



"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional


@dataclass
class    SCREAMING_SNAKE_CASE__ :
    lowercase__      =   field(
        default="codeparrot/codeparrot"      ,  metadata={"help": "Model name or path of model to be trained."}   )
    lowercase__      =   field(
        default="./"      ,  metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}   )
    lowercase__      =   field(
        default="codeparrot/codeparrot-clean-train"      ,  metadata={"help": "Name or path of training dataset."}   )
    lowercase__      =   field(
        default="codeparrot/codeparrot-clean-valid"      ,  metadata={"help": "Name or path of validation dataset."}   )
    lowercase__      =   field(default=2      ,  metadata={"help": "Batch size for training."}   )
    lowercase__      =   field(default=2      ,  metadata={"help": "Batch size for evaluation."}   )
    lowercase__      =   field(default=0.1      ,  metadata={"help": "Value of weight decay."}   )
    lowercase__      =   field(
        default=10000      ,  metadata={"help": "Size of buffer used to shuffle streaming dataset."}   )
    lowercase__      =   field(default=2e-4      ,  metadata={"help": "Learning rate fo training."}   )
    lowercase__      =   field(default="cosine"      ,  metadata={"help": "Learning rate."}   )
    lowercase__      =   field(
        default=750      ,  metadata={"help": "Number of warmup steps in the learning rate schedule."}   )
    lowercase__      =   field(
        default=16      ,  metadata={"help": "Number of gradient accumulation steps."}   )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "Use gradient checkpointing to reduce memory footprint."}   )
    lowercase__      =   field(default=50000      ,  metadata={"help": "Maximum number of training steps."}   )
    lowercase__      =   field(
        default=-1      ,  metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}   )
    lowercase__      =   field(default=1024      ,  metadata={"help": "Sequence lengths used for training."}   )
    lowercase__      =   field(default=1      ,  metadata={"help": "Training seed."}   )
    lowercase__      =   field(
        default=1024      ,  metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}      ,  )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "States path if the training should continue from a checkpoint folder."}   )
    lowercase__      =   field(default=__UpperCAmelCase      ,  metadata={"help": "If True the data is pretokenized."}   )







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







@dataclass
class    SCREAMING_SNAKE_CASE__ :
    lowercase__      =   field(
        default="codeparrot/codeparrot"      ,  metadata={"help": "Model name or path of model to be evaluated."}   )
    lowercase__      =   field(default=__UpperCAmelCase      ,  metadata={"help": "Number of workers used for code evaluation."}   )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}      ,  )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "Sample from the language model's output distribution."}   )
    lowercase__      =   field(default=0.2      ,  metadata={"help": "Sampling temperature used for generation."}   )
    lowercase__      =   field(default=256      ,  metadata={"help": "Maximum number of newly generated tokens."}   )
    lowercase__      =   field(default=0      ,  metadata={"help": "Top-k parameter used for generation."}   )
    lowercase__      =   field(default=0.95      ,  metadata={"help": "Top-p parameter used for nucleus sampling."}   )
    lowercase__      =   field(default=10      ,  metadata={"help": "Number of generations to run in parallel."}   )
    lowercase__      =   field(
        default=200      ,  metadata={"help": "Number of completions to generate for each sample."}   )
    lowercase__      =   field(default=1      ,  metadata={"help": "Random seed used for evaluation."}   )
    lowercase__      =   field(
        default="eval_results.json"      ,  metadata={"help": "Random seed used for evaluation."}   )
    lowercase__      =   field(
        default="0"      ,  metadata={"help": "Allow `code_eval` to execute Python code on machine"}   )
    lowercase__      =   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    SCREAMING_SNAKE_CASE__ :
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={
            "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
        }      ,  )
    lowercase__      =   field(
        default="transformersbook/codeparrot"      ,  metadata={"help": "Folder or name of dataset to process."}   )
    lowercase__      =   field(
        default="codeparrot-clean"      ,  metadata={"help": "Folder to save processed processed dataset."}   )
    lowercase__      =   field(
        default=100000      ,  metadata={"help": "Number of files to save per JSON output file."}   )
    lowercase__      =   field(default="content"      ,  metadata={"help": "Column containing text data to process."}   )
    lowercase__      =   field(
        default=1000      ,  metadata={"help": "Maximum line length in file, otherwise file is filtered."}   )
    lowercase__      =   field(
        default=100      ,  metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}   )
    lowercase__      =   field(
        default=0.25      ,  metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}   )
    lowercase__      =   field(
        default=1.5      ,  metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}   )
    lowercase__      =   field(
        default=0.7      ,  metadata={"help": "Probability for filtering config, test and uncommon files."}   )
    lowercase__      =   field(
        default="codeparrot/codeparrot"      ,  metadata={"help": "Name or path to the tokenizer."}      ,  )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "If True, near-duplicate samples are removed."}   )
    lowercase__      =   field(
        default=0.85      ,  metadata={"help": "Jaccard threshold for near-duplicate samples."}   )







@dataclass
class    SCREAMING_SNAKE_CASE__ :
    lowercase__      =   field(
        default="gpt2"      ,  metadata={"help": "Base tokenizer to build new tokenizer from."}   )
    lowercase__      =   field(
        default="transformersbook/codeparrot-train"      ,  metadata={"help": "Dataset to train tokenizer on."}   )
    lowercase__      =   field(default="content"      ,  metadata={"help": "Column containing text data to process."}   )
    lowercase__      =   field(default=200000      ,  metadata={"help": "Number of examples to train tokenizer on."}   )
    lowercase__      =   field(
        default=32768      ,  metadata={"help": "Number of examples to train the tokenizer on."}   )
    lowercase__      =   field(default="codeparrot"      ,  metadata={"help": "Name of new tokenizer."}   )
    lowercase__      =   field(default=__UpperCAmelCase      ,  metadata={"help": "Push saved tokenizer to the hub."}   )







@dataclass
class    SCREAMING_SNAKE_CASE__ :
    lowercase__      =   field(
        default="codeparrot/codeparrot"      ,  metadata={"help": "Name or path to the tokenizer."}   )
    lowercase__      =   field(
        default="codeparrot/codeparrot-clean-train"      ,  metadata={"help": "Name or path to the dataset to pretokenize."}   )
    lowercase__      =   field(
        default="tokenized-codeparrot-train"      ,  metadata={"help": "Repo name of the pretokenized data."}   )
    lowercase__      =   field(default=__UpperCAmelCase      ,  metadata={"help": "Number of workers used for code evaluation."}   )







@dataclass
class    SCREAMING_SNAKE_CASE__ :
    lowercase__      =   field(
        default="gpt2-large"      ,  metadata={"help": "Configuration to use for model initialization."}   )
    lowercase__      =   field(
        default="codeparrot/codeparrot"      ,  metadata={"help": "Tokenizer attached to model."}   )
    lowercase__      =   field(default="codeparrot"      ,  metadata={"help": "Name of the created model."}   )
    lowercase__      =   field(default=__UpperCAmelCase      ,  metadata={"help": "Push saved tokenizer to the hub."}   )






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase						:		Any			= {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[str]			= ["XGLMTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Dict			= ["XGLMTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Tuple			= [
           "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XGLMForCausalLM",
           "XGLMModel",
           "XGLMPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		int			= [
           "FlaxXGLMForCausalLM",
           "FlaxXGLMModel",
           "FlaxXGLMPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[str]			= [
           "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXGLMForCausalLM",
           "TFXGLMModel",
           "TFXGLMPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm import XGLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm_fast import XGLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xglm import (
                  TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXGLMForCausalLM,
                  TFXGLMModel,
                  TFXGLMPreTrainedModel,
              )


else:
       import sys

       UpperCAmelCase						:		Any			= _LazyModule(__name__, globals()["__file__"], _import_structure)

