






'''simple docstring'''







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


__lowerCAmelCase									=   os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
		from run_translation import main  # noqa


set_seed(42)
__lowerCAmelCase									=   'sshleifer/student_marian_en_ro_6_1'
__lowerCAmelCase									=   'sshleifer/tiny-mbart'





@require_torch
class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=False	,    __SCREAMING_SNAKE_CASE   :      Dict=None	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=True	,    __SCREAMING_SNAKE_CASE   :      str=True	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=True	,    __SCREAMING_SNAKE_CASE   :      int=True	,    )      ->      Dict:
							a_		:	str									=   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	,    )
							a_		:	List[str]									=   TrainerState.load_from_json(os.path.join(__SCREAMING_SNAKE_CASE	,    '''trainer_state.json'''       )       ).log_history

							if not do_eval:
											return

							a_		:	List[str]									=   [log for log in logs if '''eval_loss''' in log.keys()]

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

											a_		:	int									=   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       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      str:
							self.run_seqaseq_quick()






			@require_torch_multi_gpu
			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Optional[Any]:
							self.run_seqaseq_quick(distributed=__SCREAMING_SNAKE_CASE       )






			@require_torch_multi_gpu
			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Optional[Any]:
							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       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      Union[str, Any]:
							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       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      Dict:
							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       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      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       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      List[str]:
							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       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      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       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Any       )      ->      Optional[Any]:
							# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
							a_		:	int									=   {
							    # 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},
							}

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






			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Tuple:
							a_		:	Tuple									=   self.run_trainer(
							    eval_steps=2	,    max_len=128	,    model_name=__SCREAMING_SNAKE_CASE	,    learning_rate=3e-4	,    num_train_epochs=10	,    distributed=__SCREAMING_SNAKE_CASE	,    )

							# Check metrics
							a_		:	Optional[int]									=   TrainerState.load_from_json(os.path.join(__SCREAMING_SNAKE_CASE	,    '''trainer_state.json'''       )       ).log_history
							a_		:	List[Any]									=   [log for log in logs if '''eval_loss''' in log.keys()]
							a_		:	Optional[int]									=   eval_metrics[0]
							a_		:	Any									=   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
							a_		:	Dict									=   os.listdir(__SCREAMING_SNAKE_CASE       )
							a_		:	List[str]									=   {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       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Tuple:
							from transformers.training_args import OptimizerNames

							def train_and_return_metrics(__SCREAMING_SNAKE_CASE   :      str       ) -> Tuple[int, float]:
											a_		:	Any									=   '''--skip_memory_metrics 0'''

											a_		:	List[Any]									=   self.run_trainer(
											    max_len=128	,    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
											a_		:	Optional[int]									=   TrainerState.load_from_json(Path(__SCREAMING_SNAKE_CASE	,    '''trainer_state.json'''       )       ).log_history
											a_		:	Optional[Any]									=   int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20       )
											a_		:	Union[str, Any]									=   int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20       )

											a_		:	str									=   logs[0]['''train_loss''']
											return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

							a_					,      a_					,      a_		:	Any									=   train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value       )
							a_					,      a_					,      a_		:	List[str]									=   train_and_return_metrics(OptimizerNames.ADAMW_BNB.value       )

							a_		:	int									=   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

							a_		:	int									=   gpu_peak_mem_orig + gpu_alloc_mem_orig
							a_		:	str									=   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
							a_		:	Optional[Any]									=   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
							a_		:	Union[str, Any]									=   120

							# 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       SCREAMING_SNAKE_CASE   (			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	,    )      ->      Union[str, Any]:
							a_		:	Any									=   self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
							a_		:	Dict									=   self.get_auto_remove_tmp_dir()
							a_		:	List[Any]									=   f'\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(__SCREAMING_SNAKE_CASE       )}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(__SCREAMING_SNAKE_CASE       )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

							a_		:	Tuple									=   f'\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(__SCREAMING_SNAKE_CASE       )}\n        '.split()

							a_		:	Union[str, Any]									=   '''
            --do_predict
        '''.split()

							a_		:	Union[str, Any]									=   []
							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:
															a_		:	Dict									=   get_gpu_count()
											a_		:	int									=   get_torch_dist_unique_port()
											a_		:	Optional[int]									=   f'\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            '.split()
											a_		:	int									=   [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:
											a_		:	str									=   ['''run_translation.py'''] + args
											with patch.object(__SCREAMING_SNAKE_CASE	,    '''argv'''	,    __SCREAMING_SNAKE_CASE       ):
															main()

							return output_dir












'''simple docstring'''







import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__lowerCAmelCase									=   object()

# For specifying empty leaf dict `{}`
__lowerCAmelCase									=   object()





def 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: Tuple   ):
				a_		:	List[Any]									=   tuple((re.compile(x + '''$'''   ) for x in qs)   )
				for i in range(len(__A   ) - len(__A   ) + 1   ):
								a_		:	Union[str, Any]									=   [x.match(__A   ) for x, y in zip(__A    ,		ks[i:]   )]
								if matches and all(__A   ):
												return True
				return False





def 		_UpperCAmelCase (				__A				: List[str]   ):
				def replace(__A				: int    ,		__A				: Union[str, Any]   ):
								for rule, replacement in rules:
												if _match(__A    ,		__A   ):
																return replacement
								return val

				return replace





def 		_UpperCAmelCase (				):
				return [
				    # embeddings
				    (("transformer", "wpe", "embedding"), P('''mp'''    ,		__A   )),
				    (("transformer", "wte", "embedding"), P('''mp'''    ,		__A   )),
				    # atention
				    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__A    ,		'''mp'''   )),
				    (("attention", "out_proj", "kernel"), P('''mp'''    ,		__A   )),
				    (("attention", "out_proj", "bias"), None),
				    # mlp
				    (("mlp", "c_fc", "kernel"), P(__A    ,		'''mp'''   )),
				    (("mlp", "c_fc", "bias"), P('''mp'''   )),
				    (("mlp", "c_proj", "kernel"), P('''mp'''    ,		__A   )),
				    (("mlp", "c_proj", "bias"), None),
				    # layer norms
				    ((r"ln_\d+", "bias"), None),
				    ((r"\d+", r"ln_\d+", "scale"), None),
				    (("ln_f", "bias"), None),
				    (("ln_f", "scale"), None),
				]





def 		_UpperCAmelCase (				__A				: Union[str, Any]   ):
				a_		:	Tuple									=   _get_partition_rules()
				a_		:	Tuple									=   _replacement_rules(__A   )
				a_		:	Optional[Any]									=   {k: _unmatched for k in flatten_dict(__A   )}
				a_		:	Optional[Any]									=   {k: replace(__A    ,		__A   ) for k, v in initd.items()}
				assert _unmatched not in result.values(), "Incomplete partition spec."
				return freeze(unflatten_dict(__A   )   )




