



'''simple docstring'''




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   :			str					  =    """tf"""
else:
							a   :			Union[str, Any]					  =    """jax"""

class 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = PerceiverTokenizer
	lowercase          = False





	def  _lowercase(  self    )			->			int:
								super().setUp()
								UpperCAmelCase     :		int							     =		PerceiverTokenizer()
								tokenizer.save_pretrained(self.tmpdirname    )





	@cached_property
	def  _lowercase(  self    )			->			Any:
								return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""    )





	def  _lowercase(  self		,		**A    )			->			PerceiverTokenizer:
								return self.tokenizer_class.from_pretrained(self.tmpdirname		,		**A    )





	def  _lowercase(  self		,		A		,		A=False		,		A=20		,		A=5    )			->			Tuple[str, list]:
								# XXX The default common tokenizer tests assume that every ID is decodable on its own.
								# This assumption is invalid for Perceiver because single bytes might not be
								# valid utf-8 (byte 128 for instance).
								# Here we're overriding the smallest possible method to provide
								# a clean sequence without making the same assumption.

								UpperCAmelCase     :		Any							     =		[]
								for i in range(len(A    )    ):
															try:
																						UpperCAmelCase     :		Dict							     =		tokenizer.decode([i]		,		clean_up_tokenization_spaces=A    )
															except UnicodeDecodeError:
																						pass
															toks.append((i, tok)    )

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

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





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		str							     =		self.perceiver_tokenizer
								UpperCAmelCase     :		List[str]							     =		"""Unicode €."""
								UpperCAmelCase     :		List[Any]							     =		tokenizer(A    )
								UpperCAmelCase     :		List[str]							     =		[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
								self.assertEqual(encoded["""input_ids"""]		,		A    )

								# decoding
								UpperCAmelCase     :		Tuple							     =		tokenizer.decode(A    )
								self.assertEqual(A		,		"""[CLS]Unicode €.[SEP]"""    )

								UpperCAmelCase     :		List[str]							     =		tokenizer("""e è é ê ë"""    )
								UpperCAmelCase     :		List[Any]							     =		[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
								self.assertEqual(encoded["""input_ids"""]		,		A    )
								# decoding
								UpperCAmelCase     :		Tuple							     =		tokenizer.decode(A    )
								self.assertEqual(A		,		"""[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  _lowercase(  self    )			->			Dict:
								UpperCAmelCase     :		List[str]							     =		self.perceiver_tokenizer
								UpperCAmelCase     :		Union[str, Any]							     =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
								# fmt: off
								UpperCAmelCase     :		Dict							     =		[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
								# fmt: on
								UpperCAmelCase     :		List[Any]							     =		tokenizer(A		,		padding=A		,		return_tensors=A    )
								self.assertIsInstance(A		,		A    )

								if FRAMEWORK != "jax":
															UpperCAmelCase     :		List[str]							     =		list(batch.input_ids.numpy()[0]    )
								else:
															UpperCAmelCase     :		List[Any]							     =		list(batch.input_ids.tolist()[0]    )

								self.assertListEqual(A		,		A    )

								self.assertEqual((2, 38)		,		batch.input_ids.shape    )
								self.assertEqual((2, 38)		,		batch.attention_mask.shape    )





	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		Union[str, Any]							     =		self.perceiver_tokenizer
								UpperCAmelCase     :		Optional[Any]							     =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
								UpperCAmelCase     :		List[str]							     =		tokenizer(A		,		padding=A		,		return_tensors=A    )
								# check if input_ids are returned and no decoder_input_ids
								self.assertIn("""input_ids"""		,		A    )
								self.assertIn("""attention_mask"""		,		A    )
								self.assertNotIn("""decoder_input_ids"""		,		A    )
								self.assertNotIn("""decoder_attention_mask"""		,		A    )





	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		Tuple							     =		self.perceiver_tokenizer
								UpperCAmelCase     :		int							     =		[
								    """Summary of the text.""",
								    """Another summary.""",
								]
								UpperCAmelCase     :		List[str]							     =		tokenizer(
								    text_target=A		,		max_length=32		,		padding="""max_length"""		,		truncation=A		,		return_tensors=A    )
								self.assertEqual(32		,		targets["""input_ids"""].shape[1]    )





	def  _lowercase(  self    )			->			List[str]:
								# safety check on max_len default value so we are sure the test works
								UpperCAmelCase     :		Union[str, Any]							     =		self.get_tokenizers()
								for tokenizer in tokenizers:
															with self.subTest(f'''{tokenizer.__class__.__name__}'''    ):
																						self.assertNotEqual(tokenizer.model_max_length		,		42    )

        # Now let's start the test
								UpperCAmelCase     :		str							     =		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
																						UpperCAmelCase     :		List[Any]							     =		tempfile.mkdtemp()

																						UpperCAmelCase     :		Tuple							     =		""" He is very happy, UNwant\u00E9d,running"""
																						UpperCAmelCase     :		Optional[int]							     =		tokenizer.encode(A		,		add_special_tokens=A    )
																						tokenizer.save_pretrained(A    )

																						UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.__class__.from_pretrained(A    )
																						UpperCAmelCase     :		Tuple							     =		after_tokenizer.encode(A		,		add_special_tokens=A    )
																						self.assertListEqual(A		,		A    )

																						shutil.rmtree(A    )

								UpperCAmelCase     :		List[str]							     =		self.get_tokenizers(model_max_length=42    )
								for tokenizer in tokenizers:
															with self.subTest(f'''{tokenizer.__class__.__name__}'''    ):
																						# Isolate this from the other tests because we save additional tokens/etc
																						UpperCAmelCase     :		List[str]							     =		tempfile.mkdtemp()

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

																						UpperCAmelCase     :		Dict							     =		tokenizer.__class__.from_pretrained(A    )
																						UpperCAmelCase     :		Tuple							     =		after_tokenizer.encode(A		,		add_special_tokens=A    )
																						self.assertListEqual(A		,		A    )
																						self.assertIn("""new_additional_special_token"""		,		after_tokenizer.additional_special_tokens    )
																						self.assertEqual(after_tokenizer.model_max_length		,		42    )

																						UpperCAmelCase     :		Optional[Any]							     =		tokenizer.__class__.from_pretrained(A		,		model_max_length=43    )
																						self.assertEqual(tokenizer.model_max_length		,		43    )

																						shutil.rmtree(A    )





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		str							     =		[]
								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(A    )

																						with open(os.path.join(A		,		"""special_tokens_map.json"""    )		,		encoding="""utf-8"""    ) as json_file:
																													UpperCAmelCase     :		List[Any]							     =		json.load(A    )

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

																						UpperCAmelCase     :		Optional[Any]							     =		[f'''<extra_id_{i}>''' for i in range(125    )]

																						UpperCAmelCase     :		str							     =		added_tokens_extra_ids + [
																						    """an_additional_special_token"""
																						]
																						UpperCAmelCase     :		Dict							     =		added_tokens_extra_ids + [
																						    """an_additional_special_token"""
																						]

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

																						# 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
																						UpperCAmelCase     :		List[Any]							     =		tokenizer_class.from_pretrained(
																						    A		,		)
																						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
																						UpperCAmelCase     :		str							     =		added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""		,		lstrip=A    )]
																						UpperCAmelCase     :		List[str]							     =		tokenizer_class.from_pretrained(
																						    A		,		additional_special_tokens=A		,		)

																						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  _lowercase(  self    )			->			Dict:
								UpperCAmelCase     :		List[str]							     =		self.perceiver_tokenizer
								self.assertEqual(tokenizer.decode([178]    )		,		"""�"""    )





	def  _lowercase(  self    )			->			Optional[int]:
								pass





	def  _lowercase(  self    )			->			Any:
								pass





	def  _lowercase(  self    )			->			List[str]:
								pass





	def  _lowercase(  self    )			->			List[Any]:
								pass






	def  _lowercase(  self    )			->			Tuple:
								# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
								# strings and special added tokens as tokens
								UpperCAmelCase     :		str							     =		self.get_tokenizers(fast=A		,		do_lower_case=A    )
								for tokenizer in tokenizers:
															with self.subTest(f'''{tokenizer.__class__.__name__}'''    ):
																						UpperCAmelCase     :		Tuple							     =		["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
																						UpperCAmelCase     :		Tuple							     =		tokenizer.convert_tokens_to_string(A    )

																						self.assertIsInstance(A		,		A    )






'''simple docstring'''




from itertools import count



def       __lowerCamelCase  (					_lowercase = 5_0		)      ->			int:
							UpperCAmelCase     :		Any							     =		[1] * min_block_length

							for n in count(_lowercase		):
														fill_count_functions.append(1		)

														for block_length in range(_lowercase	,				n + 1		):
																					for block_start in range(n - block_length		):
																												fill_count_functions[n] += fill_count_functions[
																												    n - block_start - block_length - 1
																												]

																					fill_count_functions[n] += 1

														if fill_count_functions[n] > 1_0_0_0_0_0_0:
																					break

							return n


if __name__ == "__main__":
							print(F'''{solution() = }''')

