


"""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     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				tempfile.mkdtemp()

          _a    	=				[
              '''[UNK]''',
              '''[CLS]''',
              '''[SEP]''',
              '''[PAD]''',
              '''[MASK]''',
              '''want''',
              '''##want''',
              '''##ed''',
              '''wa''',
              '''un''',
              '''runn''',
              '''##ing''',
              ''',''',
              '''low''',
              '''lowest''',
          ]
          _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''': 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],
          }
          _a    	=				os.path.join(self.tmpdirname    ,				UpperCamelCase__							)
          with open(self.image_processor_file    ,				'''w'''    ,				encoding='''utf-8'''							) as fp:
               json.dump(UpperCamelCase__    ,				UpperCamelCase__							)


     def 							_UpperCAmelCase      (      self    ,				**__UpperCAmelCase							)       ->							Any:
          return BertTokenizer.from_pretrained(self.tmpdirname    ,				**UpperCamelCase__							)


     def 							_UpperCAmelCase      (      self    ,				**__UpperCAmelCase							)       ->							str:
          return BertTokenizerFast.from_pretrained(self.tmpdirname    ,				**UpperCamelCase__							)


     def 							_UpperCAmelCase      (      self    ,				**__UpperCAmelCase							)       ->							List[Any]:
          return EfficientNetImageProcessor.from_pretrained(self.tmpdirname    ,				**UpperCamelCase__							)


     def 							_UpperCAmelCase      (      self							)       ->							int:
          shutil.rmtree(self.tmpdirname							)


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


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				self.get_tokenizer()
          _a    	=				self.get_rust_tokenizer()
          _a    	=				self.get_image_processor()

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

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

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


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				AlignProcessor(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=UpperCamelCase__    ,				padding_value=1.0							)

          _a    	=				AlignProcessor.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 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				self.get_image_processor()
          _a    	=				self.get_tokenizer()

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

          _a    	=				self.prepare_image_inputs()

          _a    	=				image_processor(UpperCamelCase__    ,				return_tensors='''np'''							)
          _a    	=				processor(images=UpperCamelCase__    ,				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 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				self.get_image_processor()
          _a    	=				self.get_tokenizer()

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

          _a    	=				'''lower newer'''

          _a    	=				processor(text=UpperCamelCase__							)

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

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


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				self.get_image_processor()
          _a    	=				self.get_tokenizer()

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

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

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

          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(UpperCamelCase__							):
               processor()


     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				self.get_image_processor()
          _a    	=				self.get_tokenizer()

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

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

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

          self.assertListEqual(UpperCamelCase__    ,				UpperCamelCase__							)





     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				self.get_image_processor()
          _a    	=				self.get_tokenizer()

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

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

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

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



"""simple docstring"""






from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels