

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      (						A       ,     A       ,     A       ,     A       ,     A       ,     A = None       ,     ):
	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=A    )
	UpperCAmelCase_       				=			list(ds[list(files.keys()    )[0]].features.keys()    )
	UpperCAmelCase_       				=			features_name.pop(A    )
	UpperCAmelCase_       				=			list(set(ds[list(files.keys()    )[0]][label_name]    )    )
	UpperCAmelCase_       				=			{label: i for i, label in enumerate(A    )}
	UpperCAmelCase_       				=			tokenizer.model_input_names
	UpperCAmelCase_       				=			{}

	if len(A    ) == 1:
		for k in files.keys():
			UpperCAmelCase_       				=			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():
			UpperCAmelCase_       				=			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]:
			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(
	        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:
		UpperCAmelCase_       				=			train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]    )    )    )

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

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

	return train_ds, val_ds, test_ds, labelaid


_a:    str 							=			logging.getLogger(__name__)

@dataclass
class 						__UpperCamelCase    :
	SCREAMING_SNAKE_CASE__    				=     field(metadata={'help': 'Which column contains the label'}						)
	SCREAMING_SNAKE_CASE__    				=     field(default=lowercase     ,     metadata={'help': 'The path of the training file'}						)
	SCREAMING_SNAKE_CASE__    				=     field(default=lowercase     ,     metadata={'help': 'The path of the development file'}						)
	SCREAMING_SNAKE_CASE__    				=     field(default=lowercase     ,     metadata={'help': 'The path of the test file'}						)
	SCREAMING_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.'
	        )
	    }     ,     )
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Overwrite the cached training and evaluation sets'}						)


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



def       __lowerCAmelCase      (						):
	# 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_				,   UpperCAmelCase_				,   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_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			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       ,     )

	UpperCAmelCase_       				=			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():
		UpperCAmelCase_       				=			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:
		UpperCAmelCase_       				=			np.argmax(p.predictions       ,     axis=1    )

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

	# Initialize our Trainer
	UpperCAmelCase_       				=			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
	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(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 typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

_a:    Optional[Any] 							=			logging.get_logger(__name__)



def       __lowerCAmelCase      (						A    ):
	if isinstance(A       ,     (list, tuple)    ) and isinstance(videos[0]       ,     (list, tuple)    ) and is_valid_image(videos[0][0]    ):
		return videos

	elif isinstance(A       ,     (list, tuple)    ) and is_valid_image(videos[0]    ):
		return [videos]

	elif is_valid_image(A    ):
		return [[videos]]

	raise ValueError(F"Could not make batched video from {videos}"    )




class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     ['pixel_values']




	def __init__(		self	:						int		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = PILImageResampling.BILINEAR		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Union[int, float] = 1 / 255		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	**lowerCAmelCase	:						List[Any]		,	):


		'''simple docstring'''







		super().__init__(**lowerCAmelCase							)
		UpperCAmelCase_       				=			size if size is not None else {"shortest_edge": 256}
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	default_to_square=lowerCAmelCase							)
		UpperCAmelCase_       				=			crop_size if crop_size is not None else {"height": 224, "width": 224}
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	param_name="crop_size"							)

		UpperCAmelCase_       				=			do_resize
		UpperCAmelCase_       				=			size
		UpperCAmelCase_       				=			do_center_crop
		UpperCAmelCase_       				=			crop_size
		UpperCAmelCase_       				=			resample
		UpperCAmelCase_       				=			do_rescale
		UpperCAmelCase_       				=			rescale_factor
		UpperCAmelCase_       				=			offset
		UpperCAmelCase_       				=			do_normalize
		UpperCAmelCase_       				=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		UpperCAmelCase_       				=			image_std if image_std is not None else IMAGENET_STANDARD_STD




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Dict[str, int]		,	lowerCAmelCase	:						PILImageResampling = PILImageResampling.BILINEAR		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						List[str]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	default_to_square=lowerCAmelCase							)
		if "shortest_edge" in size:
			UpperCAmelCase_       				=			get_resize_output_image_size(lowerCAmelCase		,	size["shortest_edge"]		,	default_to_square=lowerCAmelCase							)
		elif "height" in size and "width" in size:
			UpperCAmelCase_       				=			(size["height"], size["width"])
		else:
			raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"							)
		return resize(lowerCAmelCase		,	size=lowerCAmelCase		,	resample=lowerCAmelCase		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Dict[str, int]		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						Dict		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase							)
		if "height" not in size or "width" not in size:
			raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"							)
		return center_crop(lowerCAmelCase		,	size=(size["height"], size["width"])		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Union[int, float]		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						List[str]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			image.astype(np.floataa							)
		if offset:
			UpperCAmelCase_       				=			image - (scale / 2)
		return rescale(lowerCAmelCase		,	scale=lowerCAmelCase		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Union[float, List[float]]		,	lowerCAmelCase	:						Union[float, List[float]]		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						int		,	):


		'''simple docstring'''







		return normalize(lowerCAmelCase		,	mean=lowerCAmelCase		,	std=lowerCAmelCase		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						ImageInput		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						float = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[ChannelDimension] = ChannelDimension.FIRST		,	):


		'''simple docstring'''







		if do_resize and size is None or resample is None:
			raise ValueError("Size and resample must be specified if do_resize is True."							)

		if do_center_crop and crop_size is None:
			raise ValueError("Crop size must be specified if do_center_crop is True."							)

		if do_rescale and rescale_factor is None:
			raise ValueError("Rescale factor must be specified if do_rescale is True."							)

		if do_normalize and (image_mean is None or image_std is None):
			raise ValueError("Image mean and std must be specified if do_normalize is True."							)

		if offset and not do_rescale:
			raise ValueError("For offset, do_rescale must also be set to True."							)

		# All transformations expect numpy arrays.
		UpperCAmelCase_       				=			to_numpy_array(lowerCAmelCase							)

		if do_resize:
			UpperCAmelCase_       				=			self.resize(image=lowerCAmelCase		,	size=lowerCAmelCase		,	resample=lowerCAmelCase							)

		if do_center_crop:
			UpperCAmelCase_       				=			self.center_crop(lowerCAmelCase		,	size=lowerCAmelCase							)

		if do_rescale:
			UpperCAmelCase_       				=			self.rescale(image=lowerCAmelCase		,	scale=lowerCAmelCase		,	offset=lowerCAmelCase							)

		if do_normalize:
			UpperCAmelCase_       				=			self.normalize(image=lowerCAmelCase		,	mean=lowerCAmelCase		,	std=lowerCAmelCase							)

		UpperCAmelCase_       				=			to_channel_dimension_format(lowerCAmelCase		,	lowerCAmelCase							)
		return image




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						ImageInput		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						float = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[str, TensorType]] = None		,	lowerCAmelCase	:						ChannelDimension = ChannelDimension.FIRST		,	**lowerCAmelCase	:						Dict		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			do_resize if do_resize is not None else self.do_resize
		UpperCAmelCase_       				=			resample if resample is not None else self.resample
		UpperCAmelCase_       				=			do_center_crop if do_center_crop is not None else self.do_center_crop
		UpperCAmelCase_       				=			do_rescale if do_rescale is not None else self.do_rescale
		UpperCAmelCase_       				=			rescale_factor if rescale_factor is not None else self.rescale_factor
		UpperCAmelCase_       				=			offset if offset is not None else self.offset
		UpperCAmelCase_       				=			do_normalize if do_normalize is not None else self.do_normalize
		UpperCAmelCase_       				=			image_mean if image_mean is not None else self.image_mean
		UpperCAmelCase_       				=			image_std if image_std is not None else self.image_std

		UpperCAmelCase_       				=			size if size is not None else self.size
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	default_to_square=lowerCAmelCase							)
		UpperCAmelCase_       				=			crop_size if crop_size is not None else self.crop_size
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	param_name="crop_size"							)

		if not valid_images(lowerCAmelCase							):
			raise ValueError(
			    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
			    "torch.Tensor, tf.Tensor or jax.ndarray."							)

		UpperCAmelCase_       				=			make_batched(lowerCAmelCase							)

		UpperCAmelCase_       				=			[
		    [
		        self._preprocess_image(
		            image=lowerCAmelCase		,	do_resize=lowerCAmelCase		,	size=lowerCAmelCase		,	resample=lowerCAmelCase		,	do_center_crop=lowerCAmelCase		,	crop_size=lowerCAmelCase		,	do_rescale=lowerCAmelCase		,	rescale_factor=lowerCAmelCase		,	offset=lowerCAmelCase		,	do_normalize=lowerCAmelCase		,	image_mean=lowerCAmelCase		,	image_std=lowerCAmelCase		,	data_format=lowerCAmelCase		,	)
		        for img in video
		    ]
		    for video in videos
		]

		UpperCAmelCase_       				=			{"pixel_values": videos}
		return BatchFeature(data=lowerCAmelCase		,	tensor_type=lowerCAmelCase							)