





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  (	UpperCamelCase__   :   List[Any]					)      ->    Optional[Any]:

						"""simple docstring"""





						return EnvironmentCommand()





class 				a			(      __lowercase			):






				@staticmethod
				def 			snake_case_    (  _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   parser.add_parser('''env'''					)
										download_parser.set_defaults(func=_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   huggingface_hub.__version__

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

																__SCREAMING_SNAKE_CASE:				Any           =   torch.__version__
																__SCREAMING_SNAKE_CASE:				Dict           =   torch.cuda.is_available()

										__SCREAMING_SNAKE_CASE:				Dict           =   '''not installed'''
										if is_transformers_available():
																import transformers

																__SCREAMING_SNAKE_CASE:				int           =   transformers.__version__

										__SCREAMING_SNAKE_CASE:				str           =   '''not installed'''
										if is_accelerate_available():
																import accelerate

																__SCREAMING_SNAKE_CASE:				Tuple           =   accelerate.__version__

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   '''not installed'''
										if is_xformers_available():
																import xformers

																__SCREAMING_SNAKE_CASE:				Dict           =   xformers.__version__

										__SCREAMING_SNAKE_CASE:				Optional[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(_lowerCAmelCase					)					)

										return info






				@staticmethod
				def 			snake_case_    (  _lowerCAmelCase					):

										"""simple docstring"""




										return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]					) + "\n"










from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream





class 				a			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = False			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(
										    features=_lowerCAmelCase			, cache_dir=_lowerCAmelCase			, keep_in_memory=_lowerCAmelCase			, streaming=_lowerCAmelCase			, num_proc=_lowerCAmelCase			, **_lowerCAmelCase			, )
										__SCREAMING_SNAKE_CASE:				Any           =   Generator(
										    cache_dir=_lowerCAmelCase			, features=_lowerCAmelCase			, generator=_lowerCAmelCase			, gen_kwargs=_lowerCAmelCase			, **_lowerCAmelCase			, )






				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.streaming:
																__SCREAMING_SNAKE_CASE:				List[str]           =   self.builder.as_streaming_dataset(split='''train'''					)
										# Build regular (map-style) dataset
										else:
																__SCREAMING_SNAKE_CASE:				str           =   None
																__SCREAMING_SNAKE_CASE:				List[Any]           =   None
																__SCREAMING_SNAKE_CASE:				Tuple           =   None
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   None

																self.builder.download_and_prepare(
																    download_config=_lowerCAmelCase			, download_mode=_lowerCAmelCase			, verification_mode=_lowerCAmelCase			, base_path=_lowerCAmelCase			, num_proc=self.num_proc			, )
																__SCREAMING_SNAKE_CASE:				List[str]           =   self.builder.as_dataset(
																    split='''train'''			, verification_mode=_lowerCAmelCase			, in_memory=self.keep_in_memory					)
										return dataset



