




from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
		from fairscale.optim import OSS


__A						:							Tuple							=	logging.get_logger(__name__)

__A						:							Dict							=	{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}




class 			UpperCAmelCase_      (   __a       ):




							'''simple docstring'''



							def __init__(					self       :	int       ,     a       :	Optional[int]=None       ,     a       :	Optional[int]=None       ,     *a       :	Optional[Any]       ,     **a       :	Optional[Any]			)			->					Union[str, Any]:
										super().__init__(*A__       ,     **A__			)

										if config is None:
													assert isinstance(self.model       ,     A__			), (
													    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
													    f""" {self.model.__class__}"""
													)
													SCREAMING_SNAKE_CASE           =					self.model.config
										else:
													SCREAMING_SNAKE_CASE           =					config

										SCREAMING_SNAKE_CASE           =					data_args
										SCREAMING_SNAKE_CASE           =					self.config.tgt_vocab_size if isinstance(self.config       ,     A__			) else self.config.vocab_size

										if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
													assert self.config.pad_token_id is not None, (
													    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
													    " calculation or doing label smoothing."
													)

										if self.config.pad_token_id is None and self.config.eos_token_id is not None:
													logger.warning(
													    f"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
													    """ padding.."""			)

										if self.args.label_smoothing == 0:
													SCREAMING_SNAKE_CASE           =					torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id			)
										else:
													# dynamically import label_smoothed_nll_loss
													from utils import label_smoothed_nll_loss

													SCREAMING_SNAKE_CASE           =					label_smoothed_nll_loss





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	List[str]			)			->					Tuple:
										if self.optimizer is None:
													SCREAMING_SNAKE_CASE           =					["""bias""", """LayerNorm.weight"""]
													SCREAMING_SNAKE_CASE           =					[
													    {
													        """params""": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay			)],
													        """weight_decay""": self.args.weight_decay,
													    },
													    {
													        """params""": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay			)],
													        """weight_decay""": 0.0,
													    },
													]
													SCREAMING_SNAKE_CASE           =					Adafactor if self.args.adafactor else AdamW
													if self.args.adafactor:
																SCREAMING_SNAKE_CASE           =					Adafactor
																SCREAMING_SNAKE_CASE           =					{"""scale_parameter""": False, """relative_step""": False}
													else:
																SCREAMING_SNAKE_CASE           =					AdamW
																SCREAMING_SNAKE_CASE           =					{
																    """betas""": (self.args.adam_betaa, self.args.adam_betaa),
																    """eps""": self.args.adam_epsilon,
																}
													SCREAMING_SNAKE_CASE           =					self.args.learning_rate
													if self.sharded_ddp:
																SCREAMING_SNAKE_CASE           =					OSS(
																    params=A__       ,     optim=A__       ,     **A__       ,     )
													else:
																SCREAMING_SNAKE_CASE           =					optimizer_cls(A__       ,     **A__			)

										if self.lr_scheduler is None:
													SCREAMING_SNAKE_CASE           =					self._get_lr_scheduler(A__			)
										else:  # ignoring --lr_scheduler
													logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	Optional[Any]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					arg_to_scheduler[self.args.lr_scheduler]
										if self.args.lr_scheduler == "constant":
													SCREAMING_SNAKE_CASE           =					schedule_func(self.optimizer			)
										elif self.args.lr_scheduler == "constant_w_warmup":
													SCREAMING_SNAKE_CASE           =					schedule_func(self.optimizer       ,     num_warmup_steps=self.args.warmup_steps			)
										else:
													SCREAMING_SNAKE_CASE           =					schedule_func(
													    self.optimizer       ,     num_warmup_steps=self.args.warmup_steps       ,     num_training_steps=A__			)
										return scheduler





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Optional[torch.utils.data.Sampler]:
										if isinstance(self.train_dataset       ,     torch.utils.data.IterableDataset			):
													return None
										elif is_torch_tpu_available():
													return get_tpu_sampler(self.train_dataset			)
										else:
													if self.args.sortish_sampler:
																self.train_dataset.make_sortish_sampler(
																    self.args.per_device_train_batch_size       ,     distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)       ,     )

													return (
													    RandomSampler(self.train_dataset			)
													    if self.args.local_rank == -1
													    else DistributedSampler(self.train_dataset			)
													)





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	Union[str, Any]       ,     a       :	Any       ,     a       :	Union[str, Any]			)			->					List[Any]:
										if self.args.label_smoothing == 0:
													if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																# force training to ignore pad token
																SCREAMING_SNAKE_CASE           =					model(**A__       ,     use_cache=A__			)[0]
																SCREAMING_SNAKE_CASE           =					self.loss_fn(logits.view(-1       ,     logits.shape[-1]			)       ,     labels.view(-1			)			)
													else:
																# compute usual loss via models
																SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					model(**A__       ,     labels=A__       ,     use_cache=A__			)[:2]
										else:
													# compute label smoothed loss
													SCREAMING_SNAKE_CASE           =					model(**A__       ,     use_cache=A__			)[0]
													SCREAMING_SNAKE_CASE           =					torch.nn.functional.log_softmax(A__       ,     dim=-1			)
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self.loss_fn(A__       ,     A__       ,     self.args.label_smoothing       ,     ignore_index=self.config.pad_token_id			)
										return loss, logits





							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	Optional[int]       ,     a       :	Optional[Any]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					inputs.pop("""labels"""			)
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self._compute_loss(A__       ,     A__       ,     A__			)
										return loss





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	Optional[int]       ,     a       :	Optional[Any]       ,     a       :	List[Any]       ,     a       :	Optional[Any] = None       ,     )			->					Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
										SCREAMING_SNAKE_CASE           =					self._prepare_inputs(A__			)

										SCREAMING_SNAKE_CASE           =					{
										    """max_length""": self.data_args.val_max_target_length
										    if self.data_args is not None
										    else self.config.max_length,
										    """num_beams""": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
										}

										if self.args.predict_with_generate and not self.args.prediction_loss_only:
													SCREAMING_SNAKE_CASE           =					self.model.generate(
													    inputs["""input_ids"""]       ,     attention_mask=inputs["""attention_mask"""]       ,     **A__       ,     )
													# in case the batch is shorter than max length, the output should be padded
													if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																SCREAMING_SNAKE_CASE           =					self._pad_tensors_to_max_len(A__       ,     gen_kwargs["""max_length"""]			)

										SCREAMING_SNAKE_CASE           =					inputs.pop("""labels"""			)
										with torch.no_grad():
													# compute loss on predict data
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self._compute_loss(A__       ,     A__       ,     A__			)

										SCREAMING_SNAKE_CASE           =					loss.mean().detach()
										if self.args.prediction_loss_only:
													return (loss, None, None)

										SCREAMING_SNAKE_CASE           =					generated_tokens if self.args.predict_with_generate else logits

										if labels.shape[-1] < gen_kwargs["max_length"]:
													SCREAMING_SNAKE_CASE           =					self._pad_tensors_to_max_len(A__       ,     gen_kwargs["""max_length"""]			)

										return (loss, logits, labels)







							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	Any       ,     a       :	Optional[int]			)			->					List[str]:
										# If PAD token is not defined at least EOS token has to be defined
										SCREAMING_SNAKE_CASE           =					self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

										if pad_token_id is None:
													raise ValueError(
													    """Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
													    f""" padded to `max_length`={max_length}"""			)

										SCREAMING_SNAKE_CASE           =					pad_token_id * torch.ones(
										    (tensor.shape[0], max_length)       ,     dtype=tensor.dtype       ,     device=tensor.device			)
										SCREAMING_SNAKE_CASE           =					tensor
										return padded_tensor












import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			if model.config.model_type == "gpt2":
						return model.transformer.h[0].mlp.c_fc
			return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
		import torch
		import torch.nn as nn




		class 			UpperCAmelCase_      (   nn.Module       ):




									'''simple docstring'''



									def __init__(					self       :	Optional[int]       ,     a       :	nn.Module       ,     a       :	int			)			->					Optional[Any]:
												super().__init__()
												SCREAMING_SNAKE_CASE           =					module
												SCREAMING_SNAKE_CASE           =					nn.Sequential(
												    nn.Linear(module.in_features       ,     a       ,     bias=a			)       ,     nn.Linear(a       ,     module.out_features       ,     bias=a			)       ,     )
												SCREAMING_SNAKE_CASE           =					(2.0 / (5 * min(module.in_features       ,     module.out_features			))) ** 0.5
												nn.init.normal_(self.adapter[0].weight       ,     std=a			)
												nn.init.zeros_(self.adapter[1].weight			)
												self.adapter.to(module.weight.device			)







									def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	str       ,     *a       :	List[Any]       ,     **a       :	List[Any]			)			->					List[Any]:
												return self.module(a       ,     *a       ,     **a			) + self.adapter(a			)




@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     '''bigscience/bloom-1b7'''

							# Constant values
							a__						  =     2.1_0_9_6_5_9_5_5_2_6_9_2_5_7_4

							a__						  =     '''Hello my name is'''
							a__						  =     set()
							EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I'''       )
							EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n'''       )
							EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University'''       )
							a__						  =     1_0





							def    _UpperCAmelCase     (					self       :	Dict			)			->					str:
										# Models and tokenizer
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained(self.model_name			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[int]:
										super().setUp()

										# Models and tokenizer
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
										    self.model_name       ,     torch_dtype=torch.floataa       ,     device_map="""auto"""			)
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					int:
										del self.model_fpaa
										del self.model_abit

										gc.collect()
										torch.cuda.empty_cache()





							def    _UpperCAmelCase     (					self       :	str			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.model_abit.config

										self.assertTrue(hasattr(a       ,     """quantization_config"""			)			)

										SCREAMING_SNAKE_CASE           =					config.to_dict()
										SCREAMING_SNAKE_CASE           =					config.to_diff_dict()

										SCREAMING_SNAKE_CASE           =					config.to_json_string()





							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[int]:
										from bitsandbytes.nn import Paramsabit

										SCREAMING_SNAKE_CASE           =					self.model_fpaa.get_memory_footprint()
										SCREAMING_SNAKE_CASE           =					self.model_abit.get_memory_footprint()

										self.assertAlmostEqual(mem_fpaa / mem_abit       ,     self.EXPECTED_RELATIVE_DIFFERENCE			)
										SCREAMING_SNAKE_CASE           =					get_some_linear_layer(self.model_abit			)
										self.assertTrue(linear.weight.__class__ == Paramsabit			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					List[Any]:
										from transformers import TaPreTrainedModel

										self.model_fpaa.get_memory_footprint()
										self.model_abit.get_memory_footprint()

										for name, module in self.model_abit.named_modules():
													if isinstance(a       ,     torch.nn.Linear			):
																if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
																			# 4-bit parameters are packed in uint8 variables
																			self.assertTrue(module.weight.dtype == torch.uinta			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)
										SCREAMING_SNAKE_CASE           =					self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0			)       ,     max_new_tokens=10			)

										self.assertIn(self.tokenizer.decode(output_sequences[0]       ,     skip_special_tokens=a			)       ,     self.EXPECTED_OUTPUTS			)





							def    _UpperCAmelCase     (					self       :	str			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					BitsAndBytesConfig()
										SCREAMING_SNAKE_CASE           =					True

										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
										    self.model_name       ,     quantization_config=a       ,     device_map="""auto"""			)

										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)
										SCREAMING_SNAKE_CASE           =					model_abit_from_config.generate(
										    input_ids=encoded_input["""input_ids"""].to(0			)       ,     max_new_tokens=10			)

										self.assertIn(self.tokenizer.decode(output_sequences[0]       ,     skip_special_tokens=a			)       ,     self.EXPECTED_OUTPUTS			)





							def    _UpperCAmelCase     (					self       :	str			)			->					Optional[int]:
										with self.assertRaises(a			), tempfile.TemporaryDirectory() as tmpdirname:
													self.model_abit.save_pretrained(a			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					BitsAndBytesConfig()

										with self.assertRaises(a			):
													SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
													    self.model_name       ,     quantization_config=a       ,     load_in_abit=a       ,     device_map="""auto"""       ,     bnb_abit_quant_type="""nf4"""       ,     )





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					int:
										with self.assertRaises(a			):
													# Tries with `str`
													self.model_abit.to("""cpu"""			)

										with self.assertRaises(a			):
													# Tries with a `dtype``
													self.model_abit.to(torch.floataa			)

										with self.assertRaises(a			):
													# Tries with a `device`
													self.model_abit.to(torch.device("""cuda:0"""			)			)

										with self.assertRaises(a			):
													# Tries with a `device`
													self.model_abit.float()

										with self.assertRaises(a			):
													# Tries with a `device`
													self.model_abit.half()

										# Test if we did not break anything
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)

										SCREAMING_SNAKE_CASE           =					self.model_fpaa.to(torch.floataa			)
										SCREAMING_SNAKE_CASE           =					self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0			)       ,     max_new_tokens=10			)

										# Check this does not throw an error
										SCREAMING_SNAKE_CASE           =					self.model_fpaa.to("""cpu"""			)

										# Check this does not throw an error
										SCREAMING_SNAKE_CASE           =					self.model_fpaa.half()

										# Check this does not throw an error
										SCREAMING_SNAKE_CASE           =					self.model_fpaa.float()







							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""       ,     load_in_abit=a       ,     device_map="""auto"""			)
										self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa			)





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@classmethod
							def    _UpperCAmelCase     (					cls       :	Union[str, Any]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					"""t5-small"""
										SCREAMING_SNAKE_CASE           =					"""google/flan-t5-small"""  # flan-t5 uses dense-act instead of dense-relu-dense
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained(cls.model_name			)
										SCREAMING_SNAKE_CASE           =					"""Translate in German: Hello, my dog is cute"""





							def    _UpperCAmelCase     (					self       :	Any			)			->					List[str]:
										gc.collect()
										torch.cuda.empty_cache()





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					int:
										from transformers import TaForConditionalGeneration

										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration._keep_in_fpaa_modules
										SCREAMING_SNAKE_CASE           =					None

										# test with `t5-small`
										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			).to(0			)
										SCREAMING_SNAKE_CASE           =					model.generate(**a			)

										# test with `flan-t5-small`
										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			).to(0			)
										SCREAMING_SNAKE_CASE           =					model.generate(**a			)
										SCREAMING_SNAKE_CASE           =					modules







							def    _UpperCAmelCase     (					self       :	int			)			->					int:
										import bitsandbytes as bnb

										from transformers import TaForConditionalGeneration

										# test with `t5-small`
										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)

										# there was a bug with decoders - this test checks that it is fixed
										self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q       ,     bnb.nn.Linearabit			)			)

										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			).to(0			)
										SCREAMING_SNAKE_CASE           =					model.generate(**a			)

										# test with `flan-t5-small`
										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			).to(0			)
										SCREAMING_SNAKE_CASE           =					model.generate(**a			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[str]:
										super().setUp()
										# model_name
										SCREAMING_SNAKE_CASE           =					"""bigscience/bloom-560m"""
										SCREAMING_SNAKE_CASE           =					"""t5-small"""

										# Different types of model

										SCREAMING_SNAKE_CASE           =					AutoModel.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										# Sequence classification model
										SCREAMING_SNAKE_CASE           =					AutoModelForSequenceClassification.from_pretrained(
										    self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										# CausalLM model
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										# Seq2seq model
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained(
										    self.seq_to_seq_name       ,     load_in_abit=a       ,     device_map="""auto"""			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Tuple:
										del self.base_model
										del self.sequence_model
										del self.model_abit
										del self.seq_to_seq_model

										gc.collect()
										torch.cuda.empty_cache()







							def    _UpperCAmelCase     (					self       :	Dict			)			->					List[Any]:
										from bitsandbytes.nn import Paramsabit

										self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit			)

										# Other heads should be nn.Parameter
										self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter			)
										self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter			)
										self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					str:
										super().setUp()





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Optional[Any]:
										del self.pipe

										gc.collect()
										torch.cuda.empty_cache()







							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					pipeline(
										    """text-generation"""       ,     model=self.model_name       ,     model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa}       ,     max_new_tokens=self.MAX_NEW_TOKENS       ,     )

										# Real second forward pass
										SCREAMING_SNAKE_CASE           =					self.pipe(self.input_text			)
										self.assertIn(pipeline_output[0]["""generated_text"""]       ,     self.EXPECTED_OUTPUTS			)





@require_torch_multi_gpu
class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Dict			)			->					Optional[int]:
										super().setUp()







							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
										    self.model_name       ,     load_in_abit=a       ,     device_map="""balanced"""			)

										# Check correct device map
										self.assertEqual(set(model_parallel.hf_device_map.values()			)       ,     {0, 1}			)

										# Check that inference pass works on the model
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)

										# Second real batch
										SCREAMING_SNAKE_CASE           =					model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0			)       ,     max_new_tokens=10			)
										self.assertIn(self.tokenizer.decode(output_parallel[0]       ,     skip_special_tokens=a			)       ,     self.EXPECTED_OUTPUTS			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	int			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					"""facebook/opt-350m"""
										super().setUp()







							def    _UpperCAmelCase     (					self       :	Any			)			->					Tuple:
										if version.parse(importlib.metadata.version("""bitsandbytes"""			)			) < version.parse("""0.37.0"""			):
													return

										# Step 1: freeze all parameters
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(self.model_name       ,     load_in_abit=a			)

										self.assertEqual(set(model.hf_device_map.values()			)       ,     {torch.cuda.current_device()}			)

										for param in model.parameters():
													SCREAMING_SNAKE_CASE           =					False  # freeze the model - train adapters later
													if param.ndim == 1:
																# cast the small parameters (e.g. layernorm) to fp32 for stability
																SCREAMING_SNAKE_CASE           =					param.data.to(torch.floataa			)

        # Step 2: add adapters
										for _, module in model.named_modules():
													if "OPTAttention" in repr(type(a			)			):
																SCREAMING_SNAKE_CASE           =					LoRALayer(module.q_proj       ,     rank=16			)
																SCREAMING_SNAKE_CASE           =					LoRALayer(module.k_proj       ,     rank=16			)
																SCREAMING_SNAKE_CASE           =					LoRALayer(module.v_proj       ,     rank=16			)

        # Step 3: dummy batch
										SCREAMING_SNAKE_CASE           =					self.tokenizer("""Test batch """       ,     return_tensors="""pt"""			).to(0			)

										# Step 4: Check if the gradient is not None
										with torch.cuda.amp.autocast():
													SCREAMING_SNAKE_CASE           =					model.forward(**a			)
													out.logits.norm().backward()

										for module in model.modules():
													if isinstance(a       ,     a			):
																self.assertTrue(module.adapter[1].weight.grad is not None			)
																self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0			)
													elif isinstance(a       ,     nn.Embedding			):
																self.assertTrue(module.weight.grad is None			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''gpt2-xl'''
							a__						  =     3.3_1_9_1_8_5_4_8_5_4_1_5_2_1_8_7






