
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__   :			List[str]												=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :			str												=    {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}




def  __magic_name__ (   __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						List[Any]		,  __lowerCAmelCase	:						Tuple							)			->   Optional[int]:
				for attribute in key.split('''.'''							):
								__lowerCamelCase    					=	getattr(__lowerCAmelCase		,  __lowerCAmelCase							)

				if weight_type is not None:
								__lowerCamelCase    					=	getattr(__lowerCAmelCase		,  __lowerCAmelCase							).shape
				else:
								__lowerCamelCase    					=	hf_pointer.shape

				assert hf_shape == value.shape, (
				    f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
				    f''' {value.shape} for {full_name}'''
				)

				if weight_type == "weight":
								__lowerCamelCase    					=	value
				elif weight_type == "weight_g":
								__lowerCamelCase    					=	value
				elif weight_type == "weight_v":
								__lowerCamelCase    					=	value
				elif weight_type == "bias":
								__lowerCamelCase    					=	value
				else:
								__lowerCamelCase    					=	value

				logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''							)




def  __magic_name__ (   __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						List[Any]							)			->   List[str]:
				__lowerCamelCase    					=	[]
				__lowerCamelCase    					=	fairseq_model.state_dict()

				__lowerCamelCase    					=	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								__lowerCamelCase    					=	False
								if "conv_layers" in name:
												load_conv_layer(
												    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  hf_model.config.feat_extract_norm == '''group'''		,  )
												__lowerCamelCase    					=	True
								else:
												for key, mapped_key in MAPPING.items():
																__lowerCamelCase    					=	'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
																				__lowerCamelCase    					=	True
																				if "*" in mapped_key:
																								__lowerCamelCase    					=	name.split(__lowerCAmelCase							)[0].split('''.'''							)[-2]
																								__lowerCamelCase    					=	mapped_key.replace('''*'''		,  __lowerCAmelCase							)
																				if "weight_g" in name:
																								__lowerCamelCase    					=	'''weight_g'''
																				elif "weight_v" in name:
																								__lowerCamelCase    					=	'''weight_v'''
																				elif "weight" in name:
																								__lowerCamelCase    					=	'''weight'''
																				elif "bias" in name:
																								__lowerCamelCase    					=	'''bias'''
																				else:
																								__lowerCamelCase    					=	None
																				set_recursively(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase							)
																continue
								if not is_used:
												unused_weights.append(__lowerCAmelCase							)

				logger.warning(f'''Unused weights: {unused_weights}'''							)




def  __magic_name__ (   __lowerCAmelCase	:						Optional[Any]		,  __lowerCAmelCase	:						Optional[Any]		,  __lowerCAmelCase	:						Union[str, Any]		,  __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						List[Any]							)			->   Optional[Any]:
				__lowerCamelCase    					=	full_name.split('''conv_layers.'''							)[-1]
				__lowerCamelCase    					=	name.split('''.'''							)
				__lowerCamelCase    					=	int(items[0]							)
				__lowerCamelCase    					=	int(items[1]							)

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
												)
												__lowerCamelCase    					=	value
												logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
												)
												__lowerCamelCase    					=	value
												logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
												    " found."
												)
												__lowerCamelCase    					=	value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
												)
												__lowerCamelCase    					=	value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
				else:
								unused_weights.append(__lowerCAmelCase							)




def  __magic_name__ (   __lowerCAmelCase	:						Optional[int]		,  __lowerCAmelCase	:						List[str]							)			->   List[Any]:
				__lowerCamelCase    					=	SEWConfig()
				if is_finetuned:
								__lowerCamelCase    					=	model.wav_encoder.wav_model.cfg
				else:
								__lowerCamelCase    					=	model.cfg

				__lowerCamelCase    					=	fs_config.conv_bias
				__lowerCamelCase    					=	eval(fs_config.conv_feature_layers							)
				__lowerCamelCase    					=	[x[0] for x in conv_layers]
				__lowerCamelCase    					=	[x[1] for x in conv_layers]
				__lowerCamelCase    					=	[x[2] for x in conv_layers]
				__lowerCamelCase    					=	'''gelu'''
				__lowerCamelCase    					=	'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
				__lowerCamelCase    					=	0.0
				__lowerCamelCase    					=	fs_config.activation_fn.name
				__lowerCamelCase    					=	fs_config.encoder_embed_dim
				__lowerCamelCase    					=	0.02
				__lowerCamelCase    					=	fs_config.encoder_ffn_embed_dim
				__lowerCamelCase    					=	1E-5
				__lowerCamelCase    					=	fs_config.encoder_layerdrop
				__lowerCamelCase    					=	fs_config.encoder_attention_heads
				__lowerCamelCase    					=	fs_config.conv_pos_groups
				__lowerCamelCase    					=	fs_config.conv_pos
				__lowerCamelCase    					=	len(__lowerCAmelCase							)
				__lowerCamelCase    					=	fs_config.encoder_layers
				__lowerCamelCase    					=	fs_config.squeeze_factor

				# take care of any params that are overridden by the Wav2VecCtc model
				if is_finetuned:
								__lowerCamelCase    					=	model.cfg
								__lowerCamelCase    					=	fs_config.final_dropout
								__lowerCamelCase    					=	fs_config.layerdrop
				__lowerCamelCase    					=	fs_config.activation_dropout
				__lowerCamelCase    					=	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
				__lowerCamelCase    					=	fs_config.attention_dropout
				__lowerCamelCase    					=	fs_config.dropout_input
				__lowerCamelCase    					=	fs_config.dropout
				__lowerCamelCase    					=	fs_config.mask_channel_length
				__lowerCamelCase    					=	fs_config.mask_channel_prob
				__lowerCamelCase    					=	fs_config.mask_length
				__lowerCamelCase    					=	fs_config.mask_prob

				__lowerCamelCase    					=	'''Wav2Vec2FeatureExtractor'''
				__lowerCamelCase    					=	'''Wav2Vec2CTCTokenizer'''

				return config




@torch.no_grad()
def  __magic_name__ (   __lowerCAmelCase	:						Union[str, Any]		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						Optional[Any]=None		,  __lowerCAmelCase	:						Union[str, Any]=None		,  __lowerCAmelCase	:						Tuple=True							)			->   int:

				if is_finetuned:
								__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]		,  arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
				else:
								__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

				if config_path is not None:
								__lowerCamelCase    					=	SEWConfig.from_pretrained(__lowerCAmelCase							)
				else:
								__lowerCamelCase    					=	convert_config(model[0]		,  __lowerCAmelCase							)
				__lowerCamelCase    					=	model[0].eval()

				__lowerCamelCase    					=	True if config.feat_extract_norm == '''layer''' else False
				__lowerCamelCase    					=	WavaVecaFeatureExtractor(
				    feature_size=1		,  sampling_rate=1_6000		,  padding_value=0		,  do_normalize=__lowerCAmelCase		,  return_attention_mask=__lowerCAmelCase		,  )

				if is_finetuned:
								if dict_path:
												__lowerCamelCase    					=	Dictionary.load(__lowerCAmelCase							)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												__lowerCamelCase    					=	target_dict.pad_index
												__lowerCamelCase    					=	target_dict.bos_index
												__lowerCamelCase    					=	target_dict.pad_index
												__lowerCamelCase    					=	target_dict.bos_index
												__lowerCamelCase    					=	target_dict.eos_index
												__lowerCamelCase    					=	len(target_dict.symbols							)
												__lowerCamelCase    					=	os.path.join(__lowerCAmelCase		,  '''vocab.json'''							)
												if not os.path.isdir(__lowerCAmelCase							):
																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__lowerCAmelCase							)							)
																return
												os.makedirs(__lowerCAmelCase		,  exist_ok=__lowerCAmelCase							)
												with open(__lowerCAmelCase		,  '''w'''		,  encoding='''utf-8'''							) as vocab_handle:
																json.dump(target_dict.indices		,  __lowerCAmelCase							)
												__lowerCamelCase    					=	WavaVecaCTCTokenizer(
												    __lowerCAmelCase		,  unk_token=target_dict.unk_word		,  pad_token=target_dict.pad_word		,  bos_token=target_dict.bos_word		,  eos_token=target_dict.eos_word		,  word_delimiter_token='''|'''		,  do_lower_case=__lowerCAmelCase		,  )
												__lowerCamelCase    					=	WavaVecaProcessor(feature_extractor=__lowerCAmelCase		,  tokenizer=__lowerCAmelCase							)
												processor.save_pretrained(__lowerCAmelCase							)

								__lowerCamelCase    					=	SEWForCTC(__lowerCAmelCase							)
				else:
								__lowerCamelCase    					=	SEWModel(__lowerCAmelCase							)
								feature_extractor.save_pretrained(__lowerCAmelCase							)

				recursively_load_weights(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase							)

				hf_model.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    argparse.ArgumentParser()
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
					parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
					parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
					parser.add_argument(
					    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
					)
					SCREAMING_SNAKE_CASE__   :			Optional[int]												=    parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)





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



