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




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int     )	->		Optional[Any]:

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

				def white_space_fix(SCREAMING_SNAKE_CASE			: Optional[Any]     ):
								return " ".join(text.split()     )

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

				def lower(SCREAMING_SNAKE_CASE			: Tuple     ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE     )     )     )     )




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Dict       ,				SCREAMING_SNAKE_CASE			: List[Any]     )	->		Dict:
				__lowercase								=   normalize_answer(SCREAMING_SNAKE_CASE     ).split()
				__lowercase								=   normalize_answer(SCREAMING_SNAKE_CASE     ).split()
				__lowercase								=   Counter(SCREAMING_SNAKE_CASE     ) & Counter(SCREAMING_SNAKE_CASE     )
				__lowercase								=   sum(common.values()     )
				if num_same == 0:
								return 0
				__lowercase								=   1.0 * num_same / len(SCREAMING_SNAKE_CASE     )
				__lowercase								=   1.0 * num_same / len(SCREAMING_SNAKE_CASE     )
				__lowercase								=   (2 * precision * recall) / (precision + recall)
				return fa




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Optional[Any]       ,				SCREAMING_SNAKE_CASE			: Any     )	->		Dict:
				return normalize_answer(SCREAMING_SNAKE_CASE     ) == normalize_answer(SCREAMING_SNAKE_CASE     )




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Tuple       ,				SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: List[str]     )	->		Optional[Any]:
				__lowercase								=   []
				for ground_truth in ground_truths:
								__lowercase								=   metric_fn(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
								scores_for_ground_truths.append(SCREAMING_SNAKE_CASE     )
				return max(SCREAMING_SNAKE_CASE     )




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: str       ,				SCREAMING_SNAKE_CASE			: Dict     )	->		Tuple:
				__lowercase								=   __lowercase								=   __lowercase								=   0
				for article in dataset:
								for paragraph in article["paragraphs"]:
												for qa in paragraph["qas"]:
																total += 1
																if qa["id"] not in predictions:
																				__lowercase								=   'Unanswered question ' + qa['id'] + ' will receive score 0.'
																				print(SCREAMING_SNAKE_CASE       ,				file=sys.stderr     )
																				continue
																__lowercase								=   [x['text'] for x in qa['answers']]
																__lowercase								=   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								=   100.0 * exact_match / total
				__lowercase								=   100.0 * fa / total

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__				=							"""1.1"""
							SCREAMING_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""")
							SCREAMING_SNAKE_CASE__				=							parser.parse_args()
							with open(args.dataset_file) as dataset_file:
														SCREAMING_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,
																					)
														SCREAMING_SNAKE_CASE__				=							dataset_json["""data"""]
							with open(args.prediction_file) as prediction_file:
														SCREAMING_SNAKE_CASE__				=							json.load(prediction_file)
							print(json.dumps(evaluate(dataset, predictions)))







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__				=							{
    """agents""": ["""Agent""", """AzureOpenAiAgent""", """HfAgent""", """LocalAgent""", """OpenAiAgent"""],
    """base""": ["""PipelineTool""", """RemoteTool""", """Tool""", """launch_gradio_demo""", """load_tool"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__				=							["""DocumentQuestionAnsweringTool"""]
							SCREAMING_SNAKE_CASE__				=							["""ImageCaptioningTool"""]
							SCREAMING_SNAKE_CASE__				=							["""ImageQuestionAnsweringTool"""]
							SCREAMING_SNAKE_CASE__				=							["""ImageSegmentationTool"""]
							SCREAMING_SNAKE_CASE__				=							["""SpeechToTextTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextClassificationTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextQuestionAnsweringTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextSummarizationTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextToSpeechTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TranslationTool"""]

if TYPE_CHECKING:
							from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
							from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .document_question_answering import DocumentQuestionAnsweringTool
														from .image_captioning import ImageCaptioningTool
														from .image_question_answering import ImageQuestionAnsweringTool
														from .image_segmentation import ImageSegmentationTool
														from .speech_to_text import SpeechToTextTool
														from .text_classification import TextClassificationTool
														from .text_question_answering import TextQuestionAnsweringTool
														from .text_summarization import TextSummarizationTool
														from .text_to_speech import TextToSpeechTool
														from .translation import TranslationTool
else:
							import sys

							SCREAMING_SNAKE_CASE__				=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





