




from dataclasses import dataclass, field
from typing import Optional





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





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





@dataclass
class     lowerCamelCase__:
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Model name or path of model to be evaluated.'})
      UpperCAmelCase__      :	Optional[int]    		=   field(default=__lowerCamelCase ,     metadata={'help': 'Number of workers used for code evaluation.'})
      UpperCAmelCase__      :	Optional[int]    		=   field(
          default=__lowerCamelCase ,     metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'} ,     )
      UpperCAmelCase__      :	Optional[bool]    		=   field(
          default=__lowerCamelCase ,     metadata={'help': 'Sample from the language model\'s output distribution.'})
      UpperCAmelCase__      :	Optional[float]    		=   field(default=0.2 ,     metadata={'help': 'Sampling temperature used for generation.'})
      UpperCAmelCase__      :	Optional[int]    		=   field(default=256 ,     metadata={'help': 'Maximum number of newly generated tokens.'})
      UpperCAmelCase__      :	Optional[int]    		=   field(default=0 ,     metadata={'help': 'Top-k parameter used for generation.'})
      UpperCAmelCase__      :	Optional[float]    		=   field(default=0.95 ,     metadata={'help': 'Top-p parameter used for nucleus sampling.'})
      UpperCAmelCase__      :	Optional[int]    		=   field(default=10 ,     metadata={'help': 'Number of generations to run in parallel.'})
      UpperCAmelCase__      :	Optional[int]    		=   field(
          default=200 ,     metadata={'help': 'Number of completions to generate for each sample.'})
      UpperCAmelCase__      :	Optional[int]    		=   field(default=1 ,     metadata={'help': 'Random seed used for evaluation.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default='eval_results.json' ,     metadata={'help': 'Random seed used for evaluation.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default='0' ,     metadata={'help': 'Allow `code_eval` to execute Python code on machine'})
      UpperCAmelCase__      :	Optional[int]    		=   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     lowerCamelCase__:
      UpperCAmelCase__      :	Optional[int]    		=   field(
          default=__lowerCamelCase ,     metadata={
              'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
          } ,     )
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default='transformersbook/codeparrot' ,     metadata={'help': 'Folder or name of dataset to process.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default='codeparrot-clean' ,     metadata={'help': 'Folder to save processed processed dataset.'})
      UpperCAmelCase__      :	Optional[int]    		=   field(
          default=10_0000 ,     metadata={'help': 'Number of files to save per JSON output file.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(default='content' ,     metadata={'help': 'Column containing text data to process.'})
      UpperCAmelCase__      :	Optional[float]    		=   field(
          default=1000 ,     metadata={'help': 'Maximum line length in file, otherwise file is filtered.'})
      UpperCAmelCase__      :	Optional[float]    		=   field(
          default=100 ,     metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'})
      UpperCAmelCase__      :	Optional[float]    		=   field(
          default=0.25 ,     metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'})
      UpperCAmelCase__      :	Optional[float]    		=   field(
          default=1.5 ,     metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'})
      UpperCAmelCase__      :	Optional[float]    		=   field(
          default=0.7 ,     metadata={'help': 'Probability for filtering config, test and uncommon files.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Name or path to the tokenizer.'} ,     )
      UpperCAmelCase__      :	Optional[bool]    		=   field(
          default=__lowerCamelCase ,     metadata={'help': 'If True, near-duplicate samples are removed.'})
      UpperCAmelCase__      :	Optional[float]    		=   field(
          default=0.85 ,     metadata={'help': 'Jaccard threshold for near-duplicate samples.'})





@dataclass
class     lowerCamelCase__:
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default='gpt2' ,     metadata={'help': 'Base tokenizer to build new tokenizer from.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default='transformersbook/codeparrot-train' ,     metadata={'help': 'Dataset to train tokenizer on.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(default='content' ,     metadata={'help': 'Column containing text data to process.'})
      UpperCAmelCase__      :	Optional[int]    		=   field(default=20_0000 ,     metadata={'help': 'Number of examples to train tokenizer on.'})
      UpperCAmelCase__      :	Optional[int]    		=   field(
          default=3_2768 ,     metadata={'help': 'Number of examples to train the tokenizer on.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(default='codeparrot' ,     metadata={'help': 'Name of new tokenizer.'})
      UpperCAmelCase__      :	Optional[bool]    		=   field(default=__lowerCamelCase ,     metadata={'help': 'Push saved tokenizer to the hub.'})





@dataclass
class     lowerCamelCase__:
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Name or path to the tokenizer.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default='codeparrot/codeparrot-clean-train' ,     metadata={'help': 'Name or path to the dataset to pretokenize.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default='tokenized-codeparrot-train' ,     metadata={'help': 'Repo name of the pretokenized data.'})
      UpperCAmelCase__      :	Optional[int]    		=   field(default=__lowerCamelCase ,     metadata={'help': 'Number of workers used for code evaluation.'})





@dataclass
class     lowerCamelCase__:
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default='gpt2-large' ,     metadata={'help': 'Configuration to use for model initialization.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Tokenizer attached to model.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(default='codeparrot' ,     metadata={'help': 'Name of the created model.'})
      UpperCAmelCase__      :	Optional[bool]    		=   field(default=__lowerCamelCase ,     metadata={'help': 'Push saved tokenizer to the hub.'})







from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Dict    		=   DistilBertTokenizer
      UpperCAmelCase__      :	Dict    		=   DistilBertTokenizerFast
      UpperCAmelCase__      :	Tuple    		=   True
      @slow
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""   )

             __lowerCamelCase   =						tokenizer.encode("""sequence builders"""    ,       add_special_tokens=UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.encode("""multi-sequence build"""    ,       add_special_tokens=UpperCamelCase_   )

             __lowerCamelCase   =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase_    ,       UpperCamelCase_   )

             assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
             assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
                 tokenizer.sep_token_id
             ]

