






"""simple docstring"""



from typing import Mapping

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


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

a  :						Union[str, Any]					       =							{
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}


class 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   Any											=						'longt5'
    __lowercase    :   str											=						['past_key_values']
    __lowercase    :   int											=						{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


    def __init__(      self   ,			__lowercase=32128   ,			__lowercase=512   ,			__lowercase=64   ,			__lowercase=2048   ,			__lowercase=6   ,			__lowercase=None   ,			__lowercase=8   ,			__lowercase=127   ,			__lowercase=16   ,			__lowercase=32   ,			__lowercase=128   ,			__lowercase=0.1   ,			__lowercase=1e-6   ,			__lowercase=1.0   ,			__lowercase="relu"   ,			__lowercase=True   ,			__lowercase="local"   ,			__lowercase=True   ,			__lowercase=0   ,			__lowercase=1   ,			**__lowercase   ,			):
     UpperCAmelCase__ 						=				vocab_size
     UpperCAmelCase__ 						=				d_model
     UpperCAmelCase__ 						=				d_kv
     UpperCAmelCase__ 						=				d_ff
     UpperCAmelCase__ 						=				num_layers
     # default = symmetry
     UpperCAmelCase__ 						=				num_decoder_layers if num_decoder_layers is not None else self.num_layers
     UpperCAmelCase__ 						=				num_heads
     UpperCAmelCase__ 						=				local_radius
     UpperCAmelCase__ 						=				global_block_size
     UpperCAmelCase__ 						=				relative_attention_num_buckets
     UpperCAmelCase__ 						=				relative_attention_max_distance
     UpperCAmelCase__ 						=				dropout_rate
     UpperCAmelCase__ 						=				layer_norm_epsilon
     UpperCAmelCase__ 						=				initializer_factor
     UpperCAmelCase__ 						=				feed_forward_proj
     UpperCAmelCase__ 						=				encoder_attention_type
     UpperCAmelCase__ 						=				use_cache

     UpperCAmelCase__ 						=				self.feed_forward_proj.split("""-"""			)
     UpperCAmelCase__ 						=				act_info[-1]
     UpperCAmelCase__ 						=				act_info[0] == """gated"""

     if len(__lowercase			) > 1 and act_info[0] != "gated" or len(__lowercase			) > 2:
      raise ValueError(
          F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
          """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
          """'gated-gelu' or 'relu'"""			)

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
      UpperCAmelCase__ 						=				"""gelu_new"""

     super().__init__(
         pad_token_id=__lowercase   ,			eos_token_id=__lowercase   ,			is_encoder_decoder=__lowercase   ,			**__lowercase   ,			)



class 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    @property
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				{
         """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
         """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
     }
     if self.use_past:
      UpperCAmelCase__ 						=				"""past_encoder_sequence + sequence"""
      UpperCAmelCase__ 						=				{0: """batch"""}
      UpperCAmelCase__ 						=				{0: """batch""", 1: """past_decoder_sequence + sequence"""}
     else:
      UpperCAmelCase__ 						=				{0: """batch""", 1: """decoder_sequence"""}
      UpperCAmelCase__ 						=				{0: """batch""", 1: """decoder_sequence"""}

     if self.use_past:
      self.fill_with_past_key_values_(__lowercase   ,			direction="""inputs"""			)

     return common_inputs






    @property
    def 		A__      (      self			):
     return 13











"""simple docstring"""



from string import ascii_uppercase




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = False       ,	_SCREAMING_SNAKE_CASE = ascii_uppercase			)    ->str:
 UpperCAmelCase__ 						=				keyword.upper()
 UpperCAmelCase__ 						=				plaintext.upper()
 UpperCAmelCase__ 						=				set(_SCREAMING_SNAKE_CASE			)

 # 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(_SCREAMING_SNAKE_CASE			)
    # the number of those unique characters will determine the number of rows
 UpperCAmelCase__ 						=				len(_SCREAMING_SNAKE_CASE			)

 # 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       ,	2_6       ,	_SCREAMING_SNAKE_CASE			)
 ]

 # 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(_SCREAMING_SNAKE_CASE			):
  for row in modified_alphabet:
   # if current row (the last one) is too short, break out of loop
   if len(_SCREAMING_SNAKE_CASE			) <= column:
    break

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

 if verbose:
  print(_SCREAMING_SNAKE_CASE			)
 # 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'''))



