





from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 			UpperCamelCase_		:
					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=10000							,  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=__a							,  metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}					)
					lowerCAmelCase_							=	field(default=50000							,  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=1024							,  metadata={'''help''': '''Sequence lengths used for training.'''}					)
					lowerCAmelCase_							=	field(default=1							,  metadata={'''help''': '''Training seed.'''}					)
					lowerCAmelCase_							=	field(
					    default=1024							,  metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}							,  )
					lowerCAmelCase_							=	field(
					    default=__a							,  metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}					)
					lowerCAmelCase_							=	field(default=__a							,  metadata={'''help''': '''If True the data is pretokenized.'''}					)





@dataclass
class 			UpperCamelCase_		:
					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=1024							,  metadata={'''help''': '''Length of sequences to be evaluated.'''}					)
					lowerCAmelCase_							=	field(default=1							,  metadata={'''help''': '''Random seed used for evaluation.'''}					)





@dataclass
class 			UpperCamelCase_		:
					lowerCAmelCase_							=	field(
					    default='''codeparrot/codeparrot'''							,  metadata={'''help''': '''Model name or path of model to be evaluated.'''}					)
					lowerCAmelCase_							=	field(default=__a							,  metadata={'''help''': '''Number of workers used for code evaluation.'''}					)
					lowerCAmelCase_							=	field(
					    default=__a							,  metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}							,  )
					lowerCAmelCase_							=	field(
					    default=__a							,  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 			UpperCamelCase_		:
					lowerCAmelCase_							=	field(
					    default=__a							,  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=100000							,  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=1000							,  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=__a							,  metadata={'''help''': '''If True, near-duplicate samples are removed.'''}					)
					lowerCAmelCase_							=	field(
					    default=0.85							,  metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}					)





@dataclass
class 			UpperCamelCase_		:
					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=200000							,  metadata={'''help''': '''Number of examples to train tokenizer on.'''}					)
					lowerCAmelCase_							=	field(
					    default=32768							,  metadata={'''help''': '''Number of examples to train the tokenizer on.'''}					)
					lowerCAmelCase_							=	field(default='''codeparrot'''							,  metadata={'''help''': '''Name of new tokenizer.'''}					)
					lowerCAmelCase_							=	field(default=__a							,  metadata={'''help''': '''Push saved tokenizer to the hub.'''}					)





@dataclass
class 			UpperCamelCase_		:
					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=__a							,  metadata={'''help''': '''Number of workers used for code evaluation.'''}					)





@dataclass
class 			UpperCamelCase_		:
					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=__a							,  metadata={'''help''': '''Push saved tokenizer to the hub.'''}					)










from cva import destroyAllWindows, imread, imshow, waitKey





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[int]						)			-> Optional[int]:



					'''simple docstring'''




					_snake_case							,     _snake_case									=				img.shape[0], img.shape[1]

					# converting each pixel's color to its negative
					for i in range(UpperCamelCase__						):
										for j in range(UpperCamelCase__						):
															_snake_case									=				[255, 255, 255] - img[i][j]

					return img


if __name__ == "__main__":
					# read original image
					UpperCAmelCase_      							=     imread("""image_data/lena.jpg""", 1)

					# convert to its negative
					UpperCAmelCase_      							=     convert_to_negative(img)

					# show result image
					imshow("""negative of original image""", img)
					waitKey(0)
					destroyAllWindows()



