





'''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  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      str    ,							lowercase						:      str    ,							lowercase						:      PreTrainedTokenizer    ,							lowercase						:      int    ,							lowercase						:      Optional[int] = None    ,							)	->	List[str]:
				_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=lowercase						)
				_a								= list(ds[list(files.keys()						)[0]].features.keys()						)
				_a								= features_name.pop(lowercase						)
				_a								= list(set(ds[list(files.keys()						)[0]][label_name]						)						)
				_a								= {label: i for i, label in enumerate(lowercase						)}
				_a								= tokenizer.model_input_names
				_a								= {}

				if len(lowercase						) == 1:
								for k in files.keys():
												_a								= ds[k].map(
												    lambda lowercase						: 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():
												_a								= ds[k].map(
												    lambda lowercase						: 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]:
												_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(
				        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:
								_a								= train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]						)						)						)

				_a								= (
				    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:
								_a								= val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]						)						)						)

				_a								= (
				    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:
								_a								= test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]						)						)						)

				return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase_					:    Optional[Any]      	=			logging.getLogger(__name__)
@dataclass
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				__a            =field(metadata={'help': 'Which column contains the label'}      )
				__a            =field(default=lowerCamelCase_       ,							metadata={'help': 'The path of the training file'}      )
				__a            =field(default=lowerCamelCase_       ,							metadata={'help': 'The path of the development file'}      )
				__a            =field(default=lowerCamelCase_       ,							metadata={'help': 'The path of the test file'}      )
				__a            =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.'
				        )
				    }       ,							)
				__a            =field(
				    default=lowerCamelCase_       ,							metadata={'help': 'Overwrite the cached training and evaluation sets'}      )


@dataclass
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







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






def  _lowerCamelCase				( )	->	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.
				_a								= HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
				_a	,					_a	,					_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	,					_a	,					_a	,					_a								= 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    ,							)

				_a								= 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():
								_a								= 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(lowercase						:      EvalPrediction						) -> Dict:
								_a								= np.argmax(p.predictions    ,							axis=1						)

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

				# Initialize our Trainer
				_a								= 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
				_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(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

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					str					):
								_a								= [
								    "safety_checker/pytorch_model.bin",
								    "safety_checker/model.safetensors",
								    "vae/diffusion_pytorch_model.bin",
								    "vae/diffusion_pytorch_model.safetensors",
								    "text_encoder/pytorch_model.bin",
								    "text_encoder/model.safetensors",
								    "unet/diffusion_pytorch_model.bin",
								    "unet/diffusion_pytorch_model.safetensors",
								]
								self.assertTrue(is_safetensors_compatible(__a					)					)




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= [
								    "unet/diffusion_pytorch_model.bin",
								    "unet/diffusion_pytorch_model.safetensors",
								]
								self.assertTrue(is_safetensors_compatible(__a					)					)




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= [
								    "safety_checker/pytorch_model.bin",
								    "safety_checker/model.safetensors",
								    "vae/diffusion_pytorch_model.bin",
								    "vae/diffusion_pytorch_model.safetensors",
								    "text_encoder/pytorch_model.bin",
								    "text_encoder/model.safetensors",
								    "unet/diffusion_pytorch_model.bin",
								    # Removed: 'unet/diffusion_pytorch_model.safetensors',
								]
								self.assertFalse(is_safetensors_compatible(__a					)					)




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= [
								    "text_encoder/pytorch_model.bin",
								    "text_encoder/model.safetensors",
								]
								self.assertTrue(is_safetensors_compatible(__a					)					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= [
								    "safety_checker/pytorch_model.bin",
								    "safety_checker/model.safetensors",
								    "vae/diffusion_pytorch_model.bin",
								    "vae/diffusion_pytorch_model.safetensors",
								    "text_encoder/pytorch_model.bin",
								    # Removed: 'text_encoder/model.safetensors',
								    "unet/diffusion_pytorch_model.bin",
								    "unet/diffusion_pytorch_model.safetensors",
								]
								self.assertFalse(is_safetensors_compatible(__a					)					)




				def   UpperCamelCase__   (  self     :					str					):
								_a								= [
								    "safety_checker/pytorch_model.fp16.bin",
								    "safety_checker/model.fp16.safetensors",
								    "vae/diffusion_pytorch_model.fp16.bin",
								    "vae/diffusion_pytorch_model.fp16.safetensors",
								    "text_encoder/pytorch_model.fp16.bin",
								    "text_encoder/model.fp16.safetensors",
								    "unet/diffusion_pytorch_model.fp16.bin",
								    "unet/diffusion_pytorch_model.fp16.safetensors",
								]
								_a								= "fp16"
								self.assertTrue(is_safetensors_compatible(__a					,				variant=__a					)					)




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= [
								    "unet/diffusion_pytorch_model.fp16.bin",
								    "unet/diffusion_pytorch_model.fp16.safetensors",
								]
								_a								= "fp16"
								self.assertTrue(is_safetensors_compatible(__a					,				variant=__a					)					)




				def   UpperCamelCase__   (  self     :					Any					):
								# pass variant but use the non-variant filenames
								_a								= [
								    "unet/diffusion_pytorch_model.bin",
								    "unet/diffusion_pytorch_model.safetensors",
								]
								_a								= "fp16"
								self.assertTrue(is_safetensors_compatible(__a					,				variant=__a					)					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= [
								    "safety_checker/pytorch_model.fp16.bin",
								    "safety_checker/model.fp16.safetensors",
								    "vae/diffusion_pytorch_model.fp16.bin",
								    "vae/diffusion_pytorch_model.fp16.safetensors",
								    "text_encoder/pytorch_model.fp16.bin",
								    "text_encoder/model.fp16.safetensors",
								    "unet/diffusion_pytorch_model.fp16.bin",
								    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
								]
								_a								= "fp16"
								self.assertFalse(is_safetensors_compatible(__a					,				variant=__a					)					)




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= [
								    "text_encoder/pytorch_model.fp16.bin",
								    "text_encoder/model.fp16.safetensors",
								]
								_a								= "fp16"
								self.assertTrue(is_safetensors_compatible(__a					,				variant=__a					)					)




				def   UpperCamelCase__   (  self     :					List[str]					):
								# pass variant but use the non-variant filenames
								_a								= [
								    "text_encoder/pytorch_model.bin",
								    "text_encoder/model.safetensors",
								]
								_a								= "fp16"
								self.assertTrue(is_safetensors_compatible(__a					,				variant=__a					)					)




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= [
								    "safety_checker/pytorch_model.fp16.bin",
								    "safety_checker/model.fp16.safetensors",
								    "vae/diffusion_pytorch_model.fp16.bin",
								    "vae/diffusion_pytorch_model.fp16.safetensors",
								    "text_encoder/pytorch_model.fp16.bin",
								    # 'text_encoder/model.fp16.safetensors',
								    "unet/diffusion_pytorch_model.fp16.bin",
								    "unet/diffusion_pytorch_model.fp16.safetensors",
								]
								_a								= "fp16"
								self.assertFalse(is_safetensors_compatible(__a					,				variant=__a					)					)
