
"""simple docstring"""

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
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


UpperCamelCase			:       Optional[int]									=				get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		XGLMTokenizer
     lowercase		     =		XGLMTokenizerFast
     lowercase		     =		True
     lowercase		     =		True





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      super().setUp()

      # We have a SentencePiece fixture for testing
      __UpperCamelCase       						= XGLMTokenizer(__UpperCAmelCase			,  keep_accents=__UpperCAmelCase   )
      tokenizer.save_pretrained(self.tmpdirname   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= '<pad>'
      __UpperCamelCase       						= 1

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= list(self.get_tokenizer().get_vocab().keys()   )

      self.assertEqual(vocab_keys[0]			,  '<s>'   )
      self.assertEqual(vocab_keys[1]			,  '<pad>'   )
      self.assertEqual(len(__UpperCAmelCase   )			,  1008   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self.assertEqual(self.get_tokenizer().vocab_size			,  1008   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= XGLMTokenizer(__UpperCAmelCase			,  keep_accents=__UpperCAmelCase   )

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

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__UpperCAmelCase   )			,  [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(
          __UpperCAmelCase			,  [
              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(__UpperCAmelCase   )
      self.assertListEqual(
          __UpperCAmelCase			,  [
              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]
          ]			,  )

      __UpperCamelCase       						= tokenizer.convert_ids_to_tokens(__UpperCAmelCase   )
      self.assertListEqual(
          __UpperCAmelCase			,  [
              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>',
              '.',
          ]			,  )





     @cached_property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return XGLMTokenizer.from_pretrained('facebook/xglm-564M'   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      with tempfile.NamedTemporaryFile() as f:
       shutil.copyfile(__UpperCAmelCase			,  f.name   )
       __UpperCamelCase       						= XGLMTokenizer(f.name			,  keep_accents=__UpperCAmelCase   )
       __UpperCamelCase       						= pickle.dumps(__UpperCAmelCase   )
      pickle.loads(__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      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(__UpperCAmelCase   )
      __UpperCamelCase       						= rust_tokenizer.tokenize(__UpperCAmelCase   )
      self.assertListEqual(__UpperCAmelCase			,  __UpperCAmelCase   )

      __UpperCamelCase       						= tokenizer.encode(__UpperCAmelCase			,  add_special_tokens=__UpperCAmelCase   )
      __UpperCamelCase       						= rust_tokenizer.encode(__UpperCAmelCase			,  add_special_tokens=__UpperCAmelCase   )
      self.assertListEqual(__UpperCAmelCase			,  __UpperCAmelCase   )

      __UpperCamelCase       						= self.get_rust_tokenizer()
      __UpperCamelCase       						= tokenizer.encode(__UpperCAmelCase   )
      __UpperCamelCase       						= rust_tokenizer.encode(__UpperCAmelCase   )
      self.assertListEqual(__UpperCAmelCase			,  __UpperCAmelCase   )





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= 'Hello World!'
      __UpperCamelCase       						= [2, 3_1227, 4447, 35]

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





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __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'
      )
      # fmt: off
      __UpperCamelCase       						= [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 7_1630, 2_8085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 1_3675, 377, 652, 7580, 1_0341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 20_2277, 1_7892, 33, 60, 87, 4, 3234, 157, 61, 2667, 5_2376, 19, 88, 23, 735]
      # fmt: on

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







     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= {
          'input_ids': [[2, 10_8825, 1163, 15, 8_8010, 473, 1_5898, 157, 1_3672, 1857, 312, 8, 23_8021, 1163, 53, 1_3672, 1857, 312, 8, 5_3283, 18_2396, 8, 1_8566, 16, 3_6733, 4101, 8, 230, 24_4017, 12_2553, 7, 15, 13_2597, 4, 293, 1_2511, 7610, 4, 3414, 13_2597, 9, 4, 3_2361, 362, 4, 734, 2_8512, 3_2569, 18, 4, 3_2361, 2_6096, 1_4982, 73, 1_8715, 2_1433, 23_5261, 15, 492, 1_2427, 16, 53, 1_8715, 2_1433, 6_5454, 15, 2_3659, 563, 16, 278, 597, 2843, 595, 7931, 18_2396, 6_4186, 22, 886, 595, 13_2981, 53, 2_5540, 3449, 4_3982, 3_9901, 5951, 878, 330, 4, 2_7694, 8_0269, 312, 53, 6517, 1_1780, 611, 2_0408, 5], [2, 6, 13_2597, 67, 4_2897, 33, 592, 8, 16_3729, 2_5540, 361, 13_6997, 10_9514, 17_3230, 7, 501, 60, 10_2913, 196, 5631, 235, 6_3243, 473, 6, 23_1757, 74, 5277, 7905, 53, 3095, 3_7317, 22, 454, 18_3874, 5], [2, 268, 3_1298, 4_6530, 6, 13_2935, 4_3831, 7, 597, 32, 24, 3688, 9865, 5]],
          '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, 1, 1, 1]]
      }  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=__UpperCAmelCase			,  model_name='facebook/xglm-564M'			,  padding=__UpperCAmelCase			,  )





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase			:       List[Any]									=				{
    "configuration_xlm_roberta": [
        "XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaConfig",
        "XLMRobertaOnnxConfig",
    ],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[Any]									=				["XLMRobertaTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       int									=				["XLMRobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[int]									=				[
          "XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "XLMRobertaForCausalLM",
          "XLMRobertaForMaskedLM",
          "XLMRobertaForMultipleChoice",
          "XLMRobertaForQuestionAnswering",
          "XLMRobertaForSequenceClassification",
          "XLMRobertaForTokenClassification",
          "XLMRobertaModel",
          "XLMRobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[str]									=				[
          "TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFXLMRobertaForCausalLM",
          "TFXLMRobertaForMaskedLM",
          "TFXLMRobertaForMultipleChoice",
          "TFXLMRobertaForQuestionAnswering",
          "TFXLMRobertaForSequenceClassification",
          "TFXLMRobertaForTokenClassification",
          "TFXLMRobertaModel",
          "TFXLMRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[str]									=				[
          "FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "FlaxXLMRobertaForMaskedLM",
          "FlaxXLMRobertaForCausalLM",
          "FlaxXLMRobertaForMultipleChoice",
          "FlaxXLMRobertaForQuestionAnswering",
          "FlaxXLMRobertaForSequenceClassification",
          "FlaxXLMRobertaForTokenClassification",
          "FlaxXLMRobertaModel",
          "FlaxXLMRobertaPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_xlm_roberta import (
          XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
          XLMRobertaConfig,
          XLMRobertaOnnxConfig,
      )

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_roberta import XLMRobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_roberta import (
                XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMRobertaForCausalLM,
                XLMRobertaForMaskedLM,
                XLMRobertaForMultipleChoice,
                XLMRobertaForQuestionAnswering,
                XLMRobertaForSequenceClassification,
                XLMRobertaForTokenClassification,
                XLMRobertaModel,
                XLMRobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_xlm_roberta import (
                TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFXLMRobertaForCausalLM,
                TFXLMRobertaForMaskedLM,
                TFXLMRobertaForMultipleChoice,
                TFXLMRobertaForQuestionAnswering,
                TFXLMRobertaForSequenceClassification,
                TFXLMRobertaForTokenClassification,
                TFXLMRobertaModel,
                TFXLMRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_xlm_roberta import (
                FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxXLMRobertaForCausalLM,
                FlaxXLMRobertaForMaskedLM,
                FlaxXLMRobertaForMultipleChoice,
                FlaxXLMRobertaForQuestionAnswering,
                FlaxXLMRobertaForSequenceClassification,
                FlaxXLMRobertaForTokenClassification,
                FlaxXLMRobertaModel,
                FlaxXLMRobertaPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase			:       Dict									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



