






"""simple docstring"""




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict




@pytest.mark.parametrize(
    'files'					,	[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]					,	)
def 					lowercase__				(					__snake_case   :							Tuple					,	__snake_case   :							Any  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						tmp_path_factory.mktemp('dset_infos_dir'  )
					if "full:README.md" in files:
										with open(dataset_infos_dir / 'README.md'					,	'w'  ) as f:
															f.write('---\ndataset_info:\n  dataset_size: 42\n---'  )
					if "empty:README.md" in files:
										with open(dataset_infos_dir / 'README.md'					,	'w'  ) as f:
															f.write(''  )
    # we want to support dataset_infos.json for backward compatibility
					if "dataset_infos.json" in files:
										with open(dataset_infos_dir / 'dataset_infos.json'					,	'w'  ) as f:
															f.write('{\"default\": {\"dataset_size\": 42}}'  )
					UpperCAmelCase_   :				List[str]						=						DatasetInfosDict.from_directory(a_  )
					assert dataset_infos
					assert dataset_infos["default"].dataset_size == 42




@pytest.mark.parametrize(
    'dataset_info'					,	[
        DatasetInfo(),
        DatasetInfo(
            description='foo'					,	features=Features({'a': Value('int32'  )}  )					,	builder_name='builder'					,	config_name='config'					,	version='1.0.0'					,	splits=[{'name': 'train'}]					,	download_size=42					,	),
    ]					,	)
def 					lowercase__				(					__snake_case   :							Union[str, Any]					,	__snake_case   :							DatasetInfo  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						str(a_  )
					dataset_info.write_to_directory(a_  )
					UpperCAmelCase_   :				Any						=						DatasetInfo.from_directory(a_  )
					assert dataset_info == reloaded
					assert os.path.exists(os.path.join(a_					,	'dataset_info.json'  )  )




def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[int]						=						DatasetInfo(
					    description='foo'					,	citation='bar'					,	homepage='https://foo.bar'					,	license='CC0'					,	features=Features({'a': Value('int32'  )}  )					,	post_processed={}					,	supervised_keys=()					,	task_templates=[]					,	builder_name='builder'					,	config_name='config'					,	version='1.0.0'					,	splits=[{'name': 'train', 'num_examples': 42}]					,	download_checksums={}					,	download_size=1_337					,	post_processing_size=442					,	dataset_size=1_234					,	size_in_bytes=1_337 + 442 + 1_234					,	)
					UpperCAmelCase_   :				Tuple						=						dataset_info._to_yaml_dict()
					assert sorted(a_  ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML  )
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										assert key in dataset_info_yaml_dict
										assert isinstance(dataset_info_yaml_dict[key]					,	(list, dict, int, str)  )
					UpperCAmelCase_   :				List[Any]						=						yaml.safe_dump(a_  )
					UpperCAmelCase_   :				Optional[int]						=						yaml.safe_load(a_  )
					assert dataset_info_yaml_dict == reloaded




def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				str						=						DatasetInfo()
					UpperCAmelCase_   :				List[str]						=						dataset_info._to_yaml_dict()
					assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    'dataset_infos_dict'					,	[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}  ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}  ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'					,	features=Features({'a': Value('int32'  )}  )					,	builder_name='builder'					,	config_name='config'					,	version='1.0.0'					,	splits=[{'name': 'train'}]					,	download_size=42					,	)
            }  ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42  ),
                'v2': DatasetInfo(dataset_size=1_337  ),
            }  ),
    ]					,	)
def 					lowercase__				(					__snake_case   :							str					,	__snake_case   :							DatasetInfosDict  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						str(a_  )
					dataset_infos_dict.write_to_directory(a_  )
					UpperCAmelCase_   :				Union[str, Any]						=						DatasetInfosDict.from_directory(a_  )

					# the config_name of the dataset_infos_dict take over the attribute
					for config_name, dataset_info in dataset_infos_dict.items():
										UpperCAmelCase_   :				Optional[int]						=						config_name
										# the yaml representation doesn't include fields like description or citation
										# so we just test that we can recover what we can from the yaml
										UpperCAmelCase_   :				List[str]						=						DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()  )
					assert dataset_infos_dict == reloaded

					if dataset_infos_dict:
										assert os.path.exists(os.path.join(a_					,	'README.md'  )  )














from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
		from ...processing_utils import ProcessorMixin
		from ...utils import TensorType


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Dict							=  '''layoutlmv3'''



		def __init__(							self    ,		_UpperCamelCase=5_0_2_6_5    ,		_UpperCamelCase=7_6_8    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=3_0_7_2    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=2    ,		_UpperCamelCase=0.02    ,		_UpperCamelCase=1E-5    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		_UpperCamelCase=2    ,		_UpperCamelCase=1_0_2_4    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=True    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=6_4    ,		_UpperCamelCase=2_5_6    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=2_2_4    ,		_UpperCamelCase=3    ,		_UpperCamelCase=1_6    ,		_UpperCamelCase=None    ,		**_UpperCamelCase    ,		)     ->							Optional[Any]:
							super().__init__(
							    vocab_size=_UpperCamelCase    ,		hidden_size=_UpperCamelCase    ,		num_hidden_layers=_UpperCamelCase    ,		num_attention_heads=_UpperCamelCase    ,		intermediate_size=_UpperCamelCase    ,		hidden_act=_UpperCamelCase    ,		hidden_dropout_prob=_UpperCamelCase    ,		attention_probs_dropout_prob=_UpperCamelCase    ,		max_position_embeddings=_UpperCamelCase    ,		type_vocab_size=_UpperCamelCase    ,		initializer_range=_UpperCamelCase    ,		layer_norm_eps=_UpperCamelCase    ,		pad_token_id=_UpperCamelCase    ,		bos_token_id=_UpperCamelCase    ,		eos_token_id=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				str						=						max_ad_position_embeddings
							UpperCAmelCase_   :				Union[str, Any]						=						coordinate_size
							UpperCAmelCase_   :				Union[str, Any]						=						shape_size
							UpperCAmelCase_   :				str						=						has_relative_attention_bias
							UpperCAmelCase_   :				Tuple						=						rel_pos_bins
							UpperCAmelCase_   :				Dict						=						max_rel_pos
							UpperCAmelCase_   :				Any						=						has_spatial_attention_bias
							UpperCAmelCase_   :				Optional[Any]						=						rel_ad_pos_bins
							UpperCAmelCase_   :				List[str]						=						max_rel_ad_pos
							UpperCAmelCase_   :				List[str]						=						text_embed
							UpperCAmelCase_   :				Dict						=						visual_embed
							UpperCAmelCase_   :				Optional[int]						=						input_size
							UpperCAmelCase_   :				List[Any]						=						num_channels
							UpperCAmelCase_   :				List[Any]						=						patch_size
							UpperCAmelCase_   :				Union[str, Any]						=						classifier_dropout





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  version.parse('''1.12'''      )



		@property
		def  __UpperCAmelCase      (							self       )     ->							Mapping[str, Mapping[int, str]]:
							# The order of inputs is different for question answering and sequence classification
							if self.task in ["question-answering", "sequence-classification"]:
												return OrderedDict(
												    [
												        ('input_ids', {0: 'batch', 1: 'sequence'}),
												        ('attention_mask', {0: 'batch', 1: 'sequence'}),
												        ('bbox', {0: 'batch', 1: 'sequence'}),
												        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
												    ]       )
							else:
												return OrderedDict(
												    [
												        ('input_ids', {0: 'batch', 1: 'sequence'}),
												        ('bbox', {0: 'batch', 1: 'sequence'}),
												        ('attention_mask', {0: 'batch', 1: 'sequence'}),
												        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
												    ]       )



		@property
		def  __UpperCAmelCase      (							self       )     ->							float:
							return 1E-5



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return 1_2



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = -1    ,		_UpperCamelCase = -1    ,		_UpperCamelCase = False    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 3    ,		_UpperCamelCase = 4_0    ,		_UpperCamelCase = 4_0    ,		)     ->							Mapping[str, Any]:
							setattr(processor.image_processor    ,		'apply_ocr'    ,		_UpperCamelCase       )

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							UpperCAmelCase_   :				Optional[int]						=						compute_effective_axis_dimension(
							    _UpperCamelCase    ,		fixed_dimension=OnnxConfig.default_fixed_batch    ,		num_token_to_add=0       )
							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							UpperCAmelCase_   :				Any						=						processor.tokenizer.num_special_tokens_to_add(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						compute_effective_axis_dimension(
							    _UpperCamelCase    ,		fixed_dimension=OnnxConfig.default_fixed_sequence    ,		num_token_to_add=_UpperCamelCase       )
							# Generate dummy inputs according to compute batch and sequence
							UpperCAmelCase_   :				Tuple						=						[[' '.join([processor.tokenizer.unk_token]       ) * seq_length]] * batch_size

							# Generate dummy bounding boxes
							UpperCAmelCase_   :				List[str]						=						[[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
							UpperCAmelCase_   :				str						=						self._generate_dummy_images(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						dict(
							    processor(
							        _UpperCamelCase    ,		text=_UpperCamelCase    ,		boxes=_UpperCamelCase    ,		return_tensors=_UpperCamelCase    ,		)       )

							return inputs






