

from __future__ import annotations

from collections.abc import Sequence
from typing import Literal


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							list(UpperCAmelCase__      )
					__lowerCAmelCase					=							list(UpperCAmelCase__      )
					__lowerCAmelCase					=							0
					for i in range(len(UpperCAmelCase__      )      ):
										if lista[i] != lista[i]:
															count += 1
															__lowerCAmelCase					=							'_'
					if count > 1:
										return False
					else:
										return "".join(UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[]
					while True:
										__lowerCAmelCase					=							['$'] * len(UpperCAmelCase__      )
										__lowerCAmelCase					=							[]
										for i in range(len(UpperCAmelCase__      )      ):
															for j in range(i + 1		,					len(UpperCAmelCase__      )      ):
																				__lowerCAmelCase					=							compare_string(binary[i]		,					binary[j]      )
																				if k is False:
																									__lowerCAmelCase					=							'*'
																									__lowerCAmelCase					=							'*'
																									temp.append('X'      )
										for i in range(len(UpperCAmelCase__      )      ):
															if checka[i] == "$":
																				pi.append(binary[i]      )
										if len(UpperCAmelCase__      ) == 0:
															return pi
										__lowerCAmelCase					=							list(set(UpperCAmelCase__      )      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[]
					for minterm in minterms:
										__lowerCAmelCase					=							''
										for _ in range(UpperCAmelCase__      ):
															__lowerCAmelCase					=							str(minterm % 2      ) + string
															minterm //= 2
										temp.append(UpperCAmelCase__      )
					return temp


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							list(UpperCAmelCase__      )
					__lowerCAmelCase					=							list(UpperCAmelCase__      )
					__lowerCAmelCase					=							0
					for i in range(len(UpperCAmelCase__      )      ):
										if lista[i] != lista[i]:
															count_n += 1
					return count_n == count


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[]
					__lowerCAmelCase					=							[0] * len(UpperCAmelCase__      )
					for i in range(len(chart[0]      )      ):
										__lowerCAmelCase					=							0
										__lowerCAmelCase					=							-1
										for j in range(len(UpperCAmelCase__      )      ):
															if chart[j][i] == 1:
																				count += 1
																				__lowerCAmelCase					=							j
										if count == 1:
															__lowerCAmelCase					=							1
					for i in range(len(UpperCAmelCase__      )      ):
										if select[i] == 1:
															for j in range(len(chart[0]      )      ):
																				if chart[i][j] == 1:
																									for k in range(len(UpperCAmelCase__      )      ):
																														__lowerCAmelCase					=							0
															temp.append(prime_implicants[i]      )
					while True:
										__lowerCAmelCase					=							0
										__lowerCAmelCase					=							-1
										__lowerCAmelCase					=							0
										for i in range(len(UpperCAmelCase__      )      ):
															__lowerCAmelCase					=							chart[i].count(1      )
															if count_n > max_n:
																				__lowerCAmelCase					=							count_n
																				__lowerCAmelCase					=							i

										if max_n == 0:
															return temp

										temp.append(prime_implicants[rem]      )

										for i in range(len(chart[0]      )      ):
															if chart[rem][i] == 1:
																				for j in range(len(UpperCAmelCase__      )      ):
																									__lowerCAmelCase					=							0


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[[0 for x in range(len(UpperCAmelCase__      )      )] for x in range(len(UpperCAmelCase__      )      )]
					for i in range(len(UpperCAmelCase__      )      ):
										__lowerCAmelCase					=							prime_implicants[i].count('_'      )
										for j in range(len(UpperCAmelCase__      )      ):
															if is_for_table(prime_implicants[i]		,					binary[j]		,					UpperCAmelCase__      ):
																				__lowerCAmelCase					=							1

					return chart


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							int(input('Enter the no. of variables\n'      )      )
					__lowerCAmelCase					=							[
					    float(UpperCAmelCase__      )
					    for x in input(
					        'Enter the decimal representation of Minterms \'Spaces Separated\'\n'      ).split()
					]
					__lowerCAmelCase					=							decimal_to_binary(UpperCAmelCase__		,					UpperCAmelCase__      )

					__lowerCAmelCase					=							check(UpperCAmelCase__      )
					print('Prime Implicants are:'      )
					print(UpperCAmelCase__      )
					__lowerCAmelCase					=							prime_implicant_chart(UpperCAmelCase__		,					UpperCAmelCase__      )

					__lowerCAmelCase					=							selection(UpperCAmelCase__		,					UpperCAmelCase__      )
					print('Essential Prime Implicants are:'      )
					print(UpperCAmelCase__      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()









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"""





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




							def        A__  ( self							):
												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"""





							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}							)
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """The input training data file (a text file)."""}							)
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)
							__UpperCAmelCase       =field(
							    default=5			, metadata={
							        """help""": """The percentage of the train set used as validation set in case there's no validation split"""
							    }			, )
							__UpperCAmelCase       =field(
							    default=_a			, 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."""
							        )
							    }			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """The number of processes to use for the preprocessing."""}			, )
							__UpperCAmelCase       =field(
							    default=0.15			, metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}							)
							__UpperCAmelCase       =field(
							    default=_a			, 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							):
												if self.train_file is not None:
																	__lowerCAmelCase					=							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:
																	__lowerCAmelCase					=							self.validation_file.split('.'							)[-1]
																	assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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

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


def        __lowercase    (  ):

					"""simple docstring"""






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

					# Detecting last checkpoint.
					__lowerCAmelCase					=							None
					if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
										__lowerCAmelCase					=							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.
										__lowerCAmelCase					=							load_dataset(data_args.dataset_name		,					data_args.dataset_config_name      )
										if "validation" not in datasets.keys():
															__lowerCAmelCase					=							load_dataset(
															    data_args.dataset_name		,					data_args.dataset_config_name		,					split=F"""train[:{data_args.validation_split_percentage}%]"""		,					)
															__lowerCAmelCase					=							load_dataset(
															    data_args.dataset_name		,					data_args.dataset_config_name		,					split=F"""train[{data_args.validation_split_percentage}%:]"""		,					)
					else:
										__lowerCAmelCase					=							{}
										if data_args.train_file is not None:
															__lowerCAmelCase					=							data_args.train_file
										if data_args.validation_file is not None:
															__lowerCAmelCase					=							data_args.validation_file
										__lowerCAmelCase					=							data_args.train_file.split('.'      )[-1]
										if extension == "txt":
															__lowerCAmelCase					=							'text'
										__lowerCAmelCase					=							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.
					__lowerCAmelCase					=							{
					    '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:
										__lowerCAmelCase					=							AutoConfig.from_pretrained(model_args.config_name		,					**UpperCAmelCase__      )
					elif model_args.model_name_or_path:
										__lowerCAmelCase					=							AutoConfig.from_pretrained(model_args.model_name_or_path		,					**UpperCAmelCase__      )
					else:
										__lowerCAmelCase					=							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}"""      )

					__lowerCAmelCase					=							{
					    '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:
										__lowerCAmelCase					=							AutoTokenizer.from_pretrained(model_args.tokenizer_name		,					**UpperCAmelCase__      )
					elif model_args.model_name_or_path:
										__lowerCAmelCase					=							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:
										__lowerCAmelCase					=							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'      )
										__lowerCAmelCase					=							AutoModelForMaskedLM.from_config(UpperCAmelCase__      )

					model.resize_token_embeddings(len(UpperCAmelCase__      )      )

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

					__lowerCAmelCase					=							'max_length' if data_args.pad_to_max_length else False

					def tokenize_function(UpperCAmelCase__      ):
										# Remove empty lines
										__lowerCAmelCase					=							[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      )

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

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

					# Initialize our Trainer
					__lowerCAmelCase					=							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:
															__lowerCAmelCase					=							last_checkpoint
										elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path      ):
															__lowerCAmelCase					=							model_args.model_name_or_path
										else:
															__lowerCAmelCase					=							None
										__lowerCAmelCase					=							trainer.train(resume_from_checkpoint=UpperCAmelCase__      )
										trainer.save_model()  # Saves the tokenizer too for easy upload

										__lowerCAmelCase					=							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
					__lowerCAmelCase					=							{}
					if training_args.do_eval:
										logger.info('*** Evaluate ***'      )

										__lowerCAmelCase					=							trainer.evaluate()

										__lowerCAmelCase					=							math.exp(eval_output['eval_loss']      )
										__lowerCAmelCase					=							perplexity

										__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					main()


if __name__ == "__main__":
		main()






