
"""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       UpperCamelCase   :
           @staticmethod
           def 					__SCREAMING_SNAKE_CASE				(  *snake_case__	,  **snake_case__					):




             """simple docstring"""


             pass





      def  _lowerCAmelCase     (  lowerCamelCase__				:				Any					)	->     int:
        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.
lowercase_							:    Any         =						(
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)
@is_pipeline_test
@require_torch
@require_vision
class       UpperCamelCase   (				unittest.TestCase	):
     A__			  =      MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
     @require_pytesseract
     @require_vision
     def 					__SCREAMING_SNAKE_CASE				(  self	,  snake_case__	,  snake_case__	,  snake_case__					):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE :					str				     =			pipeline(
           "document-question-answering"	,  model=_lowercase	,  tokenizer=_lowercase	,  image_processor=_lowercase					)

       _SCREAMING_SNAKE_CASE :					Tuple				     =			INVOICE_URL
       _SCREAMING_SNAKE_CASE :					Any				     =			list(zip(*apply_tesseract(load_image(_lowercase					)	,  _lowercase	,  ""					)					)					)
       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			'''What is the placebo?'''
       _SCREAMING_SNAKE_CASE :					str				     =			[
           {
               '''image''': load_image(_lowercase					),
               '''question''': question,
           },
           {
               '''image''': image,
               '''question''': question,
           },
           {
               '''image''': image,
               '''question''': question,
               '''word_boxes''': word_boxes,
           },
       ]
       return dqa_pipeline, examples
     def 					__SCREAMING_SNAKE_CASE				(  self	,  snake_case__	,  snake_case__					):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE :					List[Any]				     =			dqa_pipeline(_lowercase	,  top_k=2					)
       self.assertEqual(
           _lowercase	,  [
               [
                   {"score": ANY(_lowercase					), "answer": ANY(_lowercase					), "start": ANY(_lowercase					), "end": ANY(_lowercase					)},
                   {"score": ANY(_lowercase					), "answer": ANY(_lowercase					), "start": ANY(_lowercase					), "end": ANY(_lowercase					)},
               ]
           ]
           * 3	,  )
     @require_torch
     @require_detectrona
     @require_pytesseract
     def 					__SCREAMING_SNAKE_CASE				(  self					):




       """simple docstring"""


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

       _SCREAMING_SNAKE_CASE :					List[Any]				     =			[
           {'''score''': 0.0_001, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
           {'''score''': 0.0_001, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
       ]
       _SCREAMING_SNAKE_CASE :					Tuple				     =			dqa_pipeline(image=_lowercase	,  question=_lowercase	,  top_k=2					)
       self.assertEqual(nested_simplify(_lowercase	,  decimals=4					)	,  _lowercase					)

       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			dqa_pipeline({"image": image, "question": question}	,  top_k=2					)
       self.assertEqual(nested_simplify(_lowercase	,  decimals=4					)	,  _lowercase					)

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

       # We can optionnally pass directly the words and bounding boxes
       _SCREAMING_SNAKE_CASE :					Any				     =			'''./tests/fixtures/tests_samples/COCO/000000039769.png'''
       _SCREAMING_SNAKE_CASE :					int				     =			[]
       _SCREAMING_SNAKE_CASE :					List[str]				     =			[]
       _SCREAMING_SNAKE_CASE :					Any				     =			dqa_pipeline(image=_lowercase	,  question=_lowercase	,  words=_lowercase	,  boxes=_lowercase	,  top_k=2					)
       self.assertEqual(_lowercase	,  []					)
     @slow
     @require_torch
     @require_detectrona
     @require_pytesseract
     def 					__SCREAMING_SNAKE_CASE				(  self					):




       """simple docstring"""


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

       _SCREAMING_SNAKE_CASE :					str				     =			dqa_pipeline(image=_lowercase	,  question=_lowercase	,  top_k=2					)
       self.assertEqual(
           nested_simplify(_lowercase	,  decimals=4					)	,  [
               {"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
               {"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
           ]	,  )

       _SCREAMING_SNAKE_CASE :					Any				     =			dqa_pipeline({"image": image, "question": question}	,  top_k=2					)
       self.assertEqual(
           nested_simplify(_lowercase	,  decimals=4					)	,  [
               {"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
               {"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
           ]	,  )

       _SCREAMING_SNAKE_CASE :					Optional[int]				     =			dqa_pipeline(
           [{"image": image, "question": question}, {"image": image, "question": question}]	,  top_k=2					)
       self.assertEqual(
           nested_simplify(_lowercase	,  decimals=4					)	,  [
               [
                   {"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
                   {"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
               ],
           ]
           * 2	,  )
     @slow
     @require_torch
     @require_detectrona
     @require_pytesseract
     def 					__SCREAMING_SNAKE_CASE				(  self					):




       """simple docstring"""


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

       _SCREAMING_SNAKE_CASE :					List[Any]				     =			dqa_pipeline(image=_lowercase	,  question=_lowercase	,  top_k=2					)
       self.assertEqual(
           nested_simplify(_lowercase	,  decimals=4					)	,  [
               {"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
               {"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
           ]	,  )

       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			dqa_pipeline({"image": image, "question": question}	,  top_k=2					)
       self.assertEqual(
           nested_simplify(_lowercase	,  decimals=4					)	,  [
               {"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
               {"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
           ]	,  )

       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			dqa_pipeline(
           [{"image": image, "question": question}, {"image": image, "question": question}]	,  top_k=2					)
       self.assertEqual(
           nested_simplify(_lowercase	,  decimals=4					)	,  [
               [
                   {"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
                   {"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
               ]
           ]
           * 2	,  )
     @slow
     @require_torch
     @require_pytesseract
     @require_vision
     def 					__SCREAMING_SNAKE_CASE				(  self					):




       """simple docstring"""


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

       _SCREAMING_SNAKE_CASE :					List[str]				     =			dqa_pipeline(image=_lowercase	,  question=_lowercase	,  top_k=2					)
       self.assertEqual(
           nested_simplify(_lowercase	,  decimals=4					)	,  [
               {"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
               {"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
           ]	,  )

       _SCREAMING_SNAKE_CASE :					Dict				     =			dqa_pipeline({"image": image, "question": question}	,  top_k=2					)
       self.assertEqual(
           nested_simplify(_lowercase	,  decimals=4					)	,  [
               {"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
               {"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
           ]	,  )

       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			dqa_pipeline(
           [{"image": image, "question": question}, {"image": image, "question": question}]	,  top_k=2					)
       self.assertEqual(
           nested_simplify(_lowercase	,  decimals=4					)	,  [
               [
                   {"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
                   {"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
               ]
           ]
           * 2	,  )

       _SCREAMING_SNAKE_CASE :					List[str]				     =			list(zip(*apply_tesseract(load_image(_lowercase					)	,  _lowercase	,  ""					)					)					)

       # This model should also work if `image` is set to None
       _SCREAMING_SNAKE_CASE :					int				     =			dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}	,  top_k=2					)
       self.assertEqual(
           nested_simplify(_lowercase	,  decimals=4					)	,  [
               {"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
               {"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
           ]	,  )
     @slow
     @require_torch
     @require_pytesseract
     @require_vision
     def 					__SCREAMING_SNAKE_CASE				(  self					):




       """simple docstring"""


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

       _SCREAMING_SNAKE_CASE :					List[Any]				     =			dqa_pipeline(image=_lowercase	,  question=_lowercase	,  top_k=2					)
       self.assertEqual(
           nested_simplify(_lowercase	,  decimals=4					)	,  [
               {"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
               {"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
           ]	,  )

       _SCREAMING_SNAKE_CASE :					str				     =			dqa_pipeline(
           [{"image": image, "question": question}, {"image": image, "question": question}]	,  top_k=2					)
       self.assertEqual(
           nested_simplify(_lowercase	,  decimals=4					)	,  [
               [
                   {"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
                   {"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
               ]
           ]
           * 2	,  )

       _SCREAMING_SNAKE_CASE :					Dict				     =			list(zip(*apply_tesseract(load_image(_lowercase					)	,  _lowercase	,  ""					)					)					)

       # This model should also work if `image` is set to None
       _SCREAMING_SNAKE_CASE :					Tuple				     =			dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}	,  top_k=2					)
       self.assertEqual(
           nested_simplify(_lowercase	,  decimals=4					)	,  [
               {"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
               {"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
           ]	,  )
     @slow
     @require_torch
     def 					__SCREAMING_SNAKE_CASE				(  self					):




       """simple docstring"""


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




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




       """simple docstring"""


       pass




from pathlib import Path

import fire




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				str	,       lowercase:				int		)     ->					int:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			Path(lowercase		)
 _UpperCamelCase:  int    						=			Path(lowercase		)
 dest_dir.mkdir(exist_ok=lowercase		)
 for path in src_dir.iterdir():
  _UpperCamelCase:  List[str]    						=			[x.rstrip() for x in list(path.open().readlines()		)][:n]
  _UpperCamelCase:  int    						=			dest_dir.joinpath(path.name		)
  print(lowercase		)
  dest_path.open('''w'''		).write('''\n'''.join(lowercase		)		)


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