






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     __SCREAMING_SNAKE_CASE					:






											@staticmethod
											def       UpperCamelCase__      (	*_UpperCamelCase     ,						**_UpperCamelCase   ):






																	"""simple docstring"""
																	pass







					def 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]		)  ->   Union[str, Any]:

											"""simple docstring"""



											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.
__snake_case    :    List[str]         =     (
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)


@is_pipeline_test
@require_torch
@require_vision
class     __SCREAMING_SNAKE_CASE					(   unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       Tuple   =					MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING






						@require_pytesseract
						@require_vision
						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						pipeline(
												    'document-question-answering'     ,						model=__a     ,						tokenizer=__a     ,						image_processor=__a   )

												lowerCAmelCase__ 						=						INVOICE_URL
												lowerCAmelCase__ 						=						list(zip(*apply_tesseract(load_image(__a   )     ,						__a     ,						''   )   )   )
												lowerCAmelCase__ 						=						"""What is the placebo?"""
												lowerCAmelCase__ 						=						[
												    {
												        """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     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						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   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						pipeline('document-question-answering'     ,						model='hf-internal-testing/tiny-random-layoutlmv2'   )
												lowerCAmelCase__ 						=						INVOICE_URL
												lowerCAmelCase__ 						=						"""How many cats are there?"""

												lowerCAmelCase__ 						=						[
												    {"""score""": 0.00_01, """answer""": """oy 2312/2019""", """start""": 38, """end""": 39},
												    {"""score""": 0.00_01, """answer""": """oy 2312/2019 DUE""", """start""": 38, """end""": 40},
												]
												lowerCAmelCase__ 						=						dqa_pipeline(image=__a     ,						question=__a     ,						top_k=2   )
												self.assertEqual(nested_simplify(__a     ,						decimals=4   )     ,						__a   )

												lowerCAmelCase__ 						=						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
												lowerCAmelCase__ 						=						"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
												lowerCAmelCase__ 						=						dqa_pipeline(image=__a     ,						question=__a     ,						top_k=2   )
												self.assertEqual(__a     ,						[]   )

												# We can optionnally pass directly the words and bounding boxes
												lowerCAmelCase__ 						=						"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
												lowerCAmelCase__ 						=						[]
												lowerCAmelCase__ 						=						[]
												lowerCAmelCase__ 						=						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   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						pipeline(
												    'document-question-answering'     ,						model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'     ,						revision='9977165'     ,						)
												lowerCAmelCase__ 						=						INVOICE_URL
												lowerCAmelCase__ 						=						"""What is the invoice number?"""

												lowerCAmelCase__ 						=						dqa_pipeline(image=__a     ,						question=__a     ,						top_k=2   )
												self.assertEqual(
												    nested_simplify(__a     ,						decimals=4   )     ,						[
												        {'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
												        {'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
												    ]     ,						)

												lowerCAmelCase__ 						=						dqa_pipeline({'image': image, 'question': question}     ,						top_k=2   )
												self.assertEqual(
												    nested_simplify(__a     ,						decimals=4   )     ,						[
												        {'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
												        {'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
												    ]     ,						)

												lowerCAmelCase__ 						=						dqa_pipeline(
												    [{'image': image, 'question': question}, {'image': image, 'question': question}]     ,						top_k=2   )
												self.assertEqual(
												    nested_simplify(__a     ,						decimals=4   )     ,						[
												        [
												            {'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
												            {'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
												        ],
												    ]
												    * 2     ,						)






						@slow
						@require_torch
						@require_detectrona
						@require_pytesseract
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						pipeline(
												    'document-question-answering'     ,						model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'     ,						revision='9977165'     ,						max_seq_len=50     ,						)
												lowerCAmelCase__ 						=						INVOICE_URL
												lowerCAmelCase__ 						=						"""What is the invoice number?"""

												lowerCAmelCase__ 						=						dqa_pipeline(image=__a     ,						question=__a     ,						top_k=2   )
												self.assertEqual(
												    nested_simplify(__a     ,						decimals=4   )     ,						[
												        {'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
												        {'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
												    ]     ,						)

												lowerCAmelCase__ 						=						dqa_pipeline({'image': image, 'question': question}     ,						top_k=2   )
												self.assertEqual(
												    nested_simplify(__a     ,						decimals=4   )     ,						[
												        {'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
												        {'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
												    ]     ,						)

												lowerCAmelCase__ 						=						dqa_pipeline(
												    [{'image': image, 'question': question}, {'image': image, 'question': question}]     ,						top_k=2   )
												self.assertEqual(
												    nested_simplify(__a     ,						decimals=4   )     ,						[
												        [
												            {'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
												            {'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
												        ]
												    ]
												    * 2     ,						)






						@slow
						@require_torch
						@require_pytesseract
						@require_vision
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						AutoTokenizer.from_pretrained(
												    'impira/layoutlm-document-qa'     ,						revision='3dc6de3'     ,						add_prefix_space=__a   )
												lowerCAmelCase__ 						=						pipeline(
												    'document-question-answering'     ,						model='impira/layoutlm-document-qa'     ,						tokenizer=__a     ,						revision='3dc6de3'     ,						)
												lowerCAmelCase__ 						=						INVOICE_URL
												lowerCAmelCase__ 						=						"""What is the invoice number?"""

												lowerCAmelCase__ 						=						dqa_pipeline(image=__a     ,						question=__a     ,						top_k=2   )
												self.assertEqual(
												    nested_simplify(__a     ,						decimals=4   )     ,						[
												        {'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
												        {'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
												    ]     ,						)

												lowerCAmelCase__ 						=						dqa_pipeline({'image': image, 'question': question}     ,						top_k=2   )
												self.assertEqual(
												    nested_simplify(__a     ,						decimals=4   )     ,						[
												        {'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
												        {'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
												    ]     ,						)

												lowerCAmelCase__ 						=						dqa_pipeline(
												    [{'image': image, 'question': question}, {'image': image, 'question': question}]     ,						top_k=2   )
												self.assertEqual(
												    nested_simplify(__a     ,						decimals=4   )     ,						[
												        [
												            {'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
												            {'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
												        ]
												    ]
												    * 2     ,						)

												lowerCAmelCase__ 						=						list(zip(*apply_tesseract(load_image(__a   )     ,						__a     ,						''   )   )   )

												# This model should also work if `image` is set to None
												lowerCAmelCase__ 						=						dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}     ,						top_k=2   )
												self.assertEqual(
												    nested_simplify(__a     ,						decimals=4   )     ,						[
												        {'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
												        {'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
												    ]     ,						)






						@slow
						@require_torch
						@require_pytesseract
						@require_vision
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						AutoTokenizer.from_pretrained(
												    'impira/layoutlm-document-qa'     ,						revision='3dc6de3'     ,						add_prefix_space=__a   )
												lowerCAmelCase__ 						=						pipeline(
												    'document-question-answering'     ,						model='impira/layoutlm-document-qa'     ,						tokenizer=__a     ,						revision='3dc6de3'     ,						max_seq_len=50     ,						)
												lowerCAmelCase__ 						=						INVOICE_URL
												lowerCAmelCase__ 						=						"""What is the invoice number?"""

												lowerCAmelCase__ 						=						dqa_pipeline(image=__a     ,						question=__a     ,						top_k=2   )
												self.assertEqual(
												    nested_simplify(__a     ,						decimals=4   )     ,						[
												        {'score': 0.99_99, 'answer': 'us-001', 'start': 16, 'end': 16},
												        {'score': 0.99_98, 'answer': 'us-001', 'start': 16, 'end': 16},
												    ]     ,						)

												lowerCAmelCase__ 						=						dqa_pipeline(
												    [{'image': image, 'question': question}, {'image': image, 'question': question}]     ,						top_k=2   )
												self.assertEqual(
												    nested_simplify(__a     ,						decimals=4   )     ,						[
												        [
												            {'score': 0.99_99, 'answer': 'us-001', 'start': 16, 'end': 16},
												            {'score': 0.99_98, 'answer': 'us-001', 'start': 16, 'end': 16},
												        ]
												    ]
												    * 2     ,						)

												lowerCAmelCase__ 						=						list(zip(*apply_tesseract(load_image(__a   )     ,						__a     ,						''   )   )   )

												# This model should also work if `image` is set to None
												lowerCAmelCase__ 						=						dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}     ,						top_k=2   )
												self.assertEqual(
												    nested_simplify(__a     ,						decimals=4   )     ,						[
												        {'score': 0.99_99, 'answer': 'us-001', 'start': 16, 'end': 16},
												        {'score': 0.99_98, 'answer': 'us-001', 'start': 16, 'end': 16},
												    ]     ,						)






						@slow
						@require_torch
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						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'     ,						)

												lowerCAmelCase__ 						=						INVOICE_URL
												lowerCAmelCase__ 						=						"""What is the invoice number?"""
												lowerCAmelCase__ 						=						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   ):






												"""simple docstring"""
												pass













import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__snake_case    :    str         =     (7_20, 12_80)  # Height, Width
__snake_case    :    Dict         =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__snake_case    :    Dict         =     1 / 1_00
__snake_case    :    Optional[int]         =     """"""
__snake_case    :    Union[str, Any]         =     """"""
__snake_case    :    List[str]         =     """"""
__snake_case    :    List[str]         =     2_50







def 	_UpperCamelCase   (    )  ->   None:

						"""simple docstring"""



						lowerCAmelCase__						,       lowerCAmelCase__    						=						get_dataset(UpperCamelCase_    ,       UpperCamelCase_		)
						for index in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						random.sample(range(len(UpperCamelCase_		)		)    ,       4		)
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						update_image_and_anno(
												    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       filter_scale=UpperCamelCase_    ,       )

												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												lowerCAmelCase__    						=						random_chars(32		)
												lowerCAmelCase__    						=						path.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												lowerCAmelCase__    						=						F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
												cva.imwrite(F"{file_root}.jpg"    ,       UpperCamelCase_    ,       [cva.IMWRITE_JPEG_QUALITY, 85]		)
												print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"		)
												lowerCAmelCase__    						=						[]
												for anno in new_annos:
																		lowerCAmelCase__    						=						anno[3] - anno[1]
																		lowerCAmelCase__    						=						anno[4] - anno[2]
																		lowerCAmelCase__    						=						anno[1] + width / 2
																		lowerCAmelCase__    						=						anno[2] + height / 2
																		lowerCAmelCase__    						=						F"{anno[0]} {x_center} {y_center} {width} {height}"
																		annos_list.append(UpperCamelCase_		)
												with open(F"{file_root}.txt"    ,       'w'		) as outfile:
																		outfile.write('\n'.join(line for line in annos_list		)		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					str		)  ->   tuple[list, list]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[]
						lowerCAmelCase__    						=						[]
						for label_file in glob.glob(os.path.join(UpperCamelCase_    ,       '*.txt'		)		):
												lowerCAmelCase__    						=						label_file.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												with open(UpperCamelCase_		) as in_file:
																		lowerCAmelCase__    						=						in_file.readlines()
												lowerCAmelCase__    						=						os.path.join(UpperCamelCase_    ,       F"{label_name}.jpg"		)

												lowerCAmelCase__    						=						[]
												for obj_list in obj_lists:
																		lowerCAmelCase__    						=						obj_list.rstrip('\n'		).split(' '		)
																		lowerCAmelCase__    						=						float(obj[1]		) - float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) - float(obj[4]		) / 2
																		lowerCAmelCase__    						=						float(obj[1]		) + float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) + float(obj[4]		) / 2

																		boxes.append([int(obj[0]		), xmin, ymin, xmax, ymax]		)
												if not boxes:
																		continue
												img_paths.append(UpperCamelCase_		)
												labels.append(UpperCamelCase_		)
						return img_paths, labels







def 	_UpperCamelCase   (    UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					tuple[int, int]    ,       UpperCamelCase_       :					tuple[float, float]    ,       UpperCamelCase_       :					float = 0.0    ,       )  ->   tuple[list, list, str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.zeros([output_size[0], output_size[1], 3]    ,       dtype=np.uinta		)
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						int(scale_x * output_size[1]		)
						lowerCAmelCase__    						=						int(scale_y * output_size[0]		)

						lowerCAmelCase__    						=						[]
						lowerCAmelCase__    						=						[]
						for i, index in enumerate(UpperCamelCase_		):
												lowerCAmelCase__    						=						all_img_list[index]
												path_list.append(UpperCamelCase_		)
												lowerCAmelCase__    						=						all_annos[index]
												lowerCAmelCase__    						=						cva.imread(UpperCamelCase_		)
												if i == 0:  # top-left
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (divid_point_x, divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						bbox[1] * scale_x
																								lowerCAmelCase__    						=						bbox[2] * scale_y
																								lowerCAmelCase__    						=						bbox[3] * scale_x
																								lowerCAmelCase__    						=						bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												elif i == 1:  # top-right
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (output_size[1] - divid_point_x, divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						scale_x + bbox[1] * (1 - scale_x)
																								lowerCAmelCase__    						=						bbox[2] * scale_y
																								lowerCAmelCase__    						=						scale_x + bbox[3] * (1 - scale_x)
																								lowerCAmelCase__    						=						bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												elif i == 2:  # bottom-left
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (divid_point_x, output_size[0] - divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						bbox[1] * scale_x
																								lowerCAmelCase__    						=						scale_y + bbox[2] * (1 - scale_y)
																								lowerCAmelCase__    						=						bbox[3] * scale_x
																								lowerCAmelCase__    						=						scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												else:  # bottom-right
																		lowerCAmelCase__    						=						cva.resize(
																		    UpperCamelCase_    ,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						scale_x + bbox[1] * (1 - scale_x)
																								lowerCAmelCase__    						=						scale_y + bbox[2] * (1 - scale_y)
																								lowerCAmelCase__    						=						scale_x + bbox[3] * (1 - scale_x)
																								lowerCAmelCase__    						=						scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)

    # Remove bounding box small than scale of filter
						if filter_scale > 0:
												lowerCAmelCase__    						=						[
												    anno
												    for anno in new_anno
												    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
												]

						return output_img, new_anno, path_list[0]







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   str:

						"""simple docstring"""



						assert number_char > 1, "The number of character should greater than 1"
						lowerCAmelCase__    						=						ascii_lowercase + digits
						return "".join(random.choice(UpperCamelCase_		) for _ in range(UpperCamelCase_		)		)


if __name__ == "__main__":
					main()
					print("""DONE ✅""")





