'''simple docstring'''




from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.layoutlm.modeling_tf_layoutlm import (
						    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLayoutLMForMaskedLM,
						    TFLayoutLMForQuestionAnswering,
						    TFLayoutLMForSequenceClassification,
						    TFLayoutLMForTokenClassification,
						    TFLayoutLMModel,
						)






class 				a__:


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=13       ,    __lowerCAmelCase=7       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=99       ,    __lowerCAmelCase=32       ,    __lowerCAmelCase=2       ,    __lowerCAmelCase=4       ,    __lowerCAmelCase=37       ,    __lowerCAmelCase="gelu"       ,    __lowerCAmelCase=0.1       ,    __lowerCAmelCase=0.1       ,    __lowerCAmelCase=512       ,    __lowerCAmelCase=16       ,    __lowerCAmelCase=2       ,    __lowerCAmelCase=0.02       ,    __lowerCAmelCase=3       ,    __lowerCAmelCase=4       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=1000       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  parent
					lowerCAmelCase   					=  batch_size
					lowerCAmelCase   					=  seq_length
					lowerCAmelCase   					=  is_training
					lowerCAmelCase   					=  use_input_mask
					lowerCAmelCase   					=  use_token_type_ids
					lowerCAmelCase   					=  use_labels
					lowerCAmelCase   					=  vocab_size
					lowerCAmelCase   					=  hidden_size
					lowerCAmelCase   					=  num_hidden_layers
					lowerCAmelCase   					=  num_attention_heads
					lowerCAmelCase   					=  intermediate_size
					lowerCAmelCase   					=  hidden_act
					lowerCAmelCase   					=  hidden_dropout_prob
					lowerCAmelCase   					=  attention_probs_dropout_prob
					lowerCAmelCase   					=  max_position_embeddings
					lowerCAmelCase   					=  type_vocab_size
					lowerCAmelCase   					=  type_sequence_label_size
					lowerCAmelCase   					=  initializer_range
					lowerCAmelCase   					=  num_labels
					lowerCAmelCase   					=  num_choices
					lowerCAmelCase   					=  scope
					lowerCAmelCase   					=  range_bbox



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ids_tensor([self.batch_size, self.seq_length]       ,    self.vocab_size)

					# convert bbox to numpy since TF does not support item assignment
					lowerCAmelCase   					=  ids_tensor([self.batch_size, self.seq_length, 4]       ,    self.range_bbox).numpy()
					# Ensure that bbox is legal
					for i in range(bbox.shape[0]):
							for j in range(bbox.shape[1]):
									if bbox[i, j, 3] < bbox[i, j, 1]:
											lowerCAmelCase   					=  bbox[i, j, 3]
											lowerCAmelCase   					=  bbox[i, j, 1]
											lowerCAmelCase   					=  t
									if bbox[i, j, 2] < bbox[i, j, 0]:
											lowerCAmelCase   					=  bbox[i, j, 2]
											lowerCAmelCase   					=  bbox[i, j, 0]
											lowerCAmelCase   					=  t
					lowerCAmelCase   					=  tf.convert_to_tensor(__lowerCAmelCase)

					lowerCAmelCase   					=  None
					if self.use_input_mask:
							lowerCAmelCase   					=  random_attention_mask([self.batch_size, self.seq_length])

					lowerCAmelCase   					=  None
					if self.use_token_type_ids:
							lowerCAmelCase   					=  ids_tensor([self.batch_size, self.seq_length]       ,    self.type_vocab_size)

					lowerCAmelCase   					=  None
					lowerCAmelCase   					=  None
					lowerCAmelCase   					=  None
					if self.use_labels:
							lowerCAmelCase   					=  ids_tensor([self.batch_size]       ,    self.type_sequence_label_size)
							lowerCAmelCase   					=  ids_tensor([self.batch_size, self.seq_length]       ,    self.num_labels)
							lowerCAmelCase   					=  ids_tensor([self.batch_size]       ,    self.num_choices)

					lowerCAmelCase   					=  LayoutLMConfig(
					    vocab_size=self.vocab_size       ,    hidden_size=self.hidden_size       ,    num_hidden_layers=self.num_hidden_layers       ,    num_attention_heads=self.num_attention_heads       ,    intermediate_size=self.intermediate_size       ,    hidden_act=self.hidden_act       ,    hidden_dropout_prob=self.hidden_dropout_prob       ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,    max_position_embeddings=self.max_position_embeddings       ,    type_vocab_size=self.type_vocab_size       ,    initializer_range=self.initializer_range       ,    )

					return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  TFLayoutLMModel(config=__lowerCAmelCase)

					lowerCAmelCase   					=  model(__lowerCAmelCase       ,    __lowerCAmelCase       ,    attention_mask=__lowerCAmelCase       ,    token_type_ids=__lowerCAmelCase)
					lowerCAmelCase   					=  model(__lowerCAmelCase       ,    __lowerCAmelCase       ,    token_type_ids=__lowerCAmelCase)
					lowerCAmelCase   					=  model(__lowerCAmelCase       ,    __lowerCAmelCase)
					self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.seq_length, self.hidden_size))
					self.parent.assertEqual(result.pooler_output.shape       ,    (self.batch_size, self.hidden_size))



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  TFLayoutLMForMaskedLM(config=__lowerCAmelCase)

					lowerCAmelCase   					=  model(__lowerCAmelCase       ,    __lowerCAmelCase       ,    attention_mask=__lowerCAmelCase       ,    token_type_ids=__lowerCAmelCase       ,    labels=__lowerCAmelCase)
					self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.vocab_size))



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  self.num_labels
					lowerCAmelCase   					=  TFLayoutLMForSequenceClassification(config=__lowerCAmelCase)

					lowerCAmelCase   					=  model(__lowerCAmelCase       ,    __lowerCAmelCase       ,    attention_mask=__lowerCAmelCase       ,    token_type_ids=__lowerCAmelCase)
					self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels))



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  self.num_labels
					lowerCAmelCase   					=  TFLayoutLMForTokenClassification(config=__lowerCAmelCase)

					lowerCAmelCase   					=  model(__lowerCAmelCase       ,    __lowerCAmelCase       ,    attention_mask=__lowerCAmelCase       ,    token_type_ids=__lowerCAmelCase       ,    labels=__lowerCAmelCase)
					self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.num_labels))



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  TFLayoutLMForQuestionAnswering(config=__lowerCAmelCase)

					lowerCAmelCase   					=  model(__lowerCAmelCase       ,    __lowerCAmelCase       ,    attention_mask=__lowerCAmelCase       ,    token_type_ids=__lowerCAmelCase)
					self.parent.assertEqual(result.start_logits.shape       ,    (self.batch_size, self.seq_length))
					self.parent.assertEqual(result.end_logits.shape       ,    (self.batch_size, self.seq_length))







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.prepare_config_and_inputs()
					(
					    (
					    lowerCAmelCase
					)					,     (
					    lowerCAmelCase
					)					,     (
					    lowerCAmelCase
					)					,     (
					    lowerCAmelCase
					)					,     (
					    lowerCAmelCase
					)					,     (
					    lowerCAmelCase
					)					,     (
					    lowerCAmelCase
					)					,     (
					    lowerCAmelCase
					)					,     
					)   					=  config_and_inputs
					lowerCAmelCase   					=  {
					    """input_ids""": input_ids,
					    """bbox""": bbox,
					    """token_type_ids""": token_type_ids,
					    """attention_mask""": input_mask,
					}
					return config, inputs_dict







@require_tf
class 				a__(					lowerCAmelCase__ ,						lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[Any]      =       (
			    (
			        TFLayoutLMModel,
			        TFLayoutLMForMaskedLM,
			        TFLayoutLMForTokenClassification,
			        TFLayoutLMForSequenceClassification,
			        TFLayoutLMForQuestionAnswering,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCAmelCase_			:     str      =       (
			    {
			        '''feature-extraction''': TFLayoutLMModel,
			        '''fill-mask''': TFLayoutLMForMaskedLM,
			        '''text-classification''': TFLayoutLMForSequenceClassification,
			        '''token-classification''': TFLayoutLMForTokenClassification,
			        '''zero-shot''': TFLayoutLMForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			UpperCAmelCase_			:     List[str]      =       False
			UpperCAmelCase_			:     List[str]      =       True
			UpperCAmelCase_			:     Tuple      =       1_0



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  TFLayoutLMModelTester(self)
					lowerCAmelCase   					=  ConfigTester(self       ,    config_class=__lowerCAmelCase       ,    hidden_size=37)



			def 							a_       (   self):




					"""simple docstring"""


					self.config_tester.run_common_tests()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase)



			@slow
			def 							a_       (   self):




					"""simple docstring"""


					for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase   					=  TFLayoutLMModel.from_pretrained(__lowerCAmelCase)
							self.assertIsNotNone(__lowerCAmelCase)







			@unittest.skip("""Onnx compliancy broke with TF 2.10""")
			def 							a_       (   self):




					"""simple docstring"""


					pass







def 				snake_case__							(			)    ->	int:

		'''simple docstring'''


		lowerCAmelCase   					=  tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]] )  # noqa: E231
		lowerCAmelCase   					=  tf.convert_to_tensor([[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],] )  # noqa: E231
		lowerCAmelCase   					=  tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]] )  # noqa: E231
		lowerCAmelCase   					=  tf.convert_to_tensor([[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: E231
		# these are sequence labels (i.e. at the token level)
		lowerCAmelCase   					=  tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]] )  # noqa: E231
		# fmt: on

		return input_ids, attention_mask, bbox, token_type_ids, labels






@require_tf
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			@slow
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased""")

					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  prepare_layoutlm_batch_inputs()

					# forward pass
					lowerCAmelCase   					=  model(input_ids=__lowerCAmelCase       ,    bbox=__lowerCAmelCase       ,    attention_mask=__lowerCAmelCase       ,    token_type_ids=__lowerCAmelCase)

					# test the sequence output on [0, :3, :3]
					lowerCAmelCase   					=  tf.convert_to_tensor(
					    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]       ,    )

					self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]       ,    __lowerCAmelCase       ,    atol=1E-3))

					# test the pooled output on [1, :3]
					lowerCAmelCase   					=  tf.convert_to_tensor([-0.6580, -0.0214, 0.8552])

					self.assertTrue(np.allclose(outputs.pooler_output[1, :3]       ,    __lowerCAmelCase       ,    atol=1E-3))



			@slow
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""       ,    num_labels=2)

					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  prepare_layoutlm_batch_inputs()

					# forward pass
					lowerCAmelCase   					=  model(
					    input_ids=__lowerCAmelCase       ,    bbox=__lowerCAmelCase       ,    attention_mask=__lowerCAmelCase       ,    token_type_ids=__lowerCAmelCase       ,    labels=tf.convert_to_tensor([1, 1])       ,    )

					# test whether we get a loss as a scalar
					lowerCAmelCase   					=  outputs.loss
					lowerCAmelCase   					=  (2,)
					self.assertEqual(loss.shape       ,    __lowerCAmelCase)

					# test the shape of the logits
					lowerCAmelCase   					=  outputs.logits
					lowerCAmelCase   					=  (2, 2)
					self.assertEqual(logits.shape       ,    __lowerCAmelCase)



			@slow
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""       ,    num_labels=13)

					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  prepare_layoutlm_batch_inputs()

					# forward pass
					lowerCAmelCase   					=  model(
					    input_ids=__lowerCAmelCase       ,    bbox=__lowerCAmelCase       ,    attention_mask=__lowerCAmelCase       ,    token_type_ids=__lowerCAmelCase       ,    labels=__lowerCAmelCase)

					# test the shape of the logits
					lowerCAmelCase   					=  outputs.logits
					lowerCAmelCase   					=  tf.convert_to_tensor((2, 25, 13))
					self.assertEqual(logits.shape       ,    __lowerCAmelCase)







			@slow
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased""")

					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  prepare_layoutlm_batch_inputs()

					# forward pass
					lowerCAmelCase   					=  model(input_ids=__lowerCAmelCase       ,    bbox=__lowerCAmelCase       ,    attention_mask=__lowerCAmelCase       ,    token_type_ids=__lowerCAmelCase)

					# test the shape of the logits
					lowerCAmelCase   					=  tf.convert_to_tensor((2, 25))
					self.assertEqual(outputs.start_logits.shape       ,    __lowerCAmelCase)
					self.assertEqual(outputs.end_logits.shape       ,    __lowerCAmelCase)





'''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


__lowercase          =  get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Tuple      =       XLMRobertaTokenizer
			UpperCAmelCase_			:     int      =       XLMRobertaTokenizerFast
			UpperCAmelCase_			:     List[str]      =       True
			UpperCAmelCase_			:     Optional[int]      =       True



			def 							a_       (   self):




					"""simple docstring"""


					super().setUp()

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """<pad>"""
					lowerCAmelCase   					=  1

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  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(__lowerCAmelCase)       ,    1002)



			def 							a_       (   self):




					"""simple docstring"""


					self.assertEqual(self.get_tokenizer().vocab_size       ,    1002)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  XLMRobertaTokenizer(__lowerCAmelCase       ,    keep_accents=__lowerCAmelCase)

					lowerCAmelCase   					=  tokenizer.tokenize("""This is a test""")
					self.assertListEqual(__lowerCAmelCase       ,    ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

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

					lowerCAmelCase   					=  tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
					self.assertListEqual(
					    __lowerCAmelCase       ,    [
					        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""",
					        """.""",
					    ]       ,    )
					lowerCAmelCase   					=  tokenizer.convert_tokens_to_ids(__lowerCAmelCase)
					self.assertListEqual(
					    __lowerCAmelCase       ,    [
					        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 ^
					    ]       ,    )

					lowerCAmelCase   					=  tokenizer.convert_ids_to_tokens(__lowerCAmelCase)
					self.assertListEqual(
					    __lowerCAmelCase       ,    [
					        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 							a_       (   self):




					"""simple docstring"""


					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

					lowerCAmelCase   					=  (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})"):
									lowerCAmelCase   					=  self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  self.tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)

									lowerCAmelCase   					=  tempfile.mkdtemp()

									lowerCAmelCase   					=  tokenizer_r.save_pretrained(__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.save_pretrained(__lowerCAmelCase)

									# 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))
									lowerCAmelCase   					=  tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f)
									self.assertSequenceEqual(__lowerCAmelCase       ,    __lowerCAmelCase)

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

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

									shutil.rmtree(__lowerCAmelCase)

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

									lowerCAmelCase   					=  tokenizer_r.save_pretrained(__lowerCAmelCase       ,    legacy_format=__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.save_pretrained(__lowerCAmelCase)

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

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

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

									shutil.rmtree(__lowerCAmelCase)

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

									lowerCAmelCase   					=  tokenizer_r.save_pretrained(__lowerCAmelCase       ,    legacy_format=__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.save_pretrained(__lowerCAmelCase)

									# 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
									lowerCAmelCase   					=  tokenizer_r.from_pretrained(__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.from_pretrained(__lowerCAmelCase)

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

									shutil.rmtree(__lowerCAmelCase)



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base""")



			def 							a_       (   self):




					"""simple docstring"""


					with tempfile.NamedTemporaryFile() as f:
							shutil.copyfile(__lowerCAmelCase       ,    f.name)
							lowerCAmelCase   					=  XLMRobertaTokenizer(f.name       ,    keep_accents=__lowerCAmelCase)
							lowerCAmelCase   					=  pickle.dumps(__lowerCAmelCase)
					pickle.loads(__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					if not self.test_rust_tokenizer:
							return

					lowerCAmelCase   					=  self.get_tokenizer()
					lowerCAmelCase   					=  self.get_rust_tokenizer()

					lowerCAmelCase   					=  """I was born in 92000, and this is falsé."""

					lowerCAmelCase   					=  tokenizer.tokenize(__lowerCAmelCase)
					lowerCAmelCase   					=  rust_tokenizer.tokenize(__lowerCAmelCase)
					self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)

					lowerCAmelCase   					=  tokenizer.encode(__lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase)
					lowerCAmelCase   					=  rust_tokenizer.encode(__lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase)
					self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)

					lowerCAmelCase   					=  self.get_rust_tokenizer()
					lowerCAmelCase   					=  tokenizer.encode(__lowerCAmelCase)
					lowerCAmelCase   					=  rust_tokenizer.encode(__lowerCAmelCase)
					self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)



			@slow
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """Hello World!"""
					lowerCAmelCase   					=  [0, 35378, 6661, 38, 2]
					# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
					# xlmr.eval()
					# xlmr.encode(symbols)

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



			@slow
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  (
					    """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"""
					)
					lowerCAmelCase   					=  [
					    0,
					    3293,
					    83,
					    10,
					    4552,
					    4989,
					    7986,
					    678,
					    10,
					    5915,
					    111,
					    179459,
					    124850,
					    4,
					    6044,
					    237,
					    12,
					    6,
					    5,
					    6,
					    4,
					    6780,
					    705,
					    15,
					    1388,
					    44,
					    378,
					    10114,
					    711,
					    152,
					    20,
					    6,
					    5,
					    22376,
					    642,
					    1221,
					    15190,
					    34153,
					    450,
					    5608,
					    959,
					    1119,
					    57702,
					    136,
					    186,
					    47,
					    1098,
					    29367,
					    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,
					    50901,
					    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(__lowerCAmelCase       ,    self.big_tokenizer.encode(__lowerCAmelCase))







			@slow
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  {"""input_ids""": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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=__lowerCAmelCase       ,    model_name="""xlm-roberta-base"""       ,    revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""       ,    )




