




'''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       lowerCAmelCase_     (					__A						:							Optional[Any] ,			__A						:							Dict ,			__A						:							Optional[int] ,			__A						:							List[str]=None ,			__A						:							List[Any]=None ):




			'''simple docstring'''


			if "." in tensor_name:
						snake_case:       Any        =       tensor_name.split('.' )
						for split in splits[:-1]:
									snake_case:       Optional[Any]        =       getattr(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ )
									if new_module is None:
												raise ValueError(f"""{module} has no attribute {split}.""" )
									snake_case:       Union[str, Any]        =       new_module
						snake_case:       List[str]        =       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:       Dict        =       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:       Optional[int]        =       False
			snake_case:       List[Any]        =       False
			if is_buffer or not is_bitsandbytes_available():
						snake_case:       int        =       False
						snake_case:       List[str]        =       False
			else:
						snake_case:       List[Any]        =       hasattr(bnb.nn ,			'Params4bit' ) and isinstance(module._parameters[tensor_name] ,			bnb.nn.Paramsabit )
						snake_case:       Union[str, Any]        =       isinstance(module._parameters[tensor_name] ,			bnb.nn.IntaParams )

			if is_abit or is_abit:
						snake_case:       List[Any]        =       module._parameters[tensor_name]
						if param.device.type != "cuda":
									if value is None:
												snake_case:       Optional[Any]        =       old_value.to(SCREAMING_SNAKE_CASE_ )
									elif isinstance(SCREAMING_SNAKE_CASE_ ,			torch.Tensor ):
												snake_case:       int        =       value.to('cpu' )
												if value.dtype == torch.inta:
															snake_case:       List[str]        =       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:       Tuple        =       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:       List[Any]        =       new_value.T

									snake_case:       int        =       old_value.__dict__
									if is_abit:
												snake_case:       List[Any]        =       bnb.nn.IntaParams(SCREAMING_SNAKE_CASE_ ,			requires_grad=SCREAMING_SNAKE_CASE_ ,			**SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
									elif is_abit:
												snake_case:       Optional[int]        =       bnb.nn.Paramsabit(SCREAMING_SNAKE_CASE_ ,			requires_grad=SCREAMING_SNAKE_CASE_ ,			**SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )

									snake_case:       List[Any]        =       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:       Any        =       old_value.to(SCREAMING_SNAKE_CASE_ )
						elif isinstance(SCREAMING_SNAKE_CASE_ ,			torch.Tensor ):
									snake_case:       Dict        =       value.to(SCREAMING_SNAKE_CASE_ )
						else:
									snake_case:       List[Any]        =       torch.tensor(SCREAMING_SNAKE_CASE_ ,			device=SCREAMING_SNAKE_CASE_ )

						if is_buffer:
									snake_case:       Tuple        =       new_value
						else:
									snake_case:       Any        =       nn.Parameter(SCREAMING_SNAKE_CASE_ ,			requires_grad=old_value.requires_grad )
									snake_case:       Optional[int]        =       new_value





def       lowerCAmelCase_     (					__A						:							str ,			__A						:							int=None ,			__A						:							List[Any]=None ,			__A						:							Optional[Any]=None ,			__A						:							int=False ):




			'''simple docstring'''


			for name, module in model.named_children():
						if current_key_name is None:
									snake_case:       Any        =       []
						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:       List[str]        =       module.weight.shape
															else:
																		snake_case:       Union[str, Any]        =       module.in_features
																		snake_case:       Any        =       module.out_features

															if quantization_config.quantization_method() == "llm_int8":
																		snake_case:       str        =       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:       Any        =       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(
																					    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:       List[str]        =       True
																					      # Store the module class in case we need to transpose the weight later
															snake_case:       Union[str, Any]        =       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:       List[Any]        =       _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       lowerCAmelCase_     (					__A						:							Any ,			__A						:							Any=None ,			__A						:							Optional[Any]=None ,			__A						:							Tuple=None ):




			'''simple docstring'''


			snake_case:       Optional[Any]        =       ['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
			snake_case    ,						snake_case:       str        =       _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       lowerCAmelCase_     (					*__A						:							List[Any] ,			**__A						:							str ):




			'''simple docstring'''


			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       lowerCAmelCase_     (					*__A						:							Optional[Any] ,			**__A						:							str ):




			'''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' ,			SCREAMING_SNAKE_CASE_ ,			)
			return set_module_quantized_tensor_to_device(*SCREAMING_SNAKE_CASE_ ,			**SCREAMING_SNAKE_CASE_ )





def       lowerCAmelCase_     (					__A						:							Dict ):




			'''simple docstring'''


			snake_case:       Any        =       deepcopy(SCREAMING_SNAKE_CASE_ )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
			tied_model.tie_weights()

			snake_case:       int        =       find_tied_parameters(SCREAMING_SNAKE_CASE_ )
			# For compatibility with Accelerate < 0.18
			if isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ):
						snake_case:       List[Any]        =       sum(list(tied_params.values() ) ,			[] ) + list(tied_params.keys() )
			else:
						snake_case:       Tuple        =       sum(SCREAMING_SNAKE_CASE_ ,			[] )
			snake_case:       Tuple        =       len(SCREAMING_SNAKE_CASE_ ) > 0

			# Check if it is a base model
			snake_case:       int        =       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[str]        =       list(model.named_children() )
			snake_case:       Any        =       [list_modules[-1][0]]

			# add last module together with tied weights
			snake_case:       Any        =       set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ )
			snake_case:       List[Any]        =       list(set(SCREAMING_SNAKE_CASE_ ) ) + list(SCREAMING_SNAKE_CASE_ )

			# remove ".weight" from the keys
			snake_case:       Optional[Any]        =       ['.weight', '.bias']
			snake_case:       str        =       []
			for name in list_untouched:
						for name_to_remove in names_to_remove:
									if name_to_remove in name:
												snake_case:       Any        =       name.replace(SCREAMING_SNAKE_CASE_ ,			'' )
						filtered_module_names.append(SCREAMING_SNAKE_CASE_ )

			return filtered_module_names
'''simple docstring'''


import requests

__snake_case						  =		"""https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""

def   A_      (       SCREAMING_SNAKE_CASE_						)			->None:
  # fetching a list of articles in json format
  lowercase_     =       requests.get(_NEWS_API + bbc_news_api_key						).json()
  # each article in the list is a dict
  for i, article in enumerate(bbc_news_page["""articles"""] ,	1						):
    print(f"""{i}.) {article["title"]}"""						)


if __name__ == "__main__":
       fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")

