


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


_A			=		logging.get_logger(__name__)

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






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]              =  "table-transformer"
    UpperCAmelCase__						:		Optional[int]              =  ["past_key_values"]
    UpperCAmelCase__						:		Dict              =  {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(      self ,       A_=True ,       A_=None ,       A_=3 ,       A_=100 ,       A_=6 ,       A_=2048 ,       A_=8 ,       A_=6 ,       A_=2048 ,       A_=8 ,       A_=0.0 ,       A_=0.0 ,       A_=True ,       A_="relu" ,       A_=256 ,       A_=0.1 ,       A_=0.0 ,       A_=0.0 ,       A_=0.02 ,       A_=1.0 ,       A_=False ,       A_="sine" ,       A_="resnet50" ,       A_=True ,       A_=False ,       A_=1 ,       A_=5 ,       A_=2 ,       A_=1 ,       A_=1 ,       A_=5 ,       A_=2 ,       A_=0.1 ,       **A_ ,       )  ->	str:
         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.'   )
                   __UpperCamelCase   							=CONFIG_MAPPING['resnet'](out_features=['stage4']   )
              elif isinstance(A_ ,       A_   ):
                   __UpperCamelCase   							=backbone_config.get('model_type'   )
                   __UpperCamelCase   							=CONFIG_MAPPING[backbone_model_type]
                   __UpperCamelCase   							=config_class.from_dict(A_   )
              # set timm attributes to None
              __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=None, None, None

         __UpperCamelCase   							=use_timm_backbone
         __UpperCamelCase   							=backbone_config
         __UpperCamelCase   							=num_channels
         __UpperCamelCase   							=num_queries
         __UpperCamelCase   							=d_model
         __UpperCamelCase   							=encoder_ffn_dim
         __UpperCamelCase   							=encoder_layers
         __UpperCamelCase   							=encoder_attention_heads
         __UpperCamelCase   							=decoder_ffn_dim
         __UpperCamelCase   							=decoder_layers
         __UpperCamelCase   							=decoder_attention_heads
         __UpperCamelCase   							=dropout
         __UpperCamelCase   							=attention_dropout
         __UpperCamelCase   							=activation_dropout
         __UpperCamelCase   							=activation_function
         __UpperCamelCase   							=init_std
         __UpperCamelCase   							=init_xavier_std
         __UpperCamelCase   							=encoder_layerdrop
         __UpperCamelCase   							=decoder_layerdrop
         __UpperCamelCase   							=encoder_layers
         __UpperCamelCase   							=auxiliary_loss
         __UpperCamelCase   							=position_embedding_type
         __UpperCamelCase   							=backbone
         __UpperCamelCase   							=use_pretrained_backbone
         __UpperCamelCase   							=dilation
         # Hungarian matcher
         __UpperCamelCase   							=class_cost
         __UpperCamelCase   							=bbox_cost
         __UpperCamelCase   							=giou_cost
         # Loss coefficients
         __UpperCamelCase   							=mask_loss_coefficient
         __UpperCamelCase   							=dice_loss_coefficient
         __UpperCamelCase   							=bbox_loss_coefficient
         __UpperCamelCase   							=giou_loss_coefficient
         __UpperCamelCase   							=eos_coefficient
         super().__init__(is_encoder_decoder=A_ ,       **A_   )






    @property
    def _a (      self   )  ->	int:
         return self.encoder_attention_heads






    @property
    def _a (      self   )  ->	int:
         return self.d_model






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Dict              =  version.parse("1.11"		)






    @property
    def _a (      self   )  ->	Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                 ('pixel_mask', {0: 'batch'}),
             ]   )






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






    @property
    def _a (      self   )  ->	int:
         return 12









# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


_A			=						[
    'EAGER',
    'AOT_EAGER',
    'INDUCTOR',
    'NVFUSER',
    'AOT_NVFUSER',
    'AOT_CUDAGRAPHS',
    'OFI',
    'FX2TRT',
    'ONNXRT',
    'IPEX',
]

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]=None      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]=None      ,					SCREAMING_SNAKE_CASE__      : str=None    ):
     __UpperCamelCase   							=True
     while ask_again:
          __UpperCamelCase   							=input(SCREAMING_SNAKE_CASE__    )
          try:
               if default is not None and len(SCREAMING_SNAKE_CASE__    ) == 0:
                    return default
               return convert_value(SCREAMING_SNAKE_CASE__    ) if convert_value is not None else result
          except Exception:
               if error_message is not None:
                    print(SCREAMING_SNAKE_CASE__    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : List[str]=[]      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]=None      ,					SCREAMING_SNAKE_CASE__      : str=0    ):
     __UpperCamelCase   							=BulletMenu(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=menu.run(default_choice=SCREAMING_SNAKE_CASE__    )
     return convert_value(SCREAMING_SNAKE_CASE__    ) if convert_value is not None else result

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     __UpperCamelCase   							=int(SCREAMING_SNAKE_CASE__    )
     return ComputeEnvironment(['LOCAL_MACHINE', 'AMAZON_SAGEMAKER'][value]    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]    ):
     __UpperCamelCase   							=int(SCREAMING_SNAKE_CASE__    )
     return DistributedType(['NO', 'MULTI_CPU', 'MULTI_XPU', 'MULTI_GPU', 'MULTI_NPU', 'TPU'][value]    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[int]    ):
     __UpperCamelCase   							=int(SCREAMING_SNAKE_CASE__    )
     return DynamoBackend(DYNAMO_BACKENDS[value]    ).value

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Dict    ):
     __UpperCamelCase   							=int(SCREAMING_SNAKE_CASE__    )
     return PrecisionType(['no', 'fp16', 'bf16', 'fp8'][value]    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     __UpperCamelCase   							=int(SCREAMING_SNAKE_CASE__    )
     return SageMakerDistributedType(['NO', 'DATA_PARALLEL', 'MODEL_PARALLEL'][value]    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Any    ):
     return {"yes": True, "no": False}[value.lower()]






class    UpperCAmelCase__	( argparse.RawDescriptionHelpFormatter		):



    """simple docstring"""






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_   )  ->	Optional[int]:
         __UpperCamelCase   							=super()._format_usage(A_ ,       A_ ,       A_ ,       A_   )
         __UpperCamelCase   							=usage.replace('<command> [<args>] ' ,       ''   )
         return usage





