple ):
           return self.image_processor_tester.prepare_image_processor_dict()






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		Dict				=      self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(UpperCamelCase_    , '''do_resize''' ) )
           self.assertTrue(hasattr(UpperCamelCase_    , '''size_divisor''' ) )
           self.assertTrue(hasattr(UpperCamelCase_    , '''resample''' ) )
           self.assertTrue(hasattr(UpperCamelCase_    , '''do_rescale''' ) )






     def    lowerCamelCase__   (		self     :    List[Any] ):
           pass






     def    lowerCamelCase__   (		self     :    List[str] ):
           # Initialize image_processing
           lowerCAmelCase    :		Dict				=      self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           lowerCAmelCase    :		str				=      prepare_image_inputs(self.image_processor_tester    , equal_resolution=UpperCamelCase_ )
           for image in image_inputs:
                 self.assertIsInstance(UpperCamelCase_    , Image.Image )

           # Test not batched input (GLPNImageProcessor doesn't support batching)
           lowerCAmelCase    :		int				=      image_processing(image_inputs[0]    , return_tensors='''pt''' ).pixel_values
           self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
           self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           # Initialize image_processing
           lowerCAmelCase    :		List[str]				=      self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           lowerCAmelCase    :		Optional[Any]				=      prepare_image_inputs(self.image_processor_tester    , equal_resolution=UpperCamelCase_    , numpify=UpperCamelCase_ )
           for image in image_inputs:
                 self.assertIsInstance(UpperCamelCase_    , np.ndarray )

           # Test not batched input (GLPNImageProcessor doesn't support batching)
           lowerCAmelCase    :		Optional[Any]				=      image_processing(image_inputs[0]    , return_tensors='''pt''' ).pixel_values
           self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
           self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )






     def    lowerCamelCase__   (		self     :    List[str] ):
           # Initialize image_processing
           lowerCAmelCase    :		Tuple				=      self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           lowerCAmelCase    :		List[Any]				=      prepare_image_inputs(self.image_processor_tester    , equal_resolution=UpperCamelCase_    , torchify=UpperCamelCase_ )
           for image in image_inputs:
                 self.assertIsInstance(UpperCamelCase_    , torch.Tensor )

           # Test not batched input (GLPNImageProcessor doesn't support batching)
           lowerCAmelCase    :		Optional[int]				=      image_processing(image_inputs[0]    , return_tensors='''pt''' ).pixel_values
           self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
           self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )






"""simple docstring"""





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__						:   Optional[Any]			 = '''src/transformers'''


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

snake_case__						:   Optional[int]			 = 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__						:   Optional[int]			 = re.compile(R'''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')


snake_case__						:   int			 = {
    '''DecisionTransformerConfig''',
    '''EncoderDecoderConfig''',
    '''MusicgenConfig''',
    '''RagConfig''',
    '''SpeechEncoderDecoderConfig''',
    '''TimmBackboneConfig''',
    '''VisionEncoderDecoderConfig''',
    '''VisionTextDualEncoderConfig''',
    '''LlamaConfig''',
}


def    _snake_case  (  _snake_case :	List[str]	):
      lowerCAmelCase    :		Dict				=      None

      # source code of `config_class`
      lowerCAmelCase    :		Union[str, Any]				=      inspect.getsource(_snake_case	)
      lowerCAmelCase    :		List[Any]				=      _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('''/'''	):
                  lowerCAmelCase    :		List[str]				=      ckpt_link[:-1]

            # verify the checkpoint name corresponds to the checkpoint link
            lowerCAmelCase    :		Optional[int]				=      f'''https://huggingface.co/{ckpt_name}'''
            if ckpt_link == ckpt_link_from_name:
                  lowerCAmelCase    :		List[str]				=      ckpt_name
                  break

      return checkpoint


def    _snake_case  (  ):
      lowerCAmelCase    :		List[Any]				=      []

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

            lowerCAmelCase    :		int				=      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:
            lowerCAmelCase    :		Dict				=      '''\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()


