"""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_		(      lowerCamelCase ):
				return EnvironmentCommand()







class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			ArgumentParser    ):
									UpperCAmelCase__    =							parser.add_parser('env'    )
									download_parser.set_defaults(func=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							huggingface_hub.__version__

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

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

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

													UpperCAmelCase__    =							transformers.__version__

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

													UpperCAmelCase__    =							accelerate.__version__

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

													UpperCAmelCase__    =							xformers.__version__

									UpperCAmelCase__    =							{
									    '`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(lowerCamelCase__    )    )

									return info







					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			Any    ):
									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


lowerCAmelCase__						:						int		  =			{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__						:						Union[str, Any]		  =			[
						    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TapasForMaskedLM',
						    'TapasForQuestionAnswering',
						    'TapasForSequenceClassification',
						    'TapasModel',
						    'TapasPreTrainedModel',
						    'load_tf_weights_in_tapas',
						]
try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__						:						Optional[Any]		  =			[
						    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFTapasForMaskedLM',
						    'TFTapasForQuestionAnswering',
						    'TFTapasForSequenceClassification',
						    'TFTapasModel',
						    'TFTapasPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
						from .tokenization_tapas import TapasTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tapas import (
												    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TapasForMaskedLM,
												    TapasForQuestionAnswering,
												    TapasForSequenceClassification,
												    TapasModel,
												    TapasPreTrainedModel,
												    load_tf_weights_in_tapas,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_tapas import (
												    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFTapasForMaskedLM,
												    TFTapasForQuestionAnswering,
												    TFTapasForSequenceClassification,
												    TFTapasModel,
												    TFTapasPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase__						:						Dict		  =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



