code: 
  
  
  
  
  
  '''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
  
  
  lowercase_			       =				logging.get_logger(__name__)
  
  lowercase_			       =				{
      """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
      # See all MBART models at https://huggingface.co/models?filter=mbart
  }
  
  
  class       a_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				'''mbart'''
      UpperCamelCase									=				['''past_key_values''']
      UpperCamelCase									=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
      def __init__(	self				,	A=5_0265				,	A=1024				,	A=12				,	A=4096				,	A=16				,	A=12				,	A=4096				,	A=16				,	A=0.0				,	A=0.0				,	A=True				,	A=True				,	A="gelu"				,	A=1024				,	A=0.1				,	A=0.0				,	A=0.0				,	A=0.02				,	A=0.0				,	A=False				,	A=1				,	A=0				,	A=2				,	A=2				,	**A				,	)	->     str:
          _SCREAMING_SNAKE_CASE             =    vocab_size
          _SCREAMING_SNAKE_CASE             =    max_position_embeddings
          _SCREAMING_SNAKE_CASE             =    d_model
          _SCREAMING_SNAKE_CASE             =    encoder_ffn_dim
          _SCREAMING_SNAKE_CASE             =    encoder_layers
          _SCREAMING_SNAKE_CASE             =    encoder_attention_heads
          _SCREAMING_SNAKE_CASE             =    decoder_ffn_dim
          _SCREAMING_SNAKE_CASE             =    decoder_layers
          _SCREAMING_SNAKE_CASE             =    decoder_attention_heads
          _SCREAMING_SNAKE_CASE             =    dropout
          _SCREAMING_SNAKE_CASE             =    attention_dropout
          _SCREAMING_SNAKE_CASE             =    activation_dropout
          _SCREAMING_SNAKE_CASE             =    activation_function
          _SCREAMING_SNAKE_CASE             =    init_std
          _SCREAMING_SNAKE_CASE             =    encoder_layerdrop
          _SCREAMING_SNAKE_CASE             =    decoder_layerdrop
          _SCREAMING_SNAKE_CASE             =    classifier_dropout
          _SCREAMING_SNAKE_CASE             =    use_cache
          _SCREAMING_SNAKE_CASE             =    encoder_layers
          _SCREAMING_SNAKE_CASE             =    scale_embedding  # scale factor will be sqrt(d_model) if True
          super().__init__(
              pad_token_id=A				,	bos_token_id=A				,	eos_token_id=A				,	is_encoder_decoder=A				,	forced_eos_token_id=A				,	**A				,	)
  
  
  
  
  class       a_  (       snake_case_ ):
  
      '''simple docstring'''
  
  
      @property
      def     snake_case_(	self )	->     Mapping[str, Mapping[int, str]]:
          if self.task in ["default", "seq2seq-lm"]:
              _SCREAMING_SNAKE_CASE             =    OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ] )
  
              if self.use_past:
                  _SCREAMING_SNAKE_CASE             =    {0: """batch"""}
                  _SCREAMING_SNAKE_CASE             =    {0: """batch""", 1: """past_decoder_sequence + sequence"""}
              else:
                  _SCREAMING_SNAKE_CASE             =    {0: """batch""", 1: """decoder_sequence"""}
                  _SCREAMING_SNAKE_CASE             =    {0: """batch""", 1: """decoder_sequence"""}
  
              if self.use_past:
                  self.fill_with_past_key_values_(A				,	direction="""inputs""" )
          elif self.task == "causal-lm":
              # TODO: figure this case out.
              _SCREAMING_SNAKE_CASE             =    OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ] )
              if self.use_past:
                  _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_layers
                  for i in range(A ):
                      _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: """past_sequence + sequence"""}
                      _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: """past_sequence + sequence"""}
          else:
              _SCREAMING_SNAKE_CASE             =    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     snake_case_(	self )	->     Mapping[str, Mapping[int, str]]:
          if self.task in ["default", "seq2seq-lm"]:
              _SCREAMING_SNAKE_CASE             =    super().outputs
          else:
              _SCREAMING_SNAKE_CASE             =    super(A				,	self ).outputs
              if self.use_past:
                  _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_layers
                  for i in range(A ):
                      _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: """past_sequence + sequence"""}
                      _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: """past_sequence + sequence"""}
          return common_outputs
  
  
      def     snake_case_(	self				,	A				,	A = -1				,	A = -1				,	A = False				,	A = None				,	)	->     Mapping[str, Any]:
          _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A				,	A				,	A				,	A				,	A )
  
          # Generate decoder inputs
          _SCREAMING_SNAKE_CASE             =    seq_length if not self.use_past else 1
          _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A				,	A				,	A				,	A				,	A )
          _SCREAMING_SNAKE_CASE             =    {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
          _SCREAMING_SNAKE_CASE             =    dict(**A				,	**A )
  
          if self.use_past:
              if not is_torch_available():
                  raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
              else:
                  import torch
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    common_inputs["""input_ids"""].shape
              _SCREAMING_SNAKE_CASE             =    common_inputs["""decoder_input_ids"""].shape[1]
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_attention_heads
              _SCREAMING_SNAKE_CASE             =    (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              _SCREAMING_SNAKE_CASE             =    decoder_seq_length + 3
              _SCREAMING_SNAKE_CASE             =    (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )
  
              _SCREAMING_SNAKE_CASE             =    torch.cat(
                  [common_inputs["""decoder_attention_mask"""], torch.ones(A				,	A )]				,	dim=1 )
  
              _SCREAMING_SNAKE_CASE             =    []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_layers
              _SCREAMING_SNAKE_CASE             =    min(A				,	A )
              _SCREAMING_SNAKE_CASE             =    max(A				,	A ) - min_num_layers
              _SCREAMING_SNAKE_CASE             =    """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
  
              for _ in range(A ):
                  common_inputs["past_key_values"].append(
                      (
                          torch.zeros(A ),
                          torch.zeros(A ),
                          torch.zeros(A ),
                          torch.zeros(A ),
                      ) )
              # TODO: test this.
              _SCREAMING_SNAKE_CASE             =    encoder_shape if remaining_side_name == """encoder""" else decoder_shape
              for _ in range(A				,	A ):
                  common_inputs["past_key_values"].append((torch.zeros(A ), torch.zeros(A )) )
          return common_inputs
  
  
      def     snake_case_(	self				,	A				,	A = -1				,	A = -1				,	A = False				,	A = None				,	)	->     Mapping[str, Any]:
          _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A				,	A				,	A				,	A				,	A )
  
          if self.use_past:
              if not is_torch_available():
                  raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
              else:
                  import torch
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    common_inputs["""input_ids"""].shape
              # Not using the same length for past_key_values
              _SCREAMING_SNAKE_CASE             =    seqlen + 2
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_layers
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_attention_heads
              _SCREAMING_SNAKE_CASE             =    (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
  
              _SCREAMING_SNAKE_CASE             =    common_inputs["""attention_mask"""].dtype
              _SCREAMING_SNAKE_CASE             =    torch.cat(
                  [common_inputs["""attention_mask"""], torch.ones(A				,	A				,	dtype=A )]				,	dim=1 )
              _SCREAMING_SNAKE_CASE             =    [
                  (torch.zeros(A ), torch.zeros(A )) for _ in range(A )
              ]
          return common_inputs
  
  
      def     snake_case_(	self				,	A				,	A = -1				,	A = -1				,	A = False				,	A = None				,	)	->     Mapping[str, Any]:
          # 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
          _SCREAMING_SNAKE_CASE             =    compute_effective_axis_dimension(
              A				,	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
          _SCREAMING_SNAKE_CASE             =    tokenizer.num_special_tokens_to_add(A )
          _SCREAMING_SNAKE_CASE             =    compute_effective_axis_dimension(
              A				,	fixed_dimension=OnnxConfig.default_fixed_sequence				,	num_token_to_add=A )
  
          # Generate dummy inputs according to compute batch and sequence
          _SCREAMING_SNAKE_CASE             =    [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
          _SCREAMING_SNAKE_CASE             =    dict(tokenizer(A				,	return_tensors=A ) )
          return common_inputs
  
  
      def     snake_case_(	self				,	A				,	A = -1				,	A = -1				,	A = False				,	A = None				,	)	->     Mapping[str, Any]:
          if self.task in ["default", "seq2seq-lm"]:
              _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  A				,	batch_size=A				,	seq_length=A				,	is_pair=A				,	framework=A )
  
          elif self.task == "causal-lm":
              _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_causal_lm(
                  A				,	batch_size=A				,	seq_length=A				,	is_pair=A				,	framework=A )
          else:
              _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  A				,	batch_size=A				,	seq_length=A				,	is_pair=A				,	framework=A )
  
          return common_inputs
  
  
  
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A )	->     Optional[Any]:
          if self.task in ["default", "seq2seq-lm"]:
              _SCREAMING_SNAKE_CASE             =    super()._flatten_past_key_values_(A				,	A				,	A				,	A )
          else:
              _SCREAMING_SNAKE_CASE             =    super(A				,	self )._flatten_past_key_values_(
                  A				,	A				,	A				,	A )
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					int = 10**6					)		->int:
  
      if n <= 0:
          raise ValueError("""Please enter an integer greater than 0"""					)
  
      _SCREAMING_SNAKE_CASE             =    list(range(n + 1					)					)
      for number in range(2      , n + 1					):
          if phi[number] == number:
              phi[number] -= 1
              for multiple in range(number * 2      , n + 1      , __lowerCamelCase					):
                  _SCREAMING_SNAKE_CASE             =    (phi[multiple] // number) * (number - 1)
  
      _SCREAMING_SNAKE_CASE             =    1
      for number in range(1      , n + 1					):
          if (answer / phi[answer]) < (number / phi[number]):
              _SCREAMING_SNAKE_CASE             =    number
  
      return answer
  
  
  if __name__ == "__main__":
        print(solution())
  
  
  
  