

'''simple docstring'''






import argparse
import json
import re
import string
import sys
from collections import Counter


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Union[str, Any]    )							->   Optional[int]:
      """simple docstring"""

      def remove_articles(SCREAMING_SNAKE_CASE_     :	Tuple    ):
            return re.sub(R"\b(a|an|the)\b"  ,  " "  ,  SCREAMING_SNAKE_CASE_    )

      def white_space_fix(SCREAMING_SNAKE_CASE_     :	Union[str, Any]    ):
            return " ".join(text.split()    )

      def remove_punc(SCREAMING_SNAKE_CASE_     :	List[Any]    ):
            SCREAMING_SNAKE_CASE_      :			str     =       set(string.punctuation    )
            return "".join(ch for ch in text if ch not in exclude    )

      def lower(SCREAMING_SNAKE_CASE_     :	Any    ):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_    )    )    )    )


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	str  ,  SCREAMING_SNAKE_CASE_     :	Union[str, Any]    )							->   Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			int     =       normalize_answer(SCREAMING_SNAKE_CASE_    ).split()
      SCREAMING_SNAKE_CASE_      :			Dict     =       normalize_answer(SCREAMING_SNAKE_CASE_    ).split()
      SCREAMING_SNAKE_CASE_      :			int     =       Counter(SCREAMING_SNAKE_CASE_    ) & Counter(SCREAMING_SNAKE_CASE_    )
      SCREAMING_SNAKE_CASE_      :			Dict     =       sum(common.values()    )
      if num_same == 0:
            return 0
      SCREAMING_SNAKE_CASE_      :			int     =       1.0 * num_same / len(SCREAMING_SNAKE_CASE_    )
      SCREAMING_SNAKE_CASE_      :			Any     =       1.0 * num_same / len(SCREAMING_SNAKE_CASE_    )
      SCREAMING_SNAKE_CASE_      :			Any     =       (2 * precision * recall) / (precision + recall)
      return fa


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Any  ,  SCREAMING_SNAKE_CASE_     :	List[str]    )							->   Tuple:
      """simple docstring"""

      return normalize_answer(SCREAMING_SNAKE_CASE_    ) == normalize_answer(SCREAMING_SNAKE_CASE_    )


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Dict  ,  SCREAMING_SNAKE_CASE_     :	Optional[Any]  ,  SCREAMING_SNAKE_CASE_     :	str    )							->   Any:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Any     =       []
      for ground_truth in ground_truths:
            SCREAMING_SNAKE_CASE_      :			Tuple     =       metric_fn(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )
            scores_for_ground_truths.append(SCREAMING_SNAKE_CASE_    )
      return max(SCREAMING_SNAKE_CASE_    )


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Union[str, Any]  ,  SCREAMING_SNAKE_CASE_     :	Union[str, Any]    )							->   Optional[int]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Tuple     =       0
      for article in dataset:
            for paragraph in article["paragraphs"]:
                  for qa in paragraph["qas"]:
                        total += 1
                        if qa["id"] not in predictions:
                              SCREAMING_SNAKE_CASE_      :			int     =       "Unanswered question " + qa["id"] + " will receive score 0."
                              print(SCREAMING_SNAKE_CASE_  ,  file=sys.stderr    )
                              continue
                        SCREAMING_SNAKE_CASE_      :			str     =       [x["text"] for x in qa["answers"]]
                        SCREAMING_SNAKE_CASE_      :			int     =       predictions[qa["id"]]
                        exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )
                        fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )

      SCREAMING_SNAKE_CASE_      :			Dict     =       100.0 * exact_match / total
      SCREAMING_SNAKE_CASE_      :			int     =       100.0 * fa / total

      return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
      snake_case_       =       '1.1'
      snake_case_       =       argparse.ArgumentParser(description='Evaluation for SQuAD ' + expected_version)
      parser.add_argument('dataset_file', help='Dataset file')
      parser.add_argument('prediction_file', help='Prediction File')
      snake_case_       =       parser.parse_args()
      with open(args.dataset_file) as dataset_file:
            snake_case_       =       json.load(dataset_file)
            if dataset_json["version"] != expected_version:
                  print(
                      'Evaluation expects v-' + expected_version + ', but got dataset with v-' + dataset_json['version'],
                      file=sys.stderr,
                  )
            snake_case_       =       dataset_json['data']
      with open(args.prediction_file) as prediction_file:
            snake_case_       =       json.load(prediction_file)
      print(json.dumps(evaluate(dataset, predictions)))




'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_       =       logging.get_logger(__name__)

snake_case_       =       {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							"mobilevit"




    def __init__(					self				,     lowercase__=3				,     lowercase__=256				,     lowercase__=2				,     lowercase__=[144, 192, 240]				,     lowercase__=[16, 32, 64, 96, 128, 160, 640]				,     lowercase__=4				,     lowercase__=2.0				,     lowercase__=4.0				,     lowercase__="silu"				,     lowercase__=3				,     lowercase__=32				,     lowercase__=0.1				,     lowercase__=0.0				,     lowercase__=0.1				,     lowercase__=0.02				,     lowercase__=1e-5				,     lowercase__=True				,     lowercase__=256				,     lowercase__=[6, 12, 18]				,     lowercase__=0.1				,     lowercase__=255				,     **lowercase__				,     ):






          """simple docstring"""






          super().__init__(**lowercase__			)

          SCREAMING_SNAKE_CASE_      :			int     =       num_channels
          SCREAMING_SNAKE_CASE_      :			List[str]     =       image_size
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       patch_size
          SCREAMING_SNAKE_CASE_      :			List[str]     =       hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       neck_hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       num_attention_heads
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       mlp_ratio
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       expand_ratio
          SCREAMING_SNAKE_CASE_      :			str     =       hidden_act
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       conv_kernel_size
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       output_stride
          SCREAMING_SNAKE_CASE_      :			Tuple     =       hidden_dropout_prob
          SCREAMING_SNAKE_CASE_      :			int     =       attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Tuple     =       classifier_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       initializer_range
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       layer_norm_eps
          SCREAMING_SNAKE_CASE_      :			str     =       qkv_bias

          # decode head attributes for semantic segmentation
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       aspp_out_channels
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       atrous_rates
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       aspp_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Any     =       semantic_loss_ignore_index






class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							version.parse("1.11"      )




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]			)




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          if self.task == "image-classification":
                return OrderedDict([("logits", {0: "batch"})]			)
          else:
                return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]			)







    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 1e-4

