"""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_:       List[Any]			     =       logging.get_logger(__name__)

lowerCAmelCase_:       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  a__     ( lowerCamelCase__						):
   snake_case_				=   "mbart"
   snake_case_				=   ["past_key_values"]
   snake_case_				=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(  self,					_UpperCAmelCase=5_0265,					_UpperCAmelCase=1024,					_UpperCAmelCase=12,					_UpperCAmelCase=4096,					_UpperCAmelCase=16,					_UpperCAmelCase=12,					_UpperCAmelCase=4096,					_UpperCAmelCase=16,					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.0,					_UpperCAmelCase=True,					_UpperCAmelCase=True,					_UpperCAmelCase="gelu",					_UpperCAmelCase=1024,					_UpperCAmelCase=0.1,					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.02,					_UpperCAmelCase=0.0,					_UpperCAmelCase=False,					_UpperCAmelCase=1,					_UpperCAmelCase=0,					_UpperCAmelCase=2,					_UpperCAmelCase=2,					**_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				vocab_size
        lowercase__								=				max_position_embeddings
        lowercase__								=				d_model
        lowercase__								=				encoder_ffn_dim
        lowercase__								=				encoder_layers
        lowercase__								=				encoder_attention_heads
        lowercase__								=				decoder_ffn_dim
        lowercase__								=				decoder_layers
        lowercase__								=				decoder_attention_heads
        lowercase__								=				dropout
        lowercase__								=				attention_dropout
        lowercase__								=				activation_dropout
        lowercase__								=				activation_function
        lowercase__								=				init_std
        lowercase__								=				encoder_layerdrop
        lowercase__								=				decoder_layerdrop
        lowercase__								=				classifier_dropout
        lowercase__								=				use_cache
        lowercase__								=				encoder_layers
        lowercase__								=				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,					forced_eos_token_id=_UpperCAmelCase,					**_UpperCAmelCase,					)






class  a__     ( lowerCamelCase__						):


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        if self.task in ["default", "seq2seq-lm"]:
             lowercase__								=				OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]	)

             if self.use_past:
                  lowercase__								=				{0: '''batch'''}
                  lowercase__								=				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
             else:
                  lowercase__								=				{0: '''batch''', 1: '''decoder_sequence'''}
                  lowercase__								=				{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.
             lowercase__								=				OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]	)
             if self.use_past:
                  lowercase__								=				self.num_layers
                  for i in range(_UpperCAmelCase	):
                       lowercase__								=				{0: '''batch''', 2: '''past_sequence + sequence'''}
                       lowercase__								=				{0: '''batch''', 2: '''past_sequence + sequence'''}
        else:
             lowercase__								=				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 						snake_case__						(  self	):
        '''simple docstring'''



        if self.task in ["default", "seq2seq-lm"]:
             lowercase__								=				super().outputs
        else:
             lowercase__								=				super(_UpperCAmelCase,					self	).outputs
             if self.use_past:
                  lowercase__								=				self.num_layers
                  for i in range(_UpperCAmelCase	):
                       lowercase__								=				{0: '''batch''', 2: '''past_sequence + sequence'''}
                       lowercase__								=				{0: '''batch''', 2: '''past_sequence + sequence'''}
        return common_outputs


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = -1,					_UpperCAmelCase = -1,					_UpperCAmelCase = False,					_UpperCAmelCase = None,					):
        '''simple docstring'''



        lowercase__								=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            _UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	)

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

             lowercase__								=				torch.cat(
                 [common_inputs["decoder_attention_mask"], torch.ones(_UpperCAmelCase,					_UpperCAmelCase	)],					dim=1	)

             lowercase__								=				[]
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             lowercase__								=				self.num_layers
             lowercase__								=				min(_UpperCAmelCase,					_UpperCAmelCase	)
             lowercase__								=				max(_UpperCAmelCase,					_UpperCAmelCase	) - min_num_layers
             lowercase__								=				'''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.
             lowercase__								=				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 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = -1,					_UpperCAmelCase = -1,					_UpperCAmelCase = False,					_UpperCAmelCase = None,					):
        '''simple docstring'''



        lowercase__								=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            _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
             lowercase__								=				common_inputs['''input_ids'''].shape
             # Not using the same length for past_key_values
             lowercase__								=				seqlen + 2
             lowercase__								=				self.num_layers
             lowercase__								=				self.num_attention_heads
             lowercase__								=				(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

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


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = -1,					_UpperCAmelCase = -1,					_UpperCAmelCase = False,					_UpperCAmelCase = None,					):
        '''simple docstring'''



        lowercase__								=				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
        lowercase__								=				tokenizer.num_special_tokens_to_add(_UpperCAmelCase	)
        lowercase__								=				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
        lowercase__								=				[''' '''.join([tokenizer.unk_token]	) * seq_length] * batch_size
        lowercase__								=				dict(tokenizer(_UpperCAmelCase,					return_tensors=_UpperCAmelCase	)	)
        return common_inputs


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = -1,					_UpperCAmelCase = -1,					_UpperCAmelCase = False,					_UpperCAmelCase = None,					):
        '''simple docstring'''



        if self.task in ["default", "seq2seq-lm"]:
             lowercase__								=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _UpperCAmelCase,					batch_size=_UpperCAmelCase,					seq_length=_UpperCAmelCase,					is_pair=_UpperCAmelCase,					framework=_UpperCAmelCase	)

        elif self.task == "causal-lm":
             lowercase__								=				self._generate_dummy_inputs_for_causal_lm(
                 _UpperCAmelCase,					batch_size=_UpperCAmelCase,					seq_length=_UpperCAmelCase,					is_pair=_UpperCAmelCase,					framework=_UpperCAmelCase	)
        else:
             lowercase__								=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _UpperCAmelCase,					batch_size=_UpperCAmelCase,					seq_length=_UpperCAmelCase,					is_pair=_UpperCAmelCase,					framework=_UpperCAmelCase	)

        return common_inputs


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        if self.task in ["default", "seq2seq-lm"]:
             lowercase__								=				super()._flatten_past_key_values_(_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	)
        else:
             lowercase__								=				super(_UpperCAmelCase,					self	)._flatten_past_key_values_(
                 _UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	)







"""simple docstring"""
import math







def       UpperCamelCase_					(     lowerCamelCase							:					int   )						->     list:



       """simple docstring"""







       if n <= 0:
              raise ValueError('''Only positive integers have prime factors'''   )
       __magic_name__			:		Union[str, Any]        =     []
       while n % 2 == 0:
              pf.append(2   )
              __magic_name__			:		Union[str, Any]        =     int(n / 2   )
       for i in range(3     , int(math.sqrt(lowerCamelCase   )   ) + 1     , 2   ):
              while n % i == 0:
                     pf.append(lowerCamelCase   )
                     __magic_name__			:		Any        =     int(n / i   )
       if n > 2:
              pf.append(lowerCamelCase   )
       return pf







def       UpperCamelCase_					(     lowerCamelCase							:					int   )						->     int:



       """simple docstring"""







       if n <= 0:
              raise ValueError('''Only positive numbers are accepted'''   )
       __magic_name__			:		List[Any]        =     1
       __magic_name__			:		Dict        =     1
       while n % 2 == 0:
              temp += 1
              __magic_name__			:		Dict        =     int(n / 2   )
       div *= temp
       for i in range(3     , int(math.sqrt(lowerCamelCase   )   ) + 1     , 2   ):
              __magic_name__			:		Dict        =     1
              while n % i == 0:
                     temp += 1
                     __magic_name__			:		Union[str, Any]        =     int(n / i   )
              div *= temp
       if n > 1:
              div *= 2
       return div







def       UpperCamelCase_					(     lowerCamelCase							:					int   )						->     int:



       """simple docstring"""







       if n <= 0:
              raise ValueError('''Only positive numbers are accepted'''   )
       __magic_name__			:		int        =     1
       __magic_name__			:		Dict        =     1
       while n % 2 == 0:
              temp += 1
              __magic_name__			:		List[Any]        =     int(n / 2   )
       if temp > 1:
              s *= (2**temp - 1) / (2 - 1)
       for i in range(3     , int(math.sqrt(lowerCamelCase   )   ) + 1     , 2   ):
              __magic_name__			:		Any        =     1
              while n % i == 0:
                     temp += 1
                     __magic_name__			:		Any        =     int(n / i   )
              if temp > 1:
                     s *= (i**temp - 1) / (i - 1)
       return int(lowerCamelCase   )







def       UpperCamelCase_					(     lowerCamelCase							:					int   )						->     int:



       """simple docstring"""







       __magic_name__			:		str        =     n
       for x in set(prime_factors(lowerCamelCase   )   ):
              s *= (x - 1) / x
       return int(lowerCamelCase   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
