




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()

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

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

a_ 		=  0
a_ 		=  0
a_ 		=  0
a_ 		=  0




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Tuple:

    """simple docstring"""


    if spm_diff == list(reversed(lowercase_   )   ):
        # AAA -> AA+A vs A+AA case.
        return True
    elif len(lowercase_   ) == len(lowercase_   ) and fast.decode(lowercase_   ) == fast.decode(lowercase_   ):
        # Second order OK
        # Barrich -> Barr + ich vs Bar + rich
        return True
    __UpperCamelCase			=    slow.encode(slow.decode(lowercase_   )   )
    __UpperCamelCase			=    fast.encode(fast.decode(lowercase_   )   )
    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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """simple docstring"""


    __UpperCamelCase			=    fast.encode_plus(lowercase_   )[0]
    __UpperCamelCase			=    enc.offsets
    __UpperCamelCase		,   __UpperCamelCase			=    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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Tuple:

    """simple docstring"""


    for i, (spm_id, tok_id) in enumerate(zip(lowercase_       ,  lowercase_   )   ):
        if spm_id != tok_id:
            break
    __UpperCamelCase			=    i
    for i, (spm_id, tok_id) in enumerate(zip(reversed(lowercase_   )       ,  reversed(lowercase_   )   )   ):
        if spm_id != tok_id:
            break
    __UpperCamelCase			=    len(lowercase_   ) - i

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

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

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

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

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

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

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						str:

    """simple docstring"""


    global perfect
    global imperfect
    global wrong
    global total

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

    __UpperCamelCase			=    False
    total += 1

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

    if total % 1_00_00 == 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(lowercase_   )}\n{fast.tokenize(lowercase_   )}"




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Union[str, Any]:

    """simple docstring"""


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

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


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

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







"""simple docstring"""






import requests

try:
						from django.contrib.auth import authenticate, login
						from django.shortcuts import redirect, render
except ImportError:
						lowerCAmelCase__    :    Union[str, Any]			=      print



def     a_		(   lowerCamelCase			):
				# Enter your recaptcha secret key here
				UpperCAmelCase__			 =				'secretKey'
				UpperCAmelCase__			 =				'https://www.google.com/recaptcha/api/siteverify'

				# when method is not POST, direct user to login page
				if request.method != "POST":
								return render(lowerCamelCase   ,     'login.html'			)

				# from the frontend, get username, password, and client_key
				UpperCAmelCase__			 =				request.POST.get('username'			)
				UpperCAmelCase__			 =				request.POST.get('password'			)
				UpperCAmelCase__			 =				request.POST.get('g-recaptcha-response'			)

				# post recaptcha response to Google's recaptcha api
				UpperCAmelCase__			 =				requests.post(lowerCamelCase   ,     data={'secret': secret_key, 'response': client_key}			)
				# if the recaptcha api verified our keys
				if response.json().get('success'   ,     lowerCamelCase			):
								# authenticate the user
								UpperCAmelCase__			 =				authenticate(lowerCamelCase   ,     username=lowerCamelCase   ,     password=lowerCamelCase			)
								if user_in_database:
												login(lowerCamelCase   ,     lowerCamelCase			)
												return redirect('/your-webpage'			)
				return render(lowerCamelCase   ,     'login.html'			)



