






import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						return np.dot(_A,   _A			)





class 	snake_case__ :
	def __init__(    self      ,       *,
	    lowerCAmelCase__ = np.inf      ,       lowerCAmelCase__ = "linear"      ,       lowerCAmelCase__ = 0.0      ,       )							->				None:
							__magic_name__      :       Optional[int]       						= regularization
							__magic_name__      :       Dict       						= gamma
							if kernel == "linear":
													__magic_name__      :       str       						= self.__linear
							elif kernel == "rbf":
													if self.gamma == 0:
																			raise ValueError("""rbf kernel requires gamma"""			)
													if not isinstance(self.gamma      ,       (float, int)			):
																			raise ValueError("""gamma must be float or int"""			)
													if not self.gamma > 0:
																			raise ValueError("""gamma must be > 0"""			)
													__magic_name__      :       Optional[int]       						= self.__rbf
													# in the future, there could be a default value like in sklearn
													# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
													# previously it was 1/(n_features)
							else:
													__magic_name__      :       Tuple       						= F'Unknown kernel: {kernel}'
													raise ValueError(lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				float:
							return np.dot(lowerCAmelCase__      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				float:
							return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				None:
							__magic_name__      :       Union[str, Any]       						= observations
							__magic_name__      :       Tuple       						= classes

							# using Wolfe's Dual to calculate w.
							# Primal problem: minimize 1/2*norm_squared(w)
							#   constraint: yn(w . xn + b) >= 1
							#
							# With l a vector
							# Dual problem: maximize sum_n(ln) -
							#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
							#   constraint: self.C >= ln >= 0
							#           and sum_n(ln*yn) = 0
							# Then we get w using w = sum_n(ln*yn*xn)
							# At the end we can get b ~= mean(yn - w . xn)
							#
							# Since we use kernels, we only need l_star to calculate b
							# and to classify observations

							(__magic_name__)      :       int       						= np.shape(lowerCAmelCase__			)

							def to_minimize(lowerCAmelCase__			) -> float:
													__magic_name__      :       List[Any]       						= 0
													(__magic_name__)      :       int       						= np.shape(lowerCAmelCase__			)
													for i in range(lowerCAmelCase__			):
																			for j in range(lowerCAmelCase__			):
																									s += (
																									    candidate[i]
																									    * candidate[j]
																									    * classes[i]
																									    * classes[j]
																									    * self.kernel(observations[i]      ,       observations[j]			)
																									)
													return 1 / 2 * s - sum(lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= LinearConstraint(lowerCAmelCase__      ,       0      ,       0			)
							__magic_name__      :       Optional[Any]       						= Bounds(0      ,       self.regularization			)

							__magic_name__      :       Tuple       						= minimize(
							    lowerCAmelCase__      ,       np.ones(lowerCAmelCase__			)      ,       bounds=lowerCAmelCase__      ,       constraints=[ly_contraint]			).x
							__magic_name__      :       Union[str, Any]       						= l_star

							# calculating mean offset of separation plane to points
							__magic_name__      :       Tuple       						= 0
							for i in range(lowerCAmelCase__			):
													for j in range(lowerCAmelCase__			):
																			s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
																			    observations[i]      ,       observations[j]			)
							__magic_name__      :       Dict       						= s / n







	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				int:
							__magic_name__      :       Optional[int]       						= sum(
							    self.optimum[n]
							    * self.classes[n]
							    * self.kernel(self.observations[n]      ,       lowerCAmelCase__			)
							    for n in range(len(self.classes			)			)			)
							return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()













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


__magic_name__:					Any							=     logging.get_logger(__name__)

__magic_name__:					Dict							=     {
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					Any											=   '''blenderbot-small'''
	lowercase__						:					Optional[int]											=   ['''past_key_values''']
	lowercase__						:					Dict											=   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
	def __init__(    self      ,       lowerCAmelCase__=5_02_65      ,       lowerCAmelCase__=5_12      ,       lowerCAmelCase__=8      ,       lowerCAmelCase__=20_48      ,       lowerCAmelCase__=16      ,       lowerCAmelCase__=8      ,       lowerCAmelCase__=20_48      ,       lowerCAmelCase__=16      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__="gelu"      ,       lowerCAmelCase__=5_12      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0_2      ,       lowerCAmelCase__=1      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__=0      ,       lowerCAmelCase__=1      ,       lowerCAmelCase__=2      ,       lowerCAmelCase__=2      ,       **lowerCAmelCase__      ,       )							->				Tuple:
							__magic_name__      :       Tuple       						= vocab_size
							__magic_name__      :       List[str]       						= max_position_embeddings
							__magic_name__      :       Union[str, Any]       						= d_model
							__magic_name__      :       Optional[int]       						= encoder_ffn_dim
							__magic_name__      :       Union[str, Any]       						= encoder_layers
							__magic_name__      :       List[str]       						= encoder_attention_heads
							__magic_name__      :       List[Any]       						= decoder_ffn_dim
							__magic_name__      :       str       						= decoder_layers
							__magic_name__      :       List[str]       						= decoder_attention_heads
							__magic_name__      :       Union[str, Any]       						= dropout
							__magic_name__      :       Tuple       						= attention_dropout
							__magic_name__      :       List[Any]       						= activation_dropout
							__magic_name__      :       List[Any]       						= activation_function
							__magic_name__      :       Optional[int]       						= init_std
							__magic_name__      :       Dict       						= encoder_layerdrop
							__magic_name__      :       Union[str, Any]       						= decoder_layerdrop
							__magic_name__      :       Optional[int]       						= use_cache
							__magic_name__      :       List[Any]       						= encoder_layers
							__magic_name__      :       Dict       						= 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__      ,       decoder_start_token_id=lowerCAmelCase__      ,       forced_eos_token_id=lowerCAmelCase__      ,       **lowerCAmelCase__      ,       )





class 	snake_case__ (							_lowerCAmelCase				):
	@property
	def       __magic_name__    (    self			)							->				Mapping[str, Mapping[int, str]]:
							if self.task in ["default", "seq2seq-lm"]:
													__magic_name__      :       Optional[int]       						= OrderedDict(
													    [
													        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
													        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
													    ]			)

													if self.use_past:
																			__magic_name__      :       List[Any]       						= {0: """batch"""}
																			__magic_name__      :       Dict       						= {0: """batch""", 1: """past_decoder_sequence + sequence"""}
													else:
																			__magic_name__      :       List[str]       						= {0: """batch""", 1: """decoder_sequence"""}
																			__magic_name__      :       Any       						= {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.
													__magic_name__      :       Union[str, Any]       						= OrderedDict(
													    [
													        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
													        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
													    ]			)
													if self.use_past:
																			__magic_name__						,__magic_name__      :       Dict       						= self.num_layers
																			for i in range(lowerCAmelCase__			):
																									__magic_name__      :       Dict       						= {0: """batch""", 2: """past_sequence + sequence"""}
																									__magic_name__      :       int       						= {0: """batch""", 2: """past_sequence + sequence"""}
							else:
													__magic_name__      :       Union[str, Any]       						= 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       __magic_name__    (    self			)							->				Mapping[str, Mapping[int, str]]:
							if self.task in ["default", "seq2seq-lm"]:
													__magic_name__      :       Any       						= super().outputs
							else:
													__magic_name__      :       int       						= super(lowerCAmelCase__      ,       self			).outputs
													if self.use_past:
																			__magic_name__						,__magic_name__      :       str       						= self.num_layers
																			for i in range(lowerCAmelCase__			):
																									__magic_name__      :       Tuple       						= {0: """batch""", 2: """past_sequence + sequence"""}
																									__magic_name__      :       Dict       						= {0: """batch""", 2: """past_sequence + sequence"""}
							return common_outputs
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = None      ,       )							->				Mapping[str, Any]:
							__magic_name__      :       Tuple       						= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)

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

													__magic_name__      :       List[str]       						= torch.cat(
													    [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCAmelCase__      ,       lowerCAmelCase__			)]      ,       dim=1			)

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

													__magic_name__      :       Optional[int]       						= common_inputs["""attention_mask"""].dtype
													__magic_name__      :       Optional[Any]       						= torch.cat(
													    [common_inputs["""attention_mask"""], torch.ones(lowerCAmelCase__      ,       lowerCAmelCase__      ,       dtype=lowerCAmelCase__			)]      ,       dim=1			)
													__magic_name__      :       Tuple       						= [
													    (torch.zeros(lowerCAmelCase__			), torch.zeros(lowerCAmelCase__			)) for _ in range(lowerCAmelCase__			)
													]
							return common_inputs
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = 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
							__magic_name__      :       Tuple       						= 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
							__magic_name__      :       str       						= tokenizer.num_special_tokens_to_add(lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= 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
							__magic_name__      :       List[Any]       						= [""" """.join([tokenizer.unk_token]			) * seq_length] * batch_size
							__magic_name__      :       List[str]       						= dict(tokenizer(lowerCAmelCase__      ,       return_tensors=lowerCAmelCase__			)			)
							return common_inputs
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = None      ,       )							->				Mapping[str, Any]:
							if self.task in ["default", "seq2seq-lm"]:
													__magic_name__      :       Tuple       						= 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":
													__magic_name__      :       str       						= self._generate_dummy_inputs_for_causal_lm(
													    lowerCAmelCase__      ,       batch_size=lowerCAmelCase__      ,       seq_length=lowerCAmelCase__      ,       is_pair=lowerCAmelCase__      ,       framework=lowerCAmelCase__			)
							else:
													__magic_name__      :       Dict       						= 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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Dict:
							if self.task in ["default", "seq2seq-lm"]:
													__magic_name__      :       List[Any]       						= super()._flatten_past_key_values_(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)
							else:
													__magic_name__      :       Tuple       						= super(lowerCAmelCase__      ,       self			)._flatten_past_key_values_(
													    lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)
