'''simple docstring'''




import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_lowercase						: Optional[Any]						=						datasets.logging.get_logger(__name__)


_lowercase						: List[str]						=						"\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n"

_lowercase						: str						=						"\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

_lowercase						: Union[str, Any]						=						"\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

_lowercase						: Dict						=						{
    "bleurt-tiny-128": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip",
    "bleurt-tiny-512": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip",
    "bleurt-base-128": "https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip",
    "bleurt-base-512": "https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip",
    "bleurt-large-128": "https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip",
    "bleurt-large-512": "https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip",
    "BLEURT-20-D3": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip",
    "BLEURT-20-D6": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip",
    "BLEURT-20-D12": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip",
    "BLEURT-20": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip",
}





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,     _KWARGS_DESCRIPTION)
class     __magic_name__						(    datasets.Metric):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										return datasets.MetricInfo(
										    description=_DESCRIPTION				,			citation=_CITATION				,			homepage="""https://github.com/google-research/bleurt"""				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
										        {
										            """predictions""": datasets.Value("""string"""				,			id="""sequence"""    ),
										            """references""": datasets.Value("""string"""				,			id="""sequence"""    ),
										        }    )				,			codebase_urls=["""https://github.com/google-research/bleurt"""]				,			reference_urls=["""https://github.com/google-research/bleurt""", """https://arxiv.org/abs/2004.04696"""]				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]				,			lowercase_				:  Union[str, Any]    ):
										# check that config name specifies a valid BLEURT model
										if self.config_name == "default":
																	logger.warning(
																	    """Using default BLEURT-Base checkpoint for sequence maximum length 128. """
																	    """You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."""    )
																	lowercase_					:			Optional[Any]										=       """bleurt-base-128"""

										if self.config_name.lower() in CHECKPOINT_URLS:
																	lowercase_					:			Optional[int]										=       self.config_name.lower()

										elif self.config_name.upper() in CHECKPOINT_URLS:
																	lowercase_					:			List[Any]										=       self.config_name.upper()

										else:
																	raise KeyError(
																	    f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''    )

										# download the model checkpoint specified by self.config_name and set up the scorer
										lowercase_					:			Dict										=       dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]    )
										lowercase_					:			int										=       score.BleurtScorer(os.path.join(lowercase_				,			lowercase_    )    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  int				,			lowercase_				:  str    ):
										lowercase_					:			int										=       self.scorer.score(references=lowercase_				,			candidates=lowercase_    )
										return {"scores": scores}






'''simple docstring'''




from collections import defaultdict




def 							lowerCamelCase	(      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					str )			->      bool:
							lowercase_					:			Tuple										=       first_str.lower().strip()
							lowercase_					:			List[Any]										=       second_str.lower().strip()

							# Remove whitespace
							lowercase_					:			Dict										=       first_str.replace(""" """      ,      """""" )
							lowercase_					:			Tuple										=       second_str.replace(""" """      ,      """""" )

							# Strings of different lengths are not anagrams
							if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
														return False

							# Default values for count should be 0
							lowercase_					:			defaultdict[str, int]										=       defaultdict(UpperCAmelCase__ )

							# For each character in input strings,
							# increment count in the corresponding
							for i in range(len(UpperCAmelCase__ ) ):
														count[first_str[i]] += 1
														count[second_str[i]] -= 1

							return all(_count == 0 for _count in count.values() )


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						_lowercase						: int						=						input("Enter the first string ").strip()
						_lowercase						: Tuple						=						input("Enter the second string ").strip()

						_lowercase						: Union[str, Any]						=						check_anagrams(input_a, input_b)
						print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")





