

'''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():
							__snake_case	: Union[str, Any]       				=							"pt"
elif is_tf_available():
							__snake_case	: Dict       				=							"tf"
else:
							__snake_case	: Any       				=							"jax"
class        A  (	a  ,    unittest.TestCase ):
						__UpperCAmelCase				:	Dict   =   PerceiverTokenizer
						__UpperCAmelCase				:	Optional[int]   =   False





						def __lowerCAmelCase   (	self			)			->							str:
										super().setUp()
										_a        =   PerceiverTokenizer()
										tokenizer.save_pretrained(self.tmpdirname			)





						@cached_property
						def __lowerCAmelCase   (	self			)			->							Optional[int]:
										return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"			)





						def __lowerCAmelCase   (	self	,    **snake_case_			)			->							Any:
										return self.tokenizer_class.from_pretrained(self.tmpdirname	,    **snake_case_			)





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_=False	,    snake_case_=2_0	,    snake_case_=5			)			->							List[str]:
										# 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.

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

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

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





						def __lowerCAmelCase   (	self			)			->							Tuple:
										_a        =   self.perceiver_tokenizer
										_a        =   'Unicode €.'
										_a        =   tokenizer(snake_case_			)
										_a        =   [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"]	,    snake_case_			)

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

										_a        =   tokenizer("e è é ê ë"			)
										_a        =   [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"]	,    snake_case_			)
										# decoding
										_a        =   tokenizer.decode(snake_case_			)
										self.assertEqual(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 __lowerCAmelCase   (	self			)			->							Any:
										_a        =   self.perceiver_tokenizer
										_a        =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
										# fmt: off
										_a        =   [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
										_a        =   tokenizer(snake_case_	,    padding=snake_case_	,    return_tensors=snake_case_			)
										self.assertIsInstance(snake_case_	,    snake_case_			)

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

										self.assertListEqual(snake_case_	,    snake_case_			)

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





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





						def __lowerCAmelCase   (	self			)			->							List[str]:
										_a        =   self.perceiver_tokenizer
										_a        =   [
										    'Summary of the text.',
										    'Another summary.',
										]
										_a        =   tokenizer(
										    text_target=snake_case_	,    max_length=3_2	,    padding="max_length"	,    truncation=snake_case_	,    return_tensors=snake_case_			)
										self.assertEqual(3_2	,    targets["input_ids"].shape[1]			)





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

																		_a        =   ' He is very happy, UNwant\u00E9d,running'
																		_a        =   tokenizer.encode(snake_case_	,    add_special_tokens=snake_case_			)
																		tokenizer.save_pretrained(snake_case_			)

																		_a        =   tokenizer.__class__.from_pretrained(snake_case_			)
																		_a        =   after_tokenizer.encode(snake_case_	,    add_special_tokens=snake_case_			)
																		self.assertListEqual(snake_case_	,    snake_case_			)

																		shutil.rmtree(snake_case_			)

										_a        =   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
																		_a        =   tempfile.mkdtemp()

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

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

																		_a        =   tokenizer.__class__.from_pretrained(snake_case_	,    model_max_length=4_3			)
																		self.assertEqual(tokenizer.model_max_length	,    4_3			)

																		shutil.rmtree(snake_case_			)





						def __lowerCAmelCase   (	self			)			->							Optional[Any]:
										_a        =   []
										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(snake_case_			)

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

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

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

																		_a        =   added_tokens_extra_ids + [
																		    'an_additional_special_token'
																		]
																		_a        =   added_tokens_extra_ids + [
																		    'an_additional_special_token'
																		]

																		with open(os.path.join(snake_case_	,    "special_tokens_map.json"			)	,    "w"	,    encoding="utf-8"			) as outfile:
																						json.dump(snake_case_	,    snake_case_			)
																		with open(os.path.join(snake_case_	,    "tokenizer_config.json"			)	,    "w"	,    encoding="utf-8"			) as outfile:
																						json.dump(snake_case_	,    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
																		_a        =   tokenizer_class.from_pretrained(
																		    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
																		_a        =   added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"	,    lstrip=snake_case_			)]
																		_a        =   tokenizer_class.from_pretrained(
																		    snake_case_	,    additional_special_tokens=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 __lowerCAmelCase   (	self			)			->							Tuple:
										_a        =   self.perceiver_tokenizer
										self.assertEqual(tokenizer.decode([1_7_8]			)	,    "�"			)





						def __lowerCAmelCase   (	self			)			->							Any:
										pass





						def __lowerCAmelCase   (	self			)			->							Dict:
										pass





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





						def __lowerCAmelCase   (	self			)			->							Tuple:
										pass





						def __lowerCAmelCase   (	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
										_a        =   self.get_tokenizers(fast=snake_case_	,    do_lower_case=snake_case_			)
										for tokenizer in tokenizers:
														with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																		_a        =   ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
																		_a        =   tokenizer.convert_tokens_to_string(snake_case_			)

																		self.assertIsInstance(snake_case_	,    snake_case_			)











"""simple docstring"""






import re







def 	_snake_case     (							lowercase__						):

				if len(re.findall('[ATCG]'				,       lowercase__						)						) != len(lowercase__						):
								raise ValueError('Invalid Strand'						)

				return dna.translate(dna.maketrans('ATCG'				,       'TAGC'						)						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()