


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCAmelCase:	Dict      							=					logging.get_logger(__name__)

_lowerCAmelCase:	List[str]      							=					{
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}




class 			lowercase_				(UpperCAmelCase_      ):
    snake_case  						='m2m_100'
    snake_case  						=['past_key_values']
    snake_case  						={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





    def __init__(							self					,     lowercase_=128112					,     lowercase_=1024					,     lowercase_=12					,     lowercase_=4096					,     lowercase_=16					,     lowercase_=12					,     lowercase_=4096					,     lowercase_=16					,     lowercase_=0.05					,     lowercase_=0.05					,     lowercase_=True					,     lowercase_=True					,     lowercase_="relu"					,     lowercase_=1024					,     lowercase_=0.1					,     lowercase_=0.1					,     lowercase_=0.0					,     lowercase_=0.02					,     lowercase_=2					,     lowercase_=True					,     lowercase_=1					,     lowercase_=0					,     lowercase_=2					,     **lowercase_					,     )     ->   Optional[Any]:
           a__     						=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

           super().__init__(
               pad_token_id=__UpperCamelCase					,     bos_token_id=__UpperCamelCase					,     eos_token_id=__UpperCamelCase					,     is_encoder_decoder=__UpperCamelCase					,     decoder_start_token_id=__UpperCamelCase					,     **__UpperCamelCase					,     )







class 			lowercase_				(UpperCAmelCase_      ):





    @property
    def 	__UpperCamelCase						(							self)     ->   Union[str, Any]:
           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_(__UpperCamelCase					,     direction='inputs')
           return common_inputs





    def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_ = -1					,     lowercase_ = -1					,     lowercase_ = False					,     lowercase_ = None					,     )     ->   str:
           # 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(
               __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
           a__     						=tokenizer.num_special_tokens_to_add(__UpperCamelCase)
           a__     						=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
           a__     						=[' '.join([tokenizer.unk_token]) * seq_length] * batch_size
           a__     						=dict(tokenizer(__UpperCamelCase					,     return_tensors=__UpperCamelCase))
           return common_inputs





    def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_ = -1					,     lowercase_ = -1					,     lowercase_ = False					,     lowercase_ = None					,     )     ->   str:
           a__     						=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase)

           # Generate decoder inputs
           a__     						=seq_length if not self.use_past else 1
           a__     						=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase)
           a__     						={F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
           a__     						=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
                  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(__UpperCamelCase					,     __UpperCamelCase)]					,     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(__UpperCamelCase					,     __UpperCamelCase)
                  a__     						=max(__UpperCamelCase					,     __UpperCamelCase) - min_num_layers
                  a__     						='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.
                  a__     						=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

    snake_case  						=_generate_dummy_inputs_for_default_and_seqaseq_lm





from __future__ import annotations






def      _lowercase(				__a							:	float | int						,    __a							:	float | int						,    __a							:	float | int						,    ):
							if not all((nth_term, start_term_a, common_ratio_r)    ):
														return []
							a__     						=[]
							a__     						=1
							a__     						=common_ratio_r
							for _ in range(int(__a    )    ):
														if not series:
																					series.append(__a    )
														else:
																					power += 1
																					series.append(float(start_term_a * multiple    )    )
																					a__     						=pow(float(__a    )						,    __a    )
							return series


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowerCAmelCase:	Tuple      							=					float(input('Enter the last number (n term) of the Geometric Series'))
	_lowerCAmelCase:	Optional[int]      							=					float(input('Enter the starting term (a) of the Geometric Series'))
	_lowerCAmelCase:	List[Any]      							=					float(
	    input('Enter the common ratio between two terms (r) of the Geometric Series')
	)
	print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
	print(geometric_series(nth_term, start_term_a, common_ratio_r))

