



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
_lowercase: Tuple      			=		"src/transformers"


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

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


_lowercase: List[Any]      			=		{
    "CLIPConfigMixin",
    "DecisionTransformerConfigMixin",
    "EncoderDecoderConfigMixin",
    "RagConfigMixin",
    "SpeechEncoderDecoderConfigMixin",
    "VisionEncoderDecoderConfigMixin",
    "VisionTextDualEncoderConfigMixin",
}






def     a(      )		->       Union[str, Any]:
					"""simple docstring"""





					a					     =   []

					for config_class in list(CONFIG_MAPPING.values()       ):
										a					     =   False

										# source code of `config_class`
										a					     =   inspect.getsource(A       )
										a					     =   _re_checkpoint.findall(A       )

										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					     =   checkpoint

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

										a					     =   config_class.__name__
										if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
															configs_without_checkpoint.append(A       )

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


if __name__ == "__main__":
				check_config_docstrings_have_checkpoints()





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowercase: Union[str, Any]      			=		get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
				_lowercase: List[str]      			=		json.load(f)



@require_torch
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return FSMTTokenizer.from_pretrained(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_		).to(lowerCamelCase_		)
						if torch_device == "cuda":
											model.half()
						return model




	@parameterized.expand(
	    [
	        ["en-ru", 26.0],
	        ["ru-en", 22.0],
	        ["en-de", 22.0],
	        ["de-en", 29.0],
	    ]		)
	@slow
	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   F'''facebook/wmt19-{pair}'''
						a					     =   self.get_tokenizer(lowerCamelCase_		)
						a					     =   self.get_model(lowerCamelCase_		)

						a					     =   bleu_data[pair]["src"]
						a					     =   bleu_data[pair]["tgt"]

						a					     =   tokenizer(lowerCamelCase_		,       return_tensors="pt"		,       truncation=lowerCamelCase_		,       padding="longest"		).to(lowerCamelCase_		)
						a					     =   model.generate(
						    input_ids=batch.input_ids		,       num_beams=8		,       )
						a					     =   tokenizer.batch_decode(
						    lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       clean_up_tokenization_spaces=lowerCamelCase_		)
						a					     =   calculate_bleu(lowerCamelCase_		,       lowerCamelCase_		)
						print(lowerCamelCase_		)
						self.assertGreaterEqual(scores["bleu"]		,       lowerCamelCase_		)
