






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     _lowerCAmelCase:





          """simple docstring"""







          @staticmethod
          def       _a   ( *_lowerCamelCase  ,      **_lowerCamelCase							):
            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_		:		Any		     =			(
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)




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





       """simple docstring"""







       a    :		Union[str, Any]									=MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING





       @require_pytesseract
       @require_vision
       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: List[Any]   				=    pipeline(
             'document-question-answering'  ,      model=_lowerCamelCase  ,      tokenizer=_lowerCamelCase  ,      image_processor=_lowerCamelCase							)

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





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Optional[Any]   				=    dqa_pipeline(_lowerCamelCase  ,      top_k=2							)
         self.assertEqual(
             _lowerCamelCase  ,      [
                 [
                     {'score': ANY(_lowerCamelCase							), 'answer': ANY(_lowerCamelCase							), 'start': ANY(_lowerCamelCase							), 'end': ANY(_lowerCamelCase							)},
                     {'score': ANY(_lowerCamelCase							), 'answer': ANY(_lowerCamelCase							), 'start': ANY(_lowerCamelCase							), 'end': ANY(_lowerCamelCase							)},
                 ]
             ]
             * 3  ,      )





       @require_torch
       @require_detectrona
       @require_pytesseract
       def       _a   ( self							):
         UpperCamelCase_: Optional[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_: str   				=    [
             {'score': 0.0_0_0_1, 'answer': 'oy 2312/2019', 'start': 3_8, 'end': 3_9},
             {'score': 0.0_0_0_1, 'answer': 'oy 2312/2019 DUE', 'start': 3_8, 'end': 4_0},
         ]
         UpperCamelCase_: Dict   				=    dqa_pipeline(image=_lowerCamelCase  ,      question=_lowerCamelCase  ,      top_k=2							)
         self.assertEqual(nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      _lowerCamelCase							)

         UpperCamelCase_: List[Any]   				=    dqa_pipeline({'image': image, 'question': question}  ,      top_k=2							)
         self.assertEqual(nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      _lowerCamelCase							)

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

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





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

         UpperCamelCase_: Optional[int]   				=    dqa_pipeline(image=_lowerCamelCase  ,      question=_lowerCamelCase  ,      top_k=2							)
         self.assertEqual(
             nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [
                 {'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
             ]  ,      )

         UpperCamelCase_: str   				=    dqa_pipeline({'image': image, 'question': question}  ,      top_k=2							)
         self.assertEqual(
             nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [
                 {'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
             ]  ,      )

         UpperCamelCase_: Any   				=    dqa_pipeline(
             [{'image': image, 'question': question}, {'image': image, 'question': question}]  ,      top_k=2							)
         self.assertEqual(
             nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [
                 [
                     {'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                     {'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 ],
             ]
             * 2  ,      )





       @slow
       @require_torch
       @require_detectrona
       @require_pytesseract
       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    pipeline(
             'document-question-answering'  ,      model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'  ,      revision='9977165'  ,      max_seq_len=5_0  ,      )
         UpperCamelCase_: Union[str, Any]   				=    INVOICE_URL
         UpperCamelCase_: List[str]   				=    'What is the invoice number?'

         UpperCamelCase_: List[Any]   				=    dqa_pipeline(image=_lowerCamelCase  ,      question=_lowerCamelCase  ,      top_k=2							)
         self.assertEqual(
             nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [
                 {'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
                 {'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
             ]  ,      )

         UpperCamelCase_: Optional[int]   				=    dqa_pipeline({'image': image, 'question': question}  ,      top_k=2							)
         self.assertEqual(
             nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [
                 {'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
                 {'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
             ]  ,      )

         UpperCamelCase_: List[str]   				=    dqa_pipeline(
             [{'image': image, 'question': question}, {'image': image, 'question': question}]  ,      top_k=2							)
         self.assertEqual(
             nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [
                 [
                     {'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
                     {'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 ]
             ]
             * 2  ,      )





       @slow
       @require_torch
       @require_pytesseract
       @require_vision
       def       _a   ( self							):
         UpperCamelCase_: List[Any]   				=    AutoTokenizer.from_pretrained(
             'impira/layoutlm-document-qa'  ,      revision='3dc6de3'  ,      add_prefix_space=_lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    pipeline(
             'document-question-answering'  ,      model='impira/layoutlm-document-qa'  ,      tokenizer=_lowerCamelCase  ,      revision='3dc6de3'  ,      )
         UpperCamelCase_: Optional[Any]   				=    INVOICE_URL
         UpperCamelCase_: Tuple   				=    'What is the invoice number?'

         UpperCamelCase_: Dict   				=    dqa_pipeline(image=_lowerCamelCase  ,      question=_lowerCamelCase  ,      top_k=2							)
         self.assertEqual(
             nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [
                 {'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
             ]  ,      )

         UpperCamelCase_: Tuple   				=    dqa_pipeline({'image': image, 'question': question}  ,      top_k=2							)
         self.assertEqual(
             nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [
                 {'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
             ]  ,      )

         UpperCamelCase_: Tuple   				=    dqa_pipeline(
             [{'image': image, 'question': question}, {'image': image, 'question': question}]  ,      top_k=2							)
         self.assertEqual(
             nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [
                 [
                     {'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                     {'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
                 ]
             ]
             * 2  ,      )

         UpperCamelCase_: str   				=    list(zip(*apply_tesseract(load_image(_lowerCamelCase							)  ,      _lowerCamelCase  ,      ''							)							)							)

         # This model should also work if `image` is set to None
         UpperCamelCase_: List[str]   				=    dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}  ,      top_k=2							)
         self.assertEqual(
             nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [
                 {'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
             ]  ,      )





       @slow
       @require_torch
       @require_pytesseract
       @require_vision
       def       _a   ( self							):
         UpperCamelCase_: int   				=    AutoTokenizer.from_pretrained(
             'impira/layoutlm-document-qa'  ,      revision='3dc6de3'  ,      add_prefix_space=_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    pipeline(
             'document-question-answering'  ,      model='impira/layoutlm-document-qa'  ,      tokenizer=_lowerCamelCase  ,      revision='3dc6de3'  ,      max_seq_len=5_0  ,      )
         UpperCamelCase_: List[str]   				=    INVOICE_URL
         UpperCamelCase_: int   				=    'What is the invoice number?'

         UpperCamelCase_: List[Any]   				=    dqa_pipeline(image=_lowerCamelCase  ,      question=_lowerCamelCase  ,      top_k=2							)
         self.assertEqual(
             nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [
                 {'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
             ]  ,      )

         UpperCamelCase_: str   				=    dqa_pipeline(
             [{'image': image, 'question': question}, {'image': image, 'question': question}]  ,      top_k=2							)
         self.assertEqual(
             nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [
                 [
                     {'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                     {'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 ]
             ]
             * 2  ,      )

         UpperCamelCase_: Tuple   				=    list(zip(*apply_tesseract(load_image(_lowerCamelCase							)  ,      _lowerCamelCase  ,      ''							)							)							)

         # This model should also work if `image` is set to None
         UpperCamelCase_: Any   				=    dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}  ,      top_k=2							)
         self.assertEqual(
             nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [
                 {'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
             ]  ,      )





       @slow
       @require_torch
       def       _a   ( self							):
         UpperCamelCase_: Tuple   				=    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_: Optional[int]   				=    INVOICE_URL
         UpperCamelCase_: List[Any]   				=    'What is the invoice number?'
         UpperCamelCase_: Tuple   				=    dqa_pipeline(image=_lowerCamelCase  ,      question=_lowerCamelCase  ,      top_k=2							)
         self.assertEqual(nested_simplify(_lowerCamelCase  ,      decimals=4							)  ,      [{'answer': 'us-001'}]							)





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







import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[int]									=LongformerTokenizer
       a    :		Optional[int]									=True
       a    :		Tuple									=LongformerTokenizerFast
       a    :		Dict									=True





       def       _a   ( self							):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         UpperCamelCase_: int   				=    [
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
         ]
         UpperCamelCase_: Optional[Any]   				=    dict(zip(_lowerCamelCase  ,      range(len(_lowerCamelCase							)							)							)							)
         UpperCamelCase_: Any   				=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         UpperCamelCase_: Tuple   				=    {'unk_token': '<unk>'}

         UpperCamelCase_: str   				=    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['vocab_file']							)
         UpperCamelCase_: Tuple   				=    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['merges_file']							)
         with open(self.vocab_file  ,      'w'  ,      encoding='utf-8'							) as fp:
           fp.write(json.dumps(_lowerCamelCase							) + '\n'							)
         with open(self.merges_file  ,      'w'  ,      encoding='utf-8'							) as fp:
           fp.write('\n'.join(_lowerCamelCase							)							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         kwargs.update(self.special_tokens_map							)
         return self.tokenizer_class.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         kwargs.update(self.special_tokens_map							)
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Union[str, Any]   				=    'lower newer'
         UpperCamelCase_: Optional[Any]   				=    'lower newer'
         return input_text, output_text





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.tokenizer_class(self.vocab_file  ,      self.merges_file  ,      **self.special_tokens_map							)
         UpperCamelCase_: Any   				=    'lower newer'
         UpperCamelCase_: Any   				=    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
         UpperCamelCase_: str   				=    tokenizer.tokenize(_lowerCamelCase							)  # , add_prefix_space=True)
         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: Dict   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: int   				=    [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Tuple   				=    self.get_tokenizer()

         self.assertListEqual(tokenizer.encode('Hello world!'  ,      add_special_tokens=_lowerCamelCase							)  ,      [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]							)
         self.assertListEqual(
             tokenizer.encode('Hello world! cécé herlolip 418'  ,      add_special_tokens=_lowerCamelCase							)  ,      [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]  ,      )





       @slow
       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'							)

         UpperCamelCase_: Dict   				=    tokenizer.encode('sequence builders'  ,      add_special_tokens=_lowerCamelCase							)
         UpperCamelCase_: Any   				=    tokenizer.encode('multi-sequence build'  ,      add_special_tokens=_lowerCamelCase							)

         UpperCamelCase_: Union[str, Any]   				=    tokenizer.encode(
             'sequence builders'  ,      add_special_tokens=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase							)
         UpperCamelCase_: Any   				=    tokenizer.encode(
             'sequence builders'  ,      'multi-sequence build'  ,      add_special_tokens=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase							)

         UpperCamelCase_: int   				=    tokenizer.build_inputs_with_special_tokens(_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    tokenizer.build_inputs_with_special_tokens(_lowerCamelCase  ,      _lowerCamelCase							)

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    self.get_tokenizer()

         UpperCamelCase_: Optional[int]   				=    'Encode this sequence.'
         UpperCamelCase_: List[Any]   				=    tokenizer.byte_encoder[' '.encode('utf-8'							)[0]]

         # Testing encoder arguments
         UpperCamelCase_: Dict   				=    tokenizer.encode(_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase							)
         UpperCamelCase_: List[str]   				=    tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
         self.assertNotEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: List[Any]   				=    tokenizer.encode(_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase							)
         UpperCamelCase_: int   				=    tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
         self.assertEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         tokenizer.add_special_tokens({'bos_token': '<s>'}							)
         UpperCamelCase_: Optional[Any]   				=    tokenizer.encode(_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
         UpperCamelCase_: Any   				=    tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
         self.assertNotEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         # Testing spaces after special tokens
         UpperCamelCase_: List[Any]   				=    '<mask>'
         tokenizer.add_special_tokens(
             {'mask_token': AddedToken(_lowerCamelCase  ,      lstrip=_lowerCamelCase  ,      rstrip=_lowerCamelCase							)}							)  # mask token has a left space
         UpperCamelCase_: List[Any]   				=    tokenizer.convert_tokens_to_ids(_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    'Encode <mask> sequence'
         UpperCamelCase_: Dict   				=    'Encode <mask>sequence'

         UpperCamelCase_: Any   				=    tokenizer.encode(_lowerCamelCase							)
         UpperCamelCase_: Optional[Any]   				=    encoded.index(_lowerCamelCase							)
         UpperCamelCase_: Optional[Any]   				=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
         self.assertEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: List[str]   				=    tokenizer.encode(_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    encoded.index(_lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
         self.assertNotEqual(_lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             UpperCamelCase_: Any   				=    self.rust_tokenizer_class.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)
             UpperCamelCase_: Tuple   				=    self.tokenizer_class.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)
             UpperCamelCase_: List[Any]   				=    'A, <mask> AllenNLP sentence.'
             UpperCamelCase_: int   				=    tokenizer_r.encode_plus(_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase  ,      return_token_type_ids=_lowerCamelCase							)
             UpperCamelCase_: Any   				=    tokenizer_p.encode_plus(_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase  ,      return_token_type_ids=_lowerCamelCase							)

             # token_type_ids should put 0 everywhere
             self.assertEqual(sum(tokens_r['token_type_ids']							)  ,      sum(tokens_p['token_type_ids']							)							)

             # attention_mask should put 1 everywhere, so sum over length should be 1
             self.assertEqual(
                 sum(tokens_r['attention_mask']							) / len(tokens_r['attention_mask']							)  ,      sum(tokens_p['attention_mask']							) / len(tokens_p['attention_mask']							)  ,      )

             UpperCamelCase_: List[str]   				=    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']							)
             UpperCamelCase_: str   				=    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']							)

             # Rust correctly handles the space before the mask while python doesnt
             self.assertSequenceEqual(tokens_p['input_ids']  ,      [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]							)
             self.assertSequenceEqual(tokens_r['input_ids']  ,      [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]							)

             self.assertSequenceEqual(
                 _lowerCamelCase  ,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']							)
             self.assertSequenceEqual(
                 _lowerCamelCase  ,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']							)





       def       _a   ( self							):
         for trim_offsets, add_prefix_space in itertools.product([True, False]  ,      repeat=2							):
           UpperCamelCase_: Optional[Any]   				=    self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)

           UpperCamelCase_: Union[str, Any]   				=    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
           UpperCamelCase_: Optional[int]   				=    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

           self.assertEqual(pre_tokenizer_state['add_prefix_space']  ,      _lowerCamelCase							)

           self.assertEqual(post_processor_state['add_prefix_space']  ,      _lowerCamelCase							)
           self.assertEqual(post_processor_state['trim_offsets']  ,      _lowerCamelCase							)





       def       _a   ( self							):
         # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
         # `trim_offsets`
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             UpperCamelCase_: Optional[int]   				=    'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
             UpperCamelCase_: Union[str, Any]   				=    f'''{text_of_1_token} {text_of_1_token}'''

             UpperCamelCase_: Optional[int]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: str   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (len(_lowerCamelCase							) + 1, len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: Optional[int]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: str   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (len(_lowerCamelCase							) + 1, len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: Optional[Any]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: Dict   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (len(_lowerCamelCase							), len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: List[str]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: Optional[Any]   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (len(_lowerCamelCase							), len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: Optional[int]   				=    f''' {text}'''

             # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
             #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
             # )
             # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
             # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
             # self.assertEqual(
             #     encoding.offset_mapping[1],
             #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
             # )

             UpperCamelCase_: Optional[int]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: Tuple   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (1, 1 + len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (1 + len(_lowerCamelCase							) + 1, 1 + len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: Dict   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: Dict   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, 1 + len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (1 + len(_lowerCamelCase							), 1 + len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: Union[str, Any]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: Union[str, Any]   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, 1 + len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (1 + len(_lowerCamelCase							), 1 + len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )