


'''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


lowercase__				     =		logging.get_logger(__name__)





@dataclass
class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase=False     ,							__UpperCamelCase=False     ,							__UpperCamelCase=6.0     ,							__UpperCamelCase=None     ,							__UpperCamelCase=False     ,							__UpperCamelCase=False     ,							__UpperCamelCase=None     ,							__UpperCamelCase="fp4"     ,							__UpperCamelCase=False     ,							**__UpperCamelCase     ,							)       ->    Dict:
          _a						=		load_in_abit
          _a						=		load_in_abit
          _a						=		llm_inta_threshold
          _a						=		llm_inta_skip_modules
          _a						=		llm_inta_enable_fpaa_cpu_offload
          _a						=		llm_inta_has_fpaa_weight
          _a						=		bnb_abit_quant_type
          _a						=		bnb_abit_use_double_quant

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

          self.post_init()






     def  a_					(			self		)       ->    int:
          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  a_					(			self		)       ->    Any:
          return self.load_in_abit or self.load_in_abit






     def  a_					(			self		)       ->    str:
          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  a_					(			cls     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Optional[int]:
          _a						=		cls(**__UpperCamelCase		)

          _a						=		[]
          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  a_					(			self     ,							__UpperCamelCase		)       ->    int:
          with open(__UpperCamelCase     ,							"w"     ,							encoding="utf-8"		) as writer:
               _a						=		self.to_dict()
               _a						=		json.dumps(__UpperCamelCase     ,							indent=2     ,							sort_keys=__UpperCamelCase		) + "\n"

               writer.write(__UpperCamelCase		)






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

          return output






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






     def  a_					(			self     ,							__UpperCamelCase = True		)       ->    str:
          if use_diff is True:
               _a						=		self.to_diff_dict()
          else:
               _a						=		self.to_dict()
          return json.dumps(__UpperCamelCase     ,							indent=2     ,							sort_keys=__UpperCamelCase		) + "\n"






     def  a_					(			self		)       ->    Dict[str, Any]:
          _a						=		self.to_dict()

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

          _a						=		{}

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

          return serializable_config_dict





'''simple docstring'''

import copy

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


lowercase__				     =		logging.get_logger(__name__)





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''encoder-decoder'''
     UpperCAmelCase 					=				True






     def __init__(			self     ,							**__UpperCamelCase		)       ->    List[Any]:
          super().__init__(**__UpperCamelCase		)
          assert (
              "encoder" in kwargs and "decoder" in kwargs
          ), "Config has to be initialized with encoder and decoder config"
          _a						=		kwargs.pop("encoder"		)
          _a						=		encoder_config.pop("model_type"		)
          _a						=		kwargs.pop("decoder"		)
          _a						=		decoder_config.pop("model_type"		)

          from ..auto.configuration_auto import AutoConfig

          _a						=		AutoConfig.for_model(__UpperCamelCase     ,							**__UpperCamelCase		)
          _a						=		AutoConfig.for_model(__UpperCamelCase     ,							**__UpperCamelCase		)
          _a						=		True






     @classmethod
     def  a_					(			cls     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							**__UpperCamelCase		)       ->    PretrainedConfig:
          logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
          _a						=		True
          _a						=		True

          return cls(encoder=encoder_config.to_dict()     ,							decoder=decoder_config.to_dict()     ,							**__UpperCamelCase		)






     def  a_					(			self		)       ->    Any:
          _a						=		copy.deepcopy(self.__dict__		)
          _a						=		self.encoder.to_dict()
          _a						=		self.decoder.to_dict()
          _a						=		self.__class__.model_type
          return output

