


'''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       _UpperCamelCase (						UpperCamelCase__   ):
			return EnvironmentCommand()


class 						_snake_case   (       a__		):




					@staticmethod
					def 			snake_case__  ( _lowerCamelCase):
								UpperCAmelCase__					:   Optional[int]       				=       parser.add_parser("""env""")
								download_parser.set_defaults(func=_lowerCamelCase)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Tuple       				=       huggingface_hub.__version__

								UpperCAmelCase__					:   List[Any]       				=       """not installed"""
								UpperCAmelCase__					:   Dict       				=       """NA"""
								if is_torch_available():
											import torch

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

								UpperCAmelCase__					:   Union[str, Any]       				=       """not installed"""
								if is_transformers_available():
											import transformers

											UpperCAmelCase__					:   str       				=       transformers.__version__

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

											UpperCAmelCase__					:   Tuple       				=       accelerate.__version__

								UpperCAmelCase__					:   int       				=       """not installed"""
								if is_xformers_available():
											import xformers

											UpperCAmelCase__					:   Tuple       				=       xformers.__version__

								UpperCAmelCase__					:   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(_lowerCamelCase))

								return info




					@staticmethod
					def 			snake_case__  ( _lowerCamelCase):
								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


__A		={
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'M2M100ForConditionalGeneration',
		    'M2M100Model',
		    'M2M100PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
		from .tokenization_mam_aaa import MaMaaaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mam_aaa import (
				    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaMaaaForConditionalGeneration,
				    MaMaaaModel,
				    MaMaaaPreTrainedModel,
				)


else:
		import sys

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