



import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand

def      lowerCAmelCase_					(  _lowercase    :       Dict)							->	int:







			"""simple docstring"""
			return EnvironmentCommand()

def      lowerCAmelCase_					(  _lowercase    :       Optional[int])							->	Dict:







			"""simple docstring"""
			return EnvironmentCommand(args.accelerate_config_file)





class        snake_case__    (A_		):







						"""simple docstring"""




						@staticmethod
						def 							SCREAMING_SNAKE_CASE__(       __lowercase  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  parser.add_parser("""env"""  )
									download_parser.set_defaults(func=snake_case__  )
									download_parser.add_argument(
									    """--accelerate-config_file""" ,       default=snake_case__ ,       help="""The accelerate config file to use for the default values in the launching script.""" ,       )
									download_parser.set_defaults(func=snake_case__  )

						def __init__(       self ,       __lowercase ,       *__lowercase  ) ->				None:
									"""simple docstring"""




									a__       :						str							=  accelerate_config_file

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						List[str]							=  "not installed"
									if is_safetensors_available():
												import safetensors

												a__       :						Union[str, Any]							=  safetensors.__version__
									elif importlib.util.find_spec("""safetensors"""  ) is not None:
												import safetensors

												a__       :						Union[str, Any]							=  F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

									a__       :						Tuple							=  "not installed"
									a__       :						Dict							=  "not found"
									if is_accelerate_available():
												import accelerate
												from accelerate.commands.config import default_config_file, load_config_from_file

												a__       :						Union[str, Any]							=  accelerate.__version__
												# Get the default from the config file.
												if self._accelerate_config_file is not None or os.path.isfile(snake_case__  ):
															a__       :						Dict							=  load_config_from_file(self._accelerate_config_file  ).to_dict()

												a__       :						Any							=  (
												    "\n".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]  )
												    if isinstance(snake_case__ ,       snake_case__  )
												    else F'''\t{accelerate_config}'''
												)

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

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

									a__       :						Optional[int]							=  "not installed"
									a__       :						Any							=  "NA"
									if is_tf_available():
												import tensorflow as tf

												a__       :						Union[str, Any]							=  tf.__version__
												try:
															# deprecated in v2.1
															a__       :						str							=  tf.test.is_gpu_available()
												except AttributeError:
															# returns list of devices, convert to bool
															a__       :						Tuple							=  bool(tf.config.list_physical_devices("""GPU"""  )  )

									a__       :						Any							=  "not installed"
									a__       :						List[str]							=  "not installed"
									a__       :						Union[str, Any]							=  "not installed"
									a__       :						List[str]							=  "NA"
									if is_flax_available():
												import flax
												import jax
												import jaxlib

												a__       :						Union[str, Any]							=  flax.__version__
												a__       :						Tuple							=  jax.__version__
												a__       :						int							=  jaxlib.__version__
												a__       :						Optional[Any]							=  jax.lib.xla_bridge.get_backend().platform

									a__       :						int							=  {
									    "`transformers` version": version,
									    "Platform": platform.platform(),
									    "Python version": platform.python_version(),
									    "Huggingface_hub version": huggingface_hub.__version__,
									    "Safetensors version": F'''{safetensors_version}''',
									    "Accelerate version": F'''{accelerate_version}''',
									    "Accelerate config": F'''{accelerate_config_str}''',
									    "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
									    "Tensorflow version (GPU?)": F'''{tf_version} ({tf_cuda_available})''',
									    "Flax version (CPU?/GPU?/TPU?)": F'''{flax_version} ({jax_backend})''',
									    "Jax version": F'''{jax_version}''',
									    "JaxLib version": F'''{jaxlib_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(snake_case__  )  )

									return info

						@staticmethod
						def 							SCREAMING_SNAKE_CASE__(       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




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









from __future__ import annotations

import requests

_lowercase		:				Dict      		=set(
    "approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports".split()
)

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       int = 1						,       _lowercase    :       str = "new"						,       _lowercase    :       list | None = None)							->	dict:







			"""simple docstring"""
			a__       :						Optional[Any]							=  wanted_data or []
			if invalid_search_terms := ", ".join(sorted(set(_lowercase) - valid_terms)):
						a__       :						Any							=  F'''Invalid search term: {invalid_search_terms}'''
						raise ValueError(_lowercase)
			a__       :						int							=  requests.get(
			    F'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}'''						,       headers={"""User-agent""": """A random string"""}						,       )
			if response.status_code == 429:
						raise requests.HTTPError

			a__       :						int							=  response.json()
			if not wanted_data:
						return {id_: data["data"]["children"][id_] for id_ in range(_lowercase)}

			a__       :						int							=  {}
			for id_ in range(_lowercase):
						a__       :						List[Any]							=  {
						    item: data["""data"""]["""children"""][id_]["""data"""][item] for item in wanted_data
						}
			return data_dict


if __name__ == "__main__":
		# If you get Error 429, that means you are rate limited.Try after some time
		print(get_subreddit_data("learnpython", wanted_data=["title", "url", "selftext"]))




