






"""simple docstring"""
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    word.split()

				def justify(lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ) -> str:
								_SCREAMING_SNAKE_CASE   :		Any       		=    max_width - width
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    len(lowerCamelCase       )
								if len(lowerCamelCase       ) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												_SCREAMING_SNAKE_CASE   :		List[Any]       		=    spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												_SCREAMING_SNAKE_CASE   :		int       		=    (
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(lowerCamelCase       ):
																num_spaces_between_words_list[i] += 1
												_SCREAMING_SNAKE_CASE   :		Any       		=    []
												for i in range(lowerCamelCase       ):
																# add the word
																aligned_words_list.append(line[i]       )
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * ' '       )
												# just add the last word to the sentence
												aligned_words_list.append(line[-1]       )
												# join the aligned words list to form a justified line
												return "".join(lowerCamelCase       )

				_SCREAMING_SNAKE_CASE   :		List[str]       		=    []
				_SCREAMING_SNAKE_CASE   :		list[str]       		=    []
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    0
				for word in words:
								if width + len(lowerCamelCase       ) + len(lowerCamelCase       ) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(lowerCamelCase       )
												width += len(lowerCamelCase       )
								else:
												# justify the line and add it to result
												answer.append(justify(lowerCamelCase,						lowerCamelCase,						lowerCamelCase       )       )
												# reset new line and new width
												_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    [word], len(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Dict       		=    max_width - width - len(lowerCamelCase       )
				answer.append(' '.join(lowerCamelCase       ) + (remaining_spaces + 1) * ' '       )
				return answer


if __name__ == "__main__":
			from doctest import testmod

			testmod()









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__    						=			logging.get_logger(__name__)

lowerCAmelCase__    						=			{
    'caidas/swin2sr-classicalsr-x2-64': (
        'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
    ),
}


class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Any 					=				'swin2sr'

						SCREAMING_SNAKE_CASE_:					Dict 					=				{
						    'hidden_size': 'embed_dim',
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}







						def __init__(    self   ,				lowerCAmelCase_=6_4   ,				lowerCAmelCase_=1   ,				lowerCAmelCase_=3   ,				lowerCAmelCase_=1_8_0   ,				lowerCAmelCase_=[6, 6, 6, 6, 6, 6]   ,				lowerCAmelCase_=[6, 6, 6, 6, 6, 6]   ,				lowerCAmelCase_=8   ,				lowerCAmelCase_=2.0   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=0.0   ,				lowerCAmelCase_=0.0   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_="gelu"   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=0.02   ,				lowerCAmelCase_=1e-5   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=1.0   ,				lowerCAmelCase_="1conv"   ,				lowerCAmelCase_="pixelshuffle"   ,				**lowerCAmelCase_   ,				)		->							Dict:
										super().__init__(**lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    image_size
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    patch_size
										_SCREAMING_SNAKE_CASE   :		str       		=    num_channels
										_SCREAMING_SNAKE_CASE   :		Any       		=    embed_dim
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    depths
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    len(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_heads
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    window_size
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    mlp_ratio
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    qkv_bias
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    hidden_dropout_prob
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    drop_path_rate
										_SCREAMING_SNAKE_CASE   :		int       		=    hidden_act
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    use_absolute_embeddings
										_SCREAMING_SNAKE_CASE   :		str       		=    layer_norm_eps
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    initializer_range
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    upscale
										_SCREAMING_SNAKE_CASE   :		str       		=    img_range
										_SCREAMING_SNAKE_CASE   :		Any       		=    resi_connection
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    upsampler

