






'''simple docstring'''





from __future__ import annotations

import time

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__ :



		def __init__(     self:     Union[str, Any]					, a:     Any					, a:     List[str]					, a:     Any					, a:     Dict					, a:     int)	->Union[str, Any]:






								'''simple docstring'''


								a_					  = pos_x
								a_					  = pos_y
								a_					  = (pos_y, pos_x)
								a_					  = goal_x
								a_					  = goal_y
								a_					  = parent





class 		SCREAMING_SNAKE_CASE__ :



		def __init__(     self:     str					, a:     Union[str, Any]					, a:     Optional[Any])	->List[str]:






								'''simple docstring'''


								a_					  = Node(start[1]					, start[0]					, goal[1]					, goal[0]					, a)
								a_					  = Node(goal[1]					, goal[0]					, goal[1]					, goal[0]					, a)

								a_					  = [self.start]
								a_					  = False



		def _lowerCAmelCase  (     self:     Any)	->Path | None:






								'''simple docstring'''


								while self.node_queue:
														a_					  = self.node_queue.pop(0)

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

														a_					  = self.get_successors(a)

														for node in successors:
																				self.node_queue.append(a)

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



		def _lowerCAmelCase  (     self:     Union[str, Any]					, a:     List[Any])	->list[Node]:






								'''simple docstring'''


								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(a) - 1):
																				continue

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

														successors.append(
														    Node(a					, a					, self.target.pos_y					, self.target.pos_x					, a))
								return successors




		def _lowerCAmelCase  (     self:     Union[str, Any]					, a:     Any)	->Path:






								'''simple docstring'''


								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





class 		SCREAMING_SNAKE_CASE__ :



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






								'''simple docstring'''


								a_					  = BreadthFirstSearch(a					, a)
								a_					  = BreadthFirstSearch(a					, a)
								a_					  = False



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






								'''simple docstring'''


								while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
														a_					  = self.fwd_bfs.node_queue.pop(0)
														a_					  = self.bwd_bfs.node_queue.pop(0)

														if current_bwd_node.pos == current_fwd_node.pos:
																				a_					  = True
																				return self.retrace_bidirectional_path(
																				    a					, a)

														a_					  = current_bwd_node
														a_					  = current_fwd_node

														a_					  = {
														    self.fwd_bfs: self.fwd_bfs.get_successors(a),
														    self.bwd_bfs: self.bwd_bfs.get_successors(a),
														}

														for bfs in [self.fwd_bfs, self.bwd_bfs]:
																				for node in successors[bfs]:
																										bfs.node_queue.append(a)

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




		def _lowerCAmelCase  (     self:     str					, a:     Optional[Any]					, a:     Optional[Any])	->Path:






								'''simple docstring'''


								a_					  = self.fwd_bfs.retrace_path(a)
								a_					  = self.bwd_bfs.retrace_path(a)
								bwd_path.pop()
								bwd_path.reverse()
								a_					  = fwd_path + bwd_path
								return path


if __name__ == "__main__":
							# all coordinates are given in format [y,x]
							import doctest

							doctest.testmod()
							a_  			=			(0, 0)
							a_  			=			(len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							a_  			=			time.time()
							a_  			=			BreadthFirstSearch(init, goal)
							a_  			=			bfs.search()
							a_  			=			time.time() - start_bfs_time

							print('Unidirectional BFS computation time : ', bfs_time)

							a_  			=			time.time()
							a_  			=			BidirectionalBreadthFirstSearch(init, goal)
							a_  			=			bd_bfs.search()
							a_  			=			time.time() - start_bd_bfs_time

							print('Bidirectional BFS computation time : ', bd_bfs_time)





import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__A							=logging.getLogger(__name__)





def      lowerCamelCase_					(							):
				lowerCamelCase_         =     argparse.ArgumentParser(
				    description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."       )
				parser.add_argument(
				    "--dataset_name"      ,					type=lowerCamelCase__      ,					default="wikitext"      ,					help="Name of the training. Explore datasets at: hf.co/datasets."      ,					)
				parser.add_argument(
				    "--dataset_config"      ,					type=lowerCamelCase__      ,					default="wikitext-103-raw-v1"      ,					help="Configuration name of the dataset."       )
				parser.add_argument(
				    "--tokenizer_name_or_path"      ,					type=lowerCamelCase__      ,					default="sayakpaul/unigram-tokenizer-wikitext"      ,					help="Tokenizer identifier. Can be a local filepath or a Hub identifier."      ,					)
				parser.add_argument(
				    "--shard_size"      ,					type=lowerCamelCase__      ,					default=1_0_0_0      ,					help="Number of entries to go in a single shard."      ,					)
				parser.add_argument("--split"      ,					type=lowerCamelCase__      ,					default="train"      ,					choices=["train", "test", "validation"]       )
				parser.add_argument(
				    "--limit"      ,					default=lowerCamelCase__      ,					type=lowerCamelCase__      ,					help="Limit the number of shards (used for debugging)."      ,					)
				parser.add_argument(
				    "--max_length"      ,					type=lowerCamelCase__      ,					default=5_1_2      ,					help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
				    " sequence length that is a multiple of 8."      ,					)
				parser.add_argument(
				    "--output_dir"      ,					default="tf-tpu"      ,					type=lowerCamelCase__      ,					help="Output directory where the TFRecord shards will be saved. If the"
				    " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
				    " shards will be directly saved to a Google Cloud Storage bucket."      ,					)

				lowerCamelCase_         =     parser.parse_args()
				return args





def      lowerCamelCase_					(							lowerCamelCase__       ):
				def fn(lowerCamelCase__       ):
								return tokenizer(examples["text"]       )

				return fn





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     []
				for i in range(len(tokenized_data["input_ids"]       )       ):
								lowerCamelCase_         =     {
								    "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]       )       ),
								    "attention_mask": tf.train.Feature(
								        intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]       )       ),
								}
								lowerCamelCase_         =     tf.train.Features(feature=lowerCamelCase__       )
								lowerCamelCase_         =     tf.train.Example(features=lowerCamelCase__       )
								lowerCamelCase_         =     example.SerializeToString()
								records.append(lowerCamelCase__       )
				return records





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     datasets.load_dataset(args.dataset_name      ,					args.dataset_config      ,					split=args.split       )

				if args.limit is not None:
								lowerCamelCase_         =     min(len(lowerCamelCase__       )      ,					args.limit       )
								lowerCamelCase_         =     dataset.select(range(lowerCamelCase__       )       )
								print(F'Limiting the dataset to {args.limit} entries.'       )

				lowerCamelCase_         =     AutoTokenizer.from_pretrained(args.tokenizer_name_or_path       )

				# Handle output directory creation.
				# For serializing into a Google Cloud Storage Bucket, one needs to first
				# create a bucket.
				if "gs" not in args.output_dir:
								if not os.path.exists(args.output_dir       ):
												os.makedirs(args.output_dir       )
								lowerCamelCase_         =     os.path.join(args.output_dir      ,					args.split       )
								if not os.path.exists(lowerCamelCase__       ):
												os.makedirs(lowerCamelCase__       )
				else:
								lowerCamelCase_         =     os.path.join(args.output_dir      ,					args.split       )

				# Tokenize the whole dataset at once.
				lowerCamelCase_         =     tokenize_function(lowerCamelCase__       )
				lowerCamelCase_         =     dataset.map(lowerCamelCase__      ,					batched=lowerCamelCase__      ,					num_proc=4      ,					remove_columns=["text"]       )

				# We need to concatenate all our texts together, and then split the result
				# into chunks of a fixed size, which we will call block_size. To do this, we
				# will use the map method again, with the option batched=True. When we use batched=True,
				# the function we pass to map() will be passed multiple inputs at once, allowing us
				# to group them into more or fewer examples than we had in the input.
				# This allows us to create our new fixed-length samples. The advantage of this
				# method is that we don't lose a whole lot of content from the dataset compared to the
				# case where we simply tokenize with a pre-defined max_length.

				def group_texts(lowerCamelCase__       ):
								# Concatenate all texts.
								lowerCamelCase_         =     {k: sum(examples[k]      ,					[]       ) for k in examples.keys()}
								lowerCamelCase_         =     len(concatenated_examples[list(examples.keys()       )[0]]       )
								# We drop the small remainder, though you could add padding instead if the model supports it
								# In this, as in all things, we advise you to follow your heart 🫀
								lowerCamelCase_         =     (total_length // args.max_length) * args.max_length
								# Split by chunks of max_len.
								lowerCamelCase_         =     {
								    k: [t[i : i + args.max_length] for i in range(0      ,					lowerCamelCase__      ,					args.max_length       )]
								    for k, t in concatenated_examples.items()
								}
								return result

				lowerCamelCase_         =     dataset_tokenized.map(lowerCamelCase__      ,					batched=lowerCamelCase__      ,					batch_size=1_0_0_0      ,					num_proc=4       )

				lowerCamelCase_         =     0
				lowerCamelCase_         =     0
				for shard in range(0      ,					len(lowerCamelCase__       )      ,					args.shard_size       ):
								lowerCamelCase_         =     grouped_dataset[shard : shard + args.shard_size]
								lowerCamelCase_         =     len(dataset_snapshot["input_ids"]       )
								lowerCamelCase_         =     os.path.join(lowerCamelCase__      ,					F'dataset-{shard_count}-{records_containing}.tfrecord'       )
								lowerCamelCase_         =     get_serialized_examples(lowerCamelCase__       )

								with tf.io.TFRecordWriter(lowerCamelCase__       ) as out_file:
												for i in range(len(lowerCamelCase__       )       ):
																lowerCamelCase_         =     serialized_examples[i]
																out_file.write(lowerCamelCase__       )
												print("Wrote file {} containing {} records".format(lowerCamelCase__      ,					lowerCamelCase__       )       )

								shard_count += 1
								total_records += records_containing

				with open(F'split-{args.split}-records-count.txt'      ,					"w"       ) as f:
								print(F'Total {args.split} records: {total_records}'      ,					file=lowerCamelCase__       )


if __name__ == "__main__":
			__A							=parse_args()
			main(args)


