






"""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      SCREAMING_SNAKE_CASE  (				__UpperCAmelCase						):





	"""simple docstring"""




	@slow
	@require_torch
	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""     ,     """prajjwal1/bert-tiny"""							)
		A_		:						List[str]	    =   BertTokenizer.from_pretrained("""bert-base-uncased"""							)

		A_		:						List[Any]	    =   bertabert.config.encoder.vocab_size
		A_		:						Any	    =   tokenizer.sep_token_id
		A_		:						str	    =   tokenizer.cls_token_id
		A_		:						Optional[Any]	    =   128

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

		A_		:						int	    =   train_dataset.select(range(32							)							)
		A_		:						List[str]	    =   val_dataset.select(range(16							)							)

		A_		:						str	    =   4

		def _map_to_encoder_decoder_inputs(lowerCAmelCase_							):
			# Tokenizer will automatically set [BOS] <text> [EOS]
			A_		:						Optional[Any]	    =   tokenizer(batch["""article"""]     ,     padding="""max_length"""     ,     truncation=lowerCAmelCase_     ,     max_length=512							)
			A_		:						str	    =   tokenizer(batch["""highlights"""]     ,     padding="""max_length"""     ,     truncation=lowerCAmelCase_     ,     max_length=128							)
			A_		:						Union[str, Any]	    =   inputs.input_ids
			A_		:						Optional[int]	    =   inputs.attention_mask

			A_		:						Any	    =   outputs.input_ids
			A_		:						Optional[Any]	    =   outputs.input_ids.copy()
			A_		:						Union[str, Any]	    =   [
			    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
			]
			A_		:						Tuple	    =   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_							):
			A_		:						Dict	    =   pred.label_ids
			A_		:						List[str]	    =   pred.predictions

			# all unnecessary tokens are removed
			A_		:						Union[str, Any]	    =   tokenizer.batch_decode(lowerCAmelCase_     ,     skip_special_tokens=lowerCAmelCase_							)
			A_		:						List[Any]	    =   tokenizer.batch_decode(lowerCAmelCase_     ,     skip_special_tokens=lowerCAmelCase_							)

			A_		:						int	    =   sum([int(pred_str[i] == label_str[i]							) for i in range(len(lowerCAmelCase_							)							)]							) / len(lowerCAmelCase_							)

			return {"accuracy": accuracy}

		# map train dataset
		A_		:						Any	    =   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
		A_		:						Optional[Any]	    =   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"""]     ,     )

		A_		:						str	    =   self.get_auto_remove_tmp_dir()

		A_		:						Union[str, Any]	    =   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
		A_		:						List[str]	    =   SeqaSeqTrainer(
		    model=lowerCAmelCase_     ,     args=lowerCAmelCase_     ,     compute_metrics=_compute_metrics     ,     train_dataset=lowerCAmelCase_     ,     eval_dataset=lowerCAmelCase_     ,     tokenizer=lowerCAmelCase_     ,     )

		# start training
		trainer.train()









"""simple docstring"""
import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import 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      SCREAMING_SNAKE_CASE  :





								"""simple docstring"""




								@staticmethod
								def    lowerCamelCase(*lowerCAmelCase_     ,     **lowerCAmelCase_							):
									pass



@is_pipeline_test
@require_torch
@require_vision
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	_A       :				List[Any]    =				MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   pipeline("""visual-question-answering"""     ,     model="""hf-internal-testing/tiny-vilt-random-vqa"""							)
		A_		:						Union[str, Any]	    =   [
		    {
		        """image""": Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							),
		        """question""": """How many cats are there?""",
		    },
		    {
		        """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
		        """question""": """How many cats are there?""",
		    },
		]
		return vqa_pipeline, examples





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						str	    =   vqa_pipeline(lowerCAmelCase_     ,     top_k=1							)
		self.assertEqual(
		    lowerCAmelCase_     ,     [
		        [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}],
		        [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}],
		    ]     ,     )





	@require_torch
	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   pipeline("""visual-question-answering"""     ,     model="""hf-internal-testing/tiny-vilt-random-vqa"""							)
		A_		:						Any	    =   """./tests/fixtures/tests_samples/COCO/000000039769.png"""
		A_		:						Any	    =   """How many cats are there?"""

		A_		:						Optional[int]	    =   vqa_pipeline(image=lowerCAmelCase_     ,     question="""How many cats are there?"""     ,     top_k=2							)
		self.assertEqual(
		    lowerCAmelCase_     ,     [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}, {"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}]							)

		A_		:						Any	    =   vqa_pipeline({"""image""": image, """question""": question}     ,     top_k=2							)
		self.assertEqual(
		    lowerCAmelCase_     ,     [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}, {"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}]							)





	@slow
	@require_torch
	def    lowerCamelCase(self							):
		A_		:						int	    =   pipeline("""visual-question-answering"""     ,     model="""dandelin/vilt-b32-finetuned-vqa"""							)
		A_		:						Tuple	    =   """./tests/fixtures/tests_samples/COCO/000000039769.png"""
		A_		:						int	    =   """How many cats are there?"""

		A_		:						List[Any]	    =   vqa_pipeline(image=lowerCAmelCase_     ,     question=lowerCAmelCase_     ,     top_k=2							)
		self.assertEqual(
		    nested_simplify(lowerCAmelCase_     ,     decimals=4							)     ,     [{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]							)

		A_		:						Any	    =   vqa_pipeline({"""image""": image, """question""": question}     ,     top_k=2							)
		self.assertEqual(
		    nested_simplify(lowerCAmelCase_     ,     decimals=4							)     ,     [{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]							)

		A_		:						Tuple	    =   vqa_pipeline(
		    [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]     ,     top_k=2							)
		self.assertEqual(
		    nested_simplify(lowerCAmelCase_     ,     decimals=4							)     ,     [[{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]] * 2     ,     )





	@require_tf
	@unittest.skip("""Visual question answering not implemented in TF"""							)
	def    lowerCamelCase(self							):
		pass

