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


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




  def remove_articles(_a  ):
    return re.sub(R'''\b(a|an|the)\b'''			,   ''' '''			,   lowercase__  )

  def white_space_fix(_a  ):
    return " ".join(text.split()  )

  def remove_punc(_a  ):
    _lowerCamelCase      =						set(string.punctuation  )
    return "".join(ch for ch in text if ch not in exclude  )

  def lower(_a  ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(lowercase__  )  )  )  )


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




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


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




  return normalize_answer(lowercase__  ) == normalize_answer(lowercase__  )


def 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						[]
  for ground_truth in ground_truths:
    _lowerCamelCase      =						metric_fn(lowercase__			,   lowercase__  )
    scores_for_ground_truths.append(lowercase__  )
  return max(lowercase__  )


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




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

  _lowerCamelCase      =						100.0 * exact_match / total
  _lowerCamelCase      =						100.0 * fa / total

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


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









import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

_lowercase				:					Any     					=logging.get_logger(__name__)

_lowercase				:					Tuple     					={"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

_lowercase				:					int     					={
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

_lowercase				:					Tuple     					={
    """microsoft/deberta-base""": 5_12,
    """microsoft/deberta-large""": 5_12,
    """microsoft/deberta-xlarge""": 5_12,
    """microsoft/deberta-base-mnli""": 5_12,
    """microsoft/deberta-large-mnli""": 5_12,
    """microsoft/deberta-xlarge-mnli""": 5_12,
}

_lowercase				:					str     					={
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}




def    UpperCAmelCase					(  ):



			'''simple docstring'''





			a__					=  (
			    list(range(ord("""!"""	)				,					ord("""~"""	) + 1	)	) + list(range(ord("""¡"""	)				,					ord("""¬"""	) + 1	)	) + list(range(ord("""®"""	)				,					ord("""ÿ"""	) + 1	)	)
			)
			a__					=  bs[:]
			a__					=  0
			for b in range(2**8	):
						if b not in bs:
									bs.append(lowercase__	)
									cs.append(2**8 + n	)
									n += 1
			a__					=  [chr(lowercase__	) for n in cs]
			return dict(zip(lowercase__				,					lowercase__	)	)




def    UpperCAmelCase					(  lowercase__						:					Optional[Any]	):



			'''simple docstring'''





			a__					=  set()
			a__					=  word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)	)
						a__					=  char
			return pairs





class lowerCAmelCase_       (   A_			):







	'''simple docstring'''




	A_       :     List[str]									=		VOCAB_FILES_NAMES
	A_       :     str									=		PRETRAINED_VOCAB_FILES_MAP
	A_       :     List[str]									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A_       :     Optional[int]									=		['input_ids', 'attention_mask', 'token_type_ids']

	def __init__(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase="replace"   ,       lowerCamelCase="[CLS]"   ,       lowerCamelCase="[SEP]"   ,       lowerCamelCase="[SEP]"   ,       lowerCamelCase="[CLS]"   ,       lowerCamelCase="[UNK]"   ,       lowerCamelCase="[PAD]"   ,       lowerCamelCase="[MASK]"   ,       lowerCamelCase=False   ,       lowerCamelCase=False   ,       **lowerCamelCase   ,       ):



				'''simple docstring'''


				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else bos_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else eos_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else sep_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else cls_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else unk_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else pad_token

				# Mask token behave like a normal word, i.e. include the space before it
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else mask_token

				super().__init__(
				    errors=lowerCamelCase   ,       bos_token=lowerCamelCase   ,       eos_token=lowerCamelCase   ,       unk_token=lowerCamelCase   ,       sep_token=lowerCamelCase   ,       cls_token=lowerCamelCase   ,       pad_token=lowerCamelCase   ,       mask_token=lowerCamelCase   ,       add_prefix_space=lowerCamelCase   ,       add_bos_token=lowerCamelCase   ,       **lowerCamelCase   ,       )
				a__					=  add_bos_token

				with open(lowerCamelCase   ,       encoding="""utf-8"""       ) as vocab_handle:
							a__					=  json.load(lowerCamelCase       )
				a__					=  {v: k for k, v in self.encoder.items()}
				a__					=  errors  # how to handle errors in decoding
				a__					=  bytes_to_unicode()
				a__					=  {v: k for k, v in self.byte_encoder.items()}
				with open(lowerCamelCase   ,       encoding="""utf-8"""       ) as merges_handle:
							a__					=  merges_handle.read().split("""\n"""       )[1:-1]
				a__					=  [tuple(merge.split()       ) for merge in bpe_merges]
				a__					=  dict(zip(lowerCamelCase   ,       range(len(lowerCamelCase       )       )       )       )
				a__					=  {}
				a__					=  add_prefix_space

				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
				a__					=  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""       )

	@property
	# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
	def 				_A			(	self       ):



				'''simple docstring'''


				return len(self.encoder       )

	def 				_A			(	self       ):



				'''simple docstring'''


				return dict(self.encoder   ,       **self.added_tokens_encoder       )

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				if token in self.cache:
							return self.cache[token]
				a__					=  tuple(lowerCamelCase       )
				a__					=  get_pairs(lowerCamelCase       )

				if not pairs:
							return token

				while True:
							a__					=  min(lowerCamelCase   ,       key=lambda lowerCamelCase       : self.bpe_ranks.get(lowerCamelCase   ,       float("""inf"""       )       )       )
							if bigram not in self.bpe_ranks:
										break
							a__				,       a__					=  bigram
							a__					=  []
							a__					=  0
							while i < len(lowerCamelCase       ):
										try:
													a__					=  word.index(lowerCamelCase   ,       lowerCamelCase       )
										except ValueError:
													new_word.extend(word[i:]       )
													break
										else:
													new_word.extend(word[i:j]       )
													a__					=  j

										if word[i] == first and i < len(lowerCamelCase       ) - 1 and word[i + 1] == second:
													new_word.append(first + second       )
													i += 2
										else:
													new_word.append(word[i]       )
													i += 1
							a__					=  tuple(lowerCamelCase       )
							a__					=  new_word
							if len(lowerCamelCase       ) == 1:
										break
							else:
										a__					=  get_pairs(lowerCamelCase       )
				a__					=  """ """.join(lowerCamelCase       )
				a__					=  word
				return word

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase = None       ):



				'''simple docstring'''


				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				a__					=  [self.cls_token_id]
				a__					=  [self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase = None   ,       lowerCamelCase = False       ):



				'''simple docstring'''


				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowerCamelCase   ,       token_ids_a=lowerCamelCase   ,       already_has_special_tokens=lowerCamelCase       )

				if token_ids_a is None:
							return [1] + ([0] * len(lowerCamelCase       )) + [1]
				return [1] + ([0] * len(lowerCamelCase       )) + [1] + ([0] * len(lowerCamelCase       )) + [1]

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase = None       ):



				'''simple docstring'''


				a__					=  [self.sep_token_id]
				a__					=  [self.cls_token_id]

				if token_ids_a is None:
							return len(cls + token_ids_a + sep       ) * [0]
				return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  []
				for token in re.findall(self.pat   ,       lowerCamelCase       ):
							a__					=  """""".join(
							    self.byte_encoder[b] for b in token.encode("""utf-8"""       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase       ).split(""" """       )       )
				return bpe_tokens

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				return self.encoder.get(lowerCamelCase   ,       self.encoder.get(self.unk_token       )       )

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				return self.decoder.get(lowerCamelCase       )

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  """""".join(lowerCamelCase       )
				a__					=  bytearray([self.byte_decoder[c] for c in text]       ).decode("""utf-8"""   ,       errors=self.errors       )
				return text

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase = None       ):



				'''simple docstring'''


				if not os.path.isdir(lowerCamelCase       ):
							logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
							return
				a__					=  os.path.join(
				    lowerCamelCase   ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
				a__					=  os.path.join(
				    lowerCamelCase   ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )

				with open(lowerCamelCase   ,       """w"""   ,       encoding="""utf-8"""       ) as f:
							f.write(json.dumps(self.encoder   ,       indent=2   ,       sort_keys=lowerCamelCase   ,       ensure_ascii=lowerCamelCase       ) + """\n"""       )

				a__					=  0
				with open(lowerCamelCase   ,       """w"""   ,       encoding="""utf-8"""       ) as writer:
							writer.write("""#version: 0.2\n"""       )
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,       key=lambda lowerCamelCase       : kv[1]       ):
										if index != token_index:
													logger.warning(
													    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
													    """ Please check that the tokenizer is not corrupted!"""       )
													a__					=  token_index
										writer.write(""" """.join(lowerCamelCase       ) + """\n"""       )
										index += 1

				return vocab_file, merge_file

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase=False   ,       **lowerCamelCase       ):



				'''simple docstring'''


				a__					=  kwargs.pop("""add_prefix_space"""   ,       self.add_prefix_space       )
				if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase       ) > 0 and not text[0].isspace()):
							a__					=  """ """ + text
				return (text, kwargs)







	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  []
				for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(lowerCamelCase   ,       add_special_tokens=lowerCamelCase       ) + [self.eos_token_id]       )
				if len(lowerCamelCase       ) > self.model_max_length:
							a__					=  input_ids[-self.model_max_length :]
				return input_ids

