'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_:     int  ={
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:     Optional[Any]  =[
        'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'AdaptiveEmbedding',
        'TransfoXLForSequenceClassification',
        'TransfoXLLMHeadModel',
        'TransfoXLModel',
        'TransfoXLPreTrainedModel',
        'load_tf_weights_in_transfo_xl',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =[
        'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFAdaptiveEmbedding',
        'TFTransfoXLForSequenceClassification',
        'TFTransfoXLLMHeadModel',
        'TFTransfoXLMainLayer',
        'TFTransfoXLModel',
        'TFTransfoXLPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
    from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_transfo_xl import (
            TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            AdaptiveEmbedding,
            TransfoXLForSequenceClassification,
            TransfoXLLMHeadModel,
            TransfoXLModel,
            TransfoXLPreTrainedModel,
            load_tf_weights_in_transfo_xl,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_transfo_xl import (
            TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAdaptiveEmbedding,
            TFTransfoXLForSequenceClassification,
            TFTransfoXLLMHeadModel,
            TFTransfoXLMainLayer,
            TFTransfoXLModel,
            TFTransfoXLPreTrainedModel,
        )

else:
    import sys

    SCREAMING_SNAKE_CASE_:     Tuple  =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








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       (						lowerCAmelCase_       )->       Union[str, Any]:
				return EnvironmentCommand()







class  snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						@staticmethod
						def   lowercase_       ( __lowercase							)    ->			List[Any]:
										lowerCAmelCase_       :					List[str]			 = parser.add_parser('''env'''							)
										download_parser.set_defaults(func=__lowercase							)




						def   lowercase_       ( self							)    ->			int:
										lowerCAmelCase_       :					Optional[Any]			 = huggingface_hub.__version__

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

														lowerCAmelCase_       :					Any			 = torch.__version__
														lowerCAmelCase_       :					str			 = torch.cuda.is_available()

										lowerCAmelCase_       :					List[str]			 = '''not installed'''
										if is_transformers_available():
														import transformers

														lowerCAmelCase_       :					Any			 = transformers.__version__

										lowerCAmelCase_       :					Optional[Any]			 = '''not installed'''
										if is_accelerate_available():
														import accelerate

														lowerCAmelCase_       :					List[Any]			 = accelerate.__version__

										lowerCAmelCase_       :					List[str]			 = '''not installed'''
										if is_xformers_available():
														import xformers

														lowerCAmelCase_       :					Optional[Any]			 = xformers.__version__

										lowerCAmelCase_       :					int			 = {
										    '''`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(__lowercase							)							)

										return info




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