"""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():
    __UpperCamelCase									=			'''pt'''
elif is_tf_available():
    __UpperCamelCase									=			'''tf'''
else:
    __UpperCamelCase									=			'''jax'''




class  UpperCamelCase					(				lowerCAmelCase__    ,		unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      PerceiverTokenizer
       SCREAMING_SNAKE_CASE_			=      False







       def 		a_						( self)      ->      Union[str, Any]:
            super().setUp()
            snake_case_					       =					PerceiverTokenizer()
            tokenizer.save_pretrained(self.tmpdirname)







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







       def 		a_						( self,							**lowerCAmelCase__)      ->      PerceiverTokenizer:
            return self.tokenizer_class.from_pretrained(self.tmpdirname,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__=False,							lowerCAmelCase__=20,							lowerCAmelCase__=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(lowerCAmelCase__)):
                 try:
                      snake_case_					       =					tokenizer.decode([i],							clean_up_tokenization_spaces=lowerCAmelCase__)
                 except UnicodeDecodeError:
                      pass
                 toks.append((i, tok))

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







       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					self.perceiver_tokenizer
            snake_case_					       =					'Unicode €.'
            snake_case_					       =					tokenizer(lowerCAmelCase__)
            snake_case_					       =					[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
            self.assertEqual(encoded['input_ids'],							lowerCAmelCase__)

            # decoding
            snake_case_					       =					tokenizer.decode(lowerCAmelCase__)
            self.assertEqual(lowerCAmelCase__,							'[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'],							lowerCAmelCase__)
            # decoding
            snake_case_					       =					tokenizer.decode(lowerCAmelCase__)
            self.assertEqual(lowerCAmelCase__,							'[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 		a_						( self)      ->      List[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(lowerCAmelCase__,							padding=lowerCAmelCase__,							return_tensors=lowerCAmelCase__)
            self.assertIsInstance(lowerCAmelCase__,							lowerCAmelCase__)

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

            self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)

            self.assertEqual((2, 38),							batch.input_ids.shape)
            self.assertEqual((2, 38),							batch.attention_mask.shape)







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







       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					self.perceiver_tokenizer
            snake_case_					       =					[
                'Summary of the text.',
                'Another summary.',
            ]
            snake_case_					       =					tokenizer(
                text_target=lowerCAmelCase__,							max_length=32,							padding='max_length',							truncation=lowerCAmelCase__,							return_tensors=lowerCAmelCase__)
            self.assertEqual(32,							targets['input_ids'].shape[1])







       def 		a_						( self)      ->      Optional[int]:
            # 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(lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__)
                      tokenizer.save_pretrained(lowerCAmelCase__)

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

                      shutil.rmtree(lowerCAmelCase__)

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

                      snake_case_					       =					tokenizer.__class__.from_pretrained(lowerCAmelCase__)
                      snake_case_					       =					after_tokenizer.encode(lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__)
                      self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)
                      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(lowerCAmelCase__,							model_max_length=43)
                      self.assertEqual(tokenizer.model_max_length,							43)

                      shutil.rmtree(lowerCAmelCase__)







       def 		a_						( self)      ->      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(lowerCAmelCase__)

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

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

                      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(lowerCAmelCase__,							'special_tokens_map.json'),							'w',							encoding='utf-8') as outfile:
                           json.dump(lowerCAmelCase__,							lowerCAmelCase__)
                      with open(os.path.join(lowerCAmelCase__,							'tokenizer_config.json'),							'w',							encoding='utf-8') as outfile:
                           json.dump(lowerCAmelCase__,							lowerCAmelCase__)

                      # 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(
                          lowerCAmelCase__,							)
                      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=lowerCAmelCase__)]
                      snake_case_					       =					tokenizer_class.from_pretrained(
                          lowerCAmelCase__,							additional_special_tokens=lowerCAmelCase__,							)

                      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 		a_						( self)      ->      Optional[Any]:
            snake_case_					       =					self.perceiver_tokenizer
            self.assertEqual(tokenizer.decode([178]),							'�')







       def 		a_						( self)      ->      Optional[Any]:
            pass







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







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







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







       def 		a_						( self)      ->      Optional[Any]:
            # 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=lowerCAmelCase__,							do_lower_case=lowerCAmelCase__)
            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(lowerCAmelCase__)

                      self.assertIsInstance(lowerCAmelCase__,							lowerCAmelCase__)







import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class __snake_case			(			a		):
   UpperCAmelCase__  :    Dict          =  ['''image_processor''', '''tokenizer''']
   UpperCAmelCase__  :    Dict          =  '''FlavaImageProcessor'''
   UpperCAmelCase__  :    Dict          =  ('''BertTokenizer''', '''BertTokenizerFast''')







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						List[str]=None					,						_snake_case		:						str=None					,						**_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							None
        if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''					,						_snake_case					,						)
             UpperCAmelCase_          =							kwargs.pop('''feature_extractor''')

        UpperCAmelCase_          =							image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
             raise ValueError('''You need to specify an `image_processor`.''')
        if tokenizer is None:
             raise ValueError('''You need to specify a `tokenizer`.''')

        super().__init__(_snake_case					,						_snake_case)
        UpperCAmelCase_          =							self.image_processor







   def __call__(      self		:						List[Any]					,						_snake_case		:						Optional[ImageInput] = None					,						_snake_case		:						Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None					,						_snake_case		:						bool = True					,						_snake_case		:						Union[bool, str, PaddingStrategy] = False					,						_snake_case		:						Union[bool, str, TruncationStrategy] = False					,						_snake_case		:						Optional[int] = None					,						_snake_case		:						int = 0					,						_snake_case		:						Optional[int] = None					,						_snake_case		:						Optional[bool] = None					,						_snake_case		:						Optional[bool] = None					,						_snake_case		:						Optional[bool] = None					,						_snake_case		:						Optional[bool] = None					,						_snake_case		:						bool = False					,						_snake_case		:						bool = False					,						_snake_case		:						bool = False					,						_snake_case		:						bool = False					,						_snake_case		:						bool = True					,						_snake_case		:						Optional[Union[str, TensorType]] = None					,						**_snake_case		:						Any					,						):

        """simple docstring"""


        if text is None and images is None:
             raise ValueError('''You have to specify either text or images. Both cannot be none.''')

        if text is not None:
             UpperCAmelCase_          =							self.tokenizer(
                 text=_snake_case					,						add_special_tokens=_snake_case					,						padding=_snake_case					,						truncation=_snake_case					,						max_length=_snake_case					,						stride=_snake_case					,						pad_to_multiple_of=_snake_case					,						return_token_type_ids=_snake_case					,						return_attention_mask=_snake_case					,						return_overflowing_tokens=_snake_case					,						return_special_tokens_mask=_snake_case					,						return_offsets_mapping=_snake_case					,						return_length=_snake_case					,						verbose=_snake_case					,						return_tensors=_snake_case					,						**_snake_case					,						)
        if images is not None:
             UpperCAmelCase_          =							self.image_processor(
                 _snake_case					,						return_image_mask=_snake_case					,						return_codebook_pixels=_snake_case					,						return_tensors=_snake_case					,						**_snake_case					,						)

        if text is not None and images is not None:
             encoding.update(_snake_case)
             return encoding
        elif text is not None:
             return encoding
        else:
             return BatchEncoding(data=dict(**_snake_case)					,						tensor_type=_snake_case)







   def lowerCamelCase						(      self		:						Any					,						*_snake_case		:						Optional[Any]					,						**_snake_case		:						int):

        """simple docstring"""


        return self.tokenizer.batch_decode(*_snake_case					,						**_snake_case)







   def lowerCamelCase						(      self		:						Optional[int]					,						*_snake_case		:						int					,						**_snake_case		:						Dict):

        """simple docstring"""


        return self.tokenizer.decode(*_snake_case					,						**_snake_case)







   @property
   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.tokenizer.model_input_names
        UpperCAmelCase_          =							self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))







   @property
   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        warnings.warn(
            '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,						_snake_case					,						)
        return self.image_processor_class







   @property
   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        warnings.warn(
            '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,						_snake_case					,						)
        return self.image_processor






