


import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


__lowerCAmelCase									=				logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf





@dataclass
class     __a     (       __UpperCamelCase ):
 __lowercase					:				int  	=					'tf'
 __lowercase					:				Optional[str]  	=					field(
     default=__UpperCamelCase			,			metadata={'help': 'Name of TPU'}			,			)

 __lowercase					:				Optional[str]  	=					field(
     default=__UpperCamelCase			,			metadata={'help': 'Zone of TPU'}			,			)

 __lowercase					:				Optional[str]  	=					field(
     default=__UpperCamelCase			,			metadata={'help': 'Name of Cloud TPU-enabled project'}			,			)

 __lowercase					:				float  	=					field(
     default=1.0			,			metadata={'help': 'Power for the Polynomial decay LR scheduler.'}			,			)

 __lowercase					:				bool  	=					field(default=__UpperCamelCase			,			metadata={'help': 'Whether to activate the XLA compilation or not'} )
 @cached_property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple["tf.distribute.Strategy", int]:




        '''simple docstring'''
        requires_backends(self							,					['tf']					)
        logger.info('Tensorflow: setting up strategy'					)

        lowercase__:  Optional[Any]         =  tf.config.list_physical_devices('GPU'					)

        # Set to float16 at first
        if self.fpaa:
               tf.keras.mixed_precision.set_global_policy('mixed_float16'					)

        if self.no_cuda:
               lowercase__:  str         =  tf.distribute.OneDeviceStrategy(device='/cpu:0'					)
        else:
               try:
                      if self.tpu_name:
                             lowercase__:  Any         =  tf.distribute.cluster_resolver.TPUClusterResolver(
                                 self.tpu_name							,					zone=self.tpu_zone							,					project=self.gcp_project					)
                      else:
                             lowercase__:  Optional[int]         =  tf.distribute.cluster_resolver.TPUClusterResolver()
               except ValueError:
                      if self.tpu_name:
                             raise RuntimeError(F'Couldn\'t connect to TPU {self.tpu_name}!'					)
                      else:
                             lowercase__:  List[str]         =  None

               if tpu:
                      # Set to bfloat16 in case of TPU
                      if self.fpaa:
                             tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'					)

                      tf.config.experimental_connect_to_cluster(lowerCAmelCase__					)
                      tf.tpu.experimental.initialize_tpu_system(lowerCAmelCase__					)

                      lowercase__:  int         =  tf.distribute.TPUStrategy(lowerCAmelCase__					)

               elif len(lowerCAmelCase__					) == 0:
                      lowercase__:  List[str]         =  tf.distribute.OneDeviceStrategy(device='/cpu:0'					)
               elif len(lowerCAmelCase__					) == 1:
                      lowercase__:  Dict         =  tf.distribute.OneDeviceStrategy(device='/gpu:0'					)
               elif len(lowerCAmelCase__					) > 1:
                      # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                      lowercase__:  str         =  tf.distribute.MirroredStrategy()
               else:
                      raise ValueError('Cannot find the proper strategy, please check your environment properties.'					)

        return strategy
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							"tf.distribute.Strategy":




        '''simple docstring'''
        requires_backends(self							,					['tf']					)
        return self._setup_strategy
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							int:




        '''simple docstring'''
        requires_backends(self							,					['tf']					)
        return self._setup_strategy.num_replicas_in_sync
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        return False  # TF Logging is handled by Keras not the Trainer
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							int:




        '''simple docstring'''
        if self.per_gpu_train_batch_size:
               logger.warning(
                   'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
                   'version. Using `--per_device_train_batch_size` is preferred.'					)
        lowercase__:  List[str]         =  self.per_gpu_train_batch_size or self.per_device_train_batch_size
        return per_device_batch_size * self.n_replicas
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							int:




        '''simple docstring'''
        if self.per_gpu_eval_batch_size:
               logger.warning(
                   'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
                   'version. Using `--per_device_eval_batch_size` is preferred.'					)
        lowercase__:  Any         =  self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
        return per_device_batch_size * self.n_replicas
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							int:




        '''simple docstring'''
        requires_backends(self							,					['tf']					)
        warnings.warn(
            'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'							,					lowerCAmelCase__							,					)
        return self._setup_strategy.num_replicas_in_sync






import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


__lowerCAmelCase									=				'''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

__lowerCAmelCase									=				'''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

__lowerCAmelCase									=				'''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,			_KWARGS_DESCRIPTION )
class     __a     (       datasets.Metric ):
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        return datasets.MetricInfo(
            description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
                {
                    'predictions': {
                        'id': datasets.Value('string'					),
                        'prediction_text': datasets.Value('string'					),
                        'no_answer_probability': datasets.Value('float32'					),
                    },
                    'references': {
                        'id': datasets.Value('string'					),
                        'answers': datasets.features.Sequence(
                            {'text': datasets.Value('string'					), 'answer_start': datasets.Value('int32'					)}					),
                    },
                }					)							,					codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/']							,					reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/']							,					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__=1.0					)   ->							str:




        '''simple docstring'''
        lowercase__:  int         =  {p['id']: p['no_answer_probability'] for p in predictions}
        lowercase__:  int         =  [{'paragraphs': [{'qas': references}]}]
        lowercase__:  Optional[int]         =  {p['id']: p['prediction_text'] for p in predictions}

        lowercase__:  Any         =  make_qid_to_has_ans(lowerCAmelCase__					)  # maps qid to True/False
        lowercase__:  Optional[Any]         =  [k for k, v in qid_to_has_ans.items() if v]
        lowercase__:  Dict         =  [k for k, v in qid_to_has_ans.items() if not v]

        lowercase__     ,		lowercase__:  Dict         =  get_raw_scores(lowerCAmelCase__							,					lowerCAmelCase__					)
        lowercase__:  Optional[int]         =  apply_no_ans_threshold(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
        lowercase__:  List[Any]         =  apply_no_ans_threshold(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
        lowercase__:  Dict         =  make_eval_dict(lowerCAmelCase__							,					lowerCAmelCase__					)

        if has_ans_qids:
               lowercase__:  List[Any]         =  make_eval_dict(lowerCAmelCase__							,					lowerCAmelCase__							,					qid_list=lowerCAmelCase__					)
               merge_eval(lowerCAmelCase__							,					lowerCAmelCase__							,					'HasAns'					)
        if no_ans_qids:
               lowercase__:  int         =  make_eval_dict(lowerCAmelCase__							,					lowerCAmelCase__							,					qid_list=lowerCAmelCase__					)
               merge_eval(lowerCAmelCase__							,					lowerCAmelCase__							,					'NoAns'					)
        find_all_best_thresh(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
        return dict(lowerCAmelCase__					)


