

'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


UpperCAmelCase					 =	logging.get_logger(__name__)


class 	lowerCAmelCase  (   _lowercase ):







       def __init__(				self      :			Dict    ,   *__lowercase      :			Optional[int]    ,   **__lowercase      :			List[Any]   ):







            """simple docstring"""







            warnings.warn(
                'The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
                ' use BeitImageProcessor instead.'    ,   __SCREAMING_SNAKE_CASE    ,   )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,   **__SCREAMING_SNAKE_CASE   )




"""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   )


