


'''simple docstring'''



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
      from transformers import TvltImageProcessor

if is_speech_available():
      from transformers import TvltFeatureExtractor

from transformers import TvltProcessor



@require_torch
class 			a       (	unittest.TestCase ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	Any    )		->							Any:
          __UpperCAmelCase			:				Dict													=    '''ZinengTang/tvlt-base'''
          __UpperCAmelCase			:				Tuple													=    tempfile.mkdtemp()

     def 				lowerCamelCase__				(       self  :	int							,				**snake_case  :	str    )		->							List[Any]:
          return TvltImageProcessor.from_pretrained(self.checkpoint							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				**snake_case  :	str    )		->							Tuple:
          return TvltFeatureExtractor.from_pretrained(self.checkpoint							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							int:
          shutil.rmtree(self.tmpdirname    )

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							str:
          __UpperCAmelCase			:				List[str]													=    self.get_image_processor()
          __UpperCAmelCase			:				List[str]													=    self.get_feature_extractor()

          __UpperCAmelCase			:				List[str]													=    TvltProcessor(image_processor=snake_case							,				feature_extractor=snake_case    )
          processor.save_pretrained(self.tmpdirname    )
          __UpperCAmelCase			:				Optional[Any]													=    TvltProcessor.from_pretrained(self.tmpdirname    )

          self.assertIsInstance(processor.feature_extractor							,				snake_case    )
          self.assertIsInstance(processor.image_processor							,				snake_case    )

     def 				lowerCamelCase__				(       self  :	Any    )		->							Union[str, Any]:
          __UpperCAmelCase			:				int													=    self.get_image_processor()
          __UpperCAmelCase			:				Optional[int]													=    self.get_feature_extractor()

          __UpperCAmelCase			:				Optional[int]													=    TvltProcessor(image_processor=snake_case							,				feature_extractor=snake_case    )

          __UpperCAmelCase			:				Optional[Any]													=    np.ones([1_2000]    )

          __UpperCAmelCase			:				Dict													=    feature_extractor(snake_case							,				return_tensors='''np'''    )
          __UpperCAmelCase			:				int													=    processor(audio=snake_case							,				return_tensors='''np'''    )

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

     def 				lowerCamelCase__				(       self  :	int    )		->							Tuple:
          __UpperCAmelCase			:				int													=    self.get_image_processor()
          __UpperCAmelCase			:				Any													=    self.get_feature_extractor()

          __UpperCAmelCase			:				Union[str, Any]													=    TvltProcessor(image_processor=snake_case							,				feature_extractor=snake_case    )

          __UpperCAmelCase			:				Dict													=    np.ones([3, 224, 224]    )

          __UpperCAmelCase			:				Any													=    image_processor(snake_case							,				return_tensors='''np'''    )
          __UpperCAmelCase			:				Union[str, Any]													=    processor(images=snake_case							,				return_tensors='''np'''    )

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

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Dict:
          __UpperCAmelCase			:				Optional[int]													=    self.get_image_processor()
          __UpperCAmelCase			:				int													=    self.get_feature_extractor()

          __UpperCAmelCase			:				Dict													=    TvltProcessor(image_processor=snake_case							,				feature_extractor=snake_case    )

          __UpperCAmelCase			:				Optional[int]													=    np.ones([1_2000]    )
          __UpperCAmelCase			:				Dict													=    np.ones([3, 224, 224]    )

          __UpperCAmelCase			:				Union[str, Any]													=    processor(audio=snake_case							,				images=snake_case    )

          self.assertListEqual(list(inputs.keys()    )							,				['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']    )

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






     def 				lowerCamelCase__				(       self  :	str    )		->							List[Any]:
          __UpperCAmelCase			:				Any													=    self.get_image_processor()
          __UpperCAmelCase			:				List[Any]													=    self.get_feature_extractor()

          __UpperCAmelCase			:				str													=    TvltProcessor(image_processor=snake_case							,				feature_extractor=snake_case    )

          self.assertListEqual(
              processor.model_input_names							,				image_processor.model_input_names + feature_extractor.model_input_names							,				msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''							,				)



'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCAmelCase    :Optional[Any]				       =     {
    "configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
    "processing_layoutlmv2": ["LayoutLMv2Processor"],
    "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :Any				       =     ["LayoutLMv2TokenizerFast"]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :int				       =     ["LayoutLMv2FeatureExtractor"]
      __UpperCAmelCase    :Optional[int]				       =     ["LayoutLMv2ImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :List[Any]				       =     [
          "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LayoutLMv2ForQuestionAnswering",
          "LayoutLMv2ForSequenceClassification",
          "LayoutLMv2ForTokenClassification",
          "LayoutLMv2Layer",
          "LayoutLMv2Model",
          "LayoutLMv2PreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      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_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

      __UpperCAmelCase    :Any				       =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)