






A        =			[
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages







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						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tempfile.mkdtemp()

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

						SCREAMING_SNAKE_CASE_   :	Tuple													=   {
						    'do_resize': True,
						    'size': 20,
						    'do_center_crop': True,
						    'crop_size': 18,
						    'do_normalize': True,
						    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
						    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
						}
						SCREAMING_SNAKE_CASE_   :	str													=   os.path.join(self.tmpdirname ,    UpperCamelCase__       )
						with open(self.image_processor_file ,    'w' ,    encoding='utf-8'       ) as fp:
										json.dump(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return BertTokenizer.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return BertTokenizerFast.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self       ):
						shutil.rmtree(self.tmpdirname       )


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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.get_tokenizer()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_slow.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor.from_pretrained(self.tmpdirname ,    use_fast=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_fast.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	Union[str, 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 ,    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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=self.get_tokenizer() ,    image_processor=self.get_image_processor()       )
						processor.save_pretrained(self.tmpdirname       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_tokenizer(bos_token='(BOS)' ,    eos_token='(EOS)'       )
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_image_processor(do_normalize=UpperCamelCase__ ,    padding_value=1.0       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	str													=   self.get_tokenizer()

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

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	int													=   image_processor(UpperCamelCase__ ,    return_tensors='np'       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   'lower newer'

						SCREAMING_SNAKE_CASE_   :	Tuple													=   processor(text=UpperCamelCase__       )

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

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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_tokenizer()

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

						SCREAMING_SNAKE_CASE_   :	str													=   'lower newer'
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_tokenizer()

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

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

						SCREAMING_SNAKE_CASE_   :	List[str]													=   processor.batch_decode(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.batch_decode(UpperCamelCase__       )

						self.assertListEqual(UpperCamelCase__ ,    UpperCamelCase__       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_tokenizer()

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

						SCREAMING_SNAKE_CASE_   :	str													=   'lower newer'
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   processor(text=UpperCamelCase__ ,    images=UpperCamelCase__       )

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