




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


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

__A						:							List[Any]							=  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(r"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


__A						:							int							=  {
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					None

			# source code of `config_class`
			SCREAMING_SNAKE_CASE           =					inspect.getsource(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					_re_checkpoint.findall(SCREAMING_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("""/"""   ):
									SCREAMING_SNAKE_CASE           =					ckpt_link[:-1]

						# verify the checkpoint name corresponds to the checkpoint link
						SCREAMING_SNAKE_CASE           =					f"""https://huggingface.co/{ckpt_name}"""
						if ckpt_link == ckpt_link_from_name:
									SCREAMING_SNAKE_CASE           =					ckpt_name
									break

			return checkpoint
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					[]

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

						SCREAMING_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(SCREAMING_SNAKE_CASE   )

			if len(SCREAMING_SNAKE_CASE   ) > 0:
						SCREAMING_SNAKE_CASE           =					"""\n""".join(sorted(SCREAMING_SNAKE_CASE   )   )
						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 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""       ,     return_dict=a			).to(a			)
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained("""google/mt5-small"""			)

										SCREAMING_SNAKE_CASE           =					tokenizer("""Hello there"""       ,     return_tensors="""pt"""			).input_ids
										SCREAMING_SNAKE_CASE           =					tokenizer("""Hi I am"""       ,     return_tensors="""pt"""			).input_ids

										SCREAMING_SNAKE_CASE           =					model(input_ids.to(a			)       ,     labels=labels.to(a			)			).loss
										SCREAMING_SNAKE_CASE           =					-(labels.shape[-1] * loss.item())

										SCREAMING_SNAKE_CASE           =					-84.9127
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1E-4			)






