


"""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 ,snake_case ,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 ,snake_case ,snake_case ,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"""



import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:







  class _lowerCAmelCase    :







     @staticmethod
     def 			lowerCamelCase		(     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Union[str, Any]:

         '''simple docstring'''




         pass







@is_pipeline_test
@require_vision
@require_torch
class _lowerCAmelCase    (				unittest.TestCase     ):
   __UpperCAmelCase			:	List[Any]							  =  MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   pipeline(
           "zero-shot-object-detection"						,     model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

       snake_case   :				int				 =   [
           {
               "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
               "candidate_labels": ["cat", "remote", "couch"],
           }
       ]
       return object_detector, examples







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				str				 =   object_detector(examples[0]						,     threshold=0.0						)

       snake_case   :				str				 =   len(UpperCamelCase__						)
       self.assertGreater(UpperCamelCase__						,     0						)
       self.assertEqual(
           UpperCamelCase__						,     [
               {
                   "score": ANY(UpperCamelCase__						),
                   "label": ANY(UpperCamelCase__						),
                   "box": {"xmin": ANY(UpperCamelCase__						), "ymin": ANY(UpperCamelCase__						), "xmax": ANY(UpperCamelCase__						), "ymax": ANY(UpperCamelCase__						)},
               }
               for i in range(UpperCamelCase__						)
           ]						,     )







   @require_tf
   @unittest.skip("Zero Shot Object Detection not implemented in TF"						)
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       pass







   @require_torch
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				Dict				 =   pipeline(
           "zero-shot-object-detection"						,     model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

       snake_case   :				Optional[Any]				 =   object_detector(
           "./tests/fixtures/tests_samples/COCO/000000039769.png"						,     candidate_labels=["cat", "remote", "couch"]						,     threshold=0.64						,     )

       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
               {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
               {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
               {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
               {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
               {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
               {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
               {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
               {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
           ]						,     )

       snake_case   :				Dict				 =   object_detector(
           [
               {
                   "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                   "candidate_labels": ["cat", "remote", "couch"],
               }
           ]						,     threshold=0.64						,     )

       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               [
                   {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                   {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                   {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                   {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                   {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                   {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                   {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                   {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                   {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
               ]
           ]						,     )







   @require_torch
   @slow
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				Tuple				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
               {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
               {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
               {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
               {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
           ]						,     )

       snake_case   :				List[Any]				 =   object_detector(
           [
               {
                   "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                   "candidate_labels": ["cat", "remote", "couch"],
               },
               {
                   "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                   "candidate_labels": ["cat", "remote", "couch"],
               },
           ]						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               [
                   {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                   {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                   {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                   {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                   {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
               ],
               [
                   {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                   {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                   {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                   {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                   {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
               ],
           ]						,     )







   @require_tf
   @unittest.skip("Zero Shot Object Detection not implemented in TF"						)
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       pass







   @require_torch
   @slow
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   0.2
       snake_case   :				List[str]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				List[Any]				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     threshold=UpperCamelCase__						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
               {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
               {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
           ]						,     )







   @require_torch
   @slow
   def 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   2
       snake_case   :				Optional[Any]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				Optional[int]				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     top_k=UpperCamelCase__						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
               {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
           ]						,     )





