
'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase       =		{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase       =		["""XGLMTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase       =		["""XGLMTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase       =		[
						    """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """XGLMForCausalLM""",
						    """XGLMModel""",
						    """XGLMPreTrainedModel""",
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase       =		[
						    """FlaxXGLMForCausalLM""",
						    """FlaxXGLMModel""",
						    """FlaxXGLMPreTrainedModel""",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase       =		[
						    """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFXGLMForCausalLM""",
						    """TFXGLMModel""",
						    """TFXGLMPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xglm import XGLMTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xglm_fast import XGLMTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xglm import (
												    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXGLMForCausalLM,
												    TFXGLMModel,
												    TFXGLMPreTrainedModel,
												)


else:
						import sys

						_lowercase       =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)


'''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
_lowercase    =		"""src/transformers"""


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

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


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


def A				(__lowerCamelCase				:str		):
						_lowerCAmelCase  						=			None

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

						# 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(__lowerCamelCase		)

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

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


if __name__ == "__main__":
						check_config_docstrings_have_checkpoints()
