




"""simple docstring"""


import argparse
import hashlib  # hashlib is only used inside the Test class
import struct






class 					lowercase:







 '''simple docstring'''





 def __init__(				self:		List[Any],      a_:		List[str]		):




   '''simple docstring'''



   _snake_case							:		int								=  data
   _snake_case							:		Dict								=  [0X67452301, 0Xefcdab89, 0X98badcfe, 0X10325476, 0Xc3d2e1f0]


 @staticmethod
 def  UpperCamelCase_   (				a_:		Optional[Any],      a_:		Dict		):




   '''simple docstring'''



   return ((n << b) | (n >> (32 - b))) & 0Xffffffff


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  B"""\x80""" + B"""\x00""" * (63 - (len(self.data		) + 8) % 64)
   _snake_case							:		Optional[int]								=  self.data + padding + struct.pack(""">Q""",      8 * len(self.data		)		)
   return padded_data


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   return [
       self.padded_data[i : i + 64] for i in range(0,      len(self.padded_data		),      64		)
   ]


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  list(struct.unpack(""">16L""",      a_		)		) + [0] * 64
   for i in range(16,      80		):
     _snake_case							:		List[Any]								=  self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]),      1		)
   return w


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self.padding()
   _snake_case							:		str								=  self.split_blocks()
   for block in self.blocks:
     _snake_case							:		Any								=  self.expand_block(a_		)
     _snake_case		,					_snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		Optional[int]								=  self.h
     for i in range(0,      80		):
       if 0 <= i < 20:
         _snake_case							:		int								=  (b & c) | ((~b) & d)
         _snake_case							:		str								=  0X5a827999
       elif 20 <= i < 40:
         _snake_case							:		Optional[int]								=  b ^ c ^ d
         _snake_case							:		str								=  0X6ed9eba1
       elif 40 <= i < 60:
         _snake_case							:		List[Any]								=  (b & c) | (b & d) | (c & d)
         _snake_case							:		List[Any]								=  0X8f1bbcdc
       elif 60 <= i < 80:
         _snake_case							:		List[Any]								=  b ^ c ^ d
         _snake_case							:		int								=  0Xca62c1d6
       _snake_case		,					_snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		Optional[int]								=  (
           self.rotate(a_,      5		) + f + e + k + expanded_block[i] & 0Xffffffff,
           a,
           self.rotate(a_,      30		),
           c,
           d,
       )
     _snake_case							:		Union[str, Any]								=  (
         self.h[0] + a & 0Xffffffff,
         self.h[1] + b & 0Xffffffff,
         self.h[2] + c & 0Xffffffff,
         self.h[3] + d & 0Xffffffff,
         self.h[4] + e & 0Xffffffff,
     )
   return ("{:08x}" * 5).format(*self.h		)







def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Any								=  B"""Test String"""
  assert SHAaHash(snake_case__    ).final_hash() == hashlib.shaa(snake_case__    ).hexdigest()  # noqa: S324


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		List[Any]								=  argparse.ArgumentParser(description="""Process some strings or files"""    )
  parser.add_argument(
      """--string"""		,   dest="""input_string"""		,   default="""Hello World!! Welcome to Cryptography"""		,   help="""Hash the string"""		,   )
  parser.add_argument("""--file"""		,   dest="""input_file"""		,   help="""Hash contents of a file"""    )
  _snake_case							:		Union[str, Any]								=  parser.parse_args()
  _snake_case							:		List[Any]								=  args.input_string
  # In any case hash input should be a bytestring
  if args.input_file:
    with open(args.input_file		,   """rb"""    ) as f:
      _snake_case							:		str								=  f.read()
  else:
    _snake_case							:		int								=  bytes(snake_case__		,   """utf-8"""    )
  print(SHAaHash(snake_case__    ).final_hash()    )


if __name__ == "__main__":
     main()
     import doctest

     doctest.testmod()






'''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      snake_case    :





       """simple docstring"""




       @staticmethod
       def   snake_case		(    *UpperCamelCase			,  **UpperCamelCase     ):







        """simple docstring"""
        pass





     def __snake_case					(      UpperCAmelCase_						:						List[Any]		):
      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_  :				Dict						= (
    """https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)





@is_pipeline_test
@require_torch
@require_vision
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING






  @require_pytesseract
  @require_vision
  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  pipeline(
       "document-question-answering"			,  model=UpperCamelCase			,  tokenizer=UpperCamelCase			,  image_processor=UpperCamelCase     )

   lowerCamelCase_      =  INVOICE_URL
   lowerCamelCase_      =  list(zip(*apply_tesseract(load_image(UpperCamelCase     )			,  UpperCamelCase			,  ""     )     )     )
   lowerCamelCase_      =  "What is the placebo?"
   lowerCamelCase_      =  [
       {
           "image": load_image(UpperCamelCase     ),
           "question": question,
       },
       {
           "image": image,
           "question": question,
       },
       {
           "image": image,
           "question": question,
           "word_boxes": word_boxes,
       },
   ]
   return dqa_pipeline, examples






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  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   snake_case		(    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": 0.0_001, "answer": "oy 2312/2019", "start": 38, "end": 39},
       {"score": 0.0_001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
   ]
   lowerCamelCase_      =  dqa_pipeline(image=UpperCamelCase			,  question=UpperCamelCase			,  top_k=2     )
   self.assertEqual(nested_simplify(UpperCamelCase			,  decimals=4     )			,  UpperCamelCase     )

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

   # We can optionnally pass directly the words and bounding boxes
   lowerCamelCase_      =  "./tests/fixtures/tests_samples/COCO/000000039769.png"
   lowerCamelCase_      =  []
   lowerCamelCase_      =  []
   lowerCamelCase_      =  dqa_pipeline(image=UpperCamelCase			,  question=UpperCamelCase			,  words=UpperCamelCase			,  boxes=UpperCamelCase			,  top_k=2     )
   self.assertEqual(UpperCamelCase			,  []     )






  @slow
  @require_torch
  @require_detectrona
  @require_pytesseract
  def   snake_case		(    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=UpperCamelCase			,  question=UpperCamelCase			,  top_k=2     )
   self.assertEqual(
       nested_simplify(UpperCamelCase			,  decimals=4     )			,  [
           {"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
           {"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
       ]			,  )

   lowerCamelCase_      =  dqa_pipeline({"image": image, "question": question}			,  top_k=2     )
   self.assertEqual(
       nested_simplify(UpperCamelCase			,  decimals=4     )			,  [
           {"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
           {"score": 0.0_009, "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(UpperCamelCase			,  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   snake_case		(    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=UpperCamelCase			,  question=UpperCamelCase			,  top_k=2     )
   self.assertEqual(
       nested_simplify(UpperCamelCase			,  decimals=4     )			,  [
           {"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
           {"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
       ]			,  )

   lowerCamelCase_      =  dqa_pipeline({"image": image, "question": question}			,  top_k=2     )
   self.assertEqual(
       nested_simplify(UpperCamelCase			,  decimals=4     )			,  [
           {"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
           {"score": 0.9_948, "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(UpperCamelCase			,  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   snake_case		(    self     ):







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

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

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

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

   lowerCamelCase_      =  list(zip(*apply_tesseract(load_image(UpperCamelCase     )			,  UpperCamelCase			,  ""     )     )     )

   # 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(UpperCamelCase			,  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   snake_case		(    self     ):







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

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

   lowerCamelCase_      =  list(zip(*apply_tesseract(load_image(UpperCamelCase     )			,  UpperCamelCase			,  ""     )     )     )

   # 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(UpperCamelCase			,  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   snake_case		(    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=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   snake_case		(    self     ):







   """simple docstring"""
   pass



