



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						:List[str]				=					'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
__snake_case						:List[Any]				=					direct_transformers_import(PATH_TO_TRANSFORMERS)

__snake_case						:List[str]				=					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						:int				=					re.compile(r'''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')


__snake_case						:Union[str, Any]				=					{
    '''DecisionTransformerConfig''',
    '''EncoderDecoderConfig''',
    '''MusicgenConfig''',
    '''RagConfig''',
    '''SpeechEncoderDecoderConfig''',
    '''TimmBackboneConfig''',
    '''VisionEncoderDecoderConfig''',
    '''VisionTextDualEncoderConfig''',
    '''LlamaConfig''',
}





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    None

    # source code of `config_class`
    __a							=    inspect.getsource(_UpperCAmelCase    )
    __a							=    _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('''/'''    ):
            __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 					__snake_case     (      ):
    __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(_UpperCAmelCase    )

        __a							=    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:
        __a							=    '''\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()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__snake_case						:Tuple						=					{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:List[Any]						=					['''BloomTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:Any						=					[
     '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''BloomForCausalLM''',
     '''BloomModel''',
     '''BloomPreTrainedModel''',
     '''BloomForSequenceClassification''',
     '''BloomForTokenClassification''',
     '''BloomForQuestionAnswering''',
 ]

if TYPE_CHECKING:
 from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bloom_fast import BloomTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bloom import (
      BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
      BloomForCausalLM,
      BloomForQuestionAnswering,
      BloomForSequenceClassification,
      BloomForTokenClassification,
      BloomModel,
      BloomPreTrainedModel,
  )

else:
 import sys

 __snake_case						:Any						=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





