

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.'''}      )







from collections import OrderedDict
from typing import Mapping

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


_UpperCAmelCase		:       int						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       Optional[Any]						    =	{
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      '''roformer'''




				def __init__( self				,       snake_case=5_0000				,       snake_case=None				,       snake_case=768				,       snake_case=12				,       snake_case=12				,       snake_case=3072				,       snake_case="gelu"				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=1536				,       snake_case=2				,       snake_case=0.02				,       snake_case=1e-1_2				,       snake_case=0				,       snake_case=False				,       snake_case=True				,       **snake_case				,       ):
											super().__init__(pad_token_id=snake_case				,       **snake_case	)

											snake_case_          =   vocab_size
											snake_case_          =   hidden_size if embedding_size is None else embedding_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_          =   rotary_value
											snake_case_          =   use_cache







class 			lowercase    (	lowercase_      ):




				@property
				def    a							( self	):
											if self.task == "multiple-choice":
																		snake_case_          =   {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		snake_case_          =   {0: 'batch', 1: 'sequence'}
											snake_case_          =   {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]	)




