






import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         DebertaForMaskedLM,
         DebertaForQuestionAnswering,
         DebertaForSequenceClassification,
         DebertaForTokenClassification,
         DebertaModel,
     )
     from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST

class   __a(					__lowercase       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=13       ,_SCREAMING_SNAKE_CASE=7       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=99       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=37       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=512       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE="None"       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=None       ,)			->		List[Any]:
         UpperCAmelCase_      :							Optional[int]       =      parent
         UpperCAmelCase_      :							Union[str, Any]       =      batch_size
         UpperCAmelCase_      :							Dict       =      seq_length
         UpperCAmelCase_      :							str       =      is_training
         UpperCAmelCase_      :							List[Any]       =      use_input_mask
         UpperCAmelCase_      :							List[Any]       =      use_token_type_ids
         UpperCAmelCase_      :							int       =      use_labels
         UpperCAmelCase_      :							List[Any]       =      vocab_size
         UpperCAmelCase_      :							str       =      hidden_size
         UpperCAmelCase_      :							List[Any]       =      num_hidden_layers
         UpperCAmelCase_      :							int       =      num_attention_heads
         UpperCAmelCase_      :							Tuple       =      intermediate_size
         UpperCAmelCase_      :							Union[str, Any]       =      hidden_act
         UpperCAmelCase_      :							Tuple       =      hidden_dropout_prob
         UpperCAmelCase_      :							List[Any]       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							Dict       =      max_position_embeddings
         UpperCAmelCase_      :							int       =      type_vocab_size
         UpperCAmelCase_      :							Tuple       =      type_sequence_label_size
         UpperCAmelCase_      :							Optional[Any]       =      initializer_range
         UpperCAmelCase_      :							Union[str, Any]       =      num_labels
         UpperCAmelCase_      :							int       =      num_choices
         UpperCAmelCase_      :							Tuple       =      relative_attention
         UpperCAmelCase_      :							Union[str, Any]       =      position_biased_input
         UpperCAmelCase_      :							Union[str, Any]       =      pos_att_type
         UpperCAmelCase_      :							Optional[int]       =      scope





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							int       =      ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size  )

         UpperCAmelCase_      :							Union[str, Any]       =      None
         if self.use_input_mask:
              UpperCAmelCase_      :							int       =      ids_tensor([self.batch_size, self.seq_length]       ,vocab_size=2  )

         UpperCAmelCase_      :							Tuple       =      None
         if self.use_token_type_ids:
              UpperCAmelCase_      :							Union[str, Any]       =      ids_tensor([self.batch_size, self.seq_length]       ,self.type_vocab_size  )

         UpperCAmelCase_      :							Union[str, Any]       =      None
         UpperCAmelCase_      :							Optional[Any]       =      None
         UpperCAmelCase_      :							Optional[Any]       =      None
         if self.use_labels:
              UpperCAmelCase_      :							Any       =      ids_tensor([self.batch_size]       ,self.type_sequence_label_size  )
              UpperCAmelCase_      :							List[Any]       =      ids_tensor([self.batch_size, self.seq_length]       ,self.num_labels  )
              UpperCAmelCase_      :							Optional[int]       =      ids_tensor([self.batch_size]       ,self.num_choices  )

         UpperCAmelCase_      :							Optional[Any]       =      self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





    def 							a__				(				self  )			->		Optional[Any]:
         return DebertaConfig(
             vocab_size=self.vocab_size       ,hidden_size=self.hidden_size       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,max_position_embeddings=self.max_position_embeddings       ,type_vocab_size=self.type_vocab_size       ,initializer_range=self.initializer_range       ,relative_attention=self.relative_attention       ,position_biased_input=self.position_biased_input       ,pos_att_type=self.pos_att_type       ,)





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Any       =      self.get_config()
         UpperCAmelCase_      :							Optional[Any]       =      300
         return config





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         self.parent.assertListEqual(list(result.loss.size()  )       ,[]  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
         UpperCAmelCase_      :							Union[str, Any]       =      DebertaModel(config=snake_case_  )
         model.to(snake_case_  )
         model.eval()
         UpperCAmelCase_      :							Optional[Any]       =      model(snake_case_       ,attention_mask=snake_case_       ,token_type_ids=snake_case_  )[0]
         UpperCAmelCase_      :							str       =      model(snake_case_       ,token_type_ids=snake_case_  )[0]
         UpperCAmelCase_      :							int       =      model(snake_case_  )[0]

         self.parent.assertListEqual(list(sequence_output.size()  )       ,[self.batch_size, self.seq_length, self.hidden_size]  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         UpperCAmelCase_      :							List[Any]       =      DebertaForMaskedLM(config=snake_case_  )
         model.to(snake_case_  )
         model.eval()
         UpperCAmelCase_      :							Union[str, Any]       =      model(snake_case_       ,attention_mask=snake_case_       ,token_type_ids=snake_case_       ,labels=snake_case_  )

         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							str       =      self.num_labels
         UpperCAmelCase_      :							Optional[Any]       =      DebertaForSequenceClassification(snake_case_  )
         model.to(snake_case_  )
         model.eval()
         UpperCAmelCase_      :							Tuple       =      model(snake_case_       ,attention_mask=snake_case_       ,token_type_ids=snake_case_       ,labels=snake_case_  )
         self.parent.assertListEqual(list(result.logits.size()  )       ,[self.batch_size, self.num_labels]  )
         self.check_loss_output(snake_case_  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
         UpperCAmelCase_      :							List[str]       =      self.num_labels
         UpperCAmelCase_      :							Union[str, Any]       =      DebertaForTokenClassification(config=snake_case_  )
         model.to(snake_case_  )
         model.eval()
         UpperCAmelCase_      :							Dict       =      model(snake_case_       ,attention_mask=snake_case_       ,token_type_ids=snake_case_       ,labels=snake_case_  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.num_labels)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
         UpperCAmelCase_      :							str       =      DebertaForQuestionAnswering(config=snake_case_  )
         model.to(snake_case_  )
         model.eval()
         UpperCAmelCase_      :							Union[str, Any]       =      model(
             snake_case_       ,attention_mask=snake_case_       ,token_type_ids=snake_case_       ,start_positions=snake_case_       ,end_positions=snake_case_       ,)
         self.parent.assertEqual(result.start_logits.shape       ,(self.batch_size, self.seq_length)  )
         self.parent.assertEqual(result.end_logits.shape       ,(self.batch_size, self.seq_length)  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							Optional[int]       =      self.prepare_config_and_inputs()
         (
             UpperCAmelCase_	
         )      :							Tuple       =      config_and_inputs
         UpperCAmelCase_      :							Union[str, Any]       =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict


@require_torch
class   __a(					__lowercase    ,						__lowercase    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				(
        (
            DebertaModel,
            DebertaForMaskedLM,
            DebertaForSequenceClassification,
            DebertaForTokenClassification,
            DebertaForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    lowerCAmelCase        =				(
        {
            "feature-extraction": DebertaModel,
            "fill-mask": DebertaForMaskedLM,
            "question-answering": DebertaForQuestionAnswering,
            "text-classification": DebertaForSequenceClassification,
            "token-classification": DebertaForTokenClassification,
            "zero-shot": DebertaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    lowerCAmelCase        =				True
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							str       =      DebertaModelTester(self  )
         UpperCAmelCase_      :							List[Any]       =      ConfigTester(self       ,config_class=snake_case_       ,hidden_size=37  )





    def 							a__				(				self  )			->		Optional[Any]:
         self.config_tester.run_common_tests()





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Union[str, Any]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_model(*snake_case_  )





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							str       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_sequence_classification(*snake_case_  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_masked_lm(*snake_case_  )





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Tuple       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_question_answering(*snake_case_  )





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							List[str]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_token_classification(*snake_case_  )





    @slow
    def 							a__				(				self  )			->		Tuple:
         for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_      :							str       =      DebertaModel.from_pretrained(snake_case_  )
              self.assertIsNotNone(snake_case_  )


@require_torch
@require_sentencepiece
@require_tokenizers
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    @unittest.skip(reason='''Model not available yet'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass





    @slow
    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							int       =      DebertaModel.from_pretrained('''microsoft/deberta-base'''  )

         UpperCAmelCase_      :							Optional[int]       =      torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  )
         UpperCAmelCase_      :							Optional[int]       =      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
         with torch.no_grad():
              UpperCAmelCase_      :							str       =      model(snake_case_       ,attention_mask=snake_case_  )[0]
         # compare the actual values for a slice.
         UpperCAmelCase_      :							str       =      torch.tensor(
             [[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]]  )
         self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,snake_case_       ,atol=1e-4  )       ,f'''{output[:, 1:4, 1:4]}'''  )





from dataclasses import dataclass, field
from typing import Optional

@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Model name or path of model to be trained.'''}       )
    lowerCAmelCase        =				field(
        default='''./'''    ,						metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-train'''    ,						metadata={'''help''': '''Name or path of training dataset.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-valid'''    ,						metadata={'''help''': '''Name or path of validation dataset.'''}       )
    lowerCAmelCase        =				field(default=2    ,						metadata={'''help''': '''Batch size for training.'''}       )
    lowerCAmelCase        =				field(default=2    ,						metadata={'''help''': '''Batch size for evaluation.'''}       )
    lowerCAmelCase        =				field(default=0.1    ,						metadata={'''help''': '''Value of weight decay.'''}       )
    lowerCAmelCase        =				field(
        default=1_0000    ,						metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}       )
    lowerCAmelCase        =				field(default=2E-4    ,						metadata={'''help''': '''Learning rate fo training.'''}       )
    lowerCAmelCase        =				field(default='''cosine'''    ,						metadata={'''help''': '''Learning rate.'''}       )
    lowerCAmelCase        =				field(
        default=750    ,						metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}       )
    lowerCAmelCase        =				field(
        default=16    ,						metadata={'''help''': '''Number of gradient accumulation steps.'''}       )
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}       )
    lowerCAmelCase        =				field(default=5_0000    ,						metadata={'''help''': '''Maximum number of training steps.'''}       )
    lowerCAmelCase        =				field(
        default=-1    ,						metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}       )
    lowerCAmelCase        =				field(default=1024    ,						metadata={'''help''': '''Sequence lengths used for training.'''}       )
    lowerCAmelCase        =				field(default=1    ,						metadata={'''help''': '''Training seed.'''}       )
    lowerCAmelCase        =				field(
        default=1024    ,						metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''If True the data is pretokenized.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Model name or path of model to be evaluated.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-valid'''    ,						metadata={'''help''': '''Name or path of validation dataset.'''}       )
    lowerCAmelCase        =				field(default=2    ,						metadata={'''help''': '''Batch size used for evaluation.'''}       )
    lowerCAmelCase        =				field(
        default=-1    ,						metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}       )
    lowerCAmelCase        =				field(default=1024    ,						metadata={'''help''': '''Length of sequences to be evaluated.'''}       )
    lowerCAmelCase        =				field(default=1    ,						metadata={'''help''': '''Random seed used for evaluation.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Model name or path of model to be evaluated.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Number of workers used for code evaluation.'''}       )
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''Sample from the language model\'s output distribution.'''}       )
    lowerCAmelCase        =				field(default=0.2    ,						metadata={'''help''': '''Sampling temperature used for generation.'''}       )
    lowerCAmelCase        =				field(default=256    ,						metadata={'''help''': '''Maximum number of newly generated tokens.'''}       )
    lowerCAmelCase        =				field(default=0    ,						metadata={'''help''': '''Top-k parameter used for generation.'''}       )
    lowerCAmelCase        =				field(default=0.95    ,						metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}       )
    lowerCAmelCase        =				field(default=10    ,						metadata={'''help''': '''Number of generations to run in parallel.'''}       )
    lowerCAmelCase        =				field(
        default=200    ,						metadata={'''help''': '''Number of completions to generate for each sample.'''}       )
    lowerCAmelCase        =				field(default=1    ,						metadata={'''help''': '''Random seed used for evaluation.'''}       )
    lowerCAmelCase        =				field(
        default='''eval_results.json'''    ,						metadata={'''help''': '''Random seed used for evaluation.'''}       )
    lowerCAmelCase        =				field(
        default='''0'''    ,						metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}       )
    lowerCAmelCase        =				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   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default=_a    ,						metadata={
            '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
        }    ,						)
    lowerCAmelCase        =				field(
        default='''transformersbook/codeparrot'''    ,						metadata={'''help''': '''Folder or name of dataset to process.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot-clean'''    ,						metadata={'''help''': '''Folder to save processed processed dataset.'''}       )
    lowerCAmelCase        =				field(
        default=10_0000    ,						metadata={'''help''': '''Number of files to save per JSON output file.'''}       )
    lowerCAmelCase        =				field(default='''content'''    ,						metadata={'''help''': '''Column containing text data to process.'''}       )
    lowerCAmelCase        =				field(
        default=1000    ,						metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=100    ,						metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=0.25    ,						metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=1.5    ,						metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=0.7    ,						metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Name or path to the tokenizer.'''}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''If True, near-duplicate samples are removed.'''}       )
    lowerCAmelCase        =				field(
        default=0.85    ,						metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''gpt2'''    ,						metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}       )
    lowerCAmelCase        =				field(
        default='''transformersbook/codeparrot-train'''    ,						metadata={'''help''': '''Dataset to train tokenizer on.'''}       )
    lowerCAmelCase        =				field(default='''content'''    ,						metadata={'''help''': '''Column containing text data to process.'''}       )
    lowerCAmelCase        =				field(default=20_0000    ,						metadata={'''help''': '''Number of examples to train tokenizer on.'''}       )
    lowerCAmelCase        =				field(
        default=3_2768    ,						metadata={'''help''': '''Number of examples to train the tokenizer on.'''}       )
    lowerCAmelCase        =				field(default='''codeparrot'''    ,						metadata={'''help''': '''Name of new tokenizer.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Push saved tokenizer to the hub.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Name or path to the tokenizer.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-train'''    ,						metadata={'''help''': '''Name or path to the dataset to pretokenize.'''}       )
    lowerCAmelCase        =				field(
        default='''tokenized-codeparrot-train'''    ,						metadata={'''help''': '''Repo name of the pretokenized data.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Number of workers used for code evaluation.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''gpt2-large'''    ,						metadata={'''help''': '''Configuration to use for model initialization.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Tokenizer attached to model.'''}       )
    lowerCAmelCase        =				field(default='''codeparrot'''    ,						metadata={'''help''': '''Name of the created model.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Push saved tokenizer to the hub.'''}       )