




import functools
import operator

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


UpperCAmelCase__				     = logging.get_logger(__name__)

UpperCAmelCase__				     = {
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}







class a (     lowercase__  ):
						"""simple docstring"""

						UpperCamelCase_	:       int       	=		'sew'






						def __init__( self :     Optional[Any]				,							lowerCamelCase__ :     int=32				,							lowerCamelCase__ :     Dict=768				,							lowerCamelCase__ :     Any=12				,							lowerCamelCase__ :     int=12				,							lowerCamelCase__ :     Dict=3_072				,							lowerCamelCase__ :     List[Any]=2				,							lowerCamelCase__ :     List[Any]="gelu"				,							lowerCamelCase__ :     Tuple=0.1				,							lowerCamelCase__ :     str=0.1				,							lowerCamelCase__ :     Optional[int]=0.1				,							lowerCamelCase__ :     str=0.0				,							lowerCamelCase__ :     List[str]=0.1				,							lowerCamelCase__ :     Tuple=0.1				,							lowerCamelCase__ :     List[Any]=0.0_2				,							lowerCamelCase__ :     List[str]=1e-5				,							lowerCamelCase__ :     Optional[int]="group"				,							lowerCamelCase__ :     int="gelu"				,							lowerCamelCase__ :     Any=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,							lowerCamelCase__ :     Optional[int]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,							lowerCamelCase__ :     Any=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,							lowerCamelCase__ :     Tuple=False				,							lowerCamelCase__ :     Union[str, Any]=128				,							lowerCamelCase__ :     Dict=16				,							lowerCamelCase__ :     int=True				,							lowerCamelCase__ :     Any=0.0_5				,							lowerCamelCase__ :     int=10				,							lowerCamelCase__ :     Optional[Any]=2				,							lowerCamelCase__ :     List[Any]=0.0				,							lowerCamelCase__ :     str=10				,							lowerCamelCase__ :     List[str]=0				,							lowerCamelCase__ :     Dict="mean"				,							lowerCamelCase__ :     Union[str, Any]=False				,							lowerCamelCase__ :     Dict=False				,							lowerCamelCase__ :     Optional[Any]=256				,							lowerCamelCase__ :     Optional[int]=0				,							lowerCamelCase__ :     Dict=1				,							lowerCamelCase__ :     Any=2				,							**lowerCamelCase__ :     int				,							)    ->						Union[str, Any]:
													"""simple docstring"""


													super().__init__(**__lowerCamelCase				,							pad_token_id=__lowerCamelCase				,							bos_token_id=__lowerCamelCase				,							eos_token_id=__lowerCamelCase				)
													__lowercase      =    hidden_size
													__lowercase      =    feat_extract_norm
													__lowercase      =    feat_extract_activation
													__lowercase      =    list(__lowerCamelCase				)
													__lowercase      =    list(__lowerCamelCase				)
													__lowercase      =    list(__lowerCamelCase				)
													__lowercase      =    conv_bias
													__lowercase      =    num_conv_pos_embeddings
													__lowercase      =    num_conv_pos_embedding_groups
													__lowercase      =    len(self.conv_dim				)
													__lowercase      =    num_hidden_layers
													__lowercase      =    intermediate_size
													__lowercase      =    squeeze_factor
													__lowercase      =    hidden_act
													__lowercase      =    num_attention_heads
													__lowercase      =    hidden_dropout
													__lowercase      =    attention_dropout
													__lowercase      =    activation_dropout
													__lowercase      =    feat_proj_dropout
													__lowercase      =    final_dropout
													__lowercase      =    layerdrop
													__lowercase      =    layer_norm_eps
													__lowercase      =    initializer_range
													__lowercase      =    vocab_size

													if (
													    (len(self.conv_stride				) != self.num_feat_extract_layers)
													    or (len(self.conv_kernel				) != self.num_feat_extract_layers)
													    or (len(self.conv_dim				) != self.num_feat_extract_layers)
													):
																				raise ValueError(
																				    '''Configuration for convolutional layers is incorrect.'''
																				    '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
																				    f'but is `len(config.conv_dim) = {len(self.conv_dim				)}`, `len(config.conv_stride)'
																				    f'= {len(self.conv_stride				)}`, `len(config.conv_kernel) = {len(self.conv_kernel				)}`.'				)

													# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
													__lowercase      =    apply_spec_augment
													__lowercase      =    mask_time_prob
													__lowercase      =    mask_time_length
													__lowercase      =    mask_time_min_masks
													__lowercase      =    mask_feature_prob
													__lowercase      =    mask_feature_length
													__lowercase      =    mask_feature_min_masks

													# ctc loss
													__lowercase      =    ctc_loss_reduction
													__lowercase      =    ctc_zero_infinity

													# sequence classification
													__lowercase      =    use_weighted_layer_sum
													__lowercase      =    classifier_proj_size






						@property
						def     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


													return functools.reduce(operator.mul				,							self.conv_stride				,							1				)












import itertools
import string
from collections.abc import Generator, Iterable







def 		_A(       UpperCamelCase__							:			Iterable[str]				,       UpperCamelCase__							:			int				)    -> Generator[tuple[str, ...], None, None]:
							'''simple docstring'''


							__lowercase      =    iter(UpperCamelCase__				)
							while True:
														__lowercase      =    tuple(itertools.islice(UpperCamelCase__				,       UpperCamelCase__				)				)
														if not chunk:
																					return
														yield chunk







def 		_A(       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    ''''''.join([c.upper() for c in dirty if c in string.ascii_letters]				)
							__lowercase      =    ''''''

							if len(UpperCamelCase__				) < 2:
														return dirty

							for i in range(len(UpperCamelCase__				) - 1				):
														clean += dirty[i]

														if dirty[i] == dirty[i + 1]:
																					clean += "X"

							clean += dirty[-1]

							if len(UpperCamelCase__				) & 1:
														clean += "X"

							return clean







def 		_A(       UpperCamelCase__							:			str				)    -> list[str]:
							'''simple docstring'''


							__lowercase      =    '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
							# we're using a list instead of a '2d' array because it makes the math
							# for setting up the table and doing the actual encoding/decoding simpler
							__lowercase      =    []

							# copy key chars into the table if they are in `alphabet` ignoring duplicates
							for char in key.upper():
														if char not in table and char in alphabet:
																					table.append(UpperCamelCase__				)

    # fill the rest of the table in with the remaining alphabet chars
							for char in alphabet:
														if char not in table:
																					table.append(UpperCamelCase__				)

							return table







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    generate_table(UpperCamelCase__				)
							__lowercase      =    prepare_input(UpperCamelCase__				)
							__lowercase      =    ''''''

							# https://en.wikipedia.org/wiki/Playfair_cipher#Description
							for chara, chara in chunker(UpperCamelCase__				,       2				):
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)

														if rowa == rowa:
																					ciphertext += table[rowa * 5 + (cola + 1) % 5]
																					ciphertext += table[rowa * 5 + (cola + 1) % 5]
														elif cola == cola:
																					ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																					ciphertext += table[((rowa + 1) % 5) * 5 + cola]
														else:  # rectangle
																					ciphertext += table[rowa * 5 + cola]
																					ciphertext += table[rowa * 5 + cola]

							return ciphertext







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    generate_table(UpperCamelCase__				)
							__lowercase      =    ''''''

							# https://en.wikipedia.org/wiki/Playfair_cipher#Description
							for chara, chara in chunker(UpperCamelCase__				,       2				):
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)

														if rowa == rowa:
																					plaintext += table[rowa * 5 + (cola - 1) % 5]
																					plaintext += table[rowa * 5 + (cola - 1) % 5]
														elif cola == cola:
																					plaintext += table[((rowa - 1) % 5) * 5 + cola]
																					plaintext += table[((rowa - 1) % 5) * 5 + cola]
														else:  # rectangle
																					plaintext += table[rowa * 5 + cola]
																					plaintext += table[rowa * 5 + cola]

							return plaintext






