

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor
class  _A		:

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=32 ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=10 ,					_SCREAMING_SNAKE_CASE=[10, 20, 30, 40] ,					_SCREAMING_SNAKE_CASE=[1, 1, 2, 1] ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE="relu" ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=None ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     parent
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     batch_size
        SCREAMING_SNAKE_CASE_				:  Any           =     image_size
        SCREAMING_SNAKE_CASE_				:  List[str]           =     num_channels
        SCREAMING_SNAKE_CASE_				:  List[str]           =     embeddings_size
        SCREAMING_SNAKE_CASE_				:  Dict           =     hidden_sizes
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     depths
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     is_training
        SCREAMING_SNAKE_CASE_				:  Tuple           =     use_labels
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     hidden_act
        SCREAMING_SNAKE_CASE_				:  Any           =     num_labels
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scope
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     len(_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     None
        if self.use_labels:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     ids_tensor([self.batch_size] ,					self.num_labels					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.get_config()
        return config, pixel_values, labels

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return RegNetConfig(
            num_channels=self.num_channels ,					embeddings_size=self.embeddings_size ,					hidden_sizes=self.hidden_sizes ,					depths=self.depths ,					hidden_act=self.hidden_act ,					num_labels=self.num_labels ,					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     TFRegNetModel(config=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     model(_SCREAMING_SNAKE_CASE ,					training=_SCREAMING_SNAKE_CASE					)
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.num_labels
        SCREAMING_SNAKE_CASE_				:  int           =     TFRegNetForImageClassification(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     model(_SCREAMING_SNAKE_CASE ,					labels=_SCREAMING_SNAKE_CASE ,					training=_SCREAMING_SNAKE_CASE					)
        self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)					)



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     config_and_inputs
        SCREAMING_SNAKE_CASE_				:  int           =     {'pixel_values': pixel_values}
        return config, inputs_dict

@require_tf
class  _A		(  __magic_name__   ,     __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      Any       =  (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE    :      List[str]       =  (
      {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
      if is_tf_available()
      else {}
  )

  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  False
  SCREAMING_SNAKE_CASE    :      List[Any]       =  False
  SCREAMING_SNAKE_CASE    :      Any       =  False
  SCREAMING_SNAKE_CASE    :      List[Any]       =  False
  SCREAMING_SNAKE_CASE    :      List[Any]       =  False

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     TFRegNetModelTester(self					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ConfigTester(self ,					config_class=_SCREAMING_SNAKE_CASE ,					has_text_modality=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return

  @unittest.skip(reason='RegNet does not use inputs_embeds'					)
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices('GPU'					)					) == 0 ,					reason='TF does not support backprop for grouped convolutions on CPU.' ,					)
  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().test_keras_fit()

  @unittest.skip(reason='RegNet does not support input and output embeddings'					)
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     model_class(_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  str           =     inspect.signature(model.call					)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     [*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_				:  List[str]           =     ['pixel_values']
              self.assertListEqual(arg_names[:1] ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        def check_hidden_states_output(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  List[str]           =     model_class(_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  List[str]           =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					) ,					training=_SCREAMING_SNAKE_CASE					)

              SCREAMING_SNAKE_CASE_				:  List[Any]           =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              SCREAMING_SNAKE_CASE_				:  Dict           =     self.model_tester.num_stages
              self.assertEqual(len(_SCREAMING_SNAKE_CASE					) ,					expected_num_stages + 1					)

              # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]					) ,					[self.model_tester.image_size // 2, self.model_tester.image_size // 2] ,					)

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     ['basic', 'bottleneck']
        for model_class in self.all_model_classes:
              for layer_type in layers_type:
                    SCREAMING_SNAKE_CASE_				:  Any           =     layer_type
                    SCREAMING_SNAKE_CASE_				:  Tuple           =     True
                    check_hidden_states_output(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     True

                    check_hidden_states_output(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  int           =     self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE={}					):
              SCREAMING_SNAKE_CASE_				:  int           =     model(_SCREAMING_SNAKE_CASE ,					return_dict=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Tuple           =     model(_SCREAMING_SNAKE_CASE ,					return_dict=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).to_tuple()

              def recursive_check(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
                    if isinstance(_SCREAMING_SNAKE_CASE ,					(List, Tuple)					):
                          for tuple_iterable_value, dict_iterable_value in zip(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
                                recursive_check(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
                    elif tuple_object is None:
                          return
                    else:
                          self.assertTrue(
                              all(tf.equal(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					) ,					msg=(
                                  'Tuple and dict output are not equal. Difference:'
                                  f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object					)					)}"
                              ) ,					)

                    recursive_check(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_				:  Any           =     model_class(_SCREAMING_SNAKE_CASE					)

              SCREAMING_SNAKE_CASE_				:  str           =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              check_equivalence(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

              SCREAMING_SNAKE_CASE_				:  List[str]           =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					return_labels=_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Any           =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					return_labels=_SCREAMING_SNAKE_CASE					)
              check_equivalence(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  List[str]           =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              check_equivalence(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					{'output_hidden_states': True}					)

              SCREAMING_SNAKE_CASE_				:  Any           =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					return_labels=_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Dict           =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					return_labels=_SCREAMING_SNAKE_CASE					)
              check_equivalence(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					{'output_hidden_states': True}					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE					)



  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     TFRegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)

def    A_  (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      return image



@require_tf
@require_vision
class  _A		(  unittest.TestCase):

  @cached_property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return (
            AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
            if is_vision_available()
            else None
        )



  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.default_image_processor
        SCREAMING_SNAKE_CASE_				:  Tuple           =     prepare_img()
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     image_processor(images=_SCREAMING_SNAKE_CASE ,					return_tensors='tf'					)

        # forward pass
        SCREAMING_SNAKE_CASE_				:  int           =     model(**_SCREAMING_SNAKE_CASE ,					training=_SCREAMING_SNAKE_CASE					)

        # verify the logits
        SCREAMING_SNAKE_CASE_				:  str           =     tf.TensorShape((1, 1000)					)
        self.assertEqual(outputs.logits.shape ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     tf.constant([-0.4180, -1.5051, -3.4836]					)

        tf.debugging.assert_near(outputs.logits[0, :3] ,					_SCREAMING_SNAKE_CASE ,					atol=1e-4					)









# 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     :   Optional[int]						     =  logging.get_logger(__name__)
@dataclass
class  _A		:

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=6.0 ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE="fp4" ,					_SCREAMING_SNAKE_CASE=False ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     load_in_abit
        SCREAMING_SNAKE_CASE_				:  Tuple           =     load_in_abit
        SCREAMING_SNAKE_CASE_				:  Dict           =     llm_inta_threshold
        SCREAMING_SNAKE_CASE_				:  Tuple           =     llm_inta_skip_modules
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     llm_inta_enable_fpaa_cpu_offload
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     llm_inta_has_fpaa_weight
        SCREAMING_SNAKE_CASE_				:  List[str]           =     bnb_abit_quant_type
        SCREAMING_SNAKE_CASE_				:  int           =     bnb_abit_use_double_quant

        if bnb_abit_compute_dtype is None:
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.floataa
        elif isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     getattr(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        elif isinstance(_SCREAMING_SNAKE_CASE ,					torch.dtype					):
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     bnb_abit_compute_dtype
        else:
              raise ValueError('bnb_4bit_compute_dtype must be a string or a torch.dtype'					)

        self.post_init()

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        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  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.load_in_abit or self.load_in_abit

  def  UpperCAmelCase    (				self					):





        """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  UpperCAmelCase    (				cls ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     cls(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  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  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        with open(_SCREAMING_SNAKE_CASE ,					'w' ,					encoding='utf-8'					) as writer:
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.to_dict()
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     json.dumps(_SCREAMING_SNAKE_CASE ,					indent=2 ,					sort_keys=_SCREAMING_SNAKE_CASE					) + '\n'

              writer.write(_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

        return output

  def __repr__(				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE = True					):





        """simple docstring"""







        if use_diff is True:
              SCREAMING_SNAKE_CASE_				:  int           =     self.to_diff_dict()
        else:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     self.to_dict()
        return json.dumps(_SCREAMING_SNAKE_CASE ,					indent=2 ,					sort_keys=_SCREAMING_SNAKE_CASE					) + "\n"



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.to_dict()

        # get the default config dict
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     BitsAndBytesConfig().to_dict()

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {}

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

        return serializable_config_dict






