

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


__a  :int										=       logging.getLogger(__name__)
__a  :List[str]										=       list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__a  :Union[str, Any]										=       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class    _a :




      """simple docstring"""

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





      def 						__A     (					self				:							Dict     ):






            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 :




      """simple docstring"""

      _lowerCamelCase    :       Optional[Any]							=    field(
          default=UpperCAmelCase__			,							metadata={'help': 'The name of the dataset to use (via the datasets library).'}							)
      _lowerCamelCase    :       str							=    field(
          default=UpperCAmelCase__			,							metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}							)
      _lowerCamelCase    :       str							=    field(default=UpperCAmelCase__			,							metadata={'help': 'The input training data file (a text file).'}							)
      _lowerCamelCase    :       Optional[int]							=    field(
          default=UpperCAmelCase__			,							metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}			,							)
      _lowerCamelCase    :       Union[str, Any]							=    field(
          default=UpperCAmelCase__			,							metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}			,							)
      _lowerCamelCase    :       List[str]							=    field(
          default=UpperCAmelCase__			,							metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}			,							)
      _lowerCamelCase    :       Optional[Any]							=    field(
          default=UpperCAmelCase__			,							metadata={'help': 'Overwrite the cached training and evaluation sets'}							)
      _lowerCamelCase    :       Dict							=    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=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.'
              )
          }			,							)
      _lowerCamelCase    :       List[str]							=    field(
          default=UpperCAmelCase__			,							metadata={'help': 'The number of processes to use for the preprocessing.'}			,							)
      _lowerCamelCase    :       Optional[int]							=    field(
          default=0.1_5			,							metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}							)
      _lowerCamelCase    :       List[Any]							=    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 						__A     (					self				:							str     ):






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




def 	__snake_case		(      __UpperCamelCase     :      Any							,__UpperCamelCase     :      Dict  ):



      """simple docstring"""

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

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

def 	__snake_case		(      ):



      """simple docstring"""

      A_		   =							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.
            A_   ,			A_   ,			A_		   =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
      else:
            A_   ,			A_   ,			A_		   =							parser.parse_args_into_dataclasses()

      # Detecting last checkpoint.
      A_		   =							None
      if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
            A_		   =							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"							,_A  )

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

      A_		   =							{
          "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:
            A_		   =							AutoTokenizer.from_pretrained(model_args.tokenizer_name							,**_A  )
      elif model_args.model_name_or_path:
            A_		   =							AutoTokenizer.from_pretrained(model_args.model_name_or_path							,**_A  )
      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:
            A_		   =							AutoModelForMaskedLM.from_pretrained(
                model_args.model_name_or_path							,from_tf=bool(".ckpt" in model_args.model_name_or_path  )							,config=_A							,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"  )
            A_		   =							AutoModelForMaskedLM.from_config(_A  )

      model.resize_token_embeddings(len(_A  )  )

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

      A_		   =							"max_length" if data_args.pad_to_max_length else False

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

      A_		   =							datasets.map(
          _A							,batched=_A							,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:
            A_		   =							add_chinese_references(tokenized_datasets["train"]							,data_args.train_ref_file  )
      if data_args.validation_ref_file is not None:
            A_		   =							add_chinese_references(
                tokenized_datasets["validation"]							,data_args.validation_ref_file  )
      # If we have ref files, need to avoid it removed by trainer
      A_		   =							data_args.train_ref_file or data_args.validation_ref_file
      if has_ref:
            A_		   =							False

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

      # Initialize our Trainer
      A_		   =							Trainer(
          model=_A							,args=_A							,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=_A							,data_collator=_A							,)

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

            A_		   =							os.path.join(training_args.output_dir							,"train_results.txt"  )
            if trainer.is_world_process_zero():
                  with open(_A							,"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
      A_		   =							{}
      if training_args.do_eval:
            logger.info("*** Evaluate ***"  )

            A_		   =							trainer.evaluate()

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

            A_		   =							os.path.join(training_args.output_dir							,"eval_results_mlm_wwm.txt"  )
            if trainer.is_world_process_zero():
                  with open(_A							,"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 	__snake_case		(      __UpperCamelCase     :      Union[str, Any]  ):



      """simple docstring"""

      main()


if __name__ == "__main__":
      main()





import numpy
class 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							snake_case_		    =     input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							snake_case_		    =     numpy.random.rand(
							    self.input_array.shape[1]       ,	4						)

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							snake_case_		    =     numpy.random.rand(
							    4       ,	3						)

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							snake_case_		    =     numpy.random.rand(3       ,	1						)

							# Real output values provided.
							snake_case_		    =     output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							snake_case_		    =     numpy.zeros(output_array.shape						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     sigmoid(
							    numpy.dot(self.input_array       ,	self.input_layer_and_first_hidden_layer_weights						)						)

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,	self.first_hidden_layer_and_second_hidden_layer_weights       ,	)						)

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,	self.second_hidden_layer_and_output_layer_weights       ,	)						)

							return self.layer_between_second_hidden_layer_and_output






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							snake_case_		    =     numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T       ,	2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output						)       ,	)
							snake_case_		    =     numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T       ,	numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output						)       ,	self.second_hidden_layer_and_output_layer_weights.T       ,	)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer						)       ,	)
							snake_case_		    =     numpy.dot(
							    self.input_array.T       ,	numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output						)       ,	self.second_hidden_layer_and_output_layer_weights.T       ,	)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer						)       ,	self.first_hidden_layer_and_second_hidden_layer_weights.T       ,	)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer						)       ,	)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)






						def        snake_case__						(						self							:     Optional[Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     int       ,	__lowercase							:     bool						):






							"""simple docstring"""






							for iteration in range(1       ,	iterations + 1						):
								snake_case_		    =     self.feedforward()
								self.back_propagation()
								if give_loss:
									snake_case_		    =     numpy.mean(numpy.square(output - self.feedforward()						)						)
									print(f"Iteration {iteration} Loss: {loss}"						)






						def        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							snake_case_		    =     input_arr

							snake_case_		    =     sigmoid(
							    numpy.dot(self.array       ,	self.input_layer_and_first_hidden_layer_weights						)						)

							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,	self.first_hidden_layer_and_second_hidden_layer_weights       ,	)						)

							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,	self.second_hidden_layer_and_output_layer_weights       ,	)						)

							return int(self.layer_between_second_hidden_layer_and_output > 0.6						)







def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return 1 / (1 + numpy.exp(-value							))


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return (value) * (1 - (value))


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     numpy.array(
	    (
	        [0, 0, 0],
	        [0, 0, 1],
	        [0, 1, 0],
	        [0, 1, 1],
	        [1, 0, 0],
	        [1, 0, 1],
	        [1, 1, 0],
	        [1, 1, 1],
	    )     ,	dtype=numpy.floataa     ,	)

	# True output values for the given input values.
	snake_case_		    =     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])     ,	dtype=numpy.floataa							)

	# Calling neural network class.
	snake_case_		    =     TwoHiddenLayerNeuralNetwork(
	    input_array=_A     ,	output_array=_A							)

	# Calling training function.
	# Set give_loss to True if you want to see loss in every iteration.
	neural_network.train(output=_A     ,	iterations=10     ,	give_loss=_A							)

	return neural_network.predict(numpy.array(([1, 1, 1])     ,	dtype=numpy.floataa							)							)


if __name__ == "__main__":
		example()
