





import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__=None      ,							a__=True      ,							a__=None      ,							**a__):



             """simple docstring"""





             _lowerCamelCase							:     str	=		parent
             _lowerCamelCase							:     List[Any]	=		config_class
             _lowerCamelCase							:     List[str]	=		has_text_modality
             _lowerCamelCase							:     Optional[int]	=		kwargs
             _lowerCamelCase							:     Tuple	=		common_properties


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     int	=		self.config_class(**self.inputs_dict)
             _lowerCamelCase							:     str	=		(
                 ['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
                 if self.common_properties is None
                 else self.common_properties
             )

             # Add common fields for text models
             if self.has_text_modality:
                    common_properties.extend(['''vocab_size'''])

             # Test that config has the common properties as getters
             for prop in common_properties:
                    self.parent.assertTrue(hasattr(a__      ,							a__)      ,							msg=F"""`{prop}` does not exist""")

             # Test that config has the common properties as setter
             for idx, name in enumerate(a__):
                    try:
                           setattr(a__      ,							a__      ,							a__)
                           self.parent.assertEqual(
                               getattr(a__      ,							a__)      ,							a__      ,							msg=F"""`{name} value {idx} expected, but was {getattr(a__      ,							a__)}""")
                    except NotImplementedError:
                           # Some models might not be able to implement setters for common_properties
                           # In that case, a NotImplementedError is raised
                           pass

        # Test if config class can be called with Config(prop_name=..)
             for idx, name in enumerate(a__):
                    try:
                           _lowerCamelCase							:     int	=		self.config_class(**{name: idx})
                           self.parent.assertEqual(
                               getattr(a__      ,							a__)      ,							a__      ,							msg=F"""`{name} value {idx} expected, but was {getattr(a__      ,							a__)}""")
                    except NotImplementedError:
                           # Some models might not be able to implement setters for common_properties
                           # In that case, a NotImplementedError is raised
                           pass


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		self.config_class(**self.inputs_dict)
             _lowerCamelCase							:     Dict	=		json.loads(config.to_json_string())
             for key, value in self.inputs_dict.items():
                    self.parent.assertEqual(obj[key]      ,							a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		self.config_class(**self.inputs_dict)

             with tempfile.TemporaryDirectory() as tmpdirname:
                    _lowerCamelCase							:     Optional[int]	=		os.path.join(a__      ,							'''config.json''')
                    config_first.to_json_file(a__)
                    _lowerCamelCase							:     List[Any]	=		self.config_class.from_json_file(a__)

             self.parent.assertEqual(config_second.to_dict()      ,							config_first.to_dict())


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		self.config_class(**self.inputs_dict)

             with tempfile.TemporaryDirectory() as tmpdirname:
                    config_first.save_pretrained(a__)
                    _lowerCamelCase							:     Optional[Any]	=		self.config_class.from_pretrained(a__)

             self.parent.assertEqual(config_second.to_dict()      ,							config_first.to_dict())


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		self.config_class(**self.inputs_dict)

             _lowerCamelCase							:     Any	=		'''test'''
             with tempfile.TemporaryDirectory() as tmpdirname:
                    _lowerCamelCase							:     Dict	=		os.path.join(a__      ,							a__)
                    config_first.save_pretrained(a__)
                    _lowerCamelCase							:     Optional[int]	=		self.config_class.from_pretrained(a__      ,							subfolder=a__)

             self.parent.assertEqual(config_second.to_dict()      ,							config_first.to_dict())


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		self.config_class(**self.inputs_dict      ,							num_labels=5)
             self.parent.assertEqual(len(config.idalabel)      ,							5)
             self.parent.assertEqual(len(config.labelaid)      ,							5)

             _lowerCamelCase							:     str	=		3
             self.parent.assertEqual(len(config.idalabel)      ,							3)
             self.parent.assertEqual(len(config.labelaid)      ,							3)


      def 				__snake_case			(		self):



             """simple docstring"""





             if self.config_class.is_composition:
                    return
             _lowerCamelCase							:     int	=		self.config_class()
             self.parent.assertIsNotNone(a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		copy.deepcopy(a__)
             _lowerCamelCase							:     Optional[Any]	=		self.config_class(**a__)
             _lowerCamelCase							:     Tuple	=		[]
             for key, value in config_common_kwargs.items():
                    if key == "torch_dtype":
                           if not is_torch_available():
                                  continue
                           else:
                                  import torch

                                  if config.torch_dtype != torch.floataa:
                                         wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa))
                    elif getattr(a__      ,							a__) != value:
                           wrong_values.append((key, getattr(a__      ,							a__), value))

             if len(a__) > 0:
                    _lowerCamelCase							:     List[Any]	=		'''\n'''.join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values])
                    raise ValueError(F"""The following keys were not properly set in the config:\n{errors}""")



      def 				__snake_case			(		self):



             """simple docstring"""





             self.create_and_test_config_common_properties()
             self.create_and_test_config_to_json_string()
             self.create_and_test_config_to_json_file()
             self.create_and_test_config_from_and_save_pretrained()
             self.create_and_test_config_from_and_save_pretrained_subfolder()
             self.create_and_test_config_with_num_labels()
             self.check_config_can_be_init_without_params()
             self.check_config_arguments_init()








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__)

