





"""simple docstring"""
from ..utils import DummyObject, requires_backends






class  _SCREAMING_SNAKE_CASE(       metaclass=lowercase__  ):
       SCREAMING_SNAKE_CASE_       :				Optional[int]     						=  ['note_seq']




       def __init__(						self		,*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             requires_backends(self		,['''note_seq''']							)




       @classmethod
       def    _UpperCamelCase					(						cls		,*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






             requires_backends(cls		,['''note_seq''']							)




       @classmethod
       def    _UpperCamelCase					(						cls		,*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				Any:





             """simple docstring"""






             requires_backends(cls		,['''note_seq''']							)






"""simple docstring"""
import warnings
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_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
    # See all BART models at https://huggingface.co/models?filter=bart
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  '''bart'''
       SCREAMING_SNAKE_CASE_       :				str     						=  ['''past_key_values''']
       SCREAMING_SNAKE_CASE_       :				str     						=  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




       def __init__(						self		,SCREAMING_SNAKE_CASE__=5_02_65		,SCREAMING_SNAKE_CASE__=10_24		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=40_96		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=40_96		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=10_24		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=2		,**SCREAMING_SNAKE_CASE__		,)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :str		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  max_position_embeddings
             __SCREAMING_SNAKE_CASE       :Any		    =  d_model
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  encoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :List[str]		    =  encoder_layers
             __SCREAMING_SNAKE_CASE       :Tuple		    =  encoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  decoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :Any		    =  decoder_layers
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  decoder_attention_heads
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  dropout
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  attention_dropout
             __SCREAMING_SNAKE_CASE       :Dict		    =  activation_dropout
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  activation_function
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  init_std
             __SCREAMING_SNAKE_CASE       :int		    =  encoder_layerdrop
             __SCREAMING_SNAKE_CASE       :Any		    =  decoder_layerdrop
             __SCREAMING_SNAKE_CASE       :str		    =  classifier_dropout
             __SCREAMING_SNAKE_CASE       :List[str]		    =  use_cache
             __SCREAMING_SNAKE_CASE       :List[str]		    =  encoder_layers
             __SCREAMING_SNAKE_CASE       :Tuple		    =  scale_embedding  # scale factor will be sqrt(d_model) if True

             super().__init__(
                 num_labels=SCREAMING_SNAKE_CASE__		,pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,is_encoder_decoder=SCREAMING_SNAKE_CASE__		,decoder_start_token_id=SCREAMING_SNAKE_CASE__		,forced_eos_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)

             # ensure backward compatibility for BART CNN models
             if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''		,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :str		    =  self.bos_token_id
                   warnings.warn(
                       f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                       '''The config can simply be saved and uploaded again to be fixed.'''							)






class  _SCREAMING_SNAKE_CASE(       A  ):




       @property
       def    _UpperCamelCase					(						self							)      ->				Mapping[str, Mapping[int, str]]:





             """simple docstring"""






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

                   if self.use_past:
                         __SCREAMING_SNAKE_CASE       :int		    =  {0: '''batch'''}
                         __SCREAMING_SNAKE_CASE       :int		    =  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
                   else:
                         __SCREAMING_SNAKE_CASE       :Tuple		    =  {0: '''batch''', 1: '''decoder_sequence'''}
                         __SCREAMING_SNAKE_CASE       :Any		    =  {0: '''batch''', 1: '''decoder_sequence'''}

                   if self.use_past:
                         self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__		,direction='''inputs'''							)
             elif self.task == "causal-lm":
                   # TODO: figure this case out.
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  OrderedDict(
                       [
                           ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                           ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ]							)
                   if self.use_past:
                         __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :List[Any]		    =  self.num_layers
                         for i in range(SCREAMING_SNAKE_CASE__							):
                               __SCREAMING_SNAKE_CASE       :List[str]		    =  {0: '''batch''', 2: '''past_sequence + sequence'''}
                               __SCREAMING_SNAKE_CASE       :List[Any]		    =  {0: '''batch''', 2: '''past_sequence + sequence'''}
             else:
                   __SCREAMING_SNAKE_CASE       :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
       def    _UpperCamelCase					(						self							)      ->				Mapping[str, Mapping[int, str]]:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             # Generate decoder inputs
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  seq_length if not self.use_past else 1
             __SCREAMING_SNAKE_CASE       :Dict		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
             __SCREAMING_SNAKE_CASE       :Any		    =  dict(**SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)

             if self.use_past:
                   if not is_torch_available():
                         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
                   else:
                         import torch
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Dict		    =  common_inputs['''input_ids'''].shape
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  common_inputs['''decoder_input_ids'''].shape[1]
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.num_attention_heads
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  (
                       batch,
                       num_encoder_attention_heads,
                       encoder_seq_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  decoder_seq_length + 3
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  (
                       batch,
                       num_decoder_attention_heads,
                       decoder_past_length,
                       self._config.hidden_size // num_decoder_attention_heads,
                   )

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

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

                   for _ in range(SCREAMING_SNAKE_CASE__							):
                         common_inputs["past_key_values"].append(
                             (
                                 torch.zeros(SCREAMING_SNAKE_CASE__							),
                                 torch.zeros(SCREAMING_SNAKE_CASE__							),
                                 torch.zeros(SCREAMING_SNAKE_CASE__							),
                                 torch.zeros(SCREAMING_SNAKE_CASE__							),
                             )							)
                   # TODO: test this.
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
                   for _ in range(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							):
                         common_inputs["past_key_values"].append((torch.zeros(SCREAMING_SNAKE_CASE__							), torch.zeros(SCREAMING_SNAKE_CASE__							))							)
             return common_inputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             if self.use_past:
                   if not is_torch_available():
                         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
                   else:
                         import torch
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :str		    =  common_inputs['''input_ids'''].shape
                   # Not using the same length for past_key_values
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  seqlen + 2
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.num_layers
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :int		    =  self.num_attention_heads
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  (
                       batch,
                       num_encoder_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )

                   __SCREAMING_SNAKE_CASE       :Tuple		    =  common_inputs['''attention_mask'''].dtype
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.cat(
                       [common_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,dtype=SCREAMING_SNAKE_CASE__							)]		,dim=1							)
                   __SCREAMING_SNAKE_CASE       :str		    =  [
                       (torch.zeros(SCREAMING_SNAKE_CASE__							), torch.zeros(SCREAMING_SNAKE_CASE__							)) for _ in range(SCREAMING_SNAKE_CASE__							)
                   ]
             return common_inputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  compute_effective_axis_dimension(
                 SCREAMING_SNAKE_CASE__		,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
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  compute_effective_axis_dimension(
                 SCREAMING_SNAKE_CASE__		,fixed_dimension=OnnxConfig.default_fixed_sequence		,num_token_to_add=SCREAMING_SNAKE_CASE__							)

             # Generate dummy inputs according to compute batch and sequence
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  [''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
             __SCREAMING_SNAKE_CASE       :str		    =  dict(tokenizer(SCREAMING_SNAKE_CASE__		,return_tensors=SCREAMING_SNAKE_CASE__							)							)
             return common_inputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,)      ->				Mapping[str, Any]:





             """simple docstring"""






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

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

             return common_inputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






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