

from dataclasses import dataclass, field
from typing import Optional





@dataclass
class     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(
							    default="""codeparrot/codeparrot"""			, metadata={"""help""": """Model name or path of model to be trained."""}							)
							__UpperCAmelCase       =field(
							    default="""./"""			, metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}							)
							__UpperCAmelCase       =field(
							    default="""codeparrot/codeparrot-clean-train"""			, metadata={"""help""": """Name or path of training dataset."""}							)
							__UpperCAmelCase       =field(
							    default="""codeparrot/codeparrot-clean-valid"""			, metadata={"""help""": """Name or path of validation dataset."""}							)
							__UpperCAmelCase       =field(default=2			, metadata={"""help""": """Batch size for training."""}							)
							__UpperCAmelCase       =field(default=2			, metadata={"""help""": """Batch size for evaluation."""}							)
							__UpperCAmelCase       =field(default=0.1			, metadata={"""help""": """Value of weight decay."""}							)
							__UpperCAmelCase       =field(
							    default=1_0000			, metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}							)
							__UpperCAmelCase       =field(default=2E-4			, metadata={"""help""": """Learning rate fo training."""}							)
							__UpperCAmelCase       =field(default="""cosine"""			, metadata={"""help""": """Learning rate."""}							)
							__UpperCAmelCase       =field(
							    default=750			, metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}							)
							__UpperCAmelCase       =field(
							    default=16			, metadata={"""help""": """Number of gradient accumulation steps."""}							)
							__UpperCAmelCase       =field(
							    default=__snake_case			, metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}							)
							__UpperCAmelCase       =field(default=5_0000			, metadata={"""help""": """Maximum number of training steps."""}							)
							__UpperCAmelCase       =field(
							    default=-1			, metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}							)
							__UpperCAmelCase       =field(default=1024			, metadata={"""help""": """Sequence lengths used for training."""}							)
							__UpperCAmelCase       =field(default=1			, metadata={"""help""": """Training seed."""}							)
							__UpperCAmelCase       =field(
							    default=1024			, metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}			, )
							__UpperCAmelCase       =field(
							    default=__snake_case			, metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}							)
							__UpperCAmelCase       =field(default=__snake_case			, metadata={"""help""": """If True the data is pretokenized."""}							)





@dataclass
class     snake_case_			:


							"""simple docstring"""





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





@dataclass
class     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(
							    default="""codeparrot/codeparrot"""			, metadata={"""help""": """Model name or path of model to be evaluated."""}							)
							__UpperCAmelCase       =field(default=__snake_case			, metadata={"""help""": """Number of workers used for code evaluation."""}							)
							__UpperCAmelCase       =field(
							    default=__snake_case			, metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}			, )
							__UpperCAmelCase       =field(
							    default=__snake_case			, metadata={"""help""": """Sample from the language model\'s output distribution."""}							)
							__UpperCAmelCase       =field(default=0.2			, metadata={"""help""": """Sampling temperature used for generation."""}							)
							__UpperCAmelCase       =field(default=256			, metadata={"""help""": """Maximum number of newly generated tokens."""}							)
							__UpperCAmelCase       =field(default=0			, metadata={"""help""": """Top-k parameter used for generation."""}							)
							__UpperCAmelCase       =field(default=0.95			, metadata={"""help""": """Top-p parameter used for nucleus sampling."""}							)
							__UpperCAmelCase       =field(default=10			, metadata={"""help""": """Number of generations to run in parallel."""}							)
							__UpperCAmelCase       =field(
							    default=200			, metadata={"""help""": """Number of completions to generate for each sample."""}							)
							__UpperCAmelCase       =field(default=1			, metadata={"""help""": """Random seed used for evaluation."""}							)
							__UpperCAmelCase       =field(
							    default="""eval_results.json"""			, metadata={"""help""": """Random seed used for evaluation."""}							)
							__UpperCAmelCase       =field(
							    default="""0"""			, metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}							)
							__UpperCAmelCase       =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     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(
							    default=__snake_case			, metadata={
							        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
							    }			, )
							__UpperCAmelCase       =field(
							    default="""transformersbook/codeparrot"""			, metadata={"""help""": """Folder or name of dataset to process."""}							)
							__UpperCAmelCase       =field(
							    default="""codeparrot-clean"""			, metadata={"""help""": """Folder to save processed processed dataset."""}							)
							__UpperCAmelCase       =field(
							    default=10_0000			, metadata={"""help""": """Number of files to save per JSON output file."""}							)
							__UpperCAmelCase       =field(default="""content"""			, metadata={"""help""": """Column containing text data to process."""}							)
							__UpperCAmelCase       =field(
							    default=1000			, metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}							)
							__UpperCAmelCase       =field(
							    default=100			, metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}							)
							__UpperCAmelCase       =field(
							    default=0.25			, metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}							)
							__UpperCAmelCase       =field(
							    default=1.5			, metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}							)
							__UpperCAmelCase       =field(
							    default=0.7			, metadata={"""help""": """Probability for filtering config, test and uncommon files."""}							)
							__UpperCAmelCase       =field(
							    default="""codeparrot/codeparrot"""			, metadata={"""help""": """Name or path to the tokenizer."""}			, )
							__UpperCAmelCase       =field(
							    default=__snake_case			, metadata={"""help""": """If True, near-duplicate samples are removed."""}							)
							__UpperCAmelCase       =field(
							    default=0.85			, metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}							)





@dataclass
class     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(
							    default="""gpt2"""			, metadata={"""help""": """Base tokenizer to build new tokenizer from."""}							)
							__UpperCAmelCase       =field(
							    default="""transformersbook/codeparrot-train"""			, metadata={"""help""": """Dataset to train tokenizer on."""}							)
							__UpperCAmelCase       =field(default="""content"""			, metadata={"""help""": """Column containing text data to process."""}							)
							__UpperCAmelCase       =field(default=20_0000			, metadata={"""help""": """Number of examples to train tokenizer on."""}							)
							__UpperCAmelCase       =field(
							    default=3_2768			, metadata={"""help""": """Number of examples to train the tokenizer on."""}							)
							__UpperCAmelCase       =field(default="""codeparrot"""			, metadata={"""help""": """Name of new tokenizer."""}							)
							__UpperCAmelCase       =field(default=__snake_case			, metadata={"""help""": """Push saved tokenizer to the hub."""}							)





@dataclass
class     snake_case_			:


							"""simple docstring"""





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





@dataclass
class     snake_case_			:


							"""simple docstring"""





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









from __future__ import annotations

from collections.abc import MutableSequence





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A							):
												if len(_A							) != degree + 1:
																	raise ValueError(
																	    'The number of coefficients should be equal to the degree + 1.'							)

												__lowerCAmelCase					=							list(_A							)
												__lowerCAmelCase					=							degree




							def __add__( self					,						_A							):

												if self.degree > polynomial_a.degree:
																	__lowerCAmelCase					=							self.coefficients[:]
																	for i in range(polynomial_a.degree + 1							):
																						coefficients[i] += polynomial_a.coefficients[i]
																	return Polynomial(self.degree					,						_A							)
												else:
																	__lowerCAmelCase					=							polynomial_a.coefficients[:]
																	for i in range(self.degree + 1							):
																						coefficients[i] += self.coefficients[i]
																	return Polynomial(polynomial_a.degree					,						_A							)




							def __sub__( self					,						_A							):
												return self + polynomial_a * Polynomial(0					,						[-1]							)




							def __neg__( self							):
												return Polynomial(self.degree					,						[-c for c in self.coefficients]							)




							def __mul__( self					,						_A							):
												__lowerCAmelCase					=							[0] * (self.degree + polynomial_a.degree + 1)
												for i in range(self.degree + 1							):
																	for j in range(polynomial_a.degree + 1							):
																						coefficients[i + j] += (
																						    self.coefficients[i] * polynomial_a.coefficients[j]
																						)

												return Polynomial(self.degree + polynomial_a.degree					,						_A							)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							0
												for i in range(self.degree + 1							):
																	result += self.coefficients[i] * (substitution**i)
												return result




							def __str__( self							):
												__lowerCAmelCase					=							''
												for i in range(self.degree					,						-1					,						-1							):
																	if self.coefficients[i] == 0:
																						continue
																	elif self.coefficients[i] > 0:
																						if polynomial:
																											polynomial += " + "
																	else:
																						polynomial += " - "

																	if i == 0:
																						polynomial += str(abs(self.coefficients[i]							)							)
																	elif i == 1:
																						polynomial += str(abs(self.coefficients[i]							)							) + "x"
																	else:
																						polynomial += str(abs(self.coefficients[i]							)							) + "x^" + str(_A							)

												return polynomial




							def __repr__( self							):
												return self.__str__()




							def        A__  ( self							):
												__lowerCAmelCase					=							[0] * self.degree
												for i in range(self.degree							):
																	__lowerCAmelCase					=							self.coefficients[i + 1] * (i + 1)
												return Polynomial(self.degree - 1					,						_A							)




							def        A__  ( self					,						_A = 0							):
												__lowerCAmelCase					=							[0] * (self.degree + 2)
												__lowerCAmelCase					=							constant
												for i in range(self.degree + 1							):
																	__lowerCAmelCase					=							self.coefficients[i] / (i + 1)
												return Polynomial(self.degree + 1					,						_A							)




							def __eq__( self					,						_A							):
												if not isinstance(_A					,						_A							):
																	return False

												if self.degree != polynomial_a.degree:
																	return False

												for i in range(self.degree + 1							):
																	if self.coefficients[i] != polynomial_a.coefficients[i]:
																						return False

												return True




							def __ne__( self					,						_A							):
												return not self.__eq__(_A							)






