

from dataclasses import dataclass, field
from typing import Optional






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






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






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






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






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






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






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
			from ...onnx.config import PatchingSpec
			from ...tokenization_utils_base import PreTrainedTokenizerBase


_lowerCamelCase       :				List[Any]       =			logging.get_logger(__name__)

_lowerCamelCase       :				Union[str, Any]       =			{
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Dict       =					"longformer"



	def __init__(  self	:    Optional[Any]			,  lowercase	:    Union[List[int], int] = 512			,  lowercase	:    int = 2			,  lowercase	:    int = 1			,  lowercase	:    int = 0			,  lowercase	:    int = 2			,  lowercase	:    int = 30_522			,  lowercase	:    int = 768			,  lowercase	:    int = 12			,  lowercase	:    int = 12			,  lowercase	:    int = 3_072			,  lowercase	:    str = "gelu"			,  lowercase	:    float = 0.1			,  lowercase	:    float = 0.1			,  lowercase	:    int = 512			,  lowercase	:    int = 2			,  lowercase	:    float = 0.02			,  lowercase	:    float = 1E-12			,  lowercase	:    bool = False			,  **lowercase	:    Optional[Any]			,  ):


				'''simple docstring'''


				super().__init__(pad_token_id=lowercase			,  **lowercase	)

				_snake_case		=			attention_window
				_snake_case		=			sep_token_id
				_snake_case		=			bos_token_id
				_snake_case		=			eos_token_id
				_snake_case		=			vocab_size
				_snake_case		=			hidden_size
				_snake_case		=			num_hidden_layers
				_snake_case		=			num_attention_heads
				_snake_case		=			hidden_act
				_snake_case		=			intermediate_size
				_snake_case		=			hidden_dropout_prob
				_snake_case		=			attention_probs_dropout_prob
				_snake_case		=			max_position_embeddings
				_snake_case		=			type_vocab_size
				_snake_case		=			initializer_range
				_snake_case		=			layer_norm_eps
				_snake_case		=			onnx_export




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    int			,  lowercase	:    "PretrainedConfig"			,  lowercase	:    str = "default"			,  lowercase	:    "List[PatchingSpec]" = None	):


				'''simple docstring'''


				super().__init__(lowercase			,  lowercase			,  lowercase	)
				_snake_case		=			True



	@property
	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				if self.task == "multiple-choice":
							_snake_case		=			{0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
							_snake_case		=			{0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				        ('global_attention_mask', dynamic_axis),
				    ]	)



	@property
	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			super().outputs
				if self.task == "default":
							_snake_case		=			{0: 'batch'}
				return outputs



	@property
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				return 1E-4



	@property
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				return max(super().default_onnx_opset			,  14	)





	def  A							(  self	:    str			,  lowercase	:    "PreTrainedTokenizerBase"			,  lowercase	:    int = -1			,  lowercase	:    int = -1			,  lowercase	:    bool = False			,  lowercase	:    Optional[TensorType] = None			,  ):


				'''simple docstring'''


				_snake_case		=			super().generate_dummy_inputs(
				    preprocessor=lowercase			,  batch_size=lowercase			,  seq_length=lowercase			,  is_pair=lowercase			,  framework=lowercase	)
				import torch

				# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
				# makes the export fail randomly
				_snake_case		=			torch.zeros_like(inputs['input_ids']	)
				# make every second token global
				_snake_case		=			1

				return inputs