

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							(			lowerCAmelCase:					str					, lowerCAmelCase:					str					, lowerCAmelCase:					str					, lowerCAmelCase:					PreTrainedTokenizer					, lowerCAmelCase:					int					, lowerCAmelCase:					Optional[int] = None					, )				->    str:
    _UpperCAmelCase    :						List[Any]							=      {}

    if train_file is not None:
        _UpperCAmelCase    :						Dict							=      [train_file]
    if eval_file is not None:
        _UpperCAmelCase    :						str							=      [eval_file]
    if test_file is not None:
        _UpperCAmelCase    :						Union[str, Any]							=      [test_file]

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

    if len(lowerCAmelCase	) == 1:
        for k in files.keys():
            _UpperCAmelCase    :						List[Any]							=      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():
            _UpperCAmelCase    :						Tuple							=      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]:
            _UpperCAmelCase    :						Optional[Any]							=      {k: v for k, v in ex.items() if k in input_names}
            _UpperCAmelCase    :						Union[str, Any]							=      labelaid[ex[label_name]]
            yield (d, label)

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

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

    _UpperCAmelCase    :						Optional[int]							=      (
        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    :						str							=      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

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

    _UpperCAmelCase    :						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.TEST in transformed_ds
        else None
    )

    if test_ds is not None:
        _UpperCAmelCase    :						Dict							=      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

    return train_ds, val_ds, test_ds, labelaid


SCREAMING_SNAKE_CASE_            = logging.getLogger(__name__)

@dataclass
class 			a					:
      _lowercase        =     field(metadata={"help": "Which column contains the label"}			)
      _lowercase        =     field(default=UpperCAmelCase							,	metadata={"help": "The path of the training file"}			)
      _lowercase        =     field(default=UpperCAmelCase							,	metadata={"help": "The path of the development file"}			)
      _lowercase        =     field(default=UpperCAmelCase							,	metadata={"help": "The path of the test file"}			)
      _lowercase        =     field(
          default=1_2_8							,	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=UpperCAmelCase							,	metadata={"help": "Overwrite the cached training and evaluation sets"}			)







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







def 							__SCREAMING_SNAKE_CASE							(			)				->    Optional[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.
    _UpperCAmelCase    :						Dict							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
    _UpperCAmelCase    :						Optional[int]							=      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    :						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					, )

    _UpperCAmelCase    :						List[Any]							=      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    :						int							=      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    :						Union[str, 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:					EvalPrediction	) -> Dict:
        _UpperCAmelCase    :						List[Any]							=      np.argmax(p.predictions					, axis=1	)

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

    # Initialize our Trainer
    _UpperCAmelCase    :						Dict							=      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    :						Tuple							=      {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***"	)
        _UpperCAmelCase    :						List[Any]							=      trainer.evaluate()
        _UpperCAmelCase    :						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()





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

SCREAMING_SNAKE_CASE_            = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    Tuple:
				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												_UpperCAmelCase    :						Union[str, Any]							=      model_type_to_module_name(lowerCAmelCase	)

												_UpperCAmelCase    :						Optional[int]							=      importlib.import_module(F'.{module_name}'					, "transformers.models"	)
												try:
																return getattr(lowerCAmelCase					, lowerCAmelCase	)
												except AttributeError:
																continue

				for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
								if getattr(lowerCAmelCase					, "__name__"					, lowerCAmelCase	) == class_name:
												return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
				_UpperCAmelCase    :						Any							=      importlib.import_module("transformers"	)
				if hasattr(lowerCAmelCase					, lowerCAmelCase	):
								return getattr(lowerCAmelCase					, lowerCAmelCase	)

				return None

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Union[str, os.PathLike]					, lowerCAmelCase:					Optional[Union[str, os.PathLike]] = None					, lowerCAmelCase:					bool = False					, lowerCAmelCase:					bool = False					, lowerCAmelCase:					Optional[Dict[str, str]] = None					, lowerCAmelCase:					Optional[Union[bool, str]] = None					, lowerCAmelCase:					Optional[str] = None					, lowerCAmelCase:					bool = False					, **lowerCAmelCase:					List[Any]					, )				->    Any:
				_UpperCAmelCase    :						List[Any]							=      get_file_from_repo(
				    lowerCAmelCase					, lowerCAmelCase					, cache_dir=lowerCAmelCase					, force_download=lowerCAmelCase					, resume_download=lowerCAmelCase					, proxies=lowerCAmelCase					, use_auth_token=lowerCAmelCase					, revision=lowerCAmelCase					, local_files_only=lowerCAmelCase					, )
				if resolved_config_file is None:
								logger.info(
								    "Could not locate the image processor configuration file, will try to use the model config instead."	)
								return {}

				with open(lowerCAmelCase					, encoding="utf-8"	) as reader:
								return json.load(lowerCAmelCase	)





class 			a					:


						def __init__(    self		):







										'''simple docstring'''


										raise EnvironmentError(
										    "AutoImageProcessor is designed to be instantiated "
										    "using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method."		)


						@classmethod
						@replace_list_option_in_docstrings(A_		)
						def     _UpperCAmelCase			(    cls  ,   A_  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      kwargs.pop("config"  ,   A_		)
										_UpperCAmelCase    :						Dict							=      kwargs.pop("trust_remote_code"  ,   A_		)
										_UpperCAmelCase    :						str							=      True

										_UpperCAmelCase						,							_UpperCAmelCase    :						Tuple							=      ImageProcessingMixin.get_image_processor_dict(A_  ,   **A_		)
										_UpperCAmelCase    :						Any							=      config_dict.get("image_processor_type"  ,   A_		)
										_UpperCAmelCase    :						str							=      None
										if "AutoImageProcessor" in config_dict.get("auto_map"  ,   {}		):
														_UpperCAmelCase    :						int							=      config_dict["auto_map"]["AutoImageProcessor"]

										# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
										# and if so, infer the image processor class from there.
										if image_processor_class is None and image_processor_auto_map is None:
														_UpperCAmelCase    :						Any							=      config_dict.pop("feature_extractor_type"  ,   A_		)
														if feature_extractor_class is not None:
																		logger.warning(
																		    "Could not find image processor class in the image processor config or the model config. Loading"
																		    " based on pattern matching with the model's feature extractor configuration."		)
																		_UpperCAmelCase    :						Optional[Any]							=      feature_extractor_class.replace("FeatureExtractor"  ,   "ImageProcessor"		)
														if "AutoFeatureExtractor" in config_dict.get("auto_map"  ,   {}		):
																		_UpperCAmelCase    :						Optional[Any]							=      config_dict["auto_map"]["AutoFeatureExtractor"]
																		_UpperCAmelCase    :						List[Any]							=      feature_extractor_auto_map.replace("FeatureExtractor"  ,   "ImageProcessor"		)
																		logger.warning(
																		    "Could not find image processor auto map in the image processor config or the model config."
																		    " Loading based on pattern matching with the model's feature extractor configuration."		)

        # If we don't find the image processor class in the image processor config, let's try the model config.
										if image_processor_class is None and image_processor_auto_map is None:
														if not isinstance(A_  ,   A_		):
																		_UpperCAmelCase    :						Any							=      AutoConfig.from_pretrained(A_  ,   **A_		)
														# It could be in `config.image_processor_type``
														_UpperCAmelCase    :						Optional[Any]							=      getattr(A_  ,   "image_processor_type"  ,   A_		)
														if hasattr(A_  ,   "auto_map"		) and "AutoImageProcessor" in config.auto_map:
																		_UpperCAmelCase    :						Any							=      config.auto_map["AutoImageProcessor"]

										if image_processor_class is not None:
														_UpperCAmelCase    :						List[str]							=      image_processor_class_from_name(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      image_processor_auto_map is not None
										_UpperCAmelCase    :						Any							=      image_processor_class is not None or type(A_		) in IMAGE_PROCESSOR_MAPPING
										_UpperCAmelCase    :						List[Any]							=      resolve_trust_remote_code(
										    A_  ,   A_  ,   A_  ,   A_		)

										if has_remote_code and trust_remote_code:
														_UpperCAmelCase    :						Optional[int]							=      get_class_from_dynamic_module(
														    A_  ,   A_  ,   **A_		)
														_UpperCAmelCase    :						Optional[int]							=      kwargs.pop("code_revision"  ,   A_		)
														if os.path.isdir(A_		):
																		image_processor_class.register_for_auto_class()
														return image_processor_class.from_dict(A_  ,   **A_		)
										elif image_processor_class is not None:
														return image_processor_class.from_dict(A_  ,   **A_		)
										# Last try: we use the IMAGE_PROCESSOR_MAPPING.
										elif type(A_		) in IMAGE_PROCESSOR_MAPPING:
														_UpperCAmelCase    :						Optional[int]							=      IMAGE_PROCESSOR_MAPPING[type(A_		)]
														return image_processor_class.from_dict(A_  ,   **A_		)

										raise ValueError(
										    f'Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '
										    f'`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '
										    f'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()		)}'		)





						@staticmethod
						def     _UpperCAmelCase			(    A_  ,   A_		):







										'''simple docstring'''


										IMAGE_PROCESSOR_MAPPING.register(A_  ,   A_		)


