"""simple docstring"""





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							_UpperCamelCase     :		Dict											=					"platform"

							import jax
							import jax.numpy as jnp

							from transformers import BlenderbotTokenizer
							from transformers.models.blenderbot.modeling_flax_blenderbot import (
							    FlaxBlenderbotForConditionalGeneration,
							    FlaxBlenderbotModel,
							    shift_tokens_right,
							)





def        a_  (   _lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Tuple     ,				_lowerCAmelCase						:				str=None     ,				_lowerCAmelCase						:				List[Any]=None     ,				_lowerCAmelCase						:				Union[str, Any]=None     ,				_lowerCAmelCase						:				Tuple=None     ,				_lowerCAmelCase						:				Union[str, Any]=None     ,				_lowerCAmelCase						:				Any=None     ,				):





							'''simple docstring'''

							if attention_mask is None:
														lowercase__    :	Tuple     					=			np.where(input_ids != config.pad_token_id     ,				1     ,				0  )
							if decoder_attention_mask is None:
														lowercase__    :	int     					=			np.where(decoder_input_ids != config.pad_token_id     ,				1     ,				0  )
							if head_mask is None:
														lowercase__    :	Any     					=			np.ones((config.encoder_layers, config.encoder_attention_heads)  )
							if decoder_head_mask is None:
														lowercase__    :	List[str]     					=			np.ones((config.decoder_layers, config.decoder_attention_heads)  )
							if cross_attn_head_mask is None:
														lowercase__    :	List[str]     					=			np.ones((config.decoder_layers, config.decoder_attention_heads)  )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							}


class   UpperCAmelCase_			:


						def __init__(     self	,    a	,    a=1_3	,    a=7	,    a=True	,    a=False	,    a=9_9	,    a=1_6	,    a=2	,    a=4	,    a=4	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=3_2	,    a=2	,    a=1	,    a=0	,    a=0.02	,    ) ->      Union[str, Any]:
													lowercase__    :	Tuple     					=			parent
													lowercase__    :	int     					=			batch_size
													lowercase__    :	Dict     					=			seq_length
													lowercase__    :	int     					=			is_training
													lowercase__    :	Dict     					=			use_labels
													lowercase__    :	Union[str, Any]     					=			vocab_size
													lowercase__    :	int     					=			hidden_size
													lowercase__    :	Union[str, Any]     					=			num_hidden_layers
													lowercase__    :	Dict     					=			num_attention_heads
													lowercase__    :	int     					=			intermediate_size
													lowercase__    :	Optional[int]     					=			hidden_act
													lowercase__    :	List[Any]     					=			hidden_dropout_prob
													lowercase__    :	Union[str, Any]     					=			attention_probs_dropout_prob
													lowercase__    :	Optional[Any]     					=			max_position_embeddings
													lowercase__    :	Tuple     					=			eos_token_id
													lowercase__    :	Tuple     					=			pad_token_id
													lowercase__    :	int     					=			bos_token_id
													lowercase__    :	Optional[int]     					=			initializer_range


						def 	_UpperCAmelCase (     self							) ->      List[Any]:
													lowercase__    :	Tuple     					=			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,    self.vocab_size							)	,    3	,    self.vocab_size							)
													lowercase__    :	Union[str, Any]     					=			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,    dtype=np.intaa							))	,    -1							)

													lowercase__    :	Any     					=			shift_tokens_right(__UpperCamelCase	,    1	,    2							)

													lowercase__    :	Optional[Any]     					=			BlenderbotConfig(
													    vocab_size=self.vocab_size	,    d_model=self.hidden_size	,    encoder_layers=self.num_hidden_layers	,    decoder_layers=self.num_hidden_layers	,    encoder_attention_heads=self.num_attention_heads	,    decoder_attention_heads=self.num_attention_heads	,    encoder_ffn_dim=self.intermediate_size	,    decoder_ffn_dim=self.intermediate_size	,    dropout=self.hidden_dropout_prob	,    attention_dropout=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    eos_token_id=self.eos_token_id	,    bos_token_id=self.bos_token_id	,    pad_token_id=self.pad_token_id	,    initializer_range=self.initializer_range	,    use_cache=__UpperCamelCase	,    )
													lowercase__    :	Dict     					=			prepare_blenderbot_inputs_dict(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase							)
													return config, inputs_dict


						def 	_UpperCAmelCase (     self							) ->      List[str]:
													lowercase__					,   lowercase__    :	Union[str, Any]     					=			self.prepare_config_and_inputs()
													return config, inputs_dict


						def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      int:
													lowercase__    :	List[Any]     					=			2_0
													lowercase__    :	Tuple     					=			model_class_name(__UpperCamelCase							)

													lowercase__    :	str     					=			model.encode(inputs_dict['input_ids']							)

													lowercase__					,   lowercase__    :	List[Any]     					=			(
													    inputs_dict['decoder_input_ids'],
													    inputs_dict['decoder_attention_mask'],
													)

													lowercase__    :	Tuple     					=			model.init_cache(decoder_input_ids.shape[0]	,    __UpperCamelCase	,    __UpperCamelCase							)
													lowercase__    :	List[str]     					=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,    dtype='i4'							)

													lowercase__    :	int     					=			jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]	,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,    )
													lowercase__    :	List[str]     					=			model.decode(
													    decoder_input_ids[:, :-1]	,    __UpperCamelCase	,    decoder_attention_mask=__UpperCamelCase	,    past_key_values=__UpperCamelCase	,    decoder_position_ids=__UpperCamelCase	,    )

													lowercase__    :	Optional[Any]     					=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,    dtype='i4'							)
													lowercase__    :	Union[str, Any]     					=			model.decode(
													    decoder_input_ids[:, -1:]	,    __UpperCamelCase	,    decoder_attention_mask=__UpperCamelCase	,    past_key_values=outputs_cache.past_key_values	,    decoder_position_ids=__UpperCamelCase	,    )

													lowercase__    :	List[Any]     					=			model.decode(__UpperCamelCase	,    __UpperCamelCase							)

													lowercase__    :	Any     					=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
													self.parent.assertTrue(diff < 1e-3	,    msg=f"""Max diff is {diff}"""							)


						def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      int:
													lowercase__    :	List[Any]     					=			2_0
													lowercase__    :	Any     					=			model_class_name(__UpperCamelCase							)

													lowercase__    :	int     					=			model.encode(inputs_dict['input_ids']							)

													lowercase__					,   lowercase__    :	Optional[int]     					=			(
													    inputs_dict['decoder_input_ids'],
													    inputs_dict['decoder_attention_mask'],
													)

													lowercase__    :	int     					=			jnp.concatenate(
													    [
													        decoder_attention_mask,
													        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
													    ]	,    axis=-1	,    )

													lowercase__    :	Optional[Any]     					=			model.init_cache(decoder_input_ids.shape[0]	,    __UpperCamelCase	,    __UpperCamelCase							)
													lowercase__    :	Optional[int]     					=			jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]	,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,    )

													lowercase__    :	Union[str, Any]     					=			model.decode(
													    decoder_input_ids[:, :-1]	,    __UpperCamelCase	,    decoder_attention_mask=__UpperCamelCase	,    past_key_values=__UpperCamelCase	,    decoder_position_ids=__UpperCamelCase	,    )
													lowercase__    :	int     					=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,    dtype='i4'							)
													lowercase__    :	List[Any]     					=			model.decode(
													    decoder_input_ids[:, -1:]	,    __UpperCamelCase	,    past_key_values=outputs_cache.past_key_values	,    decoder_attention_mask=__UpperCamelCase	,    decoder_position_ids=__UpperCamelCase	,    )

													lowercase__    :	Optional[int]     					=			model.decode(__UpperCamelCase	,    __UpperCamelCase	,    decoder_attention_mask=__UpperCamelCase							)

													lowercase__    :	Dict     					=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
													self.parent.assertTrue(diff < 1e-3	,    msg=f"""Max diff is {diff}"""							)





@require_flax
class   UpperCAmelCase_			(					unittest.TestCase):
						lowerCamelCase__	:					Optional[int]										=						9_9


						def 	_UpperCAmelCase (     self							) ->      Any:
													lowercase__    :	Tuple     					=			np.array(
													    [
													        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
													        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
													        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
													        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
													        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
													        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
													        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
													        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
													        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
													        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
													        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
													        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
													        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
													    ]	,    dtype=np.intaa	,    )

													lowercase__    :	Optional[int]     					=			input_ids.shape[0]
													lowercase__    :	Any     					=			BlenderbotConfig(
													    vocab_size=self.vocab_size	,    d_model=2_4	,    encoder_layers=2	,    decoder_layers=2	,    encoder_attention_heads=2	,    decoder_attention_heads=2	,    encoder_ffn_dim=3_2	,    decoder_ffn_dim=3_2	,    max_position_embeddings=4_8	,    eos_token_id=2	,    pad_token_id=1	,    bos_token_id=0	,    )
													return config, input_ids, batch_size


						def 	_UpperCAmelCase (     self							) ->      Optional[int]:
													lowercase__					,   lowercase__					,   lowercase__    :	str     					=			self._get_config_and_data()
													lowercase__    :	Optional[Any]     					=			FlaxBlenderbotForConditionalGeneration(__UpperCamelCase							)
													lowercase__    :	Any     					=			lm_model(input_ids=__UpperCamelCase							)
													lowercase__    :	Union[str, Any]     					=			(batch_size, input_ids.shape[1], config.vocab_size)
													self.assertEqual(outputs['logits'].shape	,    __UpperCamelCase							)


						def 	_UpperCAmelCase (     self							) ->      Any:
													lowercase__    :	Dict     					=			BlenderbotConfig(
													    vocab_size=self.vocab_size	,    d_model=1_4	,    encoder_layers=2	,    decoder_layers=2	,    encoder_attention_heads=2	,    decoder_attention_heads=2	,    encoder_ffn_dim=8	,    decoder_ffn_dim=8	,    max_position_embeddings=4_8	,    )
													lowercase__    :	List[Any]     					=			FlaxBlenderbotForConditionalGeneration(__UpperCamelCase							)
													lowercase__    :	Dict     					=			np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]	,    dtype=np.intaa							)
													lowercase__    :	str     					=			np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]	,    dtype=np.intaa							)
													lowercase__    :	int     					=			lm_model(input_ids=__UpperCamelCase	,    decoder_input_ids=__UpperCamelCase							)
													lowercase__    :	Optional[Any]     					=			(*summary.shape, config.vocab_size)
													self.assertEqual(outputs['logits'].shape	,    __UpperCamelCase							)


						def 	_UpperCAmelCase (     self							) ->      str:
													lowercase__    :	Optional[int]     					=			np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]	,    dtype=np.intaa							)
													lowercase__    :	Dict     					=			shift_tokens_right(__UpperCamelCase	,    1	,    2							)
													lowercase__    :	Dict     					=			np.equal(__UpperCamelCase	,    1							).astype(np.floataa							).sum()
													lowercase__    :	List[Any]     					=			np.equal(__UpperCamelCase	,    1							).astype(np.floataa							).sum()
													self.assertEqual(shifted.shape	,    input_ids.shape							)
													self.assertEqual(__UpperCamelCase	,    n_pad_before - 1							)
													self.assertTrue(np.equal(shifted[:, 0]	,    2							).all()							)





@require_flax
class   UpperCAmelCase_			(					_a				,							unittest.TestCase				,							_a):
						lowerCamelCase__	:					Any										=						True
						lowerCamelCase__	:					Any										=						(
						    (
						        FlaxBlenderbotModel,
						        FlaxBlenderbotForConditionalGeneration,
						    )
						    if is_flax_available()
						    else ()
						)
						lowerCamelCase__	:					Optional[int]										=						(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()


						def 	_UpperCAmelCase (     self							) ->      str:
													lowercase__    :	int     					=			FlaxBlenderbotModelTester(self							)


						def 	_UpperCAmelCase (     self							) ->      int:
													lowercase__					,   lowercase__    :	Tuple     					=			self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																				self.model_tester.check_use_cache_forward(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase							)


						def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
													lowercase__					,   lowercase__    :	str     					=			self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																				self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase							)


						def 	_UpperCAmelCase (     self							) ->      List[str]:
													lowercase__					,   lowercase__    :	Any     					=			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__							):
																											lowercase__    :	Optional[int]     					=			self._prepare_for_class(__UpperCamelCase	,    __UpperCamelCase							)
																											lowercase__    :	List[Any]     					=			model_class(__UpperCamelCase							)

																											@jax.jit
																											def encode_jitted(a	,    a=None	,    **a							):
																																		return model.encode(input_ids=__UpperCamelCase	,    attention_mask=__UpperCamelCase							)

																											with self.subTest('JIT Enabled'							):
																																		lowercase__    :	Optional[int]     					=			encode_jitted(**__UpperCamelCase							).to_tuple()

																											with self.subTest('JIT Disabled'							):
																																		with jax.disable_jit():
																																									lowercase__    :	str     					=			encode_jitted(**__UpperCamelCase							).to_tuple()

																											self.assertEqual(len(__UpperCamelCase							)	,    len(__UpperCamelCase							)							)
																											for jitted_output, output in zip(__UpperCamelCase	,    __UpperCamelCase							):
																																		self.assertEqual(jitted_output.shape	,    output.shape							)


						def 	_UpperCAmelCase (     self							) ->      List[Any]:
													lowercase__					,   lowercase__    :	Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__							):
																											lowercase__    :	Optional[Any]     					=			model_class(__UpperCamelCase							)
																											lowercase__    :	Any     					=			model.encode(inputs_dict['input_ids']	,    inputs_dict['attention_mask']							)

																											lowercase__    :	Optional[Any]     					=			{
																											    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																											    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																											    'encoder_outputs': encoder_outputs,
																											}

																											@jax.jit
																											def decode_jitted(a	,    a	,    a							):
																																		return model.decode(
																																		    decoder_input_ids=__UpperCamelCase	,    decoder_attention_mask=__UpperCamelCase	,    encoder_outputs=__UpperCamelCase	,    )

																											with self.subTest('JIT Enabled'							):
																																		lowercase__    :	Optional[int]     					=			decode_jitted(**__UpperCamelCase							).to_tuple()

																											with self.subTest('JIT Disabled'							):
																																		with jax.disable_jit():
																																									lowercase__    :	Union[str, Any]     					=			decode_jitted(**__UpperCamelCase							).to_tuple()

																											self.assertEqual(len(__UpperCamelCase							)	,    len(__UpperCamelCase							)							)
																											for jitted_output, output in zip(__UpperCamelCase	,    __UpperCamelCase							):
																																		self.assertEqual(jitted_output.shape	,    output.shape							)


						@slow
						def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
													for model_class_name in self.all_model_classes:
																				lowercase__    :	Optional[Any]     					=			model_class_name.from_pretrained('facebook/blenderbot-400M-distill'							)
																				# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																				lowercase__    :	Tuple     					=			np.ones((1, 1)							) * model.config.eos_token_id
																				lowercase__    :	int     					=			model(__UpperCamelCase							)
																				self.assertIsNotNone(__UpperCamelCase							)


						@unittest.skipUnless(jax_device != 'cpu'	,    '3B test too slow on CPU.'							)
						@slow
						def 	_UpperCAmelCase (     self							) ->      int:
													lowercase__    :	Tuple     					=			{'num_beams': 1, 'early_stopping': True, 'min_length': 1_5, 'max_length': 2_5}
													lowercase__    :	List[Any]     					=			{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

													lowercase__    :	int     					=			FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'	,    from_pt=__UpperCamelCase							)
													lowercase__    :	Any     					=			BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'							)

													lowercase__    :	str     					=			['Sam']
													lowercase__    :	int     					=			tokenizer(__UpperCamelCase	,    return_tensors='jax'							)

													lowercase__    :	Tuple     					=			model.generate(**__UpperCamelCase	,    **__UpperCamelCase							)
													lowercase__    :	Any     					=			'Sam is a great name. It means \"sun\" in Gaelic.'

													lowercase__    :	Union[str, Any]     					=			tokenizer.batch_decode(__UpperCamelCase	,    **__UpperCamelCase							)
													assert generated_txt[0].strip() == tgt_text


"""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


_UpperCamelCase     :		int											=					logging.get_logger(__name__)


@dataclass
class   UpperCAmelCase_			:


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

             if bnb_abit_compute_dtype is None:
                    lowercase__    :	Dict     					=			torch.floataa
             elif isinstance(a	,    a							):
                    lowercase__    :	Any     					=			getattr(a	,    a							)
             elif isinstance(a	,    torch.dtype							):
                    lowercase__    :	Any     					=			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							) ->      str:
             if not isinstance(self.llm_inta_threshold	,    a							):
                    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	,    a							):
                    raise ValueError('llm_int8_skip_modules must be a list of strings'							)
             if not isinstance(self.llm_inta_enable_fpaa_cpu_offload	,    a							):
                    raise ValueError('llm_int8_enable_fp32_cpu_offload must be a boolean'							)

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

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


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             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	,    a	,    a	,    **a							) ->      Optional[Any]:
             lowercase__    :	List[Any]     					=			cls(**a							)

             lowercase__    :	Union[str, Any]     					=			[]
             for key, value in kwargs.items():
                    if hasattr(a	,    a							):
                           setattr(a	,    a	,    a							)
                           to_remove.append(a							)
             for key in to_remove:
                    kwargs.pop(a	,    a							)

             if return_unused_kwargs:
                    return config, kwargs
             else:
                    return config


      def 	_UpperCAmelCase (     self	,    a							) ->      Dict:
             with open(a	,    'w'	,    encoding='utf-8'							) as writer:
                    lowercase__    :	Any     					=			self.to_dict()
                    lowercase__    :	str     					=			json.dumps(a	,    indent=2	,    sort_keys=a							) + '\n'

                    writer.write(a							)


      def 	_UpperCAmelCase (     self							) ->      Dict[str, Any]:
             lowercase__    :	Optional[Any]     					=			copy.deepcopy(self.__dict__							)
             lowercase__    :	Any     					=			str(output['bnb_4bit_compute_dtype']							).split('.'							)[1]

             return output


      def __repr__(     self							) ->      Dict:
             return f"""{self.__class__.__name__} {self.to_json_string()}"""


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


      def 	_UpperCAmelCase (     self							) ->      Dict[str, Any]:
             lowercase__    :	Tuple     					=			self.to_dict()

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

             lowercase__    :	int     					=			{}

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

             return serializable_config_dict

