

from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE					:  Tuple             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      {
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "efficientformer"






  def __init__(							self						:		Tuple     ,       __lowerCamelCase						:		List[int] = [3, 2, 6, 4]     ,       __lowerCamelCase						:		List[int] = [48, 96, 224, 448]     ,       __lowerCamelCase						:		List[bool] = [True, True, True, True]     ,       __lowerCamelCase						:		int = 448     ,       __lowerCamelCase						:		int = 32     ,       __lowerCamelCase						:		int = 4     ,       __lowerCamelCase						:		int = 7     ,       __lowerCamelCase						:		int = 5     ,       __lowerCamelCase						:		int = 8     ,       __lowerCamelCase						:		int = 4     ,       __lowerCamelCase						:		float = 0.0     ,       __lowerCamelCase						:		int = 16     ,       __lowerCamelCase						:		int = 3     ,       __lowerCamelCase						:		int = 3     ,       __lowerCamelCase						:		int = 3     ,       __lowerCamelCase						:		int = 2     ,       __lowerCamelCase						:		int = 1     ,       __lowerCamelCase						:		float = 0.0     ,       __lowerCamelCase						:		int = 1     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		float = 1e-5     ,       __lowerCamelCase						:		str = "gelu"     ,       __lowerCamelCase						:		float = 0.02     ,       __lowerCamelCase						:		float = 1e-12     ,       __lowerCamelCase						:		int = 224     ,       __lowerCamelCase						:		float = 1e-05     ,       **__lowerCamelCase						:		str     ,       )		->							None:
       super().__init__(**__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   hidden_dropout_prob
       SCREAMING_SNAKE_CASE__   =   hidden_sizes
       SCREAMING_SNAKE_CASE__   =   num_hidden_layers
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   layer_norm_eps
       SCREAMING_SNAKE_CASE__   =   patch_size
       SCREAMING_SNAKE_CASE__   =   num_channels
       SCREAMING_SNAKE_CASE__   =   depths
       SCREAMING_SNAKE_CASE__   =   mlp_expansion_ratio
       SCREAMING_SNAKE_CASE__   =   downsamples
       SCREAMING_SNAKE_CASE__   =   dim
       SCREAMING_SNAKE_CASE__   =   key_dim
       SCREAMING_SNAKE_CASE__   =   attention_ratio
       SCREAMING_SNAKE_CASE__   =   resolution
       SCREAMING_SNAKE_CASE__   =   pool_size
       SCREAMING_SNAKE_CASE__   =   downsample_patch_size
       SCREAMING_SNAKE_CASE__   =   downsample_stride
       SCREAMING_SNAKE_CASE__   =   downsample_pad
       SCREAMING_SNAKE_CASE__   =   drop_path_rate
       SCREAMING_SNAKE_CASE__   =   num_metaad_blocks
       SCREAMING_SNAKE_CASE__   =   distillation
       SCREAMING_SNAKE_CASE__   =   use_layer_scale
       SCREAMING_SNAKE_CASE__   =   layer_scale_init_value
       SCREAMING_SNAKE_CASE__   =   image_size
       SCREAMING_SNAKE_CASE__   =   batch_norm_eps





import datasets

from .evaluate import evaluate


_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      '''\
@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}
}
'''

_SCREAMING_SNAKE_CASE					:  Dict             =      '''
This metric wrap the official scoring script for version 1 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.
'''

_SCREAMING_SNAKE_CASE					:  str             =      '''
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair as given in the references (see below)
        - \'prediction_text\': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair (see above),
        - \'answers\': a Dict in the SQuAD dataset format
            {
                \'text\': list of possible texts for the answer, as a list of strings
                \'answer_start\': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_metric = datasets.load_metric("squad")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 100.0, \'f1\': 100.0}
'''






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






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any] )		->							Optional[int]:
       return datasets.MetricInfo(
           description=_DESCRIPTION     ,       citation=_CITATION     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
               {
                   '''predictions''': {'''id''': datasets.Value('''string''' ), '''prediction_text''': datasets.Value('''string''' )},
                   '''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 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		List[Any] )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
       SCREAMING_SNAKE_CASE__   =   [
           {
               '''paragraphs''': [
                   {
                       '''qas''': [
                           {
                               '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                               '''id''': ref['''id'''],
                           }
                           for ref in references
                       ]
                   }
               ]
           }
       ]
       SCREAMING_SNAKE_CASE__   =   evaluate(dataset=__lowerCamelCase     ,       predictions=__lowerCamelCase )
       return score


