




# 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       a					:

     '''simple docstring'''





     def __init__(     self       :      str ,   __snake_case       :      Tuple=False ,   __snake_case       :      Dict=False ,   __snake_case       :      Any=6.0 ,   __snake_case       :      Dict=None ,   __snake_case       :      Optional[int]=False ,   __snake_case       :      str=False ,   __snake_case       :      Dict=None ,   __snake_case       :      str="fp4" ,   __snake_case       :      Any=False ,   **__snake_case       :      Union[str, Any] ,   ):
         UpperCAmelCase_										=					load_in_abit
         UpperCAmelCase_										=					load_in_abit
         UpperCAmelCase_										=					llm_inta_threshold
         UpperCAmelCase_										=					llm_inta_skip_modules
         UpperCAmelCase_										=					llm_inta_enable_fpaa_cpu_offload
         UpperCAmelCase_										=					llm_inta_has_fpaa_weight
         UpperCAmelCase_										=					bnb_abit_quant_type
         UpperCAmelCase_										=					bnb_abit_use_double_quant

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

         self.post_init()
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         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   lowerCamelCase_       (     self       :      int    ):
         return self.load_in_abit or self.load_in_abit
     def   lowerCamelCase_       (     self       :      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   lowerCamelCase_       (     cls       :      Union[str, Any] ,   __snake_case       :      str ,   __snake_case       :      Any ,   **__snake_case       :      int    ):
         UpperCAmelCase_										=					cls(**__snake_case    )

         UpperCAmelCase_										=					[]
         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   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      Union[str, os.PathLike]    ):
         with open(__snake_case ,   '''w''' ,   encoding='''utf-8'''    ) as writer:
             UpperCAmelCase_										=					self.to_dict()
             UpperCAmelCase_										=					json.dumps(__snake_case ,   indent=2 ,   sort_keys=__snake_case    ) + '''\n'''

             writer.write(__snake_case    )
     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					copy.deepcopy(self.__dict__    )
         UpperCAmelCase_										=					str(output['''bnb_4bit_compute_dtype''']    ).split('''.'''    )[1]

         return output
     def __repr__(     self       :      Union[str, Any]    ):
         return F'{self.__class__.__name__} {self.to_json_string()}'
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      bool = True    ):
         if use_diff is True:
             UpperCAmelCase_										=					self.to_diff_dict()
         else:
             UpperCAmelCase_										=					self.to_dict()
         return json.dumps(__snake_case ,   indent=2 ,   sort_keys=__snake_case    ) + "\n"




     def   lowerCamelCase_       (     self       :      List[Any]    ):
         UpperCAmelCase_										=					self.to_dict()

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

         UpperCAmelCase_										=					{}

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

         return serializable_config_dict











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase      			=						logging.get_logger(__name__)

_lowerCamelCase      			=						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  'data2vec-text'
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      Optional[int]=3_05_22 ,   __snake_case       :      List[str]=7_68 ,   __snake_case       :      Tuple=12 ,   __snake_case       :      int=12 ,   __snake_case       :      Union[str, Any]=30_72 ,   __snake_case       :      List[Any]="gelu" ,   __snake_case       :      Any=0.1 ,   __snake_case       :      Union[str, Any]=0.1 ,   __snake_case       :      Tuple=5_12 ,   __snake_case       :      str=2 ,   __snake_case       :      str=0.02 ,   __snake_case       :      List[Any]=1E-12 ,   __snake_case       :      Any=1 ,   __snake_case       :      List[Any]=0 ,   __snake_case       :      Dict=2 ,   __snake_case       :      Any="absolute" ,   __snake_case       :      Union[str, Any]=True ,   __snake_case       :      Any=None ,   **__snake_case       :      List[Any] ,   ):
         super().__init__(pad_token_id=__snake_case ,   bos_token_id=__snake_case ,   eos_token_id=__snake_case ,   **__snake_case    )

         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					hidden_size
         UpperCAmelCase_										=					num_hidden_layers
         UpperCAmelCase_										=					num_attention_heads
         UpperCAmelCase_										=					hidden_act
         UpperCAmelCase_										=					intermediate_size
         UpperCAmelCase_										=					hidden_dropout_prob
         UpperCAmelCase_										=					attention_probs_dropout_prob
         UpperCAmelCase_										=					max_position_embeddings
         UpperCAmelCase_										=					type_vocab_size
         UpperCAmelCase_										=					initializer_range
         UpperCAmelCase_										=					layer_norm_eps
         UpperCAmelCase_										=					position_embedding_type
         UpperCAmelCase_										=					use_cache
         UpperCAmelCase_										=					classifier_dropout



class       a					(       _A      ):

     '''simple docstring'''





     @property
     def   lowerCamelCase_       (     self       :      str    ):
         if self.task == "multiple-choice":
             UpperCAmelCase_										=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             UpperCAmelCase_										=					{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]    )





