




'''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


_SCREAMING_SNAKE_CASE							:       str						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       Optional[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 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Optional[Any]							=   "bart"
			lowerCAmelCase_					:				List[str]							=   ["past_key_values"]
			lowerCAmelCase_					:				List[Any]							=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




			def __init__( self    ,							a__=50_265    ,							a__=1_024    ,							a__=12    ,							a__=4_096    ,							a__=16    ,							a__=12    ,							a__=4_096    ,							a__=16    ,							a__=0.0    ,							a__=0.0    ,							a__="gelu"    ,							a__=1_024    ,							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__    ,							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				vocab_size
										snake_case_      =				max_position_embeddings
										snake_case_      =				d_model
										snake_case_      =				encoder_ffn_dim
										snake_case_      =				encoder_layers
										snake_case_      =				encoder_attention_heads
										snake_case_      =				decoder_ffn_dim
										snake_case_      =				decoder_layers
										snake_case_      =				decoder_attention_heads
										snake_case_      =				dropout
										snake_case_      =				attention_dropout
										snake_case_      =				activation_dropout
										snake_case_      =				activation_function
										snake_case_      =				init_std
										snake_case_      =				encoder_layerdrop
										snake_case_      =				decoder_layerdrop
										snake_case_      =				classifier_dropout
										snake_case_      =				use_cache
										snake_case_      =				encoder_layers
										snake_case_      =				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__							):
																	snake_case_      =				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 			_snake_case		(     lowercase_							):




			@property
			def        lowerCAmelCase__    ( self							)    ->    Mapping[str, Mapping[int, str]]:

										'''simple docstring'''




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

																	if self.use_past:
																								snake_case_      =				{0: "batch"}
																								snake_case_      =				{0: "batch", 1: "past_decoder_sequence + sequence"}
																	else:
																								snake_case_      =				{0: "batch", 1: "decoder_sequence"}
																								snake_case_      =				{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.
																	snake_case_      =				OrderedDict(
																	    [
																	        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																	        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																	    ]							)
																	if self.use_past:
																								snake_case_ ,				snake_case_      =				self.num_layers
																								for i in range(a__							):
																															snake_case_      =				{0: "batch", 2: "past_sequence + sequence"}
																															snake_case_      =				{0: "batch", 2: "past_sequence + sequence"}
										else:
																	snake_case_      =				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        lowerCAmelCase__    ( self							)    ->    Mapping[str, Mapping[int, str]]:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = -1    ,							a__ = -1    ,							a__ = False    ,							a__ = None    ,							)    ->    Mapping[str, Any]:

										'''simple docstring'''




										snake_case_      =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    a__    ,							a__    ,							a__    ,							a__    ,							a__							)

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

																	snake_case_      =				torch.cat(
																	    [common_inputs["decoder_attention_mask"], torch.ones(a__    ,							a__							)]    ,							dim=1							)

																	snake_case_      =				[]
																	# If the number of encoder and decoder layers are present in the model configuration, both are considered
																	snake_case_ ,				snake_case_      =				self.num_layers
																	snake_case_      =				min(a__    ,							a__							)
																	snake_case_      =				max(a__    ,							a__							) - min_num_layers
																	snake_case_      =				"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.
																	snake_case_      =				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        lowerCAmelCase__    ( self    ,							a__    ,							a__ = -1    ,							a__ = -1    ,							a__ = False    ,							a__ = None    ,							)    ->    Mapping[str, Any]:

										'''simple docstring'''




										snake_case_      =				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
																	snake_case_ ,				snake_case_      =				common_inputs["input_ids"].shape
																	# Not using the same length for past_key_values
																	snake_case_      =				seqlen + 2
																	snake_case_ ,				snake_case_      =				self.num_layers
																	snake_case_ ,				snake_case_      =				self.num_attention_heads
																	snake_case_      =				(
																	    batch,
																	    num_encoder_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)

																	snake_case_      =				common_inputs["attention_mask"].dtype
																	snake_case_      =				torch.cat(
																	    [common_inputs["attention_mask"], torch.ones(a__    ,							a__    ,							dtype=a__							)]    ,							dim=1							)
																	snake_case_      =				[
																	    (torch.zeros(a__							), torch.zeros(a__							)) for _ in range(a__							)
																	]
										return common_inputs




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = -1    ,							a__ = -1    ,							a__ = False    ,							a__ = None    ,							)    ->    Mapping[str, Any]:

										'''simple docstring'''




										snake_case_      =				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
										snake_case_      =				tokenizer.num_special_tokens_to_add(a__							)
										snake_case_      =				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
										snake_case_      =				[" ".join([tokenizer.unk_token]							) * seq_length] * batch_size
										snake_case_      =				dict(tokenizer(a__    ,							return_tensors=a__							)							)
										return common_inputs




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = -1    ,							a__ = -1    ,							a__ = False    ,							a__ = None    ,							)    ->    Mapping[str, Any]:

										'''simple docstring'''




										if self.task in ["default", "seq2seq-lm"]:
																	snake_case_      =				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":
																	snake_case_      =				self._generate_dummy_inputs_for_causal_lm(
																	    a__    ,							batch_size=a__    ,							seq_length=a__    ,							is_pair=a__    ,							framework=a__							)
										else:
																	snake_case_      =				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        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__    ,							a__							)    ->    List[Any]:

										'''simple docstring'''




										if self.task in ["default", "seq2seq-lm"]:
																	snake_case_      =				super()._flatten_past_key_values_(a__    ,							a__    ,							a__    ,							a__							)
										else:
																	snake_case_      =				super(a__    ,							self							)._flatten_past_key_values_(
																	    a__    ,							a__    ,							a__    ,							a__							)










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	int = 1_0_0_0					):

							'''simple docstring'''






							snake_case_      =				3
							snake_case_      =				0
							while a < n:
														if a % 3 == 0 or a % 5 == 0:
																					result += a
														elif a % 1_5 == 0:
																					result -= a
														a += 1
							return result


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




