



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


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


set_seed(42)
UpperCAmelCase_							:      Optional[Any]      =  "sshleifer/student_marian_en_ro_6_1"
UpperCAmelCase_							:      Union[str, Any]      =  "sshleifer/tiny-mbart"






@require_torch
class      UpperCamelCase    (						SCREAMING_SNAKE_CASE__     ):



 def 			__A							(       self			,	UpperCAmelCase__=False			,	UpperCAmelCase__=None			,	UpperCAmelCase__=True			,	UpperCAmelCase__=True			,	UpperCAmelCase__=True			,	UpperCAmelCase__=True			,	):
       A__							=       self.run_trainer(
           eval_steps=1			,	max_len=12			,	model_name=a_			,	num_train_epochs=1			,	distributed=a_			,	extra_args_str=a_			,	predict_with_generate=a_			,	do_train=a_			,	do_eval=a_			,	do_predict=a_			,	)
       A__							=       TrainerState.load_from_json(os.path.join(a_			,	"trainer_state.json"		)		).log_history

       if not do_eval:
             return

       A__							=       [log for log in logs if "eval_loss" in log.keys()]

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

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



 @require_torch_non_multi_gpu
 def 			__A							(       self		):
       self.run_seqaseq_quick()



 @require_torch_multi_gpu
 def 			__A							(       self		):
       self.run_seqaseq_quick(distributed=a_		)



 @require_torch_multi_gpu
 def 			__A							(       self		):
       self.run_seqaseq_quick(distributed=a_		)



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



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



 @require_apex
 @require_torch_gpu
 def 			__A							(       self		):
       self.run_seqaseq_quick(distributed=a_			,	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=a_			,	extra_args_str="--fp16 --fp16_backend=apex"		)



 @parameterized.expand(["base", "low", "high", "mixed"]		)
 @require_torch_multi_gpu
 def 			__A							(       self			,	UpperCAmelCase__		):
       A__							=       {
           # 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__							=       experiments[experiment_id]
       A__							=       {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
       A__							=       "Running training"
       with CaptureStderr() as cl:
             self.run_seqaseq_quick(**a_			,	extra_args_str=data["extra_args_str"]		)
       A__							=       len(re.findall(a_			,	cl.err		)		)
       self.assertEqual(a_			,	data["n_matches"]		)



 @slow
 def 			__A							(       self		):
       A__							=       self.run_trainer(
           eval_steps=2			,	max_len=128			,	model_name=a_			,	learning_rate=3e-4			,	num_train_epochs=10			,	distributed=a_			,	)

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

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

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



 @slow
 @require_bitsandbytes
 def 			__A							(       self		):
       from transformers.training_args import OptimizerNames

       def train_and_return_metrics(UpperCAmelCase__		) -> Tuple[int, float]:
             A__							=       "--skip_memory_metrics 0"

             A__							=       self.run_trainer(
                 max_len=128			,	model_name=a_			,	learning_rate=3e-4			,	num_train_epochs=1			,	optim=a_			,	distributed=a_			,	extra_args_str=a_			,	do_eval=a_			,	do_predict=a_			,	n_gpus_to_use=1			,	)

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

             A__							=       logs[0]["train_loss"]
             return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

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

       A__							=       gpu_alloc_mem_orig - gpu_alloc_mem_bnb

       A__							=       gpu_peak_mem_orig + gpu_alloc_mem_orig
       A__							=       gpu_peak_mem_bnb + gpu_alloc_mem_bnb
       A__							=       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__							=       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(
           a_			,	a_			,	"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(
           a_			,	a_			,	"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(
           a_			,	a_			,	F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""		)



 def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = 3e-3			,	UpperCAmelCase__ = "adafactor"			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = 0			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = None			,	):
       A__							=       self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
       A__							=       self.get_auto_remove_tmp_dir()
       A__							=       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(a_		)}\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(a_		)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        """.split()

       A__							=       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(a_		)}\n        """.split()

       A__							=       "\n            --do_predict\n        ".split()

       A__							=       []
       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__							=       get_gpu_count()
             A__							=       get_torch_dist_unique_port()
             A__							=       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__							=       [sys.executable] + distributed_args + args
             # keep for quick debug
             # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
             execute_subprocess_async(a_			,	env=self.get_env()		)
       else:
             A__							=       ["run_translation.py"] + args
             with patch.object(a_			,	"argv"			,	a_		):
                   main()

       return output_dir










from __future__ import annotations






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__=None		):
							A__							=       data
							A__							=       None



	def __repr__(       self		):
							A__							=       []
							A__							=       self
							while temp:
													string_rep.append(F"""{temp.data}"""		)
													A__							=       temp.next
							return "->".join(UpperCAmelCase__		)





def  UpperCamelCase						(       _A			:				list    )->       Dict:





						"""simple docstring"""





						if not elements_list:
												raise Exception("The Elements List is empty"    )

						A__							=       A__							=       Node(elements_list[0]    )
						for i in range(1			,							len(_A    )    ):
												A__							=       Node(elements_list[i]    )
												A__							=       current.next
						return head
def  UpperCamelCase						(       _A			:				Node    )->       None:





						"""simple docstring"""





						if head_node is not None and isinstance(_A			,							_A    ):
												print_reverse(head_node.next    )
												print(head_node.data    )
def  UpperCamelCase						(       )->       Tuple:





						"""simple docstring"""





						from doctest import testmod

						testmod()

						A__							=       make_linked_list([14, 52, 14, 12, 43]    )
						print("Linked List:"    )
						print(_A    )
						print("Elements in Reverse:"    )
						print_reverse(_A    )


if __name__ == "__main__":
						main()





