






import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase 				=	logging.get_logger(__name__)

set_seed(770)


UpperCamelCase 				=	{
    "c_attn": "att_proj",
    "c_proj": "out_proj",
    "c_fc": "in_proj",
    "transformer.": "",
    "h.": "layers.",
    "ln_1": "layernorm_1",
    "ln_2": "layernorm_2",
    "ln_f": "layernorm_final",
    "wpe": "position_embeds_layer",
    "wte": "input_embeds_layer",
}


UpperCamelCase 				=	{
    "text_small": {
        "repo_id": "suno/bark",
        "file_name": "text.pt",
    },
    "coarse_small": {
        "repo_id": "suno/bark",
        "file_name": "coarse.pt",
    },
    "fine_small": {
        "repo_id": "suno/bark",
        "file_name": "fine.pt",
    },
    "text": {
        "repo_id": "suno/bark",
        "file_name": "text_2.pt",
    },
    "coarse": {
        "repo_id": "suno/bark",
        "file_name": "coarse_2.pt",
    },
    "fine": {
        "repo_id": "suno/bark",
        "file_name": "fine_2.pt",
    },
}

UpperCamelCase 				=	os.path.dirname(os.path.abspath(__file__))
UpperCamelCase 				=	os.path.join(os.path.expanduser('''~'''), '''.cache''')
UpperCamelCase 				=	os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')

def        __lowerCamelCase			(		snake_case__       ,snake_case__=False		)							->  Optional[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  model_type
						if use_small:
												key += "_small"
						return os.path.join(__lowerCAmelCase       ,REMOTE_MODEL_PATHS[key]["""file_name"""]		)

def        __lowerCamelCase			(		snake_case__       ,snake_case__		)							->  Union[str, Any]:






						"""simple docstring"""





						os.makedirs(__lowerCAmelCase       ,exist_ok=__lowerCAmelCase		)
						hf_hub_download(repo_id=__lowerCAmelCase       ,filename=__lowerCAmelCase       ,local_dir=__lowerCAmelCase		)

def        __lowerCamelCase			(		snake_case__       ,snake_case__       ,snake_case__=False       ,snake_case__="text"		)							->  List[str]:






						"""simple docstring"""





						if model_type == "text":
												_SCREAMING_SNAKE_CASE	      =  BarkSemanticModel
												_SCREAMING_SNAKE_CASE	      =  BarkSemanticConfig
												_SCREAMING_SNAKE_CASE	      =  BarkSemanticGenerationConfig
						elif model_type == "coarse":
												_SCREAMING_SNAKE_CASE	      =  BarkCoarseModel
												_SCREAMING_SNAKE_CASE	      =  BarkCoarseConfig
												_SCREAMING_SNAKE_CASE	      =  BarkCoarseGenerationConfig
						elif model_type == "fine":
												_SCREAMING_SNAKE_CASE	      =  BarkFineModel
												_SCREAMING_SNAKE_CASE	      =  BarkFineConfig
												_SCREAMING_SNAKE_CASE	      =  BarkFineGenerationConfig
						else:
												raise NotImplementedError()
						_SCREAMING_SNAKE_CASE	      =  F'{model_type}_small' if use_small else model_type
						_SCREAMING_SNAKE_CASE	      =  REMOTE_MODEL_PATHS[model_key]
						if not os.path.exists(__lowerCAmelCase		):
												logger.info(F'{model_type} model not found, downloading into `{CACHE_DIR}`.'		)
												_download(model_info["""repo_id"""]       ,model_info["""file_name"""]		)
						_SCREAMING_SNAKE_CASE	      =  torch.load(__lowerCAmelCase       ,map_location=__lowerCAmelCase		)
						# this is a hack
						_SCREAMING_SNAKE_CASE	      =  checkpoint["""model_args"""]
						if "input_vocab_size" not in model_args:
												_SCREAMING_SNAKE_CASE	      =  model_args["""vocab_size"""]
												_SCREAMING_SNAKE_CASE	      =  model_args["""vocab_size"""]
												del model_args["vocab_size"]

						# convert Bark model arguments to HF Bark model arguments
						_SCREAMING_SNAKE_CASE	      =  model_args.pop("""n_head"""		)
						_SCREAMING_SNAKE_CASE	      =  model_args.pop("""n_embd"""		)
						_SCREAMING_SNAKE_CASE	      =  model_args.pop("""n_layer"""		)

						_SCREAMING_SNAKE_CASE	      =  ConfigClass(**checkpoint["""model_args"""]		)
						_SCREAMING_SNAKE_CASE	      =  ModelClass(config=__lowerCAmelCase		)
						_SCREAMING_SNAKE_CASE	      =  GenerationConfigClass()

						_SCREAMING_SNAKE_CASE	      =  model_generation_config
						_SCREAMING_SNAKE_CASE	      =  checkpoint["""model"""]
						# fixup checkpoint
						_SCREAMING_SNAKE_CASE	      =  """_orig_mod."""
						for k, v in list(state_dict.items()		):
												if k.startswith(__lowerCAmelCase		):
																		# replace part of the key with corresponding layer name in HF implementation
																		_SCREAMING_SNAKE_CASE	      =  k[len(__lowerCAmelCase		) :]
																		for old_layer_name in new_layer_name_dict:
																								_SCREAMING_SNAKE_CASE	      =  new_k.replace(__lowerCAmelCase       ,new_layer_name_dict[old_layer_name]		)

																		_SCREAMING_SNAKE_CASE	      =  state_dict.pop(__lowerCAmelCase		)

						_SCREAMING_SNAKE_CASE	      =  set(state_dict.keys()		) - set(model.state_dict().keys()		)
						_SCREAMING_SNAKE_CASE	      =  {k for k in extra_keys if not k.endswith(""".attn.bias"""		)}
						_SCREAMING_SNAKE_CASE	      =  set(model.state_dict().keys()		) - set(state_dict.keys()		)
						_SCREAMING_SNAKE_CASE	      =  {k for k in missing_keys if not k.endswith(""".attn.bias"""		)}
						if len(__lowerCAmelCase		) != 0:
												raise ValueError(F'extra keys found: {extra_keys}'		)
						if len(__lowerCAmelCase		) != 0:
												raise ValueError(F'missing keys: {missing_keys}'		)
						model.load_state_dict(__lowerCAmelCase       ,strict=__lowerCAmelCase		)
						_SCREAMING_SNAKE_CASE	      =  model.num_parameters(exclude_embeddings=__lowerCAmelCase		)
						_SCREAMING_SNAKE_CASE	      =  checkpoint["""best_val_loss"""].item()
						logger.info(F'model loaded: {round(n_params/1e6       ,1		)}M params, {round(__lowerCAmelCase       ,3		)} loss'		)
						model.eval()
						model.to(__lowerCAmelCase		)
						del checkpoint, state_dict

						return model

def        __lowerCamelCase			(		snake_case__       ,snake_case__=False       ,snake_case__="text"		)							->  List[str]:






						"""simple docstring"""





						if model_type not in ("text", "coarse", "fine"):
												raise NotImplementedError()

						_SCREAMING_SNAKE_CASE	      =  """cpu"""  # do conversion on cpu

						_SCREAMING_SNAKE_CASE	      =  _get_ckpt_path(__lowerCAmelCase       ,use_small=__lowerCAmelCase		)
						_SCREAMING_SNAKE_CASE	      =  _load_model(__lowerCAmelCase       ,__lowerCAmelCase       ,model_type=__lowerCAmelCase       ,use_small=__lowerCAmelCase		)

						# load bark initial model
						_SCREAMING_SNAKE_CASE	      =  _bark_load_model(__lowerCAmelCase       ,"""cpu"""       ,model_type=__lowerCAmelCase       ,use_small=__lowerCAmelCase		)

						if model_type == "text":
												_SCREAMING_SNAKE_CASE	      =  bark_model["""model"""]

						if model.num_parameters(exclude_embeddings=__lowerCAmelCase		) != bark_model.get_num_params():
												raise ValueError("""initial and new models don\'t have the same number of parameters"""		)

						# check if same output as the bark model
						_SCREAMING_SNAKE_CASE	      =  5
						_SCREAMING_SNAKE_CASE	      =  10

						if model_type in ["text", "coarse"]:
												_SCREAMING_SNAKE_CASE	      =  torch.randint(2_56       ,(batch_size, sequence_length)       ,dtype=torch.int		)
												_SCREAMING_SNAKE_CASE	      =  bark_model(__lowerCAmelCase		)[0]

												_SCREAMING_SNAKE_CASE	      =  model(__lowerCAmelCase		)

												# take last logits
												_SCREAMING_SNAKE_CASE	      =  output_new_model_total.logits[:, [-1], :]

						else:
												_SCREAMING_SNAKE_CASE	      =  3
												_SCREAMING_SNAKE_CASE	      =  8
												_SCREAMING_SNAKE_CASE	      =  torch.randint(2_56       ,(batch_size, sequence_length, n_codes_total)       ,dtype=torch.int		)

												_SCREAMING_SNAKE_CASE	      =  model(__lowerCAmelCase       ,__lowerCAmelCase		)
												_SCREAMING_SNAKE_CASE	      =  bark_model(__lowerCAmelCase       ,__lowerCAmelCase		)

												_SCREAMING_SNAKE_CASE	      =  output_new_model_total.logits

						# output difference should come from the difference of self-attention implementation design
						if output_new_model.shape != output_old_model.shape:
												raise ValueError("""initial and new outputs don\'t have the same shape"""		)
						if (output_new_model - output_old_model).abs().max().item() > 1e-3:
												raise ValueError("""initial and new outputs are not equal"""		)

						Path(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
						model.save_pretrained(__lowerCAmelCase		)

def        __lowerCamelCase			(		snake_case__       ,snake_case__       ,snake_case__       ,snake_case__       ,snake_case__       ,snake_case__       ,)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  os.path.join(__lowerCAmelCase       ,__lowerCAmelCase		)

						_SCREAMING_SNAKE_CASE	      =  BarkSemanticConfig.from_pretrained(os.path.join(__lowerCAmelCase       ,"""config.json"""		)		)
						_SCREAMING_SNAKE_CASE	      =  BarkCoarseConfig.from_pretrained(os.path.join(__lowerCAmelCase       ,"""config.json"""		)		)
						_SCREAMING_SNAKE_CASE	      =  BarkFineConfig.from_pretrained(os.path.join(__lowerCAmelCase       ,"""config.json"""		)		)
						_SCREAMING_SNAKE_CASE	      =  EncodecConfig.from_pretrained("""facebook/encodec_24khz"""		)

						_SCREAMING_SNAKE_CASE	      =  BarkSemanticModel.from_pretrained(__lowerCAmelCase		)
						_SCREAMING_SNAKE_CASE	      =  BarkCoarseModel.from_pretrained(__lowerCAmelCase		)
						_SCREAMING_SNAKE_CASE	      =  BarkFineModel.from_pretrained(__lowerCAmelCase		)
						_SCREAMING_SNAKE_CASE	      =  EncodecModel.from_pretrained("""facebook/encodec_24khz"""		)

						_SCREAMING_SNAKE_CASE	      =  BarkConfig.from_sub_model_configs(
						    __lowerCAmelCase       ,__lowerCAmelCase       ,__lowerCAmelCase       ,__lowerCAmelCase		)

						_SCREAMING_SNAKE_CASE	      =  BarkGenerationConfig.from_sub_model_configs(
						    semantic.generation_config       ,coarseAcoustic.generation_config       ,fineAcoustic.generation_config		)

						_SCREAMING_SNAKE_CASE	      =  BarkModel(__lowerCAmelCase		)

						_SCREAMING_SNAKE_CASE	      =  semantic
						_SCREAMING_SNAKE_CASE	      =  coarseAcoustic
						_SCREAMING_SNAKE_CASE	      =  fineAcoustic
						_SCREAMING_SNAKE_CASE	      =  codec

						_SCREAMING_SNAKE_CASE	      =  bark_generation_config

						Path(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
						bark.save_pretrained(__lowerCAmelCase       ,repo_id=__lowerCAmelCase       ,push_to_hub=__lowerCAmelCase		)


if __name__ == "__main__":
		UpperCamelCase 				=	argparse.ArgumentParser()
		# Required parameters

		parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
		parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

		UpperCamelCase 				=	parser.parse_args()

		load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)



import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


SCREAMING_SNAKE_CASE__   :			Optional[int]												=    os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'{bindir}/../../examples/pytorch/translation'):
					from run_translation import main  # noqa


set_seed(42)
SCREAMING_SNAKE_CASE__   :			Any												=    "sshleifer/student_marian_en_ro_6_1"
SCREAMING_SNAKE_CASE__   :			Tuple												=    "sshleifer/tiny-mbart"

@require_torch
class    lowerCAmelCase__     (	__lowercase			):






		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Tuple=False							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=None							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=True							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=True							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=True							,			)     ->							Optional[int]:
						__lowerCamelCase    					=	self.run_trainer(
						    eval_steps=1							,			max_len=12							,			model_name=SCREAMING_SNAKE_CASE__							,			num_train_epochs=1							,			distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str=SCREAMING_SNAKE_CASE__							,			predict_with_generate=SCREAMING_SNAKE_CASE__							,			do_train=SCREAMING_SNAKE_CASE__							,			do_eval=SCREAMING_SNAKE_CASE__							,			do_predict=SCREAMING_SNAKE_CASE__							,			)
						__lowerCamelCase    					=	TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE__							,			'''trainer_state.json'''  )  ).log_history

						if not do_eval:
										return

						__lowerCamelCase    					=	[log for log in logs if '''eval_loss''' in log.keys()]

						__lowerCamelCase    					=	eval_metrics[0]
						if predict_with_generate:
										assert "eval_bleu" in first_step_stats

										__lowerCamelCase    					=	eval_metrics[-1]
										assert isinstance(last_step_stats['''eval_bleu''']							,			SCREAMING_SNAKE_CASE__  )
										assert not math.isnan(float(last_step_stats['''eval_loss''']  )  ), "eval_loss must not be `nan`"






		@require_torch_non_multi_gpu
		def  __A       (		self		:							Optional[int]  )     ->							int:
						self.run_seqaseq_quick()






		@require_torch_multi_gpu
		def  __A       (		self		:							int  )     ->							List[str]:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__  )






		@require_torch_multi_gpu
		def  __A       (		self		:							Optional[Any]  )     ->							Tuple:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__  )






		@unittest.skip('''Requires an update of the env running those tests'''  )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A       (		self		:							Dict  )     ->							Tuple:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--sharded_ddp simple'''  )






		@unittest.skip('''Requires an update of the env running those tests'''  )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A       (		self		:							Optional[int]  )     ->							List[str]:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--sharded_ddp simple --fp16'''  )






		@unittest.skip('''Requires an update of the env running those tests'''  )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A       (		self		:							Tuple  )     ->							Any:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--sharded_ddp zero_dp_2'''							,			predict_with_generate=SCREAMING_SNAKE_CASE__  )






		@unittest.skip('''Requires an update of the env running those tests'''  )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A       (		self		:							Dict  )     ->							Tuple:
						self.run_seqaseq_quick(
						    distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''							,			predict_with_generate=SCREAMING_SNAKE_CASE__  )






		@require_apex
		@require_torch_gpu
		def  __A       (		self		:							Union[str, Any]  )     ->							List[str]:
						# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
						# program and it breaks other tests that run from the same pytest worker, therefore until this is
						# sorted out it must be run only in an external program, that is distributed=True in this
						# test and only under one or more gpus - if we want cpu will need to make a special test
						#
						# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
						# 2nd main() call it botches the future eval.
						#
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--fp16 --fp16_backend=apex'''  )
						# test 2nd time - was getting eval_loss': nan'
						# to reproduce the problem set distributed=False
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--fp16 --fp16_backend=apex'''  )






		@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']  )
		@require_torch_multi_gpu
		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							Optional[Any]:
						# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
						__lowerCamelCase    					=	{
						    # test with the default log_level - should be info and thus log info once
						    '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
						    # test with low log_level and log_level_replica - should be noisy on all processes
						    # now the info string should appear twice on 2 processes
						    '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
						    # test with high log_level and low log_level_replica
						    # now the info string should appear once only on the replica
						    '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
						    # test with high log_level and log_level_replica - should be quiet on all processes
						    '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
						}

						__lowerCamelCase    					=	experiments[experiment_id]
						__lowerCamelCase    					=	{'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
						__lowerCamelCase    					=	'''Running training'''
						with CaptureStderr() as cl:
										self.run_seqaseq_quick(**SCREAMING_SNAKE_CASE__							,			extra_args_str=data['''extra_args_str''']  )
						__lowerCamelCase    					=	len(re.findall(SCREAMING_SNAKE_CASE__							,			cl.err  )  )
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			data['''n_matches''']  )






		@slow
		def  __A       (		self		:							Any  )     ->							Optional[Any]:
						__lowerCamelCase    					=	self.run_trainer(
						    eval_steps=2							,			max_len=1_28							,			model_name=SCREAMING_SNAKE_CASE__							,			learning_rate=3e-4							,			num_train_epochs=10							,			distributed=SCREAMING_SNAKE_CASE__							,			)

						# Check metrics
						__lowerCamelCase    					=	TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE__							,			'''trainer_state.json'''  )  ).log_history
						__lowerCamelCase    					=	[log for log in logs if '''eval_loss''' in log.keys()]
						__lowerCamelCase    					=	eval_metrics[0]
						__lowerCamelCase    					=	eval_metrics[-1]

						assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
						assert isinstance(last_step_stats['''eval_bleu''']							,			SCREAMING_SNAKE_CASE__  )

						# test if do_predict saves generations and metrics
						__lowerCamelCase    					=	os.listdir(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	{os.path.basename(SCREAMING_SNAKE_CASE__  ) for p in contents}
						assert "generated_predictions.txt" in contents
						assert "predict_results.json" in contents






		@slow
		@require_bitsandbytes
		def  __A       (		self		:							Optional[int]  )     ->							str:
						from transformers.training_args import OptimizerNames

						def train_and_return_metrics(SCREAMING_SNAKE_CASE__		:							str  ) -> Tuple[int, float]:
										__lowerCamelCase    					=	'''--skip_memory_metrics 0'''

										__lowerCamelCase    					=	self.run_trainer(
										    max_len=1_28							,			model_name=SCREAMING_SNAKE_CASE__							,			learning_rate=3e-4							,			num_train_epochs=1							,			optim=SCREAMING_SNAKE_CASE__							,			distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str=SCREAMING_SNAKE_CASE__							,			do_eval=SCREAMING_SNAKE_CASE__							,			do_predict=SCREAMING_SNAKE_CASE__							,			n_gpus_to_use=1							,			)

										# Check metrics
										__lowerCamelCase    					=	TrainerState.load_from_json(Path(SCREAMING_SNAKE_CASE__							,			'''trainer_state.json'''  )  ).log_history
										__lowerCamelCase    					=	int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20  )
										__lowerCamelCase    					=	int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20  )

										__lowerCamelCase    					=	logs[0]['''train_loss''']
										return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value  )
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value  )

						__lowerCamelCase    					=	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

						__lowerCamelCase    					=	gpu_peak_mem_orig + gpu_alloc_mem_orig
						__lowerCamelCase    					=	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
						__lowerCamelCase    					=	gpu_total_mem_orig - gpu_total_mem_bnb

						# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
						# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
						# in 2 bytes and the diff in optim memory usage is derived as so:
						#
						# - normal 25*8=~200MB (8 bytes per param)
						# - bnb    25*2= ~50MB (2 bytes per param)
						#
						# Thus we should expect ~150MB total memory saved.
						#
						# Peak memory should be the same - the total should be different by about that same margin
						#
						# After leaving a small margin to accommodate for differences between gpus let's check
						# that we have at least 120MB in savings
						__lowerCamelCase    					=	1_20

						# uncomment the following if this test starts failing - requires py38 for a new print feature
						# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
						# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
						# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
						# print(f"{gpu_alloc_mem_diff=}MB")
						# print(f"{gpu_peak_mem_diff=}MB")
						# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
						# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

						self.assertGreater(
						    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			'''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
						    f''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
						    f''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''							,			)

						self.assertGreater(
						    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			'''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
						    f''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
						    f''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''							,			)

						self.assertEqual(
						    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''  )







		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							float = 3e-3							,			SCREAMING_SNAKE_CASE__		:							str = "adafactor"							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							str = None							,			SCREAMING_SNAKE_CASE__		:							int = 0							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							int = None							,			)     ->							List[Any]:
						__lowerCamelCase    					=	self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
						__lowerCamelCase    					=	self.get_auto_remove_tmp_dir()
						__lowerCamelCase    					=	f'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(SCREAMING_SNAKE_CASE__  )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(SCREAMING_SNAKE_CASE__  )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

						__lowerCamelCase    					=	f'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(SCREAMING_SNAKE_CASE__  )}
        '''.split()

						__lowerCamelCase    					=	'''
            --do_predict
        '''.split()

						__lowerCamelCase    					=	[]
						if do_train:
										args += args_train

						if do_eval:
										args += args_eval

						if do_predict:
										args += args_predict

						if predict_with_generate:
										args += "--predict_with_generate".split()

						if do_train:
										if optim == "adafactor":
														args += "--adafactor".split()
										else:
														args += f'''--optim {optim}'''.split()

						if extra_args_str is not None:
										args += extra_args_str.split()

						if distributed:
										if n_gpus_to_use is None:
														__lowerCamelCase    					=	get_gpu_count()
										__lowerCamelCase    					=	get_torch_dist_unique_port()
										__lowerCamelCase    					=	f'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
										__lowerCamelCase    					=	[sys.executable] + distributed_args + args
										# keep for quick debug
										# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
										execute_subprocess_async(SCREAMING_SNAKE_CASE__							,			env=self.get_env()  )
						else:
										__lowerCamelCase    					=	['''run_translation.py'''] + args
										with patch.object(SCREAMING_SNAKE_CASE__							,			'''argv'''							,			SCREAMING_SNAKE_CASE__  ):
														main()

						return output_dir



