



'''simple docstring'''



import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int




def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  int				):
			if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
						# expert layer
						__lowercase										=							flax_key_tuple[:-1] + ('''weight''',)
						__lowercase										=							torch.permute(lowerCamelCase_		,       (0, 2, 1)				)
			elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_				):
						# linear layer
						__lowercase										=							flax_key_tuple[:-1] + ('''weight''',)
						__lowercase										=							flax_tensor.T
			elif flax_key_tuple[-1] in ["scale", "embedding"]:
						__lowercase										=							flax_key_tuple[:-1] + ('''weight''',)

			return flax_key_tuple, flax_tensor




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  List[Any]		,       lowerCamelCase_			:  Union[str, Any]				):
			if "metadata" in layer:
						__lowercase										=							layer.split('''metadata'''				)
						__lowercase										=							''''''.join(split_layer[0]				)[:-1]
						__lowercase										=							[tuple(('''metadata''' + split_layer[1]).split('''/'''				)				)]
			elif "kvstore" in layer:
						__lowercase										=							layer.split('''kvstore'''				)
						__lowercase										=							''''''.join(split_layer[0]				)[:-1]
						__lowercase										=							[tuple(('''kvstore''' + split_layer[1]).split('''/'''				)				)]

			else:
						__lowercase										=							layer.split('''/'''				)
						__lowercase										=							'''/'''.join(split_layer[:-1]				)
						__lowercase										=							(split_layer[-1],)

			if "kvstore/path" in layer:
						__lowercase										=							f"{switch_checkpoint_path}/{checkpoint_info[layer]}"
			elif "kvstore/driver" in layer:
						__lowercase										=							'''file'''
			else:
						__lowercase										=							checkpoint_info[layer]

			return curr_real_layer_name, split_layer, content




def    _lowerCAmelCase						(				lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[Any]				):
			__lowercase										=							rename_keys(lowerCamelCase_				)
			__lowercase										=							{}
			for k, v in current_block.items():
						__lowercase										=							v
			__lowercase										=							new_current_block
			torch.save(lowerCamelCase_		,       lowerCamelCase_				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  List[Any]		,       lowerCamelCase_			:  str = WEIGHTS_NAME				):
			__lowercase										=							convert_file_size_to_int(lowerCamelCase_				)
			__lowercase										=							[]
			__lowercase										=							{}
			__lowercase										=							0
			__lowercase										=							0

			os.makedirs(lowerCamelCase_		,       exist_ok=lowerCamelCase_				)
			with gfile.GFile(switch_checkpoint_path + '''/checkpoint'''		,       '''rb'''				) as fp:
						__lowercase										=							serialization.msgpack_restore(fp.read()				)['''optimizer''']['''target''']
						__lowercase										=							flatten_dict(lowerCamelCase_		,       sep='''/'''				)

			__lowercase										=							{}
			for layer in checkpoint_info.keys():
						__lowercase						,		__lowercase						,		__lowercase										=							get_key_and_tensorstore_dict(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
						if curr_real_layer_name in all_layers:
									__lowercase										=							content
						else:
									__lowercase										=							{split_layer[-1]: content}

			for key in all_layers.keys():
						# open tensorstore file
						__lowercase										=							ts.open(unflatten_dict(all_layers[key]				)				).result().read().result()
						__lowercase										=							torch.tensor(lowerCamelCase_				)
						__lowercase										=							raw_weights.numel() * dtype_byte_size(raw_weights.dtype				)

						# use the renaming pattern from the small conversion scripts
						__lowercase						,		__lowercase										=							rename_base_flax_keys(tuple(key.split('''/'''				)				)		,       lowerCamelCase_				)
						__lowercase										=							'''/'''.join(lowerCamelCase_				)

						# If this weight is going to tip up over the maximal size, we split.
						if current_block_size + weight_size > max_shard_size:
									__lowercase										=							os.path.join(
									    lowerCamelCase_		,       weights_name.replace('''.bin'''		,       f"-{len(lowerCamelCase_				)+1:05d}-of-???.bin"				)				)
									rename_and_save_block(lowerCamelCase_		,       lowerCamelCase_				)
									sharded_state_dicts.append(current_block.keys()				)
									del current_block
									__lowercase										=							{}
									__lowercase										=							0

						__lowercase										=							raw_weights.to(getattr(lowerCamelCase_		,       lowerCamelCase_				)				)
						current_block_size += weight_size
						total_size += weight_size

			# Add the last block
			__lowercase										=							os.path.join(lowerCamelCase_		,       weights_name.replace('''.bin'''		,       f"-{len(lowerCamelCase_				)+1:05d}-of-???.bin"				)				)
			rename_and_save_block(lowerCamelCase_		,       lowerCamelCase_				)
			sharded_state_dicts.append(current_block.keys()				)

			# If we only have one shard, we return it
			if len(lowerCamelCase_				) == 1:
						return {weights_name: sharded_state_dicts[0]}, None

			# Otherwise, let's build the index
			__lowercase										=							{}
			__lowercase										=							{}
			for idx, shard in enumerate(lowerCamelCase_				):
						__lowercase										=							weights_name.replace(
						    '''.bin'''		,       f"-{idx+1:05d}-of-{len(lowerCamelCase_				):05d}.bin"				)  # len(sharded_state_dicts):05d}
						__lowercase										=							os.path.join(lowerCamelCase_		,       weights_name.replace('''.bin'''		,       f"-{idx+1:05d}-of-???.bin"				)				)
						os.rename(lowerCamelCase_		,       os.path.join(lowerCamelCase_		,       lowerCamelCase_				)				)
						__lowercase										=							shard
						for key in shard:
									__lowercase										=							shard_file

    # Add the metadata
			__lowercase										=							{'''total_size''': total_size}
			__lowercase										=							{'''metadata''': metadata, '''weight_map''': weight_map}

			with open(os.path.join(lowerCamelCase_		,       lowerCamelCase_				)		,       '''w'''		,       encoding='''utf-8'''				) as f:
						__lowercase										=							json.dumps(lowerCamelCase_		,       indent=2		,       sort_keys=lowerCamelCase_				) + '''\n'''
						f.write(lowerCamelCase_				)

			return metadata, index


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--switch_t5x_checkpoint_path''',
	    default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
	    type=str,
	    required=False,
	    help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
	)
	parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
	parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
	parser.add_argument(
	    '''--pytorch_dump_folder_path''',
	    default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
	    type=str,
	    required=False,
	    help='''Path to the output pytorch model.''',
	)
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	shard_on_the_fly(
	    args.switch_tax_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.max_shard_size,
	    args.dtype,
	)




def    _lowerCAmelCase						(				):
			from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

			__lowercase										=							SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''				)
			config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''				)
			__lowercase										=							SwitchTransformersForConditionalGeneration.from_pretrained(
			    '''/home/arthur_huggingface_co/transformers/switch_converted'''		,       device_map='''auto'''				)

			__lowercase										=							TaTokenizer.from_pretrained('''t5-small'''				)
			__lowercase										=							'''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

			__lowercase										=							tokenizer(lowerCamelCase_		,       return_tensors='''pt'''				).input_ids
			__lowercase										=							model.generate(lowerCamelCase_		,       decoder_start_token_id=0				)
			print(tokenizer.decode(out[0]				)				)












'''simple docstring'''



from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_SCREAMING_SNAKE_CASE					=    0

_SCREAMING_SNAKE_CASE					=    [
    [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					=    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

_SCREAMING_SNAKE_CASE					=    tuple[int, int]






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,)							->							None:







				'''simple docstring'''





				__lowercase										=							pos_x
				__lowercase										=							pos_y
				__lowercase										=							(pos_y, pos_x)
				__lowercase										=							goal_x
				__lowercase										=							goal_y
				__lowercase										=							g_cost
				__lowercase										=							parent
				__lowercase										=							self.calculate_heuristic()
				__lowercase										=							self.g_cost + self.h_cost


	def 						_UpperCAmelCase     (self )							->							float:







				'''simple docstring'''





				__lowercase										=							self.pos_x - self.goal_x
				__lowercase										=							self.pos_y - self.goal_y
				if HEURISTIC == 1:
							return abs(_lowerCamelCase ) + abs(_lowerCamelCase )
				else:
							return sqrt(dy**2 + dx**2 )


	def __lt__(self     ,_lowerCamelCase )							->							bool:







				'''simple docstring'''





				return self.f_cost < other.f_cost






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							Node(start[1]     ,start[0]     ,goal[1]     ,goal[0]     ,0     ,_lowerCamelCase )
				__lowercase										=							Node(goal[1]     ,goal[0]     ,goal[1]     ,goal[0]     ,99999     ,_lowerCamelCase )

				__lowercase										=							[self.start]
				__lowercase										=							[]

				__lowercase										=							False


	def 						_UpperCAmelCase     (self )							->							list[TPosition]:







				'''simple docstring'''





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

							if current_node.pos == self.target.pos:
										return self.retrace_path(_lowerCamelCase )

							self.closed_nodes.append(_lowerCamelCase )
							__lowercase										=							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
													__lowercase										=							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 )

				return [self.start.pos]


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							list[Node]:







				'''simple docstring'''





				__lowercase										=							[]
				for action in delta:
							__lowercase										=							parent.pos_x + action[1]
							__lowercase										=							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     ,_lowerCamelCase )							->							list[TPosition]:







				'''simple docstring'''





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






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				__lowercase										=							AStar(_lowerCamelCase     ,_lowerCamelCase )
				__lowercase										=							AStar(_lowerCamelCase     ,_lowerCamelCase )
				__lowercase										=							False


	def 						_UpperCAmelCase     (self )							->							list[TPosition]:







				'''simple docstring'''





				while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
							self.fwd_astar.open_nodes.sort()
							self.bwd_astar.open_nodes.sort()
							__lowercase										=							self.fwd_astar.open_nodes.pop(0 )
							__lowercase										=							self.bwd_astar.open_nodes.pop(0 )

							if current_bwd_node.pos == current_fwd_node.pos:
										return self.retrace_bidirectional_path(
										    _lowerCamelCase     ,_lowerCamelCase )

							self.fwd_astar.closed_nodes.append(_lowerCamelCase )
							self.bwd_astar.closed_nodes.append(_lowerCamelCase )

							__lowercase										=							current_bwd_node
							__lowercase										=							current_fwd_node

							__lowercase										=							{
							    self.fwd_astar: self.fwd_astar.get_successors(_lowerCamelCase ),
							    self.bwd_astar: self.bwd_astar.get_successors(_lowerCamelCase ),
							}

							for astar in [self.fwd_astar, self.bwd_astar]:
										for child_node in successors[astar]:
													if child_node in astar.closed_nodes:
																continue

													if child_node not in astar.open_nodes:
																astar.open_nodes.append(_lowerCamelCase )
													else:
																# retrieve the best current path
																__lowercase										=							astar.open_nodes.pop(
																    astar.open_nodes.index(_lowerCamelCase ) )

																if child_node.g_cost < better_node.g_cost:
																			astar.open_nodes.append(_lowerCamelCase )
																else:
																			astar.open_nodes.append(_lowerCamelCase )

				return [self.fwd_astar.start.pos]


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							list[TPosition]:







				'''simple docstring'''





				__lowercase										=							self.fwd_astar.retrace_path(_lowerCamelCase )
				__lowercase										=							self.bwd_astar.retrace_path(_lowerCamelCase )
				bwd_path.pop()
				bwd_path.reverse()
				__lowercase										=							fwd_path + bwd_path
				return path


if __name__ == "__main__":
	# all coordinates are given in format [y,x]
	_SCREAMING_SNAKE_CASE					=    (0, 0)
	_SCREAMING_SNAKE_CASE					=    (len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	_SCREAMING_SNAKE_CASE					=    time.time()
	_SCREAMING_SNAKE_CASE					=    AStar(init, goal)
	_SCREAMING_SNAKE_CASE					=    a_star.search()
	_SCREAMING_SNAKE_CASE					=    time.time() - start_time
	print(f'''AStar execution time = {end_time:f} seconds''')

	_SCREAMING_SNAKE_CASE					=    time.time()
	_SCREAMING_SNAKE_CASE					=    BidirectionalAStar(init, goal)
	_SCREAMING_SNAKE_CASE					=    time.time() - bd_start_time
	print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')





