






'''simple docstring'''




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       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase ) ->		Dict:

      '''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
      _A      =			slow.encode(slow.decode(__lowercase ) )
      _A      =			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       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase ) ->		Optional[int]:

      '''simple docstring'''


      _A      =			fast.encode_plus(__lowercase )[0]
      _A      =			enc.offsets
      _A  ,			_A      =			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       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase ) ->		Optional[Any]:

      '''simple docstring'''


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

      _A      =			spm_ids[first:last]
      _A      =			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
            _A      =			Counter(spm_ids[first:last] )
            _A      =			Counter(tok_ids[first:last] )

            _A      =			{spm_ for (spm_, si) in spms.items() if toks.get(spm_     ,						0 ) == si}
            _A      =			3
            for i in range(last - first - min_width ):
                  if all(spm_ids[first + i + j] in removable_tokens for j in range(__lowercase ) ):
                        _A      =			[
                            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

      _A      =			fast.decode(spm_ids[:first] )
      _A      =			fast.decode(spm_ids[last:] )
      _A      =			fast.decode(spm_ids[first:last] )
      print()
      print(__lowercase )
      return False

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase ) ->		List[Any]:

      '''simple docstring'''


      global perfect
      global imperfect
      global wrong
      global total

      _A      =			slow.encode(__lowercase )
      _A      =			fast.encode(__lowercase )

      _A      =			False
      total += 1

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

      if total % 1_0000 == 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       __lowercase		(    __lowercase     ,						__lowercase ) ->		str:

      '''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():
        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 * 1_00 / total:.2f}""")













'''simple docstring'''




from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

lowerCamelCase_							      =      '''https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'''

def       __lowercase		(    __lowercase     ,						__lowercase ) ->		list | None:

      '''simple docstring'''


      try:
            # Has user provided both inputs?
            if not drug_name or not zip_code:
                  return None

            _A      =			BASE_URL.format(__lowercase     ,						__lowercase )
            _A      =			get(__lowercase )

            # Is the response ok?
            response.raise_for_status()

            # Scrape the data using bs4
            _A      =			BeautifulSoup(response.text     ,						"html.parser" )

            # This list will store the name and price.
            _A      =			[]

            # Fetch all the grids that contains the items.
            _A      =			soup.find_all("div"     ,						{"class": "grid-x pharmCard"} )
            if grid_list and len(__lowercase ) > 0:
                  for grid in grid_list:
                        # Get the pharmacy price.
                        _A      =			grid.find("p"     ,						{"class": "list-title"} ).text

                        # Get price of the drug.
                        _A      =			grid.find("span"     ,						{"p", "price price-large"} ).text

                        pharmacy_price_list.append(
                            {
                                "pharmacy_name": pharmacy_name,
                                "price": price,
                            } )

            return pharmacy_price_list

      except (HTTPError, exceptions.RequestException, ValueError):
            return None


if __name__ == "__main__":
    # Enter a drug name and a zip code
    lowerCamelCase_							      =      input('''Enter drug name: ''').strip()
    lowerCamelCase_							      =      input('''Enter zip code: ''').strip()

    lowerCamelCase_							      =      fetch_pharmacy_and_price_list(
        drug_name, zip_code
    )

    if pharmacy_price_list:
        print(F"""\nSearch results for {drug_name} at location {zip_code}:""")
        for pharmacy_price in pharmacy_price_list:
            lowerCamelCase_							      =      pharmacy_price['''pharmacy_name''']
            lowerCamelCase_							      =      pharmacy_price['''price''']

            print(F"""Pharmacy: {name} Price: {price}""")
    else:
        print(F"""No results found for {drug_name}""")





