


'''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					(      lowerCamelCase  :      Dict							,						lowerCamelCase  :      int							,						lowerCamelCase  :      str							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Dict							,						lowerCamelCase  :      int = None							,						):
						A_						:  Tuple          =				{}

						if train_file is not None:
												A_						:  Optional[Any]          =				[train_file]
						if eval_file is not None:
												A_						:  Tuple          =				[eval_file]
						if test_file is not None:
												A_						:  Union[str, Any]          =				[test_file]

						A_						:  Dict          =				datasets.load_dataset("""csv"""							,						data_files=__lowerCAmelCase)
						A_						:  List[str]          =				list(ds[list(files.keys())[0]].features.keys())
						A_						:  str          =				features_name.pop(__lowerCAmelCase)
						A_						:  Optional[int]          =				list(set(ds[list(files.keys())[0]][label_name]))
						A_						:  List[Any]          =				{label: i for i, label in enumerate(__lowerCAmelCase)}
						A_						:  Tuple          =				tokenizer.model_input_names
						A_						:  Dict          =				{}

						if len(__lowerCAmelCase) == 1:
												for k in files.keys():
																		A_						:  Tuple          =				ds[k].map(
																		    lambda lowerCamelCase: tokenizer.batch_encode_plus(
																		        example[features_name[0]]							,						truncation=__lowerCAmelCase							,						max_length=__lowerCAmelCase							,						padding="""max_length""")							,						batched=__lowerCAmelCase							,						)
						elif len(__lowerCAmelCase) == 2:
												for k in files.keys():
																		A_						:  int          =				ds[k].map(
																		    lambda lowerCamelCase: tokenizer.batch_encode_plus(
																		        (example[features_name[0]], example[features_name[1]])							,						truncation=__lowerCAmelCase							,						max_length=__lowerCAmelCase							,						padding="""max_length"""							,						)							,						batched=__lowerCAmelCase							,						)

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

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

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

						A_						:  List[Any]          =				(
						    tf.data.Dataset.from_generator(
						        __lowerCAmelCase							,						({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_						:  Optional[Any]          =				train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

						A_						:  Optional[Any]          =				(
						    tf.data.Dataset.from_generator(
						        __lowerCAmelCase							,						({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_						:  Dict          =				val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))

						A_						:  List[Any]          =				(
						    tf.data.Dataset.from_generator(
						        __lowerCAmelCase							,						({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_						:  List[str]          =				test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))

						return train_ds, val_ds, test_ds, labelaid


__magic_name__			=      logging.getLogger(__name__)



@dataclass
class 	__lowerCAmelCase		:


		'''simple docstring'''







		a_  					=							field(metadata={"""help""": """Which column contains the label"""}    )
		a_  					=							field(default=__UpperCAmelCase  , metadata={"""help""": """The path of the training file"""}    )
		a_  					=							field(default=__UpperCAmelCase  , metadata={"""help""": """The path of the development file"""}    )
		a_  					=							field(default=__UpperCAmelCase  , 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=__UpperCAmelCase  , metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )







@dataclass
class 	__lowerCAmelCase		:


		'''simple docstring'''







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







def      lowerCamelCase					(      ):
						A_						:  Optional[Any]          =				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
						A_   ,					A_   ,					A_						:  str          =				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_						:  Optional[int]          =				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_						:  Tuple          =				get_tfds(
						    train_file=data_args.train_file							,						eval_file=data_args.dev_file							,						test_file=data_args.test_file							,						tokenizer=__lowerCAmelCase							,						label_column_id=data_args.label_column_id							,						max_seq_length=data_args.max_seq_length							,						)

						A_						:  str          =				AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,						num_labels=len(__lowerCAmelCase)							,						labelaid=__lowerCAmelCase							,						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_						:  List[Any]          =				TFAutoModelForSequenceClassification.from_pretrained(
												    model_args.model_name_or_path							,						from_pt=bool(""".bin""" in model_args.model_name_or_path)							,						config=__lowerCAmelCase							,						cache_dir=model_args.cache_dir							,						)

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

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

						# Initialize our Trainer
						A_						:  List[str]          =				TFTrainer(
						    model=__lowerCAmelCase							,						args=__lowerCAmelCase							,						train_dataset=__lowerCAmelCase							,						eval_dataset=__lowerCAmelCase							,						compute_metrics=__lowerCAmelCase							,						)

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

						# Evaluation
						A_						:  Any          =				{}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***""")
												A_						:  Optional[Any]          =				trainer.evaluate()
												A_						:  Optional[int]          =				os.path.join(training_args.output_dir							,						"""eval_results.txt""")

												with open(__lowerCAmelCase							,						"""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(__lowerCAmelCase)

						return results


if __name__ == "__main__":
					main()




'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__magic_name__			=      logging.get_logger(__name__)


if is_vision_available():
     import PIL



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""pixel_values"""]






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

        '''simple docstring'''




        super().__init__(**_a )
        A_						:  Tuple          =				size if size is not None else {"""shortest_edge""": 224}
        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a )
        A_						:  Tuple          =				crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a	,param_name="""crop_size""" )

        A_						:  Any          =				do_resize
        A_						:  List[str]          =				size
        A_						:  Union[str, Any]          =				resample
        A_						:  Dict          =				do_center_crop
        A_						:  List[str]          =				crop_size
        A_						:  Any          =				do_rescale
        A_						:  Union[str, Any]          =				rescale_factor
        A_						:  Any          =				do_normalize
        A_						:  List[str]          =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        A_						:  List[Any]          =				image_std if image_std is not None else OPENAI_CLIP_STD
        A_						:  Tuple          =				do_convert_rgb






  def        _a							(							self :       Optional[int]	,_a :       np.ndarray	,_a :       Dict[str, int]	,_a :       PILImageResampling = PILImageResampling.BICUBIC	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Optional[Any]	,):

        '''simple docstring'''




        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a )
        if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
        A_						:  Tuple          =				get_resize_output_image_size(_a	,size=size["""shortest_edge"""]	,default_to_square=_a )
        return resize(_a	,size=_a	,resample=_a	,data_format=_a	,**_a )






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

        '''simple docstring'''




        A_						:  Optional[int]          =				get_size_dict(_a )
        if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
        return center_crop(_a	,size=(size["""height"""], size["""width"""])	,data_format=_a	,**_a )






  def        _a							(							self :       Any	,_a :       np.ndarray	,_a :       Union[int, float]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Any	,):

        '''simple docstring'''




        return rescale(_a	,scale=_a	,data_format=_a	,**_a )






  def        _a							(							self :       Any	,_a :       np.ndarray	,_a :       Union[float, List[float]]	,_a :       Union[float, List[float]]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       List[str]	,):

        '''simple docstring'''




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






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

        '''simple docstring'''




        A_						:  Union[str, Any]          =				do_resize if do_resize is not None else self.do_resize
        A_						:  Tuple          =				size if size is not None else self.size
        A_						:  Optional[int]          =				get_size_dict(_a	,param_name="""size"""	,default_to_square=_a )
        A_						:  List[str]          =				resample if resample is not None else self.resample
        A_						:  int          =				do_center_crop if do_center_crop is not None else self.do_center_crop
        A_						:  Any          =				crop_size if crop_size is not None else self.crop_size
        A_						:  int          =				get_size_dict(_a	,param_name="""crop_size"""	,default_to_square=_a )
        A_						:  List[Any]          =				do_rescale if do_rescale is not None else self.do_rescale
        A_						:  int          =				rescale_factor if rescale_factor is not None else self.rescale_factor
        A_						:  Any          =				do_normalize if do_normalize is not None else self.do_normalize
        A_						:  int          =				image_mean if image_mean is not None else self.image_mean
        A_						:  int          =				image_std if image_std is not None else self.image_std
        A_						:  List[str]          =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        A_						:  int          =				make_list_of_images(_a )

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

        if do_resize and size is None:
              raise ValueError("""Size 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.""" )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
              A_						:  Optional[int]          =				[convert_to_rgb(_a ) for image in images]

        # All transformations expect numpy arrays.
        A_						:  Dict          =				[to_numpy_array(_a ) for image in images]

        if do_resize:
              A_						:  int          =				[self.resize(image=_a	,size=_a	,resample=_a ) for image in images]

        if do_center_crop:
              A_						:  Tuple          =				[self.center_crop(image=_a	,size=_a ) for image in images]

        if do_rescale:
              A_						:  List[str]          =				[self.rescale(image=_a	,scale=_a ) for image in images]

        if do_normalize:
              A_						:  Any          =				[self.normalize(image=_a	,mean=_a	,std=_a ) for image in images]

        A_						:  List[str]          =				[to_channel_dimension_format(_a	,_a ) for image in images]

        A_						:  List[str]          =				{"""pixel_values""": images}
        return BatchFeature(data=_a	,tensor_type=_a )
