

'''simple docstring'''

UpperCAmelCase					 =	[
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages




"""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		:       Dict     						=       'tf'
else:
    a		:       Optional[Any]     						=       'jax'







class     lowercase(_lowercase    ,						unittest.TestCase       ):
     __snake_case:	List[Any]						=     PerceiverTokenizer
     __snake_case:	Optional[Any]						=     False



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



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



     @cached_property
     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     PerceiverTokenizer:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=False     ,				__SCREAMING_SNAKE_CASE=2_0     ,				__SCREAMING_SNAKE_CASE=5   )		->     Tuple[str, list]:







         """simple docstring"""



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

         a__		=			list(filter(lambda __SCREAMING_SNAKE_CASE   : re.match(R'^[ a-zA-Z]+$'     ,				t[1]   )     ,				__SCREAMING_SNAKE_CASE   )   )
         a__		=			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:
             a__		=			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:
                 a__		=			toks + toks
        # toks_str = [t[1] for t in toks]
         a__		=			[t[0] for t in toks]

         # Ensure consistency
         a__		=			tokenizer.decode(__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         if " " not in output_txt and len(__SCREAMING_SNAKE_CASE   ) > 1:
             a__		=			(
                 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:
             a__		=			' ' + output_txt
         a__		=			tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
         return output_txt, output_ids



     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			'Unicode €.'
         a__		=			tokenizer(__SCREAMING_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']     ,				__SCREAMING_SNAKE_CASE   )

         # decoding
         a__		=			tokenizer.decode(__SCREAMING_SNAKE_CASE   )
         self.assertEqual(__SCREAMING_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']     ,				__SCREAMING_SNAKE_CASE   )
         # decoding
         a__		=			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 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         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(__SCREAMING_SNAKE_CASE     ,				padding=__SCREAMING_SNAKE_CASE     ,				return_tensors=__SCREAMING_SNAKE_CASE   )
         self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         if FRAMEWORK != "jax":
             a__		=			list(batch.input_ids.numpy()[0]   )
         else:
             a__		=			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 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
         a__		=			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 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			[
             'Summary of the text.',
             'Another summary.',
         ]
         a__		=			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 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         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(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE   )

                 a__		=			tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE   )
                 a__		=			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   )

         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(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE   )

                 a__		=			tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE   )
                 a__		=			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   )

                 a__		=			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 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



         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(__SCREAMING_SNAKE_CASE   )

                 with open(os.path.join(__SCREAMING_SNAKE_CASE     ,				'special_tokens_map.json'   )     ,				encoding='utf-8'   ) as json_file:
                     a__		=			json.load(__SCREAMING_SNAKE_CASE   )

                 with open(os.path.join(__SCREAMING_SNAKE_CASE     ,				'tokenizer_config.json'   )     ,				encoding='utf-8'   ) as json_file:
                     a__		=			json.load(__SCREAMING_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(__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
                 a__		=			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
                 a__		=			added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'     ,				lstrip=__SCREAMING_SNAKE_CASE   )]
                 a__		=			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 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_7_8]   )     ,				'�'   )



     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         pass




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







         """simple docstring"""



         a__		=			self.get_tokenizers(fast=__SCREAMING_SNAKE_CASE     ,				do_lower_case=__SCREAMING_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(__SCREAMING_SNAKE_CASE   )

                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )


