





'''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


lowerCAmelCase_					:    str      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Optional[int]      	=			{
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='marian'
				__a            =['past_key_values']
				__a            ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




				def __init__(  self     :					List[str]					,				__a     :					List[str]=5_81_01					,				__a     :					str=None					,				__a     :					int=10_24					,				__a     :					Optional[Any]=12					,				__a     :					int=40_96					,				__a     :					List[str]=16					,				__a     :					Optional[int]=12					,				__a     :					str=40_96					,				__a     :					Union[str, Any]=16					,				__a     :					List[str]=0.0					,				__a     :					str=0.0					,				__a     :					Optional[int]=True					,				__a     :					Optional[int]=True					,				__a     :					List[str]="gelu"					,				__a     :					Dict=10_24					,				__a     :					Optional[Any]=0.1					,				__a     :					Union[str, Any]=0.0					,				__a     :					Tuple=0.0					,				__a     :					Any=0.02					,				__a     :					str=5_81_00					,				__a     :					int=False					,				__a     :					int=5_81_00					,				__a     :					int=0					,				__a     :					Dict=0					,				__a     :					List[str]=True					,				**__a     :					Union[str, Any]					,				):
								_a								= vocab_size
								_a								= decoder_vocab_size or vocab_size
								_a								= max_position_embeddings
								_a								= d_model
								_a								= encoder_ffn_dim
								_a								= encoder_layers
								_a								= encoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= activation_function
								_a								= init_std
								_a								= encoder_layerdrop
								_a								= decoder_layerdrop
								_a								= use_cache
								_a								= encoder_layers
								_a								= scale_embedding  # scale factor will be sqrt(d_model) if True
								_a								= share_encoder_decoder_embeddings
								super().__init__(
								    pad_token_id=__a					,				eos_token_id=__a					,				is_encoder_decoder=__a					,				decoder_start_token_id=__a					,				forced_eos_token_id=__a					,				**__a					,				)


class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def   UpperCamelCase__   (  self     :					str					):
								if self.task in ["default", "seq2seq-lm"]:
												_a								= OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]					)

												if self.use_past:
																_a								= {0: "batch"}
																_a								= {0: "batch", 1: "past_decoder_sequence + sequence"}
												else:
																_a								= {0: "batch", 1: "decoder_sequence"}
																_a								= {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.
												_a								= OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]					)
												if self.use_past:
																_a	,					_a								= self.num_layers
																for i in range(__a					):
																				_a								= {0: "batch", 2: "past_sequence + sequence"}
																				_a								= {0: "batch", 2: "past_sequence + sequence"}
								else:
												_a								= 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   UpperCamelCase__   (  self     :					List[str]					):
								if self.task in ["default", "seq2seq-lm"]:
												_a								= super().outputs
								else:
												_a								= super(__a					,				self					).outputs
												if self.use_past:
																_a	,					_a								= self.num_layers
																for i in range(__a					):
																				_a								= {0: "batch", 2: "past_sequence + sequence"}
																				_a								= {0: "batch", 2: "past_sequence + sequence"}
								return common_outputs




				def   UpperCamelCase__   (  self     :					Tuple					,				__a     :					PreTrainedTokenizer					,				__a     :					int = -1					,				__a     :					int = -1					,				__a     :					bool = False					,				__a     :					Optional[TensorType] = None					,				):
								_a								= self._generate_dummy_inputs_for_encoder_and_decoder(
								    __a					,				__a					,				__a					,				__a					,				__a					)

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

												_a								= torch.cat(
												    [common_inputs["decoder_attention_mask"], torch.ones(__a					,				__a					)]					,				dim=1					)

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

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




				def   UpperCamelCase__   (  self     :					str					,				__a     :					PreTrainedTokenizer					,				__a     :					int = -1					,				__a     :					int = -1					,				__a     :					bool = False					,				__a     :					Optional[TensorType] = 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
								_a								= 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
								_a								= tokenizer.num_special_tokens_to_add(__a					)
								_a								= 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
								_a								= [" ".join([tokenizer.unk_token]					) * seq_length] * batch_size
								_a								= dict(tokenizer(__a					,				return_tensors=__a					)					)
								return common_inputs




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					PreTrainedTokenizer					,				__a     :					int = -1					,				__a     :					int = -1					,				__a     :					bool = False					,				__a     :					Optional[TensorType] = None					,				):
								if self.task in ["default", "seq2seq-lm"]:
												_a								= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
												    __a					,				batch_size=__a					,				seq_length=__a					,				is_pair=__a					,				framework=__a					)

								else:
												_a								= self._generate_dummy_inputs_for_causal_lm(
												    __a					,				batch_size=__a					,				seq_length=__a					,				is_pair=__a					,				framework=__a					)

								return common_inputs




				def   UpperCamelCase__   (  self     :					Dict					,				__a     :					Dict					,				__a     :					Optional[int]					,				__a     :					str					,				__a     :					Tuple					):
								if self.task in ["default", "seq2seq-lm"]:
												_a								= super()._flatten_past_key_values_(__a					,				__a					,				__a					,				__a					)
								else:
												_a								= super(__a					,				self					)._flatten_past_key_values_(
												    __a					,				__a					,				__a					,				__a					)




				@property
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								return 1e-4







'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      str						)	->	list:
				if n_term == "":
								return []
				_a								= []
				for temp in range(int(lowercase						)						):
								series.append(F'1/{temp + 1}' if series else "1"						)
				return series


if __name__ == "__main__":
					lowerCAmelCase_					:    Union[str, Any]      	=			input('Enter the last number (nth term) of the Harmonic Series')
					print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
					print(harmonic_series(nth_term))
