


'''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				:					Tuple    						=			logging.get_logger(__name__)


@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""


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

           if bnb_abit_compute_dtype is None:
                  _A					:     Optional[Any]     = torch.floataa
           elif isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  _A					:     Optional[int]     = getattr(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           elif isinstance(SCREAMING_SNAKE_CASE				,							torch.dtype):
                  _A					:     int     = 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      :							Optional[Any]):
           if not isinstance(self.llm_inta_threshold				,							SCREAMING_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				,							SCREAMING_SNAKE_CASE):
                  raise ValueError('llm_int8_skip_modules must be a list of strings')
           if not isinstance(self.llm_inta_enable_fpaa_cpu_offload				,							SCREAMING_SNAKE_CASE):
                  raise ValueError('llm_int8_enable_fp32_cpu_offload must be a boolean')

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

           if not isinstance(self.bnb_abit_use_double_quant				,							SCREAMING_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        A   (  self      :							List[Any]):
           return self.load_in_abit or self.load_in_abit


    def        A   (  self      :							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        A   (  cls      :							str				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Tuple				,							**SCREAMING_SNAKE_CASE      :							str):
           _A					:     List[Any]     = cls(**SCREAMING_SNAKE_CASE)

           _A					:     Optional[Any]     = []
           for key, value in kwargs.items():
                  if hasattr(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                         setattr(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                         to_remove.append(SCREAMING_SNAKE_CASE)
           for key in to_remove:
                  kwargs.pop(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           if return_unused_kwargs:
                  return config, kwargs
           else:
                  return config


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Union[str, os.PathLike]):
           with open(SCREAMING_SNAKE_CASE				,							'w'				,							encoding='utf-8') as writer:
                  _A					:     Dict     = self.to_dict()
                  _A					:     Any     = json.dumps(SCREAMING_SNAKE_CASE				,							indent=2				,							sort_keys=SCREAMING_SNAKE_CASE) + '\n'

                  writer.write(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[str]):
           _A					:     Tuple     = copy.deepcopy(self.__dict__)
           _A					:     List[str]     = str(output['bnb_4bit_compute_dtype']).split('.')[1]

           return output


    def __repr__(  self      :							Tuple):
           return F'{self.__class__.__name__} {self.to_json_string()}'


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							bool = True):
           if use_diff is True:
                  _A					:     Optional[Any]     = self.to_diff_dict()
           else:
                  _A					:     Tuple     = self.to_dict()
           return json.dumps(SCREAMING_SNAKE_CASE				,							indent=2				,							sort_keys=SCREAMING_SNAKE_CASE) + "\n"


    def        A   (  self      :							Tuple):
           _A					:     List[Any]     = self.to_dict()

           # get the default config dict
           _A					:     int     = BitsAndBytesConfig().to_dict()

           _A					:     List[str]     = {}

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

           return serializable_config_dict










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A				:					List[str]    						=			{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					int    						=			['''ElectraTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[str]    						=			[
       '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ElectraForCausalLM''',
       '''ElectraForMaskedLM''',
       '''ElectraForMultipleChoice''',
       '''ElectraForPreTraining''',
       '''ElectraForQuestionAnswering''',
       '''ElectraForSequenceClassification''',
       '''ElectraForTokenClassification''',
       '''ElectraModel''',
       '''ElectraPreTrainedModel''',
       '''load_tf_weights_in_electra''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Dict    						=			[
       '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFElectraForMaskedLM''',
       '''TFElectraForMultipleChoice''',
       '''TFElectraForPreTraining''',
       '''TFElectraForQuestionAnswering''',
       '''TFElectraForSequenceClassification''',
       '''TFElectraForTokenClassification''',
       '''TFElectraModel''',
       '''TFElectraPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''FlaxElectraForCausalLM''',
       '''FlaxElectraForMaskedLM''',
       '''FlaxElectraForMultipleChoice''',
       '''FlaxElectraForPreTraining''',
       '''FlaxElectraForQuestionAnswering''',
       '''FlaxElectraForSequenceClassification''',
       '''FlaxElectraForTokenClassification''',
       '''FlaxElectraModel''',
       '''FlaxElectraPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
   from .tokenization_electra import ElectraTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_electra_fast import ElectraTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_electra import (
          ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
          ElectraPreTrainedModel,
          load_tf_weights_in_electra,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_electra import (
          TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFElectraForMaskedLM,
          TFElectraForMultipleChoice,
          TFElectraForPreTraining,
          TFElectraForQuestionAnswering,
          TFElectraForSequenceClassification,
          TFElectraForTokenClassification,
          TFElectraModel,
          TFElectraPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
          FlaxElectraPreTrainedModel,
      )

else:
   import sys

   A				:					Tuple    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






