





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE							)       ->   str:




    """simple docstring"""




    _A    =       word.upper()
    _A    =       0
    _A    =       {}
    _A    =       []

    for letter in word:
        if letter not in letter_nums:
            _A    =       str(_SCREAMING_SNAKE_CASE							)
            next_num += 1
        word_pattern.append(letter_nums[letter]							)
    return ".".join(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
 import pprint
 import time

 __A       : Optional[Any]						    = time.time()
 with open("dictionary.txt") as in_file:
  __A       : Union[str, Any]						    = in_file.read().splitlines()

 __A       : dict						    = {}
 for word in word_list:
  __A       : str						    = get_word_pattern(word)
  if pattern in all_patterns:
   all_patterns[pattern].append(word)
  else:
   __A       : Any						    = [word]

 with open("word_patterns.txt", "w") as out_file:
  out_file.write(pprint.pformat(all_patterns))

 __A       : Any						    = round(time.time() - start_time, 2)
 print(f"Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.")
 # Done!  9,581 word patterns found in 0.58 seconds.












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A       : Dict						    = {"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       : List[str]						    = [
     "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "HubertForCTC",
     "HubertForSequenceClassification",
     "HubertModel",
     "HubertPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       : Tuple						    = [
     "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFHubertForCTC",
     "TFHubertModel",
     "TFHubertPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

 __A       : Dict						    = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





