




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



def      UpperCAmelCase_   (     __UpperCamelCase     ):

				def remove_articles(__UpperCamelCase     ):
								return re.sub(R"""\b(a|an|the)\b""",    """ """,    _lowerCAmelCase     )

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

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

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

				return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase     )     )     )     )



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
				SCREAMING_SNAKE_CASE__       =normalize_answer(_lowerCAmelCase     ).split()
				SCREAMING_SNAKE_CASE__       =normalize_answer(_lowerCAmelCase     ).split()
				SCREAMING_SNAKE_CASE__       =Counter(_lowerCAmelCase     ) & Counter(_lowerCAmelCase     )
				SCREAMING_SNAKE_CASE__       =sum(common.values()     )
				if num_same == 0:
								return 0
				SCREAMING_SNAKE_CASE__       =1.0 * num_same / len(_lowerCAmelCase     )
				SCREAMING_SNAKE_CASE__       =1.0 * num_same / len(_lowerCAmelCase     )
				SCREAMING_SNAKE_CASE__       =(2 * precision * recall) / (precision + recall)
				return fa



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
				return normalize_answer(_lowerCAmelCase     ) == normalize_answer(_lowerCAmelCase     )



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
				SCREAMING_SNAKE_CASE__       =[]
				for ground_truth in ground_truths:
								SCREAMING_SNAKE_CASE__       =metric_fn(_lowerCAmelCase,    _lowerCAmelCase     )
								scores_for_ground_truths.append(_lowerCAmelCase     )
				return max(_lowerCAmelCase     )



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

				SCREAMING_SNAKE_CASE__       =100.0 * exact_match / total
				SCREAMING_SNAKE_CASE__       =100.0 * fa / total

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


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












from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

lowerCamelCase_  						=       {
    name: (getattr(transformers, name), getattr(transformers, name + "Fast")) for name in SLOW_TO_FAST_CONVERTERS
}

lowerCamelCase_  						=       datasets.load_dataset("xnli", split="test+validation")

lowerCamelCase_  						=       0
lowerCamelCase_  						=       0
lowerCamelCase_  						=       0
lowerCamelCase_  						=       0



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    if spm_diff == list(reversed(__UpperCamelCase     )     ):
        # AAA -> AA+A vs A+AA case.
        return True
    elif len(__UpperCamelCase     ) == len(__UpperCamelCase     ) and fast.decode(__UpperCamelCase     ) == fast.decode(__UpperCamelCase     ):
        # Second order OK
        # Barrich -> Barr + ich vs Bar + rich
        return True
    SCREAMING_SNAKE_CASE__       =slow.encode(slow.decode(__UpperCamelCase     )     )
    SCREAMING_SNAKE_CASE__       =fast.encode(fast.decode(__UpperCamelCase     )     )
    if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
        # Type 3 error.
        # Snehagatha ->
        #       Sne, h, aga, th, a
        #       Sne, ha, gat, ha
        # Encoding the wrong with sp does not even recover what spm gave us
        # It fits tokenizer however...
        return True
    return False



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =fast.encode_plus(__UpperCamelCase     )[0]
    SCREAMING_SNAKE_CASE__       =enc.offsets
    SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =offsets[idx], offsets[idx - 1]
    if curr is not None and line[curr[0] : curr[1]] == "\u200f":
        return True
    if prev is not None and line[prev[0] : prev[1]] == "\u200f":
        return True



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    # Encoding can be the same with same result AAA -> A + AA vs AA + A
    # We can check that we use at least exactly the same number of tokens.
    for i, (spm_id, tok_id) in enumerate(zip(__UpperCamelCase,    __UpperCamelCase     )     ):
        if spm_id != tok_id:
            break
    SCREAMING_SNAKE_CASE__       =i
    for i, (spm_id, tok_id) in enumerate(zip(reversed(__UpperCamelCase     ),    reversed(__UpperCamelCase     )     )     ):
        if spm_id != tok_id:
            break
    SCREAMING_SNAKE_CASE__       =len(__UpperCamelCase     ) - i

    SCREAMING_SNAKE_CASE__       =spm_ids[first:last]
    SCREAMING_SNAKE_CASE__       =tok_ids[first:last]

    if check_diff(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
        return True

    if check_LTR_mark(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
        return True

    if last - first > 5:
        # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
        SCREAMING_SNAKE_CASE__       =Counter(spm_ids[first:last]     )
        SCREAMING_SNAKE_CASE__       =Counter(tok_ids[first:last]     )

        SCREAMING_SNAKE_CASE__       ={spm_ for (spm_, si) in spms.items() if toks.get(spm_,    0     ) == si}
        SCREAMING_SNAKE_CASE__       =3
        for i in range(last - first - min_width     ):
            if all(spm_ids[first + i + j] in removable_tokens for j in range(__UpperCamelCase     )     ):
                SCREAMING_SNAKE_CASE__       =[
                    k
                    for k in range(last - first - min_width     )
                    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
                ]
                for j in possible_matches:
                    if check_diff(spm_ids[first : first + i],    tok_ids[first : first + j],    __UpperCamelCase,    __UpperCamelCase     ) and check_details(
                        __UpperCamelCase,    spm_ids[first + i : last],    tok_ids[first + j : last],    __UpperCamelCase,    __UpperCamelCase,    ):
                        return True

    print(f"""Spm: {[fast.decode([spm_ids[i]]     ) for i in range(__UpperCamelCase,    __UpperCamelCase     )]}"""     )
    try:
        print(f"""Tok: {[fast.decode([tok_ids[i]]     ) for i in range(__UpperCamelCase,    __UpperCamelCase     )]}"""     )
    except Exception:
        pass

    SCREAMING_SNAKE_CASE__       =fast.decode(spm_ids[:first]     )
    SCREAMING_SNAKE_CASE__       =fast.decode(spm_ids[last:]     )
    SCREAMING_SNAKE_CASE__       =fast.decode(spm_ids[first:last]     )
    print()
    print(__UpperCamelCase     )
    return False



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    global perfect
    global imperfect
    global wrong
    global total

    SCREAMING_SNAKE_CASE__       =slow.encode(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =fast.encode(__UpperCamelCase     )

    SCREAMING_SNAKE_CASE__       =False
    total += 1

    if slow_ids != fast_ids:
        if check_details(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
            SCREAMING_SNAKE_CASE__       =True
            imperfect += 1
        else:
            wrong += 1
    else:
        perfect += 1

    if total % 10_000 == 0:
        print(f"""({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})"""     )

    if skip_assert:
        return

    assert (
        slow_ids == fast_ids
    ), f"""line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__UpperCamelCase     )}\n{fast.tokenize(__UpperCamelCase     )}"""



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    global batch_total
    for i in range(len(__UpperCamelCase     )     ):
        # premise, all languages
        for text in dataset[i]["premise"].values():
            test_string(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )

        # hypothesis, all languages
        for text in dataset[i]["hypothesis"]["translation"]:
            test_string(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )


if __name__ == "__main__":
    for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
        lowerCamelCase_  						=       list(slow_class.max_model_input_sizes.keys())
        for checkpoint in checkpoint_names:
            lowerCamelCase_  						=       0
            lowerCamelCase_  						=       0
            lowerCamelCase_  						=       0
            lowerCamelCase_  						=       0

            print(f"""========================== Checking {name}: {checkpoint} ==========================""")
            lowerCamelCase_  						=       slow_class.from_pretrained(checkpoint, force_download=True)
            lowerCamelCase_  						=       fast_class.from_pretrained(checkpoint, force_download=True)
            test_tokenizer(slow, fast)
            print(f"""Accuracy {perfect * 100 / total:.2f}""")






