


"""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__				:							Union[str, Any]						=     logging.get_logger(__name__)




@dataclass
class  UpperCamelCase_	:

	"""simple docstring"""





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

						if bnb_abit_compute_dtype is None:
											__SCREAMING_SNAKE_CASE						   =				torch.floataa
						elif isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ):
											__SCREAMING_SNAKE_CASE						   =				getattr(UpperCAmelCase__			,			UpperCAmelCase__   )
						elif isinstance(UpperCAmelCase__			,			torch.dtype   ):
											__SCREAMING_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   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	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   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	Union[str, Any]:
						return self.load_in_abit or self.load_in_abit




	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	int:
						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   UpperCAmelCase_      ( cls			:				Dict			,			UpperCAmelCase__			:				Optional[Any]			,			UpperCAmelCase__			:				Optional[Any]			,			**UpperCAmelCase__			:				List[Any]   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				cls(**UpperCAmelCase__   )

						__SCREAMING_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   UpperCAmelCase_      ( self			:				Union[str, Any]			,			UpperCAmelCase__			:				Union[str, os.PathLike]   )       ->	List[Any]:
						with open(UpperCAmelCase__			,			"w"			,			encoding="utf-8"   ) as writer:
											__SCREAMING_SNAKE_CASE						   =				self.to_dict()
											__SCREAMING_SNAKE_CASE						   =				json.dumps(UpperCAmelCase__			,			indent=2			,			sort_keys=UpperCAmelCase__   ) + "\n"

											writer.write(UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	Dict[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				copy.deepcopy(self.__dict__   )
						__SCREAMING_SNAKE_CASE						   =				str(output["bnb_4bit_compute_dtype"]   ).split("."   )[1]

						return output




	def __repr__( self			:				Optional[int]   )       ->	List[Any]:
						return F"""{self.__class__.__name__} {self.to_json_string()}"""




	def   UpperCAmelCase_      ( self			:				List[str]			,			UpperCAmelCase__			:				bool = True   )       ->	str:
						if use_diff is True:
											__SCREAMING_SNAKE_CASE						   =				self.to_diff_dict()
						else:
											__SCREAMING_SNAKE_CASE						   =				self.to_dict()
						return json.dumps(UpperCAmelCase__			,			indent=2			,			sort_keys=UpperCAmelCase__   ) + "\n"




	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	Dict[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				self.to_dict()

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

						__SCREAMING_SNAKE_CASE						   =				{}

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

						return serializable_config_dict








"""simple docstring"""




import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					'''mvp'''
 UpperCamelCase					:       Union[str, Any]			=					['''past_key_values''']
 UpperCamelCase					:       Any			=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


 def __init__(     self		:						List[str]							,						UpperCAmelCase__		:						List[str]=50267							,						UpperCAmelCase__		:						Optional[Any]=1024							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						Optional[Any]=4096							,						UpperCAmelCase__		:						int=16							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						int=4096							,						UpperCAmelCase__		:						List[Any]=16							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple="gelu"							,						UpperCAmelCase__		:						Union[str, Any]=1024							,						UpperCAmelCase__		:						List[str]=0.1							,						UpperCAmelCase__		:						Any=0.0							,						UpperCAmelCase__		:						Dict=0.0							,						UpperCAmelCase__		:						Tuple=0.0_2							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						int=True							,						UpperCAmelCase__		:						Tuple=1							,						UpperCAmelCase__		:						Dict=0							,						UpperCAmelCase__		:						Union[str, Any]=2							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Tuple=2							,						UpperCAmelCase__		:						Any=2							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						Dict=100							,						UpperCAmelCase__		:						Union[str, Any]=800							,						**UpperCAmelCase__		:						Dict							,						)					->       List[Any]:
   _a  :       Any             =	vocab_size
   _a  :       Any             =	max_position_embeddings
   _a  :       Union[str, Any]             =	d_model
   _a  :       List[str]             =	encoder_ffn_dim
   _a  :       List[Any]             =	encoder_layers
   _a  :       Dict             =	encoder_attention_heads
   _a  :       Tuple             =	decoder_ffn_dim
   _a  :       List[Any]             =	decoder_layers
   _a  :       Optional[Any]             =	decoder_attention_heads
   _a  :       Optional[Any]             =	dropout
   _a  :       str             =	attention_dropout
   _a  :       Dict             =	activation_dropout
   _a  :       Any             =	activation_function
   _a  :       Tuple             =	init_std
   _a  :       Dict             =	encoder_layerdrop
   _a  :       Optional[int]             =	decoder_layerdrop
   _a  :       Optional[Any]             =	classifier_dropout
   _a  :       List[Any]             =	use_cache
   _a  :       Dict             =	encoder_layers
   _a  :       str             =	scale_embedding  # scale factor will be sqrt(d_model) if True
   _a  :       int             =	use_prompt
   _a  :       Dict             =	prompt_length
   _a  :       Dict             =	prompt_mid_dim

   super().__init__(
       pad_token_id=UpperCAmelCase__							,						bos_token_id=UpperCAmelCase__							,						eos_token_id=UpperCAmelCase__							,						is_encoder_decoder=UpperCAmelCase__							,						decoder_start_token_id=UpperCAmelCase__							,						forced_eos_token_id=UpperCAmelCase__							,						**UpperCAmelCase__							,						)

   if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""							,						UpperCAmelCase__							):
     _a  :       List[str]             =	self.bos_token_id
     warnings.warn(
         f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
         """The config can simply be saved and uploaded again to be fixed."""							)

