

from dataclasses import dataclass, field
from typing import Optional







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







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







@dataclass
class 			lowercase    :
				__SCREAMING_SNAKE_CASE :			Optional[str]	      =      field(
				    default='''codeparrot/codeparrot'''     ,  metadata={'''help''': '''Model name or path of model to be evaluated.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      field(default=lowercase_     ,  metadata={'''help''': '''Number of workers used for code evaluation.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      field(
				    default=lowercase_     ,  metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}     ,  )
				__SCREAMING_SNAKE_CASE :			Optional[bool]	      =      field(
				    default=lowercase_     ,  metadata={'''help''': '''Sample from the language model\'s output distribution.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[float]	      =      field(default=0.2     ,  metadata={'''help''': '''Sampling temperature used for generation.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      field(default=256     ,  metadata={'''help''': '''Maximum number of newly generated tokens.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      field(default=0     ,  metadata={'''help''': '''Top-k parameter used for generation.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[float]	      =      field(default=0.95     ,  metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      field(default=10     ,  metadata={'''help''': '''Number of generations to run in parallel.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      field(
				    default=200     ,  metadata={'''help''': '''Number of completions to generate for each sample.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      field(default=1     ,  metadata={'''help''': '''Random seed used for evaluation.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[str]	      =      field(
				    default='''eval_results.json'''     ,  metadata={'''help''': '''Random seed used for evaluation.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[str]	      =      field(
				    default='''0'''     ,  metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}      )
				__SCREAMING_SNAKE_CASE :			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    :
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      field(
				    default=lowercase_     ,  metadata={
				        '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
				    }     ,  )
				__SCREAMING_SNAKE_CASE :			Optional[str]	      =      field(
				    default='''transformersbook/codeparrot'''     ,  metadata={'''help''': '''Folder or name of dataset to process.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[str]	      =      field(
				    default='''codeparrot-clean'''     ,  metadata={'''help''': '''Folder to save processed processed dataset.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      field(
				    default=100_000     ,  metadata={'''help''': '''Number of files to save per JSON output file.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[str]	      =      field(default='''content'''     ,  metadata={'''help''': '''Column containing text data to process.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[float]	      =      field(
				    default=1_000     ,  metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[float]	      =      field(
				    default=100     ,  metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[float]	      =      field(
				    default=0.25     ,  metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[float]	      =      field(
				    default=1.5     ,  metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[float]	      =      field(
				    default=0.7     ,  metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[str]	      =      field(
				    default='''codeparrot/codeparrot'''     ,  metadata={'''help''': '''Name or path to the tokenizer.'''}     ,  )
				__SCREAMING_SNAKE_CASE :			Optional[bool]	      =      field(
				    default=lowercase_     ,  metadata={'''help''': '''If True, near-duplicate samples are removed.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[float]	      =      field(
				    default=0.85     ,  metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}      )







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







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







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







def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							global f  # a global dp table for knapsack
							if f[i][j] < 0:
														if j < wt[i - 1]:
																					snake_case_          =   mf_knapsack(i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														else:
																					snake_case_          =   max(
																					    mf_knapsack(i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)       ,							mf_knapsack(i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							j - wt[i - 1]				) + val[i - 1]       ,							)
														snake_case_          =   val
							return f[i][j]





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   [[0] * (w + 1) for _ in range(n + 1				)]

							for i in range(1       ,							n + 1				):
														for w_ in range(1       ,							w + 1				):
																					if wt[i - 1] <= w_:
																												snake_case_          =   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]       ,							dp[i - 1][w_]				)
																					else:
																												snake_case_          =   dp[i - 1][w_]

							return dp[n][w_], dp





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							if not (isinstance(UpperCamelCase__       ,							(list, tuple)				) and isinstance(UpperCamelCase__       ,							(list, tuple)				)):
														raise ValueError(
														    'Both the weights and values vectors must be either lists or tuples'				)

							snake_case_          =   len(UpperCamelCase__				)
							if num_items != len(UpperCamelCase__				):
														snake_case_          =   (
														    'The number of weights must be the same as the number of values.\n'
														    F'''But got {num_items} weights and {len(UpperCamelCase__				)} values'''
														)
														raise ValueError(UpperCamelCase__				)
							for i in range(UpperCamelCase__				):
														if not isinstance(wt[i]       ,							UpperCamelCase__				):
																					snake_case_          =   (
																					    'All weights must be integers but got weight of '
																					    F'''type {type(wt[i]				)} at index {i}'''
																					)
																					raise TypeError(UpperCamelCase__				)

							snake_case_    ,   snake_case_          =   knapsack(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_          =   set()
							_construct_solution(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)

							return optimal_val, example_optional_set





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							if i > 0 and j > 0:
														if dp[i - 1][j] == dp[i][j]:
																					_construct_solution(UpperCamelCase__       ,							UpperCamelCase__       ,							i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														else:
																					optimal_set.add(UpperCamelCase__				)
																					_construct_solution(UpperCamelCase__       ,							UpperCamelCase__       ,							i - 1       ,							j - wt[i - 1]       ,							UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       int						    =	[3, 2, 4, 4]
					_UpperCAmelCase		:       Tuple						    =	[4, 3, 2, 3]
					_UpperCAmelCase		:       Dict						    =	4
					_UpperCAmelCase		:       int						    =	6
					_UpperCAmelCase		:       Union[str, Any]						    =	[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					_UpperCAmelCase    ,						_UpperCAmelCase		:       Optional[int]						    =	knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					_UpperCAmelCase    ,						_UpperCAmelCase		:       Optional[int]						    =	knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)




