
"""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

UpperCAmelCase__          = logging.get_logger(__name__)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       List[str]=None ,					__lowerCamelCase   :       List[str]=None		)     ->					Optional[Any]:
  # Recurse if needed
  if "." in tensor_name:
    _snake_case					     =       tensor_name.split('''.'''		)
    for split in splits[:-1]:
      _snake_case					     =       getattr(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE		)
      if new_module is None:
        raise ValueError(f'''{module} has no attribute {split}.'''		)
      _snake_case					     =       new_module
    _snake_case					     =       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					     =       tensor_name in module._buffers
  _snake_case					     =       getattr(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE		)

  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					     =       False
  _snake_case					     =       False
  if is_buffer or not is_bitsandbytes_available():
    _snake_case					     =       False
    _snake_case					     =       False
  else:
    _snake_case					     =       hasattr(bnb.nn ,					'''Params4bit'''		) and isinstance(module._parameters[tensor_name] ,					bnb.nn.Paramsabit		)
    _snake_case					     =       isinstance(module._parameters[tensor_name] ,					bnb.nn.IntaParams		)

  if is_abit or is_abit:
    _snake_case					     =       module._parameters[tensor_name]
    if param.device.type != "cuda":
      if value is None:
        _snake_case					     =       old_value.to(__SCREAMING_SNAKE_CASE		)
      elif isinstance(__SCREAMING_SNAKE_CASE ,					torch.Tensor		):
        _snake_case					     =       value.to('''cpu'''		)
        if value.dtype == torch.inta:
          _snake_case					     =       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					     =       torch.tensor(__SCREAMING_SNAKE_CASE ,					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 ,					__SCREAMING_SNAKE_CASE		) and fpaa_statistics is None:
        _snake_case					     =       new_value.T

      _snake_case					     =       old_value.__dict__
      if is_abit:
        _snake_case					     =       bnb.nn.IntaParams(__SCREAMING_SNAKE_CASE ,					requires_grad=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
      elif is_abit:
        _snake_case					     =       bnb.nn.Paramsabit(__SCREAMING_SNAKE_CASE ,					requires_grad=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)

      _snake_case					     =       new_value
      if fpaa_statistics is not None:
        setattr(module.weight ,					'''SCB''' ,					fpaa_statistics.to(__SCREAMING_SNAKE_CASE		)		)

  else:
    if value is None:
      _snake_case					     =       old_value.to(__SCREAMING_SNAKE_CASE		)
    elif isinstance(__SCREAMING_SNAKE_CASE ,					torch.Tensor		):
      _snake_case					     =       value.to(__SCREAMING_SNAKE_CASE		)
    else:
      _snake_case					     =       torch.tensor(__SCREAMING_SNAKE_CASE ,					device=__SCREAMING_SNAKE_CASE		)

    if is_buffer:
      _snake_case					     =       new_value
    else:
      _snake_case					     =       nn.Parameter(__SCREAMING_SNAKE_CASE ,					requires_grad=old_value.requires_grad		)
      _snake_case					     =       new_value
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Optional[Any]=None ,					__lowerCamelCase   :       int=None ,					__lowerCamelCase   :       int=None ,					__lowerCamelCase   :       Union[str, Any]=False		)     ->					Dict:
  for name, module in model.named_children():
    if current_key_name is None:
      _snake_case					     =       []
    current_key_name.append(__SCREAMING_SNAKE_CASE		)

    if (isinstance(__SCREAMING_SNAKE_CASE ,					nn.Linear		) or isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE		)) 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(__SCREAMING_SNAKE_CASE		) for key in modules_to_not_convert		):
        with init_empty_weights():
          if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE		):
            _snake_case     ,					_snake_case					     =       module.weight.shape
          else:
            _snake_case					     =       module.in_features
            _snake_case					     =       module.out_features

          if quantization_config.quantization_method() == "llm_int8":
            _snake_case					     =       bnb.nn.LinearabitLt(
                __SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					module.bias is not None ,					has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight ,					threshold=quantization_config.llm_inta_threshold ,					)
            _snake_case					     =       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					     =       bnb.nn.Linearabit(
                  __SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					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					     =       True
                    # Store the module class in case we need to transpose the weight later
          _snake_case					     =       type(__SCREAMING_SNAKE_CASE		)
          # Force requires grad to False to avoid unexpected errors
          model._modules[name].requires_grad_(__SCREAMING_SNAKE_CASE		)
    if len(list(module.children()		)		) > 0:
      _snake_case     ,					_snake_case					     =       _replace_with_bnb_linear(
          __SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					has_been_replaced=__SCREAMING_SNAKE_CASE ,					)
    # Remove the last key for recursion
    current_key_name.pop(-1		)
  return model, has_been_replaced
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict=None ,					__lowerCamelCase   :       int=None ,					__lowerCamelCase   :       Optional[int]=None		)     ->					Any:
  _snake_case					     =       ['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
  _snake_case     ,					_snake_case					     =       _replace_with_bnb_linear(
      __SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE		)

  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 					_UpperCAmelCase      ( *__lowerCamelCase   :       Optional[int] ,					**__lowerCamelCase   :       Tuple		)     ->					Any:
  warnings.warn(
      '''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead''' ,					__SCREAMING_SNAKE_CASE ,					)
  return replace_with_bnb_linear(*__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE		)
def 					_UpperCAmelCase      ( *__lowerCamelCase   :       int ,					**__lowerCamelCase   :       Optional[int]		)     ->					List[str]:
  warnings.warn(
      '''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead''' ,					__SCREAMING_SNAKE_CASE ,					)
  return set_module_quantized_tensor_to_device(*__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE		)

def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					str:
  _snake_case					     =       deepcopy(__SCREAMING_SNAKE_CASE		)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
  tied_model.tie_weights()

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

  # Check if it is a base model
  _snake_case					     =       not hasattr(__SCREAMING_SNAKE_CASE ,					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(model.named_children()		)
  _snake_case					     =       [list_modules[-1][0]]

  # add last module together with tied weights
  _snake_case					     =       set(__SCREAMING_SNAKE_CASE		) - set(__SCREAMING_SNAKE_CASE		)
  _snake_case					     =       list(set(__SCREAMING_SNAKE_CASE		)		) + list(__SCREAMING_SNAKE_CASE		)

  # remove ".weight" from the keys
  _snake_case					     =       ['''.weight''', '''.bias''']
  _snake_case					     =       []
  for name in list_untouched:
    for name_to_remove in names_to_remove:
      if name_to_remove in name:
        _snake_case					     =       name.replace(__SCREAMING_SNAKE_CASE ,					''''''		)
    filtered_module_names.append(__SCREAMING_SNAKE_CASE		)

  return filtered_module_names





import pprint

import requests

UpperCAmelCase	      = '''https://zenquotes.io/api'''






def 							UpperCAmelCase_		(							):
	return requests.get(API_ENDPOINT_URL + '/today'							).json()






def 							UpperCAmelCase_		(							):
	return requests.get(API_ENDPOINT_URL + '/random'							).json()


if __name__ == "__main__":
	UpperCAmelCase	      = random_quotes()
	pprint.pprint(response)

