





'''simple docstring'''


import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm
		import torch_xla.debug.metrics as met






class    UpperCamelCase__	(		SCREAMING_SNAKE_CASE__  ):







	def __init__(					self      : Optional[int]			,			*lowerCamelCase      : Optional[Any]			,			lowerCamelCase      : Dict=None			,			lowerCamelCase      : int=None			,			**lowerCamelCase      : Optional[int]		):




				'''simple docstring'''
				super().__init__(*__lowerCamelCase			,			**__lowerCamelCase		)
				a__	 =			eval_examples
				a__	 =			post_process_function







	def     __a (					self      : List[str]			,			lowerCamelCase      : Any = None			,			lowerCamelCase      : Optional[int]=None			,			lowerCamelCase      : Optional[Any] = None			,			lowerCamelCase      : List[Any] = "eval"			,			**lowerCamelCase      : Any			,			):




				'''simple docstring'''
				a__	 =			gen_kwargs.copy()
				a__	 =			(
				    gen_kwargs['''max_length'''] if gen_kwargs.get("max_length"		) is not None else self.args.generation_max_length
				)
				a__	 =			(
				    gen_kwargs['''num_beams'''] if gen_kwargs.get("num_beams"		) is not None else self.args.generation_num_beams
				)
				a__	 =			gen_kwargs

				a__	 =			self.eval_dataset if eval_dataset is None else eval_dataset
				a__	 =			self.get_eval_dataloader(__lowerCamelCase		)
				a__	 =			self.eval_examples if eval_examples is None else eval_examples

				# Temporarily disable metric computation, we will do it in the loop here.
				a__	 =			self.compute_metrics
				a__	 =			None
				a__	 =			time.time()
				a__	 =			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
				try:
							a__	 =			eval_loop(
							    __lowerCamelCase			,			description="Evaluation"			,			prediction_loss_only=True if compute_metrics is None else None			,			ignore_keys=__lowerCamelCase			,			metric_key_prefix=__lowerCamelCase			,			)
				finally:
							a__	 =			compute_metrics
				a__	 =			self.args.eval_batch_size * self.args.world_size
				if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
							start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
				output.metrics.update(
				    speed_metrics(
				        __lowerCamelCase			,			__lowerCamelCase			,			num_samples=output.num_samples			,			num_steps=math.ceil(output.num_samples / total_batch_size		)			,			)		)

				if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
							# Only the main node write the results by default
							a__	 =			self.post_process_function(__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase		)
							a__	 =			self.compute_metrics(__lowerCamelCase		)

							# Prefix all keys with metric_key_prefix + '_'
							for key in list(metrics.keys()		):
										if not key.startswith(F'''{metric_key_prefix}_'''		):
													a__	 =			metrics.pop(__lowerCamelCase		)

							metrics.update(output.metrics		)
				else:
							a__	 =			output.metrics

				if self.args.should_log:
							# Only the main node log the results by default
							self.log(__lowerCamelCase		)

				if self.args.tpu_metrics_debug or self.args.debug:
							# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
							xm.master_print(met.metrics_report()		)

				a__	 =			self.callback_handler.on_evaluate(self.args			,			self.state			,			self.control			,			__lowerCamelCase		)
				return metrics







	def     __a (					self      : str			,			lowerCamelCase      : Any			,			lowerCamelCase      : Dict			,			lowerCamelCase      : int=None			,			lowerCamelCase      : Tuple = "test"			,			**lowerCamelCase      : List[Any]		):




				'''simple docstring'''
				a__	 =			gen_kwargs.copy()

				a__	 =			self.get_test_dataloader(__lowerCamelCase		)

				# Temporarily disable metric computation, we will do it in the loop here.
				a__	 =			self.compute_metrics
				a__	 =			None
				a__	 =			time.time()
				a__	 =			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
				try:
							a__	 =			eval_loop(
							    __lowerCamelCase			,			description="Prediction"			,			prediction_loss_only=True if compute_metrics is None else None			,			ignore_keys=__lowerCamelCase			,			metric_key_prefix=__lowerCamelCase			,			)
				finally:
							a__	 =			compute_metrics

				a__	 =			self.args.eval_batch_size * self.args.world_size
				if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
							start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
				output.metrics.update(
				    speed_metrics(
				        __lowerCamelCase			,			__lowerCamelCase			,			num_samples=output.num_samples			,			num_steps=math.ceil(output.num_samples / total_batch_size		)			,			)		)
				if self.post_process_function is None or self.compute_metrics is None:
							return output

				a__	 =			self.post_process_function(__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase			,			"predict"		)
				a__	 =			self.compute_metrics(__lowerCamelCase		)

				# Prefix all keys with metric_key_prefix + '_'
				for key in list(metrics.keys()		):
							if not key.startswith(F'''{metric_key_prefix}_'''		):
										a__	 =			metrics.pop(__lowerCamelCase		)
				metrics.update(output.metrics		)
				return PredictionOutput(predictions=predictions.predictions			,			label_ids=predictions.label_ids			,			metrics=__lowerCamelCase		)










"""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"""))




