



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


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





@require_torch
class 				lowerCAmelCase_						(			A__		):





    def 	__snake_case  (							self							:							Any,							_snake_case							:							List[str]=False,							_snake_case							:							str=None,							_snake_case							:							List[Any]=True,							_snake_case							:							List[str]=True,							_snake_case							:							List[str]=True,							_snake_case							:							Any=True,							):



       '''simple docstring'''







       snake_case       :				Tuple		    =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_,							)
       snake_case       :				Tuple		    =TrainerState.load_from_json(os.path.join(UpperCamelCase_,							'''trainer_state.json'''      )      ).log_history

       if not do_eval:
          return

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

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

          snake_case       :				int		    =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							:							Any      ):



       '''simple docstring'''







       self.run_seqaseq_quick()





    @require_torch_multi_gpu
    def 	__snake_case  (							self							:							Any      ):



       '''simple docstring'''







       self.run_seqaseq_quick(distributed=UpperCamelCase_      )





    @require_torch_multi_gpu
    def 	__snake_case  (							self							:							int      ):



       '''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							:							List[str]      ):



       '''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							:							Dict      ):



       '''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							:							Optional[Any]      ):



       '''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							:							Any      ):



       '''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							:							Tuple      ):



       '''simple docstring'''







       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							:							List[str],							_snake_case							:							Union[str, Any]      ):



       '''simple docstring'''







       snake_case       :				Union[str, 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},
       }

       snake_case       :				Union[str, Any]		    =experiments[experiment_id]
       snake_case       :				str		    ={'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
       snake_case       :				Union[str, Any]		    ='''Running training'''
       with CaptureStderr() as cl:
          self.run_seqaseq_quick(**UpperCamelCase_,							extra_args_str=data['''extra_args_str''']      )
       snake_case       :				Union[str, Any]		    =len(re.findall(UpperCamelCase_,							cl.err      )      )
       self.assertEqual(UpperCamelCase_,							data['''n_matches''']      )





    @slow
    def 	__snake_case  (							self							:							Optional[int]      ):



       '''simple docstring'''







       snake_case       :				List[str]		    =self.run_trainer(
           eval_steps=2,							max_len=128,							model_name=UpperCamelCase_,							learning_rate=3E-4,							num_train_epochs=10,							distributed=UpperCamelCase_,							)

       # Check metrics
       snake_case       :				Any		    =TrainerState.load_from_json(os.path.join(UpperCamelCase_,							'''trainer_state.json'''      )      ).log_history
       snake_case       :				List[Any]		    =[log for log in logs if '''eval_loss''' in log.keys()]
       snake_case       :				Dict		    =eval_metrics[0]
       snake_case       :				Any		    =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
       snake_case       :				Tuple		    =os.listdir(UpperCamelCase_      )
       snake_case       :				Dict		    ={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							:							Union[str, Any]      ):



       '''simple docstring'''







       from transformers.training_args import OptimizerNames

       def train_and_return_metrics(_snake_case							:							Tuple      ) -> Tuple[int, float]:
          snake_case       :				List[str]		    ='''--skip_memory_metrics 0'''

          snake_case       :				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
          snake_case       :				List[Any]		    =TrainerState.load_from_json(Path(UpperCamelCase_,							'''trainer_state.json'''      )      ).log_history
          snake_case       :				Tuple		    =int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20      )
          snake_case       :				Optional[int]		    =int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20      )

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

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

       snake_case       :				Any		    =gpu_alloc_mem_orig - gpu_alloc_mem_bnb

       snake_case       :				Any		    =gpu_peak_mem_orig + gpu_alloc_mem_orig
       snake_case       :				int		    =gpu_peak_mem_bnb + gpu_alloc_mem_bnb
       snake_case       :				Dict		    =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       :				Optional[int]		    =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							:							List[Any],							_snake_case							:							str,							_snake_case							:							str,							_snake_case							:							List[str],							_snake_case							:							Tuple = 3E-3,							_snake_case							:							str = "adafactor",							_snake_case							:							Optional[Any] = False,							_snake_case							:							Tuple = None,							_snake_case							:							Any = 0,							_snake_case							:							Union[str, Any] = True,							_snake_case							:							Optional[Any] = True,							_snake_case							:							Union[str, Any] = True,							_snake_case							:							Dict = True,							_snake_case							:							int = None,							):



       '''simple docstring'''







       snake_case       :				Dict		    =self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
       snake_case       :				List[Any]		    =self.get_auto_remove_tmp_dir()
       snake_case       :				Optional[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()

       snake_case       :				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()

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

       snake_case       :				Dict		    =[]
       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       :				int		    =get_gpu_count()
          snake_case       :				str		    =get_torch_dist_unique_port()
          snake_case       :				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()
          snake_case       :				List[str]		    =[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:
          snake_case       :				str		    =['''run_translation.py'''] + args
          with patch.object(UpperCamelCase_,							'''argv''',							UpperCamelCase_      ):
             main()

       return output_dir









'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


__snake_case				 =	{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case				 =	{
    '''vocab_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-generator''': (
            '''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
        ),
    },
}

__snake_case				 =	{
    '''google/electra-small-generator''': 512,
    '''google/electra-base-generator''': 512,
    '''google/electra-large-generator''': 512,
    '''google/electra-small-discriminator''': 512,
    '''google/electra-base-discriminator''': 512,
    '''google/electra-large-discriminator''': 512,
}

__snake_case				 =	{
    '''google/electra-small-generator''': {'''do_lower_case''': True},
    '''google/electra-base-generator''': {'''do_lower_case''': True},
    '''google/electra-large-generator''': {'''do_lower_case''': True},
    '''google/electra-small-discriminator''': {'''do_lower_case''': True},
    '''google/electra-base-discriminator''': {'''do_lower_case''': True},
    '''google/electra-large-discriminator''': {'''do_lower_case''': True},
}
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							VOCAB_FILES_NAMES
		_a       					=							PRETRAINED_VOCAB_FILES_MAP
		_a       					=							PRETRAINED_INIT_CONFIGURATION
		_a       					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a       					=							ElectraTokenizer




		def __init__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=True       ,  UpperCamelCase_="[UNK]"       ,  UpperCamelCase_="[SEP]"       ,  UpperCamelCase_="[PAD]"       ,  UpperCamelCase_="[CLS]"       ,  UpperCamelCase_="[MASK]"       ,  UpperCamelCase_=True       ,  UpperCamelCase_=None       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							super().__init__(
							    UpperCamelCase_       ,  tokenizer_file=UpperCamelCase_       ,  do_lower_case=UpperCamelCase_       ,  unk_token=UpperCamelCase_       ,  sep_token=UpperCamelCase_       ,  pad_token=UpperCamelCase_       ,  cls_token=UpperCamelCase_       ,  mask_token=UpperCamelCase_       ,  tokenize_chinese_chars=UpperCamelCase_       ,  strip_accents=UpperCamelCase_       ,  **UpperCamelCase_       ,  )

							UpperCamelCase__  :str				   =					json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
							if (
							    normalizer_state.get('''lowercase'''       ,  UpperCamelCase_				) != do_lower_case
							    or normalizer_state.get('''strip_accents'''       ,  UpperCamelCase_				) != strip_accents
							    or normalizer_state.get('''handle_chinese_chars'''       ,  UpperCamelCase_				) != tokenize_chinese_chars
							):
												UpperCamelCase__  :Any				   =					getattr(UpperCamelCase_       ,  normalizer_state.pop('''type'''				)				)
												UpperCamelCase__  :Any				   =					do_lower_case
												UpperCamelCase__  :Dict				   =					strip_accents
												UpperCamelCase__  :Optional[int]				   =					tokenize_chinese_chars
												UpperCamelCase__  :Dict				   =					normalizer_class(**UpperCamelCase_				)

							UpperCamelCase__  :str				   =					do_lower_case




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=None				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

							return output




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					[self.sep_token_id]
							UpperCamelCase__  :str				   =					[self.cls_token_id]
							if token_ids_a is None:
												return len(cls + token_ids_a + sep				) * [0]
							return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					self._tokenizer.model.save(UpperCamelCase_       ,  name=UpperCamelCase_				)
							return tuple(UpperCamelCase_				)