

"""simple docstring"""


def    a__   (   __SCREAMING_SNAKE_CASE = 1_0 ,			__SCREAMING_SNAKE_CASE = 1_0_0_0 ,			__SCREAMING_SNAKE_CASE = True			)     ->     int:
		assert (
		    isinstance(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		    and isinstance(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		    and isinstance(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		), "Invalid type of value(s) specified to function!"

		if min_val > max_val:
				raise ValueError("Invalid value for min_val or max_val (min_value < max_value)"			)
		return min_val if option else max_val






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     int:
		return int((number_a + number_a) / 2			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     None:
		assert (
		    isinstance(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			) and isinstance(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			) and isinstance(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		), 'argument values must be type of "int"'

		if lower > higher:
				raise ValueError("argument value for lower and higher must be(lower > higher)"			)

		if not lower < to_guess < higher:
				raise ValueError(
				    "guess value must be within the range of lower and higher value"			)

		def answer(__SCREAMING_SNAKE_CASE			) -> str:
				if number > to_guess:
						return "high"
				elif number < to_guess:
						return "low"
				else:
						return "same"

		print("started..."			)

		__lowerCAmelCase:	Optional[Any]   				=      lower
		__lowerCAmelCase:	Optional[int]   				=      higher

		__lowerCAmelCase:	Dict   				=      []

		while True:
				__lowerCAmelCase:	Dict   				=      get_avg(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
				last_numbers.append(__SCREAMING_SNAKE_CASE			)

				if answer(__SCREAMING_SNAKE_CASE			) == "low":
						__lowerCAmelCase:	Optional[Any]   				=      number
				elif answer(__SCREAMING_SNAKE_CASE			) == "high":
						__lowerCAmelCase:	int   				=      number
				else:
						break

		print(F"guess the number : {last_numbers[-1]}"			)
		print(F"details : {last_numbers!s}"			)






def    a__   (   )     ->     None:
		__lowerCAmelCase:	Optional[int]   				=      int(input("Enter lower value : "			).strip()			)
		__lowerCAmelCase:	List[str]   				=      int(input("Enter high value : "			).strip()			)
		__lowerCAmelCase:	str   				=      int(input("Enter value to guess : "			).strip()			)
		guess_the_number(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
			main()





"""simple docstring"""


import os

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

import datasets


__A						       =      datasets.logging.get_logger(__name__)


__A						       =      "\\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"

__A						       =      "\\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"

__A						       =      "\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"

__A						       =      {
    "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    snake_case     (    datasets.Metric  ):

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



						'''simple docstring'''


						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 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Dict)->       List[str]:



						'''simple docstring'''


						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').")
								__lowerCAmelCase:	List[str]   				=      "bleurt-base-128"

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

						elif self.config_name.upper() in CHECKPOINT_URLS:
								__lowerCAmelCase:	Tuple   				=      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
						__lowerCAmelCase:	Union[str, Any]   				=      dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
						__lowerCAmelCase:	Dict   				=      score.BleurtScorer(os.path.join(UpperCamelCase__					,  UpperCamelCase__))




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.scorer.score(references=UpperCamelCase__					,  candidates=UpperCamelCase__)
						return {"scores": scores}


