"""simple docstring"""




from dataclasses import dataclass, field
from typing import Optional







@dataclass
class __snake_case    :
   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_00_00       ,		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=7_50       ,		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=lowerCamelCase_       ,		metadata={"help": "Use gradient checkpointing to reduce memory footprint."}		)
   lowerCAmelCase_														=							field(default=5_00_00       ,		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=10_24       ,		metadata={"help": "Sequence lengths used for training."}		)
   lowerCAmelCase_														=							field(default=1       ,		metadata={"help": "Training seed."}		)
   lowerCAmelCase_														=							field(
       default=10_24       ,		metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}       ,		)
   lowerCAmelCase_														=							field(
       default=lowerCamelCase_       ,		metadata={"help": "States path if the training should continue from a checkpoint folder."}		)
   lowerCAmelCase_														=							field(default=lowerCamelCase_       ,		metadata={"help": "If True the data is pretokenized."}		)







@dataclass
class __snake_case    :
   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=10_24       ,		metadata={"help": "Length of sequences to be evaluated."}		)
   lowerCAmelCase_														=							field(default=1       ,		metadata={"help": "Random seed used for evaluation."}		)







@dataclass
class __snake_case    :
   lowerCAmelCase_														=							field(
       default="codeparrot/codeparrot"       ,		metadata={"help": "Model name or path of model to be evaluated."}		)
   lowerCAmelCase_														=							field(default=lowerCamelCase_       ,		metadata={"help": "Number of workers used for code evaluation."}		)
   lowerCAmelCase_														=							field(
       default=lowerCamelCase_       ,		metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}       ,		)
   lowerCAmelCase_														=							field(
       default=lowerCamelCase_       ,		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=2_56       ,		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=2_00       ,		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 __snake_case    :
   lowerCAmelCase_														=							field(
       default=lowerCamelCase_       ,		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_00_00       ,		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=10_00       ,		metadata={"help": "Maximum line length in file, otherwise file is filtered."}		)
   lowerCAmelCase_														=							field(
       default=1_00       ,		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=lowerCamelCase_       ,		metadata={"help": "If True, near-duplicate samples are removed."}		)
   lowerCAmelCase_														=							field(
       default=0.85       ,		metadata={"help": "Jaccard threshold for near-duplicate samples."}		)







@dataclass
class __snake_case    :
   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_00_00       ,		metadata={"help": "Number of examples to train tokenizer on."}		)
   lowerCAmelCase_														=							field(
       default=3_27_68       ,		metadata={"help": "Number of examples to train the tokenizer on."}		)
   lowerCAmelCase_														=							field(default="codeparrot"       ,		metadata={"help": "Name of new tokenizer."}		)
   lowerCAmelCase_														=							field(default=lowerCamelCase_       ,		metadata={"help": "Push saved tokenizer to the hub."}		)







@dataclass
class __snake_case    :
   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=lowerCamelCase_       ,		metadata={"help": "Number of workers used for code evaluation."}		)







@dataclass
class __snake_case    :
   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=lowerCamelCase_       ,		metadata={"help": "Push saved tokenizer to the hub."}		)

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
   __lowerCamelCase      :			Optional[Any]							       =		{
       '''linear''': PIL.Image.Resampling.BILINEAR,
       '''bilinear''': PIL.Image.Resampling.BILINEAR,
       '''bicubic''': PIL.Image.Resampling.BICUBIC,
       '''lanczos''': PIL.Image.Resampling.LANCZOS,
       '''nearest''': PIL.Image.Resampling.NEAREST,
   }
else:
   __lowerCamelCase      :			int							       =		{
       '''linear''': PIL.Image.LINEAR,
       '''bilinear''': PIL.Image.BILINEAR,
       '''bicubic''': PIL.Image.BICUBIC,
       '''lanczos''': PIL.Image.LANCZOS,
       '''nearest''': PIL.Image.NEAREST,
   }



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				(images / 2 + 0.5).clamp(0			,					1     )
  SCREAMING_SNAKE_CASE__      							=				images.cpu().permute(0			,					2			,					3			,					1     ).float().numpy()
  SCREAMING_SNAKE_CASE__      							=				numpy_to_pil(__UpperCamelCase     )
  return images






def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> int:







  """simple docstring"""





  if images.ndim == 3:
    SCREAMING_SNAKE_CASE__      							=				images[None, ...]
  SCREAMING_SNAKE_CASE__      							=				(images * 2_55).round().astype("""uint8"""     )
  if images.shape[-1] == 1:
    # special case for grayscale (single channel) images
    SCREAMING_SNAKE_CASE__      							=				[Image.fromarray(image.squeeze()			,					mode="""L"""     ) for image in images]
  else:
    SCREAMING_SNAKE_CASE__      							=				[Image.fromarray(__UpperCamelCase     ) for image in images]

  return pil_images
