
'''simple docstring'''





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

def   A			(							A_				:       str							):

						def remove_articles(A_				:       Optional[Any]							):
												return re.sub(R'''\b(a|an|the)\b'''     ,				''' '''     ,				A_							)

						def white_space_fix(A_				:       Union[str, Any]							):
												return " ".join(text.split()							)

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

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

						return white_space_fix(remove_articles(remove_punc(lower(A_							)							)							)							)

def   A			(							A_				:       Any     ,				A_				:       Any							):
						snake_case							:  Any			=     normalize_answer(A_							).split()
						snake_case							:  Optional[Any]			=     normalize_answer(A_							).split()
						snake_case							:  Optional[int]			=     Counter(A_							) & Counter(A_							)
						snake_case							:  Optional[Any]			=     sum(common.values()							)
						if num_same == 0:
												return 0
						snake_case							:  Any			=     1.0 * num_same / len(A_							)
						snake_case							:  Any			=     1.0 * num_same / len(A_							)
						snake_case							:  List[Any]			=     (2 * precision * recall) / (precision + recall)
						return fa

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

def   A			(							A_				:       str     ,				A_				:       List[str]     ,				A_				:       int							):
						snake_case							:  Tuple			=     []
						for ground_truth in ground_truths:
												snake_case							:  int			=     metric_fn(A_     ,				A_							)
												scores_for_ground_truths.append(A_							)
						return max(A_							)

def   A			(							A_				:       str     ,				A_				:       str							):
						snake_case							:  Union[str, Any]			=     0
						for article in dataset:
												for paragraph in article["paragraphs"]:
																		for qa in paragraph["qas"]:
																								total += 1
																								if qa["id"] not in predictions:
																														snake_case							:  Tuple			=     '''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																														print(A_     ,				file=sys.stderr							)
																														continue
																								snake_case							:  Any			=     [x['''text'''] for x in qa['''answers''']]
																								snake_case							:  Dict			=     predictions[qa['''id''']]
																								exact_match += metric_max_over_ground_truths(A_     ,				A_     ,				A_							)
																								fa += metric_max_over_ground_truths(A_     ,				A_     ,				A_							)

						snake_case							:  List[Any]			=     100.0 * exact_match / total
						snake_case							:  List[str]			=     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 os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
							from .testing_utils import (
							    floats_tensor,
							    load_hf_numpy,
							    load_image,
							    load_numpy,
							    load_pt,
							    nightly,
							    parse_flag_from_env,
							    print_tensor_test,
							    require_torch_a,
							    require_torch_gpu,
							    skip_mps,
							    slow,
							    torch_all_close,
							    torch_device,
							)
							from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A_			:List[str]	    =		get_logger(__name__)






def 				A	(			a_   )	->		Tuple:
					if version.parse(a_   ) < version.parse(a_   ):
										if "dev" in min_version:
															__UpperCamelCase :   List[str]									=(
															    'This example requires a source install from HuggingFace diffusers (see '
															    '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
															)
										else:
															__UpperCamelCase :   Tuple									=F'This example requires a minimum version of {min_version},'
										error_message += F' but the version found is {__version__}.\n'
										raise ImportError(a_   )

