





"""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


UpperCamelCase_									=logging.get_logger(__name__)

UpperCamelCase_									={
    """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						(				_lowerCAmelCase    ):
				UpperCamelCase								=   '''bart'''
				UpperCamelCase								=   ['''past_key_values''']
				UpperCamelCase								=   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






				def __init__(			self		:   List[str],			lowerCAmelCase__		:   Any=5_0_2_6_5,			lowerCAmelCase__		:   Optional[Any]=1_0_2_4,			lowerCAmelCase__		:   List[Any]=1_2,			lowerCAmelCase__		:   Any=4_0_9_6,			lowerCAmelCase__		:   List[str]=1_6,			lowerCAmelCase__		:   List[Any]=1_2,			lowerCAmelCase__		:   Dict=4_0_9_6,			lowerCAmelCase__		:   Optional[Any]=1_6,			lowerCAmelCase__		:   List[Any]=0.0,			lowerCAmelCase__		:   Optional[Any]=0.0,			lowerCAmelCase__		:   str="gelu",			lowerCAmelCase__		:   str=1_0_2_4,			lowerCAmelCase__		:   int=0.1,			lowerCAmelCase__		:   Union[str, Any]=0.0,			lowerCAmelCase__		:   List[str]=0.0,			lowerCAmelCase__		:   Optional[int]=0.02,			lowerCAmelCase__		:   Optional[int]=0.0,			lowerCAmelCase__		:   List[str]=False,			lowerCAmelCase__		:   Optional[Any]=True,			lowerCAmelCase__		:   Union[str, Any]=3,			lowerCAmelCase__		:   Tuple=1,			lowerCAmelCase__		:   Dict=0,			lowerCAmelCase__		:   int=2,			lowerCAmelCase__		:   Any=True,			lowerCAmelCase__		:   Optional[int]=2,			lowerCAmelCase__		:   str=2,			**lowerCAmelCase__		:   Optional[int],			)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   vocab_size
										_UpperCamelCase      : Union[str, Any]         =   max_position_embeddings
										_UpperCamelCase      : List[str]         =   d_model
										_UpperCamelCase      : Any         =   encoder_ffn_dim
										_UpperCamelCase      : Optional[Any]         =   encoder_layers
										_UpperCamelCase      : Dict         =   encoder_attention_heads
										_UpperCamelCase      : Tuple         =   decoder_ffn_dim
										_UpperCamelCase      : List[str]         =   decoder_layers
										_UpperCamelCase      : int         =   decoder_attention_heads
										_UpperCamelCase      : int         =   dropout
										_UpperCamelCase      : Any         =   attention_dropout
										_UpperCamelCase      : Optional[int]         =   activation_dropout
										_UpperCamelCase      : List[str]         =   activation_function
										_UpperCamelCase      : str         =   init_std
										_UpperCamelCase      : Tuple         =   encoder_layerdrop
										_UpperCamelCase      : Any         =   decoder_layerdrop
										_UpperCamelCase      : Optional[Any]         =   classifier_dropout
										_UpperCamelCase      : Dict         =   use_cache
										_UpperCamelCase      : List[Any]         =   encoder_layers
										_UpperCamelCase      : Dict         =   scale_embedding  # scale factor will be sqrt(d_model) if True

										super().__init__(
										    num_labels=_lowerCAmelCase,			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,			)

										# ensure backward compatibility for BART CNN models
										if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''',			_lowerCAmelCase							):
																_UpperCamelCase      : Optional[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						(				_lowerCAmelCase    ):






				@property
				def     snake_case     (			self		:   List[Any]							)     ->				int:




										'''simple docstring'''



										if self.task in ["default", "seq2seq-lm"]:
																_UpperCamelCase      : str         =   OrderedDict(
																    [
																        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																    ]							)

																if self.use_past:
																						_UpperCamelCase      : int         =   {0: '''batch'''}
																						_UpperCamelCase      : str         =   {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																else:
																						_UpperCamelCase      : str         =   {0: '''batch''', 1: '''decoder_sequence'''}
																						_UpperCamelCase      : List[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.
																_UpperCamelCase      : List[Any]         =   OrderedDict(
																    [
																        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																    ]							)
																if self.use_past:
																						_UpperCamelCase       ,							_UpperCamelCase      : List[Any]         =   self.num_layers
																						for i in range(_lowerCAmelCase							):
																												_UpperCamelCase      : Tuple         =   {0: '''batch''', 2: '''past_sequence + sequence'''}
																												_UpperCamelCase      : int         =   {0: '''batch''', 2: '''past_sequence + sequence'''}
										else:
																_UpperCamelCase      : Optional[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     snake_case     (			self		:   Union[str, Any]							)     ->				int:




										'''simple docstring'''



										if self.task in ["default", "seq2seq-lm"]:
																_UpperCamelCase      : Union[str, Any]         =   super().outputs
										else:
																_UpperCamelCase      : List[Any]         =   super(_lowerCAmelCase,			self							).outputs
																if self.use_past:
																						_UpperCamelCase       ,							_UpperCamelCase      : Optional[Any]         =   self.num_layers
																						for i in range(_lowerCAmelCase							):
																												_UpperCamelCase      : Any         =   {0: '''batch''', 2: '''past_sequence + sequence'''}
																												_UpperCamelCase      : Union[str, Any]         =   {0: '''batch''', 2: '''past_sequence + sequence'''}
										return common_outputs






				def     snake_case     (			self		:   Tuple,			lowerCAmelCase__		:   PreTrainedTokenizer,			lowerCAmelCase__		:   int = -1,			lowerCAmelCase__		:   int = -1,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   Optional[TensorType] = None,			)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    _lowerCAmelCase,			_lowerCAmelCase,			_lowerCAmelCase,			_lowerCAmelCase,			_lowerCAmelCase							)

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

																_UpperCamelCase      : Optional[int]         =   torch.cat(
																    [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCAmelCase,			_lowerCAmelCase							)],			dim=1							)

																_UpperCamelCase      : int         =   []
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																_UpperCamelCase       ,							_UpperCamelCase      : Dict         =   self.num_layers
																_UpperCamelCase      : Union[str, Any]         =   min(_lowerCAmelCase,			_lowerCAmelCase							)
																_UpperCamelCase      : Any         =   max(_lowerCAmelCase,			_lowerCAmelCase							) - min_num_layers
																_UpperCamelCase      : List[str]         =   '''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.
																_UpperCamelCase      : 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     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   PreTrainedTokenizer,			lowerCAmelCase__		:   int = -1,			lowerCAmelCase__		:   int = -1,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   Optional[TensorType] = None,			)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   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
																_UpperCamelCase       ,							_UpperCamelCase      : Optional[int]         =   common_inputs['''input_ids'''].shape
																# Not using the same length for past_key_values
																_UpperCamelCase      : Optional[int]         =   seqlen + 2
																_UpperCamelCase       ,							_UpperCamelCase      : List[str]         =   self.num_layers
																_UpperCamelCase       ,							_UpperCamelCase      : List[Any]         =   self.num_attention_heads
																_UpperCamelCase      : List[Any]         =   (
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

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






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   PreTrainedTokenizer,			lowerCAmelCase__		:   int = -1,			lowerCAmelCase__		:   int = -1,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   Optional[TensorType] = None,			)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   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
										_UpperCamelCase      : Tuple         =   tokenizer.num_special_tokens_to_add(_lowerCAmelCase							)
										_UpperCamelCase      : Optional[Any]         =   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
										_UpperCamelCase      : str         =   [''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
										_UpperCamelCase      : int         =   dict(tokenizer(_lowerCAmelCase,			return_tensors=_lowerCAmelCase							)							)
										return common_inputs






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   PreTrainedTokenizer,			lowerCAmelCase__		:   int = -1,			lowerCAmelCase__		:   int = -1,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   Optional[TensorType] = None,			)     ->				List[Any]:




										'''simple docstring'''



										if self.task in ["default", "seq2seq-lm"]:
																_UpperCamelCase      : Dict         =   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":
																_UpperCamelCase      : Optional[Any]         =   self._generate_dummy_inputs_for_causal_lm(
																    _lowerCAmelCase,			batch_size=_lowerCAmelCase,			seq_length=_lowerCAmelCase,			is_pair=_lowerCAmelCase,			framework=_lowerCAmelCase							)
										else:
																_UpperCamelCase      : int         =   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     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   List[Any]							)     ->				Optional[int]:




										'''simple docstring'''



										if self.task in ["default", "seq2seq-lm"]:
																_UpperCamelCase      : Optional[int]         =   super()._flatten_past_key_values_(_lowerCAmelCase,			_lowerCAmelCase,			_lowerCAmelCase,			_lowerCAmelCase							)
										else:
																_UpperCamelCase      : Optional[Any]         =   super(_lowerCAmelCase,			self							)._flatten_past_key_values_(
																    _lowerCAmelCase,			_lowerCAmelCase,			_lowerCAmelCase,			_lowerCAmelCase							)










"""simple docstring"""




def 	a_    (							_lowercase = 100       ):
						_UpperCamelCase      : List[Any]         =   0
						_UpperCamelCase      : Optional[Any]         =   0
						for i in range(1	,						n + 1       ):
												sum_of_squares += i**2
												sum_of_ints += i
						return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
						print(F"{solution() = }")



