






"""simple docstring"""





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A__					: Optional[int]=	get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				Tuple 						=XLMRobertaTokenizer
   a      :				Dict 						=XLMRobertaTokenizerFast
   a      :				Tuple 						=True
   a      :				Optional[int] 						=True

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase__         =    XLMRobertaTokenizer(snake_case_ ,				keep_accents=snake_case_      )
       tokenizer.save_pretrained(self.tmpdirname      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    '<pad>'
       UpperCamelCase__         =    1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_      ) ,				snake_case_      )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_      ) ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    list(self.get_tokenizer().get_vocab().keys()      )

       self.assertEqual(vocab_keys[0] ,				'<s>'      )
       self.assertEqual(vocab_keys[1] ,				'<pad>'      )
       self.assertEqual(vocab_keys[-1] ,				'<mask>'      )
       self.assertEqual(len(snake_case_      ) ,				1002      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       self.assertEqual(self.get_tokenizer().vocab_size ,				1002      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    XLMRobertaTokenizer(snake_case_ ,				keep_accents=snake_case_      )

       UpperCamelCase__         =    tokenizer.tokenize('This is a test'      )
       self.assertListEqual(snake_case_ ,				['▁This', '▁is', '▁a', '▁t', 'est']      )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(snake_case_      ) ,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,				)

       UpperCamelCase__         =    tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
       self.assertListEqual(
           snake_case_ ,				[
               SPIECE_UNDERLINE + 'I',
               SPIECE_UNDERLINE + 'was',
               SPIECE_UNDERLINE + 'b',
               'or',
               'n',
               SPIECE_UNDERLINE + 'in',
               SPIECE_UNDERLINE + '',
               '9',
               '2',
               '0',
               '0',
               '0',
               ',',
               SPIECE_UNDERLINE + 'and',
               SPIECE_UNDERLINE + 'this',
               SPIECE_UNDERLINE + 'is',
               SPIECE_UNDERLINE + 'f',
               'al',
               's',
               'é',
               '.',
           ] ,				)
       UpperCamelCase__         =    tokenizer.convert_tokens_to_ids(snake_case_      )
       self.assertListEqual(
           snake_case_ ,				[
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
           ] ,				)

       UpperCamelCase__         =    tokenizer.convert_ids_to_tokens(snake_case_      )
       self.assertListEqual(
           snake_case_ ,				[
               SPIECE_UNDERLINE + 'I',
               SPIECE_UNDERLINE + 'was',
               SPIECE_UNDERLINE + 'b',
               'or',
               'n',
               SPIECE_UNDERLINE + 'in',
               SPIECE_UNDERLINE + '',
               '<unk>',
               '2',
               '0',
               '0',
               '0',
               ',',
               SPIECE_UNDERLINE + 'and',
               SPIECE_UNDERLINE + 'this',
               SPIECE_UNDERLINE + 'is',
               SPIECE_UNDERLINE + 'f',
               'al',
               's',
               '<unk>',
               '.',
           ] ,				)

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       if not self.test_slow_tokenizer:
           # as we don't have a slow version, we can't compare the outputs between slow and fast versions
           return

       UpperCamelCase__         =    (self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
               UpperCamelCase__         =    self.rust_tokenizer_class.from_pretrained(snake_case_ ,				**snake_case_      )
               UpperCamelCase__         =    self.tokenizer_class.from_pretrained(snake_case_ ,				**snake_case_      )

               UpperCamelCase__         =    tempfile.mkdtemp()

               UpperCamelCase__         =    tokenizer_r.save_pretrained(snake_case_      )
               UpperCamelCase__         =    tokenizer_p.save_pretrained(snake_case_      )

               # Checks it save with the same files + the tokenizer.json file for the fast one
               self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )
               UpperCamelCase__         =    tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f      )
               self.assertSequenceEqual(snake_case_ ,				snake_case_      )

               # Checks everything loads correctly in the same way
               UpperCamelCase__         =    tokenizer_r.from_pretrained(snake_case_      )
               UpperCamelCase__         =    tokenizer_p.from_pretrained(snake_case_      )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                   self.assertTrue(hasattr(snake_case_ ,				snake_case_      )      )
                   # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                   # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

               shutil.rmtree(snake_case_      )

               # Save tokenizer rust, legacy_format=True
               UpperCamelCase__         =    tempfile.mkdtemp()

               UpperCamelCase__         =    tokenizer_r.save_pretrained(snake_case_ ,				legacy_format=snake_case_      )
               UpperCamelCase__         =    tokenizer_p.save_pretrained(snake_case_      )

               # Checks it save with the same files
               self.assertSequenceEqual(snake_case_ ,				snake_case_      )

               # Checks everything loads correctly in the same way
               UpperCamelCase__         =    tokenizer_r.from_pretrained(snake_case_      )
               UpperCamelCase__         =    tokenizer_p.from_pretrained(snake_case_      )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                   self.assertTrue(hasattr(snake_case_ ,				snake_case_      )      )

               shutil.rmtree(snake_case_      )

               # Save tokenizer rust, legacy_format=False
               UpperCamelCase__         =    tempfile.mkdtemp()

               UpperCamelCase__         =    tokenizer_r.save_pretrained(snake_case_ ,				legacy_format=snake_case_      )
               UpperCamelCase__         =    tokenizer_p.save_pretrained(snake_case_      )

               # Checks it saved the tokenizer.json file
               self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )

               # Checks everything loads correctly in the same way
               UpperCamelCase__         =    tokenizer_r.from_pretrained(snake_case_      )
               UpperCamelCase__         =    tokenizer_p.from_pretrained(snake_case_      )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                   self.assertTrue(hasattr(snake_case_ ,				snake_case_      )      )

               shutil.rmtree(snake_case_      )

   @cached_property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       with tempfile.NamedTemporaryFile() as f:
           shutil.copyfile(snake_case_ ,				f.name      )
           UpperCamelCase__         =    XLMRobertaTokenizer(f.name ,				keep_accents=snake_case_      )
           UpperCamelCase__         =    pickle.dumps(snake_case_      )
       pickle.loads(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       if not self.test_rust_tokenizer:
           return

       UpperCamelCase__         =    self.get_tokenizer()
       UpperCamelCase__         =    self.get_rust_tokenizer()

       UpperCamelCase__         =    'I was born in 92000, and this is falsé.'

       UpperCamelCase__         =    tokenizer.tokenize(snake_case_      )
       UpperCamelCase__         =    rust_tokenizer.tokenize(snake_case_      )
       self.assertListEqual(snake_case_ ,				snake_case_      )

       UpperCamelCase__         =    tokenizer.encode(snake_case_ ,				add_special_tokens=snake_case_      )
       UpperCamelCase__         =    rust_tokenizer.encode(snake_case_ ,				add_special_tokens=snake_case_      )
       self.assertListEqual(snake_case_ ,				snake_case_      )

       UpperCamelCase__         =    self.get_rust_tokenizer()
       UpperCamelCase__         =    tokenizer.encode(snake_case_      )
       UpperCamelCase__         =    rust_tokenizer.encode(snake_case_      )
       self.assertListEqual(snake_case_ ,				snake_case_      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    'Hello World!'
       UpperCamelCase__         =    [0, 3_5378, 6661, 38, 2]
       # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
       # xlmr.eval()
       # xlmr.encode(symbols)

       self.assertListEqual(snake_case_ ,				self.big_tokenizer.encode(snake_case_      )      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    (
           'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
           ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
       )
       UpperCamelCase__         =    [
           0,
           3293,
           83,
           10,
           4552,
           4989,
           7986,
           678,
           10,
           5915,
           111,
           17_9459,
           12_4850,
           4,
           6044,
           237,
           12,
           6,
           5,
           6,
           4,
           6780,
           705,
           15,
           1388,
           44,
           378,
           1_0114,
           711,
           152,
           20,
           6,
           5,
           2_2376,
           642,
           1221,
           1_5190,
           3_4153,
           450,
           5608,
           959,
           1119,
           5_7702,
           136,
           186,
           47,
           1098,
           2_9367,
           47,
           # 4426, # What fairseq tokenizes from "<unk>": "_<"
           # 3678, # What fairseq tokenizes from "<unk>": "unk"
           # 2740, # What fairseq tokenizes from "<unk>": ">"
           3,  # What we tokenize from "<unk>": "<unk>"
           6,  # Residue from the tokenization: an extra sentencepiece underline
           4,
           6044,
           237,
           6284,
           5_0901,
           528,
           31,
           90,
           34,
           927,
           2,
       ]
       # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
       # xlmr.eval()
       # xlmr.encode(symbols)

       self.assertListEqual(snake_case_ ,				self.big_tokenizer.encode(snake_case_      )      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       # fmt: off
       UpperCamelCase__         =    {'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=snake_case_ ,				model_name='xlm-roberta-base' ,				revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3' ,				)












"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType


A__					: Any=	logging.get_logger(__name__)

A__					: str=	{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}
class    __lowerCamelCase    (  _a  ):
   a      :				List[str] 						="""layoutlmv3"""

   def __init__(					self ,				snake_case_=5_0265 ,				snake_case_=768 ,				snake_case_=12 ,				snake_case_=12 ,				snake_case_=3072 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=512 ,				snake_case_=2 ,				snake_case_=0.02 ,				snake_case_=1E-5 ,				snake_case_=1 ,				snake_case_=0 ,				snake_case_=2 ,				snake_case_=1024 ,				snake_case_=128 ,				snake_case_=128 ,				snake_case_=True ,				snake_case_=32 ,				snake_case_=128 ,				snake_case_=64 ,				snake_case_=256 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=True ,				snake_case_=224 ,				snake_case_=3 ,				snake_case_=16 ,				snake_case_=None ,				**snake_case_ ,				)		->       Union[str, Any]:
       super().__init__(
           vocab_size=snake_case_ ,				hidden_size=snake_case_ ,				num_hidden_layers=snake_case_ ,				num_attention_heads=snake_case_ ,				intermediate_size=snake_case_ ,				hidden_act=snake_case_ ,				hidden_dropout_prob=snake_case_ ,				attention_probs_dropout_prob=snake_case_ ,				max_position_embeddings=snake_case_ ,				type_vocab_size=snake_case_ ,				initializer_range=snake_case_ ,				layer_norm_eps=snake_case_ ,				pad_token_id=snake_case_ ,				bos_token_id=snake_case_ ,				eos_token_id=snake_case_ ,				**snake_case_ ,				)
       UpperCamelCase__         =    max_ad_position_embeddings
       UpperCamelCase__         =    coordinate_size
       UpperCamelCase__         =    shape_size
       UpperCamelCase__         =    has_relative_attention_bias
       UpperCamelCase__         =    rel_pos_bins
       UpperCamelCase__         =    max_rel_pos
       UpperCamelCase__         =    has_spatial_attention_bias
       UpperCamelCase__         =    rel_ad_pos_bins
       UpperCamelCase__         =    max_rel_ad_pos
       UpperCamelCase__         =    text_embed
       UpperCamelCase__         =    visual_embed
       UpperCamelCase__         =    input_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    patch_size
       UpperCamelCase__         =    classifier_dropout







class    __lowerCamelCase    (  _a  ):
   a      :				Tuple 						=version.parse("""1.12"""  )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Mapping[str, Mapping[int, str]]:
       # The order of inputs is different for question answering and sequence classification
       if self.task in ["question-answering", "sequence-classification"]:
           return OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'sequence'}),
                   ('bbox', {0: 'batch', 1: 'sequence'}),
                   ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
               ]      )
       else:
           return OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'sequence'}),
                   ('bbox', {0: 'batch', 1: 'sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'sequence'}),
                   ('pixel_values', {0: 'batch', 1: 'num_channels'}),
               ]      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       float:
       return 1E-5

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return 12







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = -1 ,				snake_case_ = -1 ,				snake_case_ = False ,				snake_case_ = None ,				snake_case_ = 3 ,				snake_case_ = 40 ,				snake_case_ = 40 ,				)		->       Mapping[str, Any]:
       setattr(processor.image_processor ,				'apply_ocr' ,				snake_case_      )

       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCamelCase__         =    compute_effective_axis_dimension(
           snake_case_ ,				fixed_dimension=OnnxConfig.default_fixed_batch ,				num_token_to_add=0      )
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCamelCase__         =    processor.tokenizer.num_special_tokens_to_add(snake_case_      )
       UpperCamelCase__         =    compute_effective_axis_dimension(
           snake_case_ ,				fixed_dimension=OnnxConfig.default_fixed_sequence ,				num_token_to_add=snake_case_      )
       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase__         =    [[' '.join([processor.tokenizer.unk_token]      ) * seq_length]] * batch_size

       # Generate dummy bounding boxes
       UpperCamelCase__         =    [[[48, 84, 73, 128]]] * batch_size

       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
       UpperCamelCase__         =    self._generate_dummy_images(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )

       UpperCamelCase__         =    dict(
           processor(
               snake_case_ ,				text=snake_case_ ,				boxes=snake_case_ ,				return_tensors=snake_case_ ,				)      )

       return inputs




