
from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset




class   __lowercase						(						__snake_case       ):






	def       _a(self				: Union[str, Any]    ) ->			str:
							return [
							    {"col_1": 3, "col_2": "a"},
							    {"col_1": 2, "col_2": "b"},
							    {"col_1": 1, "col_2": "c"},
							    {"col_1": 0, "col_2": "d"},
							]






	def       _a(self				: List[str]    ) ->			Dict:
							_lowercase       :	Union[str, Any]           =   {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
							return Dataset.from_dict(snake_case    )






	def       _a(self				: Tuple    ) ->			int:
							_lowercase       :	List[str]           =   self._create_example_records()
							_lowercase       :	str           =   Dataset.from_list(snake_case    )
							self.assertListEqual(dset.column_names      ,	["col_1", "col_2"]    )
							for i, r in enumerate(snake_case    ):
													self.assertDictEqual(snake_case      ,	example_records[i]    )






	def       _a(self				: Union[str, Any]    ) ->			Any:
							_lowercase       :	Dict           =   self._create_example_records()
							_lowercase       :	str           =   Dataset.from_list(snake_case    )
							_lowercase       :	List[Any]           =   Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}    )
							self.assertEqual(dset.info      ,	dset_from_dict.info    )






	def       _a(self				: Tuple    ) ->			Optional[int]:  # checks what happens with missing columns
							_lowercase       :	Tuple           =   [{"col_1": 1}, {"col_2": "x"}]
							_lowercase       :	Optional[Any]           =   Dataset.from_list(snake_case    )
							self.assertDictEqual(dset[0]      ,	{"col_1": 1}    )
							self.assertDictEqual(dset[1]      ,	{"col_1": None}    )  # NB: first record is used for columns






	def       _a(self				: Dict    ) ->			int:  # checks if the type can be inferred from the second record
							_lowercase       :	int           =   [{"col_1": []}, {"col_1": [1, 2]}]
							_lowercase       :	Optional[int]           =   Dataset.from_list(snake_case    )
							self.assertEqual(dset.info.features["col_1"]      ,	Sequence(Value("int64"    )    )    )






	def       _a(self				: int    ) ->			int:
							_lowercase       :	str           =   Dataset.from_list([]    )
							self.assertEqual(len(snake_case    )      ,	0    )
							self.assertListEqual(dset.column_names      ,	[]    )






from dataclasses import dataclass, field
from typing import Optional




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




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




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




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




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




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




