
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return EnvironmentCommand()







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							@staticmethod
							def  A__      (				A__):
												lowercase							=						parser.add_parser('''env''')
												download_parser.set_defaults(func=A__)




							def  A__      (				self):
												lowercase							=						huggingface_hub.__version__

												lowercase							=						'''not installed'''
												lowercase							=						'''NA'''
												if is_torch_available():
																	import torch

																	lowercase							=						torch.__version__
																	lowercase							=						torch.cuda.is_available()

												lowercase							=						'''not installed'''
												if is_transformers_available():
																	import transformers

																	lowercase							=						transformers.__version__

												lowercase							=						'''not installed'''
												if is_accelerate_available():
																	import accelerate

																	lowercase							=						accelerate.__version__

												lowercase							=						'''not installed'''
												if is_xformers_available():
																	import xformers

																	lowercase							=						xformers.__version__

												lowercase							=						{
												    '''`diffusers` version''': version,
												    '''Platform''': platform.platform(),
												    '''Python version''': platform.python_version(),
												    '''PyTorch version (GPU?)''': f'{pt_version} ({pt_cuda_available})',
												    '''Huggingface_hub version''': hub_version,
												    '''Transformers version''': transformers_version,
												    '''Accelerate version''': accelerate_version,
												    '''xFormers version''': xformers_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__):
												return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]) + "\n"









"""simple docstring"""
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


A:   List[str]								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Tuple								=		["DPTFeatureExtractor"]
      A:   int								=		["DPTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[Any]								=		[
          "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPTForDepthEstimation",
          "DPTForSemanticSegmentation",
          "DPTModel",
          "DPTPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_dpt import DPTFeatureExtractor
            from .image_processing_dpt import DPTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpt import (
                DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPTForDepthEstimation,
                DPTForSemanticSegmentation,
                DPTModel,
                DPTPreTrainedModel,
            )


else:
      import sys

      A:   str								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
