

import math

def      a__	(						UpperCAmelCase				:     int     )    ->		list[int]:
  if not isinstance(UpperCAmelCase							,					UpperCAmelCase     ):
    UpperCAmelCase			:				int										=					f'''Input value of [number={number}] must be an integer'''
    raise TypeError(UpperCAmelCase     )
  if number < 1:
    UpperCAmelCase			:				Union[str, Any]										=					f'''Input value of [number={number}] must be a positive integer'''
    raise ValueError(UpperCAmelCase     )
  UpperCAmelCase			:				Dict										=					[number]
  while number != 1:
    if number % 2 == 0:
      UpperCAmelCase			:				Any										=					math.floor(math.sqrt(UpperCAmelCase     )     )
    else:
      UpperCAmelCase			:				Tuple										=					math.floor(
          math.sqrt(UpperCAmelCase     ) * math.sqrt(UpperCAmelCase     ) * math.sqrt(UpperCAmelCase     )     )
    sequence.append(UpperCAmelCase     )
  return sequence


if __name__ == "__main__":
     import doctest

     doctest.testmod()








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							:  Union[str, Any]     =				logging.get_logger(__name__)

_lowerCamelCase							:  Optional[int]     =				{
    "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    __UpperCAmelCase	(			lowerCamelCase__		):
   UpperCamelCase  	=	"""m2m_100"""
   UpperCamelCase  	=	["""past_key_values"""]
   UpperCamelCase  	=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



   def __init__(				self				:	int,						__A				:	Optional[Any]=1_2_8_1_1_2,						__A				:	str=1_0_2_4,						__A				:	Tuple=1_2,						__A				:	Any=4_0_9_6,						__A				:	str=1_6,						__A				:	List[str]=1_2,						__A				:	int=4_0_9_6,						__A				:	List[Any]=1_6,						__A				:	Tuple=0.0_5,						__A				:	Tuple=0.0_5,						__A				:	List[Any]=True,						__A				:	int=True,						__A				:	str="relu",						__A				:	str=1_0_2_4,						__A				:	str=0.1,						__A				:	str=0.1,						__A				:	List[str]=0.0,						__A				:	List[str]=0.0_2,						__A				:	Any=2,						__A				:	List[str]=True,						__A				:	Tuple=1,						__A				:	Optional[Any]=0,						__A				:	Optional[Any]=2,						**__A				:	Optional[Any],						):
     UpperCAmelCase			:				str										=					vocab_size
     UpperCAmelCase			:				Optional[Any]										=					max_position_embeddings
     UpperCAmelCase			:				List[str]										=					d_model
     UpperCAmelCase			:				Any										=					encoder_ffn_dim
     UpperCAmelCase			:				Optional[int]										=					encoder_layers
     UpperCAmelCase			:				List[Any]										=					encoder_attention_heads
     UpperCAmelCase			:				str										=					decoder_ffn_dim
     UpperCAmelCase			:				Any										=					decoder_layers
     UpperCAmelCase			:				List[str]										=					decoder_attention_heads
     UpperCAmelCase			:				str										=					dropout
     UpperCAmelCase			:				int										=					attention_dropout
     UpperCAmelCase			:				Dict										=					activation_dropout
     UpperCAmelCase			:				Tuple										=					activation_function
     UpperCAmelCase			:				Any										=					init_std
     UpperCAmelCase			:				Union[str, Any]										=					encoder_layerdrop
     UpperCAmelCase			:				List[Any]										=					decoder_layerdrop
     UpperCAmelCase			:				Optional[Any]										=					use_cache
     UpperCAmelCase			:				str										=					encoder_layers
     UpperCAmelCase			:				List[Any]										=					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,						decoder_start_token_id=__A,						**__A,						)






class    __UpperCAmelCase	(			lowerCamelCase__		):



   @property
   def 			__magic_name__       (				self				:	List[str]		):
     UpperCAmelCase			:				List[Any]										=					OrderedDict(
         [
             ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
         ]		)

     if self.use_past:
       UpperCAmelCase			:				List[str]										=					{0: '''batch'''}
       UpperCAmelCase			:				Any										=					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
     else:
       UpperCAmelCase			:				Any										=					{0: '''batch''', 1: '''decoder_sequence'''}
       UpperCAmelCase			:				List[Any]										=					{0: '''batch''', 1: '''decoder_sequence'''}

     if self.use_past:
       self.fill_with_past_key_values_(__A,						direction='''inputs'''		)
     return common_inputs



   def 			__magic_name__       (				self				:	Dict,						__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
     UpperCAmelCase			:				List[str]										=					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
     UpperCAmelCase			:				Optional[int]										=					tokenizer.num_special_tokens_to_add(__A		)
     UpperCAmelCase			:				int										=					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
     UpperCAmelCase			:				List[str]										=					[''' '''.join([tokenizer.unk_token]		) * seq_length] * batch_size
     UpperCAmelCase			:				Tuple										=					dict(tokenizer(__A,						return_tensors=__A		)		)
     return common_inputs



   def 			__magic_name__       (				self				:	Any,						__A				:	PreTrainedTokenizer,						__A				:	int = -1,						__A				:	int = -1,						__A				:	bool = False,						__A				:	Optional[TensorType] = None,						):
     UpperCAmelCase			:				int										=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __A,						__A,						__A,						__A,						__A		)

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

       UpperCAmelCase			:				Union[str, Any]										=					torch.cat(
           [common_inputs['''decoder_attention_mask'''], torch.ones(__A,						__A		)],						dim=1		)

       UpperCAmelCase			:				Any										=					[]
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       UpperCAmelCase					,		UpperCAmelCase			:				str										=					self.num_layers
       UpperCAmelCase			:				Any										=					min(__A,						__A		)
       UpperCAmelCase			:				Dict										=					max(__A,						__A		) - min_num_layers
       UpperCAmelCase			:				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.
       UpperCAmelCase			:				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





   UpperCamelCase  	=	_generate_dummy_inputs_for_default_and_seqaseq_lm





