

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import VivitImageProcessor






class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			def __init__(			self							: Dict      ,      a							: int      ,      a							: str=7      ,      a							: Union[str, Any]=3      ,      a							: Optional[int]=10      ,      a							: Union[str, Any]=18      ,      a							: Dict=30      ,      a							: str=400      ,      a							: Optional[Any]=True      ,      a							: List[str]=None      ,      a							: Optional[int]=True      ,      a							: Optional[Any]=[0.5, 0.5, 0.5]      ,      a							: Union[str, Any]=[0.5, 0.5, 0.5]      ,      a							: str=None      ,      )							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       size if size is not None else {"shortest_edge": 18}
				SCREAMING_SNAKE_CASE   :      Any				     =       crop_size if crop_size is not None else {"height": 18, "width": 18}

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       parent
				SCREAMING_SNAKE_CASE   :      Tuple				     =       batch_size
				SCREAMING_SNAKE_CASE   :      int				     =       num_channels
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       num_frames
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_size
				SCREAMING_SNAKE_CASE   :      Any				     =       min_resolution
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       max_resolution
				SCREAMING_SNAKE_CASE   :      Tuple				     =       do_resize
				SCREAMING_SNAKE_CASE   :      int				     =       size
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       do_normalize
				SCREAMING_SNAKE_CASE   :      Dict				     =       image_mean
				SCREAMING_SNAKE_CASE   :      Any				     =       image_std
				SCREAMING_SNAKE_CASE   :      List[str]				     =       crop_size






			def 							__UpperCamelCase (			self							: Any	)							->       List[str]:

				"""simple docstring"""

				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "crop_size": self.crop_size,
				}






@require_torch
@require_vision
class _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =VivitImageProcessor if is_vision_available() else None
			def 							__UpperCamelCase (			self							: Tuple	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       VivitImageProcessingTester(self	)
			@property
			def 							__UpperCamelCase (			self							: Dict	)							->       int:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()
			def 							__UpperCamelCase (			self							: Tuple	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(a      ,      "image_mean"	)	)
				self.assertTrue(hasattr(a      ,      "image_std"	)	)
				self.assertTrue(hasattr(a      ,      "do_normalize"	)	)
				self.assertTrue(hasattr(a      ,      "do_resize"	)	)
				self.assertTrue(hasattr(a      ,      "do_center_crop"	)	)
				self.assertTrue(hasattr(a      ,      "size"	)	)
			def 							__UpperCamelCase (			self							: int	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.image_processing_class.from_dict(self.image_processor_dict	)
				self.assertEqual(image_processor.size      ,      {"shortest_edge": 18}	)
				self.assertEqual(image_processor.crop_size      ,      {"height": 18, "width": 18}	)

				SCREAMING_SNAKE_CASE   :      int				     =       self.image_processing_class.from_dict(self.image_processor_dict      ,      size=42      ,      crop_size=84	)
				self.assertEqual(image_processor.size      ,      {"shortest_edge": 42}	)
				self.assertEqual(image_processor.crop_size      ,      {"height": 84, "width": 84}	)
			def 							__UpperCamelCase (			self							: List[Any]	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random PIL videos
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       prepare_video_inputs(self.image_processor_tester      ,      equal_resolution=a	)
				for video in video_inputs:
					self.assertIsInstance(a      ,      a	)
					self.assertIsInstance(video[0]      ,      Image.Image	)

				# Test not batched input
				SCREAMING_SNAKE_CASE   :      int				     =       image_processing(video_inputs[0]      ,      return_tensors="pt"	).pixel_values
				self.assertEqual(
				    encoded_videos.shape      ,      (
				        1,
				        self.image_processor_tester.num_frames,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )

				# Test batched
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       image_processing(a      ,      return_tensors="pt"	).pixel_values
				self.assertEqual(
				    encoded_videos.shape      ,      (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_frames,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )
			def 							__UpperCamelCase (			self							: List[Any]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				SCREAMING_SNAKE_CASE   :      int				     =       prepare_video_inputs(self.image_processor_tester      ,      equal_resolution=a      ,      numpify=a	)
				for video in video_inputs:
					self.assertIsInstance(a      ,      a	)
					self.assertIsInstance(video[0]      ,      np.ndarray	)

				# Test not batched input
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       image_processing(video_inputs[0]      ,      return_tensors="pt"	).pixel_values
				self.assertEqual(
				    encoded_videos.shape      ,      (
				        1,
				        self.image_processor_tester.num_frames,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )

				# Test batched
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_processing(a      ,      return_tensors="pt"	).pixel_values
				self.assertEqual(
				    encoded_videos.shape      ,      (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_frames,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )






			def 							__UpperCamelCase (			self							: Any	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				SCREAMING_SNAKE_CASE   :      Tuple				     =       prepare_video_inputs(self.image_processor_tester      ,      equal_resolution=a      ,      torchify=a	)
				for video in video_inputs:
					self.assertIsInstance(a      ,      a	)
					self.assertIsInstance(video[0]      ,      torch.Tensor	)

				# Test not batched input
				SCREAMING_SNAKE_CASE   :      Any				     =       image_processing(video_inputs[0]      ,      return_tensors="pt"	).pixel_values
				self.assertEqual(
				    encoded_videos.shape      ,      (
				        1,
				        self.image_processor_tester.num_frames,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )

				# Test batched
				SCREAMING_SNAKE_CASE   :      str				     =       image_processing(a      ,      return_tensors="pt"	).pixel_values
				self.assertEqual(
				    encoded_videos.shape      ,      (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_frames,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )


import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image

			from transformers.image_utils import load_image
else:






			class _UpperCamelCase							:
						'''simple docstring'''







						@staticmethod
						def 							__UpperCamelCase (			*a							: Any      ,      **a							: Optional[int]	)							->       Optional[Any]:

							"""simple docstring"""

							pass

			def  lowerCamelCase__ (					_a):
				return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
a_						     =    (
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)






@is_pipeline_test
@require_torch
@require_vision
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
			@require_pytesseract
			@require_vision
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: Union[str, Any]      ,      a							: Any      ,      a							: Optional[Any]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       pipeline(
				    "document-question-answering"      ,      model=a      ,      tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      int				     =       list(zip(*apply_tesseract(load_image(a	)      ,      a      ,      ""	)	)	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "What is the placebo?"
				SCREAMING_SNAKE_CASE   :      List[str]				     =       [
				    {
				        "image": load_image(a	),
				        "question": question,
				    },
				    {
				        "image": image,
				        "question": question,
				    },
				    {
				        "image": image,
				        "question": question,
				        "word_boxes": word_boxes,
				    },
				]
				return dqa_pipeline, examples
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: List[Any]      ,      a							: Any	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       dqa_pipeline(a      ,      top_k=2	)
				self.assertEqual(
				    a      ,      [
				        [
				            {"score": ANY(a	), "answer": ANY(a	), "start": ANY(a	), "end": ANY(a	)},
				            {"score": ANY(a	), "answer": ANY(a	), "start": ANY(a	), "end": ANY(a	)},
				        ]
				    ]
				    * 3      ,      )
			@require_torch
			@require_detectrona
			@require_pytesseract
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       pipeline("document-question-answering"      ,      model="hf-internal-testing/tiny-random-layoutlmv2"	)
				SCREAMING_SNAKE_CASE   :      int				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      int				     =       "How many cats are there?"

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [
				    {"score": 0.0001, "answer": "oy 2312/2019", "start": 38, "end": 39},
				    {"score": 0.0001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
				]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(nested_simplify(a      ,      decimals=4	)      ,      a	)

				SCREAMING_SNAKE_CASE   :      Any				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(nested_simplify(a      ,      decimals=4	)      ,      a	)

				# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
				# Empty answer probably
				SCREAMING_SNAKE_CASE   :      Tuple				     =       "./tests/fixtures/tests_samples/COCO/000000039769.png"
				SCREAMING_SNAKE_CASE   :      str				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(a      ,      []	)

				# We can optionnally pass directly the words and bounding boxes
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "./tests/fixtures/tests_samples/COCO/000000039769.png"
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       []
				SCREAMING_SNAKE_CASE   :      int				     =       []
				SCREAMING_SNAKE_CASE   :      str				     =       dqa_pipeline(image=a      ,      question=a      ,      words=a      ,      boxes=a      ,      top_k=2	)
				self.assertEqual(a      ,      []	)
			@slow
			@require_torch
			@require_detectrona
			@require_pytesseract
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       pipeline(
				    "document-question-answering"      ,      model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"      ,      revision="9977165"      ,      )
				SCREAMING_SNAKE_CASE   :      Any				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      Any				     =       "What is the invoice number?"

				SCREAMING_SNAKE_CASE   :      Dict				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Any				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        [
				            {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
				            {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
				        ],
				    ]
				    * 2      ,      )
			@slow
			@require_torch
			@require_detectrona
			@require_pytesseract
			def 							__UpperCamelCase (			self							: Tuple	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       pipeline(
				    "document-question-answering"      ,      model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"      ,      revision="9977165"      ,      max_seq_len=50      ,      )
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      int				     =       "What is the invoice number?"

				SCREAMING_SNAKE_CASE   :      Any				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
				        {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Tuple				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
				        {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        [
				            {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
				            {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
				        ]
				    ]
				    * 2      ,      )
			@slow
			@require_torch
			@require_pytesseract
			@require_vision
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       AutoTokenizer.from_pretrained(
				    "impira/layoutlm-document-qa"      ,      revision="3dc6de3"      ,      add_prefix_space=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       pipeline(
				    "document-question-answering"      ,      model="impira/layoutlm-document-qa"      ,      tokenizer=a      ,      revision="3dc6de3"      ,      )
				SCREAMING_SNAKE_CASE   :      List[str]				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      int				     =       "What is the invoice number?"

				SCREAMING_SNAKE_CASE   :      str				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Dict				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        [
				            {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
				            {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
				        ]
				    ]
				    * 2      ,      )

				SCREAMING_SNAKE_CASE   :      int				     =       list(zip(*apply_tesseract(load_image(a	)      ,      a      ,      ""	)	)	)

				# This model should also work if `image` is set to None
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
				    ]      ,      )
			@slow
			@require_torch
			@require_pytesseract
			@require_vision
			def 							__UpperCamelCase (			self							: Tuple	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       AutoTokenizer.from_pretrained(
				    "impira/layoutlm-document-qa"      ,      revision="3dc6de3"      ,      add_prefix_space=a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       pipeline(
				    "document-question-answering"      ,      model="impira/layoutlm-document-qa"      ,      tokenizer=a      ,      revision="3dc6de3"      ,      max_seq_len=50      ,      )
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "What is the invoice number?"

				SCREAMING_SNAKE_CASE   :      Dict				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      int				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        [
				            {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
				            {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
				        ]
				    ]
				    * 2      ,      )

				SCREAMING_SNAKE_CASE   :      int				     =       list(zip(*apply_tesseract(load_image(a	)      ,      a      ,      ""	)	)	)

				# This model should also work if `image` is set to None
				SCREAMING_SNAKE_CASE   :      Any				     =       dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )
			@slow
			@require_torch
			def 							__UpperCamelCase (			self							: str	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       pipeline(
				    "document-question-answering"      ,      model="naver-clova-ix/donut-base-finetuned-docvqa"      ,      tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa"	)      ,      feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa"      ,      )

				SCREAMING_SNAKE_CASE   :      int				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "What is the invoice number?"
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(nested_simplify(a      ,      decimals=4	)      ,      [{"answer": "us-001"}]	)






			@require_tf
			@unittest.skip("Document question answering not implemented in TF"	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       int:

				"""simple docstring"""

				pass