'''simple docstring'''





import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


SCREAMING_SNAKE_CASE_:	Optional[Any]    =logging.getLogger(__name__)
SCREAMING_SNAKE_CASE_:	int    =list(MODEL_FOR_MASKED_LM_MAPPING.keys())
SCREAMING_SNAKE_CASE_:	Any    =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 			__A :
    a__			:		Optional[str]			  =    field(
        default=UpperCamelCase__						,      metadata={
            """help""": (
                """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
            )
        }						,      )
    a__			:		Optional[str]			  =    field(
        default=UpperCamelCase__						,      metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCamelCase__      )}						,      )
    a__			:		Optional[str]			  =    field(
        default=UpperCamelCase__						,      metadata={
            """help""": (
                """Override some existing default config settings when a model is trained from scratch. Example: """
                """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
            )
        }						,      )
    a__			:		Optional[str]			  =    field(
        default=UpperCamelCase__						,      metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
    a__			:		Optional[str]			  =    field(
        default=UpperCamelCase__						,      metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}      )
    a__			:		Optional[str]			  =    field(
        default=UpperCamelCase__						,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}						,      )
    a__			:		bool			  =    field(
        default=UpperCamelCase__						,      metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}						,      )
    a__			:		str			  =    field(
        default="""main"""						,      metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}						,      )
    a__			:		bool			  =    field(
        default=UpperCamelCase__						,      metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }						,      )



    def    _lowercase		(self       :       Union[str, Any]					):
         if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
              raise ValueError(
                  "--config_overrides can\'t be used in combination with --config_name or --model_name_or_path"					)




@dataclass
class 			__A :
    a__			:		Optional[str]			  =    field(
        default=UpperCamelCase__						,      metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}      )
    a__			:		Optional[str]			  =    field(
        default=UpperCamelCase__						,      metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}      )
    a__			:		Optional[str]			  =    field(default=UpperCamelCase__						,      metadata={"""help""": """The input training data file (a text file)."""}      )
    a__			:		Optional[str]			  =    field(
        default=UpperCamelCase__						,      metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}						,      )
    a__			:		Optional[str]			  =    field(
        default=UpperCamelCase__						,      metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}						,      )
    a__			:		Optional[str]			  =    field(
        default=UpperCamelCase__						,      metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}						,      )
    a__			:		bool			  =    field(
        default=UpperCamelCase__						,      metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )
    a__			:		Optional[int]			  =    field(
        default=5						,      metadata={
            """help""": """The percentage of the train set used as validation set in case there's no validation split"""
        }						,      )
    a__			:		Optional[int]			  =    field(
        default=UpperCamelCase__						,      metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated. Default to the max input length of the model."""
            )
        }						,      )
    a__			:		Optional[int]			  =    field(
        default=UpperCamelCase__						,      metadata={"""help""": """The number of processes to use for the preprocessing."""}						,      )
    a__			:		float			  =    field(
        default=0.1_5						,      metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}      )
    a__			:		bool			  =    field(
        default=UpperCamelCase__						,      metadata={
            """help""": (
                """Whether to pad all samples to `max_seq_length`. """
                """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
            )
        }						,      )



    def    _lowercase		(self       :       Any					):
         if self.train_file is not None:
              UpperCAmelCase_			       =  self.train_file.split("."					)[-1]
              assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
         if self.validation_file is not None:
              UpperCAmelCase_			       =  self.validation_file.split("."					)[-1]
              assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def       lowerCAmelCase_							(   snake_case_			:					List[str]   ,       snake_case_			:					int     )       -> Union[str, Any]:


     '''simple docstring'''




     with open(__UpperCAmelCase   ,       "r"   ,       encoding="utf-8"     ) as f:
          UpperCAmelCase_			       =  [json.loads(__UpperCAmelCase     ) for line in f.read().splitlines() if (len(__UpperCAmelCase     ) > 0 and not line.isspace())]
     assert len(__UpperCAmelCase     ) == len(__UpperCAmelCase     )

     UpperCAmelCase_			       =  {c: dataset[c] for c in dataset.column_names}
     UpperCAmelCase_			       =  refs
     return Dataset.from_dict(__UpperCAmelCase     )





def       lowerCAmelCase_							(   )       -> Dict:


     '''simple docstring'''




     UpperCAmelCase_			       =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
     if len(sys.argv     ) == 2 and sys.argv[1].endswith(".json"     ):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          UpperCAmelCase_			       =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
     else:
          UpperCAmelCase_			       =  parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     UpperCAmelCase_			       =  None
     if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
          UpperCAmelCase_			       =  get_last_checkpoint(training_args.output_dir     )
          if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
               raise ValueError(
                   f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                   "Use --overwrite_output_dir to overcome."     )
          elif last_checkpoint is not None:
               logger.info(
                   f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                   "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."     )

    # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"   ,       datefmt="%m/%d/%Y %H:%M:%S"   ,       handlers=[logging.StreamHandler(sys.stdout     )]   ,       )
     logger.setLevel(logging.INFO if is_main_process(training_args.local_rank     ) else logging.WARN     )

     # Log on each process the small summary:
     logger.warning(
         f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
         + f"""distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}"""     )
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank     ):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info("Training/evaluation parameters %s"   ,       __UpperCAmelCase     )

     # Set seed before initializing model.
     set_seed(training_args.seed     )

     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
     # (the dataset will be downloaded automatically from the datasets Hub).
     #
     # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
     # 'text' is found. You can easily tweak this behavior (see below).
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
     if data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          UpperCAmelCase_			       =  load_dataset(data_args.dataset_name   ,       data_args.dataset_config_name     )
          if "validation" not in datasets.keys():
               UpperCAmelCase_			       =  load_dataset(
                   data_args.dataset_name   ,       data_args.dataset_config_name   ,       split=f"""train[:{data_args.validation_split_percentage}%]"""   ,       )
               UpperCAmelCase_			       =  load_dataset(
                   data_args.dataset_name   ,       data_args.dataset_config_name   ,       split=f"""train[{data_args.validation_split_percentage}%:]"""   ,       )
     else:
          UpperCAmelCase_			       =  {}
          if data_args.train_file is not None:
               UpperCAmelCase_			       =  data_args.train_file
          if data_args.validation_file is not None:
               UpperCAmelCase_			       =  data_args.validation_file
          UpperCAmelCase_			       =  data_args.train_file.split("."     )[-1]
          if extension == "txt":
               UpperCAmelCase_			       =  'text'
          UpperCAmelCase_			       =  load_dataset(__UpperCAmelCase   ,       data_files=__UpperCAmelCase     )
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     UpperCAmelCase_			       =  {
         'cache_dir': model_args.cache_dir,
         'revision': model_args.model_revision,
         'use_auth_token': True if model_args.use_auth_token else None,
     }
     if model_args.config_name:
          UpperCAmelCase_			       =  AutoConfig.from_pretrained(model_args.config_name   ,       **__UpperCAmelCase     )
     elif model_args.model_name_or_path:
          UpperCAmelCase_			       =  AutoConfig.from_pretrained(model_args.model_name_or_path   ,       **__UpperCAmelCase     )
     else:
          UpperCAmelCase_			       =  CONFIG_MAPPING[model_args.model_type]()
          logger.warning("You are instantiating a new config instance from scratch."     )
          if model_args.config_overrides is not None:
               logger.info(f"""Overriding config: {model_args.config_overrides}"""     )
               config.update_from_string(model_args.config_overrides     )
               logger.info(f"""New config: {config}"""     )

     UpperCAmelCase_			       =  {
         'cache_dir': model_args.cache_dir,
         'use_fast': model_args.use_fast_tokenizer,
         'revision': model_args.model_revision,
         'use_auth_token': True if model_args.use_auth_token else None,
     }
     if model_args.tokenizer_name:
          UpperCAmelCase_			       =  AutoTokenizer.from_pretrained(model_args.tokenizer_name   ,       **__UpperCAmelCase     )
     elif model_args.model_name_or_path:
          UpperCAmelCase_			       =  AutoTokenizer.from_pretrained(model_args.model_name_or_path   ,       **__UpperCAmelCase     )
     else:
          raise ValueError(
              "You are instantiating a new tokenizer from scratch. This is not supported by this script."
              "You can do it from another script, save it, and load it from here, using --tokenizer_name."     )

     if model_args.model_name_or_path:
          UpperCAmelCase_			       =  AutoModelForMaskedLM.from_pretrained(
              model_args.model_name_or_path   ,       from_tf=bool(".ckpt" in model_args.model_name_or_path     )   ,       config=__UpperCAmelCase   ,       cache_dir=model_args.cache_dir   ,       revision=model_args.model_revision   ,       use_auth_token=True if model_args.use_auth_token else None   ,       )
     else:
          logger.info("Training new model from scratch"     )
          UpperCAmelCase_			       =  AutoModelForMaskedLM.from_config(__UpperCAmelCase     )

     model.resize_token_embeddings(len(__UpperCAmelCase     )     )

     # Preprocessing the datasets.
     # First we tokenize all the texts.
     if training_args.do_train:
          UpperCAmelCase_			       =  datasets['train'].column_names
     else:
          UpperCAmelCase_			       =  datasets['validation'].column_names
     UpperCAmelCase_			       =  'text' if 'text' in column_names else column_names[0]

     UpperCAmelCase_			       =  'max_length' if data_args.pad_to_max_length else False

     def tokenize_function(snake_case_			:					List[Any]     ):
          # Remove empty lines
          UpperCAmelCase_			       =  [line for line in examples['text'] if len(__UpperCAmelCase     ) > 0 and not line.isspace()]
          return tokenizer(examples["text"]   ,       padding=__UpperCAmelCase   ,       truncation=__UpperCAmelCase   ,       max_length=data_args.max_seq_length     )

     UpperCAmelCase_			       =  datasets.map(
         __UpperCAmelCase   ,       batched=__UpperCAmelCase   ,       num_proc=data_args.preprocessing_num_workers   ,       remove_columns=[text_column_name]   ,       load_from_cache_file=not data_args.overwrite_cache   ,       )

     # Add the chinese references if provided
     if data_args.train_ref_file is not None:
          UpperCAmelCase_			       =  add_chinese_references(tokenized_datasets["train"]   ,       data_args.train_ref_file     )
     if data_args.validation_ref_file is not None:
          UpperCAmelCase_			       =  add_chinese_references(
              tokenized_datasets["validation"]   ,       data_args.validation_ref_file     )
     # If we have ref files, need to avoid it removed by trainer
     UpperCAmelCase_			       =  data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
          UpperCAmelCase_			       =  False

     # Data collator
     # This one will take care of randomly masking the tokens.
     UpperCAmelCase_			       =  DataCollatorForWholeWordMask(tokenizer=__UpperCAmelCase   ,       mlm_probability=data_args.mlm_probability     )

     # Initialize our Trainer
     UpperCAmelCase_			       =  Trainer(
         model=__UpperCAmelCase   ,       args=__UpperCAmelCase   ,       train_dataset=tokenized_datasets["train"] if training_args.do_train else None   ,       eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None   ,       tokenizer=__UpperCAmelCase   ,       data_collator=__UpperCAmelCase   ,       )

     # Training
     if training_args.do_train:
          if last_checkpoint is not None:
               UpperCAmelCase_			       =  last_checkpoint
          elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path     ):
               UpperCAmelCase_			       =  model_args.model_name_or_path
          else:
               UpperCAmelCase_			       =  None
          UpperCAmelCase_			       =  trainer.train(resume_from_checkpoint=__UpperCAmelCase     )
          trainer.save_model()  # Saves the tokenizer too for easy upload

          UpperCAmelCase_			       =  os.path.join(training_args.output_dir   ,       "train_results.txt"     )
          if trainer.is_world_process_zero():
               with open(__UpperCAmelCase   ,       "w"     ) as writer:
                    logger.info("***** Train results *****"     )
                    for key, value in sorted(train_result.metrics.items()     ):
                         logger.info(f"""  {key} = {value}"""     )
                         writer.write(f"""{key} = {value}\n"""     )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
               trainer.state.save_to_json(os.path.join(training_args.output_dir   ,       "trainer_state.json"     )     )

    # Evaluation
     UpperCAmelCase_			       =  {}
     if training_args.do_eval:
          logger.info("*** Evaluate ***"     )

          UpperCAmelCase_			       =  trainer.evaluate()

          UpperCAmelCase_			       =  math.exp(eval_output["eval_loss"]     )
          UpperCAmelCase_			       =  perplexity

          UpperCAmelCase_			       =  os.path.join(training_args.output_dir   ,       "eval_results_mlm_wwm.txt"     )
          if trainer.is_world_process_zero():
               with open(__UpperCAmelCase   ,       "w"     ) as writer:
                    logger.info("***** Eval results *****"     )
                    for key, value in sorted(results.items()     ):
                         logger.info(f"""  {key} = {value}"""     )
                         writer.write(f"""{key} = {value}\n"""     )

     return results





def       lowerCAmelCase_							(   snake_case_			:					Optional[Any]     )       -> str:


     '''simple docstring'''




     main()


if __name__ == "__main__":
    main()









'''simple docstring'''

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 						a_		:


							def __init__(				self   :						Tuple      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						float      ,						a_   :						Node | None      ,						)			->					Optional[Any]:
										snake_case:    str      							=pos_x
										snake_case:    str      							=pos_y
										snake_case:    Optional[Any]      							=(pos_y, pos_x)
										snake_case:    Union[str, Any]      							=goal_x
										snake_case:    Tuple      							=goal_y
										snake_case:    Tuple      							=g_cost
										snake_case:    Tuple      							=parent
										snake_case:    Optional[Any]      							=self.calculate_heuristic()


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					float:
										snake_case:    Optional[Any]      							=abs(self.pos_x - self.goal_x			)
										snake_case:    Optional[int]      							=abs(self.pos_y - self.goal_y			)
										return dx + dy




							def __lt__(				self   :						Union[str, Any]      ,						a_   :						Tuple			)			->					bool:
										return self.f_cost < other.f_cost






class 						a_		:


							def __init__(				self   :						Any      ,						a_   :						tuple[int, int]      ,						a_   :						tuple[int, int]			)			->					Optional[Any]:
										snake_case:    Any      							=Node(start[1]      ,						start[0]      ,						goal[1]      ,						goal[0]      ,						0      ,						a_			)
										snake_case:    str      							=Node(goal[1]      ,						goal[0]      ,						goal[1]      ,						goal[0]      ,						9_9_9_9_9      ,						a_			)

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

										snake_case:    Tuple      							=False


							def    UpperCamelCase     (				self   :						str			)			->					Path | None:
										while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													snake_case:    Tuple      							=self.open_nodes.pop(0			)

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

													self.closed_nodes.append(a_			)
													snake_case:    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
																			snake_case:    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    UpperCamelCase     (				self   :						str      ,						a_   :						Node			)			->					list[Node]:
										snake_case:    Any      							=[]
										for action in delta:
													snake_case:    Any      							=parent.pos_x + action[1]
													snake_case:    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    UpperCamelCase     (				self   :						Dict      ,						a_   :						Node | None			)			->					Path:
										snake_case:    Dict      							=node
										snake_case:    Optional[int]      							=[]
										while current_node is not None:
													path.append((current_node.pos_y, current_node.pos_x)			)
													snake_case:    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)




