





"""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						: Dict     	=			logging.get_logger(__name__)


@dataclass
class        __UpperCamelCase				:






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

								if bnb_abit_compute_dtype is None:
									a      :		str	=				torch.floataa
								elif isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
									a      :		Optional[int]	=				getattr(lowerCAmelCase__      ,					lowerCAmelCase__      )
								elif isinstance(lowerCAmelCase__      ,					torch.dtype      ):
									a      :		List[Any]	=				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      ,					lowerCAmelCase__      ):
									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      ,					lowerCAmelCase__      ):
									raise ValueError("llm_int8_skip_modules must be a list of strings"      )
								if not isinstance(self.llm_inta_enable_fpaa_cpu_offload      ,					lowerCAmelCase__      ):
									raise ValueError("llm_int8_enable_fp32_cpu_offload must be a boolean"      )

								if not isinstance(self.llm_inta_has_fpaa_weight      ,					lowerCAmelCase__      ):
									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      ,					lowerCAmelCase__      ):
									raise ValueError("bnb_4bit_quant_type must be a string"      )

								if not isinstance(self.bnb_abit_use_double_quant      ,					lowerCAmelCase__      ):
									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      )	-> Tuple:
								return self.load_in_abit or self.load_in_abit






							def     __a   (   self      )	-> 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      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      )	-> str:
								a      :		List[Any]	=				cls(**lowerCAmelCase__      )

								a      :		Optional[int]	=				[]
								for key, value in kwargs.items():
									if hasattr(lowerCAmelCase__      ,					lowerCAmelCase__      ):
										setattr(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )
										to_remove.append(lowerCAmelCase__      )
								for key in to_remove:
									kwargs.pop(lowerCAmelCase__      ,					lowerCAmelCase__      )

								if return_unused_kwargs:
									return config, kwargs
								else:
									return config






							def     __a   (   self      ,					lowerCAmelCase__      )	-> int:
								with open(lowerCAmelCase__      ,					"w"      ,					encoding="utf-8"      ) as writer:
									a      :		List[str]	=				self.to_dict()
									a      :		Tuple	=				json.dumps(lowerCAmelCase__      ,					indent=2      ,					sort_keys=lowerCAmelCase__      ) + "\n"

									writer.write(lowerCAmelCase__      )






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

								return output






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






							def     __a   (   self      ,					lowerCAmelCase__ = True      )	-> str:
								if use_diff is True:
									a      :		List[Any]	=				self.to_diff_dict()
								else:
									a      :		Dict	=				self.to_dict()
								return json.dumps(lowerCAmelCase__      ,					indent=2      ,					sort_keys=lowerCAmelCase__      ) + "\n"






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

								# get the default config dict
								a      :		Tuple	=				BitsAndBytesConfig().to_dict()

								a      :		List[Any]	=				{}

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

								return serializable_config_dict



"""simple docstring"""






from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


__lowerCAmelCase    :       List[Any]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       Union[str, Any]					     ={
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Optional[int]							  =	'efficientnet'

      def __init__(						self		,       __lowerCAmelCase = 3		,       __lowerCAmelCase = 600		,       __lowerCAmelCase = 2.0		,       __lowerCAmelCase = 3.1		,       __lowerCAmelCase = 8		,       __lowerCAmelCase = [3, 3, 5, 3, 5, 5, 3]		,       __lowerCAmelCase = [32, 16, 24, 40, 80, 112, 192]		,       __lowerCAmelCase = [16, 24, 40, 80, 112, 192, 320]		,       __lowerCAmelCase = []		,       __lowerCAmelCase = [1, 2, 2, 2, 1, 2, 1]		,       __lowerCAmelCase = [1, 2, 2, 3, 3, 4, 1]		,       __lowerCAmelCase = [1, 6, 6, 6, 6, 6, 6]		,       __lowerCAmelCase = 0.2_5		,       __lowerCAmelCase = "swish"		,       __lowerCAmelCase = 2560		,       __lowerCAmelCase = "mean"		,       __lowerCAmelCase = 0.0_2		,       __lowerCAmelCase = 0.0_0_1		,       __lowerCAmelCase = 0.9_9		,       __lowerCAmelCase = 0.5		,       __lowerCAmelCase = 0.2		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase						)

            lowercase														=	num_channels
            lowercase														=	image_size
            lowercase														=	width_coefficient
            lowercase														=	depth_coefficient
            lowercase														=	depth_divisor
            lowercase														=	kernel_sizes
            lowercase														=	in_channels
            lowercase														=	out_channels
            lowercase														=	depthwise_padding
            lowercase														=	strides
            lowercase														=	num_block_repeats
            lowercase														=	expand_ratios
            lowercase														=	squeeze_expansion_ratio
            lowercase														=	hidden_act
            lowercase														=	hidden_dim
            lowercase														=	pooling_type
            lowercase														=	initializer_range
            lowercase														=	batch_norm_eps
            lowercase														=	batch_norm_momentum
            lowercase														=	dropout_rate
            lowercase														=	drop_connect_rate
            lowercase														=	sum(__lowerCAmelCase						) * 4






class  _A						(       lowerCAmelCase	):
      snake_case__			:   Dict							  =	version.parse('1.11'	)

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return OrderedDict(
                [
                    ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ]						)







      @property
      def 				A__       (						self						):







            """simple docstring"""



            return 1E-5



