



"""simple docstring"""





import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


lowerCamelCase  			=		logging.get_logger(__name__)

lowerCamelCase  			=		{
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	'''detr'''
       UpperCamelCase		=	['''past_key_values''']
       UpperCamelCase		=	{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
       }



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


             '''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."       )
                         UpperCAmelCase_     					=				CONFIG_MAPPING["resnet"](out_features=["stage4"]       )
                   elif isinstance(_UpperCAmelCase						,							_UpperCAmelCase       ):
                         UpperCAmelCase_     					=				backbone_config.get("model_type"       )
                         UpperCAmelCase_     					=				CONFIG_MAPPING[backbone_model_type]
                         UpperCAmelCase_     					=				config_class.from_dict(_UpperCAmelCase       )
                   # 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=_UpperCAmelCase						,							**_UpperCAmelCase       )



       @property
       def 	lowercase__							(				self     :    List[Any]       )					->		int:


             '''simple docstring'''




             return self.encoder_attention_heads



       @property
       def 	lowercase__							(				self     :    List[Any]       )					->		int:


             '''simple docstring'''




             return self.d_model



       @classmethod
       def 	lowercase__							(				cls     :    Dict						,							_UpperCAmelCase     :    PretrainedConfig						,							**_UpperCAmelCase     :    Tuple       )					->		Any:


             '''simple docstring'''




             return cls(backbone_config=_UpperCAmelCase						,							**_UpperCAmelCase       )







       def 	lowercase__							(				self     :    Optional[int]       )					->		Dict[str, any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				copy.deepcopy(self.__dict__       )
             if output["backbone_config"] is not None:
                   UpperCAmelCase_     					=				self.backbone_config.to_dict()
             UpperCAmelCase_     					=				self.__class__.model_type
             return output



class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	version.parse('''1.11''' )



       @property
       def 	lowercase__							(				self     :    str       )					->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




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



       @property
       def 	lowercase__							(				self     :    List[Any]       )					->		float:


             '''simple docstring'''




             return 1e-5







       @property
       def 	lowercase__							(				self     :    List[str]       )					->		int:


             '''simple docstring'''




             return 12








# Copyright 2022 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
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available




def       a_ (       SCREAMING_SNAKE_CASE__   : str=None	):
       '''simple docstring'''





       if subparsers is not None:
              _lowerCamelCase			:						str            =subparsers.add_parser('env'	)
       else:
              _lowerCamelCase			:						List[Any]            =argparse.ArgumentParser('Accelerate env command'	)

       parser.add_argument(
           '--config_file'						,     default=SCREAMING_SNAKE_CASE__						,     help='The config file to use for the default values in the launching script.'	)

       if subparsers is not None:
              parser.set_defaults(func=SCREAMING_SNAKE_CASE__	)
       return parser




def       a_ (       SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						int            =torch.__version__
       _lowerCamelCase			:						Optional[Any]            =torch.cuda.is_available()
       _lowerCamelCase			:						int            =is_xpu_available()
       _lowerCamelCase			:						Union[str, Any]            =is_npu_available()

       _lowerCamelCase			:						Optional[int]            ='Not found'
       # Get the default from the config file.
       if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE__	):
              _lowerCamelCase			:						Any            =load_config_from_file(args.config_file	).to_dict()

       _lowerCamelCase			:						List[str]            ={
           '`Accelerate` version': version,
           'Platform': platform.platform(),
           'Python version': platform.python_version(),
           'Numpy version': np.__version__,
           'PyTorch version (GPU?)': F'''{pt_version} ({pt_cuda_available})''',
           'PyTorch XPU available': str(SCREAMING_SNAKE_CASE__	),
           'PyTorch NPU available': str(SCREAMING_SNAKE_CASE__	),
           'System RAM': F'''{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB''',
       }
       if pt_cuda_available:
              _lowerCamelCase			:						List[Any]            =torch.cuda.get_device_name()

       print('\nCopy-and-paste the text below in your GitHub issue\n'	)
       print('\n'.join([F'''- {prop}: {val}''' for prop, val in info.items()]	)	)

       print('- `Accelerate` default config:' if args.config_file is None else '- `Accelerate` config passed:'	)
       _lowerCamelCase			:						Dict            =(
           '\n'.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]	)
           if isinstance(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
           else F'''\t{accelerate_config}'''
       )
       print(SCREAMING_SNAKE_CASE__	)

       _lowerCamelCase			:						str            =accelerate_config

       return info




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =env_command_parser()
       _lowerCamelCase			:						Any            =parser.parse_args()
       env_command(SCREAMING_SNAKE_CASE__	)
       return 0


if __name__ == "__main__":
 raise SystemExit(main())


