code: 
  """simple docstring"""
  
  
  
  
  
  from string import ascii_uppercase
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase = False,	__lowerCamelCase = ascii_uppercase						):
     UpperCAmelCase_      :      List[str]      =					keyword.upper()
     UpperCAmelCase_      :      str      =					plaintext.upper()
     UpperCAmelCase_      :      Tuple      =					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
     UpperCAmelCase_      :      Tuple      =					[]
     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
     UpperCAmelCase_      :      Union[str, Any]      =					len(__lowerCamelCase						)
  
     # create a shifted version of the alphabet
     UpperCAmelCase_      :      Tuple      =					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_      :      List[str]      =					[
         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
     UpperCAmelCase_      :      Tuple      =					{}
     UpperCAmelCase_      :      Dict      =					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
           UpperCAmelCase_      :      Tuple      =					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'))
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  _a	 =							logging.get_logger(__name__)
  
  _a	 =							{
      'google/bigbird-pegasus-large-arxiv': (
          'https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'
      ),
      'google/bigbird-pegasus-large-pubmed': (
          'https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'
      ),
      'google/bigbird-pegasus-large-bigpatent': (
          'https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'
      ),
      # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
  }
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		Optional[int]						  =	"""bigbird_pegasus"""
    SCREAMING_SNAKE_CASE__    :		Any						  =	["""past_key_values"""]
    SCREAMING_SNAKE_CASE__    :		Tuple						  =	{
        """num_attention_heads""": """encoder_attention_heads""",
        """hidden_size""": """d_model""",
        """attention_probs_dropout_prob""": """attention_dropout""",
    }
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_=9_6103       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=16       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=0.0       ,     lowercase_=0.0       ,     lowercase_=True       ,     lowercase_=True       ,     lowercase_="gelu_new"       ,     lowercase_=1024       ,     lowercase_=0.1       ,     lowercase_=0.0       ,     lowercase_=0.0       ,     lowercase_=0.02       ,     lowercase_=2       ,     lowercase_=0.0       ,     lowercase_=True       ,     lowercase_=0       ,     lowercase_=2       ,     lowercase_=1       ,     lowercase_="block_sparse"       ,     lowercase_=64       ,     lowercase_=3       ,     lowercase_=False       ,     **lowercase_       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					vocab_size
       UpperCAmelCase_      :      Optional[Any]      =					max_position_embeddings
       UpperCAmelCase_      :      List[str]      =					d_model
       UpperCAmelCase_      :      str      =					encoder_ffn_dim
       UpperCAmelCase_      :      Optional[Any]      =					encoder_layers
       UpperCAmelCase_      :      Optional[Any]      =					encoder_attention_heads
       UpperCAmelCase_      :      str      =					decoder_ffn_dim
       UpperCAmelCase_      :      Optional[Any]      =					decoder_layers
       UpperCAmelCase_      :      Optional[int]      =					decoder_attention_heads
       UpperCAmelCase_      :      Dict      =					dropout
       UpperCAmelCase_      :      Optional[int]      =					attention_dropout
       UpperCAmelCase_      :      Any      =					activation_dropout
       UpperCAmelCase_      :      str      =					activation_function
       UpperCAmelCase_      :      Tuple      =					init_std
       UpperCAmelCase_      :      Optional[int]      =					encoder_layerdrop
       UpperCAmelCase_      :      Union[str, Any]      =					decoder_layerdrop
       UpperCAmelCase_      :      int      =					classifier_dropout
       UpperCAmelCase_      :      Union[str, Any]      =					use_cache
       UpperCAmelCase_      :      List[str]      =					encoder_layers
       UpperCAmelCase_      :      str      =					scale_embedding  # scale factor will be sqrt(d_model) if True
  
       # extra config
       UpperCAmelCase_      :      List[Any]      =					attention_type
       UpperCAmelCase_      :      Any      =					block_size
       UpperCAmelCase_      :      str      =					num_random_blocks
       UpperCAmelCase_      :      Optional[Any]      =					use_bias
  
       super().__init__(
           pad_token_id=lowercase_       ,     bos_token_id=lowercase_       ,     eos_token_id=lowercase_       ,     is_encoder_decoder=lowercase_       ,     decoder_start_token_id=lowercase_       ,     **lowercase_       ,     )
  
  
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      List[str]      =					OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]						)
  
          if self.use_past:
             UpperCAmelCase_      :      List[str]      =					{0: "batch"}
             UpperCAmelCase_      :      Any      =					{0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
             UpperCAmelCase_      :      List[str]      =					{0: "batch", 1: "decoder_sequence"}
             UpperCAmelCase_      :      Optional[Any]      =					{0: "batch", 1: "decoder_sequence"}
  
          if self.use_past:
             self.fill_with_past_key_values_(lowercase_       ,     direction="inputs"						)
       elif self.task == "causal-lm":
          # TODO: figure this case out.
          UpperCAmelCase_      :      str      =					OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]						)
          if self.use_past:
             UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					self.num_layers
             for i in range(lowercase_						):
                UpperCAmelCase_      :      Tuple      =					{0: "batch", 2: "past_sequence + sequence"}
                UpperCAmelCase_      :      List[str]      =					{0: "batch", 2: "past_sequence + sequence"}
       else:
          UpperCAmelCase_      :      Optional[Any]      =					OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                  ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                  ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
              ]						)
  
       return common_inputs
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      Union[str, Any]      =					super().outputs
       else:
          UpperCAmelCase_      :      Optional[Any]      =					super(lowercase_       ,     self						).outputs
          if self.use_past:
             UpperCAmelCase_  ,     UpperCAmelCase_      :      str      =					self.num_layers
             for i in range(lowercase_						):
                UpperCAmelCase_      :      Any      =					{0: "batch", 2: "past_sequence + sequence"}
                UpperCAmelCase_      :      int      =					{0: "batch", 2: "past_sequence + sequence"}
       return common_outputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
       # Generate decoder inputs
       UpperCAmelCase_      :      Union[str, Any]      =					seq_length if not self.use_past else 1
       UpperCAmelCase_      :      str      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
       UpperCAmelCase_      :      List[Any]      =					{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       UpperCAmelCase_      :      Optional[Any]      =					dict(**lowercase_       ,     **lowercase_						)
  
       if self.use_past:
          if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
          else:
             import torch
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					common_inputs["input_ids"].shape
          UpperCAmelCase_      :      Union[str, Any]      =					common_inputs["decoder_input_ids"].shape[1]
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					self.num_attention_heads
          UpperCAmelCase_      :      Tuple      =					(
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          UpperCAmelCase_      :      Union[str, Any]      =					decoder_seq_length + 3
          UpperCAmelCase_      :      List[Any]      =					(
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )
  
          UpperCAmelCase_      :      List[str]      =					torch.cat(
              [common_inputs["decoder_attention_mask"], torch.ones(lowercase_       ,     lowercase_						)]       ,     dim=1						)
  
          UpperCAmelCase_      :      Dict      =					[]
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Union[str, Any]      =					self.num_layers
          UpperCAmelCase_      :      List[str]      =					min(lowercase_       ,     lowercase_						)
          UpperCAmelCase_      :      Optional[int]      =					max(lowercase_       ,     lowercase_						) - min_num_layers
          UpperCAmelCase_      :      List[Any]      =					"encoder" if num_encoder_layers > num_decoder_layers else "decoder"
  
          for _ in range(lowercase_						):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(lowercase_						),
                     torch.zeros(lowercase_						),
                     torch.zeros(lowercase_						),
                     torch.zeros(lowercase_						),
                 )						)
          # TODO: test this.
          UpperCAmelCase_      :      Optional[int]      =					encoder_shape if remaining_side_name == "encoder" else decoder_shape
          for _ in range(lowercase_       ,     lowercase_						):
             common_inputs["past_key_values"].append((torch.zeros(lowercase_						), torch.zeros(lowercase_						))						)
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
       if self.use_past:
          if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
          else:
             import torch
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					common_inputs["input_ids"].shape
          # Not using the same length for past_key_values
          UpperCAmelCase_      :      str      =					seqlen + 2
          UpperCAmelCase_  ,     UpperCAmelCase_      :      List[Any]      =					self.num_layers
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					self.num_attention_heads
          UpperCAmelCase_      :      Optional[int]      =					(
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
  
          UpperCAmelCase_      :      str      =					common_inputs["attention_mask"].dtype
          UpperCAmelCase_      :      Union[str, Any]      =					torch.cat(
              [common_inputs["attention_mask"], torch.ones(lowercase_       ,     lowercase_       ,     dtype=lowercase_						)]       ,     dim=1						)
          UpperCAmelCase_      :      List[str]      =					[
              (torch.zeros(lowercase_						), torch.zeros(lowercase_						)) for _ in range(lowercase_						)
          ]
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase_      :      Any      =					compute_effective_axis_dimension(
           lowercase_       ,     fixed_dimension=OnnxConfig.default_fixed_batch       ,     num_token_to_add=0						)
  
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCAmelCase_      :      List[str]      =					tokenizer.num_special_tokens_to_add(lowercase_						)
       UpperCAmelCase_      :      Union[str, Any]      =					compute_effective_axis_dimension(
           lowercase_       ,     fixed_dimension=OnnxConfig.default_fixed_sequence       ,     num_token_to_add=lowercase_						)
  
       # Generate dummy inputs according to compute batch and sequence
       UpperCAmelCase_      :      str      =					[" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
       UpperCAmelCase_      :      int      =					dict(tokenizer(lowercase_       ,     return_tensors=lowercase_						)						)
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      Dict      =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
  
       elif self.task == "causal-lm":
          UpperCAmelCase_      :      List[str]      =					self._generate_dummy_inputs_for_causal_lm(
              lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
       else:
          UpperCAmelCase_      :      int      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
  
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      Union[str, Any]      =					super()._flatten_past_key_values_(lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
       else:
          UpperCAmelCase_      :      Dict      =					super(lowercase_       ,     self						)._flatten_past_key_values_(
              lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
  
  