





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__:  List[Any]       					=					logging.get_logger(__name__)

A__:  str       					=					{
    '''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  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__       						=   """mbart"""
      UpperCamelCase__       						=   ["""past_key_values"""]
      UpperCamelCase__       						=   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






      def __init__(						self:	Tuple					,  __lowerCamelCase:	Optional[Any]=5_0265					,  __lowerCamelCase:	Dict=1024					,  __lowerCamelCase:	Dict=12					,  __lowerCamelCase:	int=4096					,  __lowerCamelCase:	List[Any]=16					,  __lowerCamelCase:	List[Any]=12					,  __lowerCamelCase:	int=4096					,  __lowerCamelCase:	Any=16					,  __lowerCamelCase:	List[str]=0.0					,  __lowerCamelCase:	Union[str, Any]=0.0					,  __lowerCamelCase:	Optional[int]=True					,  __lowerCamelCase:	Optional[int]=True					,  __lowerCamelCase:	Union[str, Any]="gelu"					,  __lowerCamelCase:	Any=1024					,  __lowerCamelCase:	Dict=0.1					,  __lowerCamelCase:	Dict=0.0					,  __lowerCamelCase:	Optional[int]=0.0					,  __lowerCamelCase:	Tuple=0.02					,  __lowerCamelCase:	List[Any]=0.0					,  __lowerCamelCase:	List[Any]=False					,  __lowerCamelCase:	int=1					,  __lowerCamelCase:	Optional[Any]=0					,  __lowerCamelCase:	Dict=2					,  __lowerCamelCase:	int=2					,  **__lowerCamelCase:	Union[str, Any]					,  ):
          '''simple docstring'''


          UpperCamelCase__:					Optional[int]												=      vocab_size
          UpperCamelCase__:					Union[str, Any]												=      max_position_embeddings
          UpperCamelCase__:					Dict												=      d_model
          UpperCamelCase__:					Tuple												=      encoder_ffn_dim
          UpperCamelCase__:					Dict												=      encoder_layers
          UpperCamelCase__:					int												=      encoder_attention_heads
          UpperCamelCase__:					Any												=      decoder_ffn_dim
          UpperCamelCase__:					List[Any]												=      decoder_layers
          UpperCamelCase__:					Dict												=      decoder_attention_heads
          UpperCamelCase__:					Dict												=      dropout
          UpperCamelCase__:					Dict												=      attention_dropout
          UpperCamelCase__:					int												=      activation_dropout
          UpperCamelCase__:					Union[str, Any]												=      activation_function
          UpperCamelCase__:					Union[str, Any]												=      init_std
          UpperCamelCase__:					Tuple												=      encoder_layerdrop
          UpperCamelCase__:					Dict												=      decoder_layerdrop
          UpperCamelCase__:					str												=      classifier_dropout
          UpperCamelCase__:					Union[str, Any]												=      use_cache
          UpperCamelCase__:					int												=      encoder_layers
          UpperCamelCase__:					List[Any]												=      scale_embedding  # scale factor will be sqrt(d_model) if True
          super().__init__(
              pad_token_id=__lowerCamelCase					,  bos_token_id=__lowerCamelCase					,  eos_token_id=__lowerCamelCase					,  is_encoder_decoder=__lowerCamelCase					,  forced_eos_token_id=__lowerCamelCase					,  **__lowerCamelCase					,  )




class    _a  (  UpperCamelCase__):

      """simple docstring"""


      @property
      def 	UpperCAmelCase_   (						self:	Union[str, Any]			):
          '''simple docstring'''


          if self.task in ["default", "seq2seq-lm"]:
              UpperCamelCase__:					int												=      OrderedDict(
                  [
                      ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                      ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                  ]			)

              if self.use_past:
                  UpperCamelCase__:					str												=      {0: "batch"}
                  UpperCamelCase__:					Dict												=      {0: "batch", 1: "past_decoder_sequence + sequence"}
              else:
                  UpperCamelCase__:					str												=      {0: "batch", 1: "decoder_sequence"}
                  UpperCamelCase__:					Tuple												=      {0: "batch", 1: "decoder_sequence"}

              if self.use_past:
                  self.fill_with_past_key_values_(__lowerCamelCase					,  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__:					int												=      self.num_layers
                  for i in range(__lowerCamelCase			):
                      UpperCamelCase__:					Union[str, Any]												=      {0: "batch", 2: "past_sequence + sequence"}
                      UpperCamelCase__:					List[str]												=      {0: "batch", 2: "past_sequence + sequence"}
          else:
              UpperCamelCase__:					List[str]												=      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:	Tuple			):
          '''simple docstring'''


          if self.task in ["default", "seq2seq-lm"]:
              UpperCamelCase__:					int												=      super().outputs
          else:
              UpperCamelCase__:					List[str]												=      super(__lowerCamelCase					,  self			).outputs
              if self.use_past:
                  UpperCamelCase__:					Optional[Any]												=      self.num_layers
                  for i in range(__lowerCamelCase			):
                      UpperCamelCase__:					Optional[int]												=      {0: "batch", 2: "past_sequence + sequence"}
                      UpperCamelCase__:					Optional[int]												=      {0: "batch", 2: "past_sequence + sequence"}
          return common_outputs






      def 	UpperCAmelCase_   (						self:	Any					,  __lowerCamelCase:	PreTrainedTokenizer					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	bool = False					,  __lowerCamelCase:	Optional[TensorType] = None					,  ):
          '''simple docstring'''


          UpperCamelCase__:					Union[str, Any]												=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)

          # Generate decoder inputs
          UpperCamelCase__:					Dict												=      seq_length if not self.use_past else 1
          UpperCamelCase__:					Any												=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)
          UpperCamelCase__:					Optional[int]												=      {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
          UpperCamelCase__:					Tuple												=      dict(**__lowerCamelCase					,  **__lowerCamelCase			)

          if self.use_past:
              if not is_torch_available():
                  raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."			)
              else:
                  import torch
              UpperCamelCase__:					Tuple												=      common_inputs["input_ids"].shape
              UpperCamelCase__:					List[Any]												=      common_inputs["decoder_input_ids"].shape[1]
              UpperCamelCase__:					Optional[int]												=      self.num_attention_heads
              UpperCamelCase__:					List[Any]												=      (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              UpperCamelCase__:					Optional[Any]												=      decoder_seq_length + 3
              UpperCamelCase__:					Any												=      (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              UpperCamelCase__:					Union[str, Any]												=      torch.cat(
                  [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase					,  __lowerCamelCase			)]					,  dim=1			)

              UpperCamelCase__:					Tuple												=      []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              UpperCamelCase__:					Optional[Any]												=      self.num_layers
              UpperCamelCase__:					Optional[Any]												=      min(__lowerCamelCase					,  __lowerCamelCase			)
              UpperCamelCase__:					Any												=      max(__lowerCamelCase					,  __lowerCamelCase			) - min_num_layers
              UpperCamelCase__:					int												=      "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

              for _ in range(__lowerCamelCase			):
                  common_inputs["past_key_values"].append(
                      (
                          torch.zeros(__lowerCamelCase			),
                          torch.zeros(__lowerCamelCase			),
                          torch.zeros(__lowerCamelCase			),
                          torch.zeros(__lowerCamelCase			),
                      )			)
              # TODO: test this.
              UpperCamelCase__:					int												=      encoder_shape if remaining_side_name == "encoder" else decoder_shape
              for _ in range(__lowerCamelCase					,  __lowerCamelCase			):
                  common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			))			)
          return common_inputs






      def 	UpperCAmelCase_   (						self:	Any					,  __lowerCamelCase:	PreTrainedTokenizer					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	bool = False					,  __lowerCamelCase:	Optional[TensorType] = None					,  ):
          '''simple docstring'''


          UpperCamelCase__:					Union[str, Any]												=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)

          if self.use_past:
              if not is_torch_available():
                  raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."			)
              else:
                  import torch
              UpperCamelCase__:					Tuple												=      common_inputs["input_ids"].shape
              # Not using the same length for past_key_values
              UpperCamelCase__:					Optional[int]												=      seqlen + 2
              UpperCamelCase__:					List[str]												=      self.num_layers
              UpperCamelCase__:					Any												=      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__:					Optional[Any]												=      common_inputs["attention_mask"].dtype
              UpperCamelCase__:					Dict												=      torch.cat(
                  [common_inputs["attention_mask"], torch.ones(__lowerCamelCase					,  __lowerCamelCase					,  dtype=__lowerCamelCase			)]					,  dim=1			)
              UpperCamelCase__:					Union[str, Any]												=      [
                  (torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			)) for _ in range(__lowerCamelCase			)
              ]
          return common_inputs






      def 	UpperCAmelCase_   (						self:	Dict					,  __lowerCamelCase:	PreTrainedTokenizer					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	bool = False					,  __lowerCamelCase:	Optional[TensorType] = None					,  ):
          '''simple docstring'''


          UpperCamelCase__:					str												=      compute_effective_axis_dimension(
              __lowerCamelCase					,  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__:					int												=      tokenizer.num_special_tokens_to_add(__lowerCamelCase			)
          UpperCamelCase__:					Optional[int]												=      compute_effective_axis_dimension(
              __lowerCamelCase					,  fixed_dimension=OnnxConfig.default_fixed_sequence					,  num_token_to_add=__lowerCamelCase			)

          # Generate dummy inputs according to compute batch and sequence
          UpperCamelCase__:					Optional[int]												=      [" ".join([tokenizer.unk_token]			) * seq_length] * batch_size
          UpperCamelCase__:					Tuple												=      dict(tokenizer(__lowerCamelCase					,  return_tensors=__lowerCamelCase			)			)
          return common_inputs






      def 	UpperCAmelCase_   (						self:	Any					,  __lowerCamelCase:	PreTrainedTokenizer					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	bool = False					,  __lowerCamelCase:	Optional[TensorType] = None					,  ):
          '''simple docstring'''


          if self.task in ["default", "seq2seq-lm"]:
              UpperCamelCase__:					str												=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  __lowerCamelCase					,  batch_size=__lowerCamelCase					,  seq_length=__lowerCamelCase					,  is_pair=__lowerCamelCase					,  framework=__lowerCamelCase			)

          elif self.task == "causal-lm":
              UpperCamelCase__:					int												=      self._generate_dummy_inputs_for_causal_lm(
                  __lowerCamelCase					,  batch_size=__lowerCamelCase					,  seq_length=__lowerCamelCase					,  is_pair=__lowerCamelCase					,  framework=__lowerCamelCase			)
          else:
              UpperCamelCase__:					List[Any]												=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  __lowerCamelCase					,  batch_size=__lowerCamelCase					,  seq_length=__lowerCamelCase					,  is_pair=__lowerCamelCase					,  framework=__lowerCamelCase			)

          return common_inputs






      def 	UpperCAmelCase_   (						self:	str					,  __lowerCamelCase:	Optional[int]					,  __lowerCamelCase:	List[str]					,  __lowerCamelCase:	Union[str, Any]					,  __lowerCamelCase:	int			):
          '''simple docstring'''


          if self.task in ["default", "seq2seq-lm"]:
              UpperCamelCase__:					Any												=      super()._flatten_past_key_values_(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)
          else:
              UpperCamelCase__:					List[str]												=      super(__lowerCamelCase					,  self			)._flatten_past_key_values_(
                  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)








def 							lowerCAmelCase_				(    A_  ,A_  ,A_):
    UpperCamelCase__:					Optional[int]												=      [1 / first_term]
    UpperCamelCase__:					int												=      1 / first_term
    for _ in range(number_of_terms - 1):
        first_term += common_difference
        arithmetic_progression.append(A_)
    UpperCamelCase__:					str												=      [1 / step for step in arithmetic_progression]
    return sum(A_)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(sum_of_harmonic_progression(1 / 2, 2, 2))

