"""simple docstring"""







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


UpperCamelCase_							:							Any				 =		logging.get_logger(__name__)

UpperCamelCase_							:							Any				 =		{
    """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       _lowercase		( __lowerCamelCase	):
				lowerCamelCase__							=							'blenderbot'
				lowerCamelCase__							=							['past_key_values']
				lowerCamelCase__							=							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

				def __init__(  self							:     Tuple							,     _lowercase							:     int=8_0_0_8							,     _lowercase							:     Tuple=1_2_8							,     _lowercase							:     str=2							,     _lowercase							:     int=1_0_2_4_0							,     _lowercase							:     str=3_2							,     _lowercase							:     List[Any]=2_4							,     _lowercase							:     str=1_0_2_4_0							,     _lowercase							:     Union[str, Any]=3_2							,     _lowercase							:     List[str]=0.0							,     _lowercase							:     Any=0.0							,     _lowercase							:     Union[str, Any]=True							,     _lowercase							:     Optional[Any]=True							,     _lowercase							:     Optional[Any]="gelu"							,     _lowercase							:     int=2_5_6_0							,     _lowercase							:     List[str]=0.1							,     _lowercase							:     Tuple=0.0							,     _lowercase							:     Dict=0.0							,     _lowercase							:     Optional[int]=0.02							,     _lowercase							:     Tuple=1							,     _lowercase							:     Tuple=False							,     _lowercase							:     List[Any]=0							,     _lowercase							:     Dict=1							,     _lowercase							:     int=2							,     _lowercase							:     int=3							,     _lowercase							:     Optional[int]=2							,     **_lowercase							:     int							,     ):


										'''simple docstring'''
										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=_lowercase							,     bos_token_id=_lowercase							,     eos_token_id=_lowercase							,     is_encoder_decoder=_lowercase							,     decoder_start_token_id=_lowercase							,     encoder_no_repeat_ngram_size=_lowercase							,     forced_eos_token_id=_lowercase							,     **_lowercase							,     )


class       _lowercase		( __lowerCamelCase	):

				@property
				def 							A			(  self							:     Any  ):


										'''simple docstring'''
										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_(_lowercase							,     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(_lowercase  ):
																												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							:     str  ):


										'''simple docstring'''
										if self.task in ["default", "seq2seq-lm"]:
																UpperCAmelCase__       						=		super().outputs
										else:
																UpperCAmelCase__       						=		super(_lowercase							,     self  ).outputs
																if self.use_past:
																						UpperCAmelCase__						,   UpperCAmelCase__       						=		self.num_layers
																						for i in range(_lowercase  ):
																												UpperCAmelCase__       						=		{0: """batch""", 2: """past_sequence + sequence"""}
																												UpperCAmelCase__       						=		{0: """batch""", 2: """past_sequence + sequence"""}
										return common_outputs

				def 							A			(  self							:     Dict							,     _lowercase							:     PreTrainedTokenizer							,     _lowercase							:     int = -1							,     _lowercase							:     int = -1							,     _lowercase							:     bool = False							,     _lowercase							:     Optional[TensorType] = None							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    _lowercase							,     _lowercase							,     _lowercase							,     _lowercase							,     _lowercase  )
										# Generate decoder inputs
										UpperCAmelCase__       						=		seq_length if not self.use_past else 1
										UpperCAmelCase__       						=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    _lowercase							,     _lowercase							,     _lowercase							,     _lowercase							,     _lowercase  )
										UpperCAmelCase__       						=		{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
										UpperCAmelCase__       						=		dict(**_lowercase							,     **_lowercase  )

										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(_lowercase							,     _lowercase  )]							,     dim=1  )
																UpperCAmelCase__       						=		[]
																UpperCAmelCase__						,   UpperCAmelCase__       						=		self.num_layers

																for _ in range(_lowercase  ):
																						common_inputs["past_key_values"].append(
																						    (
																						        torch.zeros(_lowercase  ),
																						        torch.zeros(_lowercase  ),
																						        torch.zeros(_lowercase  ),
																						        torch.zeros(_lowercase  ),
																						    )  )
										return common_inputs

				def 							A			(  self							:     List[str]							,     _lowercase							:     PreTrainedTokenizer							,     _lowercase							:     int = -1							,     _lowercase							:     int = -1							,     _lowercase							:     bool = False							,     _lowercase							:     Optional[TensorType] = None							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    _lowercase							,     _lowercase							,     _lowercase							,     _lowercase							,     _lowercase  )

										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(_lowercase							,     _lowercase							,     dtype=_lowercase  )]							,     dim=1  )
																UpperCAmelCase__       						=		[
																    (torch.zeros(_lowercase  ), torch.zeros(_lowercase  )) for _ in range(_lowercase  )
																]
										return common_inputs

				def 							A			(  self							:     List[str]							,     _lowercase							:     PreTrainedTokenizer							,     _lowercase							:     int = -1							,     _lowercase							:     int = -1							,     _lowercase							:     bool = False							,     _lowercase							:     Optional[TensorType] = None							,     ):


										'''simple docstring'''
										# 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(
										    _lowercase							,     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(_lowercase  )
										UpperCAmelCase__       						=		compute_effective_axis_dimension(
										    _lowercase							,     fixed_dimension=OnnxConfig.default_fixed_sequence							,     num_token_to_add=_lowercase  )

										# Generate dummy inputs according to compute batch and sequence
										UpperCAmelCase__       						=		[""" """.join([tokenizer.unk_token]  ) * seq_length] * batch_size
										UpperCAmelCase__       						=		dict(tokenizer(_lowercase							,     return_tensors=_lowercase  )  )
										return common_inputs

				def 							A			(  self							:     Union[str, Any]							,     _lowercase							:     PreTrainedTokenizer							,     _lowercase							:     int = -1							,     _lowercase							:     int = -1							,     _lowercase							:     bool = False							,     _lowercase							:     Optional[TensorType] = None							,     ):


										'''simple docstring'''
										if self.task in ["default", "seq2seq-lm"]:
																UpperCAmelCase__       						=		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    _lowercase							,     batch_size=_lowercase							,     seq_length=_lowercase							,     is_pair=_lowercase							,     framework=_lowercase  )

										elif self.task == "causal-lm":
																UpperCAmelCase__       						=		self._generate_dummy_inputs_for_causal_lm(
																    _lowercase							,     batch_size=_lowercase							,     seq_length=_lowercase							,     is_pair=_lowercase							,     framework=_lowercase  )
										else:
																UpperCAmelCase__       						=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																    _lowercase							,     batch_size=_lowercase							,     seq_length=_lowercase							,     is_pair=_lowercase							,     framework=_lowercase  )

										return common_inputs

				def 							A			(  self							:     List[str]							,     _lowercase							:     Dict							,     _lowercase							:     List[Any]							,     _lowercase							:     Union[str, Any]							,     _lowercase							:     Dict  ):


										'''simple docstring'''
										if self.task in ["default", "seq2seq-lm"]:
																UpperCAmelCase__       						=		super()._flatten_past_key_values_(_lowercase							,     _lowercase							,     _lowercase							,     _lowercase  )
										else:
																UpperCAmelCase__       						=		super(_lowercase							,     self  )._flatten_past_key_values_(
																    _lowercase							,     _lowercase							,     _lowercase							,     _lowercase  )

				def 							A			(  self							:     Any							,     _lowercase							:     Mapping[str, Mapping[int, str]]							,     _lowercase							:     str  ):


										'''simple docstring'''
										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(_lowercase  ):
																UpperCAmelCase__       						=		{0: """batch""", 2: decoder_sequence}
																UpperCAmelCase__       						=		{0: """batch""", 2: decoder_sequence}
																UpperCAmelCase__       						=		{0: """batch""", 2: encoder_sequence}
																UpperCAmelCase__       						=		{0: """batch""", 2: encoder_sequence}


"""simple docstring"""







def 	a__   (		UpperCAmelCase__							:      list[int]      )->						int:
						UpperCAmelCase__       						=		len(UpperCAmelCase__      )
						if n in (0, 1):
												return 0
						return sum(query * (n - i - 1) for i, query in enumerate(sorted(UpperCAmelCase__      )      )      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

