
def        lowerCAmelCase       (				snake_case__   :      str )->			str:
    A_								=					word.upper()
    A_								=					0
    A_								=					{}
    A_								=					[]

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


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

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

     __magic_name__  :  int     			=   {}
     for word in word_list:
          __magic_name__  :  Optional[Any]     			=   get_word_pattern(word)
          if pattern in all_patterns:
               all_patterns[pattern].append(word)
          else:
               __magic_name__  :  Union[str, Any]     			=   [word]

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

     __magic_name__  :  Union[str, 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.










# 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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


snake_case__  				=			{
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			[
			    'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TvltModel',
			    'TvltForPreTraining',
			    'TvltForAudioVisualClassification',
			    'TvltPreTrainedModel',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			['TvltImageProcessor']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

			snake_case__  				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


