



"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


A    =	logging.get_logger(__name__)

A    =	{
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
			import detectrona

class 			_a     (   SCREAMING_SNAKE_CASE__):
			__magic_name__    				=    """layoutlmv2"""



			def __init__( self       :							str      ,  _lowercase       :							Optional[Any]=30522      ,  _lowercase       :							List[Any]=768      ,  _lowercase       :							Union[str, Any]=12      ,  _lowercase       :							Union[str, Any]=12      ,  _lowercase       :							Any=3072      ,  _lowercase       :							Optional[Any]="gelu"      ,  _lowercase       :							Optional[Any]=0.1      ,  _lowercase       :							Union[str, Any]=0.1      ,  _lowercase       :							Tuple=512      ,  _lowercase       :							Union[str, Any]=2      ,  _lowercase       :							Dict=0.02      ,  _lowercase       :							Any=1E-12      ,  _lowercase       :							Optional[Any]=0      ,  _lowercase       :							str=1024      ,  _lowercase       :							str=128      ,  _lowercase       :							int=32      ,  _lowercase       :							List[Any]=True      ,  _lowercase       :							str=256      ,  _lowercase       :							Optional[Any]=64      ,  _lowercase       :							Optional[int]=True      ,  _lowercase       :							List[Any]=[7, 7, 256]      ,  _lowercase       :							Any=128      ,  _lowercase       :							str=128      ,  _lowercase       :							Optional[Any]=True      ,  _lowercase       :							List[str]=True      ,  _lowercase       :							Any=False      ,  _lowercase       :							Optional[Any]=None      ,  **_lowercase       :							Dict      ,  )       ->				Dict:
									super().__init__(
									    vocab_size=_lowercase      ,  hidden_size=_lowercase      ,  num_hidden_layers=_lowercase      ,  num_attention_heads=_lowercase      ,  intermediate_size=_lowercase      ,  hidden_act=_lowercase      ,  hidden_dropout_prob=_lowercase      ,  attention_probs_dropout_prob=_lowercase      ,  max_position_embeddings=_lowercase      ,  type_vocab_size=_lowercase      ,  initializer_range=_lowercase      ,  layer_norm_eps=_lowercase      ,  pad_token_id=_lowercase      ,  **_lowercase      ,  )
									snake_case      : Dict							=   max_ad_position_embeddings
									snake_case      : Dict							=   max_rel_pos
									snake_case      : List[str]							=   rel_pos_bins
									snake_case      : str							=   fast_qkv
									snake_case      : int							=   max_rel_ad_pos
									snake_case      : int							=   rel_ad_pos_bins
									snake_case      : Any							=   convert_sync_batchnorm
									snake_case      : int							=   image_feature_pool_shape
									snake_case      : Dict							=   coordinate_size
									snake_case      : Dict							=   shape_size
									snake_case      : Dict							=   has_relative_attention_bias
									snake_case      : Any							=   has_spatial_attention_bias
									snake_case      : Optional[int]							=   has_visual_segment_embedding
									snake_case      : int							=   (
									    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
									)



			@classmethod
			def 							__lowercase				( self       :							Any		)       ->				List[Any]:
									return {
									    "MODEL.MASK_ON": True,
									    "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
									    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
									    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
									    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
									    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
									    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
									    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
									    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
									    "MODEL.POST_NMS_TOPK_TEST": 1000,
									    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
									    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
									    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
									    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
									    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
									    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
									    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
									    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
									    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
									    "MODEL.RESNETS.DEPTH": 101,
									    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
									    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
									    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
									    "MODEL.RESNETS.NUM_GROUPS": 32,
									    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
									    "MODEL.RESNETS.STRIDE_IN_1X1": False,
									}



			def 							__lowercase				( self       :							Dict		)       ->				Optional[Any]:
									snake_case      : int							=   detectrona.config.get_cfg()
									for k, v in self.detectrona_config_args.items():
															snake_case      : int							=   k.split("."		)
															snake_case      : Any							=   detectrona_config
															for attribute in attributes[:-1]:
																					snake_case      : Union[str, Any]							=   getattr(_lowercase      ,  _lowercase		)
															setattr(_lowercase      ,  attributes[-1]      ,  _lowercase		)

									return detectrona_config








"""simple docstring"""







from typing import TYPE_CHECKING

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


A    =	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	['CamembertTokenizer']

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CamembertForCausalLM',
			    'CamembertForMaskedLM',
			    'CamembertForMultipleChoice',
			    'CamembertForQuestionAnswering',
			    'CamembertForSequenceClassification',
			    'CamembertForTokenClassification',
			    'CamembertModel',
			    'CamembertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFCamembertForCausalLM',
			    'TFCamembertForMaskedLM',
			    'TFCamembertForMultipleChoice',
			    'TFCamembertForQuestionAnswering',
			    'TFCamembertForSequenceClassification',
			    'TFCamembertForTokenClassification',
			    'TFCamembertModel',
			    'TFCamembertPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_camembert import CamembertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_camembert_fast import CamembertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_camembert import (
						    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CamembertForCausalLM,
						    CamembertForMaskedLM,
						    CamembertForMultipleChoice,
						    CamembertForQuestionAnswering,
						    CamembertForSequenceClassification,
						    CamembertForTokenClassification,
						    CamembertModel,
						    CamembertPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_camembert import (
						    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCamembertForCausalLM,
						    TFCamembertForMaskedLM,
						    TFCamembertForMultipleChoice,
						    TFCamembertForQuestionAnswering,
						    TFCamembertForSequenceClassification,
						    TFCamembertForTokenClassification,
						    TFCamembertModel,
						    TFCamembertPreTrainedModel,
						)

else:
			import sys

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



