


'''simple docstring'''



from dataclasses import dataclass, field
from typing import Optional





@dataclass
class 							__UpperCamelCase				:
	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=lowerCAmelCase_		,      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=lowerCAmelCase_		,      metadata={"help": "States path if the training should continue from a checkpoint folder."}					)
	A_				=    field(default=lowerCAmelCase_		,      metadata={"help": "If True the data is pretokenized."}					)





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





@dataclass
class 							__UpperCamelCase				:
	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=lowerCAmelCase_		,      metadata={"help": "Push saved tokenizer to the hub."}					)





@dataclass
class 							__UpperCamelCase				:
	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=lowerCAmelCase_		,      metadata={"help": "Number of workers used for code evaluation."}					)





@dataclass
class 							__UpperCamelCase				:
	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=lowerCAmelCase_		,      metadata={"help": "Push saved tokenizer to the hub."}					)








'''simple docstring'''




import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class 	_lowerCAmelCase							(			unittest.TestCase ):


	def     _a	(self							):
				A_	:						Optional[Any]	=     10


	def     _a	(self							):
				A_	:						Dict	=     [1, 2, 3, 4]
				A_	:						List[Any]	=     [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
				self.assertEqual(truncate_or_pad(lowercase       ,      self.block_size       ,      0							)       ,      lowercase							)


	def     _a	(self							):
				A_	:						List[Any]	=     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				A_	:						Tuple	=     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase       ,      self.block_size       ,      0							)       ,      lowercase							)


	def     _a	(self							):
				A_	:						Optional[Any]	=     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
				A_	:						Any	=     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase       ,      self.block_size       ,      0							)       ,      lowercase							)


	def     _a	(self							):
				A_	:						List[str]	=     """It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
				A_,    A_	:						Dict	=     process_story(lowercase							)
				self.assertEqual(lowercase       ,      []							)


	def     _a	(self							):
				A_	:						Optional[int]	=     """"""
				A_,    A_	:						List[str]	=     process_story(lowercase							)
				self.assertEqual(lowercase       ,      []							)
				self.assertEqual(lowercase       ,      []							)


	def     _a	(self							):
				A_	:						Optional[Any]	=     (
				    """It was the year of Our Lord one thousand seven hundred and """
				    """seventy-five\n\nSpiritual revelations were conceded to England """
				    """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
				)
				A_,    A_	:						int	=     process_story(lowercase							)

				A_	:						Optional[Any]	=     [
				    """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
				    """Spiritual revelations were conceded to England at that favoured period, as at this.""",
				]
				self.assertEqual(lowercase       ,      lowercase							)

				A_	:						Dict	=     ["""It was the best of times."""]
				self.assertEqual(lowercase       ,      lowercase							)


	def     _a	(self							):
				A_	:						Optional[int]	=     torch.tensor([1, 2, 3, 4]							)
				A_	:						Dict	=     torch.tensor([1, 1, 1, 1]							)
				np.testing.assert_array_equal(build_mask(lowercase       ,      0							).numpy()       ,      expected.numpy()							)


	def     _a	(self							):
				A_	:						str	=     torch.tensor([1, 2, 3, 4, 23, 23, 23]							)
				A_	:						str	=     torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase       ,      23							).numpy()       ,      expected.numpy()							)


	def     _a	(self							):
				A_	:						Any	=     torch.tensor([8, 2, 3, 4, 1, 1, 1]							)
				A_	:						List[str]	=     torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase       ,      1							).numpy()       ,      expected.numpy()							)


	def     _a	(self							):
				A_	:						List[Any]	=     101
				A_	:						List[Any]	=     torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]							)
				A_	:						List[str]	=     torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]							)

				A_	:						Dict	=     compute_token_type_ids(lowercase       ,      lowercase							)
				np.testing.assert_array_equal(lowercase       ,      lowercase							)