
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():
				__lowerCAmelCase												=	'pt'
elif is_tf_available():
				__lowerCAmelCase												=	'tf'
else:
				__lowerCAmelCase												=	'jax'


class       _lowerCAmelCase   (   __snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						PerceiverTokenizer
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  )					->       List[str]:
														super().setUp()
														_snake_case       =  PerceiverTokenizer()
														tokenizer.save_pretrained(self.tmpdirname  )

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

							def 	lowercase							(self  ,  **UpperCAmelCase  )					->       PerceiverTokenizer:
														return self.tokenizer_class.from_pretrained(self.tmpdirname  ,  **__A  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=False  ,  UpperCAmelCase=20  ,  UpperCAmelCase=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.

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

														_snake_case       =  list(filter(lambda UpperCAmelCase  : re.match(R"""^[ a-zA-Z]+$"""  ,  t[1]  )  ,  __A  )  )
														_snake_case       =  list(filter(lambda UpperCAmelCase  : [t[0]] == tokenizer.encode(t[1]  ,  add_special_tokens=__A  )  ,  __A  )  )
														if max_length is not None and len(__A  ) > max_length:
																					_snake_case       =  toks[:max_length]
														if min_length is not None and len(__A  ) < min_length and len(__A  ) > 0:
																					while len(__A  ) < 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(__A  ,  clean_up_tokenization_spaces=__A  )
														if " " not in output_txt and len(__A  ) > 1:
																					_snake_case       =  (
																					    tokenizer.decode([toks_ids[0]]  ,  clean_up_tokenization_spaces=__A  )
																					    + """ """
																					    + tokenizer.decode(toks_ids[1:]  ,  clean_up_tokenization_spaces=__A  )
																					)
														if with_prefix_space:
																					_snake_case       =  """ """ + output_txt
														_snake_case       =  tokenizer.encode(__A  ,  add_special_tokens=__A  )
														return output_txt, output_ids

							def 	lowercase							(self  )					->       Dict:
														_snake_case       =  self.perceiver_tokenizer
														_snake_case       =  """Unicode €."""
														_snake_case       =  tokenizer(__A  )
														_snake_case       =  [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
														self.assertEqual(encoded["""input_ids"""]  ,  __A  )

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

														_snake_case       =  tokenizer("""e è é ê ë"""  )
														_snake_case       =  [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
														self.assertEqual(encoded["""input_ids"""]  ,  __A  )
														# decoding
														_snake_case       =  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  )					->       str:
														_snake_case       =  self.perceiver_tokenizer
														_snake_case       =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														# fmt: off
														_snake_case       =  [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
														_snake_case       =  tokenizer(__A  ,  padding=__A  ,  return_tensors=__A  )
														self.assertIsInstance(__A  ,  __A  )

														if FRAMEWORK != "jax":
																					_snake_case       =  list(batch.input_ids.numpy()[0]  )
														else:
																					_snake_case       =  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  )					->       Union[str, Any]:
														_snake_case       =  self.perceiver_tokenizer
														_snake_case       =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														_snake_case       =  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  )					->       Tuple:
														_snake_case       =  self.perceiver_tokenizer
														_snake_case       =  [
														    """Summary of the text.""",
														    """Another summary.""",
														]
														_snake_case       =  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  )					->       Dict:
														# safety check on max_len default value so we are sure the test works
														_snake_case       =  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
														_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(__A  ,  add_special_tokens=__A  )
																												tokenizer.save_pretrained(__A  )

																												_snake_case       =  tokenizer.__class__.from_pretrained(__A  )
																												_snake_case       =  after_tokenizer.encode(__A  ,  add_special_tokens=__A  )
																												self.assertListEqual(__A  ,  __A  )

																												shutil.rmtree(__A  )

														_snake_case       =  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
																												_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(__A  ,  add_special_tokens=__A  )
																												tokenizer.save_pretrained(__A  )

																												_snake_case       =  tokenizer.__class__.from_pretrained(__A  )
																												_snake_case       =  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  )

																												_snake_case       =  tokenizer.__class__.from_pretrained(__A  ,  model_max_length=43  )
																												self.assertEqual(tokenizer.model_max_length  ,  43  )

																												shutil.rmtree(__A  )

							def 	lowercase							(self  )					->       List[Any]:
														_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(__A  )

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

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

																												_snake_case       =  [f"""<extra_id_{i}>""" for i in range(125  )]

																												_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(__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
																												_snake_case       =  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
																												_snake_case       =  added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""  ,  lstrip=__A  )]
																												_snake_case       =  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  )					->       List[str]:
														_snake_case       =  self.perceiver_tokenizer
														self.assertEqual(tokenizer.decode([178]  )  ,  """�"""  )

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

							def 	lowercase							(self  )					->       Any:
														pass

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

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





							def 	lowercase							(self  )					->       Optional[int]:
														# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
														# strings and special added tokens as tokens
														_snake_case       =  self.get_tokenizers(fast=__A  ,  do_lower_case=__A  )
														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(__A  )

																												self.assertIsInstance(__A  ,  __A  )

'''simple docstring'''
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):

							if not (isinstance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				) and isinstance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)):
														raise ValueError("""longest_common_substring() takes two strings for inputs"""				)

							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)

							_snake_case       =  [[0] * (texta_length + 1) for _ in range(texta_length + 1				)]
							_snake_case       =  0
							_snake_case       =  0

							for i in range(1							, texta_length + 1				):
														for j in range(1							, texta_length + 1				):
																					if texta[i - 1] == texta[j - 1]:
																												_snake_case       =  1 + dp[i - 1][j - 1]
																												if dp[i][j] > ans_length:
																																			_snake_case       =  i
																																			_snake_case       =  dp[i][j]

							return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
				import doctest

				doctest.testmod()