




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE = None					,	):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					{}

			if train_file is not None:
						SCREAMING_SNAKE_CASE           =					[train_file]
			if eval_file is not None:
						SCREAMING_SNAKE_CASE           =					[eval_file]
			if test_file is not None:
						SCREAMING_SNAKE_CASE           =					[test_file]

			SCREAMING_SNAKE_CASE           =					datasets.load_dataset("""csv"""					,	data_files=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					list(ds[list(files.keys()   )[0]].features.keys()   )
			SCREAMING_SNAKE_CASE           =					features_name.pop(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					list(set(ds[list(files.keys()   )[0]][label_name]   )   )
			SCREAMING_SNAKE_CASE           =					{label: i for i, label in enumerate(SCREAMING_SNAKE_CASE   )}
			SCREAMING_SNAKE_CASE           =					tokenizer.model_input_names
			SCREAMING_SNAKE_CASE           =					{}

			if len(SCREAMING_SNAKE_CASE   ) == 1:
						for k in files.keys():
									SCREAMING_SNAKE_CASE           =					ds[k].map(
									    lambda SCREAMING_SNAKE_CASE   : tokenizer.batch_encode_plus(
									        example[features_name[0]]					,	truncation=SCREAMING_SNAKE_CASE					,	max_length=SCREAMING_SNAKE_CASE					,	padding="""max_length"""   )					,	batched=SCREAMING_SNAKE_CASE					,	)
			elif len(SCREAMING_SNAKE_CASE   ) == 2:
						for k in files.keys():
									SCREAMING_SNAKE_CASE           =					ds[k].map(
									    lambda SCREAMING_SNAKE_CASE   : tokenizer.batch_encode_plus(
									        (example[features_name[0]], example[features_name[1]])					,	truncation=SCREAMING_SNAKE_CASE					,	max_length=SCREAMING_SNAKE_CASE					,	padding="""max_length"""					,	)					,	batched=SCREAMING_SNAKE_CASE					,	)

			def gen_train():
						for ex in transformed_ds[datasets.Split.TRAIN]:
									SCREAMING_SNAKE_CASE           =					{k: v for k, v in ex.items() if k in input_names}
									SCREAMING_SNAKE_CASE           =					labelaid[ex[label_name]]
									yield (d, label)

			def gen_val():
						for ex in transformed_ds[datasets.Split.VALIDATION]:
									SCREAMING_SNAKE_CASE           =					{k: v for k, v in ex.items() if k in input_names}
									SCREAMING_SNAKE_CASE           =					labelaid[ex[label_name]]
									yield (d, label)

			def gen_test():
						for ex in transformed_ds[datasets.Split.TEST]:
									SCREAMING_SNAKE_CASE           =					{k: v for k, v in ex.items() if k in input_names}
									SCREAMING_SNAKE_CASE           =					labelaid[ex[label_name]]
									yield (d, label)

			SCREAMING_SNAKE_CASE           =					(
			    tf.data.Dataset.from_generator(
			        SCREAMING_SNAKE_CASE					,	({k: tf.intaa for k in input_names}, tf.intaa)					,	({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))					,	)
			    if datasets.Split.TRAIN in transformed_ds
			    else None
			)

			if train_ds is not None:
						SCREAMING_SNAKE_CASE           =					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]   )   )   )

			SCREAMING_SNAKE_CASE           =					(
			    tf.data.Dataset.from_generator(
			        SCREAMING_SNAKE_CASE					,	({k: tf.intaa for k in input_names}, tf.intaa)					,	({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))					,	)
			    if datasets.Split.VALIDATION in transformed_ds
			    else None
			)

			if val_ds is not None:
						SCREAMING_SNAKE_CASE           =					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]   )   )   )

			SCREAMING_SNAKE_CASE           =					(
			    tf.data.Dataset.from_generator(
			        SCREAMING_SNAKE_CASE					,	({k: tf.intaa for k in input_names}, tf.intaa)					,	({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))					,	)
			    if datasets.Split.TEST in transformed_ds
			    else None
			)

			if test_ds is not None:
						SCREAMING_SNAKE_CASE           =					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]   )   )   )

			return train_ds, val_ds, test_ds, labelaid


__A						:							Any							=	logging.getLogger(__name__)




@dataclass
class 			UpperCAmelCase_      :




							'''simple docstring'''



							a__						  =     field(metadata={'''help''': '''Which column contains the label'''}       )
							a__						  =     field(default=A		,      metadata={'''help''': '''The path of the training file'''}       )
							a__						  =     field(default=A		,      metadata={'''help''': '''The path of the development file'''}       )
							a__						  =     field(default=A		,      metadata={'''help''': '''The path of the test file'''}       )
							a__						  =     field(
							    default=1_2_8		,      metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }		,      )
							a__						  =     field(
							    default=A		,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )





@dataclass
class 			UpperCAmelCase_      :




							'''simple docstring'''



							a__						  =     field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
							a__						  =     field(
							    default=A		,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}       )
							a__						  =     field(
							    default=A		,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}       )
							a__						  =     field(default=A		,      metadata={'''help''': '''Set this flag to use fast tokenization.'''}       )
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							a__						  =     field(
							    default=A		,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,      )





def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir   )
			    and os.listdir(training_args.output_dir   )
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
						    """ --overwrite_output_dir to overcome."""   )

			# Setup logging
			logging.basicConfig(
			    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""					,	datefmt="""%m/%d/%Y %H:%M:%S"""					,	level=logging.INFO					,	)
			logger.info(
			    f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1   )}, """
			    f"""16-bits training: {training_args.fpaa}"""   )
			logger.info(f"""Training/evaluation parameters {training_args}"""   )

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,	cache_dir=model_args.cache_dir					,	)

			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					get_tfds(
			    train_file=data_args.train_file					,	eval_file=data_args.dev_file					,	test_file=data_args.test_file					,	tokenizer=SCREAMING_SNAKE_CASE					,	label_column_id=data_args.label_column_id					,	max_seq_length=data_args.max_seq_length					,	)

			SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,	num_labels=len(SCREAMING_SNAKE_CASE   )					,	labelaid=SCREAMING_SNAKE_CASE					,	idalabel={id: label for label, id in labelaid.items()}					,	finetuning_task="""text-classification"""					,	cache_dir=model_args.cache_dir					,	)

			with training_args.strategy.scope():
						SCREAMING_SNAKE_CASE           =					TFAutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path					,	from_pt=bool(""".bin""" in model_args.model_name_or_path   )					,	config=SCREAMING_SNAKE_CASE					,	cache_dir=model_args.cache_dir					,	)

			def compute_metrics(SCREAMING_SNAKE_CASE   ) -> Dict:
						SCREAMING_SNAKE_CASE           =					np.argmax(p.predictions					,	axis=1   )

						return {"acc": (preds == p.label_ids).mean()}

			# Initialize our Trainer
			SCREAMING_SNAKE_CASE           =					TFTrainer(
			    model=SCREAMING_SNAKE_CASE					,	args=SCREAMING_SNAKE_CASE					,	train_dataset=SCREAMING_SNAKE_CASE					,	eval_dataset=SCREAMING_SNAKE_CASE					,	compute_metrics=SCREAMING_SNAKE_CASE					,	)

			# Training
			if training_args.do_train:
						trainer.train()
						trainer.save_model()
						tokenizer.save_pretrained(training_args.output_dir   )

			# Evaluation
			SCREAMING_SNAKE_CASE           =					{}
			if training_args.do_eval:
						logger.info("""*** Evaluate ***"""   )
						SCREAMING_SNAKE_CASE           =					trainer.evaluate()
						SCREAMING_SNAKE_CASE           =					os.path.join(training_args.output_dir					,	"""eval_results.txt"""   )

						with open(SCREAMING_SNAKE_CASE					,	"""w"""   ) as writer:
									logger.info("""***** Eval results *****"""   )

									for key, value in result.items():
												logger.info(f"""  {key} = {value}"""   )
												writer.write(f"""{key} = {value}\n"""   )

									results.update(SCREAMING_SNAKE_CASE   )

			return results


if __name__ == "__main__":
		main()












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






