

import logging

from transformers.configuration_utils import PretrainedConfig


a		=logging.getLogger(__name__)






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  List[Any]										=				'''masked_bert'''
							def __init__(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=3_0_5_2_2       ,SCREAMING_SNAKE_CASE__		:      str=7_6_8       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1_2       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1_2       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=3_0_7_2       ,SCREAMING_SNAKE_CASE__		:      Dict="gelu"       ,SCREAMING_SNAKE_CASE__		:      List[Any]=0.1       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=0.1       ,SCREAMING_SNAKE_CASE__		:      List[str]=5_1_2       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=2       ,SCREAMING_SNAKE_CASE__		:      List[str]=0.02       ,SCREAMING_SNAKE_CASE__		:      str=1E-12       ,SCREAMING_SNAKE_CASE__		:      List[str]=0       ,SCREAMING_SNAKE_CASE__		:      int="topK"       ,SCREAMING_SNAKE_CASE__		:      Any="constant"       ,SCREAMING_SNAKE_CASE__		:      int=0.0       ,**SCREAMING_SNAKE_CASE__		:      List[Any]       ,):
								super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    str 	=			vocab_size
								__lowerCamelCase    :    List[Any] 	=			hidden_size
								__lowerCamelCase    :    int 	=			num_hidden_layers
								__lowerCamelCase    :    int 	=			num_attention_heads
								__lowerCamelCase    :    Any 	=			hidden_act
								__lowerCamelCase    :    Union[str, Any] 	=			intermediate_size
								__lowerCamelCase    :    Optional[Any] 	=			hidden_dropout_prob
								__lowerCamelCase    :    Dict 	=			attention_probs_dropout_prob
								__lowerCamelCase    :    Optional[int] 	=			max_position_embeddings
								__lowerCamelCase    :    Tuple 	=			type_vocab_size
								__lowerCamelCase    :    str 	=			initializer_range
								__lowerCamelCase    :    Optional[Any] 	=			layer_norm_eps
								__lowerCamelCase    :    Optional[Any] 	=			pruning_method
								__lowerCamelCase    :    List[Any] 	=			mask_init
								__lowerCamelCase    :    Dict 	=			mask_scale







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a		={
    """configuration_pix2struct""": [
        """PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Pix2StructConfig""",
        """Pix2StructTextConfig""",
        """Pix2StructVisionConfig""",
    ],
    """processing_pix2struct""": ["""Pix2StructProcessor"""],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Pix2StructImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Pix2StructPreTrainedModel""",
					    """Pix2StructForConditionalGeneration""",
					    """Pix2StructVisionModel""",
					    """Pix2StructTextModel""",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




