



'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase  						=			logging.get_logger(__name__)

_UpperCamelCase  						=			{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}

class      lowerCamelCase__     (     _A				):






	'''simple docstring'''

	A__		=	'''tvlt'''




	def __init__(  self		:  int   ,		__A		:  List[Any]=224   ,		__A		:  List[str]=2048   ,		__A		:  Dict=128   ,		__A		:  List[str]=[16, 16]   ,		__A		:  int=[16, 16]   ,		__A		:  Optional[int]=3   ,		__A		:  List[Any]=1   ,		__A		:  List[str]=8   ,		__A		:  List[str]=768   ,		__A		:  Optional[int]=12   ,		__A		:  Optional[int]=12   ,		__A		:  str=3072   ,		__A		:  Tuple="gelu"   ,		__A		:  Any=0.0   ,		__A		:  List[Any]=0.0   ,		__A		:  Dict=0.0_2   ,		__A		:  Tuple=1E-6   ,		__A		:  Union[str, Any]=True   ,		__A		:  Any=False   ,		__A		:  Optional[int]=16   ,		__A		:  Any=512   ,		__A		:  Any=8   ,		__A		:  Any=2048   ,		__A		:  int=0.7_5   ,		__A		:  List[str]=0.1_5   ,		__A		:  str="frame-level"   ,		__A		:  Optional[int]=True   ,		__A		:  int=True   ,		__A		:  Tuple="classification"   ,		**__A		:  Tuple   ,		)     ->			Optional[Any]:




								'''simple docstring'''





								super().__init__(**__A					)

								if audio_mask_type not in ("frame-level", "patch_level"):
															raise ValueError(
															    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
															    f'''got {audio_mask_type}'''					)

								lowerCAmelCase__  =   image_size
								lowerCAmelCase__  =   spectrogram_length
								lowerCAmelCase__  =   frequency_length
								lowerCAmelCase__  =   image_patch_size
								lowerCAmelCase__  =   audio_patch_size
								lowerCAmelCase__  =   num_image_channels
								lowerCAmelCase__  =   num_audio_channels
								lowerCAmelCase__  =   num_frames

								lowerCAmelCase__  =   hidden_size
								lowerCAmelCase__  =   num_hidden_layers
								lowerCAmelCase__  =   num_attention_heads
								lowerCAmelCase__  =   intermediate_size
								lowerCAmelCase__  =   hidden_act
								lowerCAmelCase__  =   hidden_dropout_prob
								lowerCAmelCase__  =   attention_probs_dropout_prob
								lowerCAmelCase__  =   initializer_range
								lowerCAmelCase__  =   layer_norm_eps
								lowerCAmelCase__  =   qkv_bias
								lowerCAmelCase__  =   use_mean_pooling

								lowerCAmelCase__  =   decoder_num_attention_heads
								lowerCAmelCase__  =   decoder_hidden_size
								lowerCAmelCase__  =   decoder_num_hidden_layers
								lowerCAmelCase__  =   decoder_intermediate_size
								lowerCAmelCase__  =   pixel_mask_ratio
								lowerCAmelCase__  =   audio_mask_ratio
								lowerCAmelCase__  =   audio_mask_type

								lowerCAmelCase__  =   task_matching
								lowerCAmelCase__  =   task_mae
								lowerCAmelCase__  =   loss_type










'''simple docstring'''







def 			_lowerCAmelCase(      UpperCAmelCase_      :	str						) ->							str:
							lowerCAmelCase__  =   word.upper()
							lowerCAmelCase__  =   0
							lowerCAmelCase__  =   {}
							lowerCAmelCase__  =   []

							for letter in word:
														if letter not in letter_nums:
																					lowerCAmelCase__  =   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.





