

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

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):



					'''simple docstring'''





					def remove_articles(SCREAMING_SNAKE_CASE_	:					List[str]    ):
										return re.sub(R'\b(a|an|the)\b'			,  ' '			,  SCREAMING_SNAKE_CASE_    )

					def white_space_fix(SCREAMING_SNAKE_CASE_	:					List[str]    ):
										return " ".join(text.split()    )

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

					def lower(SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
										return text.lower()

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

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Optional[int]    ):



					'''simple docstring'''





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

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Optional[int]    ):



					'''simple docstring'''





					return normalize_answer(SCREAMING_SNAKE_CASE_    ) == normalize_answer(SCREAMING_SNAKE_CASE_    )

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					List[Any]    ):



					'''simple docstring'''





					lowercase__					:     Union[str, Any]   = []
					for ground_truth in ground_truths:
										lowercase__					:     Dict   = metric_fn(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
										scores_for_ground_truths.append(SCREAMING_SNAKE_CASE_    )
					return max(SCREAMING_SNAKE_CASE_    )

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					str    ):



					'''simple docstring'''





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

					lowercase__					:     List[str]   = 100.0 * exact_match / total
					lowercase__					:     str   = 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)))




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''GPTJForCausalLM''',
           '''GPTJForQuestionAnswering''',
           '''GPTJForSequenceClassification''',
           '''GPTJModel''',
           '''GPTJPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''TFGPTJForCausalLM''',
           '''TFGPTJForQuestionAnswering''',
           '''TFGPTJForSequenceClassification''',
           '''TFGPTJModel''',
           '''TFGPTJPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''FlaxGPTJForCausalLM''',
           '''FlaxGPTJModel''',
           '''FlaxGPTJPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptj import (
                  GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTJForCausalLM,
                  GPTJForQuestionAnswering,
                  GPTJForSequenceClassification,
                  GPTJModel,
                  GPTJPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_gptj import (
                  TFGPTJForCausalLM,
                  TFGPTJForQuestionAnswering,
                  TFGPTJForSequenceClassification,
                  TFGPTJModel,
                  TFGPTJPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
       import sys

       snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

