




import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand






def  snake_case(    __magic_name__   )			->							Union[str, Any]:





     '''simple docstring'''


     return EnvironmentCommand()






def  snake_case(    __magic_name__   )			->							int:





     '''simple docstring'''


     return EnvironmentCommand(args.accelerate_config_file   )



class 						_A (  _lowerCamelCase				):






  @staticmethod
  def  __a      (  _A		:     ArgumentParser				)		->   int:






       """simple docstring"""







       lowercase :       List[str]       =			parser.add_parser('''env'''				)
       download_parser.set_defaults(func=_A				)
       download_parser.add_argument(
           '''--accelerate-config_file'''				,						default=_A				,						help='''The accelerate config file to use for the default values in the launching script.'''				,						)
       download_parser.set_defaults(func=_A				)






  def __init__(  self		:     str				,						_A		:     Dict				,						*_A		:     int				)		->   None:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			accelerate_config_file






  def  __a      (  self		:     Any				)		->   Dict:






       """simple docstring"""







       lowercase :       Optional[Any]       =			'''not installed'''
       if is_safetensors_available():
            import safetensors

            lowercase :       List[str]       =			safetensors.__version__
       elif importlib.util.find_spec('''safetensors'''				) is not None:
            import safetensors

            lowercase :       List[str]       =			f"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

       lowercase :       List[str]       =			'''not installed'''
       lowercase :       Optional[Any]       =			'''not found'''
       if is_accelerate_available():
            import accelerate
            from accelerate.commands.config import default_config_file, load_config_from_file

            lowercase :       Tuple       =			accelerate.__version__
            # Get the default from the config file.
            if self._accelerate_config_file is not None or os.path.isfile(_A				):
                 lowercase :       List[Any]       =			load_config_from_file(self._accelerate_config_file				).to_dict()

            lowercase :       Any       =			(
                '''\n'''.join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]				)
                if isinstance(_A				,						_A				)
                else f"""\t{accelerate_config}"""
            )

       lowercase :       List[str]       =			'''not installed'''
       lowercase :       List[str]       =			'''NA'''
       if is_torch_available():
            import torch

            lowercase :       Any       =			torch.__version__
            lowercase :       int       =			torch.cuda.is_available()

       lowercase :       Dict       =			'''not installed'''
       lowercase :       Optional[int]       =			'''NA'''
       if is_tf_available():
            import tensorflow as tf

            lowercase :       List[Any]       =			tf.__version__
            try:
                 # deprecated in v2.1
                 lowercase :       Optional[int]       =			tf.test.is_gpu_available()
            except AttributeError:
                 # returns list of devices, convert to bool
                 lowercase :       Dict       =			bool(tf.config.list_physical_devices('''GPU'''				)				)

       lowercase :       Dict       =			'''not installed'''
       lowercase :       List[str]       =			'''not installed'''
       lowercase :       List[Any]       =			'''not installed'''
       lowercase :       Optional[int]       =			'''NA'''
       if is_flax_available():
            import flax
            import jax
            import jaxlib

            lowercase :       Dict       =			flax.__version__
            lowercase :       Optional[int]       =			jax.__version__
            lowercase :       Optional[int]       =			jaxlib.__version__
            lowercase :       Any       =			jax.lib.xla_bridge.get_backend().platform

       lowercase :       Union[str, Any]       =			{
           '''`transformers` version''': version,
           '''Platform''': platform.platform(),
           '''Python version''': platform.python_version(),
           '''Huggingface_hub version''': huggingface_hub.__version__,
           '''Safetensors version''': f"""{safetensors_version}""",
           '''Accelerate version''': f"""{accelerate_version}""",
           '''Accelerate config''': f"""{accelerate_config_str}""",
           '''PyTorch version (GPU?)''': f"""{pt_version} ({pt_cuda_available})""",
           '''Tensorflow version (GPU?)''': f"""{tf_version} ({tf_cuda_available})""",
           '''Flax version (CPU?/GPU?/TPU?)''': f"""{flax_version} ({jax_backend})""",
           '''Jax version''': f"""{jax_version}""",
           '''JaxLib version''': f"""{jaxlib_version}""",
           '''Using GPU in script?''': '''<fill in>''',
           '''Using distributed or parallel set-up in script?''': '''<fill in>''',
       }

       print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''				)
       print(self.format_dict(_A				)				)

       return info






  @staticmethod
  def  __a      (  _A		:     Optional[Any]				)		->   Tuple:






       """simple docstring"""







       return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]				) + "\n"





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase_						=			{
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase_						=			logging.get_logger(__name__)
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      int			=      '''maskformer'''
  _UpperCamelCase       :      Union[str, Any]			=      {'''hidden_size''': '''mask_feature_size'''}
  _UpperCamelCase       :      Dict			=      ['''resnet''', '''swin''']
  _UpperCamelCase       :      Optional[int]			=      ['''detr''']






  def __init__(  self		:     Any				,						_A		:     int = 256				,						_A		:     int = 256				,						_A		:     float = 0.1				,						_A		:     bool = False				,						_A		:     Optional[Dict] = None				,						_A		:     Optional[Dict] = None				,						_A		:     float = 0.02				,						_A		:     float = 1.0				,						_A		:     float = 1.0				,						_A		:     float = 1.0				,						_A		:     float = 20.0				,						_A		:     Optional[bool] = None				,						**_A		:     str				,						)		->   str:






       """simple docstring"""







       if backbone_config is None:
            # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
            lowercase :       List[str]       =			SwinConfig(
                image_size=384				,						in_channels=3				,						patch_size=4				,						embed_dim=128				,						depths=[2, 2, 18, 2]				,						num_heads=[4, 8, 16, 32]				,						window_size=12				,						drop_path_rate=0.3				,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,						)

       if isinstance(_A				,						_A				):
            lowercase :       Optional[int]       =			backbone_config.pop('''model_type'''				)
            lowercase :       List[str]       =			CONFIG_MAPPING[backbone_model_type]
            lowercase :       Union[str, Any]       =			config_class.from_dict(_A				)

       # verify that the backbone is supported
       if backbone_config.model_type not in self.backbones_supported:
            logger.warning_once(
                f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
                f"""Supported model types: {','.join(self.backbones_supported				)}"""				)

       if decoder_config is None:
            # fall back to https://huggingface.co/facebook/detr-resnet-50
            lowercase :       Any       =			DetrConfig()
       else:
            # verify that the decoder is supported
            lowercase :       Union[str, Any]       =			(
                decoder_config.pop('''model_type'''				) if isinstance(_A				,						_A				) else decoder_config.model_type
            )
            if decoder_type not in self.decoders_supported:
                 raise ValueError(
                     f"""Transformer Decoder {decoder_type} not supported, please use one of"""
                     f""" {','.join(self.decoders_supported				)}"""				)
            if isinstance(_A				,						_A				):
                 lowercase :       str       =			CONFIG_MAPPING[decoder_type]
                 lowercase :       Dict       =			config_class.from_dict(_A				)

       lowercase :       Tuple       =			backbone_config
       lowercase :       List[Any]       =			decoder_config
       # main feature dimension for the model
       lowercase :       Optional[int]       =			fpn_feature_size
       lowercase :       List[Any]       =			mask_feature_size
       # initializer
       lowercase :       Union[str, Any]       =			init_std
       lowercase :       Tuple       =			init_xavier_std
       # Hungarian matcher && loss
       lowercase :       List[str]       =			cross_entropy_weight
       lowercase :       int       =			dice_weight
       lowercase :       List[Any]       =			mask_weight
       lowercase :       Tuple       =			use_auxiliary_loss
       lowercase :       Tuple       =			no_object_weight
       lowercase :       int       =			output_auxiliary_logits

       lowercase :       List[Any]       =			self.decoder_config.encoder_attention_heads
       lowercase :       List[Any]       =			self.decoder_config.num_hidden_layers
       super().__init__(**_A				)






  @classmethod
  def  __a      (  cls		:     Optional[int]				,						_A		:     PretrainedConfig				,						_A		:     PretrainedConfig				,						**_A		:     str				)		->   Any:






       """simple docstring"""







       return cls(
           backbone_config=_A				,						decoder_config=_A				,						**_A				,						)






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






       """simple docstring"""







       lowercase :       str       =			copy.deepcopy(self.__dict__				)
       lowercase :       Optional[Any]       =			self.backbone_config.to_dict()
       lowercase :       List[Any]       =			self.decoder_config.to_dict()
       lowercase :       Optional[int]       =			self.__class__.model_type
       return output