"""simple docstring"""




import warnings
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      :		Union[str, Any]           =   logging.get_logger(__name__)

_a      :		Any           =   {
    'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/config.json',
    # See all BART models at https://huggingface.co/models?filter=bart
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Union[str, Any]							=		"bart"
     _UpperCamelCase							:			Tuple							=		["past_key_values"]
     _UpperCamelCase							:			Optional[int]							=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






     def __init__(		self				,     a__=50265				,     a__=1024				,     a__=12				,     a__=4096				,     a__=16				,     a__=12				,     a__=4096				,     a__=16				,     a__=0.0				,     a__=0.0				,     a__="gelu"				,     a__=1024				,     a__=0.1				,     a__=0.0				,     a__=0.0				,     a__=0.0_2				,     a__=0.0				,     a__=False				,     a__=True				,     a__=3				,     a__=1				,     a__=0				,     a__=2				,     a__=True				,     a__=2				,     a__=2				,     **a__				,     ):
       _lowerCAmelCase						:       Optional[int]  			=     vocab_size
       _lowerCAmelCase						:       Union[str, Any]  			=     max_position_embeddings
       _lowerCAmelCase						:       Dict  			=     d_model
       _lowerCAmelCase						:       List[Any]  			=     encoder_ffn_dim
       _lowerCAmelCase						:       Tuple  			=     encoder_layers
       _lowerCAmelCase						:       Any  			=     encoder_attention_heads
       _lowerCAmelCase						:       Dict  			=     decoder_ffn_dim
       _lowerCAmelCase						:       Any  			=     decoder_layers
       _lowerCAmelCase						:       Dict  			=     decoder_attention_heads
       _lowerCAmelCase						:       Union[str, Any]  			=     dropout
       _lowerCAmelCase						:       List[Any]  			=     attention_dropout
       _lowerCAmelCase						:       Union[str, Any]  			=     activation_dropout
       _lowerCAmelCase						:       Optional[int]  			=     activation_function
       _lowerCAmelCase						:       Optional[Any]  			=     init_std
       _lowerCAmelCase						:       int  			=     encoder_layerdrop
       _lowerCAmelCase						:       List[Any]  			=     decoder_layerdrop
       _lowerCAmelCase						:       int  			=     classifier_dropout
       _lowerCAmelCase						:       Optional[int]  			=     use_cache
       _lowerCAmelCase						:       str  			=     encoder_layers
       _lowerCAmelCase						:       List[str]  			=     scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           num_labels=a__				,     pad_token_id=a__				,     bos_token_id=a__				,     eos_token_id=a__				,     is_encoder_decoder=a__				,     decoder_start_token_id=a__				,     forced_eos_token_id=a__				,     **a__				,     )

       # ensure backward compatibility for BART CNN models
       if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""				,     a__  ):
         _lowerCAmelCase						:       Union[str, Any]  			=     self.bos_token_id
         warnings.warn(
             F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
             """The config can simply be saved and uploaded again to be fixed."""  )







class 						__A							( SCREAMING_SNAKE_CASE_		):






     @property
     def     __A  (		self  ):
       if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						:       Dict  			=     OrderedDict(
             [
                 ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                 ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
             ]  )

         if self.use_past:
           _lowerCAmelCase						:       Optional[Any]  			=     {0: """batch"""}
           _lowerCAmelCase						:       Optional[Any]  			=     {0: """batch""", 1: """past_decoder_sequence + sequence"""}
         else:
           _lowerCAmelCase						:       str  			=     {0: """batch""", 1: """decoder_sequence"""}
           _lowerCAmelCase						:       Dict  			=     {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.
         _lowerCAmelCase						:       str  			=     OrderedDict(
             [
                 ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                 ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
             ]  )
         if self.use_past:
           _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[Any]  			=     self.num_layers
           for i in range(a__  ):
             _lowerCAmelCase						:       int  			=     {0: """batch""", 2: """past_sequence + sequence"""}
             _lowerCAmelCase						:       int  			=     {0: """batch""", 2: """past_sequence + sequence"""}
       else:
         _lowerCAmelCase						:       int  			=     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     __A  (		self  ):
       if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						:       List[Any]  			=     super().outputs
       else:
         _lowerCAmelCase						:       List[str]  			=     super(a__				,     self  ).outputs
         if self.use_past:
           _lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     self.num_layers
           for i in range(a__  ):
             _lowerCAmelCase						:       str  			=     {0: """batch""", 2: """past_sequence + sequence"""}
             _lowerCAmelCase						:       Optional[int]  			=     {0: """batch""", 2: """past_sequence + sequence"""}
       return common_outputs






     def     __A  (		self				,     a__				,     a__ = -1				,     a__ = -1				,     a__ = False				,     a__ = None				,     ):
       _lowerCAmelCase						:       Optional[int]  			=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           a__				,     a__				,     a__				,     a__				,     a__  )

       # Generate decoder inputs
       _lowerCAmelCase						:       List[Any]  			=     seq_length if not self.use_past else 1
       _lowerCAmelCase						:       Any  			=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           a__				,     a__				,     a__				,     a__				,     a__  )
       _lowerCAmelCase						:       List[str]  			=     {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
       _lowerCAmelCase						:       Optional[int]  			=     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
         _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     common_inputs["""input_ids"""].shape
         _lowerCAmelCase						:       Tuple  			=     common_inputs["""decoder_input_ids"""].shape[1]
         _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     self.num_attention_heads
         _lowerCAmelCase						:       List[Any]  			=     (
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         _lowerCAmelCase						:       List[Any]  			=     decoder_seq_length + 3
         _lowerCAmelCase						:       Tuple  			=     (
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )

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

         _lowerCAmelCase						:       str  			=     []
         # If the number of encoder and decoder layers are present in the model configuration, both are considered
         _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     self.num_layers
         _lowerCAmelCase						:       Dict  			=     min(a__				,     a__  )
         _lowerCAmelCase						:       List[Any]  			=     max(a__				,     a__  ) - min_num_layers
         _lowerCAmelCase						:       Union[str, Any]  			=     """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.
         _lowerCAmelCase						:       List[str]  			=     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     __A  (		self				,     a__				,     a__ = -1				,     a__ = -1				,     a__ = False				,     a__ = None				,     ):
       _lowerCAmelCase						:       Any  			=     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
         _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[Any]  			=     common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         _lowerCAmelCase						:       Optional[int]  			=     seqlen + 2
         _lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     self.num_layers
         _lowerCAmelCase			,	_lowerCAmelCase						:       int  			=     self.num_attention_heads
         _lowerCAmelCase						:       Tuple  			=     (
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )

         _lowerCAmelCase						:       Optional[Any]  			=     common_inputs["""attention_mask"""].dtype
         _lowerCAmelCase						:       Any  			=     torch.cat(
             [common_inputs["""attention_mask"""], torch.ones(a__				,     a__				,     dtype=a__  )]				,     dim=1  )
         _lowerCAmelCase						:       List[Any]  			=     [
             (torch.zeros(a__  ), torch.zeros(a__  )) for _ in range(a__  )
         ]
       return common_inputs






     def     __A  (		self				,     a__				,     a__ = -1				,     a__ = -1				,     a__ = False				,     a__ = None				,     ):
       # 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
       _lowerCAmelCase						:       Tuple  			=     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
       _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer.num_special_tokens_to_add(a__  )
       _lowerCAmelCase						:       Any  			=     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
       _lowerCAmelCase						:       int  			=     [""" """.join([tokenizer.unk_token]  ) * seq_length] * batch_size
       _lowerCAmelCase						:       str  			=     dict(tokenizer(a__				,     return_tensors=a__  )  )
       return common_inputs






     def     __A  (		self				,     a__				,     a__ = -1				,     a__ = -1				,     a__ = False				,     a__ = None				,     ):
       if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						:       Optional[Any]  			=     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":
         _lowerCAmelCase						:       str  			=     self._generate_dummy_inputs_for_causal_lm(
             a__				,     batch_size=a__				,     seq_length=a__				,     is_pair=a__				,     framework=a__  )
       else:
         _lowerCAmelCase						:       Union[str, Any]  			=     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     __A  (		self				,     a__				,     a__				,     a__				,     a__  ):
       if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						:       List[str]  			=     super()._flatten_past_key_values_(a__				,     a__				,     a__				,     a__  )
       else:
         _lowerCAmelCase						:       Tuple  			=     super(a__				,     self  )._flatten_past_key_values_(
             a__				,     a__				,     a__				,     a__  )












from __future__ import annotations

from math import pi






def 				lowerCamelCase__      (    __lowerCamelCase			:     float  ,		__lowerCamelCase			:     float  ,		__lowerCamelCase			:     float		):

  if (inductance, frequency, reactance).count(0		) != 1:
    raise ValueError("""One and only one argument must be 0"""		)
  if inductance < 0:
    raise ValueError("""Inductance cannot be negative"""		)
  if frequency < 0:
    raise ValueError("""Frequency cannot be negative"""		)
  if reactance < 0:
    raise ValueError("""Inductive reactance cannot be negative"""		)
  if inductance == 0:
    return {"inductance": reactance / (2 * pi * frequency)}
  elif frequency == 0:
    return {"frequency": reactance / (2 * pi * inductance)}
  elif reactance == 0:
    return {"reactance": 2 * pi * frequency * inductance}
  else:
    raise ValueError("""Exactly one argument must be 0"""		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


