


"""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						(						lowercase  :  str						,							lowercase  :  str						,							lowercase  :  str						,							lowercase  :  PreTrainedTokenizer						,							lowercase  :  int						,							lowercase  :  Optional[int] = None						,							)     ->						Optional[int]:


				"""simple docstring"""





				snake_case   :				Any				 =   {}

				if train_file is not None:
								snake_case   :				str				 =   [train_file]
				if eval_file is not None:
								snake_case   :				Optional[Any]				 =   [eval_file]
				if test_file is not None:
								snake_case   :				Tuple				 =   [test_file]

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

				if len(lowercase			) == 1:
								for k in files.keys():
												snake_case   :				Any				 =   ds[k].map(
												    lambda lowercase			: tokenizer.batch_encode_plus(
												        example[features_name[0]]						,							truncation=lowercase						,							max_length=lowercase						,							padding="max_length"			)						,							batched=lowercase						,							)
				elif len(lowercase			) == 2:
								for k in files.keys():
												snake_case   :				Union[str, Any]				 =   ds[k].map(
												    lambda lowercase			: tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]])						,							truncation=lowercase						,							max_length=lowercase						,							padding="max_length"						,							)						,							batched=lowercase						,							)

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

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

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

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

				snake_case   :				Optional[int]				 =   (
				    tf.data.Dataset.from_generator(
				        lowercase						,							({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   :				List[str]				 =   val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]			)			)			)

				snake_case   :				Optional[Any]				 =   (
				    tf.data.Dataset.from_generator(
				        lowercase						,							({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   :				int				 =   test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]			)			)			)

				return train_ds, val_ds, test_ds, labelaid


__snake_case             =   logging.getLogger(__name__)







@dataclass
class _lowerCAmelCase    :
			__UpperCAmelCase			:	int							  =  field(metadata={'''help''': '''Which column contains the label'''}     )
			__UpperCAmelCase			:	str							  =  field(default=snake_case_   ,							metadata={'''help''': '''The path of the training file'''}     )
			__UpperCAmelCase			:	Optional[str]							  =  field(default=snake_case_   ,							metadata={'''help''': '''The path of the development file'''}     )
			__UpperCAmelCase			:	Optional[str]							  =  field(default=snake_case_   ,							metadata={'''help''': '''The path of the test file'''}     )
			__UpperCAmelCase			:	int							  =  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.'''
			        )
			    }   ,							)
			__UpperCAmelCase			:	bool							  =  field(
			    default=snake_case_   ,							metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )







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







def   __lowerCAmelCase						(						)     ->						str:


				"""simple docstring"""





				snake_case   :				List[Any]				 =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)			)
				snake_case   :				Any				 =   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   :				List[Any]				 =   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   :				Union[str, Any]				 =   get_tfds(
				    train_file=data_args.train_file						,							eval_file=data_args.dev_file						,							test_file=data_args.test_file						,							tokenizer=lowercase						,							label_column_id=data_args.label_column_id						,							max_seq_length=data_args.max_seq_length						,							)

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

				def compute_metrics(lowercase  :  EvalPrediction			) -> Dict:
								snake_case   :				Optional[Any]				 =   np.argmax(p.predictions						,							axis=1			)

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

				# Initialize our Trainer
				snake_case   :				Any				 =   TFTrainer(
				    model=lowercase						,							args=lowercase						,							train_dataset=lowercase						,							eval_dataset=lowercase						,							compute_metrics=lowercase						,							)

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

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

								with open(lowercase						,							"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(lowercase			)

				return results


if __name__ == "__main__":
		main()









"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case             =   {
    """configuration_conditional_detr""": [
        """CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConditionalDetrConfig""",
        """ConditionalDetrOnnxConfig""",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   ["""ConditionalDetrFeatureExtractor"""]
  __snake_case             =   ["""ConditionalDetrImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   [
      """CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ConditionalDetrForObjectDetection""",
      """ConditionalDetrForSegmentation""",
      """ConditionalDetrModel""",
      """ConditionalDetrPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ConditionalDetrConfig,
      ConditionalDetrOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
    from .image_processing_conditional_detr import ConditionalDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_conditional_detr import (
        CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConditionalDetrForObjectDetection,
        ConditionalDetrForSegmentation,
        ConditionalDetrModel,
        ConditionalDetrPreTrainedModel,
    )

else:
  import sys

  __snake_case             =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





