import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


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





@require_sentencepiece
@require_tokenizers
class      A							(				UpperCAmelCase_		,				unittest.TestCase     ):
   __UpperCAmelCase : int      =			ReformerTokenizer
   __UpperCAmelCase : Tuple      =			ReformerTokenizerFast
   __UpperCAmelCase : Tuple      =			True
   __UpperCAmelCase : Optional[int]      =			False
   __UpperCAmelCase : List[str]      =			True


   def 			lowercase_      (self     :						List[Any]		)						->				List[str]:
         """simple docstring"""



         super().setUp()

         UpperCAmelCase__								=  ReformerTokenizer(__UpperCAmelCase       ,  keep_accents=__UpperCAmelCase		)
         tokenizer.save_pretrained(self.tmpdirname		)


   def 			lowercase_      (self     :						Optional[int]		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  "<s>"
         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 			lowercase_      (self     :						Optional[int]		)						->				Dict:
         """simple docstring"""



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

         self.assertEqual(vocab_keys[0]       ,  "<unk>"		)
         self.assertEqual(vocab_keys[1]       ,  "<s>"		)
         self.assertEqual(vocab_keys[-1]       ,  "j"		)
         self.assertEqual(len(__UpperCAmelCase		)       ,  1_0_0_0		)


   def 			lowercase_      (self     :						Dict		)						->				int:
         """simple docstring"""



         self.assertEqual(self.get_tokenizer().vocab_size       ,  1_0_0_0		)


   def 			lowercase_      (self     :						int		)						->				Tuple:
         """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		)


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						List[str]=1_5		)						->				List[str]:
         """simple docstring"""



         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(__UpperCAmelCase       ,  **__UpperCAmelCase		)

                     # Simple input
                     UpperCAmelCase__								=  "This is a simple input"
                     UpperCAmelCase__								=  ["This is a simple input 1", "This is a simple input 2"]
                     UpperCAmelCase__								=  ("This is a simple input", "This is a pair")
                     UpperCAmelCase__								=  [
                         ("This is a simple input 1", "This is a simple input 2"),
                         ("This is a simple pair 1", "This is a simple pair 2"),
                     ]

                     # Simple input tests
                     self.assertRaises(__UpperCAmelCase       ,  tokenizer_r.encode       ,  __UpperCAmelCase       ,  max_length=__UpperCAmelCase       ,  padding="max_length"		)

                     # Simple input
                     self.assertRaises(__UpperCAmelCase       ,  tokenizer_r.encode_plus       ,  __UpperCAmelCase       ,  max_length=__UpperCAmelCase       ,  padding="max_length"		)

                     # Simple input
                     self.assertRaises(
                         __UpperCAmelCase       ,  tokenizer_r.batch_encode_plus       ,  __UpperCAmelCase       ,  max_length=__UpperCAmelCase       ,  padding="max_length"       ,  )

                     # Pair input
                     self.assertRaises(__UpperCAmelCase       ,  tokenizer_r.encode       ,  __UpperCAmelCase       ,  max_length=__UpperCAmelCase       ,  padding="max_length"		)

                     # Pair input
                     self.assertRaises(__UpperCAmelCase       ,  tokenizer_r.encode_plus       ,  __UpperCAmelCase       ,  max_length=__UpperCAmelCase       ,  padding="max_length"		)

                     # Pair input
                     self.assertRaises(
                         __UpperCAmelCase       ,  tokenizer_r.batch_encode_plus       ,  __UpperCAmelCase       ,  max_length=__UpperCAmelCase       ,  padding="max_length"       ,  )


   def 			lowercase_      (self     :						str		)						->				Optional[Any]:
         """simple docstring"""



         pass


   def 			lowercase_      (self     :						int		)						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  ReformerTokenizer(__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		)       ,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]       ,  )

         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       ,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 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 			lowercase_      (self     :						Optional[Any]		)						->				Optional[int]:
         """simple docstring"""



         return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"		)


   @slow
   def 			lowercase_      (self     :						List[Any]		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  "Hello World!"
         UpperCAmelCase__								=  [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

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


   @slow
   def 			lowercase_      (self     :						Dict		)						->				List[Any]:
         """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"
         )
         UpperCAmelCase__								=  [
             1_0_8,
             2_6_5,
             2_4,
             1_1_1,
             4,
             2_5_8,
             1_5_6,
             3_5,
             2_8,
             2_7_5,
             3,
             2_5_9,
             2_9_7,
             2_6_0,
             8_4,
             4,
             3_5,
             1_1_0,
             4_4,
             8,
             2_5_9,
             9_1,
             2_6_8,
             2_1,
             1_1,
             2_0_9,
             2_7_4,
             1_0_9,
             2_6_6,
             2_7_7,
             1_1_7,
             8_6,
             9_3,
             3_1_5,
             2_5_8,
             2_7_8,
             2_5_8,
             2_7_7,
             2_5_8,
             0,
             2_5_8,
             2_8_8,
             2_5_8,
             3_1_9,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             2_8_7,
             2_5_8,
             3_1_5,
             2_5_8,
             2_8_9,
             2_5_8,
             2_7_8,
             9_9,
             2_6_9,
             2_6_6,
             2_6_2,
             8,
             2_5_9,
             2_4_1,
             4,
             2_1_7,
             2_3_0,
             2_6_8,
             2_6_6,
             5_5,
             1_6_8,
             1_0_6,
             7_5,
             1_9_3,
             2_6_6,
             2_2_3,
             2_7,
             4_9,
             2_6,
             2_8_2,
             2_5,
             2_6_4,
             2_9_9,
             1_9,
             2_6,
             0,
             2_5_8,
             2_7_7,
             1_1_7,
             8_6,
             9_3,
             1_7_6,
             1_8_3,
             2_7_0,
             1_1,
             2_6_2,
             4_2,
             6_1,
             2_6_5,
         ]

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


   @require_torch
   @slow
   def 			lowercase_      (self     :						str		)						->				Optional[int]:
         """simple docstring"""



         import torch

         from transformers import ReformerConfig, ReformerModel

         # Build sequence
         UpperCAmelCase__								=  list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
         UpperCAmelCase__								=  " ".join(__UpperCAmelCase		)
         UpperCAmelCase__								=  self.big_tokenizer.encode_plus(__UpperCAmelCase       ,  return_tensors="pt"		)
         UpperCAmelCase__								=  self.big_tokenizer.batch_encode_plus([sequence, sequence]       ,  return_tensors="pt"		)

         UpperCAmelCase__								=  ReformerConfig()
         # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
         UpperCAmelCase__								=  encoded_sequence["input_ids"].shape
         UpperCAmelCase__								=  ReformerModel(__UpperCAmelCase		)

         # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

         with torch.no_grad():
               model(**__UpperCAmelCase		)
               model(**__UpperCAmelCase		)





   @slow
   def 			lowercase_      (self     :						Optional[Any]		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  {"input_ids": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], "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]]}  # noqa: E501
         # fmt: on

         # This tokenizer does not know some characters like ")".
         # That is the reason why we use very simple texts here.
         # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
         UpperCAmelCase__								=  [
             "This is a very simple sentence.",
             "The quick brown fox jumps over the lazy dog.",
         ]

         self.tokenizer_integration_test_util(
             expected_encoding=__UpperCAmelCase       ,  model_name="google/reformer-crime-and-punishment"       ,  revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"       ,  padding=__UpperCAmelCase       ,  sequences=__UpperCAmelCase       ,  )




import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property





@unittest.skipUnless(os.path.exists(UpperCAmelCase_     )		,				'Tatoeba directory does not exist.'     )
class      A							(				unittest.TestCase     ):


   @cached_property
   def 			lowercase_      (self     :						Optional[int]		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  tempfile.mkdtemp()
         return TatoebaConverter(save_dir=__UpperCAmelCase		)


   @slow
   def 			lowercase_      (self     :						List[Any]		)						->				Optional[int]:
         """simple docstring"""



         self.resolver.convert_models(["heb-eng"]		)





   @slow
   def 			lowercase_      (self     :						Dict		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__		,      UpperCAmelCase__								=  self.resolver.write_model_card("opus-mt-he-en"       ,  dry_run=__UpperCAmelCase		)
         assert mmeta["long_pair"] == "heb-eng"



