

import importlib
import inspect
import os
import re


# 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_	:			int  	=      'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
A_	:			Dict  	=      importlib.util.spec_from_file_location(
    'transformers',
    os.path.join(PATH_TO_TRANSFORMERS, '__init__.py'),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
A_	:			Union[str, Any]  	=      spec.loader.load_module()

A_	:			Tuple  	=      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_	:			Any  	=      re.compile('\[(.+?)\]\((https://huggingface\.co/.+?)\)')


A_	:			Optional[int]  	=      {
    'CLIPConfigMixin',
    'DecisionTransformerConfigMixin',
    'EncoderDecoderConfigMixin',
    'RagConfigMixin',
    'SpeechEncoderDecoderConfigMixin',
    'VisionEncoderDecoderConfigMixin',
    'VisionTextDualEncoderConfigMixin',
}

def 						UpperCamelCase ()					->				Any:
	A__							:   Any					       =		[]

	for config_class in list(CONFIG_MAPPING.values() ):
		A__							:   Optional[Any]					       =		False

		# source code of `config_class`
		A__							:   Union[str, Any]					       =		inspect.getsource(lowercase_ )
		A__							:   List[Any]					       =		_re_checkpoint.findall(lowercase_ )

		for checkpoint in checkpoints:
			# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
			# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
			A__       ,       A__							:   Optional[Any]					       =		checkpoint

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

		A__							:   str					       =		config_class.__name__
		if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
			configs_without_checkpoint.append(lowercase_ )

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


if __name__ == "__main__":
	check_config_docstrings_have_checkpoints()






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""   ,	return_dict=A__      ).to(A__      )
						A__							:   str					       =		AutoTokenizer.from_pretrained("""google/mt5-small"""      )

						A__							:   int					       =		tokenizer("""Hello there"""   ,	return_tensors="""pt"""      ).input_ids
						A__							:   List[Any]					       =		tokenizer("""Hi I am"""   ,	return_tensors="""pt"""      ).input_ids

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



