




"""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


a  =				logging.get_logger(__name__)

a  =				{
    '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    SCREAMING_SNAKE_CASE__       (       _a       ):
						_a         =    'bart'
						_a         =    ['past_key_values']
						_a         =    {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


						def __init__(				self					:      List[str]    ,    lowerCAmelCase					:      Any=5_0265    ,    lowerCAmelCase					:      Dict=1024    ,    lowerCAmelCase					:      Optional[int]=12    ,    lowerCAmelCase					:      str=4096    ,    lowerCAmelCase					:      List[str]=16    ,    lowerCAmelCase					:      Any=12    ,    lowerCAmelCase					:      List[Any]=4096    ,    lowerCAmelCase					:      List[str]=16    ,    lowerCAmelCase					:      Tuple=0.0    ,    lowerCAmelCase					:      List[Any]=0.0    ,    lowerCAmelCase					:      Tuple="gelu"    ,    lowerCAmelCase					:      int=1024    ,    lowerCAmelCase					:      Union[str, Any]=0.1    ,    lowerCAmelCase					:      Optional[Any]=0.0    ,    lowerCAmelCase					:      Optional[int]=0.0    ,    lowerCAmelCase					:      int=0.02    ,    lowerCAmelCase					:      List[str]=0.0    ,    lowerCAmelCase					:      str=False    ,    lowerCAmelCase					:      Dict=True    ,    lowerCAmelCase					:      int=3    ,    lowerCAmelCase					:      Tuple=1    ,    lowerCAmelCase					:      int=0    ,    lowerCAmelCase					:      Dict=2    ,    lowerCAmelCase					:      Dict=True    ,    lowerCAmelCase					:      Optional[Any]=2    ,    lowerCAmelCase					:      str=2    ,    **lowerCAmelCase					:      int    ,    ):
										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						=					classifier_dropout
										lowerCAmelCase						=					use_cache
										lowerCAmelCase						=					encoder_layers
										lowerCAmelCase						=					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       ):
														lowerCAmelCase						=					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    SCREAMING_SNAKE_CASE__       (       _a       ):


						@property
						def 	__lowercase			(				self					:      Any       ):


										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 	__lowercase			(				self					:      Optional[int]       ):


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


										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 	__lowercase			(				self					:      Optional[Any]    ,    lowerCAmelCase					:      Optional[Any]    ,    lowerCAmelCase					:      Optional[int]    ,    lowerCAmelCase					:      Optional[int]    ,    lowerCAmelCase					:      int       ):


										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       )













from __future__ import annotations

UpperCAmelCase__							  =						list[tuple[int, int]]

UpperCAmelCase__							  =						[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

UpperCAmelCase__							  =						([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		Dict					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		float					,   __UpperCAmelCase :		Node | None					,   )       ->int:




       """simple docstring"""


       a						    = pos_x
       a						    = pos_y
       a						    = (pos_y, pos_x)
       a						    = goal_x
       a						    = goal_y
       a						    = g_cost
       a						    = parent
       a						    = self.calculate_heuristic()







      def  __lowerCAmelCase   ( self :		Any    )       ->float:




       """simple docstring"""


       a						    = abs(self.pos_x - self.goal_x    )
       a						    = abs(self.pos_y - self.goal_y    )
       return dx + dy







      def __lt__( self :		Any					,   __UpperCAmelCase :		Tuple    )       ->bool:




       """simple docstring"""


       return self.f_cost < other.f_cost






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		Optional[Any]					,   __UpperCAmelCase :		tuple[int, int]					,   __UpperCAmelCase :		tuple[int, int]    )       ->Dict:




       """simple docstring"""


       a						    = Node(start[1]					,   start[0]					,   goal[1]					,   goal[0]					,   0					,   __UpperCAmelCase    )
       a						    = Node(goal[1]					,   goal[0]					,   goal[1]					,   goal[0]					,   99_999					,   __UpperCAmelCase    )

       a						    = [self.start]
       a						    = []

       a						    = False







      def  __lowerCAmelCase   ( self :		str    )       ->Path | None:




       """simple docstring"""


       while self.open_nodes:
        # Open Nodes are sorted using __lt__
        self.open_nodes.sort()
        a						    = self.open_nodes.pop(0    )

        if current_node.pos == self.target.pos:
         a						    = True
         return self.retrace_path(__UpperCAmelCase    )

        self.closed_nodes.append(__UpperCAmelCase    )
        a						    = self.get_successors(__UpperCAmelCase    )

        for child_node in successors:
         if child_node in self.closed_nodes:
          continue

         if child_node not in self.open_nodes:
          self.open_nodes.append(__UpperCAmelCase    )
         else:
          # retrieve the best current path
          a						    = self.open_nodes.pop(self.open_nodes.index(__UpperCAmelCase    )    )

          if child_node.g_cost < better_node.g_cost:
           self.open_nodes.append(__UpperCAmelCase    )
          else:
           self.open_nodes.append(__UpperCAmelCase    )

       if not self.reached:
        return [self.start.pos]
       return None







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		Node    )       ->list[Node]:




       """simple docstring"""


       a						    = []
       for action in delta:
        a						    = parent.pos_x + action[1]
        a						    = parent.pos_y + action[0]

        if not (0 <= pos_x <= len(grid[0]    ) - 1 and 0 <= pos_y <= len(__UpperCAmelCase    ) - 1):
         continue

        if grid[pos_y][pos_x] != 0:
         continue

        successors.append(
            Node(
                __UpperCAmelCase					,   __UpperCAmelCase					,   self.target.pos_y					,   self.target.pos_x					,   parent.g_cost + 1					,   __UpperCAmelCase					,   )    )
       return successors







      def  __lowerCAmelCase   ( self :		Optional[Any]					,   __UpperCAmelCase :		Node | None    )       ->Path:




       """simple docstring"""


       a						    = node
       a						    = []
       while current_node is not None:
        path.append((current_node.pos_y, current_node.pos_x)    )
        a						    = current_node.parent
       path.reverse()
       return path


if __name__ == "__main__":
     UpperCAmelCase__							  =						(0, 0)
     UpperCAmelCase__							  =						(len(grid) - 1, len(grid[0]) - 1)
     for elem in grid:
          print(elem)

     print("------")

     UpperCAmelCase__							  =						GreedyBestFirst(init, goal)
     UpperCAmelCase__							  =						greedy_bf.search()
     if path:
          for pos_x, pos_y in path:
               UpperCAmelCase__							  =						2

          for elem in grid:
               print(elem)


