






"""simple docstring"""




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 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  return EnvironmentCommand()






class        UpperCamelCase			(	snake_case_      ):


 @staticmethod
 def _lowercase	(     UpperCAmelCase__		:						ArgumentParser							)					->       Tuple:
   _a  :       List[str]             =	parser.add_parser("""env"""							)
   download_parser.set_defaults(func=UpperCAmelCase__							)


 def _lowercase	(     self		:						str							)					->       List[str]:
   _a  :       int             =	huggingface_hub.__version__

   _a  :       Any             =	"""not installed"""
   _a  :       str             =	"""NA"""
   if is_torch_available():
     import torch

     _a  :       List[Any]             =	torch.__version__
     _a  :       Dict             =	torch.cuda.is_available()

   _a  :       Any             =	"""not installed"""
   if is_transformers_available():
     import transformers

     _a  :       List[Any]             =	transformers.__version__

   _a  :       Union[str, Any]             =	"""not installed"""
   if is_accelerate_available():
     import accelerate

     _a  :       Optional[int]             =	accelerate.__version__

   _a  :       Dict             =	"""not installed"""
   if is_xformers_available():
     import xformers

     _a  :       List[Any]             =	xformers.__version__

   _a  :       Dict             =	{
       """`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(UpperCAmelCase__							)							)

   return info







 @staticmethod
 def _lowercase	(     UpperCAmelCase__		:						int							)					->       int:
   return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]							) + "\n"









"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_snake_case          =   {
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   ['PerceiverFeatureExtractor']
 _snake_case          =   ['PerceiverImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'PerceiverForImageClassificationConvProcessing',
     'PerceiverForImageClassificationFourier',
     'PerceiverForImageClassificationLearned',
     'PerceiverForMaskedLM',
     'PerceiverForMultimodalAutoencoding',
     'PerceiverForOpticalFlow',
     'PerceiverForSequenceClassification',
     'PerceiverLayer',
     'PerceiverModel',
     'PerceiverPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
 from .tokenization_perceiver import PerceiverTokenizer

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_perceiver import PerceiverFeatureExtractor
  from .image_processing_perceiver import PerceiverImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_perceiver import (
      PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
      PerceiverForImageClassificationConvProcessing,
      PerceiverForImageClassificationFourier,
      PerceiverForImageClassificationLearned,
      PerceiverForMaskedLM,
      PerceiverForMultimodalAutoencoding,
      PerceiverForOpticalFlow,
      PerceiverForSequenceClassification,
      PerceiverLayer,
      PerceiverModel,
      PerceiverPreTrainedModel,
  )

else:
 import sys

 _snake_case          =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

