
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():
		lowercase  :					Any													=       'pt'
elif is_tf_available():
		lowercase  :					Any													=       'tf'
else:
		lowercase  :					Optional[Any]													=       'jax'




class  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     PerceiverTokenizer
	_A								=     False







	def    _lowerCamelCase				(					self		:Any						)		-> List[Any]:
								super().setUp()
								__UpperCamelCase	:   Optional[Any]			   =					PerceiverTokenizer()
								tokenizer.save_pretrained(self.tmpdirname						)







	@cached_property
	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> List[str]:
								return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   **a		:Union[str, Any]						)		-> PerceiverTokenizer:
								return self.tokenizer_class.from_pretrained(self.tmpdirname     ,   **a						)







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:Any     ,   a		:Union[str, Any]=False     ,   a		:Optional[Any]=2_0     ,   a		:List[Any]=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	:   Union[str, Any]			   =					tokenizer.decode([i]     ,   clean_up_tokenization_spaces=a						)
															except UnicodeDecodeError:
																						pass
															toks.append((i, tok)						)

								__UpperCamelCase	:   List[str]			   =					list(filter(lambda a						: re.match(r"^[ a-zA-Z]+$"     ,   t[1]						)     ,   a						)						)
								__UpperCamelCase	:   Optional[int]			   =					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	:   Any			   =					toks + toks
        # toks_str = [t[1] for t in toks]
								__UpperCamelCase	:   Union[str, Any]			   =					[t[0] for t in toks]

								# Ensure consistency
								__UpperCamelCase	:   Tuple			   =					tokenizer.decode(a     ,   clean_up_tokenization_spaces=a						)
								if " " not in output_txt and len(a						) > 1:
															__UpperCamelCase	:   Dict			   =					(
															    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	:   Dict			   =					" " + output_txt
								__UpperCamelCase	:   Optional[Any]			   =					tokenizer.encode(a     ,   add_special_tokens=a						)
								return output_txt, output_ids







	def    _lowerCamelCase				(					self		:str						)		-> Dict:
								__UpperCamelCase	:   Union[str, Any]			   =					self.perceiver_tokenizer
								__UpperCamelCase	:   int			   =					"Unicode €."
								__UpperCamelCase	:   Optional[Any]			   =					tokenizer(a						)
								__UpperCamelCase	:   List[str]			   =					[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"]     ,   a						)

								# decoding
								__UpperCamelCase	:   int			   =					tokenizer.decode(a						)
								self.assertEqual(a     ,   "[CLS]Unicode €.[SEP]"						)

								__UpperCamelCase	:   str			   =					tokenizer("e è é ê ë"						)
								__UpperCamelCase	:   Optional[int]			   =					[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"]     ,   a						)
								# decoding
								__UpperCamelCase	:   List[Any]			   =					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    _lowerCamelCase				(					self		:Tuple						)		-> List[str]:
								__UpperCamelCase	:   List[str]			   =					self.perceiver_tokenizer
								__UpperCamelCase	:   Union[str, Any]			   =					["A long paragraph for summarization.", "Another paragraph for summarization."]
								# fmt: off
								__UpperCamelCase	:   Optional[Any]			   =					[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
								__UpperCamelCase	:   Optional[int]			   =					tokenizer(a     ,   padding=a     ,   return_tensors=a						)
								self.assertIsInstance(a     ,   a						)

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

								self.assertListEqual(a     ,   a						)

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







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Union[str, Any]:
								__UpperCamelCase	:   int			   =					self.perceiver_tokenizer
								__UpperCamelCase	:   str			   =					["A long paragraph for summarization.", "Another paragraph for summarization."]
								__UpperCamelCase	:   List[Any]			   =					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    _lowerCamelCase				(					self		:List[str]						)		-> Tuple:
								__UpperCamelCase	:   Dict			   =					self.perceiver_tokenizer
								__UpperCamelCase	:   Any			   =					[
								    "Summary of the text.",
								    "Another summary.",
								]
								__UpperCamelCase	:   List[str]			   =					tokenizer(
								    text_target=a     ,   max_length=3_2     ,   padding="max_length"     ,   truncation=a     ,   return_tensors=a						)
								self.assertEqual(3_2     ,   targets["input_ids"].shape[1]						)







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Optional[Any]:
								# safety check on max_len default value so we are sure the test works
								__UpperCamelCase	:   Optional[int]			   =					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
								__UpperCamelCase	:   List[Any]			   =					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	:   str			   =					tempfile.mkdtemp()

																						__UpperCamelCase	:   Any			   =					" He is very happy, UNwant\u00E9d,running"
																						__UpperCamelCase	:   Optional[int]			   =					tokenizer.encode(a     ,   add_special_tokens=a						)
																						tokenizer.save_pretrained(a						)

																						__UpperCamelCase	:   str			   =					tokenizer.__class__.from_pretrained(a						)
																						__UpperCamelCase	:   Any			   =					after_tokenizer.encode(a     ,   add_special_tokens=a						)
																						self.assertListEqual(a     ,   a						)

																						shutil.rmtree(a						)

								__UpperCamelCase	:   Any			   =					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
																						__UpperCamelCase	:   List[str]			   =					tempfile.mkdtemp()

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

																						__UpperCamelCase	:   Tuple			   =					tokenizer.__class__.from_pretrained(a						)
																						__UpperCamelCase	:   List[str]			   =					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     ,   4_2						)

																						__UpperCamelCase	:   Optional[Any]			   =					tokenizer.__class__.from_pretrained(a     ,   model_max_length=4_3						)
																						self.assertEqual(tokenizer.model_max_length     ,   4_3						)

																						shutil.rmtree(a						)







	def    _lowerCamelCase				(					self		:int						)		-> Optional[int]:
								__UpperCamelCase	:   Dict			   =					[]
								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	:   str			   =					json.load(a						)

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

																						__UpperCamelCase	:   List[Any]			   =					[f'<extra_id_{i}>' for i in range(1_2_5						)]

																						__UpperCamelCase	:   Dict			   =					added_tokens_extra_ids + [
																						    "an_additional_special_token"
																						]
																						__UpperCamelCase	:   Optional[Any]			   =					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	:   Tuple			   =					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	:   Optional[Any]			   =					added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"     ,   lstrip=a						)]
																						__UpperCamelCase	:   List[Any]			   =					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    _lowerCamelCase				(					self		:Tuple						)		-> List[Any]:
								__UpperCamelCase	:   str			   =					self.perceiver_tokenizer
								self.assertEqual(tokenizer.decode([1_7_8]						)     ,   "�"						)







	def    _lowerCamelCase				(					self		:int						)		-> Tuple:
								pass







	def    _lowerCamelCase				(					self		:Dict						)		-> Any:
								pass







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







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







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

																						self.assertIsInstance(a     ,   a						)

import math




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							__UpperCamelCase	:   List[Any]			   =					input("Enter message: ")
							__UpperCamelCase	:   Optional[int]			   =					int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
							__UpperCamelCase	:   str			   =					input("Encryption/Decryption [e/d]: ")

							if mode.lower().startswith("e"):
														__UpperCamelCase	:   List[str]			   =					encrypt_message(_lowerCamelCase						,  _lowerCamelCase)
							elif mode.lower().startswith("d"):
														__UpperCamelCase	:   Dict			   =					decrypt_message(_lowerCamelCase						,  _lowerCamelCase)

							# Append pipe symbol (vertical bar) to identify spaces at the end.
							print(F'Output:\n{text + "|"}')




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			str)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					[""] * key
							for col in range(_lowerCamelCase):
														__UpperCamelCase	:   Any			   =					col
														while pointer < len(_lowerCamelCase):
																					cipher_text[col] += message[pointer]
																					pointer += key
							return "".join(_lowerCamelCase)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			str)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					math.ceil(len(_lowerCamelCase) / key)
							__UpperCamelCase	:   Any			   =					key
							__UpperCamelCase	:   str			   =					(num_cols * num_rows) - len(_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					[""] * num_cols
							__UpperCamelCase	:   Dict			   =					0
							__UpperCamelCase	:   int			   =					0

							for symbol in message:
														plain_text[col] += symbol
														col += 1

														if (
														    (col == num_cols)
														    or (col == num_cols - 1)
														    and (row >= num_rows - num_shaded_boxes)
														):
																					__UpperCamelCase	:   List[Any]			   =					0
																					row += 1

							return "".join(_lowerCamelCase)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()