

import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


lowerCAmelCase_	     =     get_logger(__name__)


if TYPE_CHECKING:
					from .builder import DatasetBuilder


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = None	):
						snake_case_       =     importlib.import_module(SCREAMING_SNAKE_CASE__	)

						# TODO(QL): always update the module to add subsequent new authentication
						if hasattr(SCREAMING_SNAKE_CASE__  ,		'''_patched_for_streaming'''	) and module._patched_for_streaming:
												return

						def wrap_auth(SCREAMING_SNAKE_CASE__	):
												@wraps(SCREAMING_SNAKE_CASE__	)
												def wrapper(*SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__	):
																		return function(*SCREAMING_SNAKE_CASE__  ,		use_auth_token=SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__	)

												snake_case_       =     '''wrap_auth'''
												return wrapper

						# open files in a streaming fashion
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''open'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''os.listdir'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''os.walk'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''glob.glob'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)	).start()
						# allow to navigate in remote zip files
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''os.path.join'''  ,		SCREAMING_SNAKE_CASE__	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''os.path.dirname'''  ,		SCREAMING_SNAKE_CASE__	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''os.path.basename'''  ,		SCREAMING_SNAKE_CASE__	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''os.path.relpath'''  ,		SCREAMING_SNAKE_CASE__	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''os.path.split'''  ,		SCREAMING_SNAKE_CASE__	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''os.path.splitext'''  ,		SCREAMING_SNAKE_CASE__	).start()
						# allow checks on paths
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''os.path.exists'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''os.path.isdir'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''os.path.isfile'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''os.path.getsize'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''pathlib.Path'''  ,		SCREAMING_SNAKE_CASE__	).start()
						# file readers
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''gzip.open'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''numpy.load'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''pandas.read_csv'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)  ,		attrs=['''__version__''']	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''pandas.read_excel'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)  ,		attrs=['''__version__''']	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''scipy.io.loadmat'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)  ,		attrs=['''__version__''']	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''xml.etree.ElementTree.parse'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)	).start()
						patch_submodule(SCREAMING_SNAKE_CASE__  ,		'''xml.dom.minidom.parse'''  ,		wrap_auth(SCREAMING_SNAKE_CASE__	)	).start()
						snake_case_       =     True


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						extend_module_for_streaming(builder.__module__  ,		use_auth_token=builder.use_auth_token	)
						# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
						if not builder.__module__.startswith('''datasets.'''	):  # check that it's not a packaged builder like csv
												for imports in get_imports(inspect.getfile(builder.__class__	)	):
																		if imports[0] == "internal":
																								snake_case_       =     imports[1]
																								snake_case_       =     '''.'''.join(builder.__module__.split('''.'''	)[:-1] + [internal_import_name]	)
																								extend_module_for_streaming(SCREAMING_SNAKE_CASE__  ,		use_auth_token=builder.use_auth_token	)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
						from .builder import DatasetBuilder

						snake_case_       =     [
						    cls.__module__
						    for cls in type(SCREAMING_SNAKE_CASE__	).__mro__[1:]  # make sure it's not the same module we've already patched
						    if issubclass(cls  ,		SCREAMING_SNAKE_CASE__	) and cls.__module__ != DatasetBuilder.__module__
						]  # check it's not a standard builder from datasets.builder
						for module in parent_builder_modules:
												extend_module_for_streaming(SCREAMING_SNAKE_CASE__  ,		use_auth_token=builder.use_auth_token	)








import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None	):
						snake_case_       =     None
						# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
						if str(weight.dtype	) == "torch.bfloat16":
												# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
												snake_case_       =     weight.view(torch.intaa	)
												snake_case_       =     '''bfloat16'''
						snake_case_       =     weight.cpu().numpy()
						snake_case_       =     os.path.join(SCREAMING_SNAKE_CASE__  ,		F'''{weight_name}.dat'''	)
						if index is not None:
												if dtype is None:
																		snake_case_       =     str(array.dtype	)
												snake_case_       =     {'''dtype''': dtype, '''shape''': list(array.shape	)}
						if array.ndim == 0:
												snake_case_       =     array[None]
						snake_case_       =     np.memmap(SCREAMING_SNAKE_CASE__  ,		dtype=array.dtype  ,		mode='''w+'''  ,		shape=array.shape	)
						snake_case_       =     array[:]
						file_array.flush()
						return index


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tuple(weight_info['''shape''']	)
						if shape == ():
												# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
												snake_case_       =     (1,)

						snake_case_       =     weight_info['''dtype''']
						if dtype == "bfloat16":
												# NumPy does not support bfloat16 so this was saved as a int16
												snake_case_       =     '''int16'''

						snake_case_       =     np.memmap(SCREAMING_SNAKE_CASE__  ,		dtype=SCREAMING_SNAKE_CASE__  ,		shape=SCREAMING_SNAKE_CASE__  ,		mode='''r'''	)

						if len(weight_info['''shape''']	) == 0:
												snake_case_       =     weight[0]
						snake_case_       =     torch.tensor(SCREAMING_SNAKE_CASE__	)
						if weight_info["dtype"] == "bfloat16":
												snake_case_       =     weight.view(torch.bfloataa	)

						return weight


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						if index is None or len(SCREAMING_SNAKE_CASE__	) == 0:
												# Nothing to save
												return

						snake_case_       =     os.path.join(SCREAMING_SNAKE_CASE__  ,		'''index.json'''	)
						if os.path.isfile(SCREAMING_SNAKE_CASE__	):
												with open(SCREAMING_SNAKE_CASE__  ,		'''r'''  ,		encoding='''utf-8'''	) as f:
																		snake_case_       =     json.load(SCREAMING_SNAKE_CASE__	)
						else:
												snake_case_       =     {}
						current_index.update(SCREAMING_SNAKE_CASE__	)

						with open(SCREAMING_SNAKE_CASE__  ,		'''w'''  ,		encoding='''utf-8'''	) as f:
												json.dump(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		indent=2	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						os.makedirs(SCREAMING_SNAKE_CASE__  ,		exist_ok=SCREAMING_SNAKE_CASE__	)
						snake_case_       =     {}
						for name, parameter in state_dict.items():
												snake_case_       =     offload_weight(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		index=SCREAMING_SNAKE_CASE__	)

						# Update index
						save_offload_index(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	)





class      snake_case_       (		__A ):







			'''simple docstring'''

			def __init__(					self			:    Optional[Any]							,       _UpperCamelCase			:    Mapping							,       _UpperCamelCase			:    str							)				->int:
									snake_case_       =     dataset
									snake_case_       =     prefix







			def __getitem__(					self			:    List[Any]							,       _UpperCamelCase			:    Any							)				->Tuple:
									return self.dataset[f'''{self.prefix}{key}''']







			def __iter__(					self			:    Optional[int]							)				->int:
									return iter([key for key in self.dataset if key.startswith(self.prefix							)]							)







			def __len__(					self			:    Dict							)				->int:
									return len(self.dataset							)






class      snake_case_       (		__A ):







			'''simple docstring'''

			def __init__(					self			:    str							,       _UpperCamelCase			:    Dict[str, torch.Tensor] = None							,       _UpperCamelCase			:    Optional[Union[str, os.PathLike]] = None							,       _UpperCamelCase			:    Mapping = None							,       _UpperCamelCase			:    Tuple=None							,       )				->Dict:
									if state_dict is None and save_folder is None:
															raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''							)

									snake_case_       =     {} if state_dict is None else state_dict
									snake_case_       =     save_folder
									if index is None and save_folder is not None:
															with open(os.path.join(_UpperCamelCase							,       '''index.json'''							)							) as f:
																					snake_case_       =     json.load(_UpperCamelCase							)
									snake_case_       =     {} if index is None else index
									snake_case_       =     list(self.state_dict.keys()							)
									self.all_keys.extend([key for key in self.index if key not in self.all_keys]							)
									snake_case_       =     device







			def __getitem__(					self			:    int							,       _UpperCamelCase			:    str							)				->Union[str, Any]:
									# State dict gets priority
									if key in self.state_dict:
															return self.state_dict[key]
									snake_case_       =     self.index[key]
									if weight_info.get('''safetensors_file'''							) is not None:
															if not is_safetensors_available():
																					raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''							)

															from safetensors import safe_open

															snake_case_       =     '''cpu''' if self.device is None else self.device
															with safe_open(weight_info['''safetensors_file''']							,       framework='''pt'''							,       device=_UpperCamelCase							) as f:
																					snake_case_       =     f.get_tensor(weight_info.get('''weight_name'''							,       _UpperCamelCase							)							)

															if "dtype" in weight_info:
																					return tensor.to(getattr(_UpperCamelCase							,       weight_info['''dtype''']							)							)
															else:
																					return tensor

									snake_case_       =     os.path.join(self.save_folder							,       f'''{key}.dat'''							)
									return load_offloaded_weight(_UpperCamelCase							,       _UpperCamelCase							)







			def __iter__(					self			:    int							)				->List[Any]:
									return iter(self.all_keys							)







			def __len__(					self			:    Optional[Any]							)				->Optional[Any]:
									return len(self.all_keys							)






def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     {}
						for module_name in submodule_names:
												# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
												# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
												result.update(
												    {
												        key: param
												        for key, param in state_dict.items()
												        if key == module_name or key.startswith(module_name + '''.'''	)
												    }	)
						return result