





import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowerCAmelCase_    =				logging.getLogger(__name__)



class 					__lowerCAmelCase       (			_a     ):






			def __init__(self					,							__magic_name__=-1    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    int				=  label_idx






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							if isinstance(__magic_name__					,							__magic_name__    ):
											snake_case_						:    Union[str, Any]				=  mode.value
							snake_case_						:    List[Any]				=  os.path.join(__magic_name__					,							F'''{mode}.txt'''    )
							snake_case_						:    Tuple				=  1
							snake_case_						:    List[Any]				=  []
							with open(__magic_name__					,							encoding='''utf-8'''    ) as f:
											snake_case_						:    Dict				=  []
											snake_case_						:    List[Any]				=  []
											for line in f:
															if line.startswith('''-DOCSTART-'''    ) or line == "" or line == "\n":
																			if words:
																							examples.append(InputExample(guid=F'''{mode}-{guid_index}'''					,							words=__magic_name__					,							labels=__magic_name__    )    )
																							guid_index += 1
																							snake_case_						:    Union[str, Any]				=  []
																							snake_case_						:    Union[str, Any]				=  []
															else:
																			snake_case_						:    str				=  line.split(''' '''    )
																			words.append(splits[0]    )
																			if len(__magic_name__    ) > 1:
																							labels.append(splits[self.label_idx].replace('''\n'''					,							''''''    )    )
																			else:
																							# Examples could have no label for mode = "test"
																							labels.append('''O'''    )
											if words:
															examples.append(InputExample(guid=F'''{mode}-{guid_index}'''					,							words=__magic_name__					,							labels=__magic_name__    )    )
							return examples






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   str:


							'''simple docstring'''



							snake_case_						:    List[str]				=  0
							for line in test_input_reader:
											if line.startswith('''-DOCSTART-'''    ) or line == "" or line == "\n":
															writer.write(__magic_name__    )
															if not preds_list[example_id]:
																			example_id += 1
											elif preds_list[example_id]:
															snake_case_						:    Optional[int]				=  line.split()[0] + ''' ''' + preds_list[example_id].pop(0    ) + '''\n'''
															writer.write(__magic_name__    )
											else:
															logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''					,							line.split()[0]    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Optional[int]:


							'''simple docstring'''



							if path:
											with open(__magic_name__					,							'''r'''    ) as f:
															snake_case_						:    Optional[int]				=  f.read().splitlines()
											if "O" not in labels:
															snake_case_						:    Optional[int]				=  ['''O'''] + labels
											return labels
							else:
											return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class 					__lowerCAmelCase       (			_a     ):






			def __init__(self    )     ->   Optional[Any]:


							'''simple docstring'''



							super().__init__(label_idx=-2    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   str:


							'''simple docstring'''



							if path:
											with open(__magic_name__					,							'''r'''    ) as f:
															snake_case_						:    Dict				=  f.read().splitlines()
											if "O" not in labels:
															snake_case_						:    Optional[Any]				=  ['''O'''] + labels
											return labels
							else:
											return [
											    "O",
											    "B-ADVP",
											    "B-INTJ",
											    "B-LST",
											    "B-PRT",
											    "B-NP",
											    "B-SBAR",
											    "B-VP",
											    "B-ADJP",
											    "B-CONJP",
											    "B-PP",
											    "I-ADVP",
											    "I-INTJ",
											    "I-LST",
											    "I-PRT",
											    "I-NP",
											    "I-SBAR",
											    "I-VP",
											    "I-ADJP",
											    "I-CONJP",
											    "I-PP",
											]




class 					__lowerCAmelCase       (			_a     ):






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   Optional[int]:


							'''simple docstring'''



							if isinstance(__magic_name__					,							__magic_name__    ):
											snake_case_						:    Optional[int]				=  mode.value
							snake_case_						:    Optional[int]				=  os.path.join(__magic_name__					,							F'''{mode}.txt'''    )
							snake_case_						:    Any				=  1
							snake_case_						:    Dict				=  []

							with open(__magic_name__					,							encoding='''utf-8'''    ) as f:
											for sentence in parse_incr(__magic_name__    ):
															snake_case_						:    List[str]				=  []
															snake_case_						:    str				=  []
															for token in sentence:
																			words.append(token['''form''']    )
																			labels.append(token['''upos''']    )
															assert len(__magic_name__    ) == len(__magic_name__    )
															if words:
																			examples.append(InputExample(guid=F'''{mode}-{guid_index}'''					,							words=__magic_name__					,							labels=__magic_name__    )    )
																			guid_index += 1
							return examples






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    List[Any]				=  0
							for sentence in parse_incr(__magic_name__    ):
											snake_case_						:    Tuple				=  preds_list[example_id]
											snake_case_						:    List[str]				=  ''''''
											for token in sentence:
															out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0    )}) '''
											out += "\n"
											writer.write(__magic_name__    )
											example_id += 1






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   List[Any]:


							'''simple docstring'''



							if path:
											with open(__magic_name__					,							'''r'''    ) as f:
															return f.read().splitlines()
							else:
											return [
											    "ADJ",
											    "ADP",
											    "ADV",
											    "AUX",
											    "CCONJ",
											    "DET",
											    "INTJ",
											    "NOUN",
											    "NUM",
											    "PART",
											    "PRON",
											    "PROPN",
											    "PUNCT",
											    "SCONJ",
											    "SYM",
											    "VERB",
											    "X",
											]




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


set_seed(42)
a_  :				int						= """sshleifer/student_marian_en_ro_6_1"""
a_  :				str						= """sshleifer/tiny-mbart"""





@require_torch
class      snake_case    (    lowercase			):





  """simple docstring"""




  def   snake_case		(    self			,  UpperCamelCase=False			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  ):







   """simple docstring"""
   lowerCamelCase_      =  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_      =  TrainerState.load_from_json(os.path.join(UpperCamelCase			,  "trainer_state.json"     )     ).log_history

   if not do_eval:
    return

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

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

    lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick()






  @require_torch_multi_gpu
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase     )






  @require_torch_multi_gpu
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase     )






  @unittest.skip("Requires an update of the env running those tests"     )
  @require_torch_multi_gpu
  @require_fairscale
  def   snake_case		(    self     ):







   """simple docstring"""
   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   snake_case		(    self     ):







   """simple docstring"""
   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   snake_case		(    self     ):







   """simple docstring"""
   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   snake_case		(    self     ):







   """simple docstring"""
   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   snake_case		(    self     ):







   """simple docstring"""
   # 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   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
   lowerCamelCase_      =  {
       # 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_      =  experiments[experiment_id]
   lowerCamelCase_      =  {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
   lowerCamelCase_      =  "Running training"
   with CaptureStderr() as cl:
    self.run_seqaseq_quick(**UpperCamelCase			,  extra_args_str=data["extra_args_str"]     )
   lowerCamelCase_      =  len(re.findall(UpperCamelCase			,  cl.err     )     )
   self.assertEqual(UpperCamelCase			,  data["n_matches"]     )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  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_      =  TrainerState.load_from_json(os.path.join(UpperCamelCase			,  "trainer_state.json"     )     ).log_history
   lowerCamelCase_      =  [log for log in logs if "eval_loss" in log.keys()]
   lowerCamelCase_      =  eval_metrics[0]
   lowerCamelCase_      =  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_      =  os.listdir(UpperCamelCase     )
   lowerCamelCase_      =  {os.path.basename(UpperCamelCase     ) for p in contents}
   assert "generated_predictions.txt" in contents
   assert "predict_results.json" in contents






  @slow
  @require_bitsandbytes
  def   snake_case		(    self     ):







   """simple docstring"""
   from transformers.training_args import OptimizerNames

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

    lowerCamelCase_      =  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_      =  TrainerState.load_from_json(Path(UpperCamelCase			,  "trainer_state.json"     )     ).log_history
    lowerCamelCase_      =  int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20     )
    lowerCamelCase_      =  int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20     )

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

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

   lowerCamelCase_      =  gpu_alloc_mem_orig - gpu_alloc_mem_bnb

   lowerCamelCase_      =  gpu_peak_mem_orig + gpu_alloc_mem_orig
   lowerCamelCase_      =  gpu_peak_mem_bnb + gpu_alloc_mem_bnb
   lowerCamelCase_      =  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_      =  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   snake_case		(    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			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
   lowerCamelCase_      =  self.get_auto_remove_tmp_dir()
   lowerCamelCase_      =  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_      =  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_      =  "\n            --do_predict\n        ".split()

   lowerCamelCase_      =  []
   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_      =  get_gpu_count()
    lowerCamelCase_      =  get_torch_dist_unique_port()
    lowerCamelCase_      =  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_      =  [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_      =  ["run_translation.py"] + args
    with patch.object(UpperCamelCase			,  "argv"			,  UpperCamelCase     ):
     main()

   return output_dir



