
"""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


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


set_seed(42)
lowercase_       :						Union[str, Any]     					=	'''sshleifer/student_marian_en_ro_6_1'''
lowercase_       :						Union[str, Any]     					=	'''sshleifer/tiny-mbart'''
@require_torch
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=False  ,     snake_case__=None  ,     snake_case__=True  ,     snake_case__=True  ,     snake_case__=True  ,     snake_case__=True  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = self.run_trainer(
           eval_steps=1  ,     max_len=12  ,     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__  ,     )
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = TrainerState.load_from_json(os.path.join(snake_case__  ,     "trainer_state.json"				)				).log_history

       if not do_eval:
         return

       _SCREAMING_SNAKE_CASE   :				str							    = [log for log in logs if "eval_loss" in log.keys()]

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = eval_metrics[0]
       if predict_with_generate:
         assert "eval_bleu" in first_step_stats

         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = 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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       self.run_seqaseq_quick()
     @require_torch_multi_gpu
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       self.run_seqaseq_quick(distributed=snake_case__				)
     @require_torch_multi_gpu
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       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 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = {
           # 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},
       }

       _SCREAMING_SNAKE_CASE   :				int							    = experiments[experiment_id]
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = "Running training"
       with CaptureStderr() as cl:
         self.run_seqaseq_quick(**snake_case__  ,     extra_args_str=data["extra_args_str"]				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = len(re.findall(snake_case__  ,     cl.err				)				)
       self.assertEqual(snake_case__  ,     data["n_matches"]				)
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.run_trainer(
           eval_steps=2  ,     max_len=128  ,     model_name=snake_case__  ,     learning_rate=3E-4  ,     num_train_epochs=10  ,     distributed=snake_case__  ,     )

       # Check metrics
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = TrainerState.load_from_json(os.path.join(snake_case__  ,     "trainer_state.json"				)				).log_history
       _SCREAMING_SNAKE_CASE   :				int							    = [log for log in logs if "eval_loss" in log.keys()]
       _SCREAMING_SNAKE_CASE   :				int							    = eval_metrics[0]
       _SCREAMING_SNAKE_CASE   :				Tuple							    = 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
       _SCREAMING_SNAKE_CASE   :				Tuple							    = os.listdir(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				int							    = {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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       from transformers.training_args import OptimizerNames

       def train_and_return_metrics(snake_case__				) -> Tuple[int, float]:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = "--skip_memory_metrics 0"

         _SCREAMING_SNAKE_CASE   :				Tuple							    = self.run_trainer(
             max_len=128  ,     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
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = TrainerState.load_from_json(Path(snake_case__  ,     "trainer_state.json"				)				).log_history
         _SCREAMING_SNAKE_CASE   :				Any							    = int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20				)
         _SCREAMING_SNAKE_CASE   :				Dict							    = int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20				)

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

       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Dict							    = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value				)
       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Dict							    = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = gpu_alloc_mem_orig - gpu_alloc_mem_bnb

       _SCREAMING_SNAKE_CASE   :				List[str]							    = gpu_peak_mem_orig + gpu_alloc_mem_orig
       _SCREAMING_SNAKE_CASE   :				str							    = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
       _SCREAMING_SNAKE_CASE   :				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
       _SCREAMING_SNAKE_CASE   :				List[str]							    = 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(
           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 				__SCREAMING_SNAKE_CASE							(     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  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.get_auto_remove_tmp_dir()
       _SCREAMING_SNAKE_CASE   :				Dict							    = 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()

       _SCREAMING_SNAKE_CASE   :				Dict							    = 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()

       _SCREAMING_SNAKE_CASE   :				int							    = "\n            --do_predict\n        ".split()

       _SCREAMING_SNAKE_CASE   :				str							    = []
       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:
           _SCREAMING_SNAKE_CASE   :				Any							    = get_gpu_count()
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = get_torch_dist_unique_port()
         _SCREAMING_SNAKE_CASE   :				List[str]							    = 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()
         _SCREAMING_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(snake_case__  ,     env=self.get_env()				)
       else:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = ["run_translation.py"] + args
         with patch.object(snake_case__  ,     "argv"  ,     snake_case__				):
           main()

       return output_dir





"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be trained."""}  )
     A__      =			field(
         default="""./"""      ,       metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path of training dataset."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for training."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for evaluation."""}  )
     A__      =			field(default=0.1      ,       metadata={"""help""": """Value of weight decay."""}  )
     A__      =			field(
         default=10000      ,       metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}  )
     A__      =			field(default=2e-4      ,       metadata={"""help""": """Learning rate fo training."""}  )
     A__      =			field(default="""cosine"""      ,       metadata={"""help""": """Learning rate."""}  )
     A__      =			field(
         default=750      ,       metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}  )
     A__      =			field(
         default=16      ,       metadata={"""help""": """Number of gradient accumulation steps."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}  )
     A__      =			field(default=50000      ,       metadata={"""help""": """Maximum number of training steps."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Sequence lengths used for training."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Training seed."""}  )
     A__      =			field(
         default=1024      ,       metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True the data is pretokenized."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size used for evaluation."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Length of sequences to be evaluated."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Sample from the language model's output distribution."""}  )
     A__      =			field(default=0.2      ,       metadata={"""help""": """Sampling temperature used for generation."""}  )
     A__      =			field(default=256      ,       metadata={"""help""": """Maximum number of newly generated tokens."""}  )
     A__      =			field(default=0      ,       metadata={"""help""": """Top-k parameter used for generation."""}  )
     A__      =			field(default=0.95      ,       metadata={"""help""": """Top-p parameter used for nucleus sampling."""}  )
     A__      =			field(default=10      ,       metadata={"""help""": """Number of generations to run in parallel."""}  )
     A__      =			field(
         default=200      ,       metadata={"""help""": """Number of completions to generate for each sample."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""eval_results.json"""      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""0"""      ,       metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}  )
     A__      =			field(
         default=-1      ,       metadata={
             """help""": (
                 """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
                 """ number corresponds to which GPU device id to run on."""
             )
         }      ,       )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={
             """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
         }      ,       )
     A__      =			field(
         default="""transformersbook/codeparrot"""      ,       metadata={"""help""": """Folder or name of dataset to process."""}  )
     A__      =			field(
         default="""codeparrot-clean"""      ,       metadata={"""help""": """Folder to save processed processed dataset."""}  )
     A__      =			field(
         default=100000      ,       metadata={"""help""": """Number of files to save per JSON output file."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(
         default=1000      ,       metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=100      ,       metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.25      ,       metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}  )
     A__      =			field(
         default=1.5      ,       metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.7      ,       metadata={"""help""": """Probability for filtering config, test and uncommon files."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True, near-duplicate samples are removed."""}  )
     A__      =			field(
         default=0.85      ,       metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2"""      ,       metadata={"""help""": """Base tokenizer to build new tokenizer from."""}  )
     A__      =			field(
         default="""transformersbook/codeparrot-train"""      ,       metadata={"""help""": """Dataset to train tokenizer on."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(default=200000      ,       metadata={"""help""": """Number of examples to train tokenizer on."""}  )
     A__      =			field(
         default=32768      ,       metadata={"""help""": """Number of examples to train the tokenizer on."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of new tokenizer."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path to the dataset to pretokenize."""}  )
     A__      =			field(
         default="""tokenized-codeparrot-train"""      ,       metadata={"""help""": """Repo name of the pretokenized data."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2-large"""      ,       metadata={"""help""": """Configuration to use for model initialization."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Tokenizer attached to model."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of the created model."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )



