
from dataclasses import dataclass, field
from typing import Optional


@dataclass
class     lowercase :
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot"  ,       metadata={"help": "Model name or path of model to be trained."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="./"  ,       metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot-clean-train"  ,       metadata={"help": "Name or path of training dataset."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot-clean-valid"  ,       metadata={"help": "Name or path of validation dataset."}       )
		__lowercase	:	Optional[int]     					=    field(default=2  ,       metadata={"help": "Batch size for training."}       )
		__lowercase	:	Optional[int]     					=    field(default=2  ,       metadata={"help": "Batch size for evaluation."}       )
		__lowercase	:	Optional[float]     					=    field(default=0.1  ,       metadata={"help": "Value of weight decay."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=10_000  ,       metadata={"help": "Size of buffer used to shuffle streaming dataset."}       )
		__lowercase	:	Optional[float]     					=    field(default=2e-4  ,       metadata={"help": "Learning rate fo training."}       )
		__lowercase	:	Optional[str]     					=    field(default="cosine"  ,       metadata={"help": "Learning rate."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=750  ,       metadata={"help": "Number of warmup steps in the learning rate schedule."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=16  ,       metadata={"help": "Number of gradient accumulation steps."}       )
		__lowercase	:	Optional[bool]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Use gradient checkpointing to reduce memory footprint."}       )
		__lowercase	:	Optional[int]     					=    field(default=50_000  ,       metadata={"help": "Maximum number of training steps."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=-1  ,       metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}       )
		__lowercase	:	Optional[int]     					=    field(default=1_024  ,       metadata={"help": "Sequence lengths used for training."}       )
		__lowercase	:	Optional[int]     					=    field(default=1  ,       metadata={"help": "Training seed."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=1_024  ,       metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}  ,       )
		__lowercase	:	Optional[str]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "States path if the training should continue from a checkpoint folder."}       )
		__lowercase	:	Optional[bool]     					=    field(default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "If True the data is pretokenized."}       )






@dataclass
class     lowercase :
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot"  ,       metadata={"help": "Model name or path of model to be evaluated."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot-clean-valid"  ,       metadata={"help": "Name or path of validation dataset."}       )
		__lowercase	:	Optional[int]     					=    field(default=2  ,       metadata={"help": "Batch size used for evaluation."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=-1  ,       metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}       )
		__lowercase	:	Optional[int]     					=    field(default=1_024  ,       metadata={"help": "Length of sequences to be evaluated."}       )
		__lowercase	:	Optional[int]     					=    field(default=1  ,       metadata={"help": "Random seed used for evaluation."}       )






@dataclass
class     lowercase :
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot"  ,       metadata={"help": "Model name or path of model to be evaluated."}       )
		__lowercase	:	Optional[int]     					=    field(default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Number of workers used for code evaluation."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}  ,       )
		__lowercase	:	Optional[bool]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Sample from the language model's output distribution."}       )
		__lowercase	:	Optional[float]     					=    field(default=0.2  ,       metadata={"help": "Sampling temperature used for generation."}       )
		__lowercase	:	Optional[int]     					=    field(default=256  ,       metadata={"help": "Maximum number of newly generated tokens."}       )
		__lowercase	:	Optional[int]     					=    field(default=0  ,       metadata={"help": "Top-k parameter used for generation."}       )
		__lowercase	:	Optional[float]     					=    field(default=0.9_5  ,       metadata={"help": "Top-p parameter used for nucleus sampling."}       )
		__lowercase	:	Optional[int]     					=    field(default=10  ,       metadata={"help": "Number of generations to run in parallel."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=200  ,       metadata={"help": "Number of completions to generate for each sample."}       )
		__lowercase	:	Optional[int]     					=    field(default=1  ,       metadata={"help": "Random seed used for evaluation."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="eval_results.json"  ,       metadata={"help": "Random seed used for evaluation."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="0"  ,       metadata={"help": "Allow `code_eval` to execute Python code on machine"}       )
		__lowercase	:	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     lowercase :
		__lowercase	:	Optional[int]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={
		        "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
		    }  ,       )
		__lowercase	:	Optional[str]     					=    field(
		    default="transformersbook/codeparrot"  ,       metadata={"help": "Folder or name of dataset to process."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot-clean"  ,       metadata={"help": "Folder to save processed processed dataset."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=100_000  ,       metadata={"help": "Number of files to save per JSON output file."}       )
		__lowercase	:	Optional[str]     					=    field(default="content"  ,       metadata={"help": "Column containing text data to process."}       )
		__lowercase	:	Optional[float]     					=    field(
		    default=1_000  ,       metadata={"help": "Maximum line length in file, otherwise file is filtered."}       )
		__lowercase	:	Optional[float]     					=    field(
		    default=100  ,       metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}       )
		__lowercase	:	Optional[float]     					=    field(
		    default=0.2_5  ,       metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}       )
		__lowercase	:	Optional[float]     					=    field(
		    default=1.5  ,       metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}       )
		__lowercase	:	Optional[float]     					=    field(
		    default=0.7  ,       metadata={"help": "Probability for filtering config, test and uncommon files."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="codeparrot/codeparrot"  ,       metadata={"help": "Name or path to the tokenizer."}  ,       )
		__lowercase	:	Optional[bool]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "If True, near-duplicate samples are removed."}       )
		__lowercase	:	Optional[float]     					=    field(
		    default=0.8_5  ,       metadata={"help": "Jaccard threshold for near-duplicate samples."}       )






@dataclass
class     lowercase :
		__lowercase	:	Optional[str]     					=    field(
		    default="gpt2"  ,       metadata={"help": "Base tokenizer to build new tokenizer from."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="transformersbook/codeparrot-train"  ,       metadata={"help": "Dataset to train tokenizer on."}       )
		__lowercase	:	Optional[str]     					=    field(default="content"  ,       metadata={"help": "Column containing text data to process."}       )
		__lowercase	:	Optional[int]     					=    field(default=200_000  ,       metadata={"help": "Number of examples to train tokenizer on."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=32_768  ,       metadata={"help": "Number of examples to train the tokenizer on."}       )
		__lowercase	:	Optional[str]     					=    field(default="codeparrot"  ,       metadata={"help": "Name of new tokenizer."}       )
		__lowercase	:	Optional[bool]     					=    field(default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Push saved tokenizer to the hub."}       )






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






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


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel
