



# flake8: noqa
# Lint as: python3
UpperCAmelCase_		 =					[
    """VerificationMode""",
    """Version""",
    """disable_progress_bar""",
    """enable_progress_bar""",
    """is_progress_bar_enabled""",
    """experimental""",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental








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     snake_case_      (    self					:					Tuple      )   ->	Optional[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''': [0.4814_5466, 0.457_8275, 0.4082_1073],
        '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
    }
    _A      =       os.path.join(self.tmpdirname			,						__lowerCAmelCase      )
    with open(self.image_processor_file			,						'''w'''			,						encoding='''utf-8'''      ) as fp:
       json.dump(__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Dict			,						**__lowerCAmelCase					:					int      )   ->	Optional[int]:
    return BertTokenizer.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					str			,						**__lowerCAmelCase					:					Optional[Any]      )   ->	Tuple:
    return BertTokenizerFast.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple			,						**__lowerCAmelCase					:					str      )   ->	Union[str, Any]:
    return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Optional[int]:
    shutil.rmtree(self.tmpdirname      )

 def     snake_case_      (    self					:					int      )   ->	Optional[Any]:
    _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     snake_case_      (    self					:					Dict      )   ->	List[str]:
    _A      =       self.get_tokenizer()
    _A      =       self.get_rust_tokenizer()
    _A      =       self.get_image_processor()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )
    processor_slow.save_pretrained(self.tmpdirname      )
    _A      =       AlignProcessor.from_pretrained(self.tmpdirname			,						use_fast=__lowerCAmelCase      )

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )
    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			,						__lowerCAmelCase      )
    self.assertIsInstance(processor_fast.tokenizer			,						__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					List[Any]      )   ->	List[str]:
    _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=__lowerCAmelCase			,						padding_value=1.0      )

    _A      =       AlignProcessor.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			,						__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					str      )   ->	List[Any]:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

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

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Dict:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       '''lower newer'''

    _A      =       processor(text=__lowerCAmelCase      )

    _A      =       tokenizer(__lowerCAmelCase			,						padding='''max_length'''			,						max_length=64      )

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

 def     snake_case_      (    self					:					List[str]      )   ->	Any:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(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 pytest.raises(__lowerCAmelCase      ):
       processor()

 def     snake_case_      (    self					:					Optional[Any]      )   ->	str:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(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     snake_case_      (    self					:					str      )   ->	str:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(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      )



