






import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
   from test_module.custom_tokenization_fast import CustomTokenizerFast




class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       def       _a   ( self							):
         # A mock response for an HTTP head request to emulate server down
         UpperCamelCase_: Any   				=    mock.Mock()
         UpperCamelCase_: Dict   				=    5_0_0
         UpperCamelCase_: Any   				=    {}
         UpperCamelCase_: Tuple   				=    HTTPError
         UpperCamelCase_: List[str]   				=    {}

         # Download this model to make sure it's in the cache.
         UpperCamelCase_: int   				=    BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'							)

         # Under the mock environment we get a 500 error when trying to reach the tokenizer.
         with mock.patch('requests.Session.request'  ,      return_value=_lowerCamelCase							) as mock_head:
           UpperCamelCase_: Optional[int]   				=    BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'							)
           # This check we did call the fake head request
           mock_head.assert_called()





       @require_tokenizers
       def       _a   ( self							):
         # A mock response for an HTTP head request to emulate server down
         UpperCamelCase_: Union[str, Any]   				=    mock.Mock()
         UpperCamelCase_: Union[str, Any]   				=    5_0_0
         UpperCamelCase_: str   				=    {}
         UpperCamelCase_: List[str]   				=    HTTPError
         UpperCamelCase_: Optional[int]   				=    {}

         # Download this model to make sure it's in the cache.
         UpperCamelCase_: List[str]   				=    GPTaTokenizerFast.from_pretrained('gpt2'							)

         # Under the mock environment we get a 500 error when trying to reach the tokenizer.
         with mock.patch('requests.Session.request'  ,      return_value=_lowerCamelCase							) as mock_head:
           UpperCamelCase_: str   				=    GPTaTokenizerFast.from_pretrained('gpt2'							)
           # This check we did call the fake head request
           mock_head.assert_called()





       def       _a   ( self							):
         # This test is for deprecated behavior and can be removed in v5
         try:
           UpperCamelCase_: Optional[int]   				=    tempfile.mktemp()
           with open(_lowerCamelCase  ,      'wb'							) as f:
             http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'  ,      _lowerCamelCase							)

           UpperCamelCase_: Tuple   				=    AlbertTokenizer.from_pretrained(_lowerCamelCase							)
         finally:
           os.remove(_lowerCamelCase							)

         # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
         # the current folder and have the right name.
         if os.path.isfile('tokenizer.json'							):
           # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
           return
         try:
           with open('tokenizer.json'  ,      'wb'							) as f:
             http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json'  ,      _lowerCamelCase							)
           UpperCamelCase_: List[str]   				=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
           # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
           self.assertEqual(tokenizer.vocab_size  ,      1_0_0_0							)
           # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

         finally:
           os.remove('tokenizer.json'							)





       def       _a   ( self							):
         # This test is for deprecated behavior and can be removed in v5
         UpperCamelCase_: Any   				=    AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'							)







@is_staging_test
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       a    :		Dict									=['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']





       @classmethod
       def       _a   ( cls							):
         UpperCamelCase_: Optional[int]   				=    TOKEN
         HfFolder.save_token(_lowerCamelCase							)





       @classmethod
       def       _a   ( cls							):
         try:
           delete_repo(token=cls._token  ,      repo_id='test-tokenizer'							)
         except HTTPError:
           pass

         try:
           delete_repo(token=cls._token  ,      repo_id='valid_org/test-tokenizer-org'							)
         except HTTPError:
           pass

         try:
           delete_repo(token=cls._token  ,      repo_id='test-dynamic-tokenizer'							)
         except HTTPError:
           pass





       def       _a   ( self							):
         with tempfile.TemporaryDirectory() as tmp_dir:
           UpperCamelCase_: List[Any]   				=    os.path.join(_lowerCamelCase  ,      'vocab.txt'							)
           with open(_lowerCamelCase  ,      'w'  ,      encoding='utf-8'							) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]							)							)
           UpperCamelCase_: int   				=    BertTokenizer(_lowerCamelCase							)

         tokenizer.push_to_hub('test-tokenizer'  ,      use_auth_token=self._token							)
         UpperCamelCase_: Union[str, Any]   				=    BertTokenizer.from_pretrained(f'''{USER}/test-tokenizer'''							)
         self.assertDictEqual(new_tokenizer.vocab  ,      tokenizer.vocab							)

         # Reset repo
         delete_repo(token=self._token  ,      repo_id='test-tokenizer'							)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer.save_pretrained(_lowerCamelCase  ,      repo_id='test-tokenizer'  ,      push_to_hub=_lowerCamelCase  ,      use_auth_token=self._token							)

         UpperCamelCase_: List[str]   				=    BertTokenizer.from_pretrained(f'''{USER}/test-tokenizer'''							)
         self.assertDictEqual(new_tokenizer.vocab  ,      tokenizer.vocab							)





       def       _a   ( self							):
         with tempfile.TemporaryDirectory() as tmp_dir:
           UpperCamelCase_: Optional[Any]   				=    os.path.join(_lowerCamelCase  ,      'vocab.txt'							)
           with open(_lowerCamelCase  ,      'w'  ,      encoding='utf-8'							) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]							)							)
           UpperCamelCase_: Union[str, Any]   				=    BertTokenizer(_lowerCamelCase							)

         tokenizer.push_to_hub('valid_org/test-tokenizer-org'  ,      use_auth_token=self._token							)
         UpperCamelCase_: Dict   				=    BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'							)
         self.assertDictEqual(new_tokenizer.vocab  ,      tokenizer.vocab							)

         # Reset repo
         delete_repo(token=self._token  ,      repo_id='valid_org/test-tokenizer-org'							)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer.save_pretrained(
               _lowerCamelCase  ,      repo_id='valid_org/test-tokenizer-org'  ,      push_to_hub=_lowerCamelCase  ,      use_auth_token=self._token							)

         UpperCamelCase_: Optional[Any]   				=    BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'							)
         self.assertDictEqual(new_tokenizer.vocab  ,      tokenizer.vocab							)





       @require_tokenizers
       def       _a   ( self							):
         CustomTokenizer.register_for_auto_class()
         with tempfile.TemporaryDirectory() as tmp_dir:
           UpperCamelCase_: Optional[Any]   				=    os.path.join(_lowerCamelCase  ,      'vocab.txt'							)
           with open(_lowerCamelCase  ,      'w'  ,      encoding='utf-8'							) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]							)							)
           UpperCamelCase_: Optional[int]   				=    CustomTokenizer(_lowerCamelCase							)

         # No fast custom tokenizer
         tokenizer.push_to_hub('test-dynamic-tokenizer'  ,      use_auth_token=self._token							)

         UpperCamelCase_: str   				=    AutoTokenizer.from_pretrained(f'''{USER}/test-dynamic-tokenizer'''  ,      trust_remote_code=_lowerCamelCase							)
         # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
         self.assertEqual(tokenizer.__class__.__name__  ,      'CustomTokenizer'							)

         # Fast and slow custom tokenizer
         CustomTokenizerFast.register_for_auto_class()
         with tempfile.TemporaryDirectory() as tmp_dir:
           UpperCamelCase_: Optional[Any]   				=    os.path.join(_lowerCamelCase  ,      'vocab.txt'							)
           with open(_lowerCamelCase  ,      'w'  ,      encoding='utf-8'							) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]							)							)

           UpperCamelCase_: Dict   				=    BertTokenizerFast.from_pretrained(_lowerCamelCase							)
           bert_tokenizer.save_pretrained(_lowerCamelCase							)
           UpperCamelCase_: List[str]   				=    CustomTokenizerFast.from_pretrained(_lowerCamelCase							)

         tokenizer.push_to_hub('test-dynamic-tokenizer'  ,      use_auth_token=self._token							)

         UpperCamelCase_: Tuple   				=    AutoTokenizer.from_pretrained(f'''{USER}/test-dynamic-tokenizer'''  ,      trust_remote_code=_lowerCamelCase							)
         # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
         self.assertEqual(tokenizer.__class__.__name__  ,      'CustomTokenizerFast'							)
         UpperCamelCase_: int   				=    AutoTokenizer.from_pretrained(
             f'''{USER}/test-dynamic-tokenizer'''  ,      use_fast=_lowerCamelCase  ,      trust_remote_code=_lowerCamelCase							)
         # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
         self.assertEqual(tokenizer.__class__.__name__  ,      'CustomTokenizer'							)







class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    Trie()
         trie.add('Hello 友達'							)
         self.assertEqual(trie.data  ,      {'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}}							)
         trie.add('Hello'							)
         trie.data
         self.assertEqual(trie.data  ,      {'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}}							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    Trie()
         self.assertEqual(trie.split('[CLS] This is a extra_id_100'							)  ,      ['[CLS] This is a extra_id_100']							)
         trie.add('[CLS]'							)
         trie.add('extra_id_1'							)
         trie.add('extra_id_100'							)
         self.assertEqual(trie.split('[CLS] This is a extra_id_100'							)  ,      ['[CLS]', ' This is a ', 'extra_id_100']							)





       def       _a   ( self							):
         UpperCamelCase_: int   				=    Trie()
         trie.add('A'							)
         self.assertEqual(trie.split('ABC'							)  ,      ['A', 'BC']							)
         self.assertEqual(trie.split('BCA'							)  ,      ['BC', 'A']							)





       def       _a   ( self							):
         UpperCamelCase_: str   				=    Trie()
         trie.add('TOKEN]'							)
         trie.add('[SPECIAL_TOKEN]'							)
         self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'							)  ,      ['This is something ', '[SPECIAL_TOKEN]']							)





       def       _a   ( self							):
         UpperCamelCase_: Union[str, Any]   				=    Trie()
         trie.add('A'							)
         trie.add('P'							)
         trie.add('[SPECIAL_TOKEN]'							)
         self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'							)  ,      ['This is something ', '[SPECIAL_TOKEN]']							)





       def       _a   ( self							):
         UpperCamelCase_: List[str]   				=    Trie()
         trie.add('AB'							)
         trie.add('B'							)
         trie.add('C'							)
         self.assertEqual(trie.split('ABC'							)  ,      ['AB', 'C']							)





       def       _a   ( self							):
         UpperCamelCase_: List[str]   				=    Trie()
         trie.add('ABC'							)
         trie.add('B'							)
         trie.add('CD'							)
         self.assertEqual(trie.split('ABCD'							)  ,      ['ABC', 'D']							)





       def       _a   ( self							):
         # Even if the offsets are wrong, we necessarily output correct string
         # parts.
         UpperCamelCase_: Union[str, Any]   				=    Trie()
         UpperCamelCase_: Any   				=    trie.cut_text('ABC'  ,      [0, 0, 2, 1, 2, 3]							)
         self.assertEqual(_lowerCamelCase  ,      ['AB', 'C']							)







import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[Any]									=CLIPTokenizer
       a    :		List[Any]									=CLIPTokenizerFast
       a    :		str									=True
       a    :		List[Any]									={}
       a    :		str									=False





       def       _a   ( self							):
         super().setUp()

         # fmt: off
         UpperCamelCase_: str   				=    ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
         # fmt: on
         UpperCamelCase_: Any   				=    dict(zip(_lowerCamelCase  ,      range(len(_lowerCamelCase							)							)							)							)
         UpperCamelCase_: Optional[int]   				=    ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
         UpperCamelCase_: Any   				=    {'unk_token': '<unk>'}

         UpperCamelCase_: List[Any]   				=    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['vocab_file']							)
         UpperCamelCase_: Optional[int]   				=    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['merges_file']							)
         with open(self.vocab_file  ,      'w'  ,      encoding='utf-8'							) as fp:
           fp.write(json.dumps(_lowerCamelCase							) + '\n'							)
         with open(self.merges_file  ,      'w'  ,      encoding='utf-8'							) as fp:
           fp.write('\n'.join(_lowerCamelCase							)							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         kwargs.update(self.special_tokens_map							)
         return CLIPTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         kwargs.update(self.special_tokens_map							)
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Union[str, Any]   				=    'lower newer'
         UpperCamelCase_: Tuple   				=    'lower newer'
         return input_text, output_text





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    CLIPTokenizer(self.vocab_file  ,      self.merges_file  ,      **self.special_tokens_map							)
         UpperCamelCase_: Optional[int]   				=    'lower newer'
         UpperCamelCase_: Any   				=    ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
         UpperCamelCase_: Union[str, Any]   				=    tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: str   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Optional[Any]   				=    [1_0, 2, 1_6, 9, 3, 2, 1_6, 2_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       @require_ftfy
       def       _a   ( self							):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             UpperCamelCase_: Optional[int]   				=    self.tokenizer_class.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)
             UpperCamelCase_: List[str]   				=    self.rust_tokenizer_class.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)

             UpperCamelCase_: Optional[int]   				=    'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
             UpperCamelCase_: Tuple   				=    tokenizer_s.tokenize(_lowerCamelCase							)
             UpperCamelCase_: Any   				=    tokenizer_r.tokenize(_lowerCamelCase							)

             self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

             # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
             # with Tilde) encoded in 2 different ways
             UpperCamelCase_: List[str]   				=    'xa\u0303y' + ' ' + 'x\xe3y'
             UpperCamelCase_: List[Any]   				=    tokenizer_s.tokenize(_lowerCamelCase							)
             UpperCamelCase_: int   				=    tokenizer_r.tokenize(_lowerCamelCase							)

             self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

             # Test that the tokenization is identical on unicode of space type
             UpperCamelCase_: Dict   				=    [
                 '\u0009',  # (horizontal tab, '\t')
                 '\u000B',  # (vertical tab)
                 '\u000C',  # (form feed)
                 '\u0020',  # (space, ' ')
                 '\u200E',  # (left-to-right mark):w
                 '\u200F',  # (right-to-left mark)
             ]
             for unicode_seq in spaces_unicodes:
               UpperCamelCase_: int   				=    tokenizer_s.tokenize(_lowerCamelCase							)
               UpperCamelCase_: Dict   				=    tokenizer_r.tokenize(_lowerCamelCase							)

               self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

             # Test that the tokenization is identical on unicode of line break type
             UpperCamelCase_: List[str]   				=    [
                 '\u000A',  # (line feed, '\n')
                 '\r\n',  # (carriage return and line feed, '\r\n')
                 '\u000D',  # (carriage return, '\r')
                 '\r',  # (carriage return, '\r')
                 '\u000D',  # (carriage return, '\r')
                 '\u2028',  # (line separator)
                 '\u2029',  # (paragraph separator)
                 # "\u0085", # (next line)
             ]

             # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
             # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
             # space (and thus into an empty list).

             for unicode_seq in line_break_unicodes:
               UpperCamelCase_: Optional[Any]   				=    tokenizer_s.tokenize(_lowerCamelCase							)
               UpperCamelCase_: str   				=    tokenizer_r.tokenize(_lowerCamelCase							)

               self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self							):
         # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             UpperCamelCase_: str   				=    'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
             UpperCamelCase_: str   				=    f'''{text_of_1_token} {text_of_1_token}'''

             UpperCamelCase_: Optional[Any]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      )
             UpperCamelCase_: int   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (len(_lowerCamelCase							) + 1, len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: List[str]   				=    f''' {text}'''

             UpperCamelCase_: str   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      )
             UpperCamelCase_: Dict   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (1, 1 + len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (1 + len(_lowerCamelCase							) + 1, 1 + len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )





       def       _a   ( self							):
         # Test related to the breaking change introduced in transformers v4.17.0
         # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
         with self.assertRaises(_lowerCamelCase							) as context:
           self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer'							)

         self.assertTrue(
             context.exception.args[0].startswith(
                 'The `backend_tokenizer` provided does not match the expected format.'							)							)





       @require_ftfy
       def       _a   ( self							):
         super().test_tokenization_python_rust_equals()





       def       _a   ( self							):
         # CLIP always lower cases letters
         pass