


"""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


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


set_seed(42)
UpperCAmelCase__  							="sshleifer/student_marian_en_ro_6_1"
UpperCAmelCase__  							="sshleifer/tiny-mbart"





@require_torch
class    lowerCamelCase__						(		_A    ):
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       A_	:	Optional[Any]=False      ,       A_	:	str=None      ,       A_	:	List[str]=True      ,       A_	:	str=True      ,       A_	:	List[Any]=True      ,       A_	:	Optional[int]=True      ,       ):






       '''simple docstring'''


       __lowercase									=	self.run_trainer(
           eval_steps=1      ,       max_len=1_2      ,       model_name=__lowerCamelCase      ,       num_train_epochs=1      ,       distributed=__lowerCamelCase      ,       extra_args_str=__lowerCamelCase      ,       predict_with_generate=__lowerCamelCase      ,       do_train=__lowerCamelCase      ,       do_eval=__lowerCamelCase      ,       do_predict=__lowerCamelCase      ,       )
       __lowercase									=	TrainerState.load_from_json(os.path.join(__lowerCamelCase      ,       """trainer_state.json"""							)							).log_history

       if not do_eval:
        return

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

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

        __lowercase									=	eval_metrics[-1]
        assert isinstance(last_step_stats["""eval_bleu"""]      ,       __lowerCamelCase							)
        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	:	Tuple							):






       '''simple docstring'''


       self.run_seqaseq_quick()
      @require_torch_multi_gpu
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       self.run_seqaseq_quick(distributed=__lowerCamelCase							)
      @require_torch_multi_gpu
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       self.run_seqaseq_quick(distributed=__lowerCamelCase							)
      @unittest.skip("""Requires an update of the env running those tests"""							)
      @require_torch_multi_gpu
      @require_fairscale
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       self.run_seqaseq_quick(distributed=__lowerCamelCase      ,       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	:	Tuple							):






       '''simple docstring'''


       self.run_seqaseq_quick(distributed=__lowerCamelCase      ,       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	:	List[str]							):






       '''simple docstring'''


       self.run_seqaseq_quick(distributed=__lowerCamelCase      ,       extra_args_str="""--sharded_ddp zero_dp_2"""      ,       predict_with_generate=__lowerCamelCase							)
      @unittest.skip("""Requires an update of the env running those tests"""							)
      @require_torch_multi_gpu
      @require_fairscale
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       self.run_seqaseq_quick(
           distributed=__lowerCamelCase      ,       extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""      ,       predict_with_generate=__lowerCamelCase							)
      @require_apex
      @require_torch_gpu
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       self.run_seqaseq_quick(distributed=__lowerCamelCase      ,       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=__lowerCamelCase      ,       extra_args_str="""--fp16 --fp16_backend=apex"""							)
      @parameterized.expand(["""base""", """low""", """high""", """mixed"""]							)
      @require_torch_multi_gpu
      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	{
           # 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},
       }

       __lowercase									=	experiments[experiment_id]
       __lowercase									=	{"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
       __lowercase									=	"Running training"
       with CaptureStderr() as cl:
        self.run_seqaseq_quick(**__lowerCamelCase      ,       extra_args_str=data["""extra_args_str"""]							)
       __lowercase									=	len(re.findall(__lowerCamelCase      ,       cl.err							)							)
       self.assertEqual(__lowerCamelCase      ,       data["""n_matches"""]							)
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.run_trainer(
           eval_steps=2      ,       max_len=1_2_8      ,       model_name=__lowerCamelCase      ,       learning_rate=3e-4      ,       num_train_epochs=1_0      ,       distributed=__lowerCamelCase      ,       )

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

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

       # test if do_predict saves generations and metrics
       __lowercase									=	os.listdir(__lowerCamelCase							)
       __lowercase									=	{os.path.basename(__lowerCamelCase							) for p in contents}
       assert "generated_predictions.txt" in contents
       assert "predict_results.json" in contents
      @slow
      @require_bitsandbytes
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       from transformers.training_args import OptimizerNames

       def train_and_return_metrics(A_	:	str							) -> Tuple[int, float]:
        __lowercase									=	"--skip_memory_metrics 0"

        __lowercase									=	self.run_trainer(
            max_len=1_2_8      ,       model_name=__lowerCamelCase      ,       learning_rate=3e-4      ,       num_train_epochs=1      ,       optim=__lowerCamelCase      ,       distributed=__lowerCamelCase      ,       extra_args_str=__lowerCamelCase      ,       do_eval=__lowerCamelCase      ,       do_predict=__lowerCamelCase      ,       n_gpus_to_use=1      ,       )

        # Check metrics
        __lowercase									=	TrainerState.load_from_json(Path(__lowerCamelCase      ,       """trainer_state.json"""							)							).log_history
        __lowercase									=	int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**2_0							)
        __lowercase									=	int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**2_0							)

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

       __lowercase									=	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value							)
       __lowercase									=	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value							)

       __lowercase									=	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

       __lowercase									=	gpu_peak_mem_orig + gpu_alloc_mem_orig
       __lowercase									=	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
       __lowercase									=	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
       __lowercase									=	1_2_0

       # 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(
           __lowerCamelCase      ,       __lowerCamelCase      ,       """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(
           __lowerCamelCase      ,       __lowerCamelCase      ,       """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(
           __lowerCamelCase      ,       __lowerCamelCase      ,       F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''							)






      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	int      ,       A_	:	str      ,       A_	:	int      ,       A_	:	float = 3e-3      ,       A_	:	str = "adafactor"      ,       A_	:	bool = False      ,       A_	:	str = None      ,       A_	:	int = 0      ,       A_	:	bool = True      ,       A_	:	bool = True      ,       A_	:	bool = True      ,       A_	:	bool = True      ,       A_	:	int = None      ,       ):






       '''simple docstring'''


       __lowercase									=	self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
       __lowercase									=	self.get_auto_remove_tmp_dir()
       __lowercase									=	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(__lowerCamelCase							)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__lowerCamelCase							)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

       __lowercase									=	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(__lowerCamelCase							)}
        '''.split()

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

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

       return output_dir






"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: int				,   UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: str ):






 """simple docstring"""







 __lowercase									=	[False] * len(UpperCamelCase__ )
 __lowercase									=	[]
 queue.append(UpperCamelCase__ )
 __lowercase									=	True

 while queue:
  __lowercase									=	queue.pop(0 )
  for ind in range(len(graph[u] ) ):
   if visited[ind] is False and graph[u][ind] > 0:
    queue.append(UpperCamelCase__ )
    __lowercase									=	True
    __lowercase									=	u

 return visited[t]


def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: Any				,   UpperCamelCase__	: Optional[int] ):






 """simple docstring"""







 __lowercase									=	[-1] * (len(UpperCamelCase__ ))
 __lowercase									=	0
 while bfs(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ ):
  __lowercase									=	float("""Inf""" )
  __lowercase									=	sink

  while s != source:
   # Find the minimum value in select path
   __lowercase									=	min(UpperCamelCase__				,   graph[parent[s]][s] )
   __lowercase									=	parent[s]

  max_flow += path_flow
  __lowercase									=	sink

  while v != source:
   __lowercase									=	parent[v]
   graph[u][v] -= path_flow
   graph[v][u] += path_flow
   __lowercase									=	parent[v]
 return max_flow


UpperCAmelCase__  							=[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCAmelCase__							,      UpperCAmelCase__  							=0, 5

print(ford_fulkerson(graph, source, sink))
