





'''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():
  lowercase__    :      Optional[Any]						=    '''pt'''
elif is_tf_available():
  lowercase__    :      List[Any]						=    '''tf'''
else:
  lowercase__    :      Union[str, Any]						=    '''jax'''


class  SCREAMING_SNAKE_CASE    (a__					,		unittest.TestCase							):
     lowerCAmelCase					=       PerceiverTokenizer
     lowerCAmelCase					=       False


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().setUp()
          __A						: Any      =       PerceiverTokenizer()
          tokenizer.save_pretrained(self.tmpdirname)


     @cached_property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase=False						,	_UpperCAmelCase=20						,	_UpperCAmelCase=5):

          '''simple docstring'''






          __A						: Any      =       []
          for i in range(len(_UpperCAmelCase)):
               try:
                    __A						: List[Any]      =       tokenizer.decode([i]						,	clean_up_tokenization_spaces=_UpperCAmelCase)
               except UnicodeDecodeError:
                    pass
               toks.append((i, tok))

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

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       self.perceiver_tokenizer
          __A						: Optional[int]      =       'Unicode €.'
          __A						: int      =       tokenizer(_UpperCAmelCase)
          __A						: Any      =       [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
          self.assertEqual(encoded['input_ids']						,	_UpperCAmelCase)

          # decoding
          __A						: Union[str, Any]      =       tokenizer.decode(_UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,	'[CLS]Unicode €.[SEP]')

          __A						: List[str]      =       tokenizer('e è é ê ë')
          __A						: Optional[Any]      =       [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
          self.assertEqual(encoded['input_ids']						,	_UpperCAmelCase)
          # decoding
          __A						: Tuple      =       tokenizer.decode(_UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,	'[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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Tuple      =       self.perceiver_tokenizer
          __A						: Optional[Any]      =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
          # fmt: off
          __A						: Tuple      =       [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
          __A						: Optional[Any]      =       tokenizer(_UpperCAmelCase						,	padding=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase)
          self.assertIsInstance(_UpperCAmelCase						,	_UpperCAmelCase)

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

          self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       self.perceiver_tokenizer
          __A						: Dict      =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
          __A						: str      =       tokenizer(_UpperCAmelCase						,	padding=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase)
          # check if input_ids are returned and no decoder_input_ids
          self.assertIn('input_ids'						,	_UpperCAmelCase)
          self.assertIn('attention_mask'						,	_UpperCAmelCase)
          self.assertNotIn('decoder_input_ids'						,	_UpperCAmelCase)
          self.assertNotIn('decoder_attention_mask'						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.perceiver_tokenizer
          __A						: Optional[int]      =       [
              'Summary of the text.',
              'Another summary.',
          ]
          __A						: List[Any]      =       tokenizer(
              text_target=_UpperCAmelCase						,	max_length=32						,	padding='max_length'						,	truncation=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase)
          self.assertEqual(32						,	targets['input_ids'].shape[1])


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       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
          __A						: 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
                    __A						: Dict      =       tempfile.mkdtemp()

                    __A						: Any      =       ' He is very happy, UNwant\u00E9d,running'
                    __A						: Union[str, Any]      =       tokenizer.encode(_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase)
                    tokenizer.save_pretrained(_UpperCAmelCase)

                    __A						: List[str]      =       tokenizer.__class__.from_pretrained(_UpperCAmelCase)
                    __A						: Tuple      =       after_tokenizer.encode(_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase)
                    self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)

                    shutil.rmtree(_UpperCAmelCase)

          __A						: str      =       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
                    __A						: Union[str, Any]      =       tempfile.mkdtemp()

                    __A						: Any      =       ' He is very happy, UNwant\u00E9d,running'
                    tokenizer.add_tokens(['bim', 'bambam'])
                    __A						: str      =       tokenizer.additional_special_tokens
                    additional_special_tokens.append('new_additional_special_token')
                    tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens})
                    __A						: Union[str, Any]      =       tokenizer.encode(_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase)
                    tokenizer.save_pretrained(_UpperCAmelCase)

                    __A						: List[str]      =       tokenizer.__class__.from_pretrained(_UpperCAmelCase)
                    __A						: Any      =       after_tokenizer.encode(_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase)
                    self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)
                    self.assertIn('new_additional_special_token'						,	after_tokenizer.additional_special_tokens)
                    self.assertEqual(after_tokenizer.model_max_length						,	42)

                    __A						: Tuple      =       tokenizer.__class__.from_pretrained(_UpperCAmelCase						,	model_max_length=43)
                    self.assertEqual(tokenizer.model_max_length						,	43)

                    shutil.rmtree(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       []
          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(_UpperCAmelCase)

                    with open(os.path.join(_UpperCAmelCase						,	'special_tokens_map.json')						,	encoding='utf-8') as json_file:
                         __A						: Union[str, Any]      =       json.load(_UpperCAmelCase)

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

                    __A						: List[str]      =       [F'<extra_id_{i}>' for i in range(125)]

                    __A						: List[Any]      =       added_tokens_extra_ids + [
                        'an_additional_special_token'
                    ]
                    __A						: Union[str, Any]      =       added_tokens_extra_ids + [
                        'an_additional_special_token'
                    ]

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

                    # 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						: int      =       tokenizer_class.from_pretrained(
                        _UpperCAmelCase						,	)
                    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						: List[str]      =       added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'						,	lstrip=_UpperCAmelCase)]
                    __A						: Optional[Any]      =       tokenizer_class.from_pretrained(
                        _UpperCAmelCase						,	additional_special_tokens=_UpperCAmelCase						,	)

                    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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Tuple      =       self.perceiver_tokenizer
          self.assertEqual(tokenizer.decode([178])						,	'�')


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.get_tokenizers(fast=_UpperCAmelCase						,	do_lower_case=_UpperCAmelCase)
          for tokenizer in tokenizers:
               with self.subTest(F'{tokenizer.__class__.__name__}'):
                    __A						: List[str]      =       ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
                    __A						: Union[str, Any]      =       tokenizer.convert_tokens_to_string(_UpperCAmelCase)

                    self.assertIsInstance(_UpperCAmelCase						,	_UpperCAmelCase)






'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast


@require_vision
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       tempfile.mkdtemp()

          __A						: Optional[int]      =       BlipImageProcessor()
          __A						: List[str]      =       BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel')

          __A						: Any      =       BlipProcessor(_UpperCAmelCase						,	_UpperCAmelCase)

          processor.save_pretrained(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return AutoProcessor.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase).tokenizer


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return AutoProcessor.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase).image_processor


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: int      =       [np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta)]

          __A						: Union[str, Any]      =       [Image.fromarray(np.moveaxis(_UpperCAmelCase						,	0						,	-1)) for x in image_inputs]

          return image_inputs


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: int      =       BlipProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor())
          processor.save_pretrained(self.tmpdirname)

          __A						: int      =       self.get_tokenizer(bos_token='(BOS)'						,	eos_token='(EOS)')
          __A						: List[Any]      =       self.get_image_processor(do_normalize=_UpperCAmelCase						,	padding_value=1.0)

          __A						: Tuple      =       BlipProcessor.from_pretrained(
              self.tmpdirname						,	bos_token='(BOS)'						,	eos_token='(EOS)'						,	do_normalize=_UpperCAmelCase						,	padding_value=1.0)

          self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab())
          self.assertIsInstance(processor.tokenizer						,	_UpperCAmelCase)

          self.assertEqual(processor.image_processor.to_json_string()						,	image_processor_add_kwargs.to_json_string())
          self.assertIsInstance(processor.image_processor						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.get_image_processor()
          __A						: str      =       self.get_tokenizer()

          __A						: int      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: List[str]      =       self.prepare_image_inputs()

          __A						: str      =       image_processor(_UpperCAmelCase						,	return_tensors='np')
          __A						: str      =       processor(images=_UpperCAmelCase						,	return_tensors='np')

          for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()						,	input_processor[key].sum()						,	delta=1e-2)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       self.get_image_processor()
          __A						: Tuple      =       self.get_tokenizer()

          __A						: Union[str, Any]      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: str      =       'lower newer'

          __A						: Dict      =       processor(text=_UpperCAmelCase)

          __A						: Tuple      =       tokenizer(_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase)

          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]						,	encoded_processor[key])


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.get_image_processor()
          __A						: Optional[Any]      =       self.get_tokenizer()

          __A						: Optional[Any]      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Dict      =       'lower newer'
          __A						: int      =       self.prepare_image_inputs()

          __A						: List[Any]      =       processor(text=_UpperCAmelCase						,	images=_UpperCAmelCase)

          self.assertListEqual(list(inputs.keys())						,	['pixel_values', 'input_ids', 'attention_mask'])

          # test if it raises when no input is passed
          with pytest.raises(_UpperCAmelCase):
               processor()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       self.get_image_processor()
          __A						: Optional[Any]      =       self.get_tokenizer()

          __A						: Union[str, Any]      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Tuple      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __A						: List[str]      =       processor.batch_decode(_UpperCAmelCase)
          __A						: List[str]      =       tokenizer.batch_decode(_UpperCAmelCase)

          self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       self.get_image_processor()
          __A						: Union[str, Any]      =       self.get_tokenizer()

          __A						: Optional[Any]      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: str      =       'lower newer'
          __A						: str      =       self.prepare_image_inputs()

          __A						: Optional[int]      =       processor(text=_UpperCAmelCase						,	images=_UpperCAmelCase)

          # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
          self.assertListEqual(list(inputs.keys())						,	['pixel_values', 'input_ids', 'attention_mask'])