






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


lowerCamelCase__							=     logging.getLogger(__name__)
lowerCamelCase__							=     list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCamelCase__							=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 			snake_case__   :

       '''simple docstring'''
       lowerCamelCase							:				Optional[str] 				= field(
           default=lowercase_	,    metadata={
               "help": (
                   "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
               )
           }	,    )
       lowerCamelCase							:				Optional[str] 				= field(
           default=lowercase_	,    metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowercase_)}	,    )
       lowerCamelCase							:				Optional[str] 				= field(
           default=lowercase_	,    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"
               )
           }	,    )
       lowerCamelCase							:				Optional[str] 				= field(
           default=lowercase_	,    metadata={"help": "Pretrained config name or path if not the same as model_name"})
       lowerCamelCase							:				Optional[str] 				= field(
           default=lowercase_	,    metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
       lowerCamelCase							:				Optional[str] 				= field(
           default=lowercase_	,    metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	,    )
       lowerCamelCase							:				bool 				= field(
           default=lowercase_	,    metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}	,    )
       lowerCamelCase							:				str 				= field(
           default="main"	,    metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}	,    )
       lowerCamelCase							:				bool 				= field(
           default=lowercase_	,    metadata={
               "help": (
                   "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                   "with private models)."
               )
           }	,    )



       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            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 			snake_case__   :

       '''simple docstring'''
       lowerCamelCase							:				Optional[str] 				= field(
           default=lowercase_	,    metadata={"help": "The name of the dataset to use (via the datasets library)."})
       lowerCamelCase							:				Optional[str] 				= field(
           default=lowercase_	,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."})
       lowerCamelCase							:				Optional[str] 				= field(default=lowercase_	,    metadata={"help": "The input training data file (a text file)."})
       lowerCamelCase							:				Optional[str] 				= field(
           default=lowercase_	,    metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}	,    )
       lowerCamelCase							:				Optional[str] 				= field(
           default=lowercase_	,    metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}	,    )
       lowerCamelCase							:				Optional[str] 				= field(
           default=lowercase_	,    metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}	,    )
       lowerCamelCase							:				bool 				= field(
           default=lowercase_	,    metadata={"help": "Overwrite the cached training and evaluation sets"})
       lowerCamelCase							:				Optional[int] 				= field(
           default=5	,    metadata={
               "help": "The percentage of the train set used as validation set in case there's no validation split"
           }	,    )
       lowerCamelCase							:				Optional[int] 				= field(
           default=lowercase_	,    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."
               )
           }	,    )
       lowerCamelCase							:				Optional[int] 				= field(
           default=lowercase_	,    metadata={"help": "The number of processes to use for the preprocessing."}	,    )
       lowerCamelCase							:				float 				= field(
           default=0.15	,    metadata={"help": "Ratio of tokens to mask for masked language modeling loss"})
       lowerCamelCase							:				bool 				= field(
           default=lowercase_	,    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    ) ->      Optional[int]:






            '''simple docstring'''





            if self.train_file is not None:
                 __snake_case			:List[str]          =						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:
                 __snake_case			:Tuple          =						self.validation_file.split("""."""    )[-1]
                 assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."






def   UpperCamelCase  (       snake_case__     :   Optional[Any]	,snake_case__     :   Any  ):




     '''simple docstring'''






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

     __snake_case			:Optional[int]          =						{c: dataset[c] for c in dataset.column_names}
     __snake_case			:Tuple          =						refs
     return Dataset.from_dict(snake_case__  )






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:Dict          =						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.
          __snake_case			:List[str]          =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
     else:
          __snake_case			:Union[str, Any]          =						parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     __snake_case			:List[Any]          =						None
     if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
          __snake_case			:Tuple          =						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"""	,snake_case__  )

     # 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.
          __snake_case			:List[Any]          =						load_dataset(data_args.dataset_name	,data_args.dataset_config_name  )
          if "validation" not in datasets.keys():
               __snake_case			:str          =						load_dataset(
                   data_args.dataset_name	,data_args.dataset_config_name	,split=f'''train[:{data_args.validation_split_percentage}%]'''	,)
               __snake_case			:Any          =						load_dataset(
                   data_args.dataset_name	,data_args.dataset_config_name	,split=f'''train[{data_args.validation_split_percentage}%:]'''	,)
     else:
          __snake_case			:Union[str, Any]          =						{}
          if data_args.train_file is not None:
               __snake_case			:Tuple          =						data_args.train_file
          if data_args.validation_file is not None:
               __snake_case			:Dict          =						data_args.validation_file
          __snake_case			:int          =						data_args.train_file.split("""."""  )[-1]
          if extension == "txt":
               __snake_case			:str          =						"""text"""
          __snake_case			:List[Any]          =						load_dataset(snake_case__	,data_files=snake_case__  )
     # 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.
     __snake_case			:Union[str, Any]          =						{
         """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:
          __snake_case			:Tuple          =						AutoConfig.from_pretrained(model_args.config_name	,**snake_case__  )
     elif model_args.model_name_or_path:
          __snake_case			:Union[str, Any]          =						AutoConfig.from_pretrained(model_args.model_name_or_path	,**snake_case__  )
     else:
          __snake_case			:Any          =						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}'''  )

     __snake_case			:Dict          =						{
         """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:
          __snake_case			:Dict          =						AutoTokenizer.from_pretrained(model_args.tokenizer_name	,**snake_case__  )
     elif model_args.model_name_or_path:
          __snake_case			:str          =						AutoTokenizer.from_pretrained(model_args.model_name_or_path	,**snake_case__  )
     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:
          __snake_case			:List[Any]          =						AutoModelForMaskedLM.from_pretrained(
              model_args.model_name_or_path	,from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )	,config=snake_case__	,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"""  )
          __snake_case			:str          =						AutoModelForMaskedLM.from_config(snake_case__  )

     model.resize_token_embeddings(len(snake_case__  )  )

     # Preprocessing the datasets.
     # First we tokenize all the texts.
     if training_args.do_train:
          __snake_case			:str          =						datasets["""train"""].column_names
     else:
          __snake_case			:Dict          =						datasets["""validation"""].column_names
     __snake_case			:Union[str, Any]          =						"""text""" if """text""" in column_names else column_names[0]

     __snake_case			:Tuple          =						"""max_length""" if data_args.pad_to_max_length else False

     def tokenize_function(snake_case__     :   int  ):
          # Remove empty lines
          __snake_case			:Dict          =						[line for line in examples["""text"""] if len(snake_case__  ) > 0 and not line.isspace()]
          return tokenizer(examples["""text"""]	,padding=snake_case__	,truncation=snake_case__	,max_length=data_args.max_seq_length  )

     __snake_case			:List[Any]          =						datasets.map(
         snake_case__	,batched=snake_case__	,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:
          __snake_case			:List[Any]          =						add_chinese_references(tokenized_datasets["""train"""]	,data_args.train_ref_file  )
     if data_args.validation_ref_file is not None:
          __snake_case			:Optional[int]          =						add_chinese_references(
              tokenized_datasets["""validation"""]	,data_args.validation_ref_file  )
     # If we have ref files, need to avoid it removed by trainer
     __snake_case			:Tuple          =						data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
          __snake_case			:Dict          =						False

     # Data collator
     # This one will take care of randomly masking the tokens.
     __snake_case			:Union[str, Any]          =						DataCollatorForWholeWordMask(tokenizer=snake_case__	,mlm_probability=data_args.mlm_probability  )

     # Initialize our Trainer
     __snake_case			:Optional[Any]          =						Trainer(
         model=snake_case__	,args=snake_case__	,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=snake_case__	,data_collator=snake_case__	,)

     # Training
     if training_args.do_train:
          if last_checkpoint is not None:
               __snake_case			:Dict          =						last_checkpoint
          elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path  ):
               __snake_case			:Optional[int]          =						model_args.model_name_or_path
          else:
               __snake_case			:Tuple          =						None
          __snake_case			:List[str]          =						trainer.train(resume_from_checkpoint=snake_case__  )
          trainer.save_model()  # Saves the tokenizer too for easy upload

          __snake_case			:Optional[Any]          =						os.path.join(training_args.output_dir	,"""train_results.txt"""  )
          if trainer.is_world_process_zero():
               with open(snake_case__	,"""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
     __snake_case			:Any          =						{}
     if training_args.do_eval:
          logger.info("""*** Evaluate ***"""  )

          __snake_case			:Optional[Any]          =						trainer.evaluate()

          __snake_case			:List[Any]          =						math.exp(eval_output["""eval_loss"""]  )
          __snake_case			:List[Any]          =						perplexity

          __snake_case			:List[str]          =						os.path.join(training_args.output_dir	,"""eval_results_mlm_wwm.txt"""  )
          if trainer.is_world_process_zero():
               with open(snake_case__	,"""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   UpperCamelCase  (       snake_case__     :   Dict  ):




     '''simple docstring'''






     main()


if __name__ == "__main__":
     main()










from __future__ import annotations

lowerCamelCase__							=     list[list[int]]

# assigning initial values to the grid
lowerCamelCase__							=     [
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
lowerCamelCase__							=     [
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]






def   UpperCamelCase  (       snake_case__     :   Matrix	,snake_case__     :   int	,snake_case__     :   int	,snake_case__     :   int  ):




     '''simple docstring'''






     for i in range(9  ):
          if grid[row][i] == n or grid[i][column] == n:
               return False

     for i in range(3  ):
          for j in range(3  ):
               if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
                    return False

     return True






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     for i in range(9  ):
          for j in range(9  ):
               if grid[i][j] == 0:
                    return i, j
     return None






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     if location := find_empty_location(snake_case__  ):
          __snake_case       , __snake_case			:Optional[int]          =						location
     else:
          # If the location is ``None``, then the grid is solved.
          return grid

     for digit in range(1	,10  ):
          if is_safe(snake_case__	,snake_case__	,snake_case__	,snake_case__  ):
               __snake_case			:Union[str, Any]          =						digit

               if sudoku(snake_case__  ) is not None:
                    return grid

               __snake_case			:Tuple          =						0

     return None






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     for row in grid:
          for cell in row:
               print(snake_case__	,end=""" """  )
          print()


if __name__ == "__main__":
     # make a copy of grid so that you can compare with the unmodified grid
     for example_grid in (initial_grid, no_solution):
          print("""\nExample grid:\n""" + """=""" * 20)
          print_solution(example_grid)
          print("""\nExample grid solution:""")
          lowerCamelCase__							=     sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("""Cannot find a solution.""")


