



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

lowercase_	       =			logging.get_logger(__name__)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str				,   __lowerCamelCase	:    Optional[int]				,   __lowerCamelCase	:    Optional[int]				,   __lowerCamelCase	:    List[str]=None				,   __lowerCamelCase	:    Dict=None	)  ->						Optional[int]:
					# Recurse if needed
					if "." in tensor_name:
										__lowerCAmelCase	   =tensor_name.split("""."""	)
										for split in splits[:-1]:
															__lowerCAmelCase	   =getattr(__lowerCamelCase				,   __lowerCamelCase	)
															if new_module is None:
																				raise ValueError(f"""{module} has no attribute {split}."""	)
															__lowerCAmelCase	   =new_module
										__lowerCAmelCase	   =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}."""	)
					__lowerCAmelCase	   =tensor_name in module._buffers
					__lowerCAmelCase	   =getattr(__lowerCamelCase				,   __lowerCamelCase	)

					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}."""	)

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

					if is_abit or is_abit:
										__lowerCAmelCase	   =module._parameters[tensor_name]
										if param.device.type != "cuda":
															if value is None:
																				__lowerCAmelCase	   =old_value.to(__lowerCamelCase	)
															elif isinstance(__lowerCamelCase				,   torch.Tensor	):
																				__lowerCAmelCase	   =value.to("""cpu"""	)
																				if value.dtype == torch.inta:
																									__lowerCAmelCase	   =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:
																				__lowerCAmelCase	   =torch.tensor(__lowerCamelCase				,   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				,   __lowerCamelCase	) and fpaa_statistics is None:
																				__lowerCAmelCase	   =new_value.T

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

															__lowerCAmelCase	   =new_value
															if fpaa_statistics is not None:
																				setattr(module.weight				,   """SCB"""				,   fpaa_statistics.to(__lowerCamelCase	)	)

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

										if is_buffer:
															__lowerCAmelCase	   =new_value
										else:
															__lowerCAmelCase	   =nn.Parameter(__lowerCamelCase				,   requires_grad=old_value.requires_grad	)
															__lowerCAmelCase	   =new_value






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Tuple				,   __lowerCamelCase	:    str=None				,   __lowerCamelCase	:    Optional[int]=None				,   __lowerCamelCase	:    Tuple=None				,   __lowerCamelCase	:    str=False	)  ->						Optional[Any]:
					for name, module in model.named_children():
										if current_key_name is None:
															__lowerCAmelCase	   =[]
										current_key_name.append(__lowerCamelCase	)

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

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






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Dict				,   __lowerCamelCase	:    List[str]=None				,   __lowerCamelCase	:    str=None				,   __lowerCamelCase	:    int=None	)  ->						Any:
					__lowerCAmelCase	   =["""lm_head"""] if modules_to_not_convert is None else modules_to_not_convert
					__lowerCAmelCase   ,	__lowerCAmelCase	   =_replace_with_bnb_linear(
					    __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)

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






def 					__lowerCAmelCase		(  *__lowerCamelCase	:    Union[str, Any]				,   **__lowerCamelCase	:    List[Any]	)  ->						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"""				,   __lowerCamelCase				,   )
					return set_module_quantized_tensor_to_device(*__lowerCamelCase				,   **__lowerCamelCase	)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Tuple	)  ->						Optional[Any]:
					__lowerCAmelCase	   =deepcopy(__lowerCamelCase	)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
					tied_model.tie_weights()

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

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

					# add last module together with tied weights
					__lowerCAmelCase	   =set(__lowerCamelCase	) - set(__lowerCamelCase	)
					__lowerCAmelCase	   =list(set(__lowerCamelCase	)	) + list(__lowerCamelCase	)

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

					return filtered_module_names









from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str = "laptop"	)  ->						DataFrame:
					__lowerCAmelCase	   =f"""https://www.amazon.in/laptop/s?k={product}"""
					__lowerCAmelCase	   ={
					    """User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
					    """Accept-Language""": """en-US, en;q=0.5""",
					}
					__lowerCAmelCase	   =BeautifulSoup(requests.get(__lowerCamelCase				,   headers=__lowerCamelCase	).text	)
					# Initialize a Pandas dataframe with the column titles
					__lowerCAmelCase	   =DataFrame(
					    columns=[
					        """Product Title""",
					        """Product Link""",
					        """Current Price of the product""",
					        """Product Rating""",
					        """MRP of the product""",
					        """Discount""",
					    ]	)
					# Loop through each entry and store them in the dataframe
					for item, _ in zip_longest(
					    soup.find_all(
					        """div"""				,   attrs={"""class""": """s-result-item""", """data-component-type""": """s-search-result"""}				,   )				,   soup.find_all("""div"""				,   attrs={"""class""": """a-row a-size-base a-color-base"""}	)				,   ):
										try:
															__lowerCAmelCase	   =item.ha.text
															__lowerCAmelCase	   ="""https://www.amazon.in/""" + item.ha.a["""href"""]
															__lowerCAmelCase	   =item.find("""span"""				,   attrs={"""class""": """a-offscreen"""}	).text
															try:
																				__lowerCAmelCase	   =item.find("""span"""				,   attrs={"""class""": """a-icon-alt"""}	).text
															except AttributeError:
																				__lowerCAmelCase	   ="""Not available"""
															try:
																				__lowerCAmelCase	   =(
																				    """₹"""
																				    + item.find(
																				        """span"""				,   attrs={"""class""": """a-price a-text-price"""}	).text.split("""₹"""	)[1]
																				)
															except AttributeError:
																				__lowerCAmelCase	   =""""""
															try:
																				__lowerCAmelCase	   =float(
																				    (
																				        (
																				            float(product_mrp.strip("""₹"""	).replace(""","""				,   """"""	)	)
																				            - float(product_price.strip("""₹"""	).replace(""","""				,   """"""	)	)
																				        )
																				        / float(product_mrp.strip("""₹"""	).replace(""","""				,   """"""	)	)
																				    )
																				    * 100	)
															except ValueError:
																				__lowerCAmelCase	   =float("""nan"""	)
										except AttributeError:
															pass
										__lowerCAmelCase	   =[
										    product_title,
										    product_link,
										    product_price,
										    product_rating,
										    product_mrp,
										    discount,
										]
					__lowerCAmelCase	   =""" """
					__lowerCAmelCase	   =""" """
					data_frame.index += 1
					return data_frame


if __name__ == "__main__":
				lowercase_	       =			'''headphones'''
				get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")




