



'''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()

lowercase__     :  Any    					=			{
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

lowercase__     :  Any    					=			datasets.load_dataset('xnli', split='test+validation')

lowercase__     :  Optional[Any]    					=			0
lowercase__     :  Optional[Any]    					=			0
lowercase__     :  Optional[int]    					=			0
lowercase__     :  Optional[int]    					=			0





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    if spm_diff == list(reversed(lowerCAmelCase_	)	):
        # AAA -> AA+A vs A+AA case.
        return True
    elif len(lowerCAmelCase_	) == len(lowerCAmelCase_	) and fast.decode(lowerCAmelCase_	) == fast.decode(lowerCAmelCase_	):
        # Second order OK
        # Barrich -> Barr + ich vs Bar + rich
        return True
    UpperCamelCase__      =			slow.encode(slow.decode(lowerCAmelCase_	)	)
    UpperCamelCase__      =			fast.encode(fast.decode(lowerCAmelCase_	)	)
    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 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			fast.encode_plus(lowerCAmelCase_	)[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 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    # 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(lowerCAmelCase_					,  lowerCAmelCase_	)	):
        if spm_id != tok_id:
            break
    UpperCamelCase__      =			i
    for i, (spm_id, tok_id) in enumerate(zip(reversed(lowerCAmelCase_	)					,  reversed(lowerCAmelCase_	)	)	):
        if spm_id != tok_id:
            break
    UpperCamelCase__      =			len(lowerCAmelCase_	) - i

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

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

    if check_LTR_mark(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
        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(lowerCAmelCase_	)	):
                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]					,  lowerCAmelCase_					,  lowerCAmelCase_	) and check_details(
                        lowerCAmelCase_					,  spm_ids[first + i : last]					,  tok_ids[first + j : last]					,  lowerCAmelCase_					,  lowerCAmelCase_					,  ):
                        return True

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

    UpperCamelCase__      =			fast.decode(spm_ids[:first]	)
    UpperCamelCase__      =			fast.decode(spm_ids[last:]	)
    UpperCamelCase__      =			fast.decode(spm_ids[first:last]	)
    print()
    print(lowerCAmelCase_	)
    return False





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    global perfect
    global imperfect
    global wrong
    global total

    UpperCamelCase__      =			slow.encode(lowerCAmelCase_	)
    UpperCamelCase__      =			fast.encode(lowerCAmelCase_	)

    UpperCamelCase__      =			False
    total += 1

    if slow_ids != fast_ids:
        if check_details(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
            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(lowerCAmelCase_	)}\n{fast.tokenize(lowerCAmelCase_	)}'





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

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


if __name__ == "__main__":
      for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
            lowercase__     :  Tuple    					=			list(slow_class.max_model_input_sizes.keys())
            for checkpoint in checkpoint_names:
                  lowercase__     :  List[Any]    					=			0
                  lowercase__     :  Optional[Any]    					=			0
                  lowercase__     :  List[Any]    					=			0
                  lowercase__     :  Any    					=			0

                  print(F'''========================== Checking {name}: {checkpoint} ==========================''')
                  lowercase__     :  int    					=			slow_class.from_pretrained(checkpoint, force_download=True)
                  lowercase__     :  int    					=			fast_class.from_pretrained(checkpoint, force_download=True)
                  test_tokenizer(slow, fast)
                  print(F'''Accuracy {perfect * 1_00 / total:.2f}''')









'''simple docstring'''







import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
      import librosa  # noqa: E402

      lowercase__     :  Optional[Any]    					=			True
      lowercase__     :  int    					=			''
except Exception as e:
      lowercase__     :  Union[str, Any]    					=			False
      lowercase__     :  List[Any]    					=			(
          F'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
      )


from PIL import Image  # noqa: E402

class       lowerCamelCase_							(	lowerCAmelCase_					,    lowerCAmelCase_  ):






   '''simple docstring'''

   A :     int				=	"mel_config.json"


   @register_to_config
   def __init__(  self     ,						__UpperCamelCase = 2_56     ,						__UpperCamelCase = 2_56     ,						__UpperCamelCase = 2_20_50     ,						__UpperCamelCase = 20_48     ,						__UpperCamelCase = 5_12     ,						__UpperCamelCase = 80     ,						__UpperCamelCase = 32     ,						):
       UpperCamelCase__      =			hop_length
       UpperCamelCase__      =			sample_rate
       UpperCamelCase__      =			n_fft
       UpperCamelCase__      =			top_db
       UpperCamelCase__      =			n_iter
       self.set_resolution(__UpperCamelCase     ,						__UpperCamelCase )
       UpperCamelCase__      =			None

       if not _librosa_can_be_imported:
           raise ValueError(_import_error )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       UpperCamelCase__      =			x_res
       UpperCamelCase__      =			y_res
       UpperCamelCase__      =			self.y_res
       UpperCamelCase__      =			self.x_res * self.hop_length - 1


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None ):
       if audio_file is not None:
           UpperCamelCase__    ,  UpperCamelCase__      =			librosa.load(__UpperCamelCase     ,						mono=__UpperCamelCase     ,						sr=self.sr )
       else:
           UpperCamelCase__      =			raw_audio

       # Pad with silence if necessary.
       if len(self.audio ) < self.x_res * self.hop_length:
           UpperCamelCase__      =			np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio ),) )] )


   def 	_UpperCamelCase       (  self ):
       return len(self.audio ) // self.slice_size


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase = 0 ):
       return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]


   def 	_UpperCamelCase       (  self ):
       return self.sr


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			librosa.feature.melspectrogram(
           y=self.get_audio_slice(__UpperCamelCase )     ,						sr=self.sr     ,						n_fft=self.n_fft     ,						hop_length=self.hop_length     ,						n_mels=self.n_mels )
       UpperCamelCase__      =			librosa.power_to_db(__UpperCamelCase     ,						ref=np.max     ,						top_db=self.top_db )
       UpperCamelCase__      =			(((log_S + self.top_db) * 2_55 / self.top_db).clip(0     ,						2_55 ) + 0.5).astype(np.uinta )
       UpperCamelCase__      =			Image.fromarray(__UpperCamelCase )
       return image







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			np.frombuffer(image.tobytes()     ,						dtype="uint8" ).reshape((image.height, image.width) )
       UpperCamelCase__      =			bytedata.astype("float" ) * self.top_db / 2_55 - self.top_db
       UpperCamelCase__      =			librosa.db_to_power(__UpperCamelCase )
       UpperCamelCase__      =			librosa.feature.inverse.mel_to_audio(
           __UpperCamelCase     ,						sr=self.sr     ,						n_fft=self.n_fft     ,						hop_length=self.hop_length     ,						n_iter=self.n_iter )
       return audio




