

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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


a_						     =    logging.get_logger(__name__)

a_						     =    {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}



class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='gptj'
			lowerCamelCase__           ={
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}
			def __init__(			self							: Dict      ,      a							: Optional[Any]=5_0400      ,      a							: List[str]=2048      ,      a							: List[Any]=4096      ,      a							: int=28      ,      a							: Union[str, Any]=16      ,      a							: List[Any]=64      ,      a							: int=None      ,      a							: Optional[int]="gelu_new"      ,      a							: Optional[Any]=0.0      ,      a							: Any=0.0      ,      a							: Union[str, Any]=0.0      ,      a							: Union[str, Any]=1e-5      ,      a							: Any=0.02      ,      a							: Optional[int]=True      ,      a							: Tuple=5_0256      ,      a							: Union[str, Any]=5_0256      ,      a							: List[Any]=False      ,      **a							: str      ,      )							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      int				     =       n_positions
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_embd
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_layer
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       n_head
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_inner
				SCREAMING_SNAKE_CASE   :      Any				     =       rotary_dim
				SCREAMING_SNAKE_CASE   :      str				     =       activation_function
				SCREAMING_SNAKE_CASE   :      int				     =       resid_pdrop
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       embd_pdrop
				SCREAMING_SNAKE_CASE   :      Tuple				     =       attn_pdrop
				SCREAMING_SNAKE_CASE   :      List[str]				     =       layer_norm_epsilon
				SCREAMING_SNAKE_CASE   :      int				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Tuple				     =       use_cache

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       bos_token_id
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       eos_token_id

				super().__init__(
				    bos_token_id=a      ,      eos_token_id=a      ,      tie_word_embeddings=a      ,      **a	)



class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __init__(			self							: Optional[int]      ,      a							: PretrainedConfig      ,      a							: str = "default"      ,      a							: List[PatchingSpec] = None      ,      a							: bool = False      ,      )							->       Any:

				"""simple docstring"""

				super().__init__(a      ,      task=a      ,      patching_specs=a      ,      use_past=a	)
				if not getattr(self._config      ,      "pad_token_id"      ,      a	):
					# TODO: how to do that better?
					SCREAMING_SNAKE_CASE   :      Dict				     =       0
			@property
			def 							__UpperCamelCase (			self							: Any	)							->       Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
				if self.use_past:
					self.fill_with_past_key_values_(a      ,      direction="inputs"	)
					SCREAMING_SNAKE_CASE   :      int				     =       {0: "batch", 1: "past_sequence + sequence"}
				else:
					SCREAMING_SNAKE_CASE   :      Any				     =       {0: "batch", 1: "sequence"}

				return common_inputs
			@property
			def 							__UpperCamelCase (			self							: Any	)							->       int:

				"""simple docstring"""

				return self._config.n_layer
			@property
			def 							__UpperCamelCase (			self							: str	)							->       int:

				"""simple docstring"""

				return self._config.n_head
			def 							__UpperCamelCase (			self							: str      ,      a							: PreTrainedTokenizer      ,      a							: int = -1      ,      a							: int = -1      ,      a							: bool = False      ,      a							: Optional[TensorType] = None      ,      )							->       Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       super(a      ,      self	).generate_dummy_inputs(
				    a      ,      batch_size=a      ,      seq_length=a      ,      is_pair=a      ,      framework=a	)

				# We need to order the input in the way they appears in the forward()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       OrderedDict({"input_ids": common_inputs["input_ids"]}	)

				# Need to add the past_keys
				if self.use_past:
					if not is_torch_available():
						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
					else:
						import torch

						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       common_inputs["input_ids"].shape
						# Not using the same length for past_key_values
						SCREAMING_SNAKE_CASE   :      Any				     =       seqlen + 2
						SCREAMING_SNAKE_CASE   :      Dict				     =       (
						    batch,
						    self.num_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // self.num_attention_heads,
						)
						SCREAMING_SNAKE_CASE   :      str				     =       [
						    (torch.zeros(a	), torch.zeros(a	)) for _ in range(self.num_layers	)
						]

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       common_inputs["attention_mask"]
				if self.use_past:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       ordered_inputs["attention_mask"].dtype
					SCREAMING_SNAKE_CASE   :      Any				     =       torch.cat(
					    [ordered_inputs["attention_mask"], torch.ones(a      ,      a      ,      dtype=a	)]      ,      dim=1	)

				return ordered_inputs






			@property
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       int:

				"""simple docstring"""

				return 13


import numpy as np






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Dict	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       (0, 0)
				SCREAMING_SNAKE_CASE   :      Any				     =       None
				SCREAMING_SNAKE_CASE   :      Any				     =       0
				SCREAMING_SNAKE_CASE   :      List[str]				     =       0
				SCREAMING_SNAKE_CASE   :      Any				     =       0
			def __eq__(			self							: List[str]      ,      a							: Any	)							->       Tuple:

				"""simple docstring"""

				return self.position == cell.position






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       str:

				"""simple docstring"""

				print(self.position	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: List[Any]      ,      a							: List[Any]=(5, 5)	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.zeros(a	)
				SCREAMING_SNAKE_CASE   :      str				     =       world_size[0]
				SCREAMING_SNAKE_CASE   :      int				     =       world_size[1]
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[int]:

				"""simple docstring"""

				print(self.w	)






			def 							__UpperCamelCase (			self							: Dict      ,      a							: Dict	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [
				    (-1, -1),
				    (-1, 0),
				    (-1, 1),
				    (0, -1),
				    (0, 1),
				    (1, -1),
				    (1, 0),
				    (1, 1),
				]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[0]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[1]
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
				for n in neughbour_cord:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       current_x + n[0]
					SCREAMING_SNAKE_CASE   :      int				     =       current_y + n[1]
					if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
						SCREAMING_SNAKE_CASE   :      int				     =       Cell()
						SCREAMING_SNAKE_CASE   :      str				     =       (x, y)
						SCREAMING_SNAKE_CASE   :      Any				     =       cell
						neighbours.append(a	)
				return neighbours

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	_open.append(_a)

	while _open:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.argmin([n.f for n in _open])
		SCREAMING_SNAKE_CASE   :      List[str]				     =       _open[min_f]
		_closed.append(_open.pop(_a))
		if current == goal:
			break
		for n in world.get_neigbours(_a):
			for c in _closed:
				if c == n:
					continue
			SCREAMING_SNAKE_CASE   :      Tuple				     =       current.g + 1
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       n.position
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       goal.position
			SCREAMING_SNAKE_CASE   :      Any				     =       (ya - ya) ** 2 + (xa - xa) ** 2
			SCREAMING_SNAKE_CASE   :      List[str]				     =       n.h + n.g

			for c in _open:
				if c == n and c.f < n.f:
					continue
			_open.append(_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	while current.parent is not None:
		path.append(current.position)
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       current.parent
	path.append(current.position)
	return path[::-1]


if __name__ == "__main__":
			a_						     =    Gridworld()
			# Start position and goal
			a_						     =    Cell()
			a_						     =    (0, 0)
			a_						     =    Cell()
			a_						     =    (4, 4)
			print(F'''path from {start.position} to {goal.position}''')
			a_						     =    astar(world, start, goal)
			#   Just for visual reasons.
			for i in s:
						a_						     =    1
			print(world.w)