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     _A   (    unittest.TestCase							):






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      tempfile.mkdtemp()

       UpperCamelCase__										=      [
           '''[UNK]''',
           '''[CLS]''',
           '''[SEP]''',
           '''[PAD]''',
           '''[MASK]''',
           '''want''',
           '''##want''',
           '''##ed''',
           '''wa''',
           '''un''',
           '''runn''',
           '''##ing''',
           ''',''',
           '''low''',
           '''lowest''',
       ]
       UpperCamelCase__										=      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]	)	)

       UpperCamelCase__										=      {
           '''do_resize''': True,
           '''size''': 20,
           '''do_center_crop''': True,
           '''crop_size''': 18,
           '''do_normalize''': True,
           '''image_mean''': [0.48145466, 0.4578275, 0.40821073],
           '''image_std''': [0.26862954, 0.26130258, 0.27577711],
       }
       UpperCamelCase__										=      os.path.join(self.tmpdirname   ,				SCREAMING_SNAKE_CASE_	)
       with open(self.image_processor_file   ,				'''w'''   ,				encoding='''utf-8'''	) as fp:
            json.dump(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





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






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





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






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





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






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       shutil.rmtree(self.tmpdirname	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





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






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





       '''simple docstring'''





       UpperCamelCase__										=      self.get_tokenizer()
       UpperCamelCase__										=      self.get_rust_tokenizer()
       UpperCamelCase__										=      self.get_image_processor()

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)
       processor_slow.save_pretrained(self.tmpdirname	)
       UpperCamelCase__										=      AlignProcessor.from_pretrained(self.tmpdirname   ,				use_fast=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)
       processor_fast.save_pretrained(self.tmpdirname	)
       UpperCamelCase__										=      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   ,				SCREAMING_SNAKE_CASE_	)
       self.assertIsInstance(processor_fast.tokenizer   ,				SCREAMING_SNAKE_CASE_	)

       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   ,				SCREAMING_SNAKE_CASE_	)
       self.assertIsInstance(processor_fast.image_processor   ,				SCREAMING_SNAKE_CASE_	)






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





       '''simple docstring'''





       UpperCamelCase__										=      AlignProcessor(tokenizer=self.get_tokenizer()   ,				image_processor=self.get_image_processor()	)
       processor.save_pretrained(self.tmpdirname	)

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

       UpperCamelCase__										=      AlignProcessor.from_pretrained(
           self.tmpdirname   ,				bos_token='''(BOS)'''   ,				eos_token='''(EOS)'''   ,				do_normalize=SCREAMING_SNAKE_CASE_   ,				padding_value=1.0	)

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

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






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.prepare_image_inputs()

       UpperCamelCase__										=      image_processor(SCREAMING_SNAKE_CASE_   ,				return_tensors='''np'''	)
       UpperCamelCase__										=      processor(images=SCREAMING_SNAKE_CASE_   ,				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 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      '''lower newer'''

       UpperCamelCase__										=      processor(text=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      tokenizer(SCREAMING_SNAKE_CASE_   ,				padding='''max_length'''   ,				max_length=64	)

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






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





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      '''lower newer'''
       UpperCamelCase__										=      self.prepare_image_inputs()

       UpperCamelCase__										=      processor(text=SCREAMING_SNAKE_CASE_   ,				images=SCREAMING_SNAKE_CASE_	)

       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(SCREAMING_SNAKE_CASE_	):
            processor()






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

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

       UpperCamelCase__										=      processor.batch_decode(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	)

       self.assertListEqual(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      '''lower newer'''
       UpperCamelCase__										=      self.prepare_image_inputs()

       UpperCamelCase__										=      processor(text=SCREAMING_SNAKE_CASE_   ,				images=SCREAMING_SNAKE_CASE_	)

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




import warnings

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


__magic_name__								=logging.get_logger(__name__)



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      None:





       '''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_	)



