
'''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        snake_case__		(    *lowercase_ ,       **lowercase_):
       pass


  def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
      """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.
a_											=						(
    "https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
)






@is_pipeline_test
@require_torch
@require_vision
class    UpperCAmelCase_				(	unittest.TestCase					):
 UpperCAmelCase_		    =						MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING





 @require_pytesseract
 @require_vision
 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    str     			=						pipeline(
         "document-question-answering" ,       model=lowercase_ ,       tokenizer=lowercase_ ,       image_processor=lowercase_)

     snake_case_ :    Tuple     			=						INVOICE_URL
     snake_case_ :    List[Any]     			=						list(zip(*apply_tesseract(load_image(lowercase_) ,       lowercase_ ,       "")))
     snake_case_ :    Union[str, Any]     			=						"What is the placebo?"
     snake_case_ :    int     			=						[
         {
             "image": load_image(lowercase_),
             "question": question,
         },
         {
             "image": image,
             "question": question,
         },
         {
             "image": image,
             "question": question,
             "word_boxes": word_boxes,
         },
     ]
     return dqa_pipeline, examples





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):
     snake_case_ :    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        snake_case__		(    self):
     snake_case_ :    Tuple     			=						pipeline("document-question-answering" ,       model="hf-internal-testing/tiny-random-layoutlmv2")
     snake_case_ :    Optional[int]     			=						INVOICE_URL
     snake_case_ :    Union[str, Any]     			=						"How many cats are there?"

     snake_case_ :    Union[str, 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},
     ]
     snake_case_ :    Union[str, Any]     			=						dqa_pipeline(image=lowercase_ ,       question=lowercase_ ,       top_k=2)
     self.assertEqual(nested_simplify(lowercase_ ,       decimals=4) ,       lowercase_)

     snake_case_ :    Tuple     			=						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
     snake_case_ :    str     			=						"./tests/fixtures/tests_samples/COCO/000000039769.png"
     snake_case_ :    List[str]     			=						dqa_pipeline(image=lowercase_ ,       question=lowercase_ ,       top_k=2)
     self.assertEqual(lowercase_ ,       [])

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





 @slow
 @require_torch
 @require_detectrona
 @require_pytesseract
 def        snake_case__		(    self):
     snake_case_ :    str     			=						pipeline(
         "document-question-answering" ,       model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" ,       revision="9977165" ,       )
     snake_case_ :    str     			=						INVOICE_URL
     snake_case_ :    List[str]     			=						"What is the invoice number?"

     snake_case_ :    Optional[Any]     			=						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},
         ] ,       )

     snake_case_ :    Optional[int]     			=						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},
         ] ,       )

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

     snake_case_ :    Dict     			=						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},
         ] ,       )

     snake_case_ :    List[str]     			=						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},
         ] ,       )

     snake_case_ :    Dict     			=						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        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						AutoTokenizer.from_pretrained(
         "impira/layoutlm-document-qa" ,       revision="3dc6de3" ,       add_prefix_space=lowercase_)
     snake_case_ :    Optional[int]     			=						pipeline(
         "document-question-answering" ,       model="impira/layoutlm-document-qa" ,       tokenizer=lowercase_ ,       revision="3dc6de3" ,       )
     snake_case_ :    Optional[Any]     			=						INVOICE_URL
     snake_case_ :    Tuple     			=						"What is the invoice number?"

     snake_case_ :    Any     			=						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},
         ] ,       )

     snake_case_ :    Optional[Any]     			=						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},
         ] ,       )

     snake_case_ :    int     			=						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 ,       )

     snake_case_ :    int     			=						list(zip(*apply_tesseract(load_image(lowercase_) ,       lowercase_ ,       "")))

     # This model should also work if `image` is set to None
     snake_case_ :    Optional[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        snake_case__		(    self):
     snake_case_ :    Dict     			=						AutoTokenizer.from_pretrained(
         "impira/layoutlm-document-qa" ,       revision="3dc6de3" ,       add_prefix_space=lowercase_)
     snake_case_ :    str     			=						pipeline(
         "document-question-answering" ,       model="impira/layoutlm-document-qa" ,       tokenizer=lowercase_ ,       revision="3dc6de3" ,       max_seq_len=50 ,       )
     snake_case_ :    Union[str, Any]     			=						INVOICE_URL
     snake_case_ :    Optional[int]     			=						"What is the invoice number?"

     snake_case_ :    str     			=						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},
         ] ,       )

     snake_case_ :    Any     			=						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 ,       )

     snake_case_ :    Tuple     			=						list(zip(*apply_tesseract(load_image(lowercase_) ,       lowercase_ ,       "")))

     # This model should also work if `image` is set to None
     snake_case_ :    Any     			=						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        snake_case__		(    self):
     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" ,       )

     snake_case_ :    Tuple     			=						INVOICE_URL
     snake_case_ :    Any     			=						"What is the invoice number?"
     snake_case_ :    int     			=						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        snake_case__		(    self):
     pass






'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  a_											=						None


a_											=						logging.get_logger(__name__)


a_											=						"▁"

a_											=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=						{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

a_											=						{
    "google/pegasus-xsum": 512,
}






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						VOCAB_FILES_NAMES
 UpperCAmelCase_		    =						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_		    =						PegasusTokenizer
 UpperCAmelCase_		    =						["""input_ids""", """attention_mask"""]





 def __init__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_="<pad>" ,       lowercase_="</s>" ,       lowercase_="<unk>" ,       lowercase_="<mask_2>" ,       lowercase_="<mask_1>" ,       lowercase_=None ,       lowercase_=1_03 ,       **lowercase_ ,       ):
     snake_case_ :    List[Any]     			=						offset

     if additional_special_tokens is not None:
         if not isinstance(lowercase_ ,       lowercase_):
             raise TypeError(
                 F'additional_special_tokens should be of type {type(lowercase_)}, but is'
                 F' {type(lowercase_)}')

         snake_case_ :    Tuple     			=						(
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             F'<unk_{i}>' for i in range(len(lowercase_) ,       self.offset - 1)
         ]

         if len(set(lowercase_)) != len(lowercase_):
             raise ValueError(
                 "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                 F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.')
         snake_case_ :    Optional[Any]     			=						additional_special_tokens_extended
     else:
         snake_case_ :    Tuple     			=						[mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [F'<unk_{i}>' for i in range(2 ,       self.offset)]

     super().__init__(
         lowercase_ ,       tokenizer_file=lowercase_ ,       pad_token=lowercase_ ,       eos_token=lowercase_ ,       unk_token=lowercase_ ,       mask_token=lowercase_ ,       mask_token_sent=lowercase_ ,       offset=lowercase_ ,       additional_special_tokens=lowercase_ ,       **lowercase_ ,       )
     snake_case_ :    int     			=						vocab_file
     snake_case_ :    str     			=						False if not self.vocab_file else True





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Union[str, Any]     			=						set(self.all_special_ids)  # call it once instead of inside list comp
     all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

     if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
         raise ValueError(
             "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
             F' {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}')

     return [1 if x in all_special_ids else 0 for x in seq]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = False):
     if already_has_special_tokens:
         return self._special_token_mask(lowercase_)
     elif token_ids_a is None:
         return self._special_token_mask(lowercase_) + [1]
     else:
         return self._special_token_mask(token_ids_a + token_ids_a) + [1]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=None):
     if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return token_ids_a + token_ids_a + [self.eos_token_id]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     if not self.can_save_slow_tokenizer:
         raise ValueError(
             "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
             "tokenizer.")

     if not os.path.isdir(lowercase_):
         logger.error(F'Vocabulary path ({save_directory}) should be a directory')
         return
     snake_case_ :    Dict     			=						os.path.join(
         lowercase_ ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

     if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_):
         copyfile(self.vocab_file ,       lowercase_)

     return (out_vocab_file,)




