
'''simple docstring'''


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


__lowercase							:				Tuple					=       logging.get_logger(__name__)

__lowercase							:				Any					=       {
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}

class 		__lowercase			(					_lowercase      ):
			lowerCamelCase     :						Dict		 =     "mbart"
			lowerCamelCase     :						List[Any]		 =     ["past_key_values"]
			lowerCamelCase     :						Union[str, Any]		 =     {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

			def __init__(self     ,				A=5_0_2_6_5     ,				A=1_0_2_4     ,				A=1_2     ,				A=4_0_9_6     ,				A=1_6     ,				A=1_2     ,				A=4_0_9_6     ,				A=1_6     ,				A=0.0     ,				A=0.0     ,				A=True     ,				A=True     ,				A="gelu"     ,				A=1_0_2_4     ,				A=0.1     ,				A=0.0     ,				A=0.0     ,				A=0.02     ,				A=0.0     ,				A=False     ,				A=1     ,				A=0     ,				A=2     ,				A=2     ,				**A     ,				):
								lowerCamelCase_   : Optional[Any]       =						vocab_size
								lowerCamelCase_   : Dict       =						max_position_embeddings
								lowerCamelCase_   : List[str]       =						d_model
								lowerCamelCase_   : str       =						encoder_ffn_dim
								lowerCamelCase_   : Dict       =						encoder_layers
								lowerCamelCase_   : Union[str, Any]       =						encoder_attention_heads
								lowerCamelCase_   : Optional[Any]       =						decoder_ffn_dim
								lowerCamelCase_   : str       =						decoder_layers
								lowerCamelCase_   : Union[str, Any]       =						decoder_attention_heads
								lowerCamelCase_   : List[str]       =						dropout
								lowerCamelCase_   : Tuple       =						attention_dropout
								lowerCamelCase_   : str       =						activation_dropout
								lowerCamelCase_   : List[str]       =						activation_function
								lowerCamelCase_   : Union[str, Any]       =						init_std
								lowerCamelCase_   : str       =						encoder_layerdrop
								lowerCamelCase_   : List[Any]       =						decoder_layerdrop
								lowerCamelCase_   : Any       =						classifier_dropout
								lowerCamelCase_   : List[str]       =						use_cache
								lowerCamelCase_   : Any       =						encoder_layers
								lowerCamelCase_   : str       =						scale_embedding  # scale factor will be sqrt(d_model) if True
								super().__init__(
								    pad_token_id=A     ,				bos_token_id=A     ,				eos_token_id=A     ,				is_encoder_decoder=A     ,				forced_eos_token_id=A     ,				**A     ,				)

class 		__lowercase			(					_lowercase      ):

			@property
			def     UpperCAmelCase__	(self ):
								if self.task in ["default", "seq2seq-lm"]:
													lowerCamelCase_   : List[Any]       =						OrderedDict(
													    [
													        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
													        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
													    ] )

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

			def     UpperCAmelCase__	(self     ,				A     ,				A = -1     ,				A = -1     ,				A = False     ,				A = None     ,				):
								lowerCamelCase_   : List[str]       =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    A     ,				A     ,				A     ,				A     ,				A )

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

													lowerCamelCase_   : Optional[Any]       =						torch.cat(
													    [common_inputs['''decoder_attention_mask'''], torch.ones(A     ,				A )]     ,				dim=1 )

													lowerCamelCase_   : Dict       =						[]
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													lowerCamelCase_, lowerCamelCase_   : Any       =						self.num_layers
													lowerCamelCase_   : List[str]       =						min(A     ,				A )
													lowerCamelCase_   : Tuple       =						max(A     ,				A ) - min_num_layers
													lowerCamelCase_   : Tuple       =						'''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     UpperCAmelCase__	(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_   : Tuple       =						common_inputs['''input_ids'''].shape
													# Not using the same length for past_key_values
													lowerCamelCase_   : Any       =						seqlen + 2
													lowerCamelCase_, lowerCamelCase_   : int       =						self.num_layers
													lowerCamelCase_, lowerCamelCase_   : Tuple       =						self.num_attention_heads
													lowerCamelCase_   : int       =						(
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

													lowerCamelCase_   : Optional[int]       =						common_inputs['''attention_mask'''].dtype
													lowerCamelCase_   : Dict       =						torch.cat(
													    [common_inputs['''attention_mask'''], torch.ones(A     ,				A     ,				dtype=A )]     ,				dim=1 )
													lowerCamelCase_   : int       =						[
													    (torch.zeros(A ), torch.zeros(A )) for _ in range(A )
													]
								return common_inputs

			def     UpperCAmelCase__	(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_   : List[Any]       =						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_   : str       =						tokenizer.num_special_tokens_to_add(A )
								lowerCamelCase_   : Tuple       =						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_   : Optional[int]       =						[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
								lowerCamelCase_   : Optional[Any]       =						dict(tokenizer(A     ,				return_tensors=A ) )
								return common_inputs

			def     UpperCAmelCase__	(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_   : Union[str, Any]       =						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     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A ):
								if self.task in ["default", "seq2seq-lm"]:
													lowerCamelCase_   : List[Any]       =						super()._flatten_past_key_values_(A     ,				A     ,				A     ,				A )
								else:
													lowerCamelCase_   : Tuple       =						super(A     ,				self )._flatten_past_key_values_(
													    A     ,				A     ,				A     ,				A )




'''simple docstring'''


def lowercase_		(				_lowercase			)       ->							int:
					'''simple docstring'''

					if not isinstance(_lowercase     ,    _lowercase			):
										raise TypeError('''recursive_lucas_number accepts only integer arguments.'''			)
					if n_th_number == 0:
										return 2
					if n_th_number == 1:
										return 1

					return recursive_lucas_number(n_th_number - 1			) + recursive_lucas_number(
					    n_th_number - 2			)




def lowercase_		(				_lowercase			)       ->							int:
					'''simple docstring'''

					if not isinstance(_lowercase     ,    _lowercase			):
										raise TypeError('''dynamic_lucas_number accepts only integer arguments.'''			)
					lowerCamelCase_, lowerCamelCase_   : List[Any]       =						2, 1
					for _ in range(_lowercase			):
										lowerCamelCase_, lowerCamelCase_   : Dict       =						b, a + b
					return a


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					__lowercase							:				Any					=       int(input('''Enter the number of terms in lucas series:\n''').strip())
					print('''Using recursive function to calculate lucas series:''')
					print(''' '''.join(str(recursive_lucas_number(i)) for i in range(n)))
					print('''\nUsing dynamic function to calculate lucas series:''')
					print(''' '''.join(str(dynamic_lucas_number(i)) for i in range(n)))


