


'''simple docstring'''

from __future__ import annotations

from typing import Generic, TypeVar

__lowercase:		int    						=  TypeVar("T")







class 			UpperCAmelCase			(							Generic[T]):

 def __init__(			self			:						Optional[int],       a_			:						T       ):





    """simple docstring"""



    UpperCamelCase__			    =    data  # Assign data
    UpperCamelCase__			    =    None  # Initialize next as null
    UpperCamelCase__			    =    None  # Initialize prev as null







class 			UpperCAmelCase			(							Generic[T]):

 def __init__(			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    None

 def 		lowercase_    (			self			:						int,       a_			:						T       ):





    """simple docstring"""



    if self.head is None:
       UpperCamelCase__			    =    Node(a_       )
    else:
       UpperCamelCase__			    =    Node(a_       )
       UpperCamelCase__			    =    new_node
       UpperCamelCase__			    =    self.head
       UpperCamelCase__			    =    None
       UpperCamelCase__			    =    new_node

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    if self.head is None:
       return None
    else:
       assert self.head is not None
       UpperCamelCase__			    =    self.head.data
       UpperCamelCase__			    =    self.head.next
       if self.head is not None:
          UpperCamelCase__			    =    None
       return temp

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    return self.head.data if self.head is not None else None

 def __len__(			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.head
    UpperCamelCase__			    =    0
    while temp is not None:
       count += 1
       UpperCamelCase__			    =    temp.next
    return count

 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    return self.head is None

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    print("stack elements are:"       )
    UpperCamelCase__			    =    self.head
    while temp is not None:
       print(temp.data,       end="->"       )
       UpperCamelCase__			    =    temp.next


# Code execution starts here
if __name__ == "__main__":
       # Start with the empty stack
       __lowercase:		Stack[int]    						=  Stack()

       # Insert 4 at the beginning. So stack becomes 4->None
       print("Stack operations using Doubly LinkedList")
       stack.push(4)

       # Insert 5 at the beginning. So stack becomes 4->5->None
       stack.push(5)

       # Insert 6 at the beginning. So stack becomes 4->5->6->None
       stack.push(6)

       # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
       stack.push(7)

       # Print the stack
       stack.print_stack()

       # Print the top element
       print("\nTop element is ", stack.top())

       # Print the stack size
       print("Size of the stack is ", len(stack))

       # pop the top element
       stack.pop()

       # pop the top element
       stack.pop()

       # two elements have now been popped off
       stack.print_stack()

       # Print True if the stack is empty else False
       print("\nstack is empty:", stack.is_empty())



'''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


__lowercase:		int    						=  logging.get_logger(__name__)

__lowercase:		Tuple    						=  {
    "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 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       List[str]            =       'mbart'
 _lowerCamelCase			:       str            =       ['past_key_values']
 _lowerCamelCase			:       List[str]            =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

 def __init__(			self			:						Tuple,       a_			:						Union[str, Any]=5_0265,       a_			:						Union[str, Any]=1024,       a_			:						Optional[int]=12,       a_			:						str=4096,       a_			:						Optional[Any]=16,       a_			:						str=12,       a_			:						Union[str, Any]=4096,       a_			:						List[Any]=16,       a_			:						Optional[Any]=0.0,       a_			:						int=0.0,       a_			:						Any=True,       a_			:						Optional[Any]=True,       a_			:						Any="gelu",       a_			:						int=1024,       a_			:						Any=0.1,       a_			:						Optional[int]=0.0,       a_			:						Tuple=0.0,       a_			:						Optional[Any]=0.02,       a_			:						Any=0.0,       a_			:						Optional[Any]=False,       a_			:						Any=1,       a_			:						Optional[Any]=0,       a_			:						List[str]=2,       a_			:						Any=2,       **a_			:						int,       ):





    """simple docstring"""



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







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 @property
 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



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

       if self.use_past:
          UpperCamelCase__			    =    {0: "batch"}
          UpperCamelCase__			    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
          UpperCamelCase__			    =    {0: "batch", 1: "decoder_sequence"}
          UpperCamelCase__			    =    {0: "batch", 1: "decoder_sequence"}

       if self.use_past:
          self.fill_with_past_key_values_(a_,       direction="inputs"       )
    elif self.task == "causal-lm":
       # TODO: figure this case out.
       UpperCamelCase__			    =    OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
           ]       )
       if self.use_past:
          UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_layers
          for i in range(a_       ):
             UpperCamelCase__			    =    {0: "batch", 2: "past_sequence + sequence"}
             UpperCamelCase__			    =    {0: "batch", 2: "past_sequence + sequence"}
    else:
       UpperCamelCase__			    =    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 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						str,       a_			:						PreTrainedTokenizer,       a_			:						int = -1,       a_			:						int = -1,       a_			:						bool = False,       a_			:						Optional[TensorType] = None,       ):





    """simple docstring"""



    UpperCamelCase__			    =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        a_,       a_,       a_,       a_,       a_       )

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

       UpperCamelCase__			    =    torch.cat(
           [common_inputs["decoder_attention_mask"], torch.ones(a_,       a_       )],       dim=1       )

       UpperCamelCase__			    =    []
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_layers
       UpperCamelCase__			    =    min(a_,       a_       )
       UpperCamelCase__			    =    max(a_,       a_       ) - min_num_layers
       UpperCamelCase__			    =    "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__			    =    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

 def 		lowercase_    (			self			:						Optional[int],       a_			:						PreTrainedTokenizer,       a_			:						int = -1,       a_			:						int = -1,       a_			:						bool = False,       a_			:						Optional[TensorType] = None,       ):





    """simple docstring"""



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

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

 def 		lowercase_    (			self			:						List[Any],       a_			:						PreTrainedTokenizer,       a_			:						int = -1,       a_			:						int = -1,       a_			:						bool = False,       a_			:						Optional[TensorType] = None,       ):





    """simple docstring"""



    UpperCamelCase__			    =    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__			    =    tokenizer.num_special_tokens_to_add(a_       )
    UpperCamelCase__			    =    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__			    =    [" ".join([tokenizer.unk_token]       ) * seq_length] * batch_size
    UpperCamelCase__			    =    dict(tokenizer(a_,       return_tensors=a_       )       )
    return common_inputs

 def 		lowercase_    (			self			:						Union[str, Any],       a_			:						PreTrainedTokenizer,       a_			:						int = -1,       a_			:						int = -1,       a_			:						bool = False,       a_			:						Optional[TensorType] = None,       ):





    """simple docstring"""



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

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

    return common_inputs

 def 		lowercase_    (			self			:						Dict,       a_			:						int,       a_			:						str,       a_			:						List[Any],       a_			:						int       ):





    """simple docstring"""



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