

from collections import OrderedDict
from typing import Mapping

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


_a:      str								=     logging.get_logger(__name__)

_a:      Union[str, Any]								=     {
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}

class   __UpperCamelCase		(   lowercase  ):
	SCREAMING_SNAKE_CASE__      			=       'rembert'




	def __init__(						self		:  Union[str, Any]						,		lowerCAmelCase		:  Tuple=250_300						,		lowerCAmelCase		:  Tuple=1_152						,		lowerCAmelCase		:  str=32						,		lowerCAmelCase		:  Union[str, Any]=18						,		lowerCAmelCase		:  List[str]=256						,		lowerCAmelCase		:  Tuple=1_664						,		lowerCAmelCase		:  Union[str, Any]=4_608						,		lowerCAmelCase		:  Any="gelu"						,		lowerCAmelCase		:  int=0.0						,		lowerCAmelCase		:  Any=0.0						,		lowerCAmelCase		:  Optional[int]=0.1						,		lowerCAmelCase		:  List[Any]=512						,		lowerCAmelCase		:  Optional[int]=2						,		lowerCAmelCase		:  Optional[Any]=0.02						,		lowerCAmelCase		:  Any=1e-12						,		lowerCAmelCase		:  Union[str, Any]=True						,		lowerCAmelCase		:  Tuple=0						,		lowerCAmelCase		:  Optional[int]=312						,		lowerCAmelCase		:  List[str]=313						,		**lowerCAmelCase		:  int						,		):


		'''simple docstring'''







		super().__init__(pad_token_id=lowerCAmelCase						,		bos_token_id=lowerCAmelCase						,		eos_token_id=lowerCAmelCase						,		**lowerCAmelCase							)

		UpperCAmelCase_    			=     vocab_size
		UpperCAmelCase_    			=     input_embedding_size
		UpperCAmelCase_    			=     output_embedding_size
		UpperCAmelCase_    			=     max_position_embeddings
		UpperCAmelCase_    			=     hidden_size
		UpperCAmelCase_    			=     num_hidden_layers
		UpperCAmelCase_    			=     num_attention_heads
		UpperCAmelCase_    			=     intermediate_size
		UpperCAmelCase_    			=     hidden_act
		UpperCAmelCase_    			=     hidden_dropout_prob
		UpperCAmelCase_    			=     attention_probs_dropout_prob
		UpperCAmelCase_    			=     classifier_dropout_prob
		UpperCAmelCase_    			=     initializer_range
		UpperCAmelCase_    			=     type_vocab_size
		UpperCAmelCase_    			=     layer_norm_eps
		UpperCAmelCase_    			=     use_cache
		UpperCAmelCase_    			=     False


class   __UpperCamelCase		(   lowercase  ):




	@property
	def  __A   (						self		:  int							):


		'''simple docstring'''







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




	@property
	def  __A   (						self		:  str							):


		'''simple docstring'''







		return 1e-4


from string import ascii_uppercase



def  __lowerCAmelCase (  A    ,       A    ,       A = False    ,       A = ascii_uppercase      ):
	UpperCAmelCase_    			=     keyword.upper()
	UpperCAmelCase_    			=     plaintext.upper()
	UpperCAmelCase_    			=     set(A      )

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

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

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

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

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

	if verbose:
		print(A      )
	# 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"""))