

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 			_SCREAMING_SNAKE_CASE (   __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      CanineTokenizer
   lowerCamelCase__				=      False





   def      _snake_case		(       self					:			Tuple			):
       super().setUp()
       SCREAMING_SNAKE_CASE										=   CanineTokenizer()
       tokenizer.save_pretrained(self.tmpdirname			)





   @cached_property
   def      _snake_case		(       self					:			str			):
       return CanineTokenizer.from_pretrained("google/canine-s"			)





   def      _snake_case		(       self					:			Optional[int]     , **__lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE										=   self.tokenizer_class.from_pretrained(self.tmpdirname     , **__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   1024
       return tokenizer





   @require_torch
   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   self.canine_tokenizer
       SCREAMING_SNAKE_CASE										=   ["Life is like a box of chocolates.", "You never know what you're gonna get."]
       # fmt: off
       SCREAMING_SNAKE_CASE										=   [57344, 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, 57345, 0, 0, 0, 0]
       # fmt: on
       SCREAMING_SNAKE_CASE										=   tokenizer(__lowerCamelCase     , padding=__lowerCamelCase     , return_tensors="pt"			)
       self.assertIsInstance(__lowerCamelCase     , __lowerCamelCase			)

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

       self.assertListEqual(__lowerCamelCase     , __lowerCamelCase			)

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





   @require_torch
   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   self.canine_tokenizer
       SCREAMING_SNAKE_CASE										=   ["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
       SCREAMING_SNAKE_CASE										=   tokenizer(__lowerCamelCase     , padding=__lowerCamelCase     , return_tensors="pt"			)
       # check if input_ids, attention_mask and token_type_ids are returned
       self.assertIn("input_ids"     , __lowerCamelCase			)
       self.assertIn("attention_mask"     , __lowerCamelCase			)
       self.assertIn("token_type_ids"     , __lowerCamelCase			)





   @require_torch
   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   self.canine_tokenizer
       SCREAMING_SNAKE_CASE										=   [
           "What's the weater?",
           "It's about 25 degrees.",
       ]
       SCREAMING_SNAKE_CASE										=   tokenizer(
           text_target=__lowerCamelCase     , max_length=32     , padding="max_length"     , truncation=__lowerCamelCase     , return_tensors="pt"			)
       self.assertEqual(32     , targets["input_ids"].shape[1]			)





   def      _snake_case		(       self					:			List[Any]			):
       # safety check on max_len default value so we are sure the test works
       SCREAMING_SNAKE_CASE										=   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
       SCREAMING_SNAKE_CASE										=   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
               SCREAMING_SNAKE_CASE										=   tempfile.mkdtemp()

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

               SCREAMING_SNAKE_CASE										=   tokenizer.__class__.from_pretrained(__lowerCamelCase			)
               SCREAMING_SNAKE_CASE										=   after_tokenizer.encode(__lowerCamelCase     , add_special_tokens=__lowerCamelCase			)
               self.assertListEqual(__lowerCamelCase     , __lowerCamelCase			)

               shutil.rmtree(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   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
               SCREAMING_SNAKE_CASE										=   tempfile.mkdtemp()

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

               SCREAMING_SNAKE_CASE										=   tokenizer.additional_special_tokens

               # We can add a new special token for Canine as follows:
               SCREAMING_SNAKE_CASE										=   chr(0xE_007			)
               additional_special_tokens.append(__lowerCamelCase			)
               tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}			)
               SCREAMING_SNAKE_CASE										=   tokenizer.encode(__lowerCamelCase     , add_special_tokens=__lowerCamelCase			)
               tokenizer.save_pretrained(__lowerCamelCase			)

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

               SCREAMING_SNAKE_CASE										=   tokenizer.__class__.from_pretrained(__lowerCamelCase     , model_max_length=43			)
               self.assertEqual(tokenizer.model_max_length     , 43			)

               shutil.rmtree(__lowerCamelCase			)





   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   self.get_tokenizers(do_lower_case=__lowerCamelCase			)
       for tokenizer in tokenizers:
           with self.subTest(f"{tokenizer.__class__.__name__}"			):
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.get_clean_sequence(__lowerCamelCase			)

               # a special token for Canine can be defined as follows:
               SCREAMING_SNAKE_CASE										=   0xE_005
               SCREAMING_SNAKE_CASE										=   chr(__lowerCamelCase			)

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

               SCREAMING_SNAKE_CASE										=   tokenizer.decode(ids + encoded_special_token     , clean_up_tokenization_spaces=__lowerCamelCase			)
               SCREAMING_SNAKE_CASE										=   tokenizer.encode(__lowerCamelCase     , add_special_tokens=__lowerCamelCase			)

               SCREAMING_SNAKE_CASE										=   tokenizer.encode(__lowerCamelCase     , add_special_tokens=__lowerCamelCase			)
               SCREAMING_SNAKE_CASE										=   tokenizer.encode(__lowerCamelCase     , add_special_tokens=__lowerCamelCase			)
               self.assertEqual(__lowerCamelCase     , input_encoded + special_token_id			)

               SCREAMING_SNAKE_CASE										=   tokenizer.decode(__lowerCamelCase     , skip_special_tokens=__lowerCamelCase			)
               self.assertTrue(special_token not in decoded			)





   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   self.get_tokenizers(do_lower_case=__lowerCamelCase			)
       for tokenizer in tokenizers:
           with self.subTest(f"{tokenizer.__class__.__name__}"			):
               SCREAMING_SNAKE_CASE										=   chr(0xE_005			)
               SCREAMING_SNAKE_CASE										=   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=__lowerCamelCase			)
               # `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]}			)

               SCREAMING_SNAKE_CASE										=   tokenizer.tokenize(__lowerCamelCase			)
               SCREAMING_SNAKE_CASE										=   tokenizer.tokenize(__lowerCamelCase			)

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





   @require_tokenizers
   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   self.get_tokenizers(do_lower_case=__lowerCamelCase			)
       for tokenizer in tokenizers:
           with self.subTest(f"{tokenizer.__class__.__name__}"			):
               # a special token for Canine can be defined as follows:
               SCREAMING_SNAKE_CASE										=   0xE_006
               SCREAMING_SNAKE_CASE										=   chr(__lowerCamelCase			)

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

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





   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   []
       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(__lowerCamelCase			)

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

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

               # a special token for Canine can be defined as follows:
               SCREAMING_SNAKE_CASE										=   0xE_006
               SCREAMING_SNAKE_CASE										=   chr(__lowerCamelCase			)

               SCREAMING_SNAKE_CASE										=   [new_token_a]
               SCREAMING_SNAKE_CASE										=   [new_token_a]

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

               # 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
               SCREAMING_SNAKE_CASE										=   tokenizer_class.from_pretrained(__lowerCamelCase     , extra_ids=0			)
               self.assertIn(__lowerCamelCase     , 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]			)			)     , )

               SCREAMING_SNAKE_CASE										=   0xE_007
               SCREAMING_SNAKE_CASE										=   chr(__lowerCamelCase			)
               # Now we test that we can change the value of additional_special_tokens in the from_pretrained
               SCREAMING_SNAKE_CASE										=   [AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase			)]
               SCREAMING_SNAKE_CASE										=   tokenizer_class.from_pretrained(
                   __lowerCamelCase     , additional_special_tokens=__lowerCamelCase     , extra_ids=0			)

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





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

       SCREAMING_SNAKE_CASE										=   "a"
       SCREAMING_SNAKE_CASE										=   ord(__lowerCamelCase			)

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

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

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

       SCREAMING_SNAKE_CASE										=   0xE_006
       SCREAMING_SNAKE_CASE										=   chr(__lowerCamelCase			)
       setattr(__lowerCamelCase     , "additional_special_tokens_ids"     , [additional_special_token_id]			)
       self.assertListEqual(getattr(__lowerCamelCase     , "additional_special_tokens"			)     , [additional_special_token]			)
       self.assertListEqual(getattr(__lowerCamelCase     , "additional_special_tokens_ids"			)     , [additional_special_token_id]			)





   def      _snake_case		(       self					:			Optional[int]			):
       pass





   def      _snake_case		(       self					:			Any			):
       pass





   def      _snake_case		(       self					:			Optional[Any]			):
       pass





   def      _snake_case		(       self					:			Tuple			):
       pass





   def      _snake_case		(       self					:			Tuple			):
       pass





   def      _snake_case		(       self					:			Union[str, Any]			):
       pass





   def      _snake_case		(       self					:			Optional[int]			):
       pass





   def      _snake_case		(       self					:			Any			):
       pass


import pytest


__A			:					Optional[Any]						=			'__dummy_dataset1__'

__A			:					Optional[int]						=			'\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                "tokens": datasets.Sequence(datasets.Value("string")),\n                "ner_tags": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            "O",\n                            "B-PER",\n                            "I-PER",\n                            "B-ORG",\n                            "I-ORG",\n                            "B-LOC",\n                            "I-LOC",\n                        ]\n                    )\n                ),\n                "langs": datasets.Sequence(datasets.Value("string")),\n                "spans": datasets.Sequence(datasets.Value("string")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, "r", encoding="utf-8") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n'





@pytest.fixture
def 	__a     (      ):
    return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 	__a     (      ):
    return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 	__a     (      A__			:  Optional[Any]			,  A__			:  List[str]			,  A__			:  Optional[int]       ):
    SCREAMING_SNAKE_CASE										=   dataset_loading_script_name
    SCREAMING_SNAKE_CASE										=   tmp_path / "datasets" / script_name
    script_dir.mkdir(parents=A__       )
    SCREAMING_SNAKE_CASE										=   script_dir / F"{script_name}.py"
    with open(A__			,  "w"       ) as f:
        f.write(A__       )
    return str(A__       )