




# 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      :						Dict							=  logging.get_logger(__name__)


@dataclass
class 							UpperCamelCase_       :

    '''simple docstring'''






    def __init__(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      int=False   ,					UpperCAmelCase__ :      Optional[Any]=False   ,					UpperCAmelCase__ :      List[str]=6.0   ,					UpperCAmelCase__ :      Optional[int]=None   ,					UpperCAmelCase__ :      Tuple=False   ,					UpperCAmelCase__ :      Tuple=False   ,					UpperCAmelCase__ :      Dict=None   ,					UpperCAmelCase__ :      Tuple="fp4"   ,					UpperCAmelCase__ :      int=False   ,					**UpperCAmelCase__ :      Any   ,					)		->List[str]:





           '''simple docstring'''

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





           '''simple docstring'''

           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 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->Tuple:





           '''simple docstring'''

           return self.load_in_abit or self.load_in_abit


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->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 :      str   ,					UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      Optional[int]   ,					**UpperCAmelCase__ :      List[Any])		->List[str]:





           '''simple docstring'''

           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 			SCREAMING_SNAKE_CASE				(     self :      str   ,					UpperCAmelCase__ :      Union[str, os.PathLike])		->List[str]:





           '''simple docstring'''

           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 			SCREAMING_SNAKE_CASE				(     self :      str)		->Dict[str, Any]:





           '''simple docstring'''

           A__ 					=       copy.deepcopy(self.__dict__)
           A__ 					=       str(output['''bnb_4bit_compute_dtype''']).split('''.''')[1]

           return output


    def __repr__(     self :      List[Any])		->List[Any]:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      str   ,					UpperCAmelCase__ :      bool = True)		->str:





           '''simple docstring'''

           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 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Dict[str, Any]:





           '''simple docstring'''

           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




from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class       _lowerCAmelCase   (      __a			):
				_lowercase     						=42
				_lowercase     						=42


if is_transformers_available() and is_torch_available():
			from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline



