



"""simple docstring"""







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


lowerCAmelCase_   						=   logging.getLogger(__name__)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase = None						,     __lowerCamelCase = None						,     __lowerCamelCase = None						,     __lowerCamelCase = None						,     __lowerCamelCase = None						,     __lowerCamelCase = False						,     )							->      int:
		lowercase__   :    Dict				=			bnb_quantization_config.load_in_abit
		lowercase__   :    int				=			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.'''	)

		lowercase__   :    List[str]				=			[]
		# custom device map
		if isinstance(__lowerCamelCase						,     __lowerCamelCase	) and len(device_map.keys()	) > 1:
				lowercase__   :    str				=			[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:
				lowercase__   :    Any				=			get_keys_to_not_convert(__lowerCamelCase	)

		# add cpu modules to skip modules only for 4-bit modules
		if load_in_abit:
				bnb_quantization_config.skip_modules.extend(__lowerCamelCase	)
		lowercase__   :    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:
				lowercase__   :    Union[str, Any]				=			[]
		lowercase__   :    Optional[Any]				=			bnb_quantization_config.keep_in_fpaa_modules
		modules_to_not_convert.extend(__lowerCamelCase	)

		# compatibility with peft
		lowercase__   :    Optional[Any]				=			load_in_abit
		lowercase__   :    Optional[Any]				=			load_in_abit

		lowercase__   :    str				=			get_parameter_device(__lowerCamelCase	)
		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.'''	)
				lowercase__   :    Dict				=			replace_with_bnb_layers(__lowerCamelCase						,     __lowerCamelCase						,     modules_to_not_convert=__lowerCamelCase	)
				# convert param to the right dtype
				lowercase__   :    Dict				=			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:
										lowercase__   :    Any				=			name.replace('''.weight'''						,     ''''''	).replace('''.bias'''						,     ''''''	)
										lowercase__   :    Dict				=			getattr(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
										if param is not None:
												param.to(torch.floataa	)
						elif torch.is_floating_point(__lowerCamelCase	):
								param.to(__lowerCamelCase	)
				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():
						lowercase__   :    int				=			replace_with_bnb_layers(
						    __lowerCamelCase						,     __lowerCamelCase						,     modules_to_not_convert=__lowerCamelCase	)

				lowercase__   :    List[str]				=			get_quantized_model_device_map(
				    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     max_memory=__lowerCamelCase						,     no_split_module_classes=__lowerCamelCase						,     )
				if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
						lowercase__   :    List[str]				=			True

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

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



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=None	)							->      int:
		if device_map is None:
				if torch.cuda.is_available():
						lowercase__   :    Optional[int]				=			{'''''': 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(__lowerCamelCase						,     __lowerCamelCase	):
				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\'.'''	)

				lowercase__   :    List[str]				=			{}
				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	)
				    }	)

				lowercase__   :    List[str]				=			{}
				lowercase__   :    Tuple				=			special_dtypes
				lowercase__   :    Optional[int]				=			no_split_module_classes
				lowercase__   :    int				=			bnb_quantization_config.target_dtype

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

				lowercase__   :    int				=			max_memory
				lowercase__   :    List[Any]				=			infer_auto_device_map(__lowerCamelCase						,     **__lowerCamelCase	)

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

				lowercase__   :    Union[str, 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(
										    '''
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        '''	)
								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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None	)							->      str:

		if modules_to_not_convert is None:
				lowercase__   :    Optional[int]				=			[]

		lowercase__		,			lowercase__   :    Optional[Any]				=			_replace_with_bnb_layers(
		    __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.'''
				    ''' 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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     )							->      int:
		lowercase__   :    Optional[Any]				=			False
		for name, module in model.named_children():
				if current_key_name is None:
						lowercase__   :    Optional[int]				=			[]
				current_key_name.append(__lowerCamelCase	)
				if isinstance(__lowerCamelCase						,     nn.Linear	) and name not in modules_to_not_convert:
						# Check if the current key is not in the `modules_to_not_convert`
						lowercase__   :    Union[str, Any]				=			'''.'''.join(__lowerCamelCase	)
						lowercase__   :    Dict				=			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:
										lowercase__   :    int				=			False
										break
						if proceed:
								# Load bnb module with empty weight and replace ``nn.Linear` module
								if bnb_quantization_config.load_in_abit:
										lowercase__   :    Optional[int]				=			bnb.nn.LinearabitLt(
										    module.in_features						,     module.out_features						,     module.bias is not None						,     has_fpaa_weights=__lowerCamelCase						,     threshold=bnb_quantization_config.llm_inta_threshold						,     )
								elif bnb_quantization_config.load_in_abit:
										lowercase__   :    Dict				=			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'''	)
								lowercase__   :    List[str]				=			module.weight.data
								if module.bias is not None:
										lowercase__   :    Dict				=			module.bias.data
								bnb_module.requires_grad_(__lowerCamelCase	)
								setattr(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
								lowercase__   :    Tuple				=			True
				if len(list(module.children()	)	) > 0:
						lowercase__		,			lowercase__   :    int				=			_replace_with_bnb_layers(
						    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
						lowercase__   :    Union[str, Any]				=			has_been_replaced | _has_been_replaced
				# Remove the last key for recursion
				current_key_name.pop(-1	)
		return model, has_been_replaced



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      List[str]:
		# Create a copy of the model
		with init_empty_weights():
				lowercase__   :    int				=			deepcopy(__lowerCamelCase	)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

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

		# Check if it is a base model
		lowercase__   :    Any				=			False
		if hasattr(__lowerCamelCase						,     '''base_model_prefix'''	):
				lowercase__   :    Optional[int]				=			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
		lowercase__   :    int				=			list(model.named_children()	)
		lowercase__   :    List[Any]				=			[list_modules[-1][0]]

		# add last module together with tied weights
		lowercase__   :    Union[str, Any]				=			set(__lowerCamelCase	) - set(__lowerCamelCase	)
		lowercase__   :    List[str]				=			list(set(__lowerCamelCase	)	) + list(__lowerCamelCase	)

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

		return filtered_module_names



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		for m in model.modules():
				if isinstance(__lowerCamelCase						,     bnb.nn.Linearabit	):
						return True
		return False



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		return next(parameter.parameters()	).device





def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      List[str]:
		# if it is not quantized, we quantize and offload the quantized weights and the SCB stats
		if fpaa_statistics is None:
				set_module_tensor_to_device(__lowerCamelCase						,     __lowerCamelCase						,     0						,     dtype=__lowerCamelCase						,     value=__lowerCamelCase	)
				lowercase__   :    Any				=			param_name
				lowercase__   :    List[Any]				=			model
				if "." in tensor_name:
						lowercase__   :    Dict				=			tensor_name.split('''.'''	)
						for split in splits[:-1]:
								lowercase__   :    int				=			getattr(__lowerCamelCase						,     __lowerCamelCase	)
								if new_module is None:
										raise ValueError(f"""{module} has no attribute {split}."""	)
								lowercase__   :    int				=			new_module
						lowercase__   :    str				=			splits[-1]
				# offload weights
				lowercase__   :    Any				=			False
				offload_weight(module._parameters[tensor_name]						,     __lowerCamelCase						,     __lowerCamelCase						,     index=__lowerCamelCase	)
				if hasattr(module._parameters[tensor_name]						,     '''SCB'''	):
						offload_weight(
						    module._parameters[tensor_name].SCB						,     param_name.replace('''weight'''						,     '''SCB'''	)						,     __lowerCamelCase						,     index=__lowerCamelCase						,     )
		else:
				offload_weight(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     index=__lowerCamelCase	)
				offload_weight(__lowerCamelCase						,     param_name.replace('''weight'''						,     '''SCB'''	)						,     __lowerCamelCase						,     index=__lowerCamelCase	)

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






# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


UpperCAmelCase_									=	get_logger(__name__)





class     lowerCamelCase__:
      UpperCAmelCase__      :	List[Any]    		=   'dummy_data'
      UpperCAmelCase__      :	str    		=   'datasets'
      UpperCAmelCase__      :	Tuple    		=   False
      def __init__(				self: Optional[Any]    ,       UpperCamelCase_: str    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Union[Version, str]    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Optional[List[Callable]] = None    ,       ):
             __lowerCamelCase   =						0
             __lowerCamelCase   =						dataset_name
             __lowerCamelCase   =						cache_dir
             __lowerCamelCase   =						use_local_dummy_data
             __lowerCamelCase   =						config
             # download_callbacks take a single url as input
             __lowerCamelCase   =						download_callbacks or []
             # if False, it doesn't load existing files and it returns the paths of the dummy files relative
             # to the dummy_data zip file root
             __lowerCamelCase   =						load_existing_dummy_data

             # TODO(PVP, QL) might need to make this more general
             __lowerCamelCase   =						str(UpperCamelCase_   )
             # to be downloaded
             __lowerCamelCase   =						None
             __lowerCamelCase   =						None
      @property
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             if self._dummy_file is None:
                    __lowerCamelCase   =						self.download_dummy_data()
             return self._dummy_file
      @property
      def 			lowerCAmelCase__						(				self: str   ):
             if self.config is not None:
                    # structure is dummy / config_name / version_name
                    return os.path.join("""dummy"""    ,       self.config.name    ,       self.version_name   )
             # structure is dummy / version_name
             return os.path.join("""dummy"""    ,       self.version_name   )
      @property
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             return os.path.join(self.dummy_data_folder    ,       """dummy_data.zip"""   )
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						(
                 self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
             )

             __lowerCamelCase   =						cached_path(
                 UpperCamelCase_    ,       cache_dir=self.cache_dir    ,       extract_compressed_file=UpperCamelCase_    ,       force_extract=UpperCamelCase_   )

             return os.path.join(UpperCamelCase_    ,       self.dummy_file_name   )
      @property
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             return os.path.join(self.datasets_scripts_dir    ,       self.dataset_name    ,       self.dummy_zip_file   )
      @property
      def 			lowerCAmelCase__						(				self: Tuple   ):
             if self._bucket_url is None:
                    __lowerCamelCase   =						hf_github_url(self.dataset_name    ,       self.dummy_zip_file.replace(os.sep    ,       """/"""   )   )
             return self._bucket_url
      @property
      def 			lowerCAmelCase__						(				self: str   ):
             # return full path if its a dir
             if os.path.isdir(self.dummy_file   ):
                    return self.dummy_file
             # else cut off path to file -> example `xsum`.
             return "/".join(self.dummy_file.replace(os.sep    ,       """/"""   ).split("""/"""   )[:-1]   )
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Dict    ,       *UpperCamelCase_: str   ):
             if self.load_existing_dummy_data:
                    # dummy data is downloaded and tested
                    __lowerCamelCase   =						self.dummy_file
             else:
                    # dummy data cannot be downloaded and only the path to dummy file is returned
                    __lowerCamelCase   =						self.dummy_file_name

             # special case when data_url is a dict
             if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    return self.create_dummy_data_dict(UpperCamelCase_    ,       UpperCamelCase_   )
             elif isinstance(UpperCamelCase_    ,       (list, tuple)   ):
                    return self.create_dummy_data_list(UpperCamelCase_    ,       UpperCamelCase_   )
             else:
                    return self.create_dummy_data_single(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Any    ,       UpperCamelCase_: Optional[Any]    ,       *UpperCamelCase_: str   ):
             return self.download_and_extract(UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Tuple    ,       UpperCamelCase_: str   ):
             return self.download_and_extract(UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: int    ,       *UpperCamelCase_: List[str]    ,       **UpperCamelCase_: str   ):
             return path
      def 			lowerCAmelCase__						(				self: Dict   ):
             return {}
      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: Union[str, Any]   ):
             __lowerCamelCase   =						{}
             for key, single_urls in data_url.items():
                    for download_callback in self.download_callbacks:
                           if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                                  for single_url in single_urls:
                                         download_callback(UpperCamelCase_   )
                           else:
                                  __lowerCamelCase   =						single_urls
                                  download_callback(UpperCamelCase_   )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                    if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                           __lowerCamelCase   =						[os.path.join(UpperCamelCase_    ,       urllib.parse.quote_plus(Path(UpperCamelCase_   ).name   )   ) for x in single_urls]
                    else:
                           __lowerCamelCase   =						single_urls
                           __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       urllib.parse.quote_plus(Path(UpperCamelCase_   ).name   )   )
                    __lowerCamelCase   =						value

             # make sure that values are unique
             if all(isinstance(UpperCamelCase_    ,       UpperCamelCase_   ) for i in dummy_data_dict.values()   ) and len(set(dummy_data_dict.values()   )   ) < len(
                 dummy_data_dict.values()   ):
                    # append key to value to make its name unique
                    __lowerCamelCase   =						{key: value + key for key, value in dummy_data_dict.items()}

             return dummy_data_dict
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       UpperCamelCase_: Optional[Any]    ,       UpperCamelCase_: Optional[int]   ):
             __lowerCamelCase   =						[]
             # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
             __lowerCamelCase   =						all(bool(re.findall("""[0-9]{3,}-of-[0-9]{3,}"""    ,       UpperCamelCase_   )   ) for url in data_url   )
             __lowerCamelCase   =						all(
                 url.startswith("""https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"""   ) for url in data_url   )
             if data_url and (is_tf_records or is_pubmed_records):
                    __lowerCamelCase   =						[data_url[0]] * len(UpperCamelCase_   )
             for single_url in data_url:
                    for download_callback in self.download_callbacks:
                           download_callback(UpperCamelCase_   )
                    # we force the name of each key to be the last file / folder name of the url path
                    # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                    __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       urllib.parse.quote_plus(single_url.split("""/"""   )[-1]   )   )
                    dummy_data_list.append(UpperCamelCase_   )
             return dummy_data_list
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: Optional[int]    ,       UpperCamelCase_: Optional[Any]   ):
             for download_callback in self.download_callbacks:
                    download_callback(UpperCamelCase_   )
             # we force the name of each key to be the last file / folder name of the url path
             # if the url has arguments, we need to encode them with urllib.parse.quote_plus
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       urllib.parse.quote_plus(data_url.split("""/"""   )[-1]   )   )
             if os.path.exists(UpperCamelCase_   ) or not self.load_existing_dummy_data:
                    return value
             else:
                    # Backward compatibility, maybe deprecate at one point.
                    # For many datasets with single url calls to dl_manager.download_and_extract,
                    # the dummy_data.zip file is actually the zipped downloaded file
                    # while now we expected the dummy_data.zip file to be a directory containing
                    # the downloaded file.
                    return path_to_dummy_data
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             pass
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             pass
      def 			lowerCAmelCase__						(				self: Any    ,       UpperCamelCase_: Dict   ):
             def _iter_archive_members(UpperCamelCase_: Any   ):
                    # this preserves the order of the members inside the ZIP archive
                    __lowerCamelCase   =						Path(self.dummy_file   ).parent
                    __lowerCamelCase   =						path.relative_to(UpperCamelCase_   )
                    with ZipFile(self.local_path_to_dummy_data   ) as zip_file:
                           __lowerCamelCase   =						zip_file.namelist()
                    for member in members:
                           if member.startswith(relative_path.as_posix()   ):
                                  yield dummy_parent_path.joinpath(UpperCamelCase_   )

             __lowerCamelCase   =						Path(UpperCamelCase_   )
             __lowerCamelCase   =						_iter_archive_members(UpperCamelCase_   ) if self.use_local_dummy_data else path.rglob("""*"""   )
             for file_path in file_paths:
                    if file_path.is_file() and not file_path.name.startswith((""".""", """__""")   ):
                           yield file_path.relative_to(UpperCamelCase_   ).as_posix(), file_path.open("""rb"""   )




      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Dict   ):
             if not isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						[paths]
             for path in paths:
                    if os.path.isfile(UpperCamelCase_   ):
                           if os.path.basename(UpperCamelCase_   ).startswith((""".""", """__""")   ):
                                  return
                           yield path
                    else:
                           for dirpath, dirnames, filenames in os.walk(UpperCamelCase_   ):
                                  if os.path.basename(UpperCamelCase_   ).startswith((""".""", """__""")   ):
                                         continue
                                  dirnames.sort()
                                  for filename in sorted(UpperCamelCase_   ):
                                         if filename.startswith((""".""", """__""")   ):
                                                continue
                                         yield os.path.join(UpperCamelCase_    ,       UpperCamelCase_   )

