




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   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_ = None					,     )       ->	Union[str, Any]:

    """simple docstring"""


    __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=lowerCAmelCase__      )
    __UpperCamelCase  		=						list(ds[list(files.keys()      )[0]].features.keys()      )
    __UpperCamelCase  		=						features_name.pop(lowerCAmelCase__      )
    __UpperCamelCase  		=						list(set(ds[list(files.keys()      )[0]][label_name]      )      )
    __UpperCamelCase  		=						{label: i for i, label in enumerate(lowerCAmelCase__      )}
    __UpperCamelCase  		=						tokenizer.model_input_names
    __UpperCamelCase  		=						{}

    if len(lowerCAmelCase__      ) == 1:
        for k in files.keys():
            __UpperCamelCase  		=						ds[k].map(
                lambda lowercase_      : 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():
            __UpperCamelCase  		=						ds[k].map(
                lambda lowercase_      : 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]:
            __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(
            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:
        __UpperCamelCase  		=						train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

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

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

    return train_ds, val_ds, test_ds, labelaid


a_   		=     logging.getLogger(__name__)

@dataclass
class  _lowerCamelCase						:

      """simple docstring"""

      lowerCAmelCase__			: int    =					field(metadata={"help": "Which column contains the label"}   )
      lowerCAmelCase__			: str    =					field(default=__a							,			metadata={"help": "The path of the training file"}   )
      lowerCAmelCase__			: Optional[str]    =					field(default=__a							,			metadata={"help": "The path of the development file"}   )
      lowerCAmelCase__			: Optional[str]    =					field(default=__a							,			metadata={"help": "The path of the test file"}   )
      lowerCAmelCase__			: int    =					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."
              )
          }							,			)
      lowerCAmelCase__			: bool    =					field(
          default=__a							,			metadata={"help": "Overwrite the cached training and evaluation sets"}   )







@dataclass
class  _lowerCamelCase						:

      """simple docstring"""

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







def 					__SCREAMING_SNAKE_CASE   (  )       ->	Optional[int]:

    """simple docstring"""


    __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=lowerCAmelCase__					,     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(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():
        __UpperCamelCase  		=						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(lowercase_      ) -> Dict:
        __UpperCamelCase  		=						np.argmax(p.predictions					,     axis=1      )

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

    # Initialize our Trainer
    __UpperCamelCase  		=						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
    __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(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()












import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["image_processor", "tokenizer"]
      lowerCAmelCase__			: List[Any]    =					"ViTImageProcessor"
      lowerCAmelCase__			: Optional[int]    =					("CLIPTokenizer", "CLIPTokenizerFast")







      def __init__(     self					: Any ,       snake_case					: Any=None ,       snake_case					: Dict=None ,       **snake_case					: Tuple				):
          __UpperCamelCase  		=						None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                  ''' instead.''' ,       snake_case ,       )
              __UpperCamelCase  		=						kwargs.pop('''feature_extractor'''				)

          __UpperCamelCase  		=						image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError('''You need to specify an `image_processor`.'''				)
          if tokenizer is None:
              raise ValueError('''You need to specify a `tokenizer`.'''				)

          super().__init__(snake_case ,       snake_case				)







      def __call__(     self					: Dict ,       snake_case					: Union[str, Any]=None ,       snake_case					: Tuple=None ,       snake_case					: Any=None ,       snake_case					: Any=None ,       **snake_case					: Optional[int]				):
          if text is None and visual_prompt is None and images is None:
              raise ValueError('''You have to specify either text, visual prompt or images.'''				)

          if text is not None and visual_prompt is not None:
              raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

          if text is not None:
              __UpperCamelCase  		=						self.tokenizer(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if images is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None and images is not None:
              __UpperCamelCase  		=						{
                  '''pixel_values''': image_features.pixel_values,
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          elif text is not None and images is not None:
              __UpperCamelCase  		=						image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          elif visual_prompt is not None:
              __UpperCamelCase  		=						{
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          else:
              return BatchEncoding(data=dict(**snake_case				) ,       tensor_type=snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Tuple ,       **snake_case					: Any				):
          return self.tokenizer.batch_decode(*snake_case ,       **snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Dict ,       **snake_case					: List[str]				):
          return self.tokenizer.decode(*snake_case ,       **snake_case				)







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,       snake_case ,       )
          return self.image_processor_class







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,       snake_case ,       )
          return self.image_processor






