





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     __snake_case       (							__lowerCamelCase   ,			unittest.TestCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     CanineTokenizer
       lowerCAmelCase__      		=     False




       def 							UpperCAmelCase__ (  self :    Any		):
         super().setUp()
         __snake_case:						str   =						CanineTokenizer()
         tokenizer.save_pretrained(self.tmpdirname		)




       @cached_property
       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         return CanineTokenizer.from_pretrained("""google/canine-s"""		)




       def 							UpperCAmelCase__ (  self :    str							,				**A :    List[str]		):
         __snake_case:						Tuple   =						self.tokenizer_class.from_pretrained(self.tmpdirname							,				**A		)
         __snake_case:						int   =						1_024
         return tokenizer




       @require_torch
       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Dict   =						self.canine_tokenizer
         __snake_case:						int   =						["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
         # fmt: off
         __snake_case:						List[str]   =						[57_344, 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, 57_345, 0, 0, 0, 0]
         # fmt: on
         __snake_case:						int   =						tokenizer(A							,				padding=A							,				return_tensors="""pt"""		)
         self.assertIsInstance(A							,				A		)

         __snake_case:						int   =						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 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Optional[Any]   =						self.canine_tokenizer
         __snake_case:						Tuple   =						["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
         __snake_case:						Any   =						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 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						int   =						self.canine_tokenizer
         __snake_case:						Optional[int]   =						[
             """What's the weater?""",
             """It's about 25 degrees.""",
         ]
         __snake_case:						str   =						tokenizer(
             text_target=A							,				max_length=32							,				padding="""max_length"""							,				truncation=A							,				return_tensors="""pt"""		)
         self.assertEqual(32							,				targets["""input_ids"""].shape[1]		)




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

             __snake_case:						List[Any]   =						""" He is very happy, UNwant\u00E9d,running"""
             __snake_case:						Optional[Any]   =						tokenizer.encode(A							,				add_special_tokens=A		)
             tokenizer.save_pretrained(A		)

             __snake_case:						int   =						tokenizer.__class__.from_pretrained(A		)
             __snake_case:						Dict   =						after_tokenizer.encode(A							,				add_special_tokens=A		)
             self.assertListEqual(A							,				A		)

             shutil.rmtree(A		)

         __snake_case:						Union[str, Any]   =						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
             __snake_case:						str   =						tempfile.mkdtemp()

             __snake_case:						Tuple   =						""" He is very happy, UNwant\u00E9d,running"""

             __snake_case:						Dict   =						tokenizer.additional_special_tokens

             # We can add a new special token for Canine as follows:
             __snake_case:						Union[str, Any]   =						chr(0xe0_07		)
             additional_special_tokens.append(A		)
             tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}		)
             __snake_case:						Optional[int]   =						tokenizer.encode(A							,				add_special_tokens=A		)
             tokenizer.save_pretrained(A		)

             __snake_case:						str   =						tokenizer.__class__.from_pretrained(A		)
             __snake_case:						List[str]   =						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		)

             __snake_case:						Tuple   =						tokenizer.__class__.from_pretrained(A							,				model_max_length=43		)
             self.assertEqual(tokenizer.model_max_length							,				43		)

             shutil.rmtree(A		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						int   =						self.get_tokenizers(do_lower_case=A		)
         for tokenizer in tokenizers:
           with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
             __snake_case  ,   __snake_case:						List[Any]   =						self.get_clean_sequence(A		)

             # a special token for Canine can be defined as follows:
             __snake_case:						List[Any]   =						0xe0_05
             __snake_case:						Optional[int]   =						chr(A		)

             tokenizer.add_special_tokens({"""cls_token""": special_token}		)
             __snake_case:						str   =						tokenizer.encode(A							,				add_special_tokens=A		)
             self.assertEqual(len(A		)							,				1		)

             __snake_case:						Tuple   =						tokenizer.decode(ids + encoded_special_token							,				clean_up_tokenization_spaces=A		)
             __snake_case:						List[str]   =						tokenizer.encode(A							,				add_special_tokens=A		)

             __snake_case:						Dict   =						tokenizer.encode(A							,				add_special_tokens=A		)
             __snake_case:						List[Any]   =						tokenizer.encode(A							,				add_special_tokens=A		)
             self.assertEqual(A							,				input_encoded + special_token_id		)

             __snake_case:						int   =						tokenizer.decode(A							,				skip_special_tokens=A		)
             self.assertTrue(special_token not in decoded		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						str   =						self.get_tokenizers(do_lower_case=A		)
         for tokenizer in tokenizers:
           with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
             __snake_case:						Any   =						chr(0xe0_05		)
             __snake_case:						List[Any]   =						chr(0xe0_06		)

             # `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]}		)

             __snake_case:						str   =						tokenizer.tokenize(A		)
             __snake_case:						int   =						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 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						Optional[int]   =						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:
             __snake_case:						Tuple   =						0xe0_06
             __snake_case:						Union[str, Any]   =						chr(A		)

             __snake_case:						Tuple   =						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 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						List[Any]   =						[]
         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:
               __snake_case:						Optional[Any]   =						json.load(A		)

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

             # a special token for Canine can be defined as follows:
             __snake_case:						str   =						0xe0_06
             __snake_case:						int   =						chr(A		)

             __snake_case:						int   =						[new_token_a]
             __snake_case:						List[Any]   =						[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
             __snake_case:						int   =						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]		)		)							,				)

             __snake_case:						int   =						0xe0_07
             __snake_case:						List[str]   =						chr(A		)
             # Now we test that we can change the value of additional_special_tokens in the from_pretrained
             __snake_case:						int   =						[AddedToken(A							,				lstrip=A		)]
             __snake_case:						Dict   =						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 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						int   =						self.get_tokenizers(do_lower_case=A		)
         for tokenizer in tokenizers:
           with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
             __snake_case:						List[str]   =						"""hello world"""
             if self.space_between_special_tokens:
               __snake_case:						Any   =						"""[CLS] hello world [SEP]"""
             else:
               __snake_case:						Dict   =						input
             __snake_case:						Any   =						tokenizer.encode(A							,				add_special_tokens=A		)
             __snake_case:						Optional[Any]   =						tokenizer.decode(A							,				spaces_between_special_tokens=self.space_between_special_tokens		)
             self.assertIn(A							,				[output, output.lower()]		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						Optional[int]   =						self.get_tokenizers()
         for tokenizer in tokenizers:
           with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
             __snake_case:						Dict   =						[
                 """bos_token""",
                 """eos_token""",
                 """unk_token""",
                 """sep_token""",
                 """pad_token""",
                 """cls_token""",
                 """mask_token""",
             ]

         __snake_case:						Optional[int]   =						"""a"""
         __snake_case:						Tuple   =						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"""		)							,				[]		)

         __snake_case:						Tuple   =						0xe0_06
         __snake_case:						Tuple   =						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 							UpperCAmelCase__ (  self :    int		):
         pass




       def 							UpperCAmelCase__ (  self :    Dict		):
         pass




       def 							UpperCAmelCase__ (  self :    Tuple		):
         pass




       def 							UpperCAmelCase__ (  self :    Tuple		):
         pass




       def 							UpperCAmelCase__ (  self :    Tuple		):
         pass




       def 							UpperCAmelCase__ (  self :    int		):
         pass




       def 							UpperCAmelCase__ (  self :    Any		):
         pass






       def 							UpperCAmelCase__ (  self :    Any		):
         pass













import pytest


__UpperCAmelCase       :   Optional[Any]				       =							"__dummy_dataset1__"

__UpperCAmelCase       :   List[str]				       =							"\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__							(    )   ->    Optional[int]:
  return DATASET_LOADING_SCRIPT_NAME







@pytest.fixture
def 			A__							(    )   ->    Tuple:
  return DATASET_LOADING_SCRIPT_CODE







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






