



import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



class 		_lowercase		(       _lowerCamelCase,   unittest.TestCase	):
   _UpperCAmelCase     	=			CanineTokenizer
   _UpperCAmelCase     	=			False

   def 		A							(      self			:       List[Any]							)       ->				str:


    """simple docstring"""






    super().setUp()
    a						=		CanineTokenizer()
    tokenizer.save_pretrained(self.tmpdirname							)

   @cached_property
   def 		A							(      self			:       int							)       ->				str:


    """simple docstring"""






    return CanineTokenizer.from_pretrained("google/canine-s"							)

   def 		A							(      self			:       Optional[int]							,   **__lowerCAmelCase			:       int							)       ->				CanineTokenizer:


    """simple docstring"""






    a						=		self.tokenizer_class.from_pretrained(self.tmpdirname							,   **A__							)
    a						=		1024
    return tokenizer

   @require_torch
   def 		A							(      self			:       List[Any]							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		self.canine_tokenizer
    a						=		["Life is like a box of chocolates.", "You never know what you're gonna get."]
    # fmt: off
    a						=		[5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
    # fmt: on
    a						=		tokenizer(A__							,   padding=A__							,   return_tensors="pt"							)
    self.assertIsInstance(A__							,   A__							)

    a						=		list(batch.input_ids.numpy()[0]							)

    self.assertListEqual(A__							,   A__							)

    self.assertEqual((2, 39)							,   batch.input_ids.shape							)
    self.assertEqual((2, 39)							,   batch.attention_mask.shape							)

   @require_torch
   def 		A							(      self			:       List[Any]							)       ->				List[str]:


    """simple docstring"""






    a						=		self.canine_tokenizer
    a						=		["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
    a						=		tokenizer(A__							,   padding=A__							,   return_tensors="pt"							)
    # check if input_ids, attention_mask and token_type_ids are returned
    self.assertIn("input_ids"							,   A__							)
    self.assertIn("attention_mask"							,   A__							)
    self.assertIn("token_type_ids"							,   A__							)

   @require_torch
   def 		A							(      self			:       Dict							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		self.canine_tokenizer
    a						=		[
        "What's the weater?",
        "It's about 25 degrees.",
    ]
    a						=		tokenizer(
        text_target=A__							,   max_length=32							,   padding="max_length"							,   truncation=A__							,   return_tensors="pt"							)
    self.assertEqual(32							,   targets["input_ids"].shape[1]							)

   def 		A							(      self			:       int							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		self.get_tokenizers()
    for tokenizer in tokenizers:
     with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
      self.assertNotEqual(tokenizer.model_max_length							,   42							)

        # Now let's start the test
    a						=		self.get_tokenizers()
    for tokenizer in tokenizers:
     with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
      # Isolate this from the other tests because we save additional tokens/etc
      a						=		tempfile.mkdtemp()

      a						=		" He is very happy, UNwant\u00E9d,running"
      a						=		tokenizer.encode(A__							,   add_special_tokens=A__							)
      tokenizer.save_pretrained(A__							)

      a						=		tokenizer.__class__.from_pretrained(A__							)
      a						=		after_tokenizer.encode(A__							,   add_special_tokens=A__							)
      self.assertListEqual(A__							,   A__							)

      shutil.rmtree(A__							)

    a						=		self.get_tokenizers(model_max_length=42							)
    for tokenizer in tokenizers:
     with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
      # Isolate this from the other tests because we save additional tokens/etc
      a						=		tempfile.mkdtemp()

      a						=		" He is very happy, UNwant\u00E9d,running"

      a						=		tokenizer.additional_special_tokens

      # We can add a new special token for Canine as follows:
      a						=		chr(0Xe_007							)
      additional_special_tokens.append(A__							)
      tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}							)
      a						=		tokenizer.encode(A__							,   add_special_tokens=A__							)
      tokenizer.save_pretrained(A__							)

      a						=		tokenizer.__class__.from_pretrained(A__							)
      a						=		after_tokenizer.encode(A__							,   add_special_tokens=A__							)
      self.assertListEqual(A__							,   A__							)
      self.assertIn(A__							,   after_tokenizer.additional_special_tokens							)
      self.assertEqual(after_tokenizer.model_max_length							,   42							)

      a						=		tokenizer.__class__.from_pretrained(A__							,   model_max_length=43							)
      self.assertEqual(tokenizer.model_max_length							,   43							)

      shutil.rmtree(A__							)

   def 		A							(      self			:       Dict							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		self.get_tokenizers(do_lower_case=A__							)
    for tokenizer in tokenizers:
     with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
      a		,  a						=		self.get_clean_sequence(A__							)

      # a special token for Canine can be defined as follows:
      a						=		0Xe_005
      a						=		chr(A__							)

      tokenizer.add_special_tokens({"cls_token": special_token}							)
      a						=		tokenizer.encode(A__							,   add_special_tokens=A__							)
      self.assertEqual(len(A__							)							,   1							)

      a						=		tokenizer.decode(ids + encoded_special_token							,   clean_up_tokenization_spaces=A__							)
      a						=		tokenizer.encode(A__							,   add_special_tokens=A__							)

      a						=		tokenizer.encode(A__							,   add_special_tokens=A__							)
      a						=		tokenizer.encode(A__							,   add_special_tokens=A__							)
      self.assertEqual(A__							,   input_encoded + special_token_id							)

      a						=		tokenizer.decode(A__							,   skip_special_tokens=A__							)
      self.assertTrue(special_token not in decoded							)

   def 		A							(      self			:       Optional[int]							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		self.get_tokenizers(do_lower_case=A__							)
    for tokenizer in tokenizers:
     with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
      a						=		chr(0Xe_005							)
      a						=		chr(0Xe_006							)

      # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
      tokenizer.add_tokens([SPECIAL_TOKEN_1]							,   special_tokens=A__							)
      # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
      # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
      tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]}							)

      a						=		tokenizer.tokenize(A__							)
      a						=		tokenizer.tokenize(A__							)

      self.assertEqual(len(A__							)							,   1							)
      self.assertEqual(len(A__							)							,   1							)
      self.assertEqual(token_a[0]							,   A__							)
      self.assertEqual(token_a[0]							,   A__							)

   @require_tokenizers
   def 		A							(      self			:       Dict							)       ->				int:


    """simple docstring"""






    a						=		self.get_tokenizers(do_lower_case=A__							)
    for tokenizer in tokenizers:
     with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
      # a special token for Canine can be defined as follows:
      a						=		0Xe_006
      a						=		chr(A__							)

      a						=		AddedToken(A__							,   lstrip=A__							)
      tokenizer.add_special_tokens({"additional_special_tokens": [new_token]}							)

      with tempfile.TemporaryDirectory() as tmp_dir_name:
       tokenizer.save_pretrained(A__							)
       tokenizer.from_pretrained(A__							)

   def 		A							(      self			:       List[Any]							)       ->				List[Any]:


    """simple docstring"""






    a						=		[]
    if self.test_slow_tokenizer:
     tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())							)

    if self.test_rust_tokenizer:
     tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())							)

    for tokenizer_class, tokenizer_utils in tokenizer_list:
     with tempfile.TemporaryDirectory() as tmp_dir:
      tokenizer_utils.save_pretrained(A__							)

      with open(os.path.join(A__							,   "special_tokens_map.json"							)							,   encoding="utf-8"							) as json_file:
       a						=		json.load(A__							)

      with open(os.path.join(A__							,   "tokenizer_config.json"							)							,   encoding="utf-8"							) as json_file:
       a						=		json.load(A__							)

      # a special token for Canine can be defined as follows:
      a						=		0Xe_006
      a						=		chr(A__							)

      a						=		[new_token_a]
      a						=		[new_token_a]

      with open(os.path.join(A__							,   "special_tokens_map.json"							)							,   "w"							,   encoding="utf-8"							) as outfile:
       json.dump(A__							,   A__							)
      with open(os.path.join(A__							,   "tokenizer_config.json"							)							,   "w"							,   encoding="utf-8"							) as outfile:
       json.dump(A__							,   A__							)

      # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
      # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
      # "special_tokens_map.json" files
      a						=		tokenizer_class.from_pretrained(A__							,   extra_ids=0							)
      self.assertIn(A__							,   tokenizer_without_change_in_init.additional_special_tokens							)
      # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
      self.assertEqual(
          [new_token_a]							,   tokenizer_without_change_in_init.convert_ids_to_tokens(
              tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]							)							)							,   )

      a						=		0Xe_007
      a						=		chr(A__							)
      # Now we test that we can change the value of additional_special_tokens in the from_pretrained
      a						=		[AddedToken(A__							,   lstrip=A__							)]
      a						=		tokenizer_class.from_pretrained(
          A__							,   additional_special_tokens=A__							,   extra_ids=0							)

      self.assertIn(A__							,   tokenizer.additional_special_tokens							)
      # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
      self.assertEqual(
          [new_token_a]							,   tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]							)							)							)

   @require_tokenizers
   def 		A							(      self			:       List[str]							)       ->				List[str]:


    """simple docstring"""






    a						=		self.get_tokenizers(do_lower_case=A__							)
    for tokenizer in tokenizers:
     with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
      a						=		"hello world"
      if self.space_between_special_tokens:
       a						=		"[CLS] hello world [SEP]"
      else:
       a						=		input
      a						=		tokenizer.encode(A__							,   add_special_tokens=A__							)
      a						=		tokenizer.decode(A__							,   spaces_between_special_tokens=self.space_between_special_tokens							)
      self.assertIn(A__							,   [output, output.lower()]							)

   def 		A							(      self			:       Optional[int]							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		self.get_tokenizers()
    for tokenizer in tokenizers:
     with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
      a						=		[
          "bos_token",
          "eos_token",
          "unk_token",
          "sep_token",
          "pad_token",
          "cls_token",
          "mask_token",
      ]

    a						=		"a"
    a						=		ord(A__							)

    for attr in attributes_list:
     setattr(A__							,   attr + "_id"							,   A__							)
     self.assertEqual(getattr(A__							,   A__							)							,   A__							)
     self.assertEqual(getattr(A__							,   attr + "_id"							)							,   A__							)

     setattr(A__							,   attr + "_id"							,   A__							)
     self.assertEqual(getattr(A__							,   A__							)							,   A__							)
     self.assertEqual(getattr(A__							,   attr + "_id"							)							,   A__							)

    setattr(A__							,   "additional_special_tokens_ids"							,   []							)
    self.assertListEqual(getattr(A__							,   "additional_special_tokens"							)							,   []							)
    self.assertListEqual(getattr(A__							,   "additional_special_tokens_ids"							)							,   []							)

    a						=		0Xe_006
    a						=		chr(A__							)
    setattr(A__							,   "additional_special_tokens_ids"							,   [additional_special_token_id]							)
    self.assertListEqual(getattr(A__							,   "additional_special_tokens"							)							,   [additional_special_token]							)
    self.assertListEqual(getattr(A__							,   "additional_special_tokens_ids"							)							,   [additional_special_token_id]							)

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


    """simple docstring"""






    pass

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


    """simple docstring"""






    pass

   def 		A							(      self			:       List[str]							)       ->				Optional[Any]:


    """simple docstring"""






    pass

   def 		A							(      self			:       Optional[Any]							)       ->				List[Any]:


    """simple docstring"""






    pass

   def 		A							(      self			:       Union[str, Any]							)       ->				Optional[int]:


    """simple docstring"""






    pass

   def 		A							(      self			:       List[Any]							)       ->				List[Any]:


    """simple docstring"""






    pass

   def 		A							(      self			:       Tuple							)       ->				int:


    """simple docstring"""






    pass




   def 		A							(      self			:       Tuple							)       ->				Optional[int]:


    """simple docstring"""






    pass




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 		_lowercase		(       unittest.TestCase	):

   @cached_property
   def 		A							(      self			:       List[str]							)       ->				int:


    """simple docstring"""






    a						=		tempfile.mkdtemp()
    return TatoebaConverter(save_dir=__lowerCAmelCase							)

   @slow
   def 		A							(      self			:       Optional[int]							)       ->				List[str]:


    """simple docstring"""






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




   @slow
   def 		A							(      self			:       Dict							)       ->				Any:


    """simple docstring"""






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

