





def  A_(      A ):
	UpperCAmelCase_		    =  word.upper()
	UpperCAmelCase_		    =  0
	UpperCAmelCase_		    =  {}
	UpperCAmelCase_		    =  []

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


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

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

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

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

				UpperCamelCase__ :			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.









from typing import TYPE_CHECKING

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


UpperCamelCase__ :			Union[str, Any]       	=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			int       	=			[
				    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """MobileViTV2ForImageClassification""",
				    """MobileViTV2ForSemanticSegmentation""",
				    """MobileViTV2Model""",
				    """MobileViTV2PreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				UpperCamelCase__ :			List[Any]       	=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


