




"""simple docstring"""



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''dpt'''




    def __init__(   self						:	List[Any]     ,			snake_case_						:	Union[str, Any]=768     ,			snake_case_						:	Tuple=12     ,			snake_case_						:	Tuple=12     ,			snake_case_						:	List[Any]=3_072     ,			snake_case_						:	Dict="gelu"     ,			snake_case_						:	Tuple=0.0     ,			snake_case_						:	int=0.0     ,			snake_case_						:	Optional[int]=0.02     ,			snake_case_						:	Union[str, Any]=1e-12     ,			snake_case_						:	Tuple=384     ,			snake_case_						:	Tuple=16     ,			snake_case_						:	Optional[Any]=3     ,			snake_case_						:	Dict=False     ,			snake_case_						:	Any=True     ,			snake_case_						:	Any=[2, 5, 8, 11]     ,			snake_case_						:	Union[str, Any]="project"     ,			snake_case_						:	Union[str, Any]=[4, 2, 1, 0.5]     ,			snake_case_						:	List[str]=[96, 192, 384, 768]     ,			snake_case_						:	int=256     ,			snake_case_						:	Tuple=-1     ,			snake_case_						:	List[str]=False     ,			snake_case_						:	int=True     ,			snake_case_						:	List[Any]=0.4     ,			snake_case_						:	Optional[Any]=255     ,			snake_case_						:	List[str]=0.1     ,			snake_case_						:	List[str]=[1, 1_024, 24, 24]     ,			snake_case_						:	Union[str, Any]=[0, 1]     ,			snake_case_						:	Any=None     ,			**snake_case_						:	Optional[Any]     ,			)				->							Optional[int]:




     '''simple docstring'''

     super().__init__(**snake_case_				)

     A__	=    hidden_size
     A__	=    is_hybrid

     if self.is_hybrid:
      if backbone_config is None:
       logger.info("Initializing the config with a `BiT` backbone."				)
       A__	=    {
           "global_padding": "same",
           "layer_type": "bottleneck",
           "depths": [3, 4, 9],
           "out_features": ["stage1", "stage2", "stage3"],
           "embedding_dynamic_padding": True,
       }
       A__	=    BitConfig(**snake_case_				)
      elif isinstance(snake_case_     ,			snake_case_				):
       logger.info("Initializing the config with a `BiT` backbone."				)
       A__	=    BitConfig(**snake_case_				)
      elif isinstance(snake_case_     ,			snake_case_				):
       A__	=    backbone_config
      else:
       raise ValueError(
           F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""				)

      A__	=    backbone_featmap_shape
      A__	=    neck_ignore_stages

      if readout_type != "project":
       raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."				)
     else:
      A__	=    None
      A__	=    None
      A__	=    []

     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    intermediate_size
     A__	=    hidden_act
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    initializer_range
     A__	=    layer_norm_eps
     A__	=    image_size
     A__	=    patch_size
     A__	=    num_channels
     A__	=    qkv_bias
     A__	=    backbone_out_indices
     if readout_type not in ["ignore", "add", "project"]:
      raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"				)
     A__	=    readout_type
     A__	=    reassemble_factors
     A__	=    neck_hidden_sizes
     A__	=    fusion_hidden_size
     A__	=    head_in_index
     A__	=    use_batch_norm_in_fusion_residual
     # auxiliary head attributes (semantic segmentation)
     A__	=    use_auxiliary_head
     A__	=    auxiliary_loss_weight
     A__	=    semantic_loss_ignore_index
     A__	=    semantic_classifier_dropout




    def 							__magic_name__							(   self						:	List[str]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    copy.deepcopy(self.__dict__				)

     if output["backbone_config"] is not None:
      A__	=    self.backbone_config.to_dict()

     A__	=    self.__class__.model_type
     return output











"""simple docstring"""



# 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   _SCREAMING_SNAKE_CASE       (							lowercase_=None						)						->		Any:
 if subparsers is not None:
  A__	=    subparsers.add_parser("env"						)
 else:
  A__	=    argparse.ArgumentParser("Accelerate env command"						)

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

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 A__	=    torch.__version__
 A__	=    torch.cuda.is_available()
 A__	=    is_xpu_available()
 A__	=    is_npu_available()

 A__	=    "Not found"
 # Get the default from the config file.
 if args.config_file is not None or os.path.isfile(lowercase_						):
  A__	=    load_config_from_file(args.config_file						).to_dict()

 A__	=    {
     "`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(lowercase_						),
     "PyTorch NPU available": str(lowercase_						),
     "System RAM": f"""{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB""",
 }
 if pt_cuda_available:
  A__	=    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:"						)
 A__	=    (
     "\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]						)
     if isinstance(lowercase_      ,			lowercase_						)
     else f"""\t{accelerate_config}"""
 )
 print(lowercase_						)

 A__	=    accelerate_config

 return info







def   _SCREAMING_SNAKE_CASE       (							)						->		int:
 A__	=    env_command_parser()
 A__	=    parser.parse_args()
 env_command(lowercase_						)
 return 0


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





