
'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
  from PIL import Image

  from transformers import AlignProcessor, EfficientNetImageProcessor







@require_vision
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	Dict			=   tempfile.mkdtemp()

     _a				:	str			=   [
         '[UNK]',
         '[CLS]',
         '[SEP]',
         '[PAD]',
         '[MASK]',
         'want',
         '##want',
         '##ed',
         'wa',
         'un',
         'runn',
         '##ing',
         ',',
         'low',
         'lowest',
     ]
     _a				:	Dict			=   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				:	List[Any]			=   {
         'do_resize': True,
         'size': 20,
         'do_center_crop': True,
         'crop_size': 18,
         'do_normalize': True,
         'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
         'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
     }
     _a				:	Optional[int]			=   os.path.join(self.tmpdirname							,_a       )
     with open(self.image_processor_file							,'w'							,encoding='utf-8'       ) as fp:
        json.dump(_a							,_a       )


  def   __lowercase		(		self      :     Dict							,**_a      :     List[str]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     List[Any]							,**_a      :     List[str]       ):


     '''simple docstring'''






     return BertTokenizerFast.from_pretrained(self.tmpdirname							,**_a       )


  def   __lowercase		(		self      :     Union[str, Any]							,**_a      :     Union[str, Any]       ):


     '''simple docstring'''






     return EfficientNetImageProcessor.from_pretrained(self.tmpdirname							,**_a       )


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     shutil.rmtree(self.tmpdirname       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	Tuple			=   [np.random.randint(255							,size=(3, 30, 400)							,dtype=np.uinta       )]
     _a				:	Tuple			=   [Image.fromarray(np.moveaxis(_a							,0							,-1       )       ) for x in image_inputs]
     return image_inputs


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     _a				:	Tuple			=   self.get_tokenizer()
     _a				:	Optional[Any]			=   self.get_rust_tokenizer()
     _a				:	str			=   self.get_image_processor()

     _a				:	Dict			=   AlignProcessor(tokenizer=_a							,image_processor=_a       )
     processor_slow.save_pretrained(self.tmpdirname       )
     _a				:	Union[str, Any]			=   AlignProcessor.from_pretrained(self.tmpdirname							,use_fast=_a       )

     _a				:	Union[str, Any]			=   AlignProcessor(tokenizer=_a							,image_processor=_a       )
     processor_fast.save_pretrained(self.tmpdirname       )
     _a				:	Any			=   AlignProcessor.from_pretrained(self.tmpdirname       )

     self.assertEqual(processor_slow.tokenizer.get_vocab()							,tokenizer_slow.get_vocab()       )
     self.assertEqual(processor_fast.tokenizer.get_vocab()							,tokenizer_fast.get_vocab()       )
     self.assertEqual(tokenizer_slow.get_vocab()							,tokenizer_fast.get_vocab()       )
     self.assertIsInstance(processor_slow.tokenizer							,_a       )
     self.assertIsInstance(processor_fast.tokenizer							,_a       )

     self.assertEqual(processor_slow.image_processor.to_json_string()							,image_processor.to_json_string()       )
     self.assertEqual(processor_fast.image_processor.to_json_string()							,image_processor.to_json_string()       )
     self.assertIsInstance(processor_slow.image_processor							,_a       )
     self.assertIsInstance(processor_fast.image_processor							,_a       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	Optional[Any]			=   AlignProcessor(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[str]			=   self.get_image_processor(do_normalize=_a							,padding_value=1.0       )

     _a				:	Union[str, Any]			=   AlignProcessor.from_pretrained(
         self.tmpdirname							,bos_token='(BOS)'							,eos_token='(EOS)'							,do_normalize=_a							,padding_value=1.0       )

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

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


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






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

     _a				:	Optional[int]			=   AlignProcessor(tokenizer=_a							,image_processor=_a       )

     _a				:	List[Any]			=   self.prepare_image_inputs()

     _a				:	Union[str, Any]			=   image_processor(_a							,return_tensors='np'       )
     _a				:	Any			=   processor(images=_a							,return_tensors='np'       )

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


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






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

     _a				:	Optional[int]			=   AlignProcessor(tokenizer=_a							,image_processor=_a       )

     _a				:	Union[str, Any]			=   'lower newer'

     _a				:	List[Any]			=   processor(text=_a       )

     _a				:	Any			=   tokenizer(_a							,padding='max_length'							,max_length=64       )

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


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






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

     _a				:	Union[str, Any]			=   AlignProcessor(tokenizer=_a							,image_processor=_a       )

     _a				:	Any			=   'lower newer'
     _a				:	Tuple			=   self.prepare_image_inputs()

     _a				:	Dict			=   processor(text=_a							,images=_a       )

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

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


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






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

     _a				:	Any			=   AlignProcessor(tokenizer=_a							,image_processor=_a       )

     _a				:	Union[str, Any]			=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     _a				:	List[str]			=   processor.batch_decode(_a       )
     _a				:	Optional[Any]			=   tokenizer.batch_decode(_a       )

     self.assertListEqual(_a							,_a       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   self.get_image_processor()
     _a				:	int			=   self.get_tokenizer()

     _a				:	str			=   AlignProcessor(tokenizer=_a							,image_processor=_a       )

     _a				:	Tuple			=   'lower newer'
     _a				:	Union[str, Any]			=   self.prepare_image_inputs()

     _a				:	int			=   processor(text=_a							,images=_a       )

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





'''simple docstring'''







import warnings

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


__lowerCAmelCase         =    logging.get_logger(__name__)







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  def __init__(		self      :     int							,*_a      :     Optional[int]							,**_a      :     str       ):


     '''simple docstring'''






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



