




'''simple docstring'''







from string import ascii_uppercase






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_ = False						,							lowercase_ = ascii_uppercase  ):



		'''simple docstring'''







		_snake_case     	=	keyword.upper()
		_snake_case     	=	plaintext.upper()
		_snake_case     	=	set(_lowerCamelCase  )

		# create a list of unique characters in the keyword - their order matters
		# it determines how we will map plaintext characters to the ciphertext
		_snake_case     	=	[]
		for char in keyword:
				if char in alphabet_set and char not in unique_chars:
						unique_chars.append(_lowerCamelCase  )
    # the number of those unique characters will determine the number of rows
		_snake_case     	=	len(_lowerCamelCase  )

		# create a shifted version of the alphabet
		_snake_case     	=	unique_chars + [
		    char for char in alphabet if char not in unique_chars
		]

		# create a modified alphabet by splitting the shifted alphabet into rows
		_snake_case     	=	[
		    shifted_alphabet[k : k + num_unique_chars_in_keyword]
		    for k in range(0						,							26						,							_lowerCamelCase  )
		]

		# map the alphabet characters to the modified alphabet characters
		# going 'vertically' through the modified alphabet - consider columns first
		_snake_case     	=	{}
		_snake_case     	=	0
		for column in range(_lowerCamelCase  ):
				for row in modified_alphabet:
						# if current row (the last one) is too short, break out of loop
						if len(_lowerCamelCase  ) <= column:
								break

						# map current letter to letter in modified alphabet
						_snake_case     	=	row[column]
						letter_index += 1

		if verbose:
				print(_lowerCamelCase  )
		# create the encrypted text by mapping the plaintext to the modified alphabet
		return "".join(mapping[char] if char in mapping else char for char in plaintext  )


if __name__ == "__main__":
						# example use
						print(mixed_keyword('college', 'UNIVERSITY'))











'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase							:						Optional[int]							     =  logging.get_logger(__name__)

_lowerCamelCase							:						int							     =  {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__   =			"""flaubert"""
					lowercase__   =			{
					    """hidden_size""": """emb_dim""",
					    """num_attention_heads""": """n_heads""",
					    """num_hidden_layers""": """n_layers""",
					    """n_words""": """vocab_size""",  # For backward compatibility
					}






					def __init__(   self,  __UpperCamelCase=False,  __UpperCamelCase=0.0,  __UpperCamelCase=30_145,  __UpperCamelCase=2_048,  __UpperCamelCase=12,  __UpperCamelCase=16,  __UpperCamelCase=0.1,  __UpperCamelCase=0.1,  __UpperCamelCase=True,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=1,  __UpperCamelCase=True,  __UpperCamelCase=512,  __UpperCamelCase=2_048**-0.5,  __UpperCamelCase=1E-12,  __UpperCamelCase=0.02,  __UpperCamelCase=0,  __UpperCamelCase=1,  __UpperCamelCase=2,  __UpperCamelCase=3,  __UpperCamelCase=5,  __UpperCamelCase=True,  __UpperCamelCase="first",  __UpperCamelCase=True,  __UpperCamelCase=None,  __UpperCamelCase=True,  __UpperCamelCase=0.1,  __UpperCamelCase=5,  __UpperCamelCase=5,  __UpperCamelCase=0,  __UpperCamelCase=0,  __UpperCamelCase=2,  __UpperCamelCase=0,  **__UpperCamelCase,  ):
							_snake_case     	=	pre_norm
							_snake_case     	=	layerdrop
							_snake_case     	=	vocab_size
							_snake_case     	=	emb_dim
							_snake_case     	=	n_layers
							_snake_case     	=	n_heads
							_snake_case     	=	dropout
							_snake_case     	=	attention_dropout
							_snake_case     	=	gelu_activation
							_snake_case     	=	sinusoidal_embeddings
							_snake_case     	=	causal
							_snake_case     	=	asm
							_snake_case     	=	n_langs
							_snake_case     	=	use_lang_emb
							_snake_case     	=	layer_norm_eps
							_snake_case     	=	bos_index
							_snake_case     	=	eos_index
							_snake_case     	=	pad_index
							_snake_case     	=	unk_index
							_snake_case     	=	mask_index
							_snake_case     	=	is_encoder
							_snake_case     	=	max_position_embeddings
							_snake_case     	=	embed_init_std
							_snake_case     	=	init_std
							_snake_case     	=	summary_type
							_snake_case     	=	summary_use_proj
							_snake_case     	=	summary_activation
							_snake_case     	=	summary_proj_to_labels
							_snake_case     	=	summary_first_dropout
							_snake_case     	=	start_n_top
							_snake_case     	=	end_n_top
							_snake_case     	=	mask_token_id
							_snake_case     	=	lang_id

							if "n_words" in kwargs:
									_snake_case     	=	kwargs["n_words"]

							super().__init__(pad_token_id=__UpperCamelCase,  bos_token_id=__UpperCamelCase,  **__UpperCamelCase			)





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							if self.task == "multiple-choice":
									_snake_case     	=	{0: "batch", 1: "choice", 2: "sequence"}
							else:
									_snake_case     	=	{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]			)





