
'''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 (       A_							:					int       ):
						return EnvironmentCommand()





class 						a   (				__magic_name__    ):




				@staticmethod
				def 	__snake_case							(							SCREAMING_SNAKE_CASE_	:				ArgumentParser      ):
										snake_case       :     Optional[int] =    parser.add_parser('''env'''      )
										download_parser.set_defaults(func=SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     Tuple =    huggingface_hub.__version__

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

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

										snake_case       :     Tuple =    '''not installed'''
										if is_transformers_available():
																import transformers

																snake_case       :     Dict =    transformers.__version__

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

																snake_case       :     List[Any] =    accelerate.__version__

										snake_case       :     Optional[int] =    '''not installed'''
										if is_xformers_available():
																import xformers

																snake_case       :     List[Any] =    xformers.__version__

										snake_case       :     str =    {
										    '''`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(SCREAMING_SNAKE_CASE_      )      )

										return info




				@staticmethod
				def 	__snake_case							(							SCREAMING_SNAKE_CASE_	:				str      ):
										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_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase    					=   {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   ["ConvBertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvBertForMaskedLM",
						    "ConvBertForMultipleChoice",
						    "ConvBertForQuestionAnswering",
						    "ConvBertForSequenceClassification",
						    "ConvBertForTokenClassification",
						    "ConvBertLayer",
						    "ConvBertModel",
						    "ConvBertPreTrainedModel",
						    "load_tf_weights_in_convbert",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFConvBertForMaskedLM",
						    "TFConvBertForMultipleChoice",
						    "TFConvBertForQuestionAnswering",
						    "TFConvBertForSequenceClassification",
						    "TFConvBertForTokenClassification",
						    "TFConvBertLayer",
						    "TFConvBertModel",
						    "TFConvBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
						from .tokenization_convbert import ConvBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_convbert_fast import ConvBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convbert import (
												    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvBertForMaskedLM,
												    ConvBertForMultipleChoice,
												    ConvBertForQuestionAnswering,
												    ConvBertForSequenceClassification,
												    ConvBertForTokenClassification,
												    ConvBertLayer,
												    ConvBertModel,
												    ConvBertPreTrainedModel,
												    load_tf_weights_in_convbert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_convbert import (
												    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFConvBertForMaskedLM,
												    TFConvBertForMultipleChoice,
												    TFConvBertForQuestionAnswering,
												    TFConvBertForSequenceClassification,
												    TFConvBertForTokenClassification,
												    TFConvBertLayer,
												    TFConvBertModel,
												    TFConvBertPreTrainedModel,
												)


else:
						import sys

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

