






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_				   =   {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_				   =   [
        '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''WavLMForAudioFrameClassification''',
        '''WavLMForCTC''',
        '''WavLMForSequenceClassification''',
        '''WavLMForXVector''',
        '''WavLMModel''',
        '''WavLMPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_wavlm import (
            WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            WavLMForAudioFrameClassification,
            WavLMForCTC,
            WavLMForSequenceClassification,
            WavLMForXVector,
            WavLMModel,
            WavLMPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase_				   =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








# 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


UpperCamelCase__		 =     logging.get_logger(__name__)

@dataclass
class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase=False      , _UpperCamelCase=False      , _UpperCamelCase=6.0      , _UpperCamelCase=None      , _UpperCamelCase=False      , _UpperCamelCase=False      , _UpperCamelCase=None      , _UpperCamelCase="fp4"      , _UpperCamelCase=False      , **_UpperCamelCase      , )      ->							int:






              """simple docstring"""


              __snake_case       	=							load_in_abit
              __snake_case       	=							load_in_abit
              __snake_case       	=							llm_inta_threshold
              __snake_case       	=							llm_inta_skip_modules
              __snake_case       	=							llm_inta_enable_fpaa_cpu_offload
              __snake_case       	=							llm_inta_has_fpaa_weight
              __snake_case       	=							bnb_abit_quant_type
              __snake_case       	=							bnb_abit_use_double_quant

              if bnb_abit_compute_dtype is None:
                     __snake_case       	=							torch.floataa
              elif isinstance(_UpperCamelCase      , _UpperCamelCase   ):
                     __snake_case       	=							getattr(_UpperCamelCase      , _UpperCamelCase   )
              elif isinstance(_UpperCamelCase      , torch.dtype   ):
                     __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 			a		(		self   )      ->							Union[str, 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 			a		(		self   )      ->							int:






              """simple docstring"""


              return self.load_in_abit or self.load_in_abit


       def 			a		(		self   )      ->							int:






              """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 			a		(		cls      , _UpperCamelCase      , _UpperCamelCase      , **_UpperCamelCase   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							cls(**_UpperCamelCase   )

              __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 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              with open(_UpperCamelCase      , """w"""      , encoding="""utf-8"""   ) as writer:
                     __snake_case       	=							self.to_dict()
                     __snake_case       	=							json.dumps(_UpperCamelCase      , indent=2      , sort_keys=_UpperCamelCase   ) + """\n"""

                     writer.write(_UpperCamelCase   )


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






              """simple docstring"""


              __snake_case       	=							copy.deepcopy(self.__dict__   )
              __snake_case       	=							str(output["""bnb_4bit_compute_dtype"""]   ).split("""."""   )[1]

              return output


       def __repr__(		self   )      ->							Dict:






              """simple docstring"""


              return F'{self.__class__.__name__} {self.to_json_string()}'


       def 			a		(		self      , _UpperCamelCase = True   )      ->							str:






              """simple docstring"""


              if use_diff is True:
                     __snake_case       	=							self.to_diff_dict()
              else:
                     __snake_case       	=							self.to_dict()
              return json.dumps(_UpperCamelCase      , indent=2      , sort_keys=_UpperCamelCase   ) + "\n"






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






              """simple docstring"""


              __snake_case       	=							self.to_dict()

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

              __snake_case       	=							{}

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

              return serializable_config_dict

