


import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
	A							:					str     						=     'pt'
elif is_tf_available():
	A							:					Union[str, Any]     						=     'tf'
else:
	A							:					str     						=     'jax'






class     UpperCamelCase(  _a     ,							unittest.TestCase   ):
					snake_case_		:				List[str]										=	PerceiverTokenizer
					snake_case_		:				str										=	False


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




											'''simple docstring'''

											super().setUp()
											__snake_case				=		PerceiverTokenizer()
											tokenizer.save_pretrained(self.tmpdirname						)


					@cached_property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Union[str, Any]:




											'''simple docstring'''

											return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    **SCREAMING_SNAKE_CASE	:		int						)			->	PerceiverTokenizer:




											'''simple docstring'''

											return self.tokenizer_class.from_pretrained(self.tmpdirname     ,    **SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Any=False     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=2_0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=5						)			->	Tuple[str, list]:




											'''simple docstring'''

											__snake_case				=		[]
											for i in range(len(SCREAMING_SNAKE_CASE						)						):
																	try:
																							__snake_case				=		tokenizer.decode([i]     ,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE						)
																	except UnicodeDecodeError:
																							pass
																	toks.append((i, tok)						)

											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE						: re.match(R"^[ a-zA-Z]+$"     ,    t[1]						)     ,    SCREAMING_SNAKE_CASE						)						)
											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE						: [t[0]] == tokenizer.encode(t[1]     ,    add_special_tokens=SCREAMING_SNAKE_CASE						)     ,    SCREAMING_SNAKE_CASE						)						)
											if max_length is not None and len(SCREAMING_SNAKE_CASE						) > max_length:
																	__snake_case				=		toks[:max_length]
											if min_length is not None and len(SCREAMING_SNAKE_CASE						) < min_length and len(SCREAMING_SNAKE_CASE						) > 0:
																	while len(SCREAMING_SNAKE_CASE						) < min_length:
																							__snake_case				=		toks + toks
        # toks_str = [t[1] for t in toks]
											__snake_case				=		[t[0] for t in toks]

											# Ensure consistency
											__snake_case				=		tokenizer.decode(SCREAMING_SNAKE_CASE     ,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE						)
											if " " not in output_txt and len(SCREAMING_SNAKE_CASE						) > 1:
																	__snake_case				=		(
																	    tokenizer.decode([toks_ids[0]]     ,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE						)
																	    + " "
																	    + tokenizer.decode(toks_ids[1:]     ,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE						)
																	)
											if with_prefix_space:
																	__snake_case				=		" " + output_txt
											__snake_case				=		tokenizer.encode(SCREAMING_SNAKE_CASE     ,    add_special_tokens=SCREAMING_SNAKE_CASE						)
											return output_txt, output_ids


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	int:




											'''simple docstring'''

											__snake_case				=		self.perceiver_tokenizer
											__snake_case				=		"Unicode €."
											__snake_case				=		tokenizer(SCREAMING_SNAKE_CASE						)
											__snake_case				=		[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
											self.assertEqual(encoded["input_ids"]     ,    SCREAMING_SNAKE_CASE						)

											# decoding
											__snake_case				=		tokenizer.decode(SCREAMING_SNAKE_CASE						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    "[CLS]Unicode €.[SEP]"						)

											__snake_case				=		tokenizer("e è é ê ë"						)
											__snake_case				=		[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
											self.assertEqual(encoded["input_ids"]     ,    SCREAMING_SNAKE_CASE						)
											# decoding
											__snake_case				=		tokenizer.decode(SCREAMING_SNAKE_CASE						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    "[CLS]e è é ê ë[SEP]"						)

											# encode/decode, but with `encode` instead of `__call__`
											self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"						)						)     ,    "[CLS]e è é ê ë[SEP]"						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.perceiver_tokenizer
											__snake_case				=		["A long paragraph for summarization.", "Another paragraph for summarization."]
											# fmt: off
											__snake_case				=		[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
											# fmt: on
											__snake_case				=		tokenizer(SCREAMING_SNAKE_CASE     ,    padding=SCREAMING_SNAKE_CASE     ,    return_tensors=SCREAMING_SNAKE_CASE						)
											self.assertIsInstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

											if FRAMEWORK != "jax":
																	__snake_case				=		list(batch.input_ids.numpy()[0]						)
											else:
																	__snake_case				=		list(batch.input_ids.tolist()[0]						)

											self.assertListEqual(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

											self.assertEqual((2, 3_8)     ,    batch.input_ids.shape						)
											self.assertEqual((2, 3_8)     ,    batch.attention_mask.shape						)


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




											'''simple docstring'''

											__snake_case				=		self.perceiver_tokenizer
											__snake_case				=		["A long paragraph for summarization.", "Another paragraph for summarization."]
											__snake_case				=		tokenizer(SCREAMING_SNAKE_CASE     ,    padding=SCREAMING_SNAKE_CASE     ,    return_tensors=SCREAMING_SNAKE_CASE						)
											# check if input_ids are returned and no decoder_input_ids
											self.assertIn("input_ids"     ,    SCREAMING_SNAKE_CASE						)
											self.assertIn("attention_mask"     ,    SCREAMING_SNAKE_CASE						)
											self.assertNotIn("decoder_input_ids"     ,    SCREAMING_SNAKE_CASE						)
											self.assertNotIn("decoder_attention_mask"     ,    SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.perceiver_tokenizer
											__snake_case				=		[
											    "Summary of the text.",
											    "Another summary.",
											]
											__snake_case				=		tokenizer(
											    text_target=SCREAMING_SNAKE_CASE     ,    max_length=3_2     ,    padding="max_length"     ,    truncation=SCREAMING_SNAKE_CASE     ,    return_tensors=SCREAMING_SNAKE_CASE						)
											self.assertEqual(3_2     ,    targets["input_ids"].shape[1]						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.get_tokenizers()
											for tokenizer in tokenizers:
																	with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
																							self.assertNotEqual(tokenizer.model_max_length     ,    4_2						)

        # Now let's start the test
											__snake_case				=		self.get_tokenizers()
											for tokenizer in tokenizers:
																	with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
																							# Isolate this from the other tests because we save additional tokens/etc
																							__snake_case				=		tempfile.mkdtemp()

																							__snake_case				=		" He is very happy, UNwant\u00E9d,running"
																							__snake_case				=		tokenizer.encode(SCREAMING_SNAKE_CASE     ,    add_special_tokens=SCREAMING_SNAKE_CASE						)
																							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE						)

																							__snake_case				=		tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE						)
																							__snake_case				=		after_tokenizer.encode(SCREAMING_SNAKE_CASE     ,    add_special_tokens=SCREAMING_SNAKE_CASE						)
																							self.assertListEqual(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

																							shutil.rmtree(SCREAMING_SNAKE_CASE						)

											__snake_case				=		self.get_tokenizers(model_max_length=4_2						)
											for tokenizer in tokenizers:
																	with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
																							# Isolate this from the other tests because we save additional tokens/etc
																							__snake_case				=		tempfile.mkdtemp()

																							__snake_case				=		" He is very happy, UNwant\u00E9d,running"
																							tokenizer.add_tokens(["bim", "bambam"]						)
																							__snake_case				=		tokenizer.additional_special_tokens
																							additional_special_tokens.append("new_additional_special_token"						)
																							tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}						)
																							__snake_case				=		tokenizer.encode(SCREAMING_SNAKE_CASE     ,    add_special_tokens=SCREAMING_SNAKE_CASE						)
																							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE						)

																							__snake_case				=		tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE						)
																							__snake_case				=		after_tokenizer.encode(SCREAMING_SNAKE_CASE     ,    add_special_tokens=SCREAMING_SNAKE_CASE						)
																							self.assertListEqual(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																							self.assertIn("new_additional_special_token"     ,    after_tokenizer.additional_special_tokens						)
																							self.assertEqual(after_tokenizer.model_max_length     ,    4_2						)

																							__snake_case				=		tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE     ,    model_max_length=4_3						)
																							self.assertEqual(tokenizer.model_max_length     ,    4_3						)

																							shutil.rmtree(SCREAMING_SNAKE_CASE						)


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




											'''simple docstring'''

											__snake_case				=		[]
											if self.test_slow_tokenizer:
																	tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

											if self.test_rust_tokenizer:
																	tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

											for tokenizer_class, tokenizer_utils in tokenizer_list:
																	with tempfile.TemporaryDirectory() as tmp_dir:
																							tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE						)

																							with open(os.path.join(SCREAMING_SNAKE_CASE     ,    "special_tokens_map.json"						)     ,    encoding="utf-8"						) as json_file:
																													__snake_case				=		json.load(SCREAMING_SNAKE_CASE						)

																							with open(os.path.join(SCREAMING_SNAKE_CASE     ,    "tokenizer_config.json"						)     ,    encoding="utf-8"						) as json_file:
																													__snake_case				=		json.load(SCREAMING_SNAKE_CASE						)

																							__snake_case				=		[f'''<extra_id_{i}>''' for i in range(1_2_5						)]

																							__snake_case				=		added_tokens_extra_ids + [
																							    "an_additional_special_token"
																							]
																							__snake_case				=		added_tokens_extra_ids + [
																							    "an_additional_special_token"
																							]

																							with open(os.path.join(SCREAMING_SNAKE_CASE     ,    "special_tokens_map.json"						)     ,    "w"     ,    encoding="utf-8"						) as outfile:
																													json.dump(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																							with open(os.path.join(SCREAMING_SNAKE_CASE     ,    "tokenizer_config.json"						)     ,    "w"     ,    encoding="utf-8"						) as outfile:
																													json.dump(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

																							# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																							# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																							# "special_tokens_map.json" files
																							__snake_case				=		tokenizer_class.from_pretrained(
																							    SCREAMING_SNAKE_CASE     ,    )
																							self.assertIn(
																							    "an_additional_special_token"     ,    tokenizer_without_change_in_init.additional_special_tokens						)
																							self.assertEqual(
																							    ["an_additional_special_token"]     ,    tokenizer_without_change_in_init.convert_ids_to_tokens(
																							        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]						)						)     ,    )

																							# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																							__snake_case				=		added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"     ,    lstrip=SCREAMING_SNAKE_CASE						)]
																							__snake_case				=		tokenizer_class.from_pretrained(
																							    SCREAMING_SNAKE_CASE     ,    additional_special_tokens=SCREAMING_SNAKE_CASE     ,    )

																							self.assertIn("a_new_additional_special_token"     ,    tokenizer.additional_special_tokens						)
																							self.assertEqual(
																							    ["a_new_additional_special_token"]     ,    tokenizer.convert_ids_to_tokens(
																							        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]						)						)     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.perceiver_tokenizer
											self.assertEqual(tokenizer.decode([1_7_8]						)     ,    "�"						)


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




											'''simple docstring'''

											pass


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Any:




											'''simple docstring'''

											pass


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




											'''simple docstring'''

											pass


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	Dict:




											'''simple docstring'''

											pass






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Any:




											'''simple docstring'''

											__snake_case				=		self.get_tokenizers(fast=SCREAMING_SNAKE_CASE     ,    do_lower_case=SCREAMING_SNAKE_CASE						)
											for tokenizer in tokenizers:
																	with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
																							__snake_case				=		["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
																							__snake_case				=		tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE						)

																							self.assertIsInstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)









def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						__snake_case				=		0
						while n > 0:
												res += n % 10
												n //= 10
						return res





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						return n if n < 10 else n % 10 + sum_of_digits(n // 10 )





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						return sum(int(_lowerCAmelCase ) for c in str(abs(_lowerCAmelCase ) ) )





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from collections.abc import Callable
						from timeit import timeit

						def benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase ) -> None:
												__snake_case				=		F'''{func.__name__}({value})'''
												__snake_case				=		timeit(F'''__main__.{call}'''      ,	setup="import __main__" )
												print(F'''{call:56} = {func(_lowerCAmelCase )} -- {timing:.4f} seconds''' )

						for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
												for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
																		benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase )
												print()


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





