from dataclasses import dataclass, field
from typing import Optional
@dataclass
class SCREAMING_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=10_000   ,			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=__lowerCAmelCase   ,			metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}						)
   __lowerCAmelCase      =							field(default=50_000   ,			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=1_024   ,			metadata={"""help""": """Sequence lengths used for training."""}						)
   __lowerCAmelCase      =							field(default=1   ,			metadata={"""help""": """Training seed."""}						)
   __lowerCAmelCase      =							field(
       default=1_024   ,			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 SCREAMING_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=1_024   ,			metadata={"""help""": """Length of sequences to be evaluated."""}						)
   __lowerCAmelCase      =							field(default=1   ,			metadata={"""help""": """Random seed used for evaluation."""}						)





@dataclass
class SCREAMING_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=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 SCREAMING_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=100_000   ,			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=1_000   ,			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=__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 SCREAMING_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=200_000   ,			metadata={"""help""": """Number of examples to train tokenizer on."""}						)
   __lowerCAmelCase      =							field(
       default=32_768   ,			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 SCREAMING_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 SCREAMING_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."""}						)



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_       : List[str]         =			{
    'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
    'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : Optional[int]         =			[
	    'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXJapaneseForCausalLM',
	    'GPTNeoXJapaneseLayer',
	    'GPTNeoXJapaneseModel',
	    'GPTNeoXJapanesePreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	A_       : Any         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



