

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
       from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


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

lowerCamelCase           =					{'''target_lang''': '''fi''', '''source_lang''': '''en'''}
lowerCamelCase           =					'''>>zh<<'''
lowerCamelCase           =					'''Helsinki-NLP/'''

if is_torch_available():
       lowerCamelCase           =					'''pt'''
elif is_tf_available():
       lowerCamelCase           =					'''tf'''
else:
       lowerCamelCase           =					'''jax'''







@require_sentencepiece
class 						__magic_name__			(							lowerCamelCase__		,       unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	Dict    =   MarianTokenizer
   lowerCamelCase__ :	Any    =   False
   lowerCamelCase__ :	int    =   True



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     super().setUp()
     a__  =['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
     a__  =dict(zip(lowercase_,    range(len(lowercase_       )       )       )       )
     a__  =Path(self.tmpdirname       )
     save_json(lowercase_,    save_dir / VOCAB_FILES_NAMES['''vocab''']       )
     save_json(lowercase_,    save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file''']       )
     if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
       copyfile(lowercase_,    save_dir / VOCAB_FILES_NAMES['''source_spm''']       )
       copyfile(lowercase_,    save_dir / VOCAB_FILES_NAMES['''target_spm''']       )

     a__  =MarianTokenizer.from_pretrained(self.tmpdirname       )
     tokenizer.save_pretrained(self.tmpdirname       )



   def  _UpperCAmelCase (				self,    **lowercase_       )							->    MarianTokenizer:






     """simple docstring"""






     return MarianTokenizer.from_pretrained(self.tmpdirname,    **lowercase_       )



   def  _UpperCAmelCase (				self,    lowercase_       )							->    Optional[Any]:






     """simple docstring"""






     return (
         "This is a test",
         "This is a test",
     )



   def  _UpperCAmelCase (				self       )							->    Tuple:






     """simple docstring"""






     a__  ='''</s>'''
     a__  =0

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



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






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

     self.assertEqual(vocab_keys[0],    '''</s>'''       )
     self.assertEqual(vocab_keys[1],    '''<unk>'''       )
     self.assertEqual(vocab_keys[-1],    '''<pad>'''       )
     self.assertEqual(len(lowercase_       ),    9       )



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     self.assertEqual(self.get_tokenizer().vocab_size,    9       )



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__  =MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de"""       )
     a__  =en_de_tokenizer(['''I am a small frog'''],    return_tensors=lowercase_       )
     self.assertIsInstance(lowercase_,    lowercase_       )
     a__  =[38, 121, 14, 697, 38848, 0]
     self.assertListEqual(lowercase_,    batch.input_ids[0]       )

     a__  =tempfile.mkdtemp()
     en_de_tokenizer.save_pretrained(lowercase_       )
     a__  =[x.name for x in Path(lowercase_       ).glob('''*'''       )]
     self.assertIn('''source.spm''',    lowercase_       )
     MarianTokenizer.from_pretrained(lowercase_       )



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__  =self.get_tokenizer()

     a__  =tok(
         ['''I am a small frog''' * 1000, '''I am a small frog'''],    padding=lowercase_,    truncation=lowercase_,    return_tensors=lowercase_       )
     self.assertIsInstance(lowercase_,    lowercase_       )
     self.assertEqual(batch.input_ids.shape,    (2, 512)       )



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     a__  =self.get_tokenizer()
     a__  =tok(['''I am a tiny frog''', '''I am a small frog'''],    padding=lowercase_,    return_tensors=lowercase_       )
     self.assertIsInstance(lowercase_,    lowercase_       )
     self.assertEqual(batch_smaller.input_ids.shape,    (2, 10)       )



   @slow
   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__  ={'''input_ids''': [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], '''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, 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], [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]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowercase_,    model_name='''Helsinki-NLP/opus-mt-en-de''',    revision='''1a8c2263da11e68e50938f97e10cd57820bd504c''',    decode_kwargs={'''use_source_tokenizer''': True},    )



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     a__  =MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''       )

     a__  ='''Tämä on testi'''
     a__  ='''This is a test'''

     a__  =[76, 7, 2047, 2]
     a__  =[69, 12, 11, 940, 2]

     a__  =tokenizer(lowercase_       ).input_ids
     self.assertListEqual(lowercase_,    lowercase_       )

     a__  =tokenizer(text_target=lowercase_       ).input_ids
     self.assertListEqual(lowercase_,    lowercase_       )

     a__  =tokenizer.decode(lowercase_,    skip_special_tokens=lowercase_       )
     self.assertEqual(lowercase_,    lowercase_       )



from typing import TYPE_CHECKING

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


lowerCamelCase           =					{'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''XLNetTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''XLNetTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''XLNetForMultipleChoice''',
           '''XLNetForQuestionAnswering''',
           '''XLNetForQuestionAnsweringSimple''',
           '''XLNetForSequenceClassification''',
           '''XLNetForTokenClassification''',
           '''XLNetLMHeadModel''',
           '''XLNetModel''',
           '''XLNetPreTrainedModel''',
           '''load_tf_weights_in_xlnet''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFXLNetForMultipleChoice''',
           '''TFXLNetForQuestionAnsweringSimple''',
           '''TFXLNetForSequenceClassification''',
           '''TFXLNetForTokenClassification''',
           '''TFXLNetLMHeadModel''',
           '''TFXLNetMainLayer''',
           '''TFXLNetModel''',
           '''TFXLNetPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlnet import XLNetTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlnet_fast import XLNetTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlnet import (
                  XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLNetForMultipleChoice,
                  XLNetForQuestionAnswering,
                  XLNetForQuestionAnsweringSimple,
                  XLNetForSequenceClassification,
                  XLNetForTokenClassification,
                  XLNetLMHeadModel,
                  XLNetModel,
                  XLNetPreTrainedModel,
                  load_tf_weights_in_xlnet,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xlnet import (
                  TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXLNetForMultipleChoice,
                  TFXLNetForQuestionAnsweringSimple,
                  TFXLNetForSequenceClassification,
                  TFXLNetForTokenClassification,
                  TFXLNetLMHeadModel,
                  TFXLNetMainLayer,
                  TFXLNetModel,
                  TFXLNetPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
