





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


set_seed(42)
_UpperCAmelCase							:			List[Any]        =							"""sshleifer/student_marian_en_ro_6_1"""
_UpperCAmelCase							:			Optional[Any]        =							"""sshleifer/tiny-mbart"""

@require_torch
class   lowerCAmelCase				(   __UpperCamelCase     ):
 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : List[str]=False     ,	UpperCAmelCase    : Any=None     ,	UpperCAmelCase    : List[str]=True     ,	UpperCAmelCase    : Optional[int]=True     ,	UpperCAmelCase    : Union[str, Any]=True     ,	UpperCAmelCase    : Optional[int]=True     ,	)     ->      List[str]:
     lowerCamelCase__       : Dict   					=						self.run_trainer(
         eval_steps=1     ,	max_len=12     ,	model_name=UpperCAmelCase     ,	num_train_epochs=1     ,	distributed=UpperCAmelCase     ,	extra_args_str=UpperCAmelCase     ,	predict_with_generate=UpperCAmelCase     ,	do_train=UpperCAmelCase     ,	do_eval=UpperCAmelCase     ,	do_predict=UpperCAmelCase     ,	)
     lowerCamelCase__       : Tuple   					=						TrainerState.load_from_json(os.path.join(UpperCAmelCase     ,	'trainer_state.json'      )      ).log_history

     if not do_eval:
         return

     lowerCamelCase__       : Optional[Any]   					=						[log for log in logs if 'eval_loss' in log.keys()]

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

         lowerCamelCase__       : Any   					=						eval_metrics[-1]
         assert isinstance(last_step_stats['eval_bleu']     ,	UpperCAmelCase      )
         assert not math.isnan(float(last_step_stats['eval_loss']      )      ), "eval_loss must not be `nan`"
 @require_torch_non_multi_gpu
 def 	A_      ( self    : str      )     ->      int:
     self.run_seqaseq_quick()
 @require_torch_multi_gpu
 def 	A_      ( self    : Optional[Any]      )     ->      Optional[Any]:
     self.run_seqaseq_quick(distributed=UpperCAmelCase      )
 @require_torch_multi_gpu
 def 	A_      ( self    : List[str]      )     ->      Union[str, Any]:
     self.run_seqaseq_quick(distributed=UpperCAmelCase      )
 @unittest.skip('Requires an update of the env running those tests'      )
 @require_torch_multi_gpu
 @require_fairscale
 def 	A_      ( self    : Optional[Any]      )     ->      str:
     self.run_seqaseq_quick(distributed=UpperCAmelCase     ,	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    : int      )     ->      List[str]:
     self.run_seqaseq_quick(distributed=UpperCAmelCase     ,	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    : Optional[Any]      )     ->      Any:
     self.run_seqaseq_quick(distributed=UpperCAmelCase     ,	extra_args_str='--sharded_ddp zero_dp_2'     ,	predict_with_generate=UpperCAmelCase      )
 @unittest.skip('Requires an update of the env running those tests'      )
 @require_torch_multi_gpu
 @require_fairscale
 def 	A_      ( self    : List[str]      )     ->      Dict:
     self.run_seqaseq_quick(
         distributed=UpperCAmelCase     ,	extra_args_str='--sharded_ddp zero_dp_2 --fp16'     ,	predict_with_generate=UpperCAmelCase      )
 @require_apex
 @require_torch_gpu
 def 	A_      ( self    : List[str]      )     ->      Tuple:
     # 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=UpperCAmelCase     ,	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=UpperCAmelCase     ,	extra_args_str='--fp16 --fp16_backend=apex'      )
 @parameterized.expand(['base', 'low', 'high', 'mixed']      )
 @require_torch_multi_gpu
 def 	A_      ( self    : Any     ,	UpperCAmelCase    : str      )     ->      Dict:
     # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
     lowerCamelCase__       : str   					=						{
         # 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__       : Dict   					=						experiments[experiment_id]
     lowerCamelCase__       : Dict   					=						{'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
     lowerCamelCase__       : List[str]   					=						'Running training'
     with CaptureStderr() as cl:
         self.run_seqaseq_quick(**UpperCAmelCase     ,	extra_args_str=data['extra_args_str']      )
     lowerCamelCase__       : List[Any]   					=						len(re.findall(UpperCAmelCase     ,	cl.err      )      )
     self.assertEqual(UpperCAmelCase     ,	data['n_matches']      )
 @slow
 def 	A_      ( self    : Union[str, Any]      )     ->      Optional[int]:
     lowerCamelCase__       : Any   					=						self.run_trainer(
         eval_steps=2     ,	max_len=128     ,	model_name=UpperCAmelCase     ,	learning_rate=3e-4     ,	num_train_epochs=10     ,	distributed=UpperCAmelCase     ,	)

     # Check metrics
     lowerCamelCase__       : Union[str, Any]   					=						TrainerState.load_from_json(os.path.join(UpperCAmelCase     ,	'trainer_state.json'      )      ).log_history
     lowerCamelCase__       : Union[str, Any]   					=						[log for log in logs if 'eval_loss' in log.keys()]
     lowerCamelCase__       : List[Any]   					=						eval_metrics[0]
     lowerCamelCase__       : Dict   					=						eval_metrics[-1]

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

     # test if do_predict saves generations and metrics
     lowerCamelCase__       : Union[str, Any]   					=						os.listdir(UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						{os.path.basename(UpperCAmelCase      ) for p in contents}
     assert "generated_predictions.txt" in contents
     assert "predict_results.json" in contents
 @slow
 @require_bitsandbytes
 def 	A_      ( self    : List[str]      )     ->      Any:
     from transformers.training_args import OptimizerNames

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

         lowerCamelCase__       : Optional[int]   					=						self.run_trainer(
             max_len=128     ,	model_name=UpperCAmelCase     ,	learning_rate=3e-4     ,	num_train_epochs=1     ,	optim=UpperCAmelCase     ,	distributed=UpperCAmelCase     ,	extra_args_str=UpperCAmelCase     ,	do_eval=UpperCAmelCase     ,	do_predict=UpperCAmelCase     ,	n_gpus_to_use=1     ,	)

         # Check metrics
         lowerCamelCase__       : int   					=						TrainerState.load_from_json(Path(UpperCAmelCase     ,	'trainer_state.json'      )      ).log_history
         lowerCamelCase__       : Optional[Any]   					=						int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20      )
         lowerCamelCase__       : str   					=						int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20      )

         lowerCamelCase__       : Tuple   					=						logs[0]['train_loss']
         return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

     lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : int   					=						train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value      )
     lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : Tuple   					=						train_and_return_metrics(OptimizerNames.ADAMW_BNB.value      )

     lowerCamelCase__       : List[str]   					=						gpu_alloc_mem_orig - gpu_alloc_mem_bnb

     lowerCamelCase__       : Optional[int]   					=						gpu_peak_mem_orig + gpu_alloc_mem_orig
     lowerCamelCase__       : Union[str, Any]   					=						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
     lowerCamelCase__       : Tuple   					=						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__       : 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(
         UpperCAmelCase     ,	UpperCAmelCase     ,	'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(
         UpperCAmelCase     ,	UpperCAmelCase     ,	'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(
         UpperCAmelCase     ,	UpperCAmelCase     ,	F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""      )







 def 	A_      ( self    : List[Any]     ,	UpperCAmelCase    : int     ,	UpperCAmelCase    : str     ,	UpperCAmelCase    : int     ,	UpperCAmelCase    : float = 3e-3     ,	UpperCAmelCase    : str = "adafactor"     ,	UpperCAmelCase    : bool = False     ,	UpperCAmelCase    : str = None     ,	UpperCAmelCase    : int = 0     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : int = None     ,	)     ->      Optional[Any]:
     lowerCamelCase__       : Dict   					=						self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
     lowerCamelCase__       : Dict   					=						self.get_auto_remove_tmp_dir()
     lowerCamelCase__       : List[Any]   					=						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(UpperCAmelCase      )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCAmelCase      )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

     lowerCamelCase__       : Tuple   					=						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(UpperCAmelCase      )}
        """.split()

     lowerCamelCase__       : Tuple   					=						'\n            --do_predict\n        '.split()

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

     return output_dir












# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						"""philschmid/bart-large-cnn-samsum"""
 UpperCAmelCase__						=						(
     """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
     """and returns a summary of the text."""
 )
 UpperCAmelCase__						=						"""summarizer"""
 UpperCAmelCase__						=						AutoTokenizer
 UpperCAmelCase__						=						AutoModelForSeqaSeqLM

 UpperCAmelCase__						=						["""text"""]
 UpperCAmelCase__						=						["""text"""]
 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[Any]      )     ->      List[str]:
     return self.pre_processor(UpperCAmelCase     ,	return_tensors='pt'     ,	truncation=UpperCAmelCase      )
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : Dict      )     ->      str:
     return self.model.generate(**UpperCAmelCase      )[0]







 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[Any]      )     ->      Union[str, Any]:
     return self.pre_processor.decode(UpperCAmelCase     ,	skip_special_tokens=UpperCAmelCase     ,	clean_up_tokenization_spaces=UpperCAmelCase      )





