





'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
		import torch


A   : Tuple             =			logging.get_logger(__name__)







@dataclass
class 			lowerCamelCase					:


					def __init__(					self	:  Tuple       ,     __snake_case	:  str=False       ,     __snake_case	:  Tuple=False       ,     __snake_case	:  List[Any]=6.0       ,     __snake_case	:  Optional[Any]=None       ,     __snake_case	:  int=False       ,     __snake_case	:  str=False       ,     __snake_case	:  str=None       ,     __snake_case	:  Optional[Any]="fp4"       ,     __snake_case	:  Optional[int]=False       ,     **__snake_case	:  str       ,     ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		load_in_abit
											_snake_case:   List[Any]  					=		load_in_abit
											_snake_case:   Any  					=		llm_inta_threshold
											_snake_case:   str  					=		llm_inta_skip_modules
											_snake_case:   List[Any]  					=		llm_inta_enable_fpaa_cpu_offload
											_snake_case:   Tuple  					=		llm_inta_has_fpaa_weight
											_snake_case:   Tuple  					=		bnb_abit_quant_type
											_snake_case:   str  					=		bnb_abit_use_double_quant

											if bnb_abit_compute_dtype is None:
																	_snake_case:   str  					=		torch.floataa
											elif isinstance(__snake_case       ,     __snake_case       ):
																	_snake_case:   Tuple  					=		getattr(__snake_case       ,     __snake_case       )
											elif isinstance(__snake_case       ,     torch.dtype       ):
																	_snake_case:   Dict  					=		bnb_abit_compute_dtype
											else:
																	raise ValueError('bnb_4bit_compute_dtype must be a string or a torch.dtype'       )

											self.post_init()


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											if not isinstance(self.llm_inta_threshold       ,     __snake_case       ):
																	raise ValueError('llm_int8_threshold must be a float'       )

											if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules       ,     __snake_case       ):
																	raise ValueError('llm_int8_skip_modules must be a list of strings'       )
											if not isinstance(self.llm_inta_enable_fpaa_cpu_offload       ,     __snake_case       ):
																	raise ValueError('llm_int8_enable_fp32_cpu_offload must be a boolean'       )

											if not isinstance(self.llm_inta_has_fpaa_weight       ,     __snake_case       ):
																	raise ValueError('llm_int8_has_fp16_weight must be a boolean'       )

											if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype       ,     torch.dtype       ):
																	raise ValueError('bnb_4bit_compute_dtype must be torch.dtype'       )

											if not isinstance(self.bnb_abit_quant_type       ,     __snake_case       ):
																	raise ValueError('bnb_4bit_quant_type must be a string'       )

											if not isinstance(self.bnb_abit_use_double_quant       ,     __snake_case       ):
																	raise ValueError('bnb_4bit_use_double_quant must be a boolean'       )

											if self.load_in_abit and not version.parse(importlib.metadata.version('bitsandbytes'       )       ) >= version.parse(
											    '0.39.0'       ):
																	raise ValueError(
																	    '4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version'       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											return self.load_in_abit or self.load_in_abit


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											if self.load_in_abit:
																	return "llm_int8"
											elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
																	return "fp4"
											elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
																	return "nf4"
											else:
																	return None


					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  int       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  int       ,     **__snake_case	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		cls(**__snake_case       )

											_snake_case:   List[Any]  					=		[]
											for key, value in kwargs.items():
																	if hasattr(__snake_case       ,     __snake_case       ):
																							setattr(__snake_case       ,     __snake_case       ,     __snake_case       )
																							to_remove.append(__snake_case       )
											for key in to_remove:
																	kwargs.pop(__snake_case       ,     __snake_case       )

											if return_unused_kwargs:
																	return config, kwargs
											else:
																	return config


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ,     __snake_case	:  Union[str, os.PathLike]       ):





											'''simple docstring'''





											with open(__snake_case       ,     'w'       ,     encoding='utf-8'       ) as writer:
																	_snake_case:   Tuple  					=		self.to_dict()
																	_snake_case:   List[str]  					=		json.dumps(__snake_case       ,     indent=2       ,     sort_keys=__snake_case       ) + '\n'

																	writer.write(__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		copy.deepcopy(self.__dict__       )
											_snake_case:   Optional[Any]  					=		str(output['bnb_4bit_compute_dtype']       ).split('.'       )[1]

											return output


					def __repr__(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											return f'''{self.__class__.__name__} {self.to_json_string()}'''


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  bool = True       ):





											'''simple docstring'''





											if use_diff is True:
																	_snake_case:   Optional[Any]  					=		self.to_diff_dict()
											else:
																	_snake_case:   Optional[int]  					=		self.to_dict()
											return json.dumps(__snake_case       ,     indent=2       ,     sort_keys=__snake_case       ) + "\n"




					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		self.to_dict()

											# get the default config dict
											_snake_case:   Tuple  					=		BitsAndBytesConfig().to_dict()

											_snake_case:   Tuple  					=		{}

											# only serialize values that differ from the default config
											for key, value in config_dict.items():
																	if value != default_config_dict[key]:
																							_snake_case:   List[str]  					=		value

											return serializable_config_dict












'''simple docstring'''






from __future__ import annotations

A   : Union[str, Any]             =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
A   : Optional[int]             =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   Tuple  					=		[]
						_snake_case:   List[Any]  					=		len(lowercase__						)

						for i in range(lowercase__						):
												_snake_case:   float  					=		-1
												for j in range(i + 1				,  lowercase__						):
																		if arr[i] < arr[j]:
																								_snake_case:   List[str]  					=		arr[j]
																								break
												result.append(lowercase__						)
						return result





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   Tuple  					=		[]
						for i, outer in enumerate(lowercase__						):
												_snake_case:   float  					=		-1
												for inner in arr[i + 1 :]:
																		if outer < inner:
																								_snake_case:   List[Any]  					=		inner
																								break
												result.append(lowercase__						)
						return result





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   int  					=		len(lowercase__						)
						_snake_case:   list[float]  					=		[]
						_snake_case:   list[float]  					=		[-1] * arr_size

						for index in reversed(range(lowercase__						)						):
												if stack:
																		while stack[-1] <= arr[index]:
																								stack.pop()
																								if not stack:
																														break
												if stack:
																		_snake_case:   Dict  					=		stack[-1]
												stack.append(arr[index]						)
						return result


if __name__ == "__main__":
		from doctest import testmod
		from timeit import timeit

		testmod()
		print(next_greatest_element_slow(arr))
		print(next_greatest_element_fast(arr))
		print(next_greatest_element(arr))

		A   : Union[str, Any]             =			(
		    'from __main__ import arr, next_greatest_element_slow, '
		    'next_greatest_element_fast, next_greatest_element'
		)
		print(
		    'next_greatest_element_slow():',
		    timeit('next_greatest_element_slow(arr)', setup=setup),
		)
		print(
		    'next_greatest_element_fast():',
		    timeit('next_greatest_element_fast(arr)', setup=setup),
		)
		print(
		    '     next_greatest_element():',
		    timeit('next_greatest_element(arr)', setup=setup),
		)





