

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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							Tuple	,       SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							int = None	,       )			->      Union[str, Any]:
				A__        =			{}

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

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

				if len(__A      ) == 1:
								for k in files.keys():
												A__        =			ds[k].map(
												    lambda SCREAMING_SNAKE_CASE_      : tokenizer.batch_encode_plus(
												        example[features_name[0]]	,       truncation=__A	,       max_length=__A	,       padding="max_length"      )	,       batched=__A	,       )
				elif len(__A      ) == 2:
								for k in files.keys():
												A__        =			ds[k].map(
												    lambda SCREAMING_SNAKE_CASE_      : tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]])	,       truncation=__A	,       max_length=__A	,       padding="max_length"	,       )	,       batched=__A	,       )

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

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

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

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

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

				A__        =			(
				    tf.data.Dataset.from_generator(
				        __A	,       ({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:
								A__        =			test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

				return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase__	   = logging.getLogger(__name__)

@dataclass
class 					a__						:
			"""simple docstring"""







			__lowerCamelCase        =       field(metadata={'help': 'Which column contains the label'}			)
			__lowerCamelCase        =       field(default=__lowerCAmelCase							,	metadata={'help': 'The path of the training file'}			)
			__lowerCamelCase        =       field(default=__lowerCAmelCase							,	metadata={'help': 'The path of the development file'}			)
			__lowerCamelCase        =       field(default=__lowerCAmelCase							,	metadata={'help': 'The path of the test file'}			)
			__lowerCamelCase        =       field(
			    default=128							,	metadata={
			        'help': (
			            'The maximum total input sequence length after tokenization. Sequences longer '
			            'than this will be truncated, sequences shorter will be padded.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    default=__lowerCAmelCase							,	metadata={'help': 'Overwrite the cached training and evaluation sets'}			)

@dataclass
class 					a__						:
			"""simple docstring"""







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



def 					lowerCAmelCase__							(	)			->      Union[str, Any]:
				A__        =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
				A__        =			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.

				A__        =			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	,       )

				A__        =			get_tfds(
				    train_file=data_args.train_file	,       eval_file=data_args.dev_file	,       test_file=data_args.test_file	,       tokenizer=__A	,       label_column_id=data_args.label_column_id	,       max_seq_length=data_args.max_seq_length	,       )

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

				with training_args.strategy.scope():
								A__        =			TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path	,       from_pt=bool(".bin" in model_args.model_name_or_path      )	,       config=__A	,       cache_dir=model_args.cache_dir	,       )

				def compute_metrics(SCREAMING_SNAKE_CASE_:							Optional[int]      ) -> Dict:
								A__        =			np.argmax(p.predictions	,       axis=1      )

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

				# Initialize our Trainer
				A__        =			TFTrainer(
				    model=__A	,       args=__A	,       train_dataset=__A	,       eval_dataset=__A	,       compute_metrics=__A	,       )

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

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

								with open(__A	,       "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(__A      )

				return results


if __name__ == "__main__":
	main()




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			@slow
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			TFCamembertModel.from_pretrained("jplu/tf-camembert-base"       )

							A__        =			tf.convert_to_tensor(
							    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]      ,      dtype=tf.intaa      ,      )  # J'aime le camembert !"

							A__        =			model(lowercase       )["last_hidden_state"]
							A__        =			tf.TensorShape((1, 10, 768)       )
							self.assertEqual(output.shape      ,      lowercase       )
							# compare the actual values for a slice.
							A__        =			tf.convert_to_tensor(
							    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]      ,      dtype=tf.floataa      ,      )
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,      expected_slice.numpy()      ,      atol=1e-4       )       )

