

'''simple docstring'''

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast


@require_vision
class 					lowerCAmelCase       ( unittest.TestCase				):







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=tempfile.mkdtemp()

            __lowercase     						=CLIPImageProcessor()
            __lowercase     						=BertTokenizer.from_pretrained(
                'hf-internal-testing/tiny-random-BertModel'  , model_input_names=['input_ids', 'attention_mask'] )

            __lowercase     						=GitProcessor(__lowercase  , __lowercase )

            processor.save_pretrained(self.tmpdirname )







       def      snake_case				(							self     :	Dict  , **__lowercase     :	str ):







            """simple docstring"""







            return AutoProcessor.from_pretrained(self.tmpdirname  , **__lowercase ).tokenizer







       def      snake_case				(							self     :	Dict  , **__lowercase     :	Any ):







            """simple docstring"""







            return AutoProcessor.from_pretrained(self.tmpdirname  , **__lowercase ).image_processor







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            shutil.rmtree(self.tmpdirname )







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=[np.random.randint(255  , size=(3, 30, 400)  , dtype=np.uinta )]

            __lowercase     						=[Image.fromarray(np.moveaxis(__lowercase  , 0  , -1 ) ) for x in image_inputs]

            return image_inputs







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            __lowercase     						=GitProcessor(tokenizer=self.get_tokenizer()  , image_processor=self.get_image_processor() )
            processor.save_pretrained(self.tmpdirname )

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

            __lowercase     						=GitProcessor.from_pretrained(
                self.tmpdirname  , bos_token='(BOS)'  , eos_token='(EOS)'  , do_normalize=__lowercase  , padding_value=1.0 )

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

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







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            __lowercase     						=self.get_image_processor()
            __lowercase     						=self.get_tokenizer()

            __lowercase     						=GitProcessor(tokenizer=__lowercase  , image_processor=__lowercase )

            __lowercase     						=self.prepare_image_inputs()

            __lowercase     						=image_processor(__lowercase  , return_tensors='np' )
            __lowercase     						=processor(images=__lowercase  , return_tensors='np' )

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







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            __lowercase     						=self.get_image_processor()
            __lowercase     						=self.get_tokenizer()

            __lowercase     						=GitProcessor(tokenizer=__lowercase  , image_processor=__lowercase )

            __lowercase     						='lower newer'

            __lowercase     						=processor(text=__lowercase )

            __lowercase     						=tokenizer(__lowercase  , return_token_type_ids=__lowercase )

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







       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						=self.get_image_processor()
            __lowercase     						=self.get_tokenizer()

            __lowercase     						=GitProcessor(tokenizer=__lowercase  , image_processor=__lowercase )

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

            __lowercase     						=processor(text=__lowercase  , images=__lowercase )

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

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







       def      snake_case				(							self     :	int ):







            """simple docstring"""







            __lowercase     						=self.get_image_processor()
            __lowercase     						=self.get_tokenizer()

            __lowercase     						=GitProcessor(tokenizer=__lowercase  , image_processor=__lowercase )

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

            __lowercase     						=processor.batch_decode(__lowercase )
            __lowercase     						=tokenizer.batch_decode(__lowercase )

            self.assertListEqual(__lowercase  , __lowercase )







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=self.get_image_processor()
            __lowercase     						=self.get_tokenizer()

            __lowercase     						=GitProcessor(tokenizer=__lowercase  , image_processor=__lowercase )

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

            __lowercase     						=processor(text=__lowercase  , images=__lowercase )

            # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
            self.assertListEqual(list(inputs.keys() )  , ['input_ids', 'attention_mask', 'pixel_values'] )





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase							=					{
    '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					[
           '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''VideoMAEForPreTraining''',
           '''VideoMAEModel''',
           '''VideoMAEPreTrainedModel''',
           '''VideoMAEForVideoClassification''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					['''VideoMAEFeatureExtractor''']
       UpperCAmelCase							=					['''VideoMAEImageProcessor''']

if TYPE_CHECKING:
       from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_videomae import (
                  VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VideoMAEForPreTraining,
                  VideoMAEForVideoClassification,
                  VideoMAEModel,
                  VideoMAEPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_videomae import VideoMAEFeatureExtractor
              from .image_processing_videomae import VideoMAEImageProcessor

else:
       import sys

       UpperCAmelCase							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


