






from __future__ import annotations

lowerCamelCase			:					int         =	[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, ):
     __lowerCAmelCase								=					[
         [0 for col in range(len(grid[0]       )       )] for row in range(len(__snake_case       )       )
     ]  # the reference grid
     __lowerCAmelCase								=					1
     __lowerCAmelCase								=					[
         [0 for col in range(len(grid[0]       )       )] for row in range(len(__snake_case       )       )
     ]  # the action grid

     __lowerCAmelCase								=					init[0]
     __lowerCAmelCase								=					init[1]
     __lowerCAmelCase								=					0
     __lowerCAmelCase								=					g + heuristic[x][y]  # cost from starting cell to destination cell
     __lowerCAmelCase								=					[[f, g, x, y]]

     __lowerCAmelCase								=					False  # flag that is set when search is complete
     __lowerCAmelCase								=					False  # flag set if we can't find expand

     while not found and not resign:
          if len(__snake_case       ) == 0:
               raise ValueError("Algorithm is unable to find solution"       )
          else:  # to choose the least costliest action so as to move closer to the goal
               cell.sort()
               cell.reverse()
               __lowerCAmelCase								=					cell.pop()
               __lowerCAmelCase								=					next_cell[2]
               __lowerCAmelCase								=					next_cell[3]
               __lowerCAmelCase								=					next_cell[1]

               if x == goal[0] and y == goal[1]:
                    __lowerCAmelCase								=					True
               else:
                    for i in range(len(__snake_case       )       ):  # to try out different valid actions
                         __lowerCAmelCase								=					x + DIRECTIONS[i][0]
                         __lowerCAmelCase								=					y + DIRECTIONS[i][1]
                         if xa >= 0 and xa < len(__snake_case       ) and ya >= 0 and ya < len(grid[0]       ):
                              if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                                   __lowerCAmelCase								=					g + cost
                                   __lowerCAmelCase								=					ga + heuristic[xa][ya]
                                   cell.append([fa, ga, xa, ya]       )
                                   __lowerCAmelCase								=					1
                                   __lowerCAmelCase								=					i
     __lowerCAmelCase								=					[]
     __lowerCAmelCase								=					goal[0]
     __lowerCAmelCase								=					goal[1]
     invpath.append([x, y]       )  # we get the reverse path from here
     while x != init[0] or y != init[1]:
          __lowerCAmelCase								=					x - DIRECTIONS[action[x][y]][0]
          __lowerCAmelCase								=					y - DIRECTIONS[action[x][y]][1]
          __lowerCAmelCase								=					xa
          __lowerCAmelCase								=					ya
          invpath.append([x, y]       )

     __lowerCAmelCase								=					[]
     for i in range(len(__snake_case       )       ):
          path.append(invpath[len(__snake_case       ) - 1 - i]       )
     return path, action


if __name__ == "__main__":
 lowerCamelCase			:					List[Any]         =	[
     [0, 1, 0, 0, 0, 0],
     [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
     [0, 1, 0, 0, 0, 0],
     [0, 1, 0, 0, 1, 0],
     [0, 0, 0, 0, 1, 0],
 ]

 lowerCamelCase			:					List[str]         =	[0, 0]
 # all coordinates are given in format [y,x]
 lowerCamelCase			:					int         =	[len(grid) - 1, len(grid[0]) - 1]
 lowerCamelCase			:					Union[str, Any]         =	1

 # the cost map which pushes the path closer to the goal
 lowerCamelCase			:					Any         =	[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
 for i in range(len(grid)):
  for j in range(len(grid[0])):
   lowerCamelCase			:					int         =	abs(i - goal[0]) + abs(j - goal[1])
   if grid[i][j] == 1:
    # added extra penalty in the heuristic map
    lowerCamelCase			:					int         =	99

 lowerCamelCase      ,					lowerCamelCase			:					Optional[Any]         =	search(grid, init, goal, cost, heuristic)

 print('''ACTION MAP''')
 for i in range(len(action)):
  print(action[i])

 for i in range(len(path)):
  print(path[i])














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


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

lowerCamelCase			:					Union[str, 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 							_UpperCamelCase			(a_			):
      snake_case_  =							"""blenderbot-small"""
      snake_case_  =							["""past_key_values"""]
      snake_case_  =							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


      def __init__(	self					,						__UpperCamelCase=5_0_2_6_5					,						__UpperCamelCase=5_1_2					,						__UpperCamelCase=8					,						__UpperCamelCase=2_0_4_8					,						__UpperCamelCase=1_6					,						__UpperCamelCase=8					,						__UpperCamelCase=2_0_4_8					,						__UpperCamelCase=1_6					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=5_1_2					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1					,						__UpperCamelCase=False					,						__UpperCamelCase=0					,						__UpperCamelCase=1					,						__UpperCamelCase=2					,						__UpperCamelCase=2					,						**__UpperCamelCase					,						)->	Any:
           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					d_model
           __lowerCAmelCase								=					encoder_ffn_dim
           __lowerCAmelCase								=					encoder_layers
           __lowerCAmelCase								=					encoder_attention_heads
           __lowerCAmelCase								=					decoder_ffn_dim
           __lowerCAmelCase								=					decoder_layers
           __lowerCAmelCase								=					decoder_attention_heads
           __lowerCAmelCase								=					dropout
           __lowerCAmelCase								=					attention_dropout
           __lowerCAmelCase								=					activation_dropout
           __lowerCAmelCase								=					activation_function
           __lowerCAmelCase								=					init_std
           __lowerCAmelCase								=					encoder_layerdrop
           __lowerCAmelCase								=					decoder_layerdrop
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					encoder_layers
           __lowerCAmelCase								=					scale_embedding  # scale factor will be sqrt(d_model) if True

           super().__init__(
               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					,						)



class 							_UpperCamelCase			(a_			):


      @property
      def  __UpperCAmelCase		(	self	)->	Mapping[str, Mapping[int, str]]:
           if self.task in ["default", "seq2seq-lm"]:
                __lowerCAmelCase								=					OrderedDict(
                    [
                        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                    ]	)

                if self.use_past:
                     __lowerCAmelCase								=					{0: "batch"}
                     __lowerCAmelCase								=					{0: "batch", 1: "past_decoder_sequence + sequence"}
                else:
                     __lowerCAmelCase								=					{0: "batch", 1: "decoder_sequence"}
                     __lowerCAmelCase								=					{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.
                __lowerCAmelCase								=					OrderedDict(
                    [
                        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                    ]	)
                if self.use_past:
                     __lowerCAmelCase			, __lowerCAmelCase								=					self.num_layers
                     for i in range(__UpperCamelCase	):
                          __lowerCAmelCase								=					{0: "batch", 2: "past_sequence + sequence"}
                          __lowerCAmelCase								=					{0: "batch", 2: "past_sequence + sequence"}
           else:
                __lowerCAmelCase								=					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]]:
           if self.task in ["default", "seq2seq-lm"]:
                __lowerCAmelCase								=					super().outputs
           else:
                __lowerCAmelCase								=					super(__UpperCamelCase					,						self	).outputs
                if self.use_past:
                     __lowerCAmelCase			, __lowerCAmelCase								=					self.num_layers
                     for i in range(__UpperCamelCase	):
                          __lowerCAmelCase								=					{0: "batch", 2: "past_sequence + sequence"}
                          __lowerCAmelCase								=					{0: "batch", 2: "past_sequence + sequence"}
           return common_outputs


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = -1					,						__UpperCamelCase = -1					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						)->	Mapping[str, Any]:
           __lowerCAmelCase								=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)

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

                __lowerCAmelCase								=					torch.cat(
                    [common_inputs["decoder_attention_mask"], torch.ones(__UpperCamelCase					,						__UpperCamelCase	)]					,						dim=1	)

                __lowerCAmelCase								=					[]
                # If the number of encoder and decoder layers are present in the model configuration, both are considered
                __lowerCAmelCase			, __lowerCAmelCase								=					self.num_layers
                __lowerCAmelCase								=					min(__UpperCamelCase					,						__UpperCamelCase	)
                __lowerCAmelCase								=					max(__UpperCamelCase					,						__UpperCamelCase	) - min_num_layers
                __lowerCAmelCase								=					"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.
                __lowerCAmelCase								=					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]:
           __lowerCAmelCase								=					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
                __lowerCAmelCase			, __lowerCAmelCase								=					common_inputs["input_ids"].shape
                # Not using the same length for past_key_values
                __lowerCAmelCase								=					seqlen + 2
                __lowerCAmelCase			, __lowerCAmelCase								=					self.num_layers
                __lowerCAmelCase			, __lowerCAmelCase								=					self.num_attention_heads
                __lowerCAmelCase								=					(
                    batch,
                    num_encoder_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // num_encoder_attention_heads,
                )

                __lowerCAmelCase								=					common_inputs["attention_mask"].dtype
                __lowerCAmelCase								=					torch.cat(
                    [common_inputs["attention_mask"], torch.ones(__UpperCamelCase					,						__UpperCamelCase					,						dtype=__UpperCamelCase	)]					,						dim=1	)
                __lowerCAmelCase								=					[
                    (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]:
           # 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
           __lowerCAmelCase								=					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
           __lowerCAmelCase								=					tokenizer.num_special_tokens_to_add(__UpperCamelCase	)
           __lowerCAmelCase								=					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
           __lowerCAmelCase								=					[" ".join([tokenizer.unk_token]	) * seq_length] * batch_size
           __lowerCAmelCase								=					dict(tokenizer(__UpperCamelCase					,						return_tensors=__UpperCamelCase	)	)
           return common_inputs


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = -1					,						__UpperCamelCase = -1					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						)->	Mapping[str, Any]:
           if self.task in ["default", "seq2seq-lm"]:
                __lowerCAmelCase								=					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":
                __lowerCAmelCase								=					self._generate_dummy_inputs_for_causal_lm(
                    __UpperCamelCase					,						batch_size=__UpperCamelCase					,						seq_length=__UpperCamelCase					,						is_pair=__UpperCamelCase					,						framework=__UpperCamelCase	)
           else:
                __lowerCAmelCase								=					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	)->	List[Any]:
           if self.task in ["default", "seq2seq-lm"]:
                __lowerCAmelCase								=					super()._flatten_past_key_values_(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)
           else:
                __lowerCAmelCase								=					super(__UpperCamelCase					,						self	)._flatten_past_key_values_(
                    __UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)






