"""simple docstring"""


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

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__UpperCamelCase								=    logging.get_logger(__name__)

__UpperCamelCase								=    {
    '''microsoft/layoutlm-base-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'''
    ),
    '''microsoft/layoutlm-large-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'''
    ),
}




class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "layoutlm"







       def __init__(				self,							lowerCAmelCase__=3_0522,							lowerCAmelCase__=768,							lowerCAmelCase__=12,							lowerCAmelCase__=12,							lowerCAmelCase__=3072,							lowerCAmelCase__="gelu",							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=512,							lowerCAmelCase__=2,							lowerCAmelCase__=0.02,							lowerCAmelCase__=1e-12,							lowerCAmelCase__=0,							lowerCAmelCase__="absolute",							lowerCAmelCase__=True,							lowerCAmelCase__=1024,							**lowerCAmelCase__,							)   ->   str:
            super().__init__(pad_token_id=lowerCAmelCase__,							**lowerCAmelCase__)
            snake_case_       =      vocab_size
            snake_case_       =      hidden_size
            snake_case_       =      num_hidden_layers
            snake_case_       =      num_attention_heads
            snake_case_       =      hidden_act
            snake_case_       =      intermediate_size
            snake_case_       =      hidden_dropout_prob
            snake_case_       =      attention_probs_dropout_prob
            snake_case_       =      max_position_embeddings
            snake_case_       =      type_vocab_size
            snake_case_       =      initializer_range
            snake_case_       =      layer_norm_eps
            snake_case_       =      position_embedding_type
            snake_case_       =      use_cache
            snake_case_       =      max_ad_position_embeddings




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__,							lowerCAmelCase__ = "default",							lowerCAmelCase__ = None,							)   ->   Optional[int]:
            super().__init__(lowerCAmelCase__,							task=lowerCAmelCase__,							patching_specs=lowerCAmelCase__)
            snake_case_       =      config.max_ad_position_embeddings - 1







       @property
       def 	a_		(				self)   ->   Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'sequence'}),
                    ('bbox', {0: 'batch', 1: 'sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'sequence'}),
                    ('token_type_ids', {0: 'batch', 1: 'sequence'}),
                ])







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__ = -1,							lowerCAmelCase__ = -1,							lowerCAmelCase__ = False,							lowerCAmelCase__ = None,							)   ->   Mapping[str, Any]:
            snake_case_       =      super().generate_dummy_inputs(
                lowerCAmelCase__,							batch_size=lowerCAmelCase__,							seq_length=lowerCAmelCase__,							is_pair=lowerCAmelCase__,							framework=lowerCAmelCase__)

            # Generate a dummy bbox
            snake_case_       =      [48, 84, 73, 128]

            if not framework == TensorType.PYTORCH:
                 raise NotImplementedError('Exporting LayoutLM to ONNX is currently only supported for PyTorch.')

            if not is_torch_available():
                 raise ValueError('Cannot generate dummy inputs without PyTorch installed.')
            import torch

            snake_case_ ,			snake_case_       =      input_dict['input_ids'].shape
            snake_case_       =      torch.tensor([*[box] * seq_length]).tile(lowerCAmelCase__,							1,							1)
            return input_dict


"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase								=    logging.get_logger(__name__)

__UpperCamelCase								=    {
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=256,							lowerCAmelCase__=1024,							lowerCAmelCase__=16,							lowerCAmelCase__=16,							lowerCAmelCase__=4,							lowerCAmelCase__="gelu",							lowerCAmelCase__=1e-6,							**lowerCAmelCase__,							)   ->   int:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      hidden_size
            snake_case_       =      image_size
            snake_case_       =      patch_size
            snake_case_       =      image_size // patch_size
            snake_case_       =      mask_input_channels
            snake_case_       =      num_point_embeddings
            snake_case_       =      hidden_act
            snake_case_       =      layer_norm_eps




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=256,							lowerCAmelCase__="relu",							lowerCAmelCase__=2048,							lowerCAmelCase__=2,							lowerCAmelCase__=8,							lowerCAmelCase__=2,							lowerCAmelCase__=3,							lowerCAmelCase__=3,							lowerCAmelCase__=256,							lowerCAmelCase__=1e-6,							**lowerCAmelCase__,							)   ->   List[str]:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      hidden_size
            snake_case_       =      hidden_act
            snake_case_       =      mlp_dim
            snake_case_       =      num_hidden_layers
            snake_case_       =      num_attention_heads
            snake_case_       =      attention_downsample_rate
            snake_case_       =      num_multimask_outputs
            snake_case_       =      iou_head_depth
            snake_case_       =      iou_head_hidden_dim
            snake_case_       =      layer_norm_eps




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=768,							lowerCAmelCase__=256,							lowerCAmelCase__=12,							lowerCAmelCase__=12,							lowerCAmelCase__=3,							lowerCAmelCase__=1024,							lowerCAmelCase__=16,							lowerCAmelCase__="gelu",							lowerCAmelCase__=1e-06,							lowerCAmelCase__=0.0,							lowerCAmelCase__=1e-10,							lowerCAmelCase__=True,							lowerCAmelCase__=4.0,							lowerCAmelCase__=True,							lowerCAmelCase__=True,							lowerCAmelCase__=14,							lowerCAmelCase__=[2, 5, 8, 11],							lowerCAmelCase__=128,							lowerCAmelCase__=None,							**lowerCAmelCase__,							)   ->   List[str]:
            super().__init__(**lowerCAmelCase__)

            snake_case_       =      hidden_size
            snake_case_       =      output_channels
            snake_case_       =      num_hidden_layers
            snake_case_       =      num_attention_heads
            snake_case_       =      num_channels
            snake_case_       =      image_size
            snake_case_       =      patch_size
            snake_case_       =      hidden_act
            snake_case_       =      layer_norm_eps
            snake_case_       =      attention_dropout
            snake_case_       =      initializer_range
            snake_case_       =      qkv_bias
            snake_case_       =      mlp_ratio
            snake_case_       =      use_abs_pos
            snake_case_       =      use_rel_pos
            snake_case_       =      window_size
            snake_case_       =      global_attn_indexes
            snake_case_       =      num_pos_feats
            snake_case_       =      int(hidden_size * mlp_ratio) if mlp_dim is None else mlp_dim




class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "sam"
       SCREAMING_SNAKE_CASE_				=   True







       def __init__(				self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=0.02,							**lowerCAmelCase__,							)   ->   Any:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      vision_config if vision_config is not None else {}
            snake_case_       =      prompt_encoder_config if prompt_encoder_config is not None else {}
            snake_case_       =      mask_decoder_config if mask_decoder_config is not None else {}

            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      vision_config.to_dict()
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      prompt_encoder_config.to_dict()
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      mask_decoder_config.to_dict()

            snake_case_       =      SamVisionConfig(**lowerCAmelCase__)
            snake_case_       =      SamPromptEncoderConfig(**lowerCAmelCase__)
            snake_case_       =      SamMaskDecoderConfig(**lowerCAmelCase__)
            snake_case_       =      initializer_range







       def 	a_		(				self)   ->   Dict:
            snake_case_       =      copy.deepcopy(self.__dict__)
            snake_case_       =      self.vision_config.to_dict()
            snake_case_       =      self.prompt_encoder_config.to_dict()
            snake_case_       =      self.mask_decoder_config.to_dict()
            snake_case_       =      self.__class__.model_type
            return output

