
"""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 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							# A mock response for an HTTP head request to emulate server down
							A     = mock.Mock()
							A     = 500
							A     = {}
							A     = HTTPError
							A     = {}

							# Download this model to make sure it's in the cache.
							A     = 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=A_   ) as mock_head:
								A     = 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  :   int   )  -> Optional[int]:
							# A mock response for an HTTP head request to emulate server down
							A     = mock.Mock()
							A     = 500
							A     = {}
							A     = HTTPError
							A     = {}

							# Download this model to make sure it's in the cache.
							A     = 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=A_   ) as mock_head:
								A     = GPTaTokenizerFast.from_pretrained('gpt2'   )
								# This check we did call the fake head request
								mock_head.assert_called()


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

								A     = AlbertTokenizer.from_pretrained(A_   )
							finally:
								os.remove(A_   )

							# 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'	,A_   )
								A     = 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  :   List[Any]   )  -> Any:
							# This test is for deprecated behavior and can be removed in v5
							A     = AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'   )







@is_staging_test
class 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   Optional[Any]   )  -> Optional[Any]:
							A     = TOKEN
							HfFolder.save_token(A_   )


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   str   )  -> List[Any]:
							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  :   Any   )  -> List[Any]:
							with tempfile.TemporaryDirectory() as tmp_dir:
								A     = os.path.join(A_	,'vocab.txt'   )
								with open(A_	,'w'	,encoding='utf-8'   ) as vocab_writer:
									vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]   )   )
								A     = BertTokenizer(A_   )

							tokenizer.push_to_hub('test-tokenizer'	,use_auth_token=self._token   )
							A     = 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(A_	,repo_id='test-tokenizer'	,push_to_hub=A_	,use_auth_token=self._token   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Tuple:
							with tempfile.TemporaryDirectory() as tmp_dir:
								A     = os.path.join(A_	,'vocab.txt'   )
								with open(A_	,'w'	,encoding='utf-8'   ) as vocab_writer:
									vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]   )   )
								A     = BertTokenizer(A_   )

							tokenizer.push_to_hub('valid_org/test-tokenizer-org'	,use_auth_token=self._token   )
							A     = 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(
								    A_	,repo_id='valid_org/test-tokenizer-org'	,push_to_hub=A_	,use_auth_token=self._token   )

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




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

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

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

								A     = BertTokenizerFast.from_pretrained(A_   )
								bert_tokenizer.save_pretrained(A_   )
								A     = CustomTokenizerFast.from_pretrained(A_   )

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

							A     = AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer'	,trust_remote_code=A_   )
							# 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     = AutoTokenizer.from_pretrained(
							    F'{USER}/test-dynamic-tokenizer'	,use_fast=A_	,trust_remote_code=A_   )
							# 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     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Dict:
							A     = 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  :   Union[str, Any]   )  -> List[Any]:
							A     = 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  :   str   )  -> List[str]:
							A     = Trie()
							trie.add('A'   )
							self.assertEqual(trie.split('ABC'   )	,['A', 'BC']   )
							self.assertEqual(trie.split('BCA'   )	,['BC', 'A']   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Union[str, Any]:
							A     = 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  :   Any   )  -> Tuple:
							A     = 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  :   Tuple   )  -> int:
							A     = Trie()
							trie.add('AB'   )
							trie.add('B'   )
							trie.add('C'   )
							self.assertEqual(trie.split('ABC'   )	,['AB', 'C']   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[Any]:
							A     = Trie()
							trie.add('ABC'   )
							trie.add('B'   )
							trie.add('CD'   )
							self.assertEqual(trie.split('ABCD'   )	,['ABC', 'D']   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> List[str]:
							# Even if the offsets are wrong, we necessarily output correct string
							# parts.
							A     = Trie()
							A     = trie.cut_text('ABC'	,[0, 0, 2, 1, 2, 3]   )
							self.assertEqual(A_	,['AB', 'C']   )



"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		HfArgumentParser(__snake_case   )
   _lowerCamelCase	:					int   =		parser.parse_args_into_dataclasses()[0]
   _lowerCamelCase	:					Dict   =		TensorFlowBenchmark(args=__snake_case   )
   try:
      _lowerCamelCase	:					Optional[int]   =		parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      _lowerCamelCase	:					Union[str, Any]   =		"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
      _lowerCamelCase	:					List[str]   =		""" """.join(str(__snake_case   ).split(""" """   )[:-1]   )
      _lowerCamelCase	:					Dict   =		""""""
      _lowerCamelCase	:					List[Any]   =		eval(str(__snake_case   ).split(""" """   )[-1]   )
      _lowerCamelCase	:					Tuple   =		[]
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]   )
         else:
            wrong_args.append(__snake_case   )
      if len(__snake_case   ) > 0:
         _lowerCamelCase	:					Tuple   =		full_error_msg + begin_error_msg + str(__snake_case   )
      raise ValueError(__snake_case   )
   benchmark.run()


if __name__ == "__main__":
 main()

