



"""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 			A__   (  unittest.TestCase							):




	'''simple docstring'''





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

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       mock.Mock()
				__lowerCAmelCase      :  Optional[int]												=       500
				__lowerCAmelCase      :  str												=       {}
				__lowerCAmelCase      :  Dict												=       HTTPError
				__lowerCAmelCase      :  Tuple												=       {}

				# Download this model to make sure it's in the cache.
				__lowerCAmelCase      :  Optional[Any]												=       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=_SCREAMING_SNAKE_CASE) as mock_head:
							__lowerCAmelCase      :  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 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       mock.Mock()
				__lowerCAmelCase      :  Any												=       500
				__lowerCAmelCase      :  Union[str, Any]												=       {}
				__lowerCAmelCase      :  int												=       HTTPError
				__lowerCAmelCase      :  Any												=       {}

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

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

				"""simple docstring"""

				try:
							__lowerCAmelCase      :  List[str]												=       tempfile.mktemp()
							with open(_SCREAMING_SNAKE_CASE       ,			"wb") as f:
										http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"       ,			_SCREAMING_SNAKE_CASE)

							__lowerCAmelCase      :  int												=       AlbertTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)
				finally:
							os.remove(_SCREAMING_SNAKE_CASE)

				# 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"       ,			_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase      :  int												=       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:		Optional[Any])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model")






@is_staging_test
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]

	@classmethod
	def 							_SCREAMING_SNAKE_CASE   (     cls:		Optional[Any])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       TOKEN
				HfFolder.save_token(_SCREAMING_SNAKE_CASE)

	@classmethod
	def 							_SCREAMING_SNAKE_CASE   (     cls:		Union[str, Any])				->       Union[str, Any]:

				"""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:		Optional[int])				->       Optional[Any]:

				"""simple docstring"""

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

				tokenizer.push_to_hub("test-tokenizer"       ,			use_auth_token=self._token)
				__lowerCAmelCase      :  int												=       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(_SCREAMING_SNAKE_CASE       ,			repo_id="test-tokenizer"       ,			push_to_hub=_SCREAMING_SNAKE_CASE       ,			use_auth_token=self._token)

				__lowerCAmelCase      :  int												=       BertTokenizer.from_pretrained(F"""{USER}/test-tokenizer""")
				self.assertDictEqual(new_tokenizer.vocab       ,			tokenizer.vocab)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       int:

				"""simple docstring"""

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

				tokenizer.push_to_hub("valid_org/test-tokenizer-org"       ,			use_auth_token=self._token)
				__lowerCAmelCase      :  Union[str, 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(
							    _SCREAMING_SNAKE_CASE       ,			repo_id="valid_org/test-tokenizer-org"       ,			push_to_hub=_SCREAMING_SNAKE_CASE       ,			use_auth_token=self._token)

				__lowerCAmelCase      :  Dict												=       BertTokenizer.from_pretrained("valid_org/test-tokenizer-org")
				self.assertDictEqual(new_tokenizer.vocab       ,			tokenizer.vocab)







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

				"""simple docstring"""

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

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

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

							__lowerCAmelCase      :  List[Any]												=       BertTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE)
							bert_tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase      :  int												=       CustomTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE)

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

				__lowerCAmelCase      :  str												=       AutoTokenizer.from_pretrained(F"""{USER}/test-dynamic-tokenizer"""       ,			trust_remote_code=_SCREAMING_SNAKE_CASE)
				# 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")
				__lowerCAmelCase      :  Union[str, Any]												=       AutoTokenizer.from_pretrained(
				    F"""{USER}/test-dynamic-tokenizer"""       ,			use_fast=_SCREAMING_SNAKE_CASE       ,			trust_remote_code=_SCREAMING_SNAKE_CASE)
				# 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 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  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:		Dict)				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  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 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       Trie()
				trie.add("A")
				self.assertEqual(trie.split("ABC")       ,			["A", "BC"])
				self.assertEqual(trie.split("BCA")       ,			["BC", "A"])

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       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:		str)				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       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:		Optional[int])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       Trie()
				trie.add("AB")
				trie.add("B")
				trie.add("C")
				self.assertEqual(trie.split("ABC")       ,			["AB", "C"])

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       str:

				"""simple docstring"""

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







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

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       Trie()
				__lowerCAmelCase      :  Dict												=       trie.cut_text("ABC"       ,			[0, 0, 2, 1, 2, 3])
				self.assertEqual(_SCREAMING_SNAKE_CASE       ,			["AB", "C"])







"""simple docstring"""



from __future__ import annotations

__A		:       List[Any]  				=		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A		:       str  				=		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		lowercase_ : List[Any]							       =   len(__snake_case	)

		for i in range(__snake_case	):
				lowercase_ : float							       =   -1
				for j in range(i + 1		,					__snake_case	):
						if arr[i] < arr[j]:
								lowercase_ : List[str]							       =   arr[j]
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		for i, outer in enumerate(__snake_case	):
				lowercase_ : float							       =   -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								lowercase_ : List[Any]							       =   inner
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   len(__snake_case	)
		lowercase_ : list[float]							       =   []
		lowercase_ : list[float]							       =   [-1] * arr_size

		for index in reversed(range(__snake_case	)	):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						lowercase_ : Optional[Any]							       =   stack[-1]
				stack.append(arr[index]	)
		return result


if __name__ == "__main__":
						from doctest import testmod
						from timeit import timeit

						testmod()
						print(next_greatest_element_slow(arr))
						print(next_greatest_element_fast(arr))
						print(next_greatest_element(arr))

						__A		:       int  				=		(
						    '''from __main__ import arr, next_greatest_element_slow, '''
						    '''next_greatest_element_fast, next_greatest_element'''
						)
						print(
						    '''next_greatest_element_slow():''',
						    timeit('''next_greatest_element_slow(arr)''', setup=setup),
						)
						print(
						    '''next_greatest_element_fast():''',
						    timeit('''next_greatest_element_fast(arr)''', setup=setup),
						)
						print(
						    '''     next_greatest_element():''',
						    timeit('''next_greatest_element(arr)''', setup=setup),
						)



