






"""simple docstring"""





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase					=     {
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     [
      """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ConvNextV2ForImageClassification""",
      """ConvNextV2Model""",
      """ConvNextV2PreTrainedModel""",
      """ConvNextV2Backbone""",
  ]


if TYPE_CHECKING:
  from .configuration_convnextva import (
      CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ConvNextVaConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_convnextva import (
        CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConvNextVaBackbone,
        ConvNextVaForImageClassification,
        ConvNextVaModel,
        ConvNextVaPreTrainedModel,
    )

else:
  import sys

  UpperCamelCase					=     _LazyModule(__name__, globals()["""__file__"""], _import_structure)













def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		str:
       UpperCamelCase_ 							=	word.upper()
       UpperCamelCase_ 							=	0
       UpperCamelCase_ 							=	{}
       UpperCamelCase_ 							=	[]

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


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

     _UpperCAmelCase										=					time.time()
     with open('dictionary.txt') as in_file:
          _UpperCAmelCase										=					in_file.read().splitlines()

     _UpperCAmelCase										=					{}
     for word in word_list:
          _UpperCAmelCase										=					get_word_pattern(word)
          if pattern in all_patterns:
               all_patterns[pattern].append(word)
          else:
               _UpperCAmelCase										=					[word]

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

     _UpperCAmelCase										=					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.

