






"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=1_024   ,   snake_case__=1_024   ,   snake_case__=False   ,   **snake_case__					):
	A_		:						Any	    =   AutoTokenizer.from_pretrained(snake_case__					)
	A_		:						Tuple	    =   SeqaSeqDataset(snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   type_path="""train"""   ,   **snake_case__					)
	A_		:						List[str]	    =   tok.pad_token_id

	def get_lens(snake_case__					):
		A_		:						Optional[int]	    =   tqdm(
		    DataLoader(snake_case__   ,   batch_size=512   ,   num_workers=8   ,   shuffle=snake_case__   ,   collate_fn=ds.collate_fn					)   ,   desc=str(ds.len_file					)   ,   )
		A_		:						List[str]	    =   []
		for batch in dl:
			A_		:						str	    =   batch["""input_ids"""].ne(snake_case__					).sum(1					).tolist()
			A_		:						Any	    =   batch["""labels"""].ne(snake_case__					).sum(1					).tolist()
			if consider_target:
				for src, tgt in zip(snake_case__   ,   snake_case__					):
					max_lens.append(max(snake_case__   ,   snake_case__					)					)
			else:
				max_lens.extend(snake_case__					)
		return max_lens

	A_		:						int	    =   get_lens(snake_case__					)
	A_		:						List[Any]	    =   SeqaSeqDataset(snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   type_path="""val"""   ,   **snake_case__					)
	A_		:						Optional[Any]	    =   get_lens(snake_case__					)
	pickle_save(snake_case__   ,   train_ds.len_file					)
	pickle_save(snake_case__   ,   val_ds.len_file					)


if __name__ == "__main__":
							fire.Fire(save_len_file)









"""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

