


"""simple docstring"""


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   a__			(					__lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase=None )						->						Dict:
				_A       	=      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.
								_A       	=      weight.view(torch.intaa )
								_A       	=      "bfloat16"
				_A       	=      weight.cpu().numpy()
				_A       	=      os.path.join(__lowercase  ,      f"""{weight_name}.dat""" )
				if index is not None:
								if dtype is None:
												_A       	=      str(array.dtype )
								_A       	=      {"dtype": dtype, "shape": list(array.shape )}
				if array.ndim == 0:
								_A       	=      array[None]
				_A       	=      np.memmap(__lowercase  ,      dtype=array.dtype  ,      mode="w+"  ,      shape=array.shape )
				_A       	=      array[:]
				file_array.flush()
				return index






def   a__			(					__lowercase  ,      __lowercase )						->						str:
				_A       	=      tuple(weight_info["shape"] )
				if shape == ():
								# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
								_A       	=      (1,)

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

				_A       	=      np.memmap(__lowercase  ,      dtype=__lowercase  ,      shape=__lowercase  ,      mode="r" )

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

				return weight






def   a__			(					__lowercase  ,      __lowercase )						->						Optional[int]:
				if index is None or len(__lowercase ) == 0:
								# Nothing to save
								return

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

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






def   a__			(					__lowercase  ,      __lowercase )						->						int:
				os.makedirs(__lowercase  ,      exist_ok=__lowercase )
				_A       	=      {}
				for name, parameter in state_dict.items():
								_A       	=      offload_weight(__lowercase  ,      __lowercase  ,      __lowercase  ,      index=__lowercase )

				# Update index
				save_offload_index(__lowercase  ,      __lowercase )
class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Optional[Any]     , a__   :							Mapping     , a__   :							str		)							->    List[Any]:





						'''simple docstring'''







						_A       	=      dataset
						_A       	=      prefix







		def __getitem__(       self   :							Union[str, Any]     , a__   :							int		)							->    List[str]:





						'''simple docstring'''







						return self.dataset[F"""{self.prefix}{key}"""]







		def __iter__(       self   :							Optional[Any]		)							->    List[str]:





						'''simple docstring'''







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







		def __len__(       self   :							List[str]		)							->    Any:





						'''simple docstring'''







						return len(self.dataset		)





class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Any     , a__   :							Dict[str, torch.Tensor] = None     , a__   :							Optional[Union[str, os.PathLike]] = None     , a__   :							Mapping = None     , a__   :							Union[str, Any]=None     , )							->    Optional[int]:





						'''simple docstring'''







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

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







		def __getitem__(       self   :							Dict     , a__   :							str		)							->    Union[str, Any]:





						'''simple docstring'''







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

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

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

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







		def __iter__(       self   :							Any		)							->    Tuple:





						'''simple docstring'''







						return iter(self.all_keys		)







		def __len__(       self   :							int		)							->    str:





						'''simple docstring'''







						return len(self.all_keys		)






def   a__			(					__lowercase  ,      __lowercase )						->						List[Any]:
				_A       	=      {}
				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



"""simple docstring"""


def   a__			(					__lowercase )						->						bool:
				# must be
				_A       	=      [list(__lowercase ) for row in matrix]
				if matrix and isinstance(__lowercase  ,      __lowercase ):
								if isinstance(matrix[0]  ,      __lowercase ):
												_A       	=      0
												for row in matrix:
																if prev_len == 0:
																				_A       	=      len(__lowercase )
																				_A       	=      True
																else:
																				_A       	=      prev_len == len(__lowercase )
								else:
												_A       	=      True
				else:
								_A       	=      False

				return result






def   a__			(					__lowercase )						->						None:
				if check_matrix(__lowercase ) and len(__lowercase ) > 0:
								_A       	=      [list(__lowercase ) for row in a]
								_A       	=      len(__lowercase )
								if isinstance(a[0]  ,      __lowercase ):
												_A       	=      len(a[0] )
								else:
												for dat in a:
																print(__lowercase )
												return

								# horizotal printing increasing
								for i in range(0  ,      __lowercase ):
												print(a[0][i] )
								# vertical printing down
								for i in range(1  ,      __lowercase ):
												print(a[i][mat_col - 1] )
								# horizotal printing decreasing
								if mat_row > 1:
												for i in range(mat_col - 2  ,      -1  ,      -1 ):
																print(a[mat_row - 1][i] )
        # vertical printing up
								for i in range(mat_row - 2  ,      0  ,      -1 ):
												print(a[i][0] )
								_A       	=      [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
								if len(__lowercase ) > 0:
												spiral_print_clockwise(__lowercase )
								else:
												return
				else:
								print("Not a valid matrix" )
								return






def   a__			(					__lowercase )						->						list[int]:
				if matrix:
								return list(matrix.pop(0 ) ) + spiral_traversal(list(zip(*__lowercase ) )[::-1] )
				else:
								return []


# driver code
if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a_											=     [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
		spiral_print_clockwise(a)