
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     lowercase							:
												@staticmethod
												def lowercase_      (		*SCREAMING_SNAKE_CASE__			,						**SCREAMING_SNAKE_CASE__     ):
															"""simple docstring"""







															pass





@is_pipeline_test
@require_vision
@require_torch
class     lowercase							(	unittest.TestCase       ):
					__a											=     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								lowerCAmelCase__			:     int					   =      pipeline(
								    '''zero-shot-object-detection'''			,						model='''hf-internal-testing/tiny-random-owlvit-object-detection'''     )

								lowerCAmelCase__			:     Union[str, Any]					   =      [
								    {
								        '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
								        '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
								    }
								]
								return object_detector, examples
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Dict					   =      object_detector(examples[0]			,						threshold=0.0     )

								lowerCAmelCase__			:     str					   =      len(__lowerCAmelCase     )
								self.assertGreater(__lowerCAmelCase			,						0     )
								self.assertEqual(
								    __lowerCAmelCase			,						[
								        {
								            '''score''': ANY(__lowerCAmelCase     ),
								            '''label''': ANY(__lowerCAmelCase     ),
								            '''box''': {'''xmin''': ANY(__lowerCAmelCase     ), '''ymin''': ANY(__lowerCAmelCase     ), '''xmax''': ANY(__lowerCAmelCase     ), '''ymax''': ANY(__lowerCAmelCase     )},
								        }
								        for i in range(__lowerCAmelCase     )
								    ]			,						)
					@require_tf
					@unittest.skip('''Zero Shot Object Detection not implemented in TF'''     )
					def lowercase_      (		self     ):
								"""simple docstring"""







								pass
					@require_torch
					def lowercase_      (		self     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Optional[int]					   =      pipeline(
								    '''zero-shot-object-detection'''			,						model='''hf-internal-testing/tiny-random-owlvit-object-detection'''     )

								lowerCAmelCase__			:     Tuple					   =      object_detector(
								    '''./tests/fixtures/tests_samples/COCO/000000039769.png'''			,						candidate_labels=['''cat''', '''remote''', '''couch''']			,						threshold=0.64			,						)

								self.assertEqual(
								    nested_simplify(__lowerCAmelCase			,						decimals=4     )			,						[
								        {'''score''': 0.7_235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
								        {'''score''': 0.7_218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
								        {'''score''': 0.7_184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
								        {'''score''': 0.6_748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
								        {'''score''': 0.6_656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
								        {'''score''': 0.6_614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
								        {'''score''': 0.6_456, '''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''': 0.6_419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
								    ]			,						)

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

								self.assertEqual(
								    nested_simplify(__lowerCAmelCase			,						decimals=4     )			,						[
								        [
								            {'''score''': 0.7_235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
								            {'''score''': 0.7_218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
								            {'''score''': 0.7_184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
								            {'''score''': 0.6_748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
								            {'''score''': 0.6_656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
								            {'''score''': 0.6_614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
								            {'''score''': 0.6_456, '''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''': 0.6_419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
								        ]
								    ]			,						)
					@require_torch
					@slow
					def lowercase_      (		self     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Any					   =      pipeline('''zero-shot-object-detection'''     )

								lowerCAmelCase__			:     Any					   =      object_detector(
								    '''http://images.cocodataset.org/val2017/000000039769.jpg'''			,						candidate_labels=['''cat''', '''remote''', '''couch''']			,						)
								self.assertEqual(
								    nested_simplify(__lowerCAmelCase			,						decimals=4     )			,						[
								        {'''score''': 0.2_868, '''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''': 0.2_537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
								        {'''score''': 0.1_474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
								        {'''score''': 0.1_208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
								    ]			,						)

								lowerCAmelCase__			:     List[str]					   =      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(__lowerCAmelCase			,						decimals=4     )			,						[
								        [
								            {'''score''': 0.2_868, '''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''': 0.2_537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
								            {'''score''': 0.1_474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
								            {'''score''': 0.1_208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
								        ],
								        [
								            {'''score''': 0.2_868, '''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''': 0.2_537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
								            {'''score''': 0.1_474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
								            {'''score''': 0.1_208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
								        ],
								    ]			,						)
					@require_tf
					@unittest.skip('''Zero Shot Object Detection not implemented in TF'''     )
					def lowercase_      (		self     ):
								"""simple docstring"""







								pass
					@require_torch
					@slow
					def lowercase_      (		self     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Any					   =      0.2
								lowerCAmelCase__			:     Optional[Any]					   =      pipeline('''zero-shot-object-detection'''     )

								lowerCAmelCase__			:     Any					   =      object_detector(
								    '''http://images.cocodataset.org/val2017/000000039769.jpg'''			,						candidate_labels=['''cat''', '''remote''', '''couch''']			,						threshold=__lowerCAmelCase			,						)
								self.assertEqual(
								    nested_simplify(__lowerCAmelCase			,						decimals=4     )			,						[
								        {'''score''': 0.2_868, '''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''': 0.2_537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
								    ]			,						)
					@require_torch
					@slow
					def lowercase_      (		self     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Union[str, Any]					   =      2
								lowerCAmelCase__			:     Dict					   =      pipeline('''zero-shot-object-detection'''     )

								lowerCAmelCase__			:     Tuple					   =      object_detector(
								    '''http://images.cocodataset.org/val2017/000000039769.jpg'''			,						candidate_labels=['''cat''', '''remote''', '''couch''']			,						top_k=__lowerCAmelCase			,						)
								self.assertEqual(
								    nested_simplify(__lowerCAmelCase			,						decimals=4     )			,						[
								        {'''score''': 0.2_868, '''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}},
								    ]			,						)




"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
    import datasets


class 							a					(		lowerCAmelCase_ ):


    @slow
    @require_torch
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,					"""prajjwal1/bert-tiny"""       )
      _UpperCAmelCase    			=   BertTokenizer.from_pretrained("""bert-base-uncased"""       )

      _UpperCAmelCase    			=   bertabert.config.encoder.vocab_size
      _UpperCAmelCase    			=   tokenizer.sep_token_id
      _UpperCAmelCase    			=   tokenizer.cls_token_id
      _UpperCAmelCase    			=   128

      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""train[:1%]"""       )
      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""validation[:1%]"""       )

      _UpperCAmelCase    			=   train_dataset.select(range(32       )       )
      _UpperCAmelCase    			=   val_dataset.select(range(16       )       )

      _UpperCAmelCase    			=   4

      def _map_to_encoder_decoder_inputs(__lowerCAmelCase						:	Dict       ):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        _UpperCAmelCase    			=   tokenizer(batch["""article"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=512       )
        _UpperCAmelCase    			=   tokenizer(batch["""highlights"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=128       )
        _UpperCAmelCase    			=   inputs.input_ids
        _UpperCAmelCase    			=   inputs.attention_mask

        _UpperCAmelCase    			=   outputs.input_ids
        _UpperCAmelCase    			=   outputs.input_ids.copy()
        _UpperCAmelCase    			=   [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
        ]
        _UpperCAmelCase    			=   outputs.attention_mask

        assert all(len(__lowerCAmelCase       ) == 512 for x in inputs.input_ids       )
        assert all(len(__lowerCAmelCase       ) == 128 for x in outputs.input_ids       )

        return batch

      def _compute_metrics(__lowerCAmelCase						:	int       ):
        _UpperCAmelCase    			=   pred.label_ids
        _UpperCAmelCase    			=   pred.predictions

        # all unnecessary tokens are removed
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )

        _UpperCAmelCase    			=   sum([int(pred_str[i] == label_str[i]       ) for i in range(len(__lowerCAmelCase       )       )]       ) / len(__lowerCAmelCase       )

        return {"accuracy": accuracy}

      # map train dataset
      _UpperCAmelCase    			=   train_dataset.map(
          _map_to_encoder_decoder_inputs ,					batched=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					remove_columns=["""article""", """highlights"""] ,					)
      train_dataset.set_format(
          type="""torch""" ,					columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,					)

      # same for validation dataset
      _UpperCAmelCase    			=   val_dataset.map(
          _map_to_encoder_decoder_inputs ,					batched=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					remove_columns=["""article""", """highlights"""] ,					)
      val_dataset.set_format(
          type="""torch""" ,					columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,					)

      _UpperCAmelCase    			=   self.get_auto_remove_tmp_dir()

      _UpperCAmelCase    			=   SeqaSeqTrainingArguments(
          output_dir=__lowerCAmelCase ,					per_device_train_batch_size=__lowerCAmelCase ,					per_device_eval_batch_size=__lowerCAmelCase ,					predict_with_generate=__lowerCAmelCase ,					evaluation_strategy="""steps""" ,					do_train=__lowerCAmelCase ,					do_eval=__lowerCAmelCase ,					warmup_steps=0 ,					eval_steps=2 ,					logging_steps=2 ,					)

      # instantiate trainer
      _UpperCAmelCase    			=   SeqaSeqTrainer(
          model=__lowerCAmelCase ,					args=__lowerCAmelCase ,					compute_metrics=_compute_metrics ,					train_dataset=__lowerCAmelCase ,					eval_dataset=__lowerCAmelCase ,					tokenizer=__lowerCAmelCase ,					)

      # start training
      trainer.train()
