





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, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast







@require_vision
class 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		tempfile.mkdtemp()

             _lowerCamelCase							:     Tuple	=		BlipImageProcessor()
             _lowerCamelCase							:     Optional[int]	=		BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel''')

             _lowerCamelCase							:     int	=		BlipProcessor(a__      ,							a__)

             processor.save_pretrained(self.tmpdirname)


      def 				__snake_case			(		self      ,							**a__):



             """simple docstring"""





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


      def 				__snake_case			(		self      ,							**a__):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             shutil.rmtree(self.tmpdirname)


      def 				__snake_case			(		self):



             """simple docstring"""





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

             _lowerCamelCase							:     Any	=		[Image.fromarray(np.moveaxis(a__      ,							0      ,							-1)) for x in image_inputs]

             return image_inputs


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		BlipProcessor(tokenizer=self.get_tokenizer()      ,							image_processor=self.get_image_processor())
             processor.save_pretrained(self.tmpdirname)

             _lowerCamelCase							:     Optional[int]	=		self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)''')
             _lowerCamelCase							:     List[str]	=		self.get_image_processor(do_normalize=a__      ,							padding_value=1.0)

             _lowerCamelCase							:     Optional[int]	=		BlipProcessor.from_pretrained(
                 self.tmpdirname      ,							bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''      ,							do_normalize=a__      ,							padding_value=1.0)

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

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		self.get_image_processor()
             _lowerCamelCase							:     Optional[int]	=		self.get_tokenizer()

             _lowerCamelCase							:     Optional[Any]	=		BlipProcessor(tokenizer=a__      ,							image_processor=a__)

             _lowerCamelCase							:     Optional[int]	=		self.prepare_image_inputs()

             _lowerCamelCase							:     int	=		image_processor(a__      ,							return_tensors='''np''')
             _lowerCamelCase							:     Any	=		processor(images=a__      ,							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):



             """simple docstring"""





             _lowerCamelCase							:     int	=		self.get_image_processor()
             _lowerCamelCase							:     Any	=		self.get_tokenizer()

             _lowerCamelCase							:     Union[str, Any]	=		BlipProcessor(tokenizer=a__      ,							image_processor=a__)

             _lowerCamelCase							:     Any	=		'''lower newer'''

             _lowerCamelCase							:     List[str]	=		processor(text=a__)

             _lowerCamelCase							:     Union[str, Any]	=		tokenizer(a__      ,							return_token_type_ids=a__)

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     str	=		self.get_image_processor()
             _lowerCamelCase							:     Dict	=		self.get_tokenizer()

             _lowerCamelCase							:     Optional[int]	=		BlipProcessor(tokenizer=a__      ,							image_processor=a__)

             _lowerCamelCase							:     Dict	=		'''lower newer'''
             _lowerCamelCase							:     Union[str, Any]	=		self.prepare_image_inputs()

             _lowerCamelCase							:     int	=		processor(text=a__      ,							images=a__)

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

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.get_image_processor()
             _lowerCamelCase							:     Union[str, Any]	=		self.get_tokenizer()

             _lowerCamelCase							:     List[str]	=		BlipProcessor(tokenizer=a__      ,							image_processor=a__)

             _lowerCamelCase							:     Optional[int]	=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _lowerCamelCase							:     Union[str, Any]	=		processor.batch_decode(a__)
             _lowerCamelCase							:     List[str]	=		tokenizer.batch_decode(a__)

             self.assertListEqual(a__      ,							a__)



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		self.get_image_processor()
             _lowerCamelCase							:     List[Any]	=		self.get_tokenizer()

             _lowerCamelCase							:     Union[str, Any]	=		BlipProcessor(tokenizer=a__      ,							image_processor=a__)

             _lowerCamelCase							:     int	=		'''lower newer'''
             _lowerCamelCase							:     Tuple	=		self.prepare_image_inputs()

             _lowerCamelCase							:     List[Any]	=		processor(text=a__      ,							images=a__)

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase			       =      {
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      ['LayoutLMv3TokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
           'LayoutLMv3ForQuestionAnswering',
           'LayoutLMv3ForSequenceClassification',
           'LayoutLMv3ForTokenClassification',
           'LayoutLMv3Model',
           'LayoutLMv3PreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFLayoutLMv3ForQuestionAnswering',
           'TFLayoutLMv3ForSequenceClassification',
           'TFLayoutLMv3ForTokenClassification',
           'TFLayoutLMv3Model',
           'TFLayoutLMv3PreTrainedModel',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      ['LayoutLMv3FeatureExtractor']
       _lowerCamelCase			       =      ['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
       from .configuration_layoutlmva import (
           LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LayoutLMvaConfig,
           LayoutLMvaOnnxConfig,
       )
       from .processing_layoutlmva import LayoutLMvaProcessor
       from .tokenization_layoutlmva import LayoutLMvaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_layoutlmva import (
                  LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LayoutLMvaForQuestionAnswering,
                  LayoutLMvaForSequenceClassification,
                  LayoutLMvaForTokenClassification,
                  LayoutLMvaModel,
                  LayoutLMvaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_layoutlmva import (
                  TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLayoutLMvaForQuestionAnswering,
                  TFLayoutLMvaForSequenceClassification,
                  TFLayoutLMvaForTokenClassification,
                  TFLayoutLMvaModel,
                  TFLayoutLMvaPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
              from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
       import sys

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

