


import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
   import bitsandbytes as bnb

from copy import deepcopy


UpperCAmelCase_      : Any  					=       logging.getLogger(__name__)







def        SCREAMING_SNAKE_CASE_						(			__A :			torch.nn.Module  ,    __A :			BnbQuantizationConfig  ,    __A :			Union[str, os.PathLike] = None  ,    __A :			Optional[Dict[str, Union[int, str, torch.device]]] = None  ,    __A :			Optional[List[str]] = None  ,    __A :			Optional[Dict[Union[int, str], Union[int, str]]] = None  ,    __A :			Optional[Union[str, os.PathLike]] = None  ,    __A :			bool = False  ,    )	->		Dict:

       """simple docstring"""

       a_					:		Optional[int]							=  bnb_quantization_config.load_in_abit
       a_					:		Any							=  bnb_quantization_config.load_in_abit

       if load_in_abit and not is_abit_bnb_available():
              raise ImportError(
                  'You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'
                  ' make sure you have the latest version of `bitsandbytes` installed.'				)
       if load_in_abit and not is_abit_bnb_available():
              raise ValueError(
                  'You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'
                  'make sure you have the latest version of `bitsandbytes` installed.'				)

       a_					:		List[Any]							=  []
       # custom device map
       if isinstance(__A  ,    __A				) and len(device_map.keys()				) > 1:
              a_					:		Optional[int]							=  [key for key, value in device_map.items() if value in ['disk', 'cpu']]

       # We keep some modules such as the lm_head in their original dtype for numerical stability reasons
       if bnb_quantization_config.skip_modules is None:
              a_					:		List[str]							=  get_keys_to_not_convert(__A				)

       # add cpu modules to skip modules only for 4-bit modules
       if load_in_abit:
              bnb_quantization_config.skip_modules.extend(__A				)
       a_					:		Union[str, Any]							=  bnb_quantization_config.skip_modules

       # We add the modules we want to keep in full precision
       if bnb_quantization_config.keep_in_fpaa_modules is None:
              a_					:		List[Any]							=  []
       a_					:		str							=  bnb_quantization_config.keep_in_fpaa_modules
       modules_to_not_convert.extend(__A				)

       # compatibility with peft
       a_					:		Any							=  load_in_abit
       a_					:		List[Any]							=  load_in_abit

       a_					:		List[Any]							=  get_parameter_device(__A				)
       if model_device.type != "meta":
              # quantization of an already loaded model
              logger.warning(
                  'It is not recommended to quantize a loaded model. '
                  'The model should be instantiated under the `init_empty_weights` context manager.'				)
              a_					:		Any							=  replace_with_bnb_layers(__A  ,    __A  ,    modules_to_not_convert=__A				)
              # convert param to the right dtype
              a_					:		Union[str, Any]							=  bnb_quantization_config.torch_dtype
              for name, param in model.state_dict().items():
                     if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules				):
                            param.to(torch.floataa				)
                            if param.dtype != torch.floataa:
                                   a_					:		int							=  name.replace('.weight'  ,    ''				).replace('.bias'  ,    ''				)
                                   a_					:		int							=  getattr(__A  ,    __A  ,    __A				)
                                   if param is not None:
                                          param.to(torch.floataa				)
                     elif torch.is_floating_point(__A				):
                            param.to(__A				)
              if model_device.type == "cuda":
                     # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
                     model.cuda(torch.cuda.current_device()				)
                     torch.cuda.empty_cache()
              elif torch.cuda.is_available():
                     model.to(torch.cuda.current_device()				)
              else:
                     raise RuntimeError('No GPU found. A GPU is needed for quantization.'				)
              logger.info(
                  F"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
                  'We move the model to cuda.'				)
              return model

       elif weights_location is None:
              raise RuntimeError(
                  F"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """				)

       else:
              with init_empty_weights():
                     a_					:		List[Any]							=  replace_with_bnb_layers(
                         __A  ,    __A  ,    modules_to_not_convert=__A				)

              a_					:		Any							=  get_quantized_model_device_map(
                  __A  ,    __A  ,    __A  ,    max_memory=__A  ,    no_split_module_classes=__A  ,    )
              if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
                     a_					:		Dict							=  True

              a_					:		Any							=  any(x in list(device_map.values()				) for x in ['cpu', 'disk']				)

              load_checkpoint_in_model(
                  __A  ,    __A  ,    __A  ,    dtype=bnb_quantization_config.torch_dtype  ,    offload_folder=__A  ,    offload_state_dict=__A  ,    keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules  ,    offload_abit_bnb=load_in_abit and offload  ,    )
              return dispatch_model(__A  ,    device_map=__A  ,    offload_dir=__A				)







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]  ,    __A :			Dict  ,    __A :			List[Any]=None  ,    __A :			Union[str, Any]=None  ,    __A :			Optional[Any]=None				)	->		str:

       """simple docstring"""

       if device_map is None:
              if torch.cuda.is_available():
                     a_					:		Dict							=  {'': torch.cuda.current_device()}
              else:
                     raise RuntimeError('No GPU found. A GPU is needed for quantization.'				)
              logger.info('The device_map was not initialized.' 'Setting device_map to `{\'\':torch.cuda.current_device()}`.'				)

       if isinstance(__A  ,    __A				):
              if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
                     raise ValueError(
                         'If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '
                         '\'sequential\'.'				)

              a_					:		Any							=  {}
              special_dtypes.update(
                  {
                      name: bnb_quantization_config.torch_dtype
                      for name, _ in model.named_parameters()
                      if any(m in name for m in bnb_quantization_config.skip_modules				)
                  }				)
              special_dtypes.update(
                  {
                      name: torch.floataa
                      for name, _ in model.named_parameters()
                      if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules				)
                  }				)

              a_					:		Optional[Any]							=  {}
              a_					:		Union[str, Any]							=  special_dtypes
              a_					:		Optional[int]							=  no_split_module_classes
              a_					:		str							=  bnb_quantization_config.target_dtype

              # get max_memory for each device.
              if device_map != "sequential":
                     a_					:		Optional[Any]							=  get_balanced_memory(
                         __A  ,    low_zero=(device_map == 'balanced_low_0')  ,    max_memory=__A  ,    **__A  ,    )

              a_					:		int							=  max_memory
              a_					:		Optional[int]							=  infer_auto_device_map(__A  ,    **__A				)

       if isinstance(__A  ,    __A				):
              # check if don't have any quantized module on the cpu
              a_					:		str							=  bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

              a_					:		Optional[Any]							=  {
                  key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
              }
              for device in ["cpu", "disk"]:
                     if device in device_map_without_some_modules.values():
                            if bnb_quantization_config.load_in_abit:
                                   raise ValueError(
                                       '\n                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n                        these modules in `torch_dtype`, you need to pass a custom `device_map` to\n                        `load_and_quantize_model`. Check\n                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n                        for more details.\n                        '				)
                            else:
                                   logger.info(
                                       'Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit'				)
              del device_map_without_some_modules
       return device_map







def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple  ,    __A :			List[str]  ,    __A :			List[Any]=None  ,    __A :			List[Any]=None				)	->		Any:

       """simple docstring"""

       if modules_to_not_convert is None:
              a_					:		Union[str, Any]							=  []

       a_							,		a_					:		List[str]							=  _replace_with_bnb_layers(
           __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.'
                  ' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'
                  ' Please double check your model architecture, or submit an issue on github if you think this is'
                  ' a bug.'				)
       return model







def        SCREAMING_SNAKE_CASE_						(			__A :			int  ,    __A :			int  ,    __A :			Tuple=None  ,    __A :			Any=None  ,    )	->		Any:

       """simple docstring"""

       a_					:		int							=  False
       for name, module in model.named_children():
              if current_key_name is None:
                     a_					:		List[str]							=  []
              current_key_name.append(__A				)
              if isinstance(__A  ,    nn.Linear				) and name not in modules_to_not_convert:
                     # Check if the current key is not in the `modules_to_not_convert`
                     a_					:		Union[str, Any]							=  '.'.join(__A				)
                     a_					:		Any							=  True
                     for key in modules_to_not_convert:
                            if (
                                (key in current_key_name_str) and (key + "." in current_key_name_str)
                            ) or key == current_key_name_str:
                                   a_					:		int							=  False
                                   break
                     if proceed:
                            # Load bnb module with empty weight and replace ``nn.Linear` module
                            if bnb_quantization_config.load_in_abit:
                                   a_					:		Union[str, Any]							=  bnb.nn.LinearabitLt(
                                       module.in_features  ,    module.out_features  ,    module.bias is not None  ,    has_fpaa_weights=__A  ,    threshold=bnb_quantization_config.llm_inta_threshold  ,    )
                            elif bnb_quantization_config.load_in_abit:
                                   a_					:		Optional[int]							=  bnb.nn.Linearabit(
                                       module.in_features  ,    module.out_features  ,    module.bias is not None  ,    bnb_quantization_config.bnb_abit_compute_dtype  ,    compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant  ,    quant_type=bnb_quantization_config.bnb_abit_quant_type  ,    )
                            else:
                                   raise ValueError('load_in_8bit and load_in_4bit can\'t be both False'				)
                            a_					:		Union[str, Any]							=  module.weight.data
                            if module.bias is not None:
                                   a_					:		str							=  module.bias.data
                            bnb_module.requires_grad_(__A				)
                            setattr(__A  ,    __A  ,    __A				)
                            a_					:		int							=  True
              if len(list(module.children()				)				) > 0:
                     a_							,		a_					:		List[str]							=  _replace_with_bnb_layers(
                         __A  ,    __A  ,    __A  ,    __A				)
                     a_					:		List[str]							=  has_been_replaced | _has_been_replaced
              # Remove the last key for recursion
              current_key_name.pop(-1				)
       return model, has_been_replaced







def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple				)	->		Optional[int]:

       """simple docstring"""

       with init_empty_weights():
              a_					:		List[str]							=  deepcopy(__A				)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

       a_					:		List[str]							=  find_tied_parameters(__A				)
       # For compatibility with Accelerate < 0.18
       if isinstance(__A  ,    __A				):
              a_					:		Union[str, Any]							=  sum(list(tied_params.values()				)  ,    []				) + list(tied_params.keys()				)
       else:
              a_					:		Any							=  sum(__A  ,    []				)
       a_					:		Optional[int]							=  len(__A				) > 0

       # Check if it is a base model
       a_					:		Union[str, Any]							=  False
       if hasattr(__A  ,    'base_model_prefix'				):
              a_					:		Union[str, 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
       a_					:		Tuple							=  list(model.named_children()				)
       a_					:		str							=  [list_modules[-1][0]]

       # add last module together with tied weights
       a_					:		List[str]							=  set(__A				) - set(__A				)
       a_					:		Dict							=  list(set(__A				)				) + list(__A				)

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

       return filtered_module_names







def        SCREAMING_SNAKE_CASE_						(			__A :			Any				)	->		Any:

       """simple docstring"""

       for m in model.modules():
              if isinstance(__A  ,    bnb.nn.Linearabit				):
                     return True
       return False







def        SCREAMING_SNAKE_CASE_						(			__A :			nn.Module				)	->		Union[str, Any]:

       """simple docstring"""

       return next(parameter.parameters()				).device







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]  ,    __A :			int  ,    __A :			Optional[Any]  ,    __A :			List[str]  ,    __A :			Dict  ,    __A :			Tuple  ,    __A :			Optional[Any]				)	->		Dict:

       """simple docstring"""

       if fpaa_statistics is None:
              set_module_tensor_to_device(__A  ,    __A  ,    0  ,    dtype=__A  ,    value=__A				)
              a_					:		Optional[int]							=  param_name
              a_					:		List[str]							=  model
              if "." in tensor_name:
                     a_					:		int							=  tensor_name.split('.'				)
                     for split in splits[:-1]:
                            a_					:		int							=  getattr(__A  ,    __A				)
                            if new_module is None:
                                   raise ValueError(F"""{module} has no attribute {split}."""				)
                            a_					:		Optional[Any]							=  new_module
                     a_					:		List[str]							=  splits[-1]
              # offload weights
              a_					:		Union[str, Any]							=  False
              offload_weight(module._parameters[tensor_name]  ,    __A  ,    __A  ,    index=__A				)
              if hasattr(module._parameters[tensor_name]  ,    'SCB'				):
                     offload_weight(
                         module._parameters[tensor_name].SCB  ,    param_name.replace('weight'  ,    'SCB'				)  ,    __A  ,    index=__A  ,    )
       else:
              offload_weight(__A  ,    __A  ,    __A  ,    index=__A				)
              offload_weight(__A  ,    param_name.replace('weight'  ,    'SCB'				)  ,    __A  ,    index=__A				)

       set_module_tensor_to_device(__A  ,    __A  ,    'meta'  ,    dtype=__A  ,    value=torch.empty(*param.size()				)				)






from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase_      : Any  					=       {'UserAgent': UserAgent().random}







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]				)	->		dict:

       """simple docstring"""

       a_					:		Tuple							=  script.contents[0]
       a_					:		int							=  json.loads(data[data.find('{"config"'				) : -1]				)
       return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Dict     )		->			Optional[Any]:
             a_					:		Tuple							=  F"""https://www.instagram.com/{username}/"""
             a_					:		Optional[Any]							=  self.get_json()
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			dict:
             a_					:		Any							=  requests.get(self.url     ,							headers=SCREAMING_SNAKE_CASE__     ).text
             a_					:		Dict							=  BeautifulSoup(SCREAMING_SNAKE_CASE__     ,							'html.parser'     ).find_all('script'     )
             try:
                    return extract_user_profile(scripts[4]     )
             except (json.decoder.JSONDecodeError, KeyError):
                    return extract_user_profile(scripts[3]     )
      def __repr__(       self				:       Union[str, Any]     )		->			str:
             return F"""{self.__class__.__name__}('{self.username}')"""
      def __str__(       self				:       Optional[int]     )		->			str:
             return F"""{self.fullname} ({self.username}) is {self.biography}"""
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			str:
             return self.user_data["username"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			str:
             return self.user_data["full_name"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			str:
             return self.user_data["biography"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			str:
             return self.user_data["business_email"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			str:
             return self.user_data["external_url"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			int:
             return self.user_data["edge_followed_by"]["count"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			int:
             return self.user_data["edge_follow"]["count"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			int:
             return self.user_data["edge_owner_to_timeline_media"]["count"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			str:
             return self.user_data["profile_pic_url_hd"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			bool:
             return self.user_data["is_verified"]

      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			bool:
             return self.user_data["is_private"]







def        SCREAMING_SNAKE_CASE_						(			__A :			str = "github"				)	->		None:

       """simple docstring"""

       import os

       if os.environ.get('CI'				):
              return  # test failing on GitHub Actions
       a_					:		int							=  InstagramUser(__A				)
       assert instagram_user.user_data
       assert isinstance(instagram_user.user_data  ,    __A				)
       assert instagram_user.username == username
       if username != "github":
              return
       assert instagram_user.fullname == "GitHub"
       assert instagram_user.biography == "Built for developers."
       assert instagram_user.number_of_posts > 1_50
       assert instagram_user.number_of_followers > 12_00_00
       assert instagram_user.number_of_followings > 15
       assert instagram_user.email == "support@github.com"
       assert instagram_user.website == "https://github.com/readme"
       assert instagram_user.profile_picture_url.startswith('https://instagram.'				)
       assert instagram_user.is_verified is True
       assert instagram_user.is_private is False


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   UpperCAmelCase_      : Union[str, Any]  					=       InstagramUser('github')
   print(instagram_user)
   print(F'{instagram_user.number_of_posts = }')
   print(F'{instagram_user.number_of_followers = }')
   print(F'{instagram_user.number_of_followings = }')
   print(F'{instagram_user.email = }')
   print(F'{instagram_user.website = }')
   print(F'{instagram_user.profile_picture_url = }')
   print(F'{instagram_user.is_verified = }')
   print(F'{instagram_user.is_private = }')


