code: 
  
  
  
  
  
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...file_utils import TensorType, is_torch_available
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import logging
  
  
  __A  							=  logging.get_logger(__name__)
  
  __A  							=  {
      "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
      # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
  }
  
  
  
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Tuple   =   'blenderbot'
      lowerCamelCase  :       List[str]   =   ['past_key_values']
      lowerCamelCase  :       int   =   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
  
  
  
      def __init__(							self			:    int   ,    __SCREAMING_SNAKE_CASE			:    str=8008   ,    __SCREAMING_SNAKE_CASE			:    int=128   ,    __SCREAMING_SNAKE_CASE			:    List[str]=2   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=10240   ,    __SCREAMING_SNAKE_CASE			:    str=32   ,    __SCREAMING_SNAKE_CASE			:    Dict=24   ,    __SCREAMING_SNAKE_CASE			:    int=10240   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=32   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=0.0   ,    __SCREAMING_SNAKE_CASE			:    Tuple=0.0   ,    __SCREAMING_SNAKE_CASE			:    List[str]=True   ,    __SCREAMING_SNAKE_CASE			:    Tuple=True   ,    __SCREAMING_SNAKE_CASE			:    str="gelu"   ,    __SCREAMING_SNAKE_CASE			:    str=2560   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=0.1   ,    __SCREAMING_SNAKE_CASE			:    Any=0.0   ,    __SCREAMING_SNAKE_CASE			:    List[str]=0.0   ,    __SCREAMING_SNAKE_CASE			:    int=0.02   ,    __SCREAMING_SNAKE_CASE			:    Any=1   ,    __SCREAMING_SNAKE_CASE			:    str=False   ,    __SCREAMING_SNAKE_CASE			:    str=0   ,    __SCREAMING_SNAKE_CASE			:    Dict=1   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=2   ,    __SCREAMING_SNAKE_CASE			:    int=3   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=2   ,    **__SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    )			->       List[str]:
             __UpperCAmelCase    							=vocab_size
             __UpperCAmelCase    							=max_position_embeddings
             __UpperCAmelCase    							=d_model
             __UpperCAmelCase    							=encoder_ffn_dim
             __UpperCAmelCase    							=encoder_layers
             __UpperCAmelCase    							=encoder_attention_heads
             __UpperCAmelCase    							=decoder_ffn_dim
             __UpperCAmelCase    							=decoder_layers
             __UpperCAmelCase    							=decoder_attention_heads
             __UpperCAmelCase    							=dropout
             __UpperCAmelCase    							=attention_dropout
             __UpperCAmelCase    							=activation_dropout
             __UpperCAmelCase    							=activation_function
             __UpperCAmelCase    							=init_std
             __UpperCAmelCase    							=encoder_layerdrop
             __UpperCAmelCase    							=decoder_layerdrop
             __UpperCAmelCase    							=use_cache
             __UpperCAmelCase    							=encoder_layers
             __UpperCAmelCase    							=scale_embedding  # scale factor will be sqrt(d_model) if True
  
             super().__init__(
                 pad_token_id=__SCREAMING_SNAKE_CASE   ,    bos_token_id=__SCREAMING_SNAKE_CASE   ,    eos_token_id=__SCREAMING_SNAKE_CASE   ,    is_encoder_decoder=__SCREAMING_SNAKE_CASE   ,    decoder_start_token_id=__SCREAMING_SNAKE_CASE   ,    encoder_no_repeat_ngram_size=__SCREAMING_SNAKE_CASE   ,    forced_eos_token_id=__SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE   ,    )
  
  
  
  
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      @property
      def _a       (							self			:    Any			)			->       Mapping[str, Mapping[int, str]]:
             if self.task in ["default", "seq2seq-lm"]:
                    __UpperCAmelCase    							=OrderedDict(
                        [
                            ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                            ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                        ]			)
                    if self.use_past:
                           __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_(__SCREAMING_SNAKE_CASE   ,    direction="""inputs"""			)
             elif self.task == "causal-lm":
                    __UpperCAmelCase    							=OrderedDict(
                        [
                            ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                            ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                        ]			)
                    if self.use_past:
                           __UpperCAmelCase							,   __UpperCAmelCase    							=self.num_layers
                           for i in range(__SCREAMING_SNAKE_CASE			):
                                  __UpperCAmelCase    							={0: """batch""", 2: """past_sequence + sequence"""}
                                  __UpperCAmelCase    							={0: """batch""", 2: """past_sequence + sequence"""}
             else:
                    __UpperCAmelCase    							=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
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
      def _a       (							self			:    Union[str, Any]			)			->       Mapping[str, Mapping[int, str]]:
             if self.task in ["default", "seq2seq-lm"]:
                    __UpperCAmelCase    							=super().outputs
             else:
                    __UpperCAmelCase    							=super(__SCREAMING_SNAKE_CASE   ,    self			).outputs
                    if self.use_past:
                           __UpperCAmelCase							,   __UpperCAmelCase    							=self.num_layers
                           for i in range(__SCREAMING_SNAKE_CASE			):
                                  __UpperCAmelCase    							={0: """batch""", 2: """past_sequence + sequence"""}
                                  __UpperCAmelCase    							={0: """batch""", 2: """past_sequence + sequence"""}
             return common_outputs
  
  
  
      def _a       (							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    PreTrainedTokenizer   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    bool = False   ,    __SCREAMING_SNAKE_CASE			:    Optional[TensorType] = None   ,    )			->       Mapping[str, Any]:
             __UpperCAmelCase    							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
             # Generate decoder inputs
             __UpperCAmelCase    							=seq_length if not self.use_past else 1
             __UpperCAmelCase    							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							={f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
             __UpperCAmelCase    							=dict(**__SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			)
  
             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    							=common_inputs["""input_ids"""].shape
                    __UpperCAmelCase    							=common_inputs["""decoder_input_ids"""].shape[1]
                    __UpperCAmelCase							,   __UpperCAmelCase    							=self.num_attention_heads
                    __UpperCAmelCase    							=(
                        batch,
                        num_encoder_attention_heads,
                        encoder_seq_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )
                    __UpperCAmelCase    							=decoder_seq_length
                    __UpperCAmelCase    							=(
                        batch,
                        num_decoder_attention_heads,
                        decoder_past_length,
                        self._config.hidden_size // num_decoder_attention_heads,
                    )
                    __UpperCAmelCase    							=torch.cat(
                        [common_inputs["""decoder_attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)]   ,    dim=1			)
                    __UpperCAmelCase    							=[]
                    __UpperCAmelCase							,   __UpperCAmelCase    							=self.num_layers
  
                    for _ in range(__SCREAMING_SNAKE_CASE			):
                           common_inputs["past_key_values"].append(
                               (
                                   torch.zeros(__SCREAMING_SNAKE_CASE			),
                                   torch.zeros(__SCREAMING_SNAKE_CASE			),
                                   torch.zeros(__SCREAMING_SNAKE_CASE			),
                                   torch.zeros(__SCREAMING_SNAKE_CASE			),
                               )			)
             return common_inputs
  
  
  
      def _a       (							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    PreTrainedTokenizer   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    bool = False   ,    __SCREAMING_SNAKE_CASE			:    Optional[TensorType] = None   ,    )			->       Mapping[str, Any]:
             __UpperCAmelCase    							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
  
             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    							=common_inputs["""input_ids"""].shape
                    __UpperCAmelCase    							=seqlen
                    __UpperCAmelCase							,   __UpperCAmelCase    							=self.num_layers
                    __UpperCAmelCase							,   __UpperCAmelCase    							=self.num_attention_heads
                    __UpperCAmelCase    							=(
                        batch,
                        num_encoder_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )
                    __UpperCAmelCase    							=common_inputs["""attention_mask"""].dtype
                    __UpperCAmelCase    							=torch.cat(
                        [common_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    dtype=__SCREAMING_SNAKE_CASE			)]   ,    dim=1			)
                    __UpperCAmelCase    							=[
                        (torch.zeros(__SCREAMING_SNAKE_CASE			), torch.zeros(__SCREAMING_SNAKE_CASE			)) for _ in range(__SCREAMING_SNAKE_CASE			)
                    ]
             return common_inputs
  
  
  
      def _a       (							self			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    PreTrainedTokenizer   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    bool = False   ,    __SCREAMING_SNAKE_CASE			:    Optional[TensorType] = 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
             __UpperCAmelCase    							=compute_effective_axis_dimension(
                 __SCREAMING_SNAKE_CASE   ,    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    							=tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=compute_effective_axis_dimension(
                 __SCREAMING_SNAKE_CASE   ,    fixed_dimension=OnnxConfig.default_fixed_sequence   ,    num_token_to_add=__SCREAMING_SNAKE_CASE			)
  
             # Generate dummy inputs according to compute batch and sequence
             __UpperCAmelCase    							=[""" """.join([tokenizer.unk_token]			) * seq_length] * batch_size
             __UpperCAmelCase    							=dict(tokenizer(__SCREAMING_SNAKE_CASE   ,    return_tensors=__SCREAMING_SNAKE_CASE			)			)
             return common_inputs
  
  
  
      def _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    PreTrainedTokenizer   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    bool = False   ,    __SCREAMING_SNAKE_CASE			:    Optional[TensorType] = None   ,    )			->       Mapping[str, Any]:
             if self.task in ["default", "seq2seq-lm"]:
                    __UpperCAmelCase    							=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                        __SCREAMING_SNAKE_CASE   ,    batch_size=__SCREAMING_SNAKE_CASE   ,    seq_length=__SCREAMING_SNAKE_CASE   ,    is_pair=__SCREAMING_SNAKE_CASE   ,    framework=__SCREAMING_SNAKE_CASE			)
  
             elif self.task == "causal-lm":
                    __UpperCAmelCase    							=self._generate_dummy_inputs_for_causal_lm(
                        __SCREAMING_SNAKE_CASE   ,    batch_size=__SCREAMING_SNAKE_CASE   ,    seq_length=__SCREAMING_SNAKE_CASE   ,    is_pair=__SCREAMING_SNAKE_CASE   ,    framework=__SCREAMING_SNAKE_CASE			)
             else:
                    __UpperCAmelCase    							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                        __SCREAMING_SNAKE_CASE   ,    batch_size=__SCREAMING_SNAKE_CASE   ,    seq_length=__SCREAMING_SNAKE_CASE   ,    is_pair=__SCREAMING_SNAKE_CASE   ,    framework=__SCREAMING_SNAKE_CASE			)
  
             return common_inputs
  
  
  
      def _a       (							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       List[str]:
             if self.task in ["default", "seq2seq-lm"]:
                    __UpperCAmelCase    							=super()._flatten_past_key_values_(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
             else:
                    __UpperCAmelCase    							=super(__SCREAMING_SNAKE_CASE   ,    self			)._flatten_past_key_values_(
                        __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Mapping[str, Mapping[int, str]]   ,    __SCREAMING_SNAKE_CASE			:    str			)			->       Optional[Any]:
             if direction not in ["inputs", "outputs"]:
                    raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''			)
  
             __UpperCAmelCase    							="""past_key_values""" if direction == """inputs""" else """present"""
             __UpperCAmelCase							,   __UpperCAmelCase    							=self.num_layers
  
             __UpperCAmelCase    							="""past_encoder_sequence"""
             __UpperCAmelCase    							="""past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""
  
             for i in range(__SCREAMING_SNAKE_CASE			):
                    __UpperCAmelCase    							={0: """batch""", 2: decoder_sequence}
                    __UpperCAmelCase    							={0: """batch""", 2: decoder_sequence}
                    __UpperCAmelCase    							={0: """batch""", 2: encoder_sequence}
                    __UpperCAmelCase    							={0: """batch""", 2: encoder_sequence}
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import math as m
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Tuple     ,			A_:	str     ,			A_:	Union[str, Any]=0.0_0_1		)  ->							List[str]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return (f(a + h		) - f(a - h		)) / (2 * h)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	Union[str, Any]=0     ,			A_:	List[Any]=100     ,			A_:	Optional[Any]=0.0_0_0_1     ,			A_:	Tuple=1e-6     ,			A_:	Union[str, Any]=False		)  ->							Union[str, Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=xa  # set the initial guess
         __UpperCAmelCase    							=[a]
         __UpperCAmelCase    							=abs(f(A_		)		)
         __UpperCAmelCase    							=lambda A_		: calc_derivative(A_     ,			A_     ,			h=A_		)  # noqa: E731  Derivative of f(x)
         for _ in range(A_		):
                if fa(A_		) == 0:
                       raise ValueError("""No converging solution found"""		)
                __UpperCAmelCase    							=a - f(A_		) / fa(A_		)  # Calculate the next estimate
                if logsteps:
                       steps.append(A_		)
                if error < maxerror:
                       break
         else:
                raise ValueError("""Iteration limit reached, no converging solution found"""		)
         if logsteps:
                # If logstep is true, then log intermediate steps
                return a, error, steps
         return a, error
  
  
  if __name__ == "__main__":
     from matplotlib import pyplot as plt
  
     __A  							=  lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
     __A							,    __A							,    __A  							=  newton_raphson(
         f, xa=10, maxiter=10_00, step=1E-6, logsteps=True
     )
     plt.plot([abs(f(x)) for x in steps])
     plt.xlabel("step")
     plt.ylabel("error")
     plt.show()
     print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")
  
  
  
  
  