



"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__snake_case							:     Optional[Any]   =					logging.get_logger(__name__)

__snake_case							:     str   =					{
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}



class     A__     (						__lowerCamelCase							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			'table-transformer'
 SCREAMING_SNAKE_CASE					   =			['past_key_values']
 SCREAMING_SNAKE_CASE					   =			{
     'hidden_size': 'd_model',
     'num_attention_heads': 'encoder_attention_heads',
 }

 def __init__(  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	str=True ,    _SCREAMING_SNAKE_CASE:	Any=None ,    _SCREAMING_SNAKE_CASE:	List[Any]=3 ,    _SCREAMING_SNAKE_CASE:	str=100 ,    _SCREAMING_SNAKE_CASE:	int=6 ,    _SCREAMING_SNAKE_CASE:	Dict=2048 ,    _SCREAMING_SNAKE_CASE:	List[Any]=8 ,    _SCREAMING_SNAKE_CASE:	List[str]=6 ,    _SCREAMING_SNAKE_CASE:	int=2048 ,    _SCREAMING_SNAKE_CASE:	Dict=8 ,    _SCREAMING_SNAKE_CASE:	Any=0.0 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=0.0 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=True ,    _SCREAMING_SNAKE_CASE:	List[str]="relu" ,    _SCREAMING_SNAKE_CASE:	int=256 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.1 ,    _SCREAMING_SNAKE_CASE:	str=0.0 ,    _SCREAMING_SNAKE_CASE:	str=0.0 ,    _SCREAMING_SNAKE_CASE:	List[Any]=0.02 ,    _SCREAMING_SNAKE_CASE:	List[str]=1.0 ,    _SCREAMING_SNAKE_CASE:	int=False ,    _SCREAMING_SNAKE_CASE:	str="sine" ,    _SCREAMING_SNAKE_CASE:	Optional[Any]="resnet50" ,    _SCREAMING_SNAKE_CASE:	List[str]=True ,    _SCREAMING_SNAKE_CASE:	Tuple=False ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=1 ,    _SCREAMING_SNAKE_CASE:	List[str]=5 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=2 ,    _SCREAMING_SNAKE_CASE:	List[str]=1 ,    _SCREAMING_SNAKE_CASE:	Tuple=1 ,    _SCREAMING_SNAKE_CASE:	int=5 ,    _SCREAMING_SNAKE_CASE:	Tuple=2 ,    _SCREAMING_SNAKE_CASE:	List[str]=0.1 ,    **_SCREAMING_SNAKE_CASE:	List[str] ,    )       -> Dict:

    """simple docstring"""

    if backbone_config is not None and use_timm_backbone:
       raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")

    if not use_timm_backbone:
       if backbone_config is None:
          logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
          __lowerCAmelCase   :						Any				= CONFIG_MAPPING["resnet"](out_features=["stage4"])
       elif isinstance(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_):
          __lowerCAmelCase   :						Dict				= backbone_config.get("model_type")
          __lowerCAmelCase   :						Dict				= CONFIG_MAPPING[backbone_model_type]
          __lowerCAmelCase   :						List[Any]				= config_class.from_dict(SCREAMING_SNAKE_CASE_)
       # set timm attributes to None
       __lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[int]				= None, None, None

    __lowerCAmelCase   :						int				= use_timm_backbone
    __lowerCAmelCase   :						Union[str, Any]				= backbone_config
    __lowerCAmelCase   :						Dict				= num_channels
    __lowerCAmelCase   :						Dict				= num_queries
    __lowerCAmelCase   :						Tuple				= d_model
    __lowerCAmelCase   :						Union[str, Any]				= encoder_ffn_dim
    __lowerCAmelCase   :						int				= encoder_layers
    __lowerCAmelCase   :						Any				= encoder_attention_heads
    __lowerCAmelCase   :						int				= decoder_ffn_dim
    __lowerCAmelCase   :						Any				= decoder_layers
    __lowerCAmelCase   :						Tuple				= decoder_attention_heads
    __lowerCAmelCase   :						Optional[int]				= dropout
    __lowerCAmelCase   :						int				= attention_dropout
    __lowerCAmelCase   :						Optional[Any]				= activation_dropout
    __lowerCAmelCase   :						Union[str, Any]				= activation_function
    __lowerCAmelCase   :						Any				= init_std
    __lowerCAmelCase   :						Optional[Any]				= init_xavier_std
    __lowerCAmelCase   :						Dict				= encoder_layerdrop
    __lowerCAmelCase   :						Union[str, Any]				= decoder_layerdrop
    __lowerCAmelCase   :						str				= encoder_layers
    __lowerCAmelCase   :						Optional[Any]				= auxiliary_loss
    __lowerCAmelCase   :						int				= position_embedding_type
    __lowerCAmelCase   :						Dict				= backbone
    __lowerCAmelCase   :						Dict				= use_pretrained_backbone
    __lowerCAmelCase   :						Any				= dilation
    # Hungarian matcher
    __lowerCAmelCase   :						Union[str, Any]				= class_cost
    __lowerCAmelCase   :						Optional[int]				= bbox_cost
    __lowerCAmelCase   :						str				= giou_cost
    # Loss coefficients
    __lowerCAmelCase   :						Any				= mask_loss_coefficient
    __lowerCAmelCase   :						Union[str, Any]				= dice_loss_coefficient
    __lowerCAmelCase   :						str				= bbox_loss_coefficient
    __lowerCAmelCase   :						Any				= giou_loss_coefficient
    __lowerCAmelCase   :						List[str]				= eos_coefficient
    super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_ ,    **SCREAMING_SNAKE_CASE_)

 @property
 def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> str:

    """simple docstring"""

    return self.encoder_attention_heads







 @property
 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Dict:

    """simple docstring"""

    return self.d_model






class     A__     (						__lowerCamelCase							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			version.parse('1.11'							)

 @property
 def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Optional[int]:

    """simple docstring"""

    return OrderedDict(
        [
            ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ("pixel_mask", {0: "batch"}),
        ])

 @property
 def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> int:

    """simple docstring"""

    return 1e-5







 @property
 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Dict:

    """simple docstring"""

    return 12




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						Dict				= Namespace(**checkpoint["cfg"]["model"]			)
			__lowerCAmelCase   :						Dict				= checkpoint["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase   :						Optional[int]				= {key.replace("decoder"      ,"model"			): val for key, val in state_dict.items()}

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__snake_case      ,max_position_embeddings=args.max_target_positions      ,num_layers=args.decoder_layers      ,attention_heads=args.decoder_attention_heads      ,ffn_dim=args.decoder_ffn_embed_dim      ,d_model=args.decoder_embed_dim      ,layerdrop=args.decoder_layerdrop      ,dropout=args.dropout      ,attention_dropout=args.attention_dropout      ,activation_dropout=args.activation_dropout      ,activation_function="gelu"      ,scale_embedding=not args.no_scale_embedding      ,tie_word_embeddings=args.share_decoder_input_output_embed      ,)

			__lowerCAmelCase   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
	__snake_case							:     str   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	__snake_case							:     Optional[Any]   =					parser.parse_args()
	__snake_case							:     Optional[Any]   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)