
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
    import bitsandbytes as bnb
    import torch
    import torch.nn as nn

    from ..pytorch_utils import ConvaD

if is_accelerate_available():
    from accelerate import init_empty_weights
    from accelerate.utils import find_tied_parameters

_a      :			Tuple					=							logging.get_logger(__name__)
def 				a__   (   a   :				Dict       ,     a   :				str       ,     a   :				Optional[int]       ,     a   :				Optional[Any]=None       ,     a   :				Optional[Any]=None		):

  """simple docstring"""







  if "." in tensor_name:
    _snake_case     :						Any						=   tensor_name.split("."		)
    for split in splits[:-1]:
      _snake_case     :						int						=   getattr(a       ,     a		)
      if new_module is None:
        raise ValueError(f'{module} has no attribute {split}.'		)
      _snake_case     :						List[Any]						=   new_module
    _snake_case     :						Dict						=   splits[-1]

  if tensor_name not in module._parameters and tensor_name not in module._buffers:
    raise ValueError(f'{module} does not have a parameter or a buffer named {tensor_name}.'		)
  _snake_case     :						str						=   tensor_name in module._buffers
  _snake_case     :						int						=   getattr(a       ,     a		)

  if old_value.device == torch.device("meta"		) and device not in ["meta", torch.device("meta"		)] and value is None:
    raise ValueError(f'{tensor_name} is on the meta device, we need a `value` to put in on {device}.'		)

  _snake_case     :						Union[str, Any]						=   False
  _snake_case     :						Any						=   False
  if is_buffer or not is_bitsandbytes_available():
    _snake_case     :						Union[str, Any]						=   False
    _snake_case     :						Optional[int]						=   False
  else:
    _snake_case     :						List[Any]						=   hasattr(bnb.nn       ,     "Params4bit"		) and isinstance(module._parameters[tensor_name]       ,     bnb.nn.Paramsabit		)
    _snake_case     :						Tuple						=   isinstance(module._parameters[tensor_name]       ,     bnb.nn.IntaParams		)

  if is_abit or is_abit:
    _snake_case     :						Optional[int]						=   module._parameters[tensor_name]
    if param.device.type != "cuda":
      if value is None:
        _snake_case     :						Any						=   old_value.to(a		)
      elif isinstance(a       ,     torch.Tensor		):
        _snake_case     :						Optional[int]						=   value.to("cpu"		)
        if value.dtype == torch.inta:
          _snake_case     :						Tuple						=   version.parse(importlib.metadata.version("bitsandbytes"		)		) > version.parse(
              "0.37.2"		)
          if not is_abit_serializable:
            raise ValueError(
                "Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
                "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."		)
      else:
        _snake_case     :						Optional[Any]						=   torch.tensor(a       ,     device="cpu"		)

      # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
      # Since weights are saved in the correct "orientation", we skip transposing when loading.
      if issubclass(module.source_cls       ,     a		) and fpaa_statistics is None:
        _snake_case     :						Tuple						=   new_value.T

      _snake_case     :						int						=   old_value.__dict__
      if is_abit:
        _snake_case     :						Dict						=   bnb.nn.IntaParams(a       ,     requires_grad=a       ,     **a		).to(a		)
      elif is_abit:
        _snake_case     :						Optional[Any]						=   bnb.nn.Paramsabit(a       ,     requires_grad=a       ,     **a		).to(a		)

      _snake_case     :						Optional[Any]						=   new_value
      if fpaa_statistics is not None:
        setattr(module.weight       ,     "SCB"       ,     fpaa_statistics.to(a		)		)

  else:
    if value is None:
      _snake_case     :						Tuple						=   old_value.to(a		)
    elif isinstance(a       ,     torch.Tensor		):
      _snake_case     :						List[str]						=   value.to(a		)
    else:
      _snake_case     :						List[Any]						=   torch.tensor(a       ,     device=a		)

    if is_buffer:
      _snake_case     :						Tuple						=   new_value
    else:
      _snake_case     :						Optional[int]						=   nn.Parameter(a       ,     requires_grad=old_value.requires_grad		)
      _snake_case     :						int						=   new_value
def 				a__   (   a   :				List[str]       ,     a   :				List[str]=None       ,     a   :				Tuple=None       ,     a   :				Optional[int]=None       ,     a   :				List[Any]=False		):

  """simple docstring"""







  for name, module in model.named_children():
    if current_key_name is None:
      _snake_case     :						Any						=   []
    current_key_name.append(a		)

    if (isinstance(a       ,     nn.Linear		) or isinstance(a       ,     a		)) and name not in modules_to_not_convert:
      # Check if the current key is not in the `modules_to_not_convert`
      if not any(key in ".".join(a		) for key in modules_to_not_convert		):
        with init_empty_weights():
          if isinstance(a       ,     a		):
            _snake_case     :						List[str]						=   module.weight.shape
          else:
            _snake_case     :						List[str]						=   module.in_features
            _snake_case     :						Tuple						=   module.out_features

          if quantization_config.quantization_method() == "llm_int8":
            _snake_case     :						Optional[int]						=   bnb.nn.LinearabitLt(
                a       ,     a       ,     module.bias is not None       ,     has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight       ,     threshold=quantization_config.llm_inta_threshold       ,     )
            _snake_case     :						List[str]						=   True
          else:
            if (
                quantization_config.llm_inta_skip_modules is not None
                and name in quantization_config.llm_inta_skip_modules
            ):
              pass
            else:
              _snake_case     :						str						=   bnb.nn.Linearabit(
                  a       ,     a       ,     module.bias is not None       ,     quantization_config.bnb_abit_compute_dtype       ,     compress_statistics=quantization_config.bnb_abit_use_double_quant       ,     quant_type=quantization_config.bnb_abit_quant_type       ,     )
              _snake_case     :						Dict						=   True
                    # Store the module class in case we need to transpose the weight later
          _snake_case     :						Optional[int]						=   type(a		)
          # Force requires grad to False to avoid unexpected errors
          model._modules[name].requires_grad_(a		)
    if len(list(module.children()		)		) > 0:
      _snake_case     :						Dict						=   _replace_with_bnb_linear(
          a       ,     a       ,     a       ,     a       ,     has_been_replaced=a       ,     )
    # Remove the last key for recursion
    current_key_name.pop(-1		)
  return model, has_been_replaced
def 				a__   (   a   :				Dict       ,     a   :				str=None       ,     a   :				str=None       ,     a   :				int=None		):

  """simple docstring"""







  _snake_case     :						Any						=   ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
  _snake_case     :						List[str]						=   _replace_with_bnb_linear(
      a       ,     a       ,     a       ,     a		)

  if not has_been_replaced:
    logger.warning(
        "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
        " Please double check your model architecture, or submit an issue on github if you think this is"
        " a bug."		)

  return model
def 				a__   (   *a   :				Dict       ,     **a   :				Dict		):

  """simple docstring"""







  warnings.warn(
      "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead"       ,     a       ,     )
  return replace_with_bnb_linear(*a       ,     **a		)
def 				a__   (   *a   :				Optional[Any]       ,     **a   :				Optional[Any]		):

  """simple docstring"""







  warnings.warn(
      "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead"       ,     a       ,     )
  return set_module_quantized_tensor_to_device(*a       ,     **a		)



def 				a__   (   a   :				Union[str, Any]		):

  """simple docstring"""







  _snake_case     :						Optional[int]						=   deepcopy(a		)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
  tied_model.tie_weights()

  _snake_case     :						Optional[int]						=   find_tied_parameters(a		)
  # For compatibility with Accelerate < 0.18
  if isinstance(a       ,     a		):
    _snake_case     :						Dict						=   sum(list(tied_params.values()		)       ,     []		) + list(tied_params.keys()		)
  else:
    _snake_case     :						Dict						=   sum(a       ,     []		)
  _snake_case     :						Optional[Any]						=   len(a		) > 0

  # Check if it is a base model
  _snake_case     :						List[Any]						=   not hasattr(a       ,     model.base_model_prefix		)

  # Ignore this for base models (BertModel, GPT2Model, etc.)
  if (not has_tied_params) and is_base_model:
    return []

  # otherwise they have an attached head
  _snake_case     :						List[str]						=   list(model.named_children()		)
  _snake_case     :						Union[str, Any]						=   [list_modules[-1][0]]

  # add last module together with tied weights
  _snake_case     :						Optional[int]						=   set(a		) - set(a		)
  _snake_case     :						Tuple						=   list(set(a		)		) + list(a		)

  # remove ".weight" from the keys
  _snake_case     :						Optional[int]						=   [".weight", ".bias"]
  _snake_case     :						List[str]						=   []
  for name in list_untouched:
    for name_to_remove in names_to_remove:
      if name_to_remove in name:
        _snake_case     :						Tuple						=   name.replace(a       ,     ""		)
    filtered_module_names.append(a		)

  return filtered_module_names



"""simple docstring"""
from __future__ import annotations

import requests

_a      :			List[str]					=							set(
    """approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def 				a__   (   a   :				str       ,     a   :				int = 1       ,     a   :				str = "new"       ,     a   :				list | None = None		):

  """simple docstring"""







  _snake_case     :						Any						=   wanted_data or []
  if invalid_search_terms := ", ".join(sorted(set(a		) - valid_terms		)		):
    _snake_case     :						Optional[int]						=   f'Invalid search term: {invalid_search_terms}'
    raise ValueError(a		)
  _snake_case     :						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

  _snake_case     :						Optional[Any]						=   response.json()
  if not wanted_data:
    return {id_: data["data"]["children"][id_] for id_ in range(a		)}

  _snake_case     :						Tuple						=   {}
  for id_ in range(a		):
    _snake_case     :						List[str]						=   {
        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"""]))

