'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class       A   (		unittest.TestCase   ):







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							Dict:


														"""simple docstring"""


														_a     					=    tempfile.mkdtemp()

														# fmt: off
														_a     					=    ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
														# fmt: on
														_a     					=    os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES['''vocab_file'''] )
														with open(self.vocab_file							,      '''w'''							,      encoding='''utf-8''' ) as vocab_writer:
																					vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )

														_a     					=    {
														    """do_resize""": True,
														    """size""": {"""height""": 18, """width""": 18},
														    """do_normalize""": True,
														    """image_mean""": [0.5, 0.5, 0.5],
														    """image_std""": [0.5, 0.5, 0.5],
														}
														_a     					=    os.path.join(self.tmpdirname							,      _lowerCAmelCase )
														with open(self.image_processor_file							,      '''w'''							,      encoding='''utf-8''' ) as fp:
																					json.dump(_lowerCAmelCase							,      _lowerCAmelCase )







							def        __lowerCAmelCase      (  self	:  Dict							,      **lowerCAmelCase_	:  Dict )      ->							Any:


														"""simple docstring"""


														return BertTokenizer.from_pretrained(self.tmpdirname							,      **_lowerCAmelCase )







							def        __lowerCAmelCase      (  self	:  Dict							,      **lowerCAmelCase_	:  List[Any] )      ->							Any:


														"""simple docstring"""


														return ViTImageProcessor.from_pretrained(self.tmpdirname							,      **_lowerCAmelCase )







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


														"""simple docstring"""


														shutil.rmtree(self.tmpdirname )







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							int:


														"""simple docstring"""


														_a     					=    [np.random.randint(2_55							,      size=(3, 30, 4_00)							,      dtype=np.uinta )]

														_a     					=    [Image.fromarray(np.moveaxis(_lowerCAmelCase							,      0							,      -1 ) ) for x in image_inputs]

														return image_inputs







							def        __lowerCAmelCase      (  self	:  Any )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    self.get_tokenizer()
														_a     					=    self.get_image_processor()

														_a     					=    VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase							,      image_processor=_lowerCAmelCase )

														processor.save_pretrained(self.tmpdirname )
														_a     					=    VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

														self.assertEqual(processor.tokenizer.get_vocab()							,      tokenizer.get_vocab() )
														self.assertIsInstance(processor.tokenizer							,      (BertTokenizer, BertTokenizerFast) )

														self.assertEqual(processor.image_processor.to_json_string()							,      image_processor.to_json_string() )
														self.assertIsInstance(processor.image_processor							,      _lowerCAmelCase )







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							str:


														"""simple docstring"""


														_a     					=    VisionTextDualEncoderProcessor(
														    tokenizer=self.get_tokenizer()							,      image_processor=self.get_image_processor() )
														processor.save_pretrained(self.tmpdirname )

														_a     					=    self.get_tokenizer(bos_token='''(BOS)'''							,      eos_token='''(EOS)''' )
														_a     					=    self.get_image_processor(do_normalize=_lowerCAmelCase							,      padding_value=1.0 )

														_a     					=    VisionTextDualEncoderProcessor.from_pretrained(
														    self.tmpdirname							,      bos_token='''(BOS)'''							,      eos_token='''(EOS)'''							,      do_normalize=_lowerCAmelCase							,      padding_value=1.0 )

														self.assertEqual(processor.tokenizer.get_vocab()							,      tokenizer_add_kwargs.get_vocab() )
														self.assertIsInstance(processor.tokenizer							,      (BertTokenizer, BertTokenizerFast) )

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







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    self.get_image_processor()
														_a     					=    self.get_tokenizer()

														_a     					=    VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase							,      image_processor=_lowerCAmelCase )

														_a     					=    self.prepare_image_inputs()

														_a     					=    image_processor(_lowerCAmelCase							,      return_tensors='''np''' )
														_a     					=    processor(images=_lowerCAmelCase							,      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        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    self.get_image_processor()
														_a     					=    self.get_tokenizer()

														_a     					=    VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase							,      image_processor=_lowerCAmelCase )

														_a     					=    """lower newer"""

														_a     					=    processor(text=_lowerCAmelCase )

														_a     					=    tokenizer(_lowerCAmelCase )

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







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


														"""simple docstring"""


														_a     					=    self.get_image_processor()
														_a     					=    self.get_tokenizer()

														_a     					=    VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase							,      image_processor=_lowerCAmelCase )

														_a     					=    """lower newer"""
														_a     					=    self.prepare_image_inputs()

														_a     					=    processor(text=_lowerCAmelCase							,      images=_lowerCAmelCase )

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

														# test if it raises when no input is passed
														with self.assertRaises(_lowerCAmelCase ):
																					processor()







							def        __lowerCAmelCase      (  self	:  int )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    self.get_image_processor()
														_a     					=    self.get_tokenizer()

														_a     					=    VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase							,      image_processor=_lowerCAmelCase )

														_a     					=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														_a     					=    processor.batch_decode(_lowerCAmelCase )
														_a     					=    tokenizer.batch_decode(_lowerCAmelCase )

														self.assertListEqual(_lowerCAmelCase							,      _lowerCAmelCase )







							def        __lowerCAmelCase      (  self	:  Any )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    self.get_image_processor()
														_a     					=    self.get_tokenizer()

														_a     					=    VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase							,      image_processor=_lowerCAmelCase )

														_a     					=    """lower newer"""
														_a     					=    self.prepare_image_inputs()

														_a     					=    processor(text=_lowerCAmelCase							,      images=_lowerCAmelCase )

														self.assertListEqual(list(inputs.keys() )							,      processor.model_input_names )







'''simple docstring'''


import copy
import random

from transformers import CLIPTokenizer

class       A   (		_a   ):







							def __init__(  self	:  str							,      *lowerCAmelCase_	:  int							,      **lowerCAmelCase_	:  List[str] )      ->							List[Any]:


														"""simple docstring"""


														super().__init__(*lowerCAmelCase_							,      **lowerCAmelCase_ )
														_a     					=    {}







							def        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  Tuple							,      *lowerCAmelCase_	:  Any							,      **lowerCAmelCase_	:  Tuple )      ->							str:


														"""simple docstring"""


														_a     					=    super().add_tokens(lowerCAmelCase_							,      *lowerCAmelCase_							,      **lowerCAmelCase_ )
														if num_added_tokens == 0:
																					raise ValueError(
																					    F'The tokenizer already contains the token {placeholder_token}. Please pass a different'
																					    ''' `placeholder_token` that is not already in the tokenizer.''' )







							def        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  Dict							,      *lowerCAmelCase_	:  Tuple							,      lowerCAmelCase_	:  Tuple=1							,      **lowerCAmelCase_	:  int )      ->							Any:


														"""simple docstring"""


														_a     					=    []
														if num_vec_per_token == 1:
																					self.try_adding_tokens(lowerCAmelCase_							,      *lowerCAmelCase_							,      **lowerCAmelCase_ )
																					output.append(lowerCAmelCase_ )
														else:
																					_a     					=    []
																					for i in range(lowerCAmelCase_ ):
																												_a     					=    placeholder_token + F'_{i}'
																												self.try_adding_tokens(lowerCAmelCase_							,      *lowerCAmelCase_							,      **lowerCAmelCase_ )
																												output.append(lowerCAmelCase_ )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
														for token in self.token_map:
																					if token in placeholder_token:
																												raise ValueError(
																												    F'The tokenizer already has placeholder token {token} that can get confused with'
																												    F' {placeholder_token}keep placeholder tokens independent' )
														_a     					=    output







							def        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  List[Any]							,      lowerCAmelCase_	:  Tuple=False							,      lowerCAmelCase_	:  Optional[int]=1.0 )      ->							Tuple:


														"""simple docstring"""


														if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ):
																					_a     					=    []
																					for i in range(len(lowerCAmelCase_ ) ):
																												output.append(self.replace_placeholder_tokens_in_text(text[i]							,      vector_shuffle=lowerCAmelCase_ ) )
																					return output
														for placeholder_token in self.token_map:
																					if placeholder_token in text:
																												_a     					=    self.token_map[placeholder_token]
																												_a     					=    tokens[: 1 + int(len(lowerCAmelCase_ ) * prop_tokens_to_load )]
																												if vector_shuffle:
																																			_a     					=    copy.copy(lowerCAmelCase_ )
																																			random.shuffle(lowerCAmelCase_ )
																												_a     					=    text.replace(lowerCAmelCase_							,      ''' '''.join(lowerCAmelCase_ ) )
														return text







							def __call__(  self	:  List[str]							,      lowerCAmelCase_	:  str							,      *lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  Optional[int]=False							,      lowerCAmelCase_	:  Union[str, Any]=1.0							,      **lowerCAmelCase_	:  Union[str, Any] )      ->							Union[str, Any]:


														"""simple docstring"""


														return super().__call__(
														    self.replace_placeholder_tokens_in_text(
														        lowerCAmelCase_							,      vector_shuffle=lowerCAmelCase_							,      prop_tokens_to_load=lowerCAmelCase_ )							,      *lowerCAmelCase_							,      **lowerCAmelCase_							,      )







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  int							,      *lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Any=False							,      lowerCAmelCase_	:  List[Any]=1.0							,      **lowerCAmelCase_	:  Union[str, Any] )      ->							Dict:


														"""simple docstring"""


														return super().encode(
														    self.replace_placeholder_tokens_in_text(
														        lowerCAmelCase_							,      vector_shuffle=lowerCAmelCase_							,      prop_tokens_to_load=lowerCAmelCase_ )							,      *lowerCAmelCase_							,      **lowerCAmelCase_							,      )

