






'''simple docstring'''




import os
import sys
import unittest


__lowerCamelCase   :					Any   							=					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__lowerCamelCase   :					int   							=					os.path.join(git_repo_path, '''src''', '''transformers''')

__lowerCamelCase   :					str   							=					"""
{0} = None
"""

__lowerCamelCase   :					Union[str, Any]   							=					"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


__lowerCamelCase   :					Optional[Any]   							=					"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""







class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:Any				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					Any   =       find_backend("    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"				)
										self.assertIsNone(lowerCAmelCase__				)

										snake_case_				:					Dict   =       find_backend("    if not is_tokenizers_available():"				)
										self.assertEqual(lowerCAmelCase__       ,							"tokenizers"				)

										snake_case_				:					int   =       find_backend("    if not is_tensorflow_text_available():"				)
										self.assertEqual(lowerCAmelCase__       ,							"tensorflow_text"				)

										snake_case_				:					int   =       find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):"				)
										self.assertEqual(lowerCAmelCase__       ,							"sentencepiece_and_tokenizers"				)

										snake_case_				:					Optional[Any]   =       find_backend(
										    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"				)
										self.assertEqual(lowerCAmelCase__       ,							"sentencepiece_and_tensorflow_text"				)

										snake_case_				:					int   =       find_backend(
										    "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"				)
										self.assertEqual(lowerCAmelCase__       ,							"sentencepiece_and_tokenizers_and_vision"				)

							def 							_A	(		self			:List[Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Dict   =       read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn("torch"       ,							lowerCAmelCase__				)
										self.assertIn("tensorflow_text"       ,							lowerCAmelCase__				)
										self.assertIn("sentencepiece_and_tokenizers"       ,							lowerCAmelCase__				)

										# Likewise, we can't assert on the exact content of a key
										self.assertIn("BertModel"       ,							objects["torch"]				)
										self.assertIn("TFBertModel"       ,							objects["tf"]				)
										self.assertIn("FlaxBertModel"       ,							objects["flax"]				)
										self.assertIn("BertModel"       ,							objects["torch"]				)
										self.assertIn("TFBertTokenizer"       ,							objects["tensorflow_text"]				)
										self.assertIn("convert_slow_tokenizer"       ,							objects["sentencepiece_and_tokenizers"]				)

							def 							_A	(		self			:int				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       create_dummy_object("CONSTANT"       ,							"\'torch\'"				)
										self.assertEqual(lowerCAmelCase__       ,							"\nCONSTANT = None\n"				)

										snake_case_				:					List[str]   =       create_dummy_object("function"       ,							"\'torch\'"				)
										self.assertEqual(
										    lowerCAmelCase__       ,							"\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n"				)

										snake_case_				:					Dict   =       "\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n"
										snake_case_				:					Any   =       create_dummy_object("FakeClass"       ,							"\'torch\'"				)
										self.assertEqual(lowerCAmelCase__       ,							lowerCAmelCase__				)






							def 							_A	(		self			:Any				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Any   =       "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n"
										snake_case_				:					List[str]   =       create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}				)
										self.assertEqual(dummy_files["torch"]       ,							lowerCAmelCase__				)












'''simple docstring'''




import fire

from utils import calculate_rouge, save_json

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__=None     ,**__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					int   =       [x.strip() for x in open(__magic_name__						).readlines()]
			snake_case_				:					Optional[int]   =       [x.strip() for x in open(__magic_name__						).readlines()][: len(__magic_name__						)]
			snake_case_				:					List[Any]   =       calculate_rouge(__magic_name__     ,__magic_name__     ,**__magic_name__						)
			if save_path is not None:
						save_json(__magic_name__     ,__magic_name__     ,indent=__magic_name__						)
			return metrics  # these print nicely


if __name__ == "__main__":
		fire.Fire(calculate_rouge_path)




