


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      :			Dict      				=   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      :			int      				=   "sshleifer/student_marian_en_ro_6_1"
_SCREAMING_SNAKE_CASE      :			List[str]      				=   "sshleifer/tiny-mbart"

@require_torch
class   A__ (   snake_case__     ):







    """simple docstring"""

    def 		a_      (	self      , __snake_case=False      , __snake_case=None      , __snake_case=True      , __snake_case=True      , __snake_case=True      , __snake_case=True      , ):
         snake_case					=    self.run_trainer(
             eval_steps=1      , max_len=1_2      , model_name=__snake_case      , num_train_epochs=1      , distributed=__snake_case      , extra_args_str=__snake_case      , predict_with_generate=__snake_case      , do_train=__snake_case      , do_eval=__snake_case      , do_predict=__snake_case      , )
         snake_case					=    TrainerState.load_from_json(os.path.join(__snake_case      , '''trainer_state.json'''   )   ).log_history

         if not do_eval:
              return

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

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

              snake_case					=    eval_metrics[-1]
              assert isinstance(last_step_stats['''eval_bleu''']      , __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   ):
         self.run_seqaseq_quick()
    @require_torch_multi_gpu
    def 		a_      (	self   ):
         self.run_seqaseq_quick(distributed=__snake_case   )
    @require_torch_multi_gpu
    def 		a_      (	self   ):
         self.run_seqaseq_quick(distributed=__snake_case   )
    @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=__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   ):
         self.run_seqaseq_quick(distributed=__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   ):
         self.run_seqaseq_quick(distributed=__snake_case      , extra_args_str='''--sharded_ddp zero_dp_2'''      , predict_with_generate=__snake_case   )
    @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=__snake_case      , extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''      , predict_with_generate=__snake_case   )
    @require_apex
    @require_torch_gpu
    def 		a_      (	self   ):
         # 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=__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=__snake_case      , extra_args_str='''--fp16 --fp16_backend=apex'''   )
    @parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']   )
    @require_torch_multi_gpu
    def 		a_      (	self      , __snake_case   ):
         # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
         snake_case					=    {
             # 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},
         }

         snake_case					=    experiments[experiment_id]
         snake_case					=    {'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
         snake_case					=    '''Running training'''
         with CaptureStderr() as cl:
              self.run_seqaseq_quick(**__snake_case      , extra_args_str=data['''extra_args_str''']   )
         snake_case					=    len(re.findall(__snake_case      , cl.err   )   )
         self.assertEqual(__snake_case      , data['''n_matches''']   )
    @slow
    def 		a_      (	self   ):
         snake_case					=    self.run_trainer(
             eval_steps=2      , max_len=1_2_8      , model_name=__snake_case      , learning_rate=3E-4      , num_train_epochs=1_0      , distributed=__snake_case      , )

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

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

         # test if do_predict saves generations and metrics
         snake_case					=    os.listdir(__snake_case   )
         snake_case					=    {os.path.basename(__snake_case   ) 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(__snake_case   ) -> Tuple[int, float]:
              snake_case					=    '''--skip_memory_metrics 0'''

              snake_case					=    self.run_trainer(
                  max_len=1_2_8      , model_name=__snake_case      , learning_rate=3E-4      , num_train_epochs=1      , optim=__snake_case      , distributed=__snake_case      , extra_args_str=__snake_case      , do_eval=__snake_case      , do_predict=__snake_case      , n_gpus_to_use=1      , )

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

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

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

         snake_case					=    gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         snake_case					=    gpu_peak_mem_orig + gpu_alloc_mem_orig
         snake_case					=    gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         snake_case					=    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
         snake_case					=    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(
             __snake_case      , __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(
             __snake_case      , __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(
             __snake_case      , __snake_case      , F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''   )




    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case = 3E-3      , __snake_case = "adafactor"      , __snake_case = False      , __snake_case = None      , __snake_case = 0      , __snake_case = True      , __snake_case = True      , __snake_case = True      , __snake_case = True      , __snake_case = None      , ):
         snake_case					=    self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
         snake_case					=    self.get_auto_remove_tmp_dir()
         snake_case					=    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(__snake_case   )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__snake_case   )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

         snake_case					=    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(__snake_case   )}
        '''.split()

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

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

         return output_dir








import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


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

class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , *__snake_case      , **__snake_case   ):
         warnings.warn(
             '''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use BeitImageProcessor instead.'''      , __snake_case      , )
         super().__init__(*__snake_case      , **__snake_case   )




