





from __future__ import annotations

__A							   =							list[tuple[int, int]]

__A							   =							[
    [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],
]

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



class   _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

 def __init__(self							:  Tuple			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  float			,					UpperCAmelCase_							:  Node | None			,					)			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: str         =pos_x
   lowerCamelCase__: str         =pos_y
   lowerCamelCase__: Optional[Any]         =(pos_y, pos_x)
   lowerCamelCase__: Union[str, Any]         =goal_x
   lowerCamelCase__: Tuple         =goal_y
   lowerCamelCase__: Tuple         =g_cost
   lowerCamelCase__: Tuple         =parent
   lowerCamelCase__: Optional[Any]         =self.calculate_heuristic()

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any])			->float:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =abs(self.pos_x - self.goal_x)
   lowerCamelCase__: Optional[int]         =abs(self.pos_y - self.goal_y)
   return dx + dy







 def __lt__(self							:  Union[str, Any]			,					UpperCAmelCase_							:  Tuple)			->bool:



   '''simple docstring'''



   return self.f_cost < other.f_cost



class   _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

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



   '''simple docstring'''



   lowerCamelCase__: Any         =Node(start[1]			,					start[0]			,					goal[1]			,					goal[0]			,					0			,					a_)
   lowerCamelCase__: str         =Node(goal[1]			,					goal[0]			,					goal[1]			,					goal[0]			,					99_999			,					a_)

   lowerCamelCase__: int         =[self.start]
   lowerCamelCase__: list[Node]         =[]

   lowerCamelCase__: Tuple         =False

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



   '''simple docstring'''



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

     if current_node.pos == self.target.pos:
       lowerCamelCase__: Tuple         =True
       return self.retrace_path(a_)

     self.closed_nodes.append(a_)
     lowerCamelCase__: Optional[Any]         =self.get_successors(a_)

     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(a_)
       else:
         # retrieve the best current path
         lowerCamelCase__: List[Any]         =self.open_nodes.pop(self.open_nodes.index(a_))

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

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

 def 				SCREAMING_SNAKE_CASE_   (self							:  str			,					UpperCAmelCase_							:  Node)			->list[Node]:



   '''simple docstring'''



   lowerCamelCase__: Any         =[]
   for action in delta:
     lowerCamelCase__: Any         =parent.pos_x + action[1]
     lowerCamelCase__: str         =parent.pos_y + action[0]

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

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

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







 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  Node | None)			->Path:



   '''simple docstring'''



   lowerCamelCase__: Dict         =node
   lowerCamelCase__: Optional[int]         =[]
   while current_node is not None:
     path.append((current_node.pos_y, current_node.pos_x))
     lowerCamelCase__: List[str]         =current_node.parent
   path.reverse()
   return path


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

     print("------")

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

          for elem in grid:
               print(elem)






'''simple docstring'''

import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    List[str]      							=checkpoints.load_tax_checkpoint(__UpperCAmelCase					)
			snake_case:    str      							=flatten_dict(__UpperCAmelCase					)
			return flax_params







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    int      							={}

			snake_case:    Any      							={
			    'token_embedder': 'embeddings',
			    'encoder_norm': 'layernorm',
			    'kernel': 'weight',
			    '.out': '.output',
			    'scale': 'weight',
			    'embedders_0.pos_embedding': 'row_embedder.weight',
			    'embedders_1.pos_embedding': 'column_embedder.weight',
			}

			snake_case:    Union[str, Any]      							={
			    'query': 'attention.query',
			    'key': 'attention.key',
			    'value': 'attention.value',
			    'output.dense': 'output',
			    'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
			    'pre_self_attention_layer_norm': 'self_attention.layer_norm',
			    'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
			    'mlp.': 'mlp.DenseReluDense.',
			    'pre_mlp_layer_norm': 'mlp.layer_norm',
			    'self_attention.o': 'self_attention.attention.o',
			    'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
			    'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
			    'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
			    'decoder.logits_dense.weight': 'decoder.lm_head.weight',
			}

			for key in flax_dict.keys():
						if "target" in key:
									# remove the first prefix from the key
									snake_case:    Dict      							='.'.join(key[1:]					)

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												snake_case:    Union[str, Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															snake_case:    Optional[Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "layers" in new_key and "decoder" not in new_key:
												# use regex to replace the layer number
												snake_case:    Any      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)
												snake_case:    Optional[int]      							=new_key.replace('encoder'				,							'encoder.encoder'					)

									elif "layers" in new_key and "decoder" in new_key:
												# use regex to replace the layer number
												snake_case:    Optional[Any]      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)

									snake_case:    Optional[int]      							=flax_dict[key]

			snake_case:    Optional[int]      							={}
			# convert converted_dict into torch format
			for key in converted_dict.keys():
						if ("embed_tokens" not in key) and ("embedder" not in key):
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key].T					)
						else:
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key]					)

			return converted_torch_dict







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Any      							=get_flax_param(__UpperCAmelCase					)

			if not use_large:
						snake_case:    Optional[int]      							=PixaStructVisionConfig()
						snake_case:    int      							=PixaStructTextConfig()
			else:
						snake_case:    Tuple      							=PixaStructVisionConfig(
						    hidden_size=15_36				,							d_ff=39_68				,							num_attention_heads=24				,							num_hidden_layers=18					)
						snake_case:    List[Any]      							=PixaStructTextConfig(hidden_size=15_36				,							d_ff=39_68				,							num_heads=24				,							num_layers=18					)
			snake_case:    Any      							=PixaStructConfig(
			    vision_config=encoder_config.to_dict()				,							text_config=decoder_config.to_dict()				,							is_vqa=__UpperCAmelCase					)

			snake_case:    List[Any]      							=PixaStructForConditionalGeneration(__UpperCAmelCase					)

			snake_case:    str      							=rename_and_convert_flax_params(__UpperCAmelCase					)
			model.load_state_dict(__UpperCAmelCase					)

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'					)
			snake_case:    Optional[Any]      							=PixaStructImageProcessor()
			snake_case:    Optional[int]      							=PixaStructProcessor(image_processor=__UpperCAmelCase				,							tokenizer=__UpperCAmelCase					)

			if use_large:
						snake_case:    Optional[Any]      							=40_96

			snake_case:    str      							=True

			# mkdir if needed
			os.makedirs(__UpperCAmelCase				,							exist_ok=__UpperCAmelCase					)

			model.save_pretrained(__UpperCAmelCase					)
			processor.save_pretrained(__UpperCAmelCase					)

			print('Model saved in {}'.format(__UpperCAmelCase					)					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--use_large', action='store_true', help='Use large model.')
	parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
	a							   =				parser.parse_args()

	convert_pixastruct_original_pytorch_checkpoint_to_hf(
	    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
	)




