






import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available

def   snake_case (*UpperCAmelCase__  )	->    int:





		if not isinstance(__lowerCAmelCase     ,   __lowerCAmelCase  ):
				UpperCamelCase_: int   				=    list(__lowerCAmelCase  )
		for i in range(len(__lowerCAmelCase  )  ):
				UpperCamelCase_: Union[str, Any]   				=    None
		gc.collect()
		if is_xpu_available():
				torch.xpu.empty_cache()
		elif is_npu_available():
				torch.npu.empty_cache()
		else:
				torch.cuda.empty_cache()
		return objects

def   snake_case (UpperCAmelCase__  )	->    bool:
		UpperCamelCase_: Tuple   				=    [
		    '''CUDA out of memory.''',  # CUDA OOM
		    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
		    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
		]
		if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase  ) and len(exception.args  ) == 1:
				return any(err in exception.args[0] for err in _statements  )
		return False

def   snake_case (UpperCAmelCase__ = None     ,   UpperCAmelCase__ = 1_2_8  )	->    Dict:





		if function is None:
				return functools.partial(__lowerCAmelCase     ,   starting_batch_size=__lowerCAmelCase  )

		UpperCamelCase_: List[Any]   				=    starting_batch_size

		def decorator(*UpperCAmelCase__     ,   **UpperCAmelCase__  ):
				nonlocal batch_size
				gc.collect()
				if is_xpu_available():
						torch.xpu.empty_cache()
				elif is_npu_available():
						torch.npu.empty_cache()
				else:
						torch.cuda.empty_cache()
				UpperCamelCase_: Union[str, Any]   				=    list(inspect.signature(__lowerCAmelCase  ).parameters.keys()  )
				# Guard against user error
				if len(__lowerCAmelCase  ) < (len(__lowerCAmelCase  ) + 1):
						UpperCamelCase_: Union[str, Any]   				=    ''', '''.join([F'''{arg}={value}''' for arg, value in zip(params[1:]     ,   args[1:]  )]  )
						raise TypeError(
						    F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
						    F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''  )
				while True:
						if batch_size == 0:
								raise RuntimeError('No executable batch size found, reached zero.'  )
						try:
								return function(__lowerCAmelCase     ,   *__lowerCAmelCase     ,   **__lowerCAmelCase  )
						except Exception as e:
								if should_reduce_batch_size(__lowerCAmelCase  ):
										gc.collect()
										if is_xpu_available():
												torch.xpu.empty_cache()
										elif is_npu_available():
												torch.npu.empty_cache()
										else:
												torch.cuda.empty_cache()
										batch_size //= 2
								else:
										raise

		return decorator







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']							)