





"""simple docstring"""




import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
						from test_module.custom_tokenization_fast import CustomTokenizerFast




class 			_a						(				unittest.TestCase    ):






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   0






				@slow
				def     snake_case     (			self		:   Dict							)     ->				Union[str, Any]:




										'''simple docstring'''



										for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
																_UpperCamelCase      : List[str]         =   AutoTokenizer.from_pretrained(lowerCAmelCase__							)
																self.assertIsNotNone(lowerCAmelCase__							)
																self.assertIsInstance(lowerCAmelCase__,			(BertTokenizer, BertTokenizerFast)							)
																self.assertGreater(len(lowerCAmelCase__							),			0							)

										for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
																_UpperCamelCase      : Optional[Any]         =   AutoTokenizer.from_pretrained(lowerCAmelCase__							)
																self.assertIsNotNone(lowerCAmelCase__							)
																self.assertIsInstance(lowerCAmelCase__,			(GPTaTokenizer, GPTaTokenizerFast)							)
																self.assertGreater(len(lowerCAmelCase__							),			0							)






				def     snake_case     (			self		:   Union[str, Any]							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   AutoTokenizer.from_pretrained(lowerCAmelCase__							)
										self.assertIsInstance(lowerCAmelCase__,			(BertTokenizer, BertTokenizerFast)							)
										self.assertEqual(tokenizer.vocab_size,			1_2							)






				def     snake_case     (			self		:   List[Any]							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   AutoTokenizer.from_pretrained(lowerCAmelCase__							)
										self.assertIsInstance(lowerCAmelCase__,			(RobertaTokenizer, RobertaTokenizerFast)							)
										self.assertEqual(tokenizer.vocab_size,			2_0							)






				def     snake_case     (			self		:   List[Any]							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   AutoConfig.from_pretrained(lowerCAmelCase__							)
										self.assertIsInstance(lowerCAmelCase__,			lowerCAmelCase__							)
										# Check that tokenizer_type ≠ model_type
										_UpperCamelCase      : Optional[int]         =   AutoTokenizer.from_pretrained(lowerCAmelCase__,			config=lowerCAmelCase__							)
										self.assertIsInstance(lowerCAmelCase__,			(BertTokenizer, BertTokenizerFast)							)
										self.assertEqual(tokenizer.vocab_size,			1_2							)






				def     snake_case     (			self		:   Tuple							)     ->				List[Any]:




										'''simple docstring'''



										with tempfile.TemporaryDirectory() as tmp_dir:
																shutil.copy('''./tests/fixtures/vocab.txt''',			os.path.join(lowerCAmelCase__,			'''vocab.txt'''							)							)

																_UpperCamelCase      : Tuple         =   AutoTokenizer.from_pretrained(lowerCAmelCase__,			tokenizer_type='''bert''',			use_fast=lowerCAmelCase__							)
																self.assertIsInstance(lowerCAmelCase__,			lowerCAmelCase__							)

										with tempfile.TemporaryDirectory() as tmp_dir:
																shutil.copy('''./tests/fixtures/vocab.json''',			os.path.join(lowerCAmelCase__,			'''vocab.json'''							)							)
																shutil.copy('''./tests/fixtures/merges.txt''',			os.path.join(lowerCAmelCase__,			'''merges.txt'''							)							)

																_UpperCamelCase      : Tuple         =   AutoTokenizer.from_pretrained(lowerCAmelCase__,			tokenizer_type='''gpt2''',			use_fast=lowerCAmelCase__							)
																self.assertIsInstance(lowerCAmelCase__,			lowerCAmelCase__							)






				@require_tokenizers
				def     snake_case     (			self		:   int							)     ->				List[str]:




										'''simple docstring'''



										with tempfile.TemporaryDirectory() as tmp_dir:
																shutil.copy('''./tests/fixtures/vocab.txt''',			os.path.join(lowerCAmelCase__,			'''vocab.txt'''							)							)

																_UpperCamelCase      : str         =   AutoTokenizer.from_pretrained(lowerCAmelCase__,			tokenizer_type='''bert'''							)
																self.assertIsInstance(lowerCAmelCase__,			lowerCAmelCase__							)

										with tempfile.TemporaryDirectory() as tmp_dir:
																shutil.copy('''./tests/fixtures/vocab.json''',			os.path.join(lowerCAmelCase__,			'''vocab.json'''							)							)
																shutil.copy('''./tests/fixtures/merges.txt''',			os.path.join(lowerCAmelCase__,			'''merges.txt'''							)							)

																_UpperCamelCase      : List[str]         =   AutoTokenizer.from_pretrained(lowerCAmelCase__,			tokenizer_type='''gpt2'''							)
																self.assertIsInstance(lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   List[str]							)     ->				int:




										'''simple docstring'''



										with pytest.raises(lowerCAmelCase__							):
																AutoTokenizer.from_pretrained('''./''',			tokenizer_type='''xxx'''							)






				@require_tokenizers
				def     snake_case     (			self		:   int							)     ->				List[Any]:




										'''simple docstring'''



										for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
																_UpperCamelCase      : Optional[Any]         =   tokenizer_class.from_pretrained('''wietsedv/bert-base-dutch-cased'''							)
																self.assertIsInstance(lowerCAmelCase__,			(BertTokenizer, BertTokenizerFast)							)

																if isinstance(lowerCAmelCase__,			lowerCAmelCase__							):
																						self.assertEqual(tokenizer.basic_tokenizer.do_lower_case,			lowerCAmelCase__							)
																else:
																						self.assertEqual(tokenizer.do_lower_case,			lowerCAmelCase__							)

																self.assertEqual(tokenizer.model_max_length,			5_1_2							)






				@require_tokenizers
				def     snake_case     (			self		:   List[str]							)     ->				Optional[Any]:




										'''simple docstring'''



										for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
																with self.assertRaisesRegex(
																    lowerCAmelCase__,			'''julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier''',			):
																						_UpperCamelCase      : Dict         =   tokenizer_class.from_pretrained('''julien-c/herlolip-not-exists'''							)






				def     snake_case     (			self		:   Optional[int]							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   TOKENIZER_MAPPING.values()
										_UpperCamelCase      : List[str]         =   []

										for slow_tok, fast_tok in tokenizers:
																if slow_tok is not None:
																						tokenizer_names.append(slow_tok.__name__							)

																if fast_tok is not None:
																						tokenizer_names.append(fast_tok.__name__							)

										for tokenizer_name in tokenizer_names:
																# must find the right class
																tokenizer_class_from_name(lowerCAmelCase__							)






				@require_tokenizers
				def     snake_case     (			self		:   str							)     ->				int:




										'''simple docstring'''



										self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased''',			use_fast=lowerCAmelCase__							),			lowerCAmelCase__							)
										self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased'''							),			lowerCAmelCase__							)






				@require_tokenizers
				def     snake_case     (			self		:   Tuple							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   AutoTokenizer.from_pretrained('''distilbert-base-uncased''',			do_lower_case=lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   '''Hello, world. How are you?'''
										_UpperCamelCase      : Tuple         =   tokenizer.tokenize(lowerCAmelCase__							)
										self.assertEqual('''[UNK]''',			tokens[0]							)

										_UpperCamelCase      : Tuple         =   AutoTokenizer.from_pretrained('''microsoft/mpnet-base''',			do_lower_case=lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   tokenizer.tokenize(lowerCAmelCase__							)
										self.assertEqual('''[UNK]''',			tokens[0]							)






				@require_tokenizers
				def     snake_case     (			self		:   Dict							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   AutoTokenizer.from_pretrained('''robot-test/dummy-tokenizer-fast-with-model-config'''							)
										self.assertEqual(type(lowerCAmelCase__							),			lowerCAmelCase__							)
										self.assertEqual(tokenizer.model_max_length,			5_1_2							)
										self.assertEqual(tokenizer.vocab_size,			3_0_0_0_0							)
										self.assertEqual(tokenizer.unk_token,			'''[UNK]'''							)
										self.assertEqual(tokenizer.padding_side,			'''right'''							)
										self.assertEqual(tokenizer.truncation_side,			'''right'''							)






				def     snake_case     (			self		:   str							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   AutoTokenizer.from_pretrained(lowerCAmelCase__							)
										self.assertIsInstance(lowerCAmelCase__,			(BertTokenizer, BertTokenizerFast)							)
										with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer.save_pretrained(lowerCAmelCase__							)
																_UpperCamelCase      : Union[str, Any]         =   AutoTokenizer.from_pretrained(lowerCAmelCase__							)

										self.assertIsInstance(lowerCAmelCase__,			tokenizer.__class__							)
										self.assertEqual(tokenizera.vocab_size,			1_2							)






				def     snake_case     (			self		:   str							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   AutoTokenizer.from_pretrained('''ctrl'''							)
										# There is no fast CTRL so this always gives us a slow tokenizer.
										self.assertIsInstance(lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   Any							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   get_tokenizer_config('''bert-base-cased'''							)
										_UpperCamelCase      : List[Any]         =   config.pop('''_commit_hash''',			lowerCAmelCase__							)
										# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
										self.assertEqual(lowerCAmelCase__,			{'''do_lower_case''': False}							)

										# This model does not have a tokenizer_config so we get back an empty dict.
										_UpperCamelCase      : List[str]         =   get_tokenizer_config(lowerCAmelCase__							)
										self.assertDictEqual(lowerCAmelCase__,			{}							)

										# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
										_UpperCamelCase      : Tuple         =   AutoTokenizer.from_pretrained(lowerCAmelCase__							)
										with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer.save_pretrained(lowerCAmelCase__							)
																_UpperCamelCase      : Optional[int]         =   get_tokenizer_config(lowerCAmelCase__							)

										# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
										self.assertEqual(config['''tokenizer_class'''],			'''BertTokenizer'''							)






				def     snake_case     (			self		:   int							)     ->				Dict:




										'''simple docstring'''



										try:
																AutoConfig.register('''custom''',			lowerCAmelCase__							)

																AutoTokenizer.register(lowerCAmelCase__,			slow_tokenizer_class=lowerCAmelCase__							)
																# Trying to register something existing in the Transformers library will raise an error
																with self.assertRaises(lowerCAmelCase__							):
																						AutoTokenizer.register(lowerCAmelCase__,			slow_tokenizer_class=lowerCAmelCase__							)

																_UpperCamelCase      : str         =   CustomTokenizer.from_pretrained(lowerCAmelCase__							)
																with tempfile.TemporaryDirectory() as tmp_dir:
																						tokenizer.save_pretrained(lowerCAmelCase__							)

																						_UpperCamelCase      : List[str]         =   AutoTokenizer.from_pretrained(lowerCAmelCase__							)
																						self.assertIsInstance(lowerCAmelCase__,			lowerCAmelCase__							)

										finally:
																if "custom" in CONFIG_MAPPING._extra_content:
																						del CONFIG_MAPPING._extra_content["custom"]
																if CustomConfig in TOKENIZER_MAPPING._extra_content:
																						del TOKENIZER_MAPPING._extra_content[CustomConfig]






				@require_tokenizers
				def     snake_case     (			self		:   List[Any]							)     ->				Tuple:




										'''simple docstring'''



										try:
																AutoConfig.register('''custom''',			lowerCAmelCase__							)

																# Can register in two steps
																AutoTokenizer.register(lowerCAmelCase__,			slow_tokenizer_class=lowerCAmelCase__							)
																self.assertEqual(TOKENIZER_MAPPING[CustomConfig],			(CustomTokenizer, None)							)
																AutoTokenizer.register(lowerCAmelCase__,			fast_tokenizer_class=lowerCAmelCase__							)
																self.assertEqual(TOKENIZER_MAPPING[CustomConfig],			(CustomTokenizer, CustomTokenizerFast)							)

																del TOKENIZER_MAPPING._extra_content[CustomConfig]
																# Can register in one step
																AutoTokenizer.register(
																    lowerCAmelCase__,			slow_tokenizer_class=lowerCAmelCase__,			fast_tokenizer_class=lowerCAmelCase__							)
																self.assertEqual(TOKENIZER_MAPPING[CustomConfig],			(CustomTokenizer, CustomTokenizerFast)							)

																# Trying to register something existing in the Transformers library will raise an error
																with self.assertRaises(lowerCAmelCase__							):
																						AutoTokenizer.register(lowerCAmelCase__,			fast_tokenizer_class=lowerCAmelCase__							)

																# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
																# and that model does not have a tokenizer.json
																with tempfile.TemporaryDirectory() as tmp_dir:
																						_UpperCamelCase      : Optional[int]         =   BertTokenizerFast.from_pretrained(lowerCAmelCase__							)
																						bert_tokenizer.save_pretrained(lowerCAmelCase__							)
																						_UpperCamelCase      : Optional[Any]         =   CustomTokenizerFast.from_pretrained(lowerCAmelCase__							)

																with tempfile.TemporaryDirectory() as tmp_dir:
																						tokenizer.save_pretrained(lowerCAmelCase__							)

																						_UpperCamelCase      : Any         =   AutoTokenizer.from_pretrained(lowerCAmelCase__							)
																						self.assertIsInstance(lowerCAmelCase__,			lowerCAmelCase__							)

																						_UpperCamelCase      : Dict         =   AutoTokenizer.from_pretrained(lowerCAmelCase__,			use_fast=lowerCAmelCase__							)
																						self.assertIsInstance(lowerCAmelCase__,			lowerCAmelCase__							)

										finally:
																if "custom" in CONFIG_MAPPING._extra_content:
																						del CONFIG_MAPPING._extra_content["custom"]
																if CustomConfig in TOKENIZER_MAPPING._extra_content:
																						del TOKENIZER_MAPPING._extra_content[CustomConfig]






				def     snake_case     (			self		:   Optional[Any]							)     ->				Tuple:




										'''simple docstring'''



										with self.assertRaises(lowerCAmelCase__							):
																_UpperCamelCase      : List[Any]         =   AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''							)
										# If remote code is disabled, we can't load this config.
										with self.assertRaises(lowerCAmelCase__							):
																_UpperCamelCase      : Optional[int]         =   AutoTokenizer.from_pretrained(
																    '''hf-internal-testing/test_dynamic_tokenizer''',			trust_remote_code=lowerCAmelCase__							)

										_UpperCamelCase      : Tuple         =   AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''',			trust_remote_code=lowerCAmelCase__							)
										self.assertTrue(tokenizer.special_attribute_present							)
										# Test tokenizer can be reloaded.
										with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer.save_pretrained(lowerCAmelCase__							)
																_UpperCamelCase      : Optional[int]         =   AutoTokenizer.from_pretrained(lowerCAmelCase__,			trust_remote_code=lowerCAmelCase__							)
										self.assertTrue(reloaded_tokenizer.special_attribute_present							)

										if is_tokenizers_available():
																self.assertEqual(tokenizer.__class__.__name__,			'''NewTokenizerFast'''							)
																self.assertEqual(reloaded_tokenizer.__class__.__name__,			'''NewTokenizerFast'''							)

																# Test we can also load the slow version
																_UpperCamelCase      : Optional[Any]         =   AutoTokenizer.from_pretrained(
																    '''hf-internal-testing/test_dynamic_tokenizer''',			trust_remote_code=lowerCAmelCase__,			use_fast=lowerCAmelCase__							)
																self.assertTrue(tokenizer.special_attribute_present							)
																self.assertEqual(tokenizer.__class__.__name__,			'''NewTokenizer'''							)
																# Test tokenizer can be reloaded.
																with tempfile.TemporaryDirectory() as tmp_dir:
																						tokenizer.save_pretrained(lowerCAmelCase__							)
																						_UpperCamelCase      : Any         =   AutoTokenizer.from_pretrained(lowerCAmelCase__,			trust_remote_code=lowerCAmelCase__,			use_fast=lowerCAmelCase__							)
																self.assertEqual(reloaded_tokenizer.__class__.__name__,			'''NewTokenizer'''							)
																self.assertTrue(reloaded_tokenizer.special_attribute_present							)
										else:
																self.assertEqual(tokenizer.__class__.__name__,			'''NewTokenizer'''							)
																self.assertEqual(reloaded_tokenizer.__class__.__name__,			'''NewTokenizer'''							)






				@require_tokenizers
				def     snake_case     (			self		:   Any							)     ->				List[str]:




										'''simple docstring'''



										class 			_a						(				_lowerCAmelCase    ):
														UpperCamelCase								=   False




										class 			_a						(				_lowerCAmelCase    ):
														UpperCamelCase								=   NewTokenizer
														UpperCamelCase								=   False

										try:
																AutoConfig.register('''custom''',			lowerCAmelCase__							)
																AutoTokenizer.register(lowerCAmelCase__,			slow_tokenizer_class=lowerCAmelCase__							)
																AutoTokenizer.register(lowerCAmelCase__,			fast_tokenizer_class=lowerCAmelCase__							)
																# If remote code is not set, the default is to use local
																_UpperCamelCase      : Dict         =   AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''							)
																self.assertEqual(tokenizer.__class__.__name__,			'''NewTokenizerFast'''							)
																self.assertFalse(tokenizer.special_attribute_present							)
																_UpperCamelCase      : List[str]         =   AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''',			use_fast=lowerCAmelCase__							)
																self.assertEqual(tokenizer.__class__.__name__,			'''NewTokenizer'''							)
																self.assertFalse(tokenizer.special_attribute_present							)

																# If remote code is disabled, we load the local one.
																_UpperCamelCase      : List[str]         =   AutoTokenizer.from_pretrained(
																    '''hf-internal-testing/test_dynamic_tokenizer''',			trust_remote_code=lowerCAmelCase__							)
																self.assertEqual(tokenizer.__class__.__name__,			'''NewTokenizerFast'''							)
																self.assertFalse(tokenizer.special_attribute_present							)
																_UpperCamelCase      : Tuple         =   AutoTokenizer.from_pretrained(
																    '''hf-internal-testing/test_dynamic_tokenizer''',			trust_remote_code=lowerCAmelCase__,			use_fast=lowerCAmelCase__							)
																self.assertEqual(tokenizer.__class__.__name__,			'''NewTokenizer'''							)
																self.assertFalse(tokenizer.special_attribute_present							)

																# If remote is enabled, we load from the Hub
																_UpperCamelCase      : List[str]         =   AutoTokenizer.from_pretrained(
																    '''hf-internal-testing/test_dynamic_tokenizer''',			trust_remote_code=lowerCAmelCase__							)
																self.assertEqual(tokenizer.__class__.__name__,			'''NewTokenizerFast'''							)
																self.assertTrue(tokenizer.special_attribute_present							)
																_UpperCamelCase      : List[Any]         =   AutoTokenizer.from_pretrained(
																    '''hf-internal-testing/test_dynamic_tokenizer''',			trust_remote_code=lowerCAmelCase__,			use_fast=lowerCAmelCase__							)
																self.assertEqual(tokenizer.__class__.__name__,			'''NewTokenizer'''							)
																self.assertTrue(tokenizer.special_attribute_present							)

										finally:
																if "custom" in CONFIG_MAPPING._extra_content:
																						del CONFIG_MAPPING._extra_content["custom"]
																if CustomConfig in TOKENIZER_MAPPING._extra_content:
																						del TOKENIZER_MAPPING._extra_content[CustomConfig]






				def     snake_case     (			self		:   Optional[int]							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : str         =   AutoTokenizer.from_pretrained(
										    '''hf-internal-testing/test_dynamic_tokenizer_legacy''',			trust_remote_code=lowerCAmelCase__							)
										self.assertTrue(tokenizer.special_attribute_present							)
										if is_tokenizers_available():
																self.assertEqual(tokenizer.__class__.__name__,			'''NewTokenizerFast'''							)

																# Test we can also load the slow version
																_UpperCamelCase      : List[str]         =   AutoTokenizer.from_pretrained(
																    '''hf-internal-testing/test_dynamic_tokenizer_legacy''',			trust_remote_code=lowerCAmelCase__,			use_fast=lowerCAmelCase__							)
																self.assertTrue(tokenizer.special_attribute_present							)
																self.assertEqual(tokenizer.__class__.__name__,			'''NewTokenizer'''							)
										else:
																self.assertEqual(tokenizer.__class__.__name__,			'''NewTokenizer'''							)






				def     snake_case     (			self		:   Any							)     ->				str:




										'''simple docstring'''



										with self.assertRaisesRegex(
										    lowerCAmelCase__,			'''bert-base is not a local folder and is not a valid model identifier'''							):
																_UpperCamelCase      : int         =   AutoTokenizer.from_pretrained('''bert-base'''							)






				def     snake_case     (			self		:   Tuple							)     ->				Union[str, Any]:




										'''simple docstring'''



										with self.assertRaisesRegex(
										    lowerCAmelCase__,			R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''							):
																_UpperCamelCase      : str         =   AutoTokenizer.from_pretrained(lowerCAmelCase__,			revision='''aaaaaa'''							)






				def     snake_case     (			self		:   int							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''							)
										with RequestCounter() as counter:
																_UpperCamelCase      : List[Any]         =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''							)
																self.assertEqual(counter.get_request_count,			0							)
																self.assertEqual(counter.head_request_count,			1							)
																self.assertEqual(counter.other_request_count,			0							)










"""simple docstring"""




from __future__ import annotations

import math





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):

						if depth < 0:
												raise ValueError('''Depth cannot be less than 0'''       )

						if len(_lowercase       ) == 0:
												raise ValueError('''Scores cannot be empty'''       )

						if depth == height:
												return scores[node_index]

						if is_max:
												return max(
												    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)

						return min(
						    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)





def 	a_    (							):
						_UpperCamelCase      : Dict         =   [90, 23, 6, 33, 21, 65, 123, 3_4423]
						_UpperCamelCase      : int         =   math.log(len(_lowercase       )	,						2       )
						print('''Optimal value : '''	,						end=''''''       )
						print(minimax(0	,						0	,						_lowercase	,						_lowercase	,						_lowercase       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()



