



'''simple docstring'''



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    ["""image_processor""", """tokenizer"""]
				lowerCAmelCase__					    =    """Pix2StructImageProcessor"""
				lowerCAmelCase__					    =    ("""T5Tokenizer""", """T5TokenizerFast""")

				def __init__(							self  :  Optional[Any]  ,       _lowerCAmelCase  :  List[Any]  ,       _lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=False
											super().__init__(_lowerCAmelCase  ,       _lowerCAmelCase)

				def __call__(							self  :  int  ,       _lowerCAmelCase  :  Tuple=None  ,       _lowerCAmelCase  :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,       _lowerCAmelCase  :  bool = True  ,       _lowerCAmelCase  :  Union[bool, str, PaddingStrategy] = False  ,       _lowerCAmelCase  :  Union[bool, str, TruncationStrategy] = None  ,       _lowerCAmelCase  :  Optional[int] = None  ,       _lowerCAmelCase  :  Optional[int] = 2_0_4_8  ,       _lowerCAmelCase  :  int = 0  ,       _lowerCAmelCase  :  Optional[int] = None  ,       _lowerCAmelCase  :  Optional[bool] = None  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = True  ,       _lowerCAmelCase  :  Optional[Union[str, TensorType]] = None  ,       **_lowerCAmelCase  :  Optional[int]  ,       ):







											'''simple docstring'''

											if images is None and text is None:
																		raise ValueError('You have to specify either images or text.')

											# Get only text
											if images is None and not self.image_processor.is_vqa:
																		__lowercase       	=self.tokenizer
																		__lowercase       	=self.tokenizer(
																		    text=_lowerCAmelCase  ,       add_special_tokens=_lowerCAmelCase  ,       padding=_lowerCAmelCase  ,       truncation=_lowerCAmelCase  ,       max_length=_lowerCAmelCase  ,       stride=_lowerCAmelCase  ,       pad_to_multiple_of=_lowerCAmelCase  ,       return_attention_mask=_lowerCAmelCase  ,       return_overflowing_tokens=_lowerCAmelCase  ,       return_special_tokens_mask=_lowerCAmelCase  ,       return_offsets_mapping=_lowerCAmelCase  ,       return_token_type_ids=_lowerCAmelCase  ,       return_length=_lowerCAmelCase  ,       verbose=_lowerCAmelCase  ,       return_tensors=_lowerCAmelCase  ,       **_lowerCAmelCase  ,       )
																		return text_encoding

											if not self.image_processor.is_vqa:
																		# add pixel_values
																		__lowercase       	=self.image_processor(
																		    _lowerCAmelCase  ,       return_tensors=_lowerCAmelCase  ,       max_patches=_lowerCAmelCase  ,       **_lowerCAmelCase)
											else:
																		# add pixel_values and bbox
																		__lowercase       	=self.image_processor(
																		    _lowerCAmelCase  ,       return_tensors=_lowerCAmelCase  ,       max_patches=_lowerCAmelCase  ,       header_text=_lowerCAmelCase  ,       **_lowerCAmelCase)

											if text is not None and not self.image_processor.is_vqa:
																		__lowercase       	=self.tokenizer(
																		    text=_lowerCAmelCase  ,       add_special_tokens=_lowerCAmelCase  ,       padding=_lowerCAmelCase  ,       truncation=_lowerCAmelCase  ,       max_length=_lowerCAmelCase  ,       stride=_lowerCAmelCase  ,       pad_to_multiple_of=_lowerCAmelCase  ,       return_attention_mask=_lowerCAmelCase  ,       return_overflowing_tokens=_lowerCAmelCase  ,       return_special_tokens_mask=_lowerCAmelCase  ,       return_offsets_mapping=_lowerCAmelCase  ,       return_token_type_ids=_lowerCAmelCase  ,       return_length=_lowerCAmelCase  ,       verbose=_lowerCAmelCase  ,       return_tensors=_lowerCAmelCase  ,       **_lowerCAmelCase  ,       )

																		if "attention_mask" in text_encoding:
																									__lowercase       	=text_encoding.pop('attention_mask')
																		if "input_ids" in text_encoding:
																									__lowercase       	=text_encoding.pop('input_ids')
											else:
																		__lowercase       	=None

											if text_encoding is not None:
																		encoding_image_processor.update(_lowerCAmelCase)

											return encoding_image_processor

				def 				__lowerCamelCase     (							self  :  Optional[Any]  ,       *_lowerCAmelCase  :  Any  ,       **_lowerCAmelCase  :  Any):







											'''simple docstring'''

											return self.tokenizer.batch_decode(*_lowerCAmelCase  ,       **_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Optional[int]  ,       *_lowerCAmelCase  :  str  ,       **_lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											return self.tokenizer.decode(*_lowerCAmelCase  ,       **_lowerCAmelCase)

				@property
				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

											__lowercase       	=self.tokenizer.model_input_names
											__lowercase       	=self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))








'''simple docstring'''



import numpy as np





class _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Dict):







											'''simple docstring'''

											__lowercase       	=(0, 0)
											__lowercase       	=None
											__lowercase       	=0
											__lowercase       	=0
											__lowercase       	=0

				def __eq__(							self  :  List[str]  ,       _lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											return self.position == cell.position

				def 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

											print(self.position)





class _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Any  ,       _lowerCAmelCase  :  List[str]=(5, 5)):







											'''simple docstring'''

											__lowercase       	=np.zeros(_lowerCAmelCase)
											__lowercase       	=world_size[0]
											__lowercase       	=world_size[1]

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											print(self.w)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  List[str]):







											'''simple docstring'''

											__lowercase       	=[
											    (-1, -1),
											    (-1, 0),
											    (-1, 1),
											    (0, -1),
											    (0, 1),
											    (1, -1),
											    (1, 0),
											    (1, 1),
											]
											__lowercase       	=cell.position[0]
											__lowercase       	=cell.position[1]
											__lowercase       	=[]
											for n in neughbour_cord:
																		__lowercase       	=current_x + n[0]
																		__lowercase       	=current_y + n[1]
																		if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																									__lowercase       	=Cell()
																									__lowercase       	=(x, y)
																									__lowercase       	=cell
																									neighbours.append(_lowerCAmelCase)
											return neighbours



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=[]
							__lowercase       	=[]
							_open.append(_lowerCAmelCase					)

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

																					for c in _open:
																												if c == n and c.f < n.f:
																																			continue
																					_open.append(_lowerCAmelCase					)
							__lowercase       	=[]
							while current.parent is not None:
														path.append(current.position					)
														__lowercase       	=current.parent
							path.append(current.position					)
							return path[::-1]


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



