


import heapq
import sys

import numpy as np

A   :					List[Any]	=      tuple[int, int]







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						[]
             lowerCamelCase__	:       Any					=						set()





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             if not self.empty():
                   return self.elements[0][0]
             else:
                   return float("inf" )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             return len(self.elements ) == 0





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             if item not in self.set:
                   heapq.heappush(self.elements      ,   (priority, item) )
                   self.set.add(__lowerCamelCase )
             else:
                   # update
                   # print("update", item)
                   lowerCamelCase__	:       Tuple					=						[]
                   ((lowerCamelCase__)					,     (lowerCamelCase__))	:       Tuple					=						heapq.heappop(self.elements )
                   while x != item:
                         temp.append((pri, x) )
                         ((lowerCamelCase__)					,     (lowerCamelCase__))	:       Union[str, Any]					=						heapq.heappop(self.elements )
                   temp.append((priority, item) )
                   for pro, xxx in temp:
                         heapq.heappush(self.elements      ,   (pro, xxx) )





       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   __lowerCamelCase							:    Dict ):


             '''simple docstring'''



             if item in self.set:
                   self.set.remove(__lowerCamelCase )
                   lowerCamelCase__	:       str					=						[]
                   ((lowerCamelCase__)					,     (lowerCamelCase__))	:       Optional[int]					=						heapq.heappop(self.elements )
                   while x != item:
                         temp.append((pro, x) )
                         ((lowerCamelCase__)					,     (lowerCamelCase__))	:       Optional[Any]					=						heapq.heappop(self.elements )
                   for prito, yyy in temp:
                         heapq.heappush(self.elements      ,   (prito, yyy) )





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             return self.elements[0][1]





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             ((lowerCamelCase__)					,     (lowerCamelCase__))	:       Tuple					=						heapq.heappop(self.elements )
             self.set.remove(__lowerCamelCase )
             return (priority, item)



def    lowercase_	(	_A    :					TPos					,      _A    :					TPos		):

      """simple docstring"""




      lowerCamelCase__	:       Dict					=						np.array(_A		)
      lowerCamelCase__	:       str					=						np.array(_A		)
      return np.linalg.norm(a - b		)

def    lowercase_	(	_A    :					TPos					,      _A    :					TPos		):

      """simple docstring"""




      return consistent_heuristic(_A					,      _A		) // t

def    lowercase_	(	_A    :					TPos					,      _A    :					TPos		):

      """simple docstring"""




      return abs(p[0] - goal[0]		) + abs(p[1] - goal[1]		)

def    lowercase_	(	_A    :					TPos					,      _A    :					int					,      _A    :					TPos					,      _A    :					dict[TPos, float]		):

      """simple docstring"""




      lowerCamelCase__	:       Optional[int]					=						g_function[start] + Wa * heuristics[i](_A					,      _A		)
      return ans

def    lowercase_	(	_A    :					Dict					,      _A    :					Dict					,      _A    :					Optional[int]		):

      """simple docstring"""




      lowerCamelCase__	:       List[str]					=						np.chararray((n, n)		)
      for i in range(_A		):
            for j in range(_A		):
                  lowerCamelCase__	:       Any					=						"*"

      for i in range(_A		):
            for j in range(_A		):
                  if (j, (n - 1) - i) in blocks:
                        lowerCamelCase__	:       Optional[Any]					=						"#"

      lowerCamelCase__	:       Union[str, Any]					=						"-"
      lowerCamelCase__	:       Union[str, Any]					=						back_pointer[goal]
      while x != start:
            ((lowerCamelCase__)					,     (lowerCamelCase__))	:       str					=						x
            # print(x)
            lowerCamelCase__	:       int					=						"-"
            lowerCamelCase__	:       Dict					=						back_pointer[x]
      lowerCamelCase__	:       Union[str, Any]					=						"-"

      for i in range(_A		):
            for j in range(_A		):
                  if (i, j) == (0, n - 1):
                        print(grid[i][j]					,      end=" "		)
                        print("<-- End position"					,      end=" "		)
                  else:
                        print(grid[i][j]					,      end=" "		)
            print()
      print("^"		)
      print("Start position"		)
      print()
      print("# is an obstacle"		)
      print("- is the path taken by algorithm"		)
      print("PATH TAKEN BY THE ALGORITHM IS:-"		)
      lowerCamelCase__	:       Optional[int]					=						back_pointer[goal]
      while x != start:
            print(_A					,      end=" "		)
            lowerCamelCase__	:       Any					=						back_pointer[x]
      print(_A		)
      sys.exit()

def    lowercase_	(	_A    :					TPos		):

      """simple docstring"""




      if p[0] < 0 or p[0] > n - 1:
            return False
      if p[1] < 0 or p[1] > n - 1:
            return False
      return True

def    lowercase_	(	_A    :					List[str]					,      _A    :					List[str]					,      _A    :					Any					,      _A    :					Any					,      _A    :					Optional[Any]					,      _A    :					List[str]					,      _A    :					Dict					,      _A    :					int					,      ):

      """simple docstring"""




      for itera in range(_A		):
            open_list[itera].remove_element(_A		)
      # print("s", s)
      # print("j", j)
      ((lowerCamelCase__)					,     (lowerCamelCase__))	:       Union[str, Any]					=						s
      lowerCamelCase__	:       Any					=						(x - 1, y)
      lowerCamelCase__	:       List[Any]					=						(x + 1, y)
      lowerCamelCase__	:       Any					=						(x, y + 1)
      lowerCamelCase__	:       List[str]					=						(x, y - 1)

      for neighbours in [left, right, up, down]:
            if neighbours not in blocks:
                  if valid(_A		) and neighbours not in visited:
                        # print("neighbour", neighbours)
                        visited.add(_A		)
                        lowerCamelCase__	:       Optional[Any]					=						-1
                        lowerCamelCase__	:       Optional[Any]					=						float("inf"		)

                  if valid(_A		) and g_function[neighbours] > g_function[s] + 1:
                        lowerCamelCase__	:       Tuple					=						g_function[s] + 1
                        lowerCamelCase__	:       List[Any]					=						s
                        if neighbours not in close_list_anchor:
                              open_list[0].put(_A					,      key(_A					,      0					,      _A					,      _A		)		)
                              if neighbours not in close_list_inad:
                                    for var in range(1					,      _A		):
                                          if key(_A					,      _A					,      _A					,      _A		) <= Wa * key(
                                              _A					,      0					,      _A					,      _A		):
                                                open_list[j].put(
                                                    _A					,      key(_A					,      _A					,      _A					,      _A		)		)

def    lowercase_	(	):

      """simple docstring"""




      lowerCamelCase__	:       Optional[int]					=						[]
      for x in range(1					,      5		):
            for y in range(1					,      6		):
                  some_list.append((x, y)		)

      for x in range(15					,      20		):
            some_list.append((x, 17)		)

      for x in range(10					,      19		):
            for y in range(1					,      15		):
                  some_list.append((x, y)		)

    # L block
      for x in range(1					,      4		):
            for y in range(12					,      19		):
                  some_list.append((x, y)		)
      for x in range(3					,      13		):
            for y in range(16					,      19		):
                  some_list.append((x, y)		)
      return some_list


A   :					Optional[int]	=      {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

A   :					Union[str, Any]	=      [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
A   :					Optional[Any]	=      make_common_ground()


A   :					Optional[Any]	=      blocks_blk
# hyper parameters
A   :					List[Any]	=      1
A   :					str	=      1
A   :					Tuple	=      20
A   :					int	=      3  # one consistent and two other inconsistent

# start and end destination
A   :					str	=      (0, 0)
A   :					Union[str, Any]	=      (n - 1, n - 1)

A   :					Tuple	=      1

def    lowercase_	(	_A    :					TPos					,      _A    :					TPos					,      _A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       Union[str, Any]					=						{start: 0, goal: float("inf"		)}
      lowerCamelCase__	:       List[Any]					=						{start: -1, goal: -1}
      lowerCamelCase__	:       Union[str, Any]					=						[]
      lowerCamelCase__	:       str					=						set()

      for i in range(_A		):
            open_list.append(PriorityQueue()		)
            open_list[i].put(_A					,      key(_A					,      _A					,      _A					,      _A		)		)

      lowerCamelCase__	:       list[int]					=						[]
      lowerCamelCase__	:       list[int]					=						[]
      while open_list[0].minkey() < float("inf"		):
            for i in range(1					,      _A		):
                  # print(open_list[0].minkey(), open_list[i].minkey())
                  if open_list[i].minkey() <= Wa * open_list[0].minkey():
                        global t
                        t += 1
                        if g_function[goal] <= open_list[i].minkey():
                              if g_function[goal] < float("inf"		):
                                    do_something(_A					,      _A					,      _A		)
                        else:
                              lowerCamelCase__					,     lowerCamelCase__	:       List[str]					=						open_list[i].top_show()
                              visited.add(_A		)
                              expand_state(
                                  _A					,      _A					,      _A					,      _A					,      _A					,      _A					,      _A					,      _A					,      )
                              close_list_inad.append(_A		)
                  else:
                        if g_function[goal] <= open_list[0].minkey():
                              if g_function[goal] < float("inf"		):
                                    do_something(_A					,      _A					,      _A		)
                        else:
                              lowerCamelCase__	:       Any					=						open_list[0].top_show()
                              visited.add(_A		)
                              expand_state(
                                  _A					,      0					,      _A					,      _A					,      _A					,      _A					,      _A					,      _A					,      )
                              close_list_anchor.append(_A		)
      print("No path found to goal"		)
      print()
      for i in range(n - 1					,      -1					,      -1		):
            for j in range(_A		):
                  if (j, i) in blocks:
                        print("#"					,      end=" "		)
                  elif (j, i) in back_pointer:
                        if (j, i) == (n - 1, n - 1):
                              print("*"					,      end=" "		)
                        else:
                              print("-"					,      end=" "		)
                  else:
                        print("*"					,      end=" "		)
                  if (j, i) == (n - 1, n - 1):
                        print("<-- End position"					,      end=" "		)
            print()
      print("^"		)
      print("Start position"		)
      print()
      print("# is an obstacle"		)
      print("- is the path taken by algorithm"		)


if __name__ == "__main__":
    multi_a_star(start, goal, n_heuristic)






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


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

A   :					Any	=      {
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "blenderbot-small"
       A__      		=    ["past_key_values"]
       A__      		=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





       def __init__(     self							:    Dict      ,   __lowerCamelCase							:    List[str]=50265      ,   __lowerCamelCase							:    str=512      ,   __lowerCamelCase							:    Tuple=8      ,   __lowerCamelCase							:    str=2048      ,   __lowerCamelCase							:    str=16      ,   __lowerCamelCase							:    List[Any]=8      ,   __lowerCamelCase							:    Any=2048      ,   __lowerCamelCase							:    List[str]=16      ,   __lowerCamelCase							:    Dict=0.0      ,   __lowerCamelCase							:    List[Any]=0.0      ,   __lowerCamelCase							:    Optional[int]=True      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    Tuple="gelu"      ,   __lowerCamelCase							:    Tuple=512      ,   __lowerCamelCase							:    Dict=0.1      ,   __lowerCamelCase							:    int=0.0      ,   __lowerCamelCase							:    Union[str, Any]=0.0      ,   __lowerCamelCase							:    Any=0.0_2      ,   __lowerCamelCase							:    str=1      ,   __lowerCamelCase							:    Dict=False      ,   __lowerCamelCase							:    int=0      ,   __lowerCamelCase							:    Optional[Any]=1      ,   __lowerCamelCase							:    str=2      ,   __lowerCamelCase							:    Any=2      ,   **__lowerCamelCase							:    int      ,   ):


             '''simple docstring'''



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

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







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



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

                   if self.use_past:
                         lowerCamelCase__	:       Union[str, Any]					=						{0: "batch"}
                         lowerCamelCase__	:       int					=						{0: "batch", 1: "past_decoder_sequence + sequence"}
                   else:
                         lowerCamelCase__	:       Tuple					=						{0: "batch", 1: "decoder_sequence"}
                         lowerCamelCase__	:       str					=						{0: "batch", 1: "decoder_sequence"}

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


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Union[str, Any]					=						super().outputs
             else:
                   lowerCamelCase__	:       int					=						super(__lowerCamelCase      ,   self ).outputs
                   if self.use_past:
                         lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						self.num_layers
                         for i in range(__lowerCamelCase ):
                               lowerCamelCase__	:       Tuple					=						{0: "batch", 2: "past_sequence + sequence"}
                               lowerCamelCase__	:       Any					=						{0: "batch", 2: "past_sequence + sequence"}
             return common_outputs





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


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )

             # Generate decoder inputs
             lowerCamelCase__	:       List[str]					=						seq_length if not self.use_past else 1
             lowerCamelCase__	:       List[Any]					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
             lowerCamelCase__	:       Optional[Any]					=						{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
             lowerCamelCase__	:       Optional[Any]					=						dict(**__lowerCamelCase      ,   **__lowerCamelCase )

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

                   lowerCamelCase__	:       List[Any]					=						torch.cat(
                       [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase      ,   __lowerCamelCase )]      ,   dim=1 )

                   lowerCamelCase__	:       Optional[Any]					=						[]
                   # If the number of encoder and decoder layers are present in the model configuration, both are considered
                   lowerCamelCase__					,     lowerCamelCase__	:       str					=						self.num_layers
                   lowerCamelCase__	:       Union[str, Any]					=						min(__lowerCamelCase      ,   __lowerCamelCase )
                   lowerCamelCase__	:       Union[str, Any]					=						max(__lowerCamelCase      ,   __lowerCamelCase ) - min_num_layers
                   lowerCamelCase__	:       str					=						"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )

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

                   lowerCamelCase__	:       Union[str, Any]					=						common_inputs["attention_mask"].dtype
                   lowerCamelCase__	:       List[str]					=						torch.cat(
                       [common_inputs["attention_mask"], torch.ones(__lowerCamelCase      ,   __lowerCamelCase      ,   dtype=__lowerCamelCase )]      ,   dim=1 )
                   lowerCamelCase__	:       Tuple					=						[
                       (torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(__lowerCamelCase )
                   ]
             return common_inputs





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


             '''simple docstring'''



             lowerCamelCase__	:       str					=						compute_effective_axis_dimension(
                 __lowerCamelCase      ,   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
             lowerCamelCase__	:       List[str]					=						tokenizer.num_special_tokens_to_add(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						compute_effective_axis_dimension(
                 __lowerCamelCase      ,   fixed_dimension=OnnxConfig.default_fixed_sequence      ,   num_token_to_add=__lowerCamelCase )

             # Generate dummy inputs according to compute batch and sequence
             lowerCamelCase__	:       Optional[int]					=						[" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
             lowerCamelCase__	:       Optional[Any]					=						dict(tokenizer(__lowerCamelCase      ,   return_tensors=__lowerCamelCase ) )
             return common_inputs





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Optional[int]					=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                       __lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   seq_length=__lowerCamelCase      ,   is_pair=__lowerCamelCase      ,   framework=__lowerCamelCase )

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

             return common_inputs





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



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


