





"""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  __A   (_SCREAMING_SNAKE_CASE			)					->Optional[int]:





  """simple docstring"""


  return EnvironmentCommand()






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       @staticmethod
       def      snake_case						(    __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							parser.add_parser('env'      )
         download_parser.set_defaults(func=__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							huggingface_hub.__version__

         lowerCAmelCase__			:Any											=							'not installed'
         lowerCAmelCase__			:Optional[int]											=							'NA'
         if is_torch_available():
           import torch

           lowerCAmelCase__			:List[str]											=							torch.__version__
           lowerCAmelCase__			:Union[str, Any]											=							torch.cuda.is_available()

         lowerCAmelCase__			:Optional[int]											=							'not installed'
         if is_transformers_available():
           import transformers

           lowerCAmelCase__			:str											=							transformers.__version__

         lowerCAmelCase__			:str											=							'not installed'
         if is_accelerate_available():
           import accelerate

           lowerCAmelCase__			:Optional[int]											=							accelerate.__version__

         lowerCAmelCase__			:Dict											=							'not installed'
         if is_xformers_available():
           import xformers

           lowerCAmelCase__			:Any											=							xformers.__version__

         lowerCAmelCase__			:Union[str, Any]											=							{
             '`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      snake_case						(    __UpperCAmelCase      ):



         '''simple docstring'''

         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_tf_available, is_torch_available


__A											=		{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecAudioForAudioFrameClassification""",
      """Data2VecAudioForCTC""",
      """Data2VecAudioForSequenceClassification""",
      """Data2VecAudioForXVector""",
      """Data2VecAudioModel""",
      """Data2VecAudioPreTrainedModel""",
  ]
  __A											=		[
      """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecTextForCausalLM""",
      """Data2VecTextForMaskedLM""",
      """Data2VecTextForMultipleChoice""",
      """Data2VecTextForQuestionAnswering""",
      """Data2VecTextForSequenceClassification""",
      """Data2VecTextForTokenClassification""",
      """Data2VecTextModel""",
      """Data2VecTextPreTrainedModel""",
  ]
  __A											=		[
      """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecVisionForImageClassification""",
      """Data2VecVisionForMaskedImageModeling""",
      """Data2VecVisionForSemanticSegmentation""",
      """Data2VecVisionModel""",
      """Data2VecVisionPreTrainedModel""",
  ]

if is_tf_available():
  __A											=		[
      """TFData2VecVisionForImageClassification""",
      """TFData2VecVisionForSemanticSegmentation""",
      """TFData2VecVisionModel""",
      """TFData2VecVisionPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
  from .configuration_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DataaVecTextConfig,
      DataaVecTextOnnxConfig,
  )
  from .configuration_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DataaVecVisionConfig,
      DataaVecVisionOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dataavec_audio import (
        DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecAudioForAudioFrameClassification,
        DataaVecAudioForCTC,
        DataaVecAudioForSequenceClassification,
        DataaVecAudioForXVector,
        DataaVecAudioModel,
        DataaVecAudioPreTrainedModel,
    )
    from .modeling_dataavec_text import (
        DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecTextForCausalLM,
        DataaVecTextForMaskedLM,
        DataaVecTextForMultipleChoice,
        DataaVecTextForQuestionAnswering,
        DataaVecTextForSequenceClassification,
        DataaVecTextForTokenClassification,
        DataaVecTextModel,
        DataaVecTextPreTrainedModel,
    )
    from .modeling_dataavec_vision import (
        DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecVisionForImageClassification,
        DataaVecVisionForMaskedImageModeling,
        DataaVecVisionForSemanticSegmentation,
        DataaVecVisionModel,
        DataaVecVisionPreTrainedModel,
    )
  if is_tf_available():
    from .modeling_tf_dataavec_vision import (
        TFDataaVecVisionForImageClassification,
        TFDataaVecVisionForSemanticSegmentation,
        TFDataaVecVisionModel,
        TFDataaVecVisionPreTrainedModel,
    )

else:
  import sys

  __A											=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
