


'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		AutoConfig.from_pretrained(UpperCamelCase_ )
							A_			    =		FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase_ )
							A_			    =		checkpoints.load_tax_checkpoint(UpperCamelCase_ )

							A_			    =		"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

							if config.model_type == "t5":
														A_			    =		"SelfAttention"
							if config.model_type == "longt5" and config.encoder_attention_type == "local":
														A_			    =		"LocalSelfAttention"
							elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														A_			    =		"TransientGlobalSelfAttention"
							else:
														raise ValueError(
														    "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
														    " attribute with a value from ['local', 'transient-global]." )

							# Encoder
							for layer_index in range(config.num_layers ):
														A_			    =		f"layers_{str(UpperCamelCase_ )}"

														# Self-Attention
														A_			    =		tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
														A_			    =		tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
														A_			    =		tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
														A_			    =		tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					A_			    =		tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

														# Layer Normalization
														A_			    =		tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

														if split_mlp_wi:
																					A_			    =		tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
																					A_			    =		tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
														else:
																					A_			    =		tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

														A_			    =		tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

														# Layer Normalization
														A_			    =		tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

														# Assigning
														A_			    =		flax_model.params["encoder"]["block"][str(UpperCamelCase_ )]["layer"]
														A_			    =		tax_attention_key
														A_			    =		tax_attention_out
														A_			    =		tax_attention_query
														A_			    =		tax_attention_value

														A_			    =		tax_attention_layer_norm

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					A_			    =		tax_global_layer_norm

														if split_mlp_wi:
																					A_			    =		tax_mlp_wi_a
																					A_			    =		tax_mlp_wi_a
														else:
																					A_			    =		tax_mlp_wi

														A_			    =		tax_mlp_wo
														A_			    =		tax_mlp_layer_norm

														A_			    =		flax_model_encoder_layer_block

							# Only for layer 0:
							A_			    =		tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
							A_			    =		tax_encoder_rel_embedding

							# Side/global relative position_bias + layer norm
							if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														A_			    =		tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
														A_			    =		tax_encoder_global_rel_embedding

							# Assigning
							A_			    =		tax_model["target"]["encoder"]["encoder_norm"]["scale"]
							A_			    =		tax_encoder_norm

							# Decoder
							for layer_index in range(config.num_layers ):
														A_			    =		f"layers_{str(UpperCamelCase_ )}"

														# Self-Attention
														A_			    =		tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
														A_			    =		tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
														A_			    =		tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
														A_			    =		tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

														# Layer Normalization
														A_			    =		tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
														    "scale"
														]

														# Encoder-Decoder-Attention
														A_			    =		tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
														A_			    =		tax_enc_dec_attention_module["key"]["kernel"]
														A_			    =		tax_enc_dec_attention_module["out"]["kernel"]
														A_			    =		tax_enc_dec_attention_module["query"]["kernel"]
														A_			    =		tax_enc_dec_attention_module["value"]["kernel"]

														# Layer Normalization
														A_			    =		tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

														# MLP
														if split_mlp_wi:
																					A_			    =		tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
																					A_			    =		tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
														else:
																					A_			    =		tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

														A_			    =		tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

														# Layer Normalization
														A_			    =		tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

														# Assigning
														A_			    =		flax_model.params["decoder"]["block"][str(UpperCamelCase_ )]["layer"]
														A_			    =		tax_attention_key
														A_			    =		tax_attention_out
														A_			    =		tax_attention_query
														A_			    =		tax_attention_value

														A_			    =		tax_pre_attention_layer_norm

														A_			    =		tax_enc_dec_attention_key
														A_			    =		tax_enc_dec_attention_out
														A_			    =		tax_enc_dec_attention_query
														A_			    =		tax_enc_dec_attention_value

														A_			    =		tax_cross_layer_norm

														if split_mlp_wi:
																					A_			    =		tax_mlp_wi_a
																					A_			    =		tax_mlp_wi_a
														else:
																					A_			    =		tax_mlp_wi

														A_			    =		tax_mlp_wo

														A_			    =		txa_mlp_layer_norm

														A_			    =		flax_model_decoder_layer_block

							# Decoder Normalization
							A_			    =		tax_model["target"]["decoder"]["decoder_norm"]["scale"]
							A_			    =		txa_decoder_norm

							# Only for layer 0:
							A_			    =		tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
							A_			    =		tax_decoder_rel_embedding

							# Token Embeddings
							A_			    =		tax_model["target"]["token_embedder"]["embedding"]
							A_			    =		txa_token_embeddings

							# LM Head (only in v1.1 and LongT5 checkpoints)
							if "logits_dense" in tax_model["target"]["decoder"]:
														A_			    =		tax_model["target"]["decoder"]["logits_dense"]["kernel"]

							flax_model.save_pretrained(UpperCamelCase_ )
							print("T5X Model was sucessfully converted!" )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				Tuple									=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
							)
							parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
							parser.add_argument(
							    '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
							)
							__SCREAMING_SNAKE_CASE					:				Any									=		parser.parse_args()
							convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









'''simple docstring'''
from __future__ import annotations

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		list[tuple[int, int]]

__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		[
    [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],
]

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      List[str] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      float ,  lowerCAmelCase							:      Node | None ,  ):
													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 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		abs(self.pos_x - self.goal_x	)
													A_			    =		abs(self.pos_y - self.goal_y	)
													return dx + dy






						def __lt__(  self							:      Tuple ,  lowerCAmelCase							:      Any	):
													return self.f_cost < other.f_cost






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      Union[str, Any] ,  lowerCAmelCase							:      tuple[int, int] ,  lowerCAmelCase							:      tuple[int, int]	):
													A_			    =		Node(start[1] ,  start[0] ,  goal[1] ,  goal[0] ,  0 ,  lowerCAmelCase	)
													A_			    =		Node(goal[1] ,  goal[0] ,  goal[1] ,  goal[0] ,  9_99_99 ,  lowerCAmelCase	)

													A_			    =		[self.start]
													A_			    =		[]

													A_			    =		False






						def 				_UpperCAmelCase       (  self							:      str	):
													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(lowerCAmelCase	)

																				self.closed_nodes.append(lowerCAmelCase	)
																				A_			    =		self.get_successors(lowerCAmelCase	)

																				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(lowerCAmelCase	)
																											else:
																																		# retrieve the best current path
																																		A_			    =		self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase	)	)

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

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






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      Node	):
													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(lowerCAmelCase	) - 1):
																											continue

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

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






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      Node | None	):
													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__":
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		(0, 0)
							__SCREAMING_SNAKE_CASE					:				Dict									=		(len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							print('''------''')

							__SCREAMING_SNAKE_CASE					:				int									=		GreedyBestFirst(init, goal)
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		greedy_bf.search()
							if path:
														for pos_x, pos_y in path:
																					__SCREAMING_SNAKE_CASE					:				List[Any]									=		2

														for elem in grid:
																					print(elem)





