
"""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


SCREAMING_SNAKE_CASE_         =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_         =  {
    '''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_							):
      __SCREAMING_SNAKE_CASE				:							Tuple							 =		"mbart"
      __SCREAMING_SNAKE_CASE				:							Union[str, Any]							 =		["past_key_values"]
      __SCREAMING_SNAKE_CASE				:							List[str]							 =		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




      def __init__(							self				,    lowercase_=5_0_2_6_5				,    lowercase_=1_0_2_4				,    lowercase_=1_2				,    lowercase_=4_0_9_6				,    lowercase_=1_6				,    lowercase_=1_2				,    lowercase_=4_0_9_6				,    lowercase_=1_6				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=True				,    lowercase_=True				,    lowercase_="gelu"				,    lowercase_=1_0_2_4				,    lowercase_=0.1				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=0.0_2				,    lowercase_=0.0				,    lowercase_=False				,    lowercase_=1				,    lowercase_=0				,    lowercase_=2				,    lowercase_=2				,    **lowercase_				,    )  ->   Union[str, Any]:
          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=lowercase_				,    bos_token_id=lowercase_				,    eos_token_id=lowercase_				,    is_encoder_decoder=lowercase_				,    forced_eos_token_id=lowercase_				,    **lowercase_				,    )

class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):




      @property
      def       a_ (							self							)  ->   Mapping[str, Mapping[int, str]]:
          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_(lowercase_				,    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(lowercase_							):
                      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       a_ (							self							)  ->   Mapping[str, Mapping[int, str]]:
          if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase       =   super().outputs
          else:
              UpperCAmelCase       =   super(lowercase_				,    self							).outputs
              if self.use_past:
                  UpperCAmelCase     ,							UpperCAmelCase       =   self.num_layers
                  for i in range(lowercase_							):
                      UpperCAmelCase       =   {0: 'batch', 2: 'past_sequence + sequence'}
                      UpperCAmelCase       =   {0: 'batch', 2: 'past_sequence + sequence'}
          return common_outputs




      def       a_ (							self				,    lowercase_				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = False				,    lowercase_ = None				,    )  ->   Mapping[str, Any]:
          UpperCAmelCase       =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)

          # Generate decoder inputs
          UpperCAmelCase       =   seq_length if not self.use_past else 1
          UpperCAmelCase       =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)
          UpperCAmelCase       =   {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
          UpperCAmelCase       =   dict(**lowercase_				,    **lowercase_							)

          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(lowercase_				,    lowercase_							)]				,    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(lowercase_				,    lowercase_							)
              UpperCAmelCase       =   max(lowercase_				,    lowercase_							) - min_num_layers
              UpperCAmelCase       =   'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

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




      def       a_ (							self				,    lowercase_				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = False				,    lowercase_ = None				,    )  ->   Mapping[str, Any]:
          UpperCAmelCase       =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)

          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(lowercase_				,    lowercase_				,    dtype=lowercase_							)]				,    dim=1							)
              UpperCAmelCase       =   [
                  (torch.zeros(lowercase_							), torch.zeros(lowercase_							)) for _ in range(lowercase_							)
              ]
          return common_inputs




      def       a_ (							self				,    lowercase_				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = False				,    lowercase_ = None				,    )  ->   Mapping[str, Any]:
          # 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       =   compute_effective_axis_dimension(
              lowercase_				,    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(lowercase_							)
          UpperCAmelCase       =   compute_effective_axis_dimension(
              lowercase_				,    fixed_dimension=OnnxConfig.default_fixed_sequence				,    num_token_to_add=lowercase_							)

          # Generate dummy inputs according to compute batch and sequence
          UpperCAmelCase       =   [' '.join([tokenizer.unk_token]							) * seq_length] * batch_size
          UpperCAmelCase       =   dict(tokenizer(lowercase_				,    return_tensors=lowercase_							)							)
          return common_inputs




      def       a_ (							self				,    lowercase_				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = False				,    lowercase_ = None				,    )  ->   Mapping[str, Any]:
          if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase       =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  lowercase_				,    batch_size=lowercase_				,    seq_length=lowercase_				,    is_pair=lowercase_				,    framework=lowercase_							)

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

          return common_inputs




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase       =   super()._flatten_past_key_values_(lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)
          else:
              UpperCAmelCase       =   super(lowercase_				,    self							)._flatten_past_key_values_(
                  lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)





"""simple docstring"""

import math
import sys
class        _UpperCAmelCase							:




      def __init__(							self							)  ->   List[str]:
          UpperCAmelCase       =   0
          UpperCAmelCase       =   []
          UpperCAmelCase       =   {}  # To store the pos of node in array




      def       a_ (							self							)  ->   Dict:
          return self.cur_size == 0




      def       a_ (							self				,    lowercase_							)  ->   List[str]:
          UpperCAmelCase       =   self.left(lowercase_							)
          UpperCAmelCase       =   self.right(lowercase_							)
          if lc < self.cur_size and self.array(lowercase_							)[0] < self.array(lowercase_							)[0]:
              UpperCAmelCase       =   lc
          else:
              UpperCAmelCase       =   idx
          if rc < self.cur_size and self.array(lowercase_							)[0] < self.array(lowercase_							)[0]:
              UpperCAmelCase       =   rc
          if smallest != idx:
              self.swap(lowercase_				,    lowercase_							)
              self.min_heapify(lowercase_							)




      def       a_ (							self				,    lowercase_							)  ->   List[str]:
          # Inserts a node into the Priority Queue
          UpperCAmelCase       =   self.cur_size
          self.cur_size += 1
          self.array.append((sys.maxsize, tup[1])							)
          self.decrease_key((sys.maxsize, tup[1])				,    tup[0]							)




      def       a_ (							self							)  ->   int:
          # Removes and returns the min element at top of priority queue
          UpperCAmelCase       =   self.array[0][1]
          UpperCAmelCase       =   self.array[self.cur_size - 1]
          self.cur_size -= 1
          self.min_heapify(1							)
          del self.pos[min_node]
          return min_node




      def       a_ (							self				,    lowercase_							)  ->   Optional[int]:
          # returns the index of left child
          return 2 * i + 1




      def       a_ (							self				,    lowercase_							)  ->   List[Any]:
          # returns the index of right child
          return 2 * i + 2




      def       a_ (							self				,    lowercase_							)  ->   List[Any]:
          # returns the index of parent
          return math.floor(i / 2							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   List[Any]:
          # swaps array elements at indices i and j
          # update the pos{}
          UpperCAmelCase       =   j
          UpperCAmelCase       =   i
          UpperCAmelCase       =   self.array[i]
          UpperCAmelCase       =   self.array[j]
          UpperCAmelCase       =   temp




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   int:
          UpperCAmelCase       =   self.pos[tup[1]]
          # assuming the new_d is atmost old_d
          UpperCAmelCase       =   (new_d, tup[1])
          while idx > 0 and self.array[self.par(lowercase_							)][0] > self.array[idx][0]:
              self.swap(lowercase_				,    self.par(lowercase_							)							)
              UpperCAmelCase       =   self.par(lowercase_							)

class        _UpperCAmelCase							:




      def __init__(							self				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   {}  # To store graph: u -> (v,w)
          UpperCAmelCase       =   num  # Number of nodes in graph
          # To store the distance from source vertex
          UpperCAmelCase       =   [0] * self.num_nodes
          UpperCAmelCase       =   [-1] * self.num_nodes  # To store the path




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Any:
          #  Edge going from node u to v and v to u with weight w
          # u (w)-> v, v (w) -> u
          # Check if u already in graph
          if u in self.adjList:
              self.adjList[u].append((v, w)							)
          else:
              UpperCAmelCase       =   [(v, w)]

          # Assuming undirected graph
          if v in self.adjList:
              self.adjList[v].append((u, w)							)
          else:
              UpperCAmelCase       =   [(u, w)]




      def       a_ (							self							)  ->   Optional[Any]:
          # u -> v(w)
          for u in self.adjList:
              print(lowercase_				,    '->'				,    ' -> '.join(str(F"{v}({w})"							) for v, w in self.adjList[u]							)							)




      def       a_ (							self				,    lowercase_							)  ->   int:
          # Flush old junk values in par[]
          UpperCAmelCase       =   [-1] * self.num_nodes
          # src is the source node
          UpperCAmelCase       =   0
          UpperCAmelCase       =   PriorityQueue()
          q.insert((0, src)							)  # (dist from src, node)
          for u in self.adjList:
              if u != src:
                  UpperCAmelCase       =   sys.maxsize  # Infinity
                  UpperCAmelCase       =   -1

          while not q.is_empty():
              UpperCAmelCase       =   q.extract_min()  # Returns node with the min dist from source
              # Update the distance of all the neighbours of u and
              # if their prev dist was INFINITY then push them in Q
              for v, w in self.adjList[u]:
                  UpperCAmelCase       =   self.dist[u] + w
                  if self.dist[v] > new_dist:
                      if self.dist[v] == sys.maxsize:
                          q.insert((new_dist, v)							)
                      else:
                          q.decrease_key((self.dist[v], v)				,    lowercase_							)
                      UpperCAmelCase       =   new_dist
                      UpperCAmelCase       =   u

        # Show the shortest distances from src
          self.show_distances(lowercase_							)




      def       a_ (							self				,    lowercase_							)  ->   Any:
          print(F"Distance from node: {src}"							)
          for u in range(self.num_nodes							):
              print(F"Node {u} has distance: {self.dist[u]}"							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   List[Any]:
          # To show the shortest path from src to dest
          # WARNING: Use it *after* calling dijkstra
          UpperCAmelCase       =   []
          UpperCAmelCase       =   0
          UpperCAmelCase       =   dest
          # Backtracking from dest to src
          while self.par[temp] != -1:
              path.append(lowercase_							)
              if temp != src:
                  for v, w in self.adjList[temp]:
                      if v == self.par[temp]:
                          cost += w
                          break
              UpperCAmelCase       =   self.par[temp]
          path.append(lowercase_							)
          path.reverse()

          print(F"----Path to reach {dest} from {src}----"							)
          for u in path:
              print(F"{u}"				,    end=' '							)
              if u != dest:
                  print('-> '				,    end=''							)

          print('\nTotal cost of path: '				,    lowercase_							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_         =  Graph(9)
   graph.add_edge(0, 1, 4)
   graph.add_edge(0, 7, 8)
   graph.add_edge(1, 2, 8)
   graph.add_edge(1, 7, 11)
   graph.add_edge(2, 3, 7)
   graph.add_edge(2, 8, 2)
   graph.add_edge(2, 5, 4)
   graph.add_edge(3, 4, 9)
   graph.add_edge(3, 5, 14)
   graph.add_edge(4, 5, 10)
   graph.add_edge(5, 6, 2)
   graph.add_edge(6, 7, 1)
   graph.add_edge(6, 8, 6)
   graph.add_edge(7, 8, 7)
   graph.show_graph()
   graph.dijkstra(0)
   graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21



