





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=10000		,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=_UpperCamelCase		,metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=50000		,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=_UpperCamelCase		,metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}  )
   _UpperCamelCase :						Optional[bool]     		=				field(default=_UpperCamelCase		,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=_UpperCamelCase		,metadata={"""help""": """Number of workers used for code evaluation."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(
       default=_UpperCamelCase		,metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}		,)
   _UpperCamelCase :						Optional[bool]     		=				field(
       default=_UpperCamelCase		,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=_UpperCamelCase		,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=100000		,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=_UpperCamelCase		,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=200000		,metadata={"""help""": """Number of examples to train tokenizer on."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(
       default=32768		,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=_UpperCamelCase		,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=_UpperCamelCase		,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=_UpperCamelCase		,metadata={"""help""": """Push saved tokenizer to the hub."""}  )








# Lint as: python3
import itertools
import os
import re


SCREAMING_SNAKE_CASE__       :			Tuple			      =						re.compile(R"""([A-Z]+)([A-Z][a-z])""")
SCREAMING_SNAKE_CASE__       :			List[str]			      =						re.compile(R"""([a-z\d])([A-Z])""")

SCREAMING_SNAKE_CASE__       :			Tuple			      =						re.compile(R"""(?<!_)_(?!_)""")
SCREAMING_SNAKE_CASE__       :			Dict			      =						re.compile(R"""(_{2,})""")

SCREAMING_SNAKE_CASE__       :			List[Any]			      =						R"""^\w+(\.\w+)*$"""

SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						R"""<>:/\|?*"""






def       _A  (      lowerCamelCase   ):
   a__	:						List[str]      		=  _uppercase_uppercase_re.sub(r"\1_\2"							, lowerCamelCase   )
   a__	:						Dict      		=  _lowercase_uppercase_re.sub(r"\1_\2"							, lowerCamelCase   )
   return name.lower()






def       _A  (      lowerCamelCase   ):
   a__	:						Tuple      		=  _single_underscore_re.split(lowerCamelCase   )
   a__	:						Any      		=  [_multiple_underscores_re.split(lowerCamelCase   ) for n in name]
   return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowerCamelCase   ) if n != ""   )






def       _A  (      lowerCamelCase   ):
   if os.path.basename(lowerCamelCase   ) != name:
      raise ValueError(F"""Should be a dataset name, not a path: {name}"""   )
   return camelcase_to_snakecase(lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if os.path.basename(lowerCamelCase   ) != name:
      raise ValueError(F"""Should be a dataset name, not a path: {name}"""   )
   if not re.match(_split_re							, lowerCamelCase   ):
      raise ValueError(F"""Split name should match '{_split_re}'' but got '{split}'."""   )
   return F"""{filename_prefix_for_name(lowerCamelCase   )}-{split}"""






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase=None   ):
   a__	:						Union[str, Any]      		=  filename_prefix_for_split(lowerCamelCase							, lowerCamelCase   )
   if filetype_suffix:
      prefix += F""".{filetype_suffix}"""
   a__	:						Any      		=  os.path.join(lowerCamelCase							, lowerCamelCase   )
   return F"""{filepath}*"""






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase=None							, lowerCamelCase=None   ):
   a__	:						List[str]      		=  filename_prefix_for_split(lowerCamelCase							, lowerCamelCase   )
   a__	:						Tuple      		=  os.path.join(lowerCamelCase							, lowerCamelCase   )

   if shard_lengths:
      a__	:						List[str]      		=  len(lowerCamelCase   )
      a__	:						str      		=  [F"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(lowerCamelCase   )]
      if filetype_suffix:
         a__	:						Optional[Any]      		=  [filename + F""".{filetype_suffix}""" for filename in filenames]
      return filenames
   else:
      a__	:						Optional[int]      		=  prefix
      if filetype_suffix:
         filename += F""".{filetype_suffix}"""
      return [filename]

