
"""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


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


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

@require_torch
class 		a__						(      a_						):
   def 			__magic_name__			(			self	,    _a=False	,    _a=None	,    _a=True	,    _a=True	,    _a=True	,    _a=True	,    ):
          lowercase    :				Union[str, Any]       		=							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	,    )
          lowercase    :				Optional[Any]       		=							TrainerState.load_from_json(os.path.join(_a	,    "trainer_state.json"				)				).log_history

          if not do_eval:
                 return

          lowercase    :				Dict       		=							[log for log in logs if "eval_loss" in log.keys()]

          lowercase    :				Union[str, Any]       		=							eval_metrics[0]
          if predict_with_generate:
                 assert "eval_bleu" in first_step_stats

                 lowercase    :				Any       		=							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 			__magic_name__			(			self				):
          self.run_seqaseq_quick()
   @require_torch_multi_gpu
   def 			__magic_name__			(			self				):
          self.run_seqaseq_quick(distributed=_a				)
   @require_torch_multi_gpu
   def 			__magic_name__			(			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 			__magic_name__			(			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 			__magic_name__			(			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 			__magic_name__			(			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 			__magic_name__			(			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 			__magic_name__			(			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=_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 			__magic_name__			(			self	,    _a				):
          # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
          lowercase    :				Optional[Any]       		=							{
              # 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    :				Union[str, Any]       		=							experiments[experiment_id]
          lowercase    :				str       		=							{"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
          lowercase    :				int       		=							"Running training"
          with CaptureStderr() as cl:
                 self.run_seqaseq_quick(**_a	,    extra_args_str=data["extra_args_str"]				)
          lowercase    :				Optional[int]       		=							len(re.findall(_a	,    cl.err				)				)
          self.assertEqual(_a	,    data["n_matches"]				)
   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.run_trainer(
              eval_steps=2	,    max_len=128	,    model_name=_a	,    learning_rate=3E-4	,    num_train_epochs=10	,    distributed=_a	,    )

          # Check metrics
          lowercase    :				List[str]       		=							TrainerState.load_from_json(os.path.join(_a	,    "trainer_state.json"				)				).log_history
          lowercase    :				Union[str, Any]       		=							[log for log in logs if "eval_loss" in log.keys()]
          lowercase    :				Optional[int]       		=							eval_metrics[0]
          lowercase    :				Optional[int]       		=							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
          lowercase    :				Optional[int]       		=							os.listdir(_a				)
          lowercase    :				List[str]       		=							{os.path.basename(_a				) for p in contents}
          assert "generated_predictions.txt" in contents
          assert "predict_results.json" in contents
   @slow
   @require_bitsandbytes
   def 			__magic_name__			(			self				):
          from transformers.training_args import OptimizerNames

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

                 lowercase    :				Any       		=							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
                 lowercase    :				Any       		=							TrainerState.load_from_json(Path(_a	,    "trainer_state.json"				)				).log_history
                 lowercase    :				Dict       		=							int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20				)
                 lowercase    :				Any       		=							int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20				)

                 lowercase    :				str       		=							logs[0]["train_loss"]
                 return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

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

          lowercase    :				Any       		=							gpu_alloc_mem_orig - gpu_alloc_mem_bnb

          lowercase    :				List[str]       		=							gpu_peak_mem_orig + gpu_alloc_mem_orig
          lowercase    :				Tuple       		=							gpu_peak_mem_bnb + gpu_alloc_mem_bnb
          lowercase    :				str       		=							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    :				Optional[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(
              _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 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a = 3E-3	,    _a = "adafactor"	,    _a = False	,    _a = None	,    _a = 0	,    _a = True	,    _a = True	,    _a = True	,    _a = True	,    _a = None	,    ):
          lowercase    :				List[Any]       		=							self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
          lowercase    :				Union[str, Any]       		=							self.get_auto_remove_tmp_dir()
          lowercase    :				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(_a				)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(_a				)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

          lowercase    :				str       		=							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(_a				)}
        """.split()

          lowercase    :				Any       		=							"\n            --do_predict\n        ".split()

          lowercase    :				Tuple       		=							[]
          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    :				Optional[int]       		=							get_gpu_count()
                 lowercase    :				Dict       		=							get_torch_dist_unique_port()
                 lowercase    :				Optional[int]       		=							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    :				int       		=							[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:
                 lowercase    :				Optional[int]       		=							["run_translation.py"] + args
                 with patch.object(_a	,    "argv"	,    _a				):
                        main()

          return output_dir




"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							"laion/clap-htsat-unfused"
          lowercase    :				Optional[int]       		=							tempfile.mkdtemp()
   def 			__magic_name__			(			self	,    **_a				):
          return RobertaTokenizer.from_pretrained(self.checkpoint	,    **_a				)
   def 			__magic_name__			(			self	,    **_a				):
          return ClapFeatureExtractor.from_pretrained(self.checkpoint	,    **_a				)
   def 			__magic_name__			(			self				):
          shutil.rmtree(self.tmpdirname				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_tokenizer()
          lowercase    :				List[Any]       		=							self.get_feature_extractor()

          lowercase    :				Dict       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          processor.save_pretrained(self.tmpdirname				)
          lowercase    :				int       		=							ClapProcessor.from_pretrained(self.tmpdirname				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    _a				)

          self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor.to_json_string()				)
          self.assertIsInstance(processor.feature_extractor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							ClapProcessor(tokenizer=self.get_tokenizer()	,    feature_extractor=self.get_feature_extractor()				)
          processor.save_pretrained(self.tmpdirname				)

          lowercase    :				Union[str, Any]       		=							self.get_tokenizer(bos_token="(BOS)"	,    eos_token="(EOS)"				)
          lowercase    :				Optional[int]       		=							self.get_feature_extractor(do_normalize=_a	,    padding_value=1.0				)

          lowercase    :				Dict       		=							ClapProcessor.from_pretrained(
              self.tmpdirname	,    bos_token="(BOS)"	,    eos_token="(EOS)"	,    do_normalize=_a	,    padding_value=1.0				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    _a				)

          self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor_add_kwargs.to_json_string()				)
          self.assertIsInstance(processor.feature_extractor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.get_feature_extractor()
          lowercase    :				List[str]       		=							self.get_tokenizer()

          lowercase    :				int       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Dict       		=							floats_list((3, 1_000)				)

          lowercase    :				str       		=							feature_extractor(_a	,    return_tensors="np"				)
          lowercase    :				Dict       		=							processor(audios=_a	,    return_tensors="np"				)

          for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()	,    input_processor[key].sum()	,    delta=1E-2				)
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.get_feature_extractor()
          lowercase    :				int       		=							self.get_tokenizer()

          lowercase    :				Dict       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Optional[Any]       		=							"This is a test string"

          lowercase    :				Any       		=							processor(text=_a				)

          lowercase    :				List[Any]       		=							tokenizer(_a				)

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_feature_extractor()
          lowercase    :				Any       		=							self.get_tokenizer()

          lowercase    :				Union[str, Any]       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Union[str, Any]       		=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          lowercase    :				str       		=							processor.batch_decode(_a				)
          lowercase    :				Optional[int]       		=							tokenizer.batch_decode(_a				)

          self.assertListEqual(_a	,    _a				)

   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.get_feature_extractor()
          lowercase    :				Union[str, Any]       		=							self.get_tokenizer()

          lowercase    :				Any       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          self.assertListEqual(
              processor.model_input_names[2:]	,    feature_extractor.model_input_names	,    msg="`processor` and `feature_extractor` model input names do not match"	,    )


