





'''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_  :					Union[str, Any]    ):



    '''simple docstring'''





    return EnvironmentCommand()





class    lowerCAmelCase_		(		__magic_name__		):




 @staticmethod
 def 							_snake_case   (	_lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      parser.add_parser("env"		)
     download_parser.set_defaults(func=_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      huggingface_hub.__version__

     _lowerCAmelCase				=      "not installed"
     _lowerCAmelCase				=      "NA"
     if is_torch_available():
         import torch

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

     _lowerCAmelCase				=      "not installed"
     if is_transformers_available():
         import transformers

         _lowerCAmelCase				=      transformers.__version__

     _lowerCAmelCase				=      "not installed"
     if is_accelerate_available():
         import accelerate

         _lowerCAmelCase				=      accelerate.__version__

     _lowerCAmelCase				=      "not installed"
     if is_xformers_available():
         import xformers

         _lowerCAmelCase				=      xformers.__version__

     _lowerCAmelCase				=      {
         "`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		)    ->							Optional[int]:
     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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE	 =							{"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							["XGLMTokenizer"]

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XGLMForCausalLM",
           "XGLMModel",
           "XGLMPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "FlaxXGLMForCausalLM",
           "FlaxXGLMModel",
           "FlaxXGLMPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXGLMForCausalLM",
           "TFXGLMModel",
           "TFXGLMPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm import XGLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm_fast import XGLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xglm import (
                  TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXGLMForCausalLM,
                  TFXGLMModel,
                  TFXGLMPreTrainedModel,
              )


else:
       import sys

       _SCREAMING_SNAKE_CASE	 =							_LazyModule(__name__, globals()["__file__"], _import_structure)


