
'''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        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A = None    ,       ) ->			Union[str, Any]:
	_snake_case     		=	{}

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

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

	if len(__A			) == 1:
		for k in files.keys():
			_snake_case     		=	ds[k].map(
			    lambda __A			: 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():
			_snake_case     		=	ds[k].map(
			    lambda __A			: 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]:
			_snake_case     		=	{k: v for k, v in ex.items() if k in input_names}
			_snake_case     		=	labelaid[ex[label_name]]
			yield (d, label)

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

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

	_snake_case     		=	(
	    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:
		_snake_case     		=	train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]			)			)			)

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

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

	return train_ds, val_ds, test_ds, labelaid


lowercase					:							Tuple   =      logging.getLogger(__name__)


@dataclass
class 		__UpperCAmelCase   :
							__lowercase   	= field(metadata={"""help""": """Which column contains the label"""}		)
							__lowercase   	= field(default=_lowerCamelCase		,     metadata={"""help""": """The path of the training file"""}		)
							__lowercase   	= field(default=_lowerCamelCase		,     metadata={"""help""": """The path of the development file"""}		)
							__lowercase   	= field(default=_lowerCamelCase		,     metadata={"""help""": """The path of the test file"""}		)
							__lowercase   	= 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."""
							        )
							    }		,     )
							__lowercase   	= field(
							    default=_lowerCamelCase		,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}		)


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







def        SCREAMING_SNAKE_CASE__			(     ) ->			Union[str, 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.
	_snake_case     		=	HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)			)
	_snake_case		,				_snake_case		,				_snake_case     		=	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.

	_snake_case     		=	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    ,       )

	_snake_case		,				_snake_case		,				_snake_case		,				_snake_case     		=	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    ,       )

	_snake_case     		=	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():
		_snake_case     		=	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(__A			) -> Dict:
		_snake_case     		=	np.argmax(p.predictions    ,       axis=1			)

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

	# Initialize our Trainer
	_snake_case     		=	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
	_snake_case     		=	{}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'			)
		_snake_case     		=	trainer.evaluate()
		_snake_case     		=	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()



'''simple docstring'''







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel

