





"""simple docstring"""





import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


lowerCAmelCase__     			=			{
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
		assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
		if args.mlm:
				assert os.path.isfile(args.token_counts    )
				assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
		else:
				assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

		assert args.teacher_type == args.student_type or (
		    args.student_type == "distilbert" and args.teacher_type == "bert"
		)
		assert os.path.isfile(args.student_config    )
		if args.student_pretrained_weights is not None:
				assert os.path.isfile(args.student_pretrained_weights    )

		if args.freeze_token_type_embds:
				assert args.student_type in ["roberta"]

		assert args.alpha_ce >= 0.0
		assert args.alpha_mlm >= 0.0
		assert args.alpha_clm >= 0.0
		assert args.alpha_mse >= 0.0
		assert args.alpha_cos >= 0.0
		assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if args.student_type == "roberta":
				UpperCamelCase   =	False
		elif args.student_type == "gpt2":
				UpperCamelCase   =	False


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if args.student_type == "roberta":
				UpperCamelCase   =	False


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	argparse.ArgumentParser(description="Training"    )
		parser.add_argument("--force"  ,	action="store_true"  ,	help="Overwrite dump_path if it already exists."    )

		parser.add_argument(
		    "--dump_path"  ,	type=_SCREAMING_SNAKE_CASE  ,	required=_SCREAMING_SNAKE_CASE  ,	help="The output directory (log, checkpoints, parameters, etc.)"    )
		parser.add_argument(
		    "--data_file"  ,	type=_SCREAMING_SNAKE_CASE  ,	required=_SCREAMING_SNAKE_CASE  ,	help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."  ,	)

		parser.add_argument(
		    "--student_type"  ,	type=_SCREAMING_SNAKE_CASE  ,	choices=["distilbert", "roberta", "gpt2"]  ,	required=_SCREAMING_SNAKE_CASE  ,	help="The student type (DistilBERT, RoBERTa)."  ,	)
		parser.add_argument("--student_config"  ,	type=_SCREAMING_SNAKE_CASE  ,	required=_SCREAMING_SNAKE_CASE  ,	help="Path to the student configuration."    )
		parser.add_argument(
		    "--student_pretrained_weights"  ,	default=_SCREAMING_SNAKE_CASE  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Load student initialization checkpoint."    )

		parser.add_argument(
		    "--teacher_type"  ,	choices=["bert", "roberta", "gpt2"]  ,	required=_SCREAMING_SNAKE_CASE  ,	help="Teacher type (BERT, RoBERTa)."    )
		parser.add_argument("--teacher_name"  ,	type=_SCREAMING_SNAKE_CASE  ,	required=_SCREAMING_SNAKE_CASE  ,	help="The teacher model."    )

		parser.add_argument("--temperature"  ,	default=2.0  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Temperature for the softmax temperature."    )
		parser.add_argument(
		    "--alpha_ce"  ,	default=0.5  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Linear weight for the distillation loss. Must be >=0."    )
		parser.add_argument(
		    "--alpha_mlm"  ,	default=0.0  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."  ,	)
		parser.add_argument("--alpha_clm"  ,	default=0.5  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Linear weight for the CLM loss. Must be >=0."    )
		parser.add_argument("--alpha_mse"  ,	default=0.0  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Linear weight of the MSE loss. Must be >=0."    )
		parser.add_argument(
		    "--alpha_cos"  ,	default=0.0  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Linear weight of the cosine embedding loss. Must be >=0."    )

		parser.add_argument(
		    "--mlm"  ,	action="store_true"  ,	help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."    )
		parser.add_argument(
		    "--mlm_mask_prop"  ,	default=0.15  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Proportion of tokens for which we need to make a prediction."  ,	)
		parser.add_argument("--word_mask"  ,	default=0.8  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Proportion of tokens to mask out."    )
		parser.add_argument("--word_keep"  ,	default=0.1  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Proportion of tokens to keep."    )
		parser.add_argument("--word_rand"  ,	default=0.1  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Proportion of tokens to randomly replace."    )
		parser.add_argument(
		    "--mlm_smoothing"  ,	default=0.7  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."  ,	)
		parser.add_argument("--token_counts"  ,	type=_SCREAMING_SNAKE_CASE  ,	help="The token counts in the data_file for MLM."    )

		parser.add_argument(
		    "--restrict_ce_to_mask"  ,	action="store_true"  ,	help="If true, compute the distillation loss only the [MLM] prediction distribution."  ,	)
		parser.add_argument(
		    "--freeze_pos_embs"  ,	action="store_true"  ,	help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."  ,	)
		parser.add_argument(
		    "--freeze_token_type_embds"  ,	action="store_true"  ,	help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."  ,	)

		parser.add_argument("--n_epoch"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=3  ,	help="Number of pass on the whole dataset."    )
		parser.add_argument("--batch_size"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=5  ,	help="Batch size (for each process)."    )
		parser.add_argument(
		    "--group_by_size"  ,	action="store_false"  ,	help="If true, group sequences that have similar length into the same batch. Default is true."  ,	)

		parser.add_argument(
		    "--gradient_accumulation_steps"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=50  ,	help="Gradient accumulation for larger training batches."  ,	)
		parser.add_argument("--warmup_prop"  ,	default=0.05  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Linear warmup proportion."    )
		parser.add_argument("--weight_decay"  ,	default=0.0  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Weight decay if we apply some."    )
		parser.add_argument("--learning_rate"  ,	default=5e-4  ,	type=_SCREAMING_SNAKE_CASE  ,	help="The initial learning rate for Adam."    )
		parser.add_argument("--adam_epsilon"  ,	default=1e-6  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Epsilon for Adam optimizer."    )
		parser.add_argument("--max_grad_norm"  ,	default=5.0  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Max gradient norm."    )
		parser.add_argument("--initializer_range"  ,	default=0.02  ,	type=_SCREAMING_SNAKE_CASE  ,	help="Random initialization range."    )

		parser.add_argument(
		    "--fp16"  ,	action="store_true"  ,	help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"  ,	)
		parser.add_argument(
		    "--fp16_opt_level"  ,	type=_SCREAMING_SNAKE_CASE  ,	default="O1"  ,	help=(
		        "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
		        "See details at https://nvidia.github.io/apex/amp.html"
		    )  ,	)
		parser.add_argument("--n_gpu"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=1  ,	help="Number of GPUs in the node."    )
		parser.add_argument("--local_rank"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=-1  ,	help="Distributed training - Local rank"    )
		parser.add_argument("--seed"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=56  ,	help="Random seed"    )

		parser.add_argument("--log_interval"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=500  ,	help="Tensorboard logging interval."    )
		parser.add_argument("--checkpoint_interval"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=4_000  ,	help="Checkpoint interval."    )
		UpperCamelCase   =	parser.parse_args()
		sanity_checks(_SCREAMING_SNAKE_CASE    )

		# ARGS #
		init_gpu_params(_SCREAMING_SNAKE_CASE    )
		set_seed(_SCREAMING_SNAKE_CASE    )
		if args.is_master:
				if os.path.exists(args.dump_path    ):
						if not args.force:
								raise ValueError(
								    F"Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"
								    " itUse `--force` if you want to overwrite it"    )
						else:
								shutil.rmtree(args.dump_path    )

				if not os.path.exists(args.dump_path    ):
						os.makedirs(args.dump_path    )
				logger.info(F"Experiment will be dumped and logged in {args.dump_path}"    )

				# SAVE PARAMS #
				logger.info(F"Param: {args}"    )
				with open(os.path.join(args.dump_path  ,	"parameters.json"    )  ,	"w"    ) as f:
						json.dump(vars(_SCREAMING_SNAKE_CASE    )  ,	_SCREAMING_SNAKE_CASE  ,	indent=4    )
				git_log(args.dump_path    )

		UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	MODEL_CLASSES[args.student_type]
		UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	MODEL_CLASSES[args.teacher_type]

		# TOKENIZER #
		UpperCamelCase   =	teacher_tokenizer_class.from_pretrained(args.teacher_name    )
		UpperCamelCase   =	{}
		for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
				UpperCamelCase   =	tokenizer.all_special_tokens.index(_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	tokenizer.all_special_ids[idx]
		logger.info(F"Special tokens {special_tok_ids}"    )
		UpperCamelCase   =	special_tok_ids
		UpperCamelCase   =	tokenizer.max_model_input_sizes[args.teacher_name]

		# DATA LOADER #
		logger.info(F"Loading data from {args.data_file}"    )
		with open(args.data_file  ,	"rb"    ) as fp:
				UpperCamelCase   =	pickle.load(_SCREAMING_SNAKE_CASE    )

		if args.mlm:
				logger.info(F"Loading token counts from {args.token_counts} (already pre-computed)"    )
				with open(args.token_counts  ,	"rb"    ) as fp:
						UpperCamelCase   =	pickle.load(_SCREAMING_SNAKE_CASE    )

				UpperCamelCase   =	np.maximum(_SCREAMING_SNAKE_CASE  ,	1    ) ** -args.mlm_smoothing
				for idx in special_tok_ids.values():
						UpperCamelCase   =	0.0  # do not predict special tokens
				UpperCamelCase   =	torch.from_numpy(_SCREAMING_SNAKE_CASE    )
		else:
				UpperCamelCase   =	None

		UpperCamelCase   =	LmSeqsDataset(params=_SCREAMING_SNAKE_CASE  ,	data=_SCREAMING_SNAKE_CASE    )
		logger.info("Data loader created."    )

		# STUDENT #
		logger.info(F"Loading student config from {args.student_config}"    )
		UpperCamelCase   =	student_config_class.from_pretrained(args.student_config    )
		UpperCamelCase   =	True

		if args.student_pretrained_weights is not None:
				logger.info(F"Loading pretrained weights from {args.student_pretrained_weights}"    )
				UpperCamelCase   =	student_model_class.from_pretrained(args.student_pretrained_weights  ,	config=_SCREAMING_SNAKE_CASE    )
		else:
				UpperCamelCase   =	student_model_class(_SCREAMING_SNAKE_CASE    )

		if args.n_gpu > 0:
				student.to(F"cuda:{args.local_rank}"    )
		logger.info("Student loaded."    )

		# TEACHER #
		UpperCamelCase   =	teacher_model_class.from_pretrained(args.teacher_name  ,	output_hidden_states=_SCREAMING_SNAKE_CASE    )
		if args.n_gpu > 0:
				teacher.to(F"cuda:{args.local_rank}"    )
		logger.info(F"Teacher loaded from {args.teacher_name}."    )

		# FREEZING #
		if args.freeze_pos_embs:
				freeze_pos_embeddings(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
		if args.freeze_token_type_embds:
				freeze_token_type_embeddings(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		# SANITY CHECKS #
		assert student.config.vocab_size == teacher.config.vocab_size
		assert student.config.hidden_size == teacher.config.hidden_size
		assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
		if args.mlm:
				assert token_probs.size(0    ) == stu_architecture_config.vocab_size

		# DISTILLER #
		torch.cuda.empty_cache()
		UpperCamelCase   =	Distiller(
		    params=_SCREAMING_SNAKE_CASE  ,	dataset=_SCREAMING_SNAKE_CASE  ,	token_probs=_SCREAMING_SNAKE_CASE  ,	student=_SCREAMING_SNAKE_CASE  ,	teacher=_SCREAMING_SNAKE_CASE    )
		distiller.train()
		logger.info("Let's go get some drinks."    )


if __name__ == "__main__":
						main()






"""simple docstring"""





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       a__					(					_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        _lowerCamelCase      (       nn.Module ):


												def __init__(self   ,							__a   ,							__a		) ->   Tuple:
														super().__init__()
														UpperCamelCase   =	module
														UpperCamelCase   =	nn.Sequential(
														    nn.Linear(module.in_features   ,							__a   ,							bias=__a		)   ,							nn.Linear(__a   ,							module.out_features   ,							bias=__a		)   ,							)
														UpperCamelCase   =	(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     snake_case_   (self   ,							__a   ,							*__a   ,							**__a		) ->   int:
														return self.module(__a   ,							*__a   ,							**__a		) + self.adapter(__a		)





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class        _lowerCamelCase      (       unittest.TestCase ):
						# We keep the constants inside the init function and model loading inside setUp function

						# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
						# Therefore here we use only bloom-1b3 to test our module
						UpperCAmelCase_ =			"bigscience/bloom-1b7"

						# Constant values
						UpperCAmelCase_ =			2.109659552692574

						UpperCAmelCase_ =			"Hello my name is"
						UpperCAmelCase_ =			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" )
						UpperCAmelCase_ =			10


						def     snake_case_   (self		) ->   str:
								# Models and tokenizer
								UpperCamelCase   =	AutoTokenizer.from_pretrained(self.model_name		)





class        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   Any:
								super().setUp()

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


						def     snake_case_   (self		) ->   str:
								del self.model_fpaa
								del self.model_abit

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


						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	self.model_abit.config

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

								UpperCamelCase   =	config.to_dict()
								UpperCamelCase   =	config.to_diff_dict()

								UpperCamelCase   =	config.to_json_string()


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

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

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


						def     snake_case_   (self		) ->   Dict:
								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     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	self.tokenizer(self.input_text   ,							return_tensors="pt"		)
								UpperCamelCase   =	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     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	BitsAndBytesConfig()
								UpperCamelCase   =	True

								UpperCamelCase   =	AutoModelForCausalLM.from_pretrained(
								    self.model_name   ,							quantization_config=__a   ,							device_map="auto"		)

								UpperCamelCase   =	self.tokenizer(self.input_text   ,							return_tensors="pt"		)
								UpperCamelCase   =	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     snake_case_   (self		) ->   Dict:
								with self.assertRaises(__a		), tempfile.TemporaryDirectory() as tmpdirname:
										self.model_abit.save_pretrained(__a		)


						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	BitsAndBytesConfig()

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


						def     snake_case_   (self		) ->   Any:
								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
								UpperCamelCase   =	self.tokenizer(self.input_text   ,							return_tensors="pt"		)

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

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

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

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




						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	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        _lowerCamelCase      (       unittest.TestCase ):


						@classmethod
						def     snake_case_   (cls		) ->   Tuple:
								UpperCamelCase   =	"t5-small"
								UpperCamelCase   =	"google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
								UpperCamelCase   =	AutoTokenizer.from_pretrained(cls.model_name		)
								UpperCamelCase   =	"Translate in German: Hello, my dog is cute"


						def     snake_case_   (self		) ->   Any:
								gc.collect()
								torch.cuda.empty_cache()


						def     snake_case_   (self		) ->   Optional[Any]:
								from transformers import TaForConditionalGeneration

								UpperCamelCase   =	TaForConditionalGeneration._keep_in_fpaa_modules
								UpperCamelCase   =	None

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

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




						def     snake_case_   (self		) ->   str:
								import bitsandbytes as bnb

								from transformers import TaForConditionalGeneration

								# test with `t5-small`
								UpperCamelCase   =	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		)		)

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

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





class        _lowerCamelCase      (       _lowercase ):


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

								# Different types of model

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


						def     snake_case_   (self		) ->   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     snake_case_   (self		) ->   Optional[int]:
								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        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   int:
								super().setUp()


						def     snake_case_   (self		) ->   Any:
								del self.pipe

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




						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	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
								UpperCamelCase   =	self.pipe(self.input_text		)
								self.assertIn(pipeline_output[0]["generated_text"]   ,							self.EXPECTED_OUTPUTS		)





@require_torch_multi_gpu
class        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   List[Any]:
								super().setUp()




						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	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
								UpperCamelCase   =	self.tokenizer(self.input_text   ,							return_tensors="pt"		)

								# Second real batch
								UpperCamelCase   =	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        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	"facebook/opt-350m"
								super().setUp()




						def     snake_case_   (self		) ->   List[Any]:
								if version.parse(importlib.metadata.version("bitsandbytes"		)		) < version.parse("0.37.0"		):
										return

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

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

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

								# Step 4: Check if the gradient is not None
								with torch.cuda.amp.autocast():
										UpperCamelCase   =	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        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"gpt2-xl"
						UpperCAmelCase_ =			3.3191854854152187






