"""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


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={
    "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  UpperCamelCase	(    lowercase_						):
   lowercase							=						'bart'
   lowercase							=						['past_key_values']
   lowercase							=						{'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="gelu"  ,__UpperCamelCase=1024  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.02  ,__UpperCamelCase=0.0  ,__UpperCamelCase=False  ,__UpperCamelCase=True  ,__UpperCamelCase=3  ,__UpperCamelCase=1  ,__UpperCamelCase=0  ,__UpperCamelCase=2  ,__UpperCamelCase=True  ,__UpperCamelCase=2  ,__UpperCamelCase=2  ,**__UpperCamelCase  ,)			->   Tuple:


        '''simple docstring'''


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

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

        # ensure backward compatibility for BART CNN models
        if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'  ,__UpperCamelCase    ):
             lowercase_			:       Optional[Any]									=			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  UpperCamelCase	(    lowercase_						):
   @property
   def     _UpperCAmelCase     (    self    )			->   Mapping[str, Mapping[int, str]]:


        '''simple docstring'''


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

             if self.use_past:
                  lowercase_			:       Optional[Any]									=			{0: 'batch'}
                  lowercase_			:       Optional[int]									=			{0: 'batch', 1: 'past_decoder_sequence + sequence'}
             else:
                  lowercase_			:       Optional[int]									=			{0: 'batch', 1: 'decoder_sequence'}
                  lowercase_			:       Optional[Any]									=			{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_			:       Union[str, Any]									=			OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]    )
             if self.use_past:
                  lowercase_							,						lowercase_			:       Union[str, Any]									=			self.num_layers
                  for i in range(__UpperCamelCase    ):
                       lowercase_			:       Union[str, Any]									=			{0: 'batch', 2: 'past_sequence + sequence'}
                       lowercase_			:       List[Any]									=			{0: 'batch', 2: 'past_sequence + sequence'}
        else:
             lowercase_			:       List[str]									=			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"]:
             lowercase_			:       Union[str, Any]									=			super().outputs
        else:
             lowercase_			:       Tuple									=			super(__UpperCamelCase  ,self    ).outputs
             if self.use_past:
                  lowercase_							,						lowercase_			:       Optional[Any]									=			self.num_layers
                  for i in range(__UpperCamelCase    ):
                       lowercase_			:       Any									=			{0: 'batch', 2: 'past_sequence + sequence'}
                       lowercase_			:       List[str]									=			{0: 'batch', 2: 'past_sequence + sequence'}
        return common_outputs
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = -1  ,__UpperCamelCase = -1  ,__UpperCamelCase = False  ,__UpperCamelCase = None  ,)			->   Mapping[str, Any]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )

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

             lowercase_			:       List[str]									=			torch.cat(
                 [common_inputs['decoder_attention_mask'], torch.ones(__UpperCamelCase  ,__UpperCamelCase    )]  ,dim=1    )

             lowercase_			:       Optional[int]									=			[]
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             lowercase_							,						lowercase_			:       str									=			self.num_layers
             lowercase_			:       Optional[Any]									=			min(__UpperCamelCase  ,__UpperCamelCase    )
             lowercase_			:       Any									=			max(__UpperCamelCase  ,__UpperCamelCase    ) - min_num_layers
             lowercase_			:       Union[str, Any]									=			'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_			:       List[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     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = -1  ,__UpperCamelCase = -1  ,__UpperCamelCase = False  ,__UpperCamelCase = None  ,)			->   Mapping[str, Any]:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			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_							,						lowercase_			:       Dict									=			common_inputs['input_ids'].shape
             # Not using the same length for past_key_values
             lowercase_			:       Union[str, Any]									=			seqlen + 2
             lowercase_							,						lowercase_			:       Tuple									=			self.num_layers
             lowercase_							,						lowercase_			:       Union[str, Any]									=			self.num_attention_heads
             lowercase_			:       Any									=			(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             lowercase_			:       List[Any]									=			common_inputs['attention_mask'].dtype
             lowercase_			:       Optional[Any]									=			torch.cat(
                 [common_inputs['attention_mask'], torch.ones(__UpperCamelCase  ,__UpperCamelCase  ,dtype=__UpperCamelCase    )]  ,dim=1    )
             lowercase_			:       Any									=			[
                 (torch.zeros(__UpperCamelCase    ), torch.zeros(__UpperCamelCase    )) for _ in range(__UpperCamelCase    )
             ]
        return common_inputs
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = -1  ,__UpperCamelCase = -1  ,__UpperCamelCase = False  ,__UpperCamelCase = None  ,)			->   Mapping[str, Any]:


        '''simple docstring'''


        lowercase_			:       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
        lowercase_			:       int									=			tokenizer.num_special_tokens_to_add(__UpperCamelCase    )
        lowercase_			:       Optional[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
        lowercase_			:       Tuple									=			[' '.join([tokenizer.unk_token]    ) * seq_length] * batch_size
        lowercase_			:       int									=			dict(tokenizer(__UpperCamelCase  ,return_tensors=__UpperCamelCase    )    )
        return common_inputs
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = -1  ,__UpperCamelCase = -1  ,__UpperCamelCase = False  ,__UpperCamelCase = None  ,)			->   Mapping[str, Any]:


        '''simple docstring'''


        if self.task in ["default", "seq2seq-lm"]:
             lowercase_			:       List[Any]									=			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_			:       List[Any]									=			self._generate_dummy_inputs_for_causal_lm(
                 __UpperCamelCase  ,batch_size=__UpperCamelCase  ,seq_length=__UpperCamelCase  ,is_pair=__UpperCamelCase  ,framework=__UpperCamelCase    )
        else:
             lowercase_			:       Dict									=			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     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


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


"""simple docstring"""







from random import randint, random



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  bool = False  ,    __SCREAMING_SNAKE_CASE							:  bool = False  ,    __SCREAMING_SNAKE_CASE							:  int = 5  ,    ):
     lowercase_			:       Union[str, Any]									=			[[-1] * number_of_cells]  # Create a highway without any car
     lowercase_			:       str									=			0
     lowercase_			:       List[Any]									=			max(__SCREAMING_SNAKE_CASE  ,    0   )
     while i < number_of_cells:
          lowercase_			:       Union[str, Any]									=			(
              randint(0  ,    __SCREAMING_SNAKE_CASE   ) if random_speed else initial_speed
          )  # Place the cars
          i += (
              randint(1  ,    max_speed * 2   ) if random_frequency else frequency
          )  # Arbitrary number, may need tuning
     return highway



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       Union[str, Any]									=			0
     lowercase_			:       Dict									=			highway_now[car_index + 1 :]
     for cell in range(len(__SCREAMING_SNAKE_CASE   )   ):  # May need a better name for this
          if cells[cell] != -1:  # If the cell is not empty then
               return distance  # we have the distance we wanted
          distance += 1
     # Here if the car is near the end of the highway
     return distance + get_distance(__SCREAMING_SNAKE_CASE  ,    -1   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  float  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       List[Any]									=			len(__SCREAMING_SNAKE_CASE   )
     # Beforce calculations, the highway is empty
     lowercase_			:       Dict									=			[-1] * number_of_cells

     for car_index in range(__SCREAMING_SNAKE_CASE   ):
          if highway_now[car_index] != -1:
               # Add 1 to the current speed of the car and cap the speed
               lowercase_			:       Any									=			min(highway_now[car_index] + 1  ,    __SCREAMING_SNAKE_CASE   )
               # Number of empty cell before the next car
               lowercase_			:       List[Any]									=			get_distance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ) - 1
               # We can't have the car causing an accident
               lowercase_			:       Dict									=			min(next_highway[car_index]  ,    __SCREAMING_SNAKE_CASE   )
               if random() < probability:
                    # Randomly, a driver will slow down
                    lowercase_			:       Optional[Any]									=			max(next_highway[car_index] - 1  ,    0   )
     return next_highway



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  float  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       Union[str, Any]									=			len(highway[0]   )

     for i in range(__SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Optional[Any]									=			update(highway[i]  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
          lowercase_			:       List[str]									=			[-1] * number_of_cells

          for car_index in range(__SCREAMING_SNAKE_CASE   ):
               lowercase_			:       Optional[Any]									=			next_speeds_calculated[car_index]
               if speed != -1:
                    # Change the position based on the speed (with % to create the loop)
                    lowercase_			:       Any									=			(car_index + speed) % number_of_cells
                    # Commit the change of position
                    lowercase_			:       Optional[Any]									=			speed
          highway.append(__SCREAMING_SNAKE_CASE   )

     return highway


if __name__ == "__main__":
       import doctest

       doctest.testmod()

