




from dataclasses import dataclass, field
from typing import Optional




@dataclass
class 			UpperCAmelCase_      :




							'''simple docstring'''



							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=1_0_0_0_0		,      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=7_5_0		,      metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}       )
							a__						  =     field(
							    default=1_6		,      metadata={'''help''': '''Number of gradient accumulation steps.'''}       )
							a__						  =     field(
							    default=A		,      metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}       )
							a__						  =     field(default=5_0_0_0_0		,      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=1_0_2_4		,      metadata={'''help''': '''Sequence lengths used for training.'''}       )
							a__						  =     field(default=1		,      metadata={'''help''': '''Training seed.'''}       )
							a__						  =     field(
							    default=1_0_2_4		,      metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}		,      )
							a__						  =     field(
							    default=A		,      metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}       )
							a__						  =     field(default=A		,      metadata={'''help''': '''If True the data is pretokenized.'''}       )





@dataclass
class 			UpperCAmelCase_      :




							'''simple docstring'''



							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=1_0_2_4		,      metadata={'''help''': '''Length of sequences to be evaluated.'''}       )
							a__						  =     field(default=1		,      metadata={'''help''': '''Random seed used for evaluation.'''}       )





@dataclass
class 			UpperCAmelCase_      :




							'''simple docstring'''



							a__						  =     field(
							    default='''codeparrot/codeparrot'''		,      metadata={'''help''': '''Model name or path of model to be evaluated.'''}       )
							a__						  =     field(default=A		,      metadata={'''help''': '''Number of workers used for code evaluation.'''}       )
							a__						  =     field(
							    default=A		,      metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}		,      )
							a__						  =     field(
							    default=A		,      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=2_5_6		,      metadata={'''help''': '''Maximum number of newly generated tokens.'''}       )
							a__						  =     field(default=0		,      metadata={'''help''': '''Top-k parameter used for generation.'''}       )
							a__						  =     field(default=0.9_5		,      metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}       )
							a__						  =     field(default=1_0		,      metadata={'''help''': '''Number of generations to run in parallel.'''}       )
							a__						  =     field(
							    default=2_0_0		,      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_      :




							'''simple docstring'''



							a__						  =     field(
							    default=A		,      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=1_0_0_0_0_0		,      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=1_0_0_0		,      metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}       )
							a__						  =     field(
							    default=1_0_0		,      metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}       )
							a__						  =     field(
							    default=0.2_5		,      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=A		,      metadata={'''help''': '''If True, near-duplicate samples are removed.'''}       )
							a__						  =     field(
							    default=0.8_5		,      metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}       )





@dataclass
class 			UpperCAmelCase_      :




							'''simple docstring'''



							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=2_0_0_0_0_0		,      metadata={'''help''': '''Number of examples to train tokenizer on.'''}       )
							a__						  =     field(
							    default=3_2_7_6_8		,      metadata={'''help''': '''Number of examples to train the tokenizer on.'''}       )
							a__						  =     field(default='''codeparrot'''		,      metadata={'''help''': '''Name of new tokenizer.'''}       )
							a__						  =     field(default=A		,      metadata={'''help''': '''Push saved tokenizer to the hub.'''}       )





@dataclass
class 			UpperCAmelCase_      :




							'''simple docstring'''



							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=A		,      metadata={'''help''': '''Number of workers used for code evaluation.'''}       )





@dataclass
class 			UpperCAmelCase_      :




							'''simple docstring'''



							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=A		,      metadata={'''help''': '''Push saved tokenizer to the hub.'''}       )












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""       ,     return_dict=a			).to(a			)
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained("""google/mt5-small"""			)

										SCREAMING_SNAKE_CASE           =					tokenizer("""Hello there"""       ,     return_tensors="""pt"""			).input_ids
										SCREAMING_SNAKE_CASE           =					tokenizer("""Hi I am"""       ,     return_tensors="""pt"""			).input_ids

										SCREAMING_SNAKE_CASE           =					model(input_ids.to(a			)       ,     labels=labels.to(a			)			).loss
										SCREAMING_SNAKE_CASE           =					-(labels.shape[-1] * loss.item())

										SCREAMING_SNAKE_CASE           =					-84.9127
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1E-4			)






