



'''simple docstring'''
from typing import TYPE_CHECKING

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


snake_case_				:			Union[str, Any]						 =						{
    'configuration_flava': [
        'FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'FlavaConfig',
        'FlavaImageCodebookConfig',
        'FlavaImageConfig',
        'FlavaMultimodalConfig',
        'FlavaTextConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_				:			str						 =						['FlavaFeatureExtractor']
					snake_case_				:			Dict						 =						['FlavaImageProcessor']
					snake_case_				:			Union[str, Any]						 =						['FlavaProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_				:			int						 =						[
					    'FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlavaForPreTraining',
					    'FlavaImageCodebook',
					    'FlavaImageModel',
					    'FlavaModel',
					    'FlavaMultimodalModel',
					    'FlavaPreTrainedModel',
					    'FlavaTextModel',
					]

if TYPE_CHECKING:
					from .configuration_flava import (
					    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    FlavaConfig,
					    FlavaImageCodebookConfig,
					    FlavaImageConfig,
					    FlavaMultimodalConfig,
					    FlavaTextConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_flava import FlavaFeatureExtractor
										from .image_processing_flava import FlavaImageProcessor
										from .processing_flava import FlavaProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flava import (
										    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlavaForPreTraining,
										    FlavaImageCodebook,
										    FlavaImageModel,
										    FlavaModel,
										    FlavaMultimodalModel,
										    FlavaPreTrainedModel,
										    FlavaTextModel,
										)

else:
					import sys

					snake_case_				:			Union[str, Any]						 =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''
def A__					(  UpperCAmelCase_							):
						_UpperCamelCase      :	Optional[Any]				       =     word.upper()
						_UpperCamelCase      :	Optional[Any]				       =     0
						_UpperCamelCase      :	Dict				       =     {}
						_UpperCamelCase      :	List[str]				       =     []

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


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

					snake_case_				:			List[Any]						 =						time.time()
					with open('dictionary.txt') as in_file:
										snake_case_				:			Any						 =						in_file.read().splitlines()

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

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

					snake_case_				:			List[str]						 =						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.


