
"""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()

_a   :      Union[str, Any]       =				{
    name: (getattr(transformers, name), getattr(transformers, name + """Fast""")) for name in SLOW_TO_FAST_CONVERTERS
}

_a   :      Any       =				datasets.load_dataset("""xnli""", split="""test+validation""")

_a   :      Any       =				0
_a   :      int       =				0
_a   :      Optional[Any]       =				0
_a   :      Dict       =				0
def 					a__ ( a :					Dict							,  a :					Tuple							,  a :					Optional[Any]							,  a :					Optional[int]		):

  """simple docstring"""







  if spm_diff == list(reversed(a		)		):
    # AAA -> AA+A vs A+AA case.
    return True
  elif len(a		) == len(a		) and fast.decode(a		) == fast.decode(a		):
    # Second order OK
    # Barrich -> Barr + ich vs Bar + rich
    return True
  _snake_case  :    Any            = slow.encode(slow.decode(a		)		)
  _snake_case  :    Union[str, Any]            = fast.encode(fast.decode(a		)		)
  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 					a__ ( a :					str							,  a :					str							,  a :					Dict		):

  """simple docstring"""







  _snake_case  :    Optional[int]            = fast.encode_plus(a		)[0]
  _snake_case  :    List[Any]            = enc.offsets
  _snake_case      ,       _snake_case  :    Dict            = 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 					a__ ( a :					Optional[Any]							,  a :					List[str]							,  a :					List[Any]							,  a :					Optional[Any]							,  a :					Any		):

  """simple docstring"""







  for i, (spm_id, tok_id) in enumerate(zip(a							,  a		)		):
    if spm_id != tok_id:
      break
  _snake_case  :    Any            = i
  for i, (spm_id, tok_id) in enumerate(zip(reversed(a		)							,  reversed(a		)		)		):
    if spm_id != tok_id:
      break
  _snake_case  :    Dict            = len(a		) - i

  _snake_case  :    int            = spm_ids[first:last]
  _snake_case  :    Union[str, Any]            = tok_ids[first:last]

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

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

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

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

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

  _snake_case  :    str            = fast.decode(spm_ids[:first]		)
  _snake_case  :    Optional[Any]            = fast.decode(spm_ids[last:]		)
  _snake_case  :    Optional[Any]            = fast.decode(spm_ids[first:last]		)
  print()
  print(a		)
  return False
def 					a__ ( a :					str							,  a :					Optional[int]							,  a :					Optional[int]		):

  """simple docstring"""







  global perfect
  global imperfect
  global wrong
  global total

  _snake_case  :    List[str]            = slow.encode(a		)
  _snake_case  :    List[str]            = fast.encode(a		)

  _snake_case  :    List[str]            = False
  total += 1

  if slow_ids != fast_ids:
    if check_details(a							,  a							,  a							,  a							,  a		):
      _snake_case  :    Optional[int]            = 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(a		)}\n{fast.tokenize(a		)}'
def 					a__ ( a :					str							,  a :					Any		):

  """simple docstring"""







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

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


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

            print(f'========================== Checking {name}: {checkpoint} ==========================')
            _a   :      Union[str, Any]       =				slow_class.from_pretrained(checkpoint, force_download=True)
            _a   :      int       =				fast_class.from_pretrained(checkpoint, force_download=True)
            test_tokenizer(slow, fast)
            print(f'Accuracy {perfect * 100 / total:.2f}')





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a					:				Dict       =      logging.get_logger(__name__)

a					:				str       =      {
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hubert"""

       def __init__(      self						,    a__=32						,    a__=7_68						,    a__=12						,    a__=12						,    a__=30_72						,    a__="gelu"						,    a__=0.1						,    a__=0.1						,    a__=0.1						,    a__=True						,    a__=0.0						,    a__=0.1						,    a__=0.1						,    a__=0.02						,    a__=1e-5						,    a__="group"						,    a__="gelu"						,    a__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)						,    a__=(5, 2, 2, 2, 2, 2, 2)						,    a__=(10, 3, 3, 3, 3, 2, 2)						,    a__=False						,    a__=1_28						,    a__=16						,    a__=False						,    a__=True						,    a__=0.05						,    a__=10						,    a__=2						,    a__=0.0						,    a__=10						,    a__=0						,    a__="sum"						,    a__=False						,    a__=False						,    a__=2_56						,    a__=0						,    a__=1						,    a__=2						,    **a__						,    ):






        '''simple docstring'''






        super().__init__(**a__						,    pad_token_id=a__						,    bos_token_id=a__						,    eos_token_id=a__       )
        snake_case :			List[str]							=   hidden_size
        snake_case :			List[str]							=   feat_extract_norm
        snake_case :			List[str]							=   feat_extract_activation
        snake_case :			Dict							=   list(a__       )
        snake_case :			Tuple							=   list(a__       )
        snake_case :			Dict							=   list(a__       )
        snake_case :			str							=   conv_bias
        snake_case :			Dict							=   num_conv_pos_embeddings
        snake_case :			Union[str, Any]							=   num_conv_pos_embedding_groups
        snake_case :			Optional[int]							=   len(self.conv_dim       )
        snake_case :			Tuple							=   num_hidden_layers
        snake_case :			Any							=   intermediate_size
        snake_case :			Optional[Any]							=   hidden_act
        snake_case :			str							=   num_attention_heads
        snake_case :			List[Any]							=   hidden_dropout
        snake_case :			Union[str, Any]							=   attention_dropout
        snake_case :			Dict							=   activation_dropout
        snake_case :			List[str]							=   feat_proj_layer_norm
        snake_case :			List[str]							=   feat_proj_dropout
        snake_case :			Union[str, Any]							=   final_dropout
        snake_case :			List[str]							=   layerdrop
        snake_case :			str							=   layer_norm_eps
        snake_case :			Tuple							=   initializer_range
        snake_case :			str							=   vocab_size
        snake_case :			Tuple							=   do_stable_layer_norm
        snake_case :			Dict							=   use_weighted_layer_sum
        snake_case :			Optional[Any]							=   classifier_proj_size

        if (
            (len(self.conv_stride       ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
            or (len(self.conv_dim       ) != self.num_feat_extract_layers)
        ):
         raise ValueError(
             '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
             ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
             f''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
             f''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        snake_case :			Optional[int]							=   apply_spec_augment
        snake_case :			int							=   mask_time_prob
        snake_case :			Dict							=   mask_time_length
        snake_case :			Any							=   mask_time_min_masks
        snake_case :			Any							=   mask_feature_prob
        snake_case :			Union[str, Any]							=   mask_feature_length
        snake_case :			List[Any]							=   mask_feature_min_masks

        # ctc loss
        snake_case :			List[str]							=   ctc_loss_reduction
        snake_case :			Dict							=   ctc_zero_infinity



       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return functools.reduce(operator.mul						,    self.conv_stride						,    1       )






