"""simple docstring"""

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		(						a__	:		str ,  a__	:		List[Any] ,  a__	:		List[Any] ,  a__	:		List[Any] ,  a__	:		Tuple ,  a__	:		Optional[int] = None ,  )						->   Any:
					_UpperCamelCase      						=	{}

					if train_file is not None:
										_UpperCamelCase      						=	[train_file]
					if eval_file is not None:
										_UpperCamelCase      						=	[eval_file]
					if test_file is not None:
										_UpperCamelCase      						=	[test_file]

					_UpperCamelCase      						=	datasets.load_dataset('''csv''' ,  data_files=_lowercase     )
					_UpperCamelCase      						=	list(ds[list(files.keys()     )[0]].features.keys()     )
					_UpperCamelCase      						=	features_name.pop(_lowercase     )
					_UpperCamelCase      						=	list(set(ds[list(files.keys()     )[0]][label_name]     )     )
					_UpperCamelCase      						=	{label: i for i, label in enumerate(_lowercase     )}
					_UpperCamelCase      						=	tokenizer.model_input_names
					_UpperCamelCase      						=	{}

					if len(_lowercase     ) == 1:
										for k in files.keys():
															_UpperCamelCase      						=	ds[k].map(
															    lambda a__     : tokenizer.batch_encode_plus(
															        example[features_name[0]] ,  truncation=_lowercase ,  max_length=_lowercase ,  padding='''max_length'''     ) ,  batched=_lowercase ,  )
					elif len(_lowercase     ) == 2:
										for k in files.keys():
															_UpperCamelCase      						=	ds[k].map(
															    lambda a__     : tokenizer.batch_encode_plus(
															        (example[features_name[0]], example[features_name[1]]) ,  truncation=_lowercase ,  max_length=_lowercase ,  padding='''max_length''' ,  ) ,  batched=_lowercase ,  )

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

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

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

					_UpperCamelCase      						=	(
					    tf.data.Dataset.from_generator(
					        _lowercase ,  ({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      						=	train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]     )     )     )

					_UpperCamelCase      						=	(
					    tf.data.Dataset.from_generator(
					        _lowercase ,  ({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      						=	val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]     )     )     )

					_UpperCamelCase      						=	(
					    tf.data.Dataset.from_generator(
					        _lowercase ,  ({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      						=	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      UpperCAmelCase_       :
							snake_case__        =      field(metadata={'''help''': '''Which column contains the label'''})
							snake_case__        =      field(default=_lowercase			,							metadata={'''help''': '''The path of the training file'''})
							snake_case__        =      field(default=_lowercase			,							metadata={'''help''': '''The path of the development file'''})
							snake_case__        =      field(default=_lowercase			,							metadata={'''help''': '''The path of the test file'''})
							snake_case__        =      field(
							    default=1_28			,							metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }			,							)
							snake_case__        =      field(
							    default=_lowercase			,							metadata={'''help''': '''Overwrite the cached training and evaluation sets'''})







@dataclass
class      UpperCAmelCase_       :
							snake_case__        =      field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
							snake_case__        =      field(
							    default=_lowercase			,							metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
							snake_case__        =      field(
							    default=_lowercase			,							metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
							snake_case__        =      field(default=_lowercase			,							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__        =      field(
							    default=_lowercase			,							metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,							)






def      lowercase		(						)						->   List[Any]:
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.
					_UpperCamelCase      						=	HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
					_UpperCamelCase      						=	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      						=	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      						=	get_tfds(
					    train_file=data_args.train_file ,  eval_file=data_args.dev_file ,  test_file=data_args.test_file ,  tokenizer=_lowercase ,  label_column_id=data_args.label_column_id ,  max_seq_length=data_args.max_seq_length ,  )

					_UpperCamelCase      						=	AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,  num_labels=len(_lowercase     ) ,  labelaid=_lowercase ,  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      						=	TFAutoModelForSequenceClassification.from_pretrained(
										    model_args.model_name_or_path ,  from_pt=bool('''.bin''' in model_args.model_name_or_path     ) ,  config=_lowercase ,  cache_dir=model_args.cache_dir ,  )

					def compute_metrics(a__	:		Union[str, Any]     ) -> Dict:
										_UpperCamelCase      						=	np.argmax(p.predictions ,  axis=1     )

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

					# Initialize our Trainer
					_UpperCamelCase      						=	TFTrainer(
					    model=_lowercase ,  args=_lowercase ,  train_dataset=_lowercase ,  eval_dataset=_lowercase ,  compute_metrics=_lowercase ,  )

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

					# Evaluation
					_UpperCamelCase      						=	{}
					if training_args.do_eval:
										logger.info('''*** Evaluate ***'''     )
										_UpperCamelCase      						=	trainer.evaluate()
										_UpperCamelCase      						=	os.path.join(training_args.output_dir ,  '''eval_results.txt'''     )

										with open(_lowercase ,  '''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(_lowercase     )

					return results


if __name__ == "__main__":
		main()





'''simple docstring'''




import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def       __lowerCamelCase  (					)      ->			Any:
							raise RuntimeError("""CUDA out of memory."""		)

class 			UpperCamelCase_ (							nn.Module			):





	def __init__(  self    )			->			Any:
								super().__init__()
								UpperCAmelCase     :		Tuple							     =		nn.Linear(3		,		4    )
								UpperCAmelCase     :		Tuple							     =		nn.BatchNormad(4    )
								UpperCAmelCase     :		int							     =		nn.Linear(4		,		5    )






	def  _lowercase(  self		,		A    )			->			Any:
								return self.lineara(self.batchnorm(self.lineara(A    )    )    )




class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[int]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()
															return batch_size, arga

								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		mock_training_loop_function("""hello"""    )
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )
								self.assertListEqual([bs, arga]		,		[8, """hello"""]    )





	def  _lowercase(  self    )			->			Any:
								@find_executable_batch_size(starting_batch_size=0    )
								def mock_training_loop_function(A    ):
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[int]:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															if batch_size > 0:
																						raise_fake_out_of_memory()
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[Any]:
								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A		,		A    ):
															if batch_size != 8:
																						raise raise_fake_out_of_memory()

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function(128		,		"""hello"""		,		"""world"""    )
															self.assertIn("""Batch size was passed into `f`"""		,		cm.exception.args[0]    )
															self.assertIn("""`f(arg1='hello', arg2='world')"""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			int:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															raise ValueError("""Oops, we had an error!"""    )

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""Oops, we had an error!"""		,		cm.exception.args[0]    )






	@require_cuda
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		torch.cuda.memory_allocated()
								UpperCAmelCase     :		List[str]							     =		ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()		,		A    )
								UpperCAmelCase     :		Tuple							     =		release_memory(A    )
								self.assertEqual(torch.cuda.memory_allocated()		,		A    )

