






import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()




def      lowercase__      (     __snake_case    :							str     ,       __snake_case    :							str     ,       __snake_case    :							str     ,       __snake_case    :							PreTrainedTokenizer     ,       __snake_case    :							int     ,       __snake_case    :							Optional[int] = None     ,       ):


					'''simple docstring'''


					UpperCAmelCase_    :      Any            =						{}

					if train_file is not None:
										UpperCAmelCase_    :      Any            =						[train_file]
					if eval_file is not None:
										UpperCAmelCase_    :      int            =						[eval_file]
					if test_file is not None:
										UpperCAmelCase_    :      str            =						[test_file]

					UpperCAmelCase_    :      int            =						datasets.load_dataset('csv'     ,       data_files=__snake_case  )
					UpperCAmelCase_    :      List[Any]            =						list(ds[list(files.keys()  )[0]].features.keys()  )
					UpperCAmelCase_    :      List[str]            =						features_name.pop(__snake_case  )
					UpperCAmelCase_    :      Any            =						list(set(ds[list(files.keys()  )[0]][label_name]  )  )
					UpperCAmelCase_    :      List[str]            =						{label: i for i, label in enumerate(__snake_case  )}
					UpperCAmelCase_    :      Dict            =						tokenizer.model_input_names
					UpperCAmelCase_    :      Optional[int]            =						{}

					if len(__snake_case  ) == 1:
										for k in files.keys():
															UpperCAmelCase_    :      List[Any]            =						ds[k].map(
															    lambda __snake_case  : tokenizer.batch_encode_plus(
															        example[features_name[0]]     ,       truncation=__snake_case     ,       max_length=__snake_case     ,       padding='max_length'  )     ,       batched=__snake_case     ,       )
					elif len(__snake_case  ) == 2:
										for k in files.keys():
															UpperCAmelCase_    :      Union[str, Any]            =						ds[k].map(
															    lambda __snake_case  : tokenizer.batch_encode_plus(
															        (example[features_name[0]], example[features_name[1]])     ,       truncation=__snake_case     ,       max_length=__snake_case     ,       padding='max_length'     ,       )     ,       batched=__snake_case     ,       )

					def gen_train():
										for ex in transformed_ds[datasets.Split.TRAIN]:
															UpperCAmelCase_    :      Tuple            =						{k: v for k, v in ex.items() if k in input_names}
															UpperCAmelCase_    :      str            =						labelaid[ex[label_name]]
															yield (d, label)

					def gen_val():
										for ex in transformed_ds[datasets.Split.VALIDATION]:
															UpperCAmelCase_    :      Any            =						{k: v for k, v in ex.items() if k in input_names}
															UpperCAmelCase_    :      List[str]            =						labelaid[ex[label_name]]
															yield (d, label)

					def gen_test():
										for ex in transformed_ds[datasets.Split.TEST]:
															UpperCAmelCase_    :      Any            =						{k: v for k, v in ex.items() if k in input_names}
															UpperCAmelCase_    :      Dict            =						labelaid[ex[label_name]]
															yield (d, label)

					UpperCAmelCase_    :      str            =						(
					    tf.data.Dataset.from_generator(
					        __snake_case     ,       ({k: tf.intaa for k in input_names}, tf.intaa)     ,       ({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))     ,       )
					    if datasets.Split.TRAIN in transformed_ds
					    else None
					)

					if train_ds is not None:
										UpperCAmelCase_    :      Tuple            =						train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]  )  )  )

					UpperCAmelCase_    :      str            =						(
					    tf.data.Dataset.from_generator(
					        __snake_case     ,       ({k: tf.intaa for k in input_names}, tf.intaa)     ,       ({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))     ,       )
					    if datasets.Split.VALIDATION in transformed_ds
					    else None
					)

					if val_ds is not None:
										UpperCAmelCase_    :      int            =						val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]  )  )  )

					UpperCAmelCase_    :      List[str]            =						(
					    tf.data.Dataset.from_generator(
					        __snake_case     ,       ({k: tf.intaa for k in input_names}, tf.intaa)     ,       ({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))     ,       )
					    if datasets.Split.TEST in transformed_ds
					    else None
					)

					if test_ds is not None:
										UpperCAmelCase_    :      List[Any]            =						test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]  )  )  )

					return train_ds, val_ds, test_ds, labelaid


__UpperCAmelCase								=						logging.getLogger(__name__)



@dataclass
class   lowerCamelCase      :







		'''simple docstring'''



		_snake_case    :    int       						=  field(metadata={'''help''': '''Which column contains the label'''}			)
		_snake_case    :    str       						=  field(default=_snake_case ,  metadata={'''help''': '''The path of the training file'''}			)
		_snake_case    :    Optional[str]       						=  field(default=_snake_case ,  metadata={'''help''': '''The path of the development file'''}			)
		_snake_case    :    Optional[str]       						=  field(default=_snake_case ,  metadata={'''help''': '''The path of the test file'''}			)
		_snake_case    :    int       						=  field(
		    default=1_2_8 ,  metadata={
		        '''help''': (
		            '''The maximum total input sequence length after tokenization. Sequences longer '''
		            '''than this will be truncated, sequences shorter will be padded.'''
		        )
		    } ,  )
		_snake_case    :    bool       						=  field(
		    default=_snake_case ,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}			)





@dataclass
class   lowerCamelCase      :







		'''simple docstring'''



		_snake_case    :    str       						=  field(
		    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}			)
		_snake_case    :    Optional[str]       						=  field(
		    default=_snake_case ,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
		_snake_case    :    Optional[str]       						=  field(
		    default=_snake_case ,  metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}			)
		_snake_case    :    bool       						=  field(default=_snake_case ,  metadata={'''help''': '''Set this flag to use fast tokenization.'''}			)
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		_snake_case    :    Optional[str]       						=  field(
		    default=_snake_case ,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,  )





def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Dict            =						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
					UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir  )
					    and os.listdir(training_args.output_dir  )
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
										    ' --overwrite_output_dir to overcome.'  )

					# Setup logging
					logging.basicConfig(
					    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'     ,       datefmt='%m/%d/%Y %H:%M:%S'     ,       level=logging.INFO     ,       )
					logger.info(
					    F"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1  )}, "
					    F"16-bits training: {training_args.fpaa}"  )
					logger.info(F"Training/evaluation parameters {training_args}"  )

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					UpperCAmelCase_    :      List[str]            =						AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,       cache_dir=model_args.cache_dir     ,       )

					UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Dict            =						get_tfds(
					    train_file=data_args.train_file     ,       eval_file=data_args.dev_file     ,       test_file=data_args.test_file     ,       tokenizer=__snake_case     ,       label_column_id=data_args.label_column_id     ,       max_seq_length=data_args.max_seq_length     ,       )

					UpperCAmelCase_    :      Any            =						AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,       num_labels=len(__snake_case  )     ,       labelaid=__snake_case     ,       idalabel={id: label for label, id in labelaid.items()}     ,       finetuning_task='text-classification'     ,       cache_dir=model_args.cache_dir     ,       )

					with training_args.strategy.scope():
										UpperCAmelCase_    :      Dict            =						TFAutoModelForSequenceClassification.from_pretrained(
										    model_args.model_name_or_path     ,       from_pt=bool('.bin' in model_args.model_name_or_path  )     ,       config=__snake_case     ,       cache_dir=model_args.cache_dir     ,       )

					def compute_metrics(__snake_case    :							EvalPrediction  ) -> Dict:
										UpperCAmelCase_    :      Tuple            =						np.argmax(p.predictions     ,       axis=1  )

										return {"acc": (preds == p.label_ids).mean()}

					# Initialize our Trainer
					UpperCAmelCase_    :      List[str]            =						TFTrainer(
					    model=__snake_case     ,       args=__snake_case     ,       train_dataset=__snake_case     ,       eval_dataset=__snake_case     ,       compute_metrics=__snake_case     ,       )

					# Training
					if training_args.do_train:
										trainer.train()
										trainer.save_model()
										tokenizer.save_pretrained(training_args.output_dir  )

					# Evaluation
					UpperCAmelCase_    :      List[Any]            =						{}
					if training_args.do_eval:
										logger.info('*** Evaluate ***'  )
										UpperCAmelCase_    :      Dict            =						trainer.evaluate()
										UpperCAmelCase_    :      Dict            =						os.path.join(training_args.output_dir     ,       'eval_results.txt'  )

										with open(__snake_case     ,       'w'  ) as writer:
															logger.info('***** Eval results *****'  )

															for key, value in result.items():
																				logger.info(F"  {key} = {value}"  )
																				writer.write(F"{key} = {value}\n"  )

															results.update(__snake_case  )

					return results


if __name__ == "__main__":
		main()













'''simple docstring'''

from __future__ import annotations

import time

import numpy as np

__lowerCAmelCase							:	Optional[Any]			=[8, 5, 9, 7]
__lowerCAmelCase							:	Optional[int]			=[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__lowerCAmelCase							:	List[Any]			=[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class    UpperCAmelCase	:
 def __init__(				self      :Optional[int]						,       lowercase_      :list[int]						,       lowercase_      :list[list[int]]						,       lowercase_      :list[list[int]]						,       )->							None:
     A__				=			claim_vector
     A__				=			allocated_resources_table
     A__				=			maximum_claim_table
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							list[int]:
     return [
         sum(p_item[i] for p_item in self.__allocated_resources_table							)
         for i in range(len(self.__allocated_resources_table[0]							)							)
     ]
 def      UpperCAmelCase_							(				self      :Tuple							)->							list[int]:
     return np.array(self.__claim_vector							) - np.array(
         self.__processes_resource_summation()							)
 def      UpperCAmelCase_							(				self      :Dict							)->							list[list[int]]:
     return [
         list(np.array(self.__maximum_claim_table[i]							) - np.array(lowercase_							)							)
         for i, allocated_resource in enumerate(self.__allocated_resources_table							)
     ]
 def      UpperCAmelCase_							(				self      :Any							)->							dict[int, list[int]]:
     return {self.__need().index(lowercase_							): i for i in self.__need()}
 def      UpperCAmelCase_							(				self      :Tuple						,       **lowercase_      :List[str]							)->							None:
     A__				=			self.__need()
     A__				=			self.__allocated_resources_table
     A__				=			self.__available_resources()
     A__				=			self.__need_index_manager()
     for kw, val in kwargs.items():
         if kw and val is True:
             self.__pretty_data()
     print("_" * 50 + "\n"							)
     while need_list:
         A__				=			False
         for each_need in need_list:
             A__				=			True
             for index, need in enumerate(lowercase_							):
                 if need > available_resources[index]:
                     A__				=			False
                     break
             if execution:
                 A__				=			True
                 # get the original index of the process from ind_ctrl db
                 for original_need_index, need_clone in need_index_manager.items():
                     if each_need == need_clone:
                         A__				=			original_need_index
                 print(F"Process {process_number + 1} is executing."							)
                 # remove the process run from stack
                 need_list.remove(lowercase_							)
                 # update available/freed resources stack
                 A__				=			np.array(lowercase_							) + np.array(
                     alloc_resources_table[process_number]							)
                 print(
                     "Updated available resource stack for processes: "
                     + " ".join([str(lowercase_							) for x in available_resources]							)							)
                 break
         if safe:
             print("The process is in a safe state.\n"							)
         else:
             print("System in unsafe state. Aborting...\n"							)
             break



 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Tuple:
     print(" " * 9 + "Allocated Resource Table"							)
     for item in self.__allocated_resources_table:
         print(
             F"P{self.__allocated_resources_table.index(lowercase_							) + 1}"
             + " ".join(F"{it:>8}" for it in item							)
             + "\n"							)
     print(" " * 9 + "System Resource Table"							)
     for item in self.__maximum_claim_table:
         print(
             F"P{self.__maximum_claim_table.index(lowercase_							) + 1}"
             + " ".join(F"{it:>8}" for it in item							)
             + "\n"							)
     print(
         "Current Usage by Active Processes: "
         + " ".join(str(lowercase_							) for x in self.__claim_vector							)							)
     print(
         "Initial Available Resources:       "
         + " ".join(str(lowercase_							) for x in self.__available_resources()							)							)
     time.sleep(1							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



