

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 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=None   ):

					"""simple docstring"""






					__lowerCAmelCase       =				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.
										__lowerCAmelCase       =				weight.view(torch.intaa   )
										__lowerCAmelCase       =				'bfloat16'
					__lowerCAmelCase       =				weight.cpu().numpy()
					__lowerCAmelCase       =				os.path.join(UpperCAmelCase__		,       F"""{weight_name}.dat"""   )
					if index is not None:
										if dtype is None:
															__lowerCAmelCase       =				str(array.dtype   )
										__lowerCAmelCase       =				{'dtype': dtype, 'shape': list(array.shape   )}
					if array.ndim == 0:
										__lowerCAmelCase       =				array[None]
					__lowerCAmelCase       =				np.memmap(UpperCAmelCase__		,       dtype=array.dtype		,       mode='w+'		,       shape=array.shape   )
					__lowerCAmelCase       =				array[:]
					file_array.flush()
					return index


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






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

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

					__lowerCAmelCase       =				np.memmap(UpperCAmelCase__		,       dtype=UpperCAmelCase__		,       shape=UpperCAmelCase__		,       mode='r'   )

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

					return weight


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					if index is None or len(UpperCAmelCase__   ) == 0:
										# Nothing to save
										return

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

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


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					os.makedirs(UpperCAmelCase__		,       exist_ok=UpperCAmelCase__   )
					__lowerCAmelCase       =				{}
					for name, parameter in state_dict.items():
										__lowerCAmelCase       =				offload_weight(UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__		,       index=UpperCAmelCase__   )

					# Update index
					save_offload_index(UpperCAmelCase__		,       UpperCAmelCase__   )





class      snake_case_      (					_a				):


							"""simple docstring"""





							def __init__(			self       ,    _A       ,    _A				):
												__lowerCAmelCase       =				dataset
												__lowerCAmelCase       =				prefix




							def __getitem__(			self       ,    _A				):
												return self.dataset[F"""{self.prefix}{key}"""]




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




							def __len__(			self				):
												return len(self.dataset				)





class      snake_case_      (					_a				):


							"""simple docstring"""





							def __init__(			self       ,    _A = None       ,    _A = None       ,    _A = None       ,    _A=None       ,    ):

												if state_dict is None and save_folder is None:
																	raise ValueError('Need either a `state_dict` or a `save_folder` containing offloaded weights.'				)

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




							def __getitem__(			self       ,    _A				):

												if key in self.state_dict:
																	return self.state_dict[key]
												__lowerCAmelCase       =				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

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

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

												__lowerCAmelCase       =				os.path.join(self.save_folder       ,    F"""{key}.dat"""				)
												return load_offloaded_weight(lowerCamelCase_       ,    lowerCamelCase_				)




							def __iter__(			self				):
												return iter(self.all_keys				)




							def __len__(			self				):
												return len(self.all_keys				)


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					__lowerCAmelCase       =				{}
					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











import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand






def        a( A			:       Dict			)						->			Optional[int]:
					"""simple docstring"""





					return EnvironmentCommand()







class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@staticmethod
	def 	UpperCamelCase_			(lowerCamelCase_						):
						"""simple docstring"""

						a   	=  parser.add_parser("env"						,			help="Print relevant system environment info."						)
						download_parser.set_defaults(func=lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  {
						    "`datasets` version": version,
						    "Platform": platform.platform(),
						    "Python version": platform.python_version(),
						    "Huggingface_hub version": huggingface_hub.__version__,
						    "PyArrow version": pyarrow.__version__,
						    "Pandas version": pandas.__version__,
						}

						print("\nCopy-and-paste the text below in your GitHub issue.\n"						)
						print(self.format_dict(lowerCamelCase_						)						)

						return info




	@staticmethod
	def 	UpperCamelCase_			(lowerCamelCase_						):
						"""simple docstring"""

						return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]						) + "\n"
