





'''simple docstring'''




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 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					str						=			mock.Mock()
          __A		:					List[Any]						=			500
          __A		:					Optional[int]						=			{}
          __A		:					str						=			HTTPError
          __A		:					Any						=			{}

          # Download this model to make sure it's in the cache.
          __A		:					Tuple						=			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=_UpperCAmelCase) as mock_head:
               __A		:					Optional[Any]						=			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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			mock.Mock()
          __A		:					Union[str, Any]						=			500
          __A		:					Any						=			{}
          __A		:					Tuple						=			HTTPError
          __A		:					Tuple						=			{}

          # Download this model to make sure it's in the cache.
          __A		:					Optional[int]						=			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=_UpperCAmelCase) as mock_head:
               __A		:					str						=			GPTaTokenizerFast.from_pretrained('gpt2')
               # This check we did call the fake head request
               mock_head.assert_called()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          try:
               __A		:					Any						=			tempfile.mktemp()
               with open(_UpperCAmelCase		,      'wb') as f:
                    http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'		,      _UpperCAmelCase)

               __A		:					Tuple						=			AlbertTokenizer.from_pretrained(_UpperCAmelCase)
          finally:
               os.remove(_UpperCAmelCase)

          # 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'		,      _UpperCAmelCase)
               __A		:					Any						=			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		,      1000)
               # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model')




@is_staging_test
class 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):
     lowerCAmelCase						     =			['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']


     @classmethod
     def 				SCREAMING_SNAKE_CASE    (		cls):

          '''simple docstring'''






          __A		:					List[Any]						=			TOKEN
          HfFolder.save_token(_UpperCAmelCase)


     @classmethod
     def 				SCREAMING_SNAKE_CASE    (		cls):

          '''simple docstring'''






          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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          tokenizer.push_to_hub('test-tokenizer'		,      use_auth_token=self._token)
          __A		:					Dict						=			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(_UpperCAmelCase		,      repo_id='test-tokenizer'		,      push_to_hub=_UpperCAmelCase		,      use_auth_token=self._token)

          __A		:					Any						=			BertTokenizer.from_pretrained(F'{USER}/test-tokenizer')
          self.assertDictEqual(new_tokenizer.vocab		,      tokenizer.vocab)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          tokenizer.push_to_hub('valid_org/test-tokenizer-org'		,      use_auth_token=self._token)
          __A		:					List[Any]						=			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(
                   _UpperCAmelCase		,      repo_id='valid_org/test-tokenizer-org'		,      push_to_hub=_UpperCAmelCase		,      use_auth_token=self._token)

          __A		:					str						=			BertTokenizer.from_pretrained('valid_org/test-tokenizer-org')
          self.assertDictEqual(new_tokenizer.vocab		,      tokenizer.vocab)


     @require_tokenizers
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          CustomTokenizer.register_for_auto_class()
          with tempfile.TemporaryDirectory() as tmp_dir:
               __A		:					str						=			os.path.join(_UpperCAmelCase		,      'vocab.txt')
               with open(_UpperCAmelCase		,      'w'		,      encoding='utf-8') as vocab_writer:
                    vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]))
               __A		:					Any						=			CustomTokenizer(_UpperCAmelCase)

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

          __A		:					List[Any]						=			AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer'		,      trust_remote_code=_UpperCAmelCase)
          # 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:
               __A		:					List[str]						=			os.path.join(_UpperCAmelCase		,      'vocab.txt')
               with open(_UpperCAmelCase		,      'w'		,      encoding='utf-8') as vocab_writer:
                    vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]))

               __A		:					Tuple						=			BertTokenizerFast.from_pretrained(_UpperCAmelCase)
               bert_tokenizer.save_pretrained(_UpperCAmelCase)
               __A		:					Any						=			CustomTokenizerFast.from_pretrained(_UpperCAmelCase)

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

          __A		:					Tuple						=			AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer'		,      trust_remote_code=_UpperCAmelCase)
          # 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')
          __A		:					Union[str, Any]						=			AutoTokenizer.from_pretrained(
              F'{USER}/test-dynamic-tokenizer'		,      use_fast=_UpperCAmelCase		,      trust_remote_code=_UpperCAmelCase)
          # 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 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					str						=			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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			Trie()
          trie.add('A')
          self.assertEqual(trie.split('ABC')		,      ['A', 'BC'])
          self.assertEqual(trie.split('BCA')		,      ['BC', 'A'])


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			Trie()
          trie.add('AB')
          trie.add('B')
          trie.add('C')
          self.assertEqual(trie.split('ABC')		,      ['AB', 'C'])


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[Any]						=			Trie()
          trie.add('ABC')
          trie.add('B')
          trie.add('CD')
          self.assertEqual(trie.split('ABCD')		,      ['ABC', 'D'])


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			Trie()
          __A		:					Optional[int]						=			trie.cut_text('ABC'		,      [0, 0, 2, 1, 2, 3])
          self.assertEqual(_UpperCAmelCase		,      ['AB', 'C'])


import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def      A__					(			__A  :						List[str]			)					->str:
      __A    =[]
      for line in lines:
            __A    =re.sub(r'''#.*'''     ,				''''''     ,				__A			)  # remove comments
            if line:
                  filtered_lines.append(__A			)
      __A    ='''\n'''.join(__A			)

      # Make a hash from all this code
      __A    =full_str.encode('''utf-8'''			)
      return shaaaa(__A			).hexdigest()


# get importable module names and hash for caching
_lowerCamelCase      :					Dict					    =      {
    '''csv''': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    '''json''': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    '''pandas''': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    '''parquet''': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    '''arrow''': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    '''text''': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    '''imagefolder''': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    '''audiofolder''': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_lowerCamelCase      :					int					    =      {
    '''.csv''': ('''csv''', {}),
    '''.tsv''': ('''csv''', {'''sep''': '''\t'''}),
    '''.json''': ('''json''', {}),
    '''.jsonl''': ('''json''', {}),
    '''.parquet''': ('''parquet''', {}),
    '''.arrow''': ('''arrow''', {}),
    '''.txt''': ('''text''', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_lowerCamelCase      :					List[str]					    =      {'''imagefolder''', '''audiofolder'''}

# Used to filter data files based on extensions given a module name
_lowerCamelCase      :					Dict[str, List[str]]					    =      {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('''.zip''')


_MODULE_TO_EXTENSIONS["audiofolder"].append('''.zip''')


