

# 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


lowerCAmelCase_			      =      logging.get_logger(__name__)





@dataclass
class       snake_case_  :







			'''simple docstring'''

			def __init__(	self					:							Any				,  _UpperCamelCase					:							Any=False				,  _UpperCamelCase					:							Union[str, Any]=False				,  _UpperCamelCase					:							Optional[int]=6.0				,  _UpperCamelCase					:							Union[str, Any]=None				,  _UpperCamelCase					:							Optional[int]=False				,  _UpperCamelCase					:							Optional[Any]=False				,  _UpperCamelCase					:							int=None				,  _UpperCamelCase					:							Union[str, Any]="fp4"				,  _UpperCamelCase					:							List[str]=False				,  **_UpperCamelCase					:							int				,  ) ->Optional[Any]:
									snake_case_  =      load_in_abit
									snake_case_  =      load_in_abit
									snake_case_  =      llm_inta_threshold
									snake_case_  =      llm_inta_skip_modules
									snake_case_  =      llm_inta_enable_fpaa_cpu_offload
									snake_case_  =      llm_inta_has_fpaa_weight
									snake_case_  =      bnb_abit_quant_type
									snake_case_  =      bnb_abit_use_double_quant

									if bnb_abit_compute_dtype is None:
															snake_case_  =      torch.floataa
									elif isinstance(_UpperCamelCase				,  _UpperCamelCase				):
															snake_case_  =      getattr(_UpperCamelCase				,  _UpperCamelCase				)
									elif isinstance(_UpperCamelCase				,  torch.dtype				):
															snake_case_  =      bnb_abit_compute_dtype
									else:
															raise ValueError('''bnb_4bit_compute_dtype must be a string or a torch.dtype'''				)

									self.post_init()







			def 							snake_case__(	self					:							Tuple				) ->List[Any]:
									if not isinstance(self.llm_inta_threshold				,  _UpperCamelCase				):
															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				,  _UpperCamelCase				):
															raise ValueError('''llm_int8_skip_modules must be a list of strings'''				)
									if not isinstance(self.llm_inta_enable_fpaa_cpu_offload				,  _UpperCamelCase				):
															raise ValueError('''llm_int8_enable_fp32_cpu_offload must be a boolean'''				)

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

									if not isinstance(self.bnb_abit_use_double_quant				,  _UpperCamelCase				):
															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 							snake_case__(	self					:							Union[str, Any]				) ->Dict:
									return self.load_in_abit or self.load_in_abit







			def 							snake_case__(	self					:							Optional[int]				) ->Union[str, Any]:
									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 							snake_case__(	cls					:							Any				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							List[Any]				,  **_UpperCamelCase					:							Union[str, Any]				) ->Optional[Any]:
									snake_case_  =      cls(**_UpperCamelCase				)

									snake_case_  =      []
									for key, value in kwargs.items():
															if hasattr(_UpperCamelCase				,  _UpperCamelCase				):
																					setattr(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)
																					to_remove.append(_UpperCamelCase				)
									for key in to_remove:
															kwargs.pop(_UpperCamelCase				,  _UpperCamelCase				)

									if return_unused_kwargs:
															return config, kwargs
									else:
															return config







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							Union[str, os.PathLike]				) ->Optional[int]:
									with open(_UpperCamelCase				,  '''w'''				,  encoding='''utf-8'''				) as writer:
															snake_case_  =      self.to_dict()
															snake_case_  =      json.dumps(_UpperCamelCase				,  indent=2				,  sort_keys=_UpperCamelCase				) + '''\n'''

															writer.write(_UpperCamelCase				)







			def 							snake_case__(	self					:							Any				) ->Dict[str, Any]:
									snake_case_  =      copy.deepcopy(self.__dict__				)
									snake_case_  =      str(output['''bnb_4bit_compute_dtype''']				).split('''.'''				)[1]

									return output







			def __repr__(	self					:							Optional[Any]				) ->List[str]:
									return f'''{self.__class__.__name__} {self.to_json_string()}'''







			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							bool = True				) ->str:
									if use_diff is True:
															snake_case_  =      self.to_diff_dict()
									else:
															snake_case_  =      self.to_dict()
									return json.dumps(_UpperCamelCase				,  indent=2				,  sort_keys=_UpperCamelCase				) + "\n"







			def 							snake_case__(	self					:							List[str]				) ->Dict[str, Any]:
									snake_case_  =      self.to_dict()

									# get the default config dict
									snake_case_  =      BitsAndBytesConfig().to_dict()

									snake_case_  =      {}

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

									return serializable_config_dict







"""simple docstring"""







def      UpperCamelCase       ( _A  ,				_A )     ->       None:
				lowercase    :      List[Any]       = len(_A )
				print("""The following activities are selected:""" )

				# The first activity is always selected
				lowercase    :      Optional[int]       = 0
				print(_A  ,				end=""",""" )

				# Consider rest of the activities
				for j in range(_A ):
								# If this activity has start time greater than
								# or equal to the finish time of previously
								# selected activity, then select it
								if start[j] >= finish[i]:
												print(_A  ,				end=""",""" )
												lowercase    :      str       = j


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCAmelCase            =	[1, 3, 0, 5, 8, 5]
				_lowerCAmelCase            =	[2, 4, 6, 7, 9, 9]
				print_max_activities(start, finish)
