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


__lowerCamelCase	:					Union[str, Any]			  =					get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
@require_tokenizers
class  A__       (	__snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :Tuple						   =       XLMRobertaTokenizer
      _UpperCAmelCase     :Tuple						   =       XLMRobertaTokenizerFast
      _UpperCAmelCase     :List[Any]						   =       True
      _UpperCAmelCase     :List[Any]						   =       True





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase     : List[str]	     =       XLMRobertaTokenizer(_lowerCAmelCase	,							keep_accents=_lowerCAmelCase       )
       tokenizer.save_pretrained(self.tmpdirname       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       "<pad>"
       UpperCamelCase     : Any	     =       1

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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       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 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       XLMRobertaTokenizer(_lowerCAmelCase	,							keep_accents=_lowerCAmelCase       )

       UpperCamelCase     : Optional[int]	     =       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]]	,							)

       UpperCamelCase     : str	     =       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",
               ".",
           ]	,							)
       UpperCamelCase     : List[str]	     =       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 ^
           ]	,							)

       UpperCamelCase     : Optional[int]	     =       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 	__UpperCamelCase(			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

       UpperCamelCase     : Optional[Any]	     =       (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     : Optional[int]	     =       self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase	,							**_lowerCAmelCase       )
         UpperCamelCase     : Dict	     =       self.tokenizer_class.from_pretrained(_lowerCAmelCase	,							**_lowerCAmelCase       )

         UpperCamelCase     : List[Any]	     =       tempfile.mkdtemp()

         UpperCamelCase     : Any	     =       tokenizer_r.save_pretrained(_lowerCAmelCase       )
         UpperCamelCase     : str	     =       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       )       )
         UpperCamelCase     : Union[str, Any]	     =       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
         UpperCamelCase     : Optional[Any]	     =       tokenizer_r.from_pretrained(_lowerCAmelCase       )
         UpperCamelCase     : List[str]	     =       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
         UpperCamelCase     : str	     =       tempfile.mkdtemp()

         UpperCamelCase     : Optional[int]	     =       tokenizer_r.save_pretrained(_lowerCAmelCase	,							legacy_format=_lowerCAmelCase       )
         UpperCamelCase     : Any	     =       tokenizer_p.save_pretrained(_lowerCAmelCase       )

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

         # Checks everything loads correctly in the same way
         UpperCamelCase     : Dict	     =       tokenizer_r.from_pretrained(_lowerCAmelCase       )
         UpperCamelCase     : Dict	     =       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
         UpperCamelCase     : Optional[int]	     =       tempfile.mkdtemp()

         UpperCamelCase     : Optional[int]	     =       tokenizer_r.save_pretrained(_lowerCAmelCase	,							legacy_format=_lowerCAmelCase       )
         UpperCamelCase     : Dict	     =       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
         UpperCamelCase     : List[str]	     =       tokenizer_r.from_pretrained(_lowerCAmelCase       )
         UpperCamelCase     : Dict	     =       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 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       with tempfile.NamedTemporaryFile() as f:
        shutil.copyfile(_lowerCAmelCase	,							f.name       )
        UpperCamelCase     : List[Any]	     =       XLMRobertaTokenizer(f.name	,							keep_accents=_lowerCAmelCase       )
        UpperCamelCase     : Optional[int]	     =       pickle.dumps(_lowerCAmelCase       )
       pickle.loads(_lowerCAmelCase       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       if not self.test_rust_tokenizer:
        return

       UpperCamelCase     : Dict	     =       self.get_tokenizer()
       UpperCamelCase     : Any	     =       self.get_rust_tokenizer()

       UpperCamelCase     : Any	     =       "I was born in 92000, and this is falsé."

       UpperCamelCase     : Any	     =       tokenizer.tokenize(_lowerCAmelCase       )
       UpperCamelCase     : Tuple	     =       rust_tokenizer.tokenize(_lowerCAmelCase       )
       self.assertListEqual(_lowerCAmelCase	,							_lowerCAmelCase       )

       UpperCamelCase     : Dict	     =       tokenizer.encode(_lowerCAmelCase	,							add_special_tokens=_lowerCAmelCase       )
       UpperCamelCase     : str	     =       rust_tokenizer.encode(_lowerCAmelCase	,							add_special_tokens=_lowerCAmelCase       )
       self.assertListEqual(_lowerCAmelCase	,							_lowerCAmelCase       )

       UpperCamelCase     : Optional[int]	     =       self.get_rust_tokenizer()
       UpperCamelCase     : Union[str, Any]	     =       tokenizer.encode(_lowerCAmelCase       )
       UpperCamelCase     : List[str]	     =       rust_tokenizer.encode(_lowerCAmelCase       )
       self.assertListEqual(_lowerCAmelCase	,							_lowerCAmelCase       )





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       "Hello World!"
       UpperCamelCase     : List[Any]	     =       [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(_lowerCAmelCase	,							self.big_tokenizer.encode(_lowerCAmelCase       )       )





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       (
           "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     : Tuple	     =       [
           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(_lowerCAmelCase	,							self.big_tokenizer.encode(_lowerCAmelCase       )       )





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       {"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=_lowerCAmelCase	,							model_name="xlm-roberta-base"	,							revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"	,							)







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import tensorflow as tf

    from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class  A__       (	unittest.TestCase      ):





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       )
       UpperCamelCase     : Tuple	     =       AutoTokenizer.from_pretrained("google/mt5-small"       )

       UpperCamelCase     : Dict	     =       tokenizer("Hello there"	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : int	     =       tokenizer("Hi I am"	,							return_tensors="tf"       ).input_ids

       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							labels=A_       ).loss
       UpperCamelCase     : List[str]	     =       -tf.math.reduce_mean(A_       ).numpy()

       UpperCamelCase     : Union[str, Any]	     =       -21.22_81_68
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 2e-4       )
