






'''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


_A     :      Optional[Any]   					=logging.get_logger(__name__)

_A     :      List[str]   					={
    '''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 _lowercase     (    _lowercase		):
							a    				=	"""marian"""
							a    				=	["""past_key_values"""]
							a    				=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




							def __init__(							self:    Tuple      ,							UpperCamelCase__:    Optional[Any]=58_101      ,							UpperCamelCase__:    Optional[int]=None      ,							UpperCamelCase__:    Union[str, Any]=1_024      ,							UpperCamelCase__:    Any=12      ,							UpperCamelCase__:    Optional[int]=4_096      ,							UpperCamelCase__:    Tuple=16      ,							UpperCamelCase__:    Dict=12      ,							UpperCamelCase__:    Optional[Any]=4_096      ,							UpperCamelCase__:    Any=16      ,							UpperCamelCase__:    List[str]=0.0      ,							UpperCamelCase__:    Tuple=0.0      ,							UpperCamelCase__:    str=True      ,							UpperCamelCase__:    Optional[int]=True      ,							UpperCamelCase__:    Optional[int]="gelu"      ,							UpperCamelCase__:    Union[str, Any]=1_024      ,							UpperCamelCase__:    Optional[int]=0.1      ,							UpperCamelCase__:    Optional[Any]=0.0      ,							UpperCamelCase__:    Optional[Any]=0.0      ,							UpperCamelCase__:    Optional[int]=0.02      ,							UpperCamelCase__:    str=58_100      ,							UpperCamelCase__:    Tuple=False      ,							UpperCamelCase__:    Optional[Any]=58_100      ,							UpperCamelCase__:    int=0      ,							UpperCamelCase__:    Union[str, Any]=0      ,							UpperCamelCase__:    List[str]=True      ,							**UpperCamelCase__:    str      ,							):
														lowerCamelCase__ :     int	  =	vocab_size
														lowerCamelCase__ :     Tuple	  =	decoder_vocab_size or vocab_size
														lowerCamelCase__ :     List[str]	  =	max_position_embeddings
														lowerCamelCase__ :     Optional[Any]	  =	d_model
														lowerCamelCase__ :     int	  =	encoder_ffn_dim
														lowerCamelCase__ :     Union[str, Any]	  =	encoder_layers
														lowerCamelCase__ :     Dict	  =	encoder_attention_heads
														lowerCamelCase__ :     Optional[int]	  =	decoder_ffn_dim
														lowerCamelCase__ :     List[str]	  =	decoder_layers
														lowerCamelCase__ :     Dict	  =	decoder_attention_heads
														lowerCamelCase__ :     int	  =	dropout
														lowerCamelCase__ :     str	  =	attention_dropout
														lowerCamelCase__ :     Dict	  =	activation_dropout
														lowerCamelCase__ :     List[str]	  =	activation_function
														lowerCamelCase__ :     Union[str, Any]	  =	init_std
														lowerCamelCase__ :     str	  =	encoder_layerdrop
														lowerCamelCase__ :     Any	  =	decoder_layerdrop
														lowerCamelCase__ :     List[str]	  =	use_cache
														lowerCamelCase__ :     List[str]	  =	encoder_layers
														lowerCamelCase__ :     int	  =	scale_embedding  # scale factor will be sqrt(d_model) if True
														lowerCamelCase__ :     str	  =	share_encoder_decoder_embeddings
														super().__init__(
														    pad_token_id=UpperCamelCase__      ,							eos_token_id=UpperCamelCase__      ,							is_encoder_decoder=UpperCamelCase__      ,							decoder_start_token_id=UpperCamelCase__      ,							forced_eos_token_id=UpperCamelCase__      ,							**UpperCamelCase__      ,							)



class _lowercase     (    _lowercase		):




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

																					if self.use_past:
																												lowerCamelCase__ :     Dict	  =	{0: """batch"""}
																												lowerCamelCase__ :     Union[str, Any]	  =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
																					else:
																												lowerCamelCase__ :     Any	  =	{0: """batch""", 1: """decoder_sequence"""}
																												lowerCamelCase__ :     Dict	  =	{0: """batch""", 1: """decoder_sequence"""}

																					if self.use_past:
																												self.fill_with_past_key_values_(UpperCamelCase__      ,							direction="""inputs"""					)
														elif self.task == "causal-lm":
																					# TODO: figure this case out.
																					lowerCamelCase__ :     Union[str, Any]	  =	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(UpperCamelCase__					):
																																			lowerCamelCase__ :     Union[str, Any]	  =	{0: """batch""", 2: """past_sequence + sequence"""}
																																			lowerCamelCase__ :     List[str]	  =	{0: """batch""", 2: """past_sequence + sequence"""}
														else:
																					lowerCamelCase__ :     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
							# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														if self.task in ["default", "seq2seq-lm"]:
																					lowerCamelCase__ :     Dict	  =	super().outputs
														else:
																					lowerCamelCase__ :     Any	  =	super(UpperCamelCase__      ,							self					).outputs
																					if self.use_past:
																												lowerCamelCase__  ,		lowerCamelCase__ :     str	  =	self.num_layers
																												for i in range(UpperCamelCase__					):
																																			lowerCamelCase__ :     Tuple	  =	{0: """batch""", 2: """past_sequence + sequence"""}
																																			lowerCamelCase__ :     Union[str, Any]	  =	{0: """batch""", 2: """past_sequence + sequence"""}
														return common_outputs




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    PreTrainedTokenizer      ,							UpperCamelCase__:    int = -1      ,							UpperCamelCase__:    int = -1      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    Optional[TensorType] = None      ,							):
														lowerCamelCase__ :     Union[str, Any]	  =	self._generate_dummy_inputs_for_encoder_and_decoder(
														    UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)

														# Generate decoder inputs
														lowerCamelCase__ :     Any	  =	seq_length if not self.use_past else 1
														lowerCamelCase__ :     Optional[Any]	  =	self._generate_dummy_inputs_for_encoder_and_decoder(
														    UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)
														lowerCamelCase__ :     str	  =	{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
														lowerCamelCase__ :     Optional[int]	  =	dict(**UpperCamelCase__      ,							**UpperCamelCase__					)

														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__ :     Optional[Any]	  =	common_inputs["""input_ids"""].shape
																					lowerCamelCase__ :     Tuple	  =	common_inputs["""decoder_input_ids"""].shape[1]
																					lowerCamelCase__  ,		lowerCamelCase__ :     List[str]	  =	self.num_attention_heads
																					lowerCamelCase__ :     Any	  =	(
																					    batch,
																					    num_encoder_attention_heads,
																					    encoder_seq_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)
																					lowerCamelCase__ :     Tuple	  =	decoder_seq_length + 3
																					lowerCamelCase__ :     int	  =	(
																					    batch,
																					    num_decoder_attention_heads,
																					    decoder_past_length,
																					    self._config.hidden_size // num_decoder_attention_heads,
																					)

																					lowerCamelCase__ :     Optional[int]	  =	torch.cat(
																					    [common_inputs["""decoder_attention_mask"""], torch.ones(UpperCamelCase__      ,							UpperCamelCase__					)]      ,							dim=1					)

																					lowerCamelCase__ :     Any	  =	[]
																					# If the number of encoder and decoder layers are present in the model configuration, both are considered
																					lowerCamelCase__  ,		lowerCamelCase__ :     Any	  =	self.num_layers
																					lowerCamelCase__ :     str	  =	min(UpperCamelCase__      ,							UpperCamelCase__					)
																					lowerCamelCase__ :     str	  =	max(UpperCamelCase__      ,							UpperCamelCase__					) - min_num_layers
																					lowerCamelCase__ :     int	  =	"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

																					for _ in range(UpperCamelCase__					):
																												common_inputs["past_key_values"].append(
																												    (
																												        torch.zeros(UpperCamelCase__					),
																												        torch.zeros(UpperCamelCase__					),
																												        torch.zeros(UpperCamelCase__					),
																												        torch.zeros(UpperCamelCase__					),
																												    )					)
																					# TODO: test this.
																					lowerCamelCase__ :     Union[str, Any]	  =	encoder_shape if remaining_side_name == """encoder""" else decoder_shape
																					for _ in range(UpperCamelCase__      ,							UpperCamelCase__					):
																												common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase__					), torch.zeros(UpperCamelCase__					))					)
														return common_inputs




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    PreTrainedTokenizer      ,							UpperCamelCase__:    int = -1      ,							UpperCamelCase__:    int = -1      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    Optional[TensorType] = None      ,							):
														lowerCamelCase__ :     Any	  =	self._generate_dummy_inputs_for_encoder_and_decoder(
														    UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)

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

																					lowerCamelCase__ :     Optional[Any]	  =	common_inputs["""attention_mask"""].dtype
																					lowerCamelCase__ :     int	  =	torch.cat(
																					    [common_inputs["""attention_mask"""], torch.ones(UpperCamelCase__      ,							UpperCamelCase__      ,							dtype=UpperCamelCase__					)]      ,							dim=1					)
																					lowerCamelCase__ :     int	  =	[
																					    (torch.zeros(UpperCamelCase__					), torch.zeros(UpperCamelCase__					)) for _ in range(UpperCamelCase__					)
																					]
														return common_inputs




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    PreTrainedTokenizer      ,							UpperCamelCase__:    int = -1      ,							UpperCamelCase__:    int = -1      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    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
														lowerCamelCase__ :     List[Any]	  =	compute_effective_axis_dimension(
														    UpperCamelCase__      ,							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__ :     Union[str, Any]	  =	tokenizer.num_special_tokens_to_add(UpperCamelCase__					)
														lowerCamelCase__ :     Any	  =	compute_effective_axis_dimension(
														    UpperCamelCase__      ,							fixed_dimension=OnnxConfig.default_fixed_sequence      ,							num_token_to_add=UpperCamelCase__					)

														# Generate dummy inputs according to compute batch and sequence
														lowerCamelCase__ :     Union[str, Any]	  =	[""" """.join([tokenizer.unk_token]					) * seq_length] * batch_size
														lowerCamelCase__ :     str	  =	dict(tokenizer(UpperCamelCase__      ,							return_tensors=UpperCamelCase__					)					)
														return common_inputs




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

														else:
																					lowerCamelCase__ :     Tuple	  =	self._generate_dummy_inputs_for_causal_lm(
																					    UpperCamelCase__      ,							batch_size=UpperCamelCase__      ,							seq_length=UpperCamelCase__      ,							is_pair=UpperCamelCase__      ,							framework=UpperCamelCase__					)

														return common_inputs




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Optional[Any]					):
														if self.task in ["default", "seq2seq-lm"]:
																					lowerCamelCase__ :     Dict	  =	super()._flatten_past_key_values_(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)
														else:
																					lowerCamelCase__ :     List[Any]	  =	super(UpperCamelCase__      ,							self					)._flatten_past_key_values_(
																					    UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)




							@property
							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														return 1e-4









'''simple docstring'''







from __future__ import annotations





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	list[str]:
							if nth_term == "":
														return [""]
							lowerCamelCase__ :     str	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     Union[str, Any]	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     list[str]	  =	[]
							for temp in range(int(UpperCamelCase				)				):
														series.append(f'''1 / {pow(temp + 1    ,      int(UpperCamelCase				)				)}''' if series else """1"""				)
							return series


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			_A     :      Optional[Any]   					=int(input('''Enter the last number (nth term) of the P-Series'''))
			_A     :      List[str]   					=int(input('''Enter the power for  P-Series'''))
			print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
			print(p_series(nth_term, power))

