


'''simple docstring'''
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      lowercase			:

				"""simple docstring"""

				@staticmethod
				def 					lowerCAmelCase__       (	*UpperCamelCase_					,     **UpperCamelCase_							):

									'''simple docstring'''


									pass



		def     a   (    __a     )						->			Dict:



							'''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							   =  (
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)
@is_pipeline_test
@require_torch
@require_vision
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		_a									=       MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING




		@require_pytesseract
		@require_vision
		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				pipeline(
							    '''document-question-answering'''					,     model=UpperCamelCase_					,     tokenizer=UpperCamelCase_					,     image_processor=UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				INVOICE_URL
							UpperCamelCase__     :int							      =				list(zip(*apply_tesseract(load_image(UpperCamelCase_							)					,     UpperCamelCase_					,     ''''''							)							)							)
							UpperCamelCase__     :Optional[int]							      =				'''What is the placebo?'''
							UpperCamelCase__     :int							      =				[
							    {
							        '''image''': load_image(UpperCamelCase_							),
							        '''question''': question,
							    },
							    {
							        '''image''': image,
							        '''question''': question,
							    },
							    {
							        '''image''': image,
							        '''question''': question,
							        '''word_boxes''': word_boxes,
							    },
							]
							return dqa_pipeline, examples




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				dqa_pipeline(UpperCamelCase_					,     top_k=2							)
							self.assertEqual(
							    UpperCamelCase_					,     [
							        [
							            {'''score''': ANY(UpperCamelCase_							), '''answer''': ANY(UpperCamelCase_							), '''start''': ANY(UpperCamelCase_							), '''end''': ANY(UpperCamelCase_							)},
							            {'''score''': ANY(UpperCamelCase_							), '''answer''': ANY(UpperCamelCase_							), '''start''': ANY(UpperCamelCase_							), '''end''': ANY(UpperCamelCase_							)},
							        ]
							    ]
							    * 3					,     )




		@require_torch
		@require_detectrona
		@require_pytesseract
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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

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

							UpperCamelCase__     :Dict							      =				dqa_pipeline({'''image''': image, '''question''': question}					,     top_k=2							)
							self.assertEqual(nested_simplify(UpperCamelCase_					,     decimals=4							)					,     UpperCamelCase_							)

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

							# We can optionnally pass directly the words and bounding boxes
							UpperCamelCase__     :List[Any]							      =				'''./tests/fixtures/tests_samples/COCO/000000039769.png'''
							UpperCamelCase__     :Optional[int]							      =				[]
							UpperCamelCase__     :Optional[Any]							      =				[]
							UpperCamelCase__     :Optional[Any]							      =				dqa_pipeline(image=UpperCamelCase_					,     question=UpperCamelCase_					,     words=UpperCamelCase_					,     boxes=UpperCamelCase_					,     top_k=2							)
							self.assertEqual(UpperCamelCase_					,     []							)




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

							'''simple docstring'''


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

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

							UpperCamelCase__     :int							      =				dqa_pipeline({'''image''': image, '''question''': question}					,     top_k=2							)
							self.assertEqual(
							    nested_simplify(UpperCamelCase_					,     decimals=4							)					,     [
							        {'''score''': 0.9944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
							        {'''score''': 0.0009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
							    ]					,     )

							UpperCamelCase__     :Tuple							      =				dqa_pipeline(
							    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]					,     top_k=2							)
							self.assertEqual(
							    nested_simplify(UpperCamelCase_					,     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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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

							UpperCamelCase__     :Optional[int]							      =				dqa_pipeline(image=UpperCamelCase_					,     question=UpperCamelCase_					,     top_k=2							)
							self.assertEqual(
							    nested_simplify(UpperCamelCase_					,     decimals=4							)					,     [
							        {'''score''': 0.9974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
							        {'''score''': 0.9948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
							    ]					,     )

							UpperCamelCase__     :Optional[int]							      =				dqa_pipeline({'''image''': image, '''question''': question}					,     top_k=2							)
							self.assertEqual(
							    nested_simplify(UpperCamelCase_					,     decimals=4							)					,     [
							        {'''score''': 0.9974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
							        {'''score''': 0.9948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
							    ]					,     )

							UpperCamelCase__     :int							      =				dqa_pipeline(
							    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]					,     top_k=2							)
							self.assertEqual(
							    nested_simplify(UpperCamelCase_					,     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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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

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

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

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

							UpperCamelCase__     :Optional[Any]							      =				list(zip(*apply_tesseract(load_image(UpperCamelCase_							)					,     UpperCamelCase_					,     ''''''							)							)							)

							# This model should also work if `image` is set to None
							UpperCamelCase__     :Optional[int]							      =				dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}					,     top_k=2							)
							self.assertEqual(
							    nested_simplify(UpperCamelCase_					,     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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				AutoTokenizer.from_pretrained(
							    '''impira/layoutlm-document-qa'''					,     revision='''3dc6de3'''					,     add_prefix_space=UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				pipeline(
							    '''document-question-answering'''					,     model='''impira/layoutlm-document-qa'''					,     tokenizer=UpperCamelCase_					,     revision='''3dc6de3'''					,     max_seq_len=50					,     )
							UpperCamelCase__     :Optional[int]							      =				INVOICE_URL
							UpperCamelCase__     :Any							      =				'''What is the invoice number?'''

							UpperCamelCase__     :Tuple							      =				dqa_pipeline(image=UpperCamelCase_					,     question=UpperCamelCase_					,     top_k=2							)
							self.assertEqual(
							    nested_simplify(UpperCamelCase_					,     decimals=4							)					,     [
							        {'''score''': 0.9999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
							        {'''score''': 0.9998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
							    ]					,     )

							UpperCamelCase__     :Union[str, Any]							      =				dqa_pipeline(
							    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]					,     top_k=2							)
							self.assertEqual(
							    nested_simplify(UpperCamelCase_					,     decimals=4							)					,     [
							        [
							            {'''score''': 0.9999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
							            {'''score''': 0.9998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
							        ]
							    ]
							    * 2					,     )

							UpperCamelCase__     :Optional[Any]							      =				list(zip(*apply_tesseract(load_image(UpperCamelCase_							)					,     UpperCamelCase_					,     ''''''							)							)							)

							# This model should also work if `image` is set to None
							UpperCamelCase__     :Optional[Any]							      =				dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}					,     top_k=2							)
							self.assertEqual(
							    nested_simplify(UpperCamelCase_					,     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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				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'''					,     )

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




		@require_tf
		@unittest.skip('''Document question answering not implemented in TF'''							)
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							pass



'''simple docstring'''
import json
import sys


def     a   (    __a				,      __a     )						->			str:



					'''simple docstring'''






					with open(__a				,      encoding='''utf-8'''     ) as f:
										UpperCamelCase__     :List[str]							      =				json.load(__a     )

					UpperCamelCase__     :int							      =				['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']

					for benchmark_name in sorted(__a     ):
										UpperCamelCase__     :Optional[Any]							      =				results[benchmark_name]

										UpperCamelCase__     :int							      =				benchmark_name.split('''/'''     )[-1]
										output_md.append(f'''### Benchmark: {benchmark_file_name}'''     )

										UpperCamelCase__     :List[str]							      =				'''| metric |'''
										UpperCamelCase__     :str							      =				'''|--------|'''
										UpperCamelCase__     :Union[str, Any]							      =				'''| new / old (diff) |'''
										for metric_name in sorted(__a     ):
															UpperCamelCase__     :List[Any]							      =				benchmark_res[metric_name]
															UpperCamelCase__     :Optional[int]							      =				metric_vals['''new''']
															UpperCamelCase__     :Any							      =				metric_vals.get('''old'''				,      __a     )
															UpperCamelCase__     :Optional[int]							      =				metric_vals.get('''diff'''				,      __a     )

															UpperCamelCase__     :List[str]							      =				f''' {new_val:f}''' if isinstance(__a				,      (int, float)     ) else '''None'''

															if old_val is not None:
																				val_str += f''' / {old_val:f}''' if isinstance(__a				,      (int, float)     ) else "None"
															if dif_val is not None:
																				val_str += f''' ({dif_val:f})''' if isinstance(__a				,      (int, float)     ) else "None"

															title += " " + metric_name + " |"
															lines += "---|"
															value += val_str + " |"

										output_md += [title, lines, value, " "]

					output_md.append('''</details>'''     )

					with open(__a				,      '''w'''				,      encoding='''utf-8'''     ) as f:
										f.writelines('''\n'''.join(__a     )     )


if __name__ == "__main__":
		__snake_case							   =  sys.argv[1]
		__snake_case							   =  sys.argv[2]

		format_json_to_md(input_json_file, output_md_file)