

'''simple docstring'''




from typing import Mapping

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


_a			:	Optional[int]								=  logging.get_logger(__name__)



class     lowercase_		(						a	):

		'''simple docstring'''





		__lowerCAmelCase   :	Optional[int]      					=				"mt5"
		__lowerCAmelCase   :	Union[str, Any]      					=				["past_key_values"]
		def __init__(       self				,	a_=2_5_0_1_1_2				,	a_=5_1_2				,	a_=6_4				,	a_=1_0_2_4				,	a_=8				,	a_=None				,	a_=6				,	a_=3_2				,	a_=1_2_8				,	a_=0.1				,	a_=1E-6				,	a_=1.0				,	a_="gated-gelu"				,	a_=True				,	a_=True				,	a_="T5Tokenizer"				,	a_=False				,	a_=0				,	a_=1				,	a_=0				,	**a_				,	)							->			Any:


				"""simple docstring"""
				super().__init__(
				    is_encoder_decoder=a_				,	tokenizer_class=a_				,	tie_word_embeddings=a_				,	pad_token_id=a_				,	eos_token_id=a_				,	decoder_start_token_id=a_				,	**a_				,	)
				UpperCAmelCase  							=		vocab_size
				UpperCAmelCase  							=		d_model
				UpperCAmelCase  							=		d_kv
				UpperCAmelCase  							=		d_ff
				UpperCAmelCase  							=		num_layers
				UpperCAmelCase  							=		(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				UpperCAmelCase  							=		num_heads
				UpperCAmelCase  							=		relative_attention_num_buckets
				UpperCAmelCase  							=		relative_attention_max_distance
				UpperCAmelCase  							=		dropout_rate
				UpperCAmelCase  							=		layer_norm_epsilon
				UpperCAmelCase  							=		initializer_factor
				UpperCAmelCase  							=		feed_forward_proj
				UpperCAmelCase  							=		use_cache

				UpperCAmelCase  							=		self.feed_forward_proj.split('-'       )
				UpperCAmelCase  							=		act_info[-1]
				UpperCAmelCase  							=		act_info[0] == 'gated'

				if len(a_       ) > 1 and act_info[0] != "gated" or len(a_       ) > 2:
						raise ValueError(
						    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
						    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
						    '\'gated-gelu\' or \'relu\''       )

				# for backwards compatibility
				if feed_forward_proj == "gated-gelu":
						UpperCAmelCase  							=		'gelu_new'
		@property
		def   snake_case_     (       self       )							->			Union[str, Any]:


				"""simple docstring"""
				return self.d_model
		@property
		def   snake_case_     (       self       )							->			List[Any]:


				"""simple docstring"""
				return self.num_heads





		@property
		def   snake_case_     (       self       )							->			Optional[Any]:


				"""simple docstring"""
				return self.num_layers




class     lowercase_		(						a	):

		'''simple docstring'''





		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def   snake_case_     (       self       )							->			Mapping[str, Mapping[int, str]]:


				"""simple docstring"""
				UpperCAmelCase  							=		{
				    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
				    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
				}
				if self.use_past:
						UpperCAmelCase  							=		'past_encoder_sequence + sequence'
						UpperCAmelCase  							=		{0: 'batch'}
						UpperCAmelCase  							=		{0: 'batch', 1: 'past_decoder_sequence + sequence'}
				else:
						UpperCAmelCase  							=		{0: 'batch', 1: 'decoder_sequence'}
						UpperCAmelCase  							=		{0: 'batch', 1: 'decoder_sequence'}

				if self.use_past:
						self.fill_with_past_key_values_(a_				,	direction='inputs'       )

				return common_inputs
		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def   snake_case_     (       self       )							->			int:


				"""simple docstring"""
				return 1_3





		@property
		def   snake_case_     (       self       )							->			float:


				"""simple docstring"""
				return 5E-4





'''simple docstring'''




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


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]  ):

  def remove_articles(SCREAMING_SNAKE_CASE :				Union[str, Any]  ):
    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]  ):
    UpperCAmelCase  							=		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 :				Tuple    ,				SCREAMING_SNAKE_CASE :				Dict  ):
  UpperCAmelCase  							=		normalize_answer(SCREAMING_SNAKE_CASE  ).split()
  UpperCAmelCase  							=		normalize_answer(SCREAMING_SNAKE_CASE  ).split()
  UpperCAmelCase  							=		Counter(SCREAMING_SNAKE_CASE  ) & Counter(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		sum(common.values()  )
  if num_same == 0:
    return 0
  UpperCAmelCase  							=		1.0 * num_same / len(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		1.0 * num_same / len(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		(2 * precision * recall) / (precision + recall)
  return fa


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				int  ):
  return normalize_answer(SCREAMING_SNAKE_CASE  ) == normalize_answer(SCREAMING_SNAKE_CASE  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict    ,				SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				Dict  ):
  UpperCAmelCase  							=		[]
  for ground_truth in ground_truths:
    UpperCAmelCase  							=		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 :				Optional[Any]    ,				SCREAMING_SNAKE_CASE :				Optional[int]  ):
  UpperCAmelCase  							=		UpperCAmelCase  							=		UpperCAmelCase  							=		0
  for article in dataset:
    for paragraph in article["paragraphs"]:
      for qa in paragraph["qas"]:
        total += 1
        if qa["id"] not in predictions:
          UpperCAmelCase  							=		'Unanswered question ' + qa['id'] + ' will receive score 0.'
          print(SCREAMING_SNAKE_CASE    ,				file=sys.stderr  )
          continue
        UpperCAmelCase  							=		[x['text'] for x in qa['answers']]
        UpperCAmelCase  							=		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  )

  UpperCAmelCase  							=		100.0 * exact_match / total
  UpperCAmelCase  							=		100.0 * fa / total

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


if __name__ == "__main__":
  _a			:	Tuple								=  '1.1'
  _a			:	int								=  argparse.ArgumentParser(description='Evaluation for SQuAD ' + expected_version)
  parser.add_argument('dataset_file', help='Dataset file')
  parser.add_argument('prediction_file', help='Prediction File')
  _a			:	List[Any]								=  parser.parse_args()
  with open(args.dataset_file) as dataset_file:
    _a			:	Optional[int]								=  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,
      )
    _a			:	Optional[Any]								=  dataset_json['data']
  with open(args.prediction_file) as prediction_file:
    _a			:	Optional[Any]								=  json.load(prediction_file)
  print(json.dumps(evaluate(dataset, predictions)))


