






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}












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}}}""")




