
'''simple docstring'''
import argparse
import json
import re
import string
import sys
from collections import Counter







def 						A_(			A		:						Any):

							def remove_articles(A		:						Any):
														return re.sub(r'\b(a|an|the)\b'						,			' '						,			A)

							def white_space_fix(A		:						Optional[int]):
														return " ".join(text.split())

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

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

							return white_space_fix(remove_articles(remove_punc(lower(A))))







def 						A_(			A		:						Tuple						,			A		:						Any):
							UpperCamelCase					       =			normalize_answer(A).split()
							UpperCamelCase					       =			normalize_answer(A).split()
							UpperCamelCase					       =			Counter(A) & Counter(A)
							UpperCamelCase					       =			sum(common.values())
							if num_same == 0:
														return 0
							UpperCamelCase					       =			1.0 * num_same / len(A)
							UpperCamelCase					       =			1.0 * num_same / len(A)
							UpperCamelCase					       =			(2 * precision * recall) / (precision + recall)
							return fa







def 						A_(			A		:						Union[str, Any]						,			A		:						str):
							return normalize_answer(A) == normalize_answer(A)







def 						A_(			A		:						int						,			A		:						Any						,			A		:						Any):
							UpperCamelCase					       =			[]
							for ground_truth in ground_truths:
														UpperCamelCase					       =			metric_fn(A						,			A)
														scores_for_ground_truths.append(A)
							return max(A)







def 						A_(			A		:						List[Any]						,			A		:						Union[str, Any]):
							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(A						,			file=sys.stderr)
																																			continue
																												UpperCamelCase					       =			[x['text'] for x in qa['answers']]
																												UpperCamelCase					       =			predictions[qa['id']]
																												exact_match += metric_max_over_ground_truths(A						,			A						,			A)
																												fa += metric_max_over_ground_truths(A						,			A						,			A)

							UpperCamelCase					       =			100.0 * exact_match / total
							UpperCamelCase					       =			100.0 * fa / total

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


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






'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm







def 						A_(			A		:						int						,			A		:						bool = False):
							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(A)
							np.random.seed(A)
							torch.manual_seed(A)
							if is_xpu_available():
														torch.xpu.manual_seed_all(A)
							elif is_npu_available():
														torch.npu.manual_seed_all(A)
							else:
														torch.cuda.manual_seed_all(A)
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(A)







def 						A_(			A		:						Optional[RNGType] = None						,			A		:						Optional[torch.Generator] = None):
							# Get the proper rng state
							if rng_type == RNGType.TORCH:
														UpperCamelCase					       =			torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														UpperCamelCase					       =			torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														UpperCamelCase					       =			torch.tensor(xm.get_rng_state())
							elif rng_type == RNGType.NPU:
														assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
														UpperCamelCase					       =			torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														UpperCamelCase					       =			torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														UpperCamelCase					       =			generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							UpperCamelCase					       =			AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														UpperCamelCase					       =			rng_state.to(xm.xla_device())
														xm.collective_broadcast([rng_state])
														xm.mark_step()
														UpperCamelCase					       =			rng_state.cpu()
							elif (
							    state.distributed_type in CUDA_DISTRIBUTED_TYPES
							    or state.distributed_type == DistributedType.MULTI_NPU
							    or state.distributed_type == DistributedType.MULTI_XPU
							):
														UpperCamelCase					       =			rng_state.to(state.device)
														torch.distributed.broadcast(A						,			0)
														UpperCamelCase					       =			rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(A						,			0)

							# Set the broadcast rng state
							if rng_type == RNGType.TORCH:
														torch.set_rng_state(A)
							elif rng_type == RNGType.CUDA:
														torch.cuda.set_rng_state(A)
							elif rng_type == RNGType.NPU:
														torch.npu.set_rng_state(A)
							elif rng_type == RNGType.XPU:
														torch.xpu.set_rng_state(A)
							elif rng_type == RNGType.XLA:
														xm.set_rng_state(rng_state.item())
							elif rng_type == RNGType.GENERATOR:
														generator.set_state(A)







def 						A_(			A		:						List[Union[str, RNGType]]						,			A		:						Optional[torch.Generator] = None):
							for rng_type in rng_types:
														synchronize_rng_state(RNGType(A)						,			generator=A)




