




"""simple docstring"""





import os
import sys
import unittest


A				=							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
A				=							os.path.join(git_repo_path, """src""", """transformers""")

A				=							"""
{0} = None
"""

A				=							"""
class {0}(metaclass=DummyObject):
    _backends = {1}

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


A				=							"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



class   a__					(						unittest.TestCase ):


			def    a_  (   self   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       find_backend("    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")")
						self.assertIsNone(UpperCamelCase_)

						__UpperCAmelCase						:			List[Any]												=       find_backend("    if not is_tokenizers_available():")
						self.assertEqual(UpperCamelCase_   ,	"tokenizers")

						__UpperCAmelCase						:			Optional[Any]												=       find_backend("    if not is_tensorflow_text_available():")
						self.assertEqual(UpperCamelCase_   ,	"tensorflow_text")

						__UpperCAmelCase						:			str												=       find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):")
						self.assertEqual(UpperCamelCase_   ,	"sentencepiece_and_tokenizers")

						__UpperCAmelCase						:			Optional[int]												=       find_backend(
						    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):")
						self.assertEqual(UpperCamelCase_   ,	"sentencepiece_and_tensorflow_text")

						__UpperCAmelCase						:			Optional[Any]												=       find_backend(
						    "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):")
						self.assertEqual(UpperCamelCase_   ,	"sentencepiece_and_tokenizers_and_vision")


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




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

						# 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   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       create_dummy_object("CONSTANT"   ,	"'torch'")
						self.assertEqual(UpperCamelCase_   ,	"\nCONSTANT = None\n")

						__UpperCAmelCase						:			Optional[Any]												=       create_dummy_object("function"   ,	"'torch'")
						self.assertEqual(
						    UpperCamelCase_   ,	"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n")

						__UpperCAmelCase						:			Optional[Any]												=       "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n"
						__UpperCAmelCase						:			List[Any]												=       create_dummy_object("FakeClass"   ,	"'torch'")
						self.assertEqual(UpperCamelCase_   ,	UpperCamelCase_)


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			List[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"
						__UpperCAmelCase						:			str												=       create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]})
						self.assertEqual(dummy_files["torch"]   ,	UpperCamelCase_)









"""simple docstring"""



def 						__lowercase	(  snake_case_    :    str							,snake_case_    :    str		)  ->float:



    '''simple docstring'''







    def get_matched_characters(snake_case_    :    str							,snake_case_    :    str		) -> str:
        __A       :		Any							=	[]
        __A       :		Any							=	min(len(_stra		)							,len(_stra		)		) // 2
        for i, l in enumerate(_stra		):
            __A       :		Dict							=	int(max(0							,i - limit		)		)
            __A       :		Tuple							=	int(min(i + limit + 1							,len(_stra		)		)		)
            if l in _stra[left:right]:
                matched.append(snake_case_		)
                __A       :		Any							=	F"""{_stra[0:_stra.index(snake_case_		)]} {_stra[_stra.index(snake_case_		) + 1:]}"""

        return "".join(snake_case_		)

    # matching characters
    __A       :		int							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		Tuple							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		str							=	len(snake_case_		)

    # transposition
    __A       :		Dict							=	(
        len([(ca, ca) for ca, ca in zip(snake_case_							,snake_case_		) if ca != ca]		) // 2
    )

    if not match_count:
        __A       :		List[str]							=	0.0
    else:
        __A       :		Tuple							=	(
            1
            / 3
            * (
                match_count / len(snake_case_		)
                + match_count / len(snake_case_		)
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    __A       :		Tuple							=	0
    for ca, ca in zip(stra[:4]							,stra[:4]		):
        if ca == ca:
            prefix_len += 1
        else:
            break

    return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(jaro_winkler("""hello""", """world"""))




