



'''simple docstring'''



from dataclasses import dataclass, field
from typing import Optional





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__            =     field(
				    default="""codeparrot/codeparrot"""   ,							metadata={"""help""": """Model name or path of model to be trained."""}		)
				lowerCAmelCase__            =     field(
				    default="""./"""   ,							metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}		)
				lowerCAmelCase__            =     field(
				    default="""codeparrot/codeparrot-clean-train"""   ,							metadata={"""help""": """Name or path of training dataset."""}		)
				lowerCAmelCase__            =     field(
				    default="""codeparrot/codeparrot-clean-valid"""   ,							metadata={"""help""": """Name or path of validation dataset."""}		)
				lowerCAmelCase__            =     field(default=2   ,							metadata={"""help""": """Batch size for training."""}		)
				lowerCAmelCase__            =     field(default=2   ,							metadata={"""help""": """Batch size for evaluation."""}		)
				lowerCAmelCase__            =     field(default=0.1   ,							metadata={"""help""": """Value of weight decay."""}		)
				lowerCAmelCase__            =     field(
				    default=1_00_00   ,							metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}		)
				lowerCAmelCase__            =     field(default=2E-4   ,							metadata={"""help""": """Learning rate fo training."""}		)
				lowerCAmelCase__            =     field(default="""cosine"""   ,							metadata={"""help""": """Learning rate."""}		)
				lowerCAmelCase__            =     field(
				    default=7_50   ,							metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}		)
				lowerCAmelCase__            =     field(
				    default=16   ,							metadata={"""help""": """Number of gradient accumulation steps."""}		)
				lowerCAmelCase__            =     field(
				    default=SCREAMING_SNAKE_CASE_   ,							metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}		)
				lowerCAmelCase__            =     field(default=5_00_00   ,							metadata={"""help""": """Maximum number of training steps."""}		)
				lowerCAmelCase__            =     field(
				    default=-1   ,							metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}		)
				lowerCAmelCase__            =     field(default=10_24   ,							metadata={"""help""": """Sequence lengths used for training."""}		)
				lowerCAmelCase__            =     field(default=1   ,							metadata={"""help""": """Training seed."""}		)
				lowerCAmelCase__            =     field(
				    default=10_24   ,							metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}   ,							)
				lowerCAmelCase__            =     field(
				    default=SCREAMING_SNAKE_CASE_   ,							metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}		)
				lowerCAmelCase__            =     field(default=SCREAMING_SNAKE_CASE_   ,							metadata={"""help""": """If True the data is pretokenized."""}		)





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__            =     field(
				    default="""codeparrot/codeparrot"""   ,							metadata={"""help""": """Model name or path of model to be evaluated."""}		)
				lowerCAmelCase__            =     field(
				    default="""codeparrot/codeparrot-clean-valid"""   ,							metadata={"""help""": """Name or path of validation dataset."""}		)
				lowerCAmelCase__            =     field(default=2   ,							metadata={"""help""": """Batch size used for evaluation."""}		)
				lowerCAmelCase__            =     field(
				    default=-1   ,							metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}		)
				lowerCAmelCase__            =     field(default=10_24   ,							metadata={"""help""": """Length of sequences to be evaluated."""}		)
				lowerCAmelCase__            =     field(default=1   ,							metadata={"""help""": """Random seed used for evaluation."""}		)





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__            =     field(
				    default="""codeparrot/codeparrot"""   ,							metadata={"""help""": """Model name or path of model to be evaluated."""}		)
				lowerCAmelCase__            =     field(default=SCREAMING_SNAKE_CASE_   ,							metadata={"""help""": """Number of workers used for code evaluation."""}		)
				lowerCAmelCase__            =     field(
				    default=SCREAMING_SNAKE_CASE_   ,							metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}   ,							)
				lowerCAmelCase__            =     field(
				    default=SCREAMING_SNAKE_CASE_   ,							metadata={"""help""": """Sample from the language model's output distribution."""}		)
				lowerCAmelCase__            =     field(default=0.2   ,							metadata={"""help""": """Sampling temperature used for generation."""}		)
				lowerCAmelCase__            =     field(default=2_56   ,							metadata={"""help""": """Maximum number of newly generated tokens."""}		)
				lowerCAmelCase__            =     field(default=0   ,							metadata={"""help""": """Top-k parameter used for generation."""}		)
				lowerCAmelCase__            =     field(default=0.95   ,							metadata={"""help""": """Top-p parameter used for nucleus sampling."""}		)
				lowerCAmelCase__            =     field(default=10   ,							metadata={"""help""": """Number of generations to run in parallel."""}		)
				lowerCAmelCase__            =     field(
				    default=2_00   ,							metadata={"""help""": """Number of completions to generate for each sample."""}		)
				lowerCAmelCase__            =     field(default=1   ,							metadata={"""help""": """Random seed used for evaluation."""}		)
				lowerCAmelCase__            =     field(
				    default="""eval_results.json"""   ,							metadata={"""help""": """Random seed used for evaluation."""}		)
				lowerCAmelCase__            =     field(
				    default="""0"""   ,							metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}		)
				lowerCAmelCase__            =     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'''





				lowerCAmelCase__            =     field(
				    default=SCREAMING_SNAKE_CASE_   ,							metadata={
				        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
				    }   ,							)
				lowerCAmelCase__            =     field(
				    default="""transformersbook/codeparrot"""   ,							metadata={"""help""": """Folder or name of dataset to process."""}		)
				lowerCAmelCase__            =     field(
				    default="""codeparrot-clean"""   ,							metadata={"""help""": """Folder to save processed processed dataset."""}		)
				lowerCAmelCase__            =     field(
				    default=10_00_00   ,							metadata={"""help""": """Number of files to save per JSON output file."""}		)
				lowerCAmelCase__            =     field(default="""content"""   ,							metadata={"""help""": """Column containing text data to process."""}		)
				lowerCAmelCase__            =     field(
				    default=10_00   ,							metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}		)
				lowerCAmelCase__            =     field(
				    default=1_00   ,							metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}		)
				lowerCAmelCase__            =     field(
				    default=0.25   ,							metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}		)
				lowerCAmelCase__            =     field(
				    default=1.5   ,							metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}		)
				lowerCAmelCase__            =     field(
				    default=0.7   ,							metadata={"""help""": """Probability for filtering config, test and uncommon files."""}		)
				lowerCAmelCase__            =     field(
				    default="""codeparrot/codeparrot"""   ,							metadata={"""help""": """Name or path to the tokenizer."""}   ,							)
				lowerCAmelCase__            =     field(
				    default=SCREAMING_SNAKE_CASE_   ,							metadata={"""help""": """If True, near-duplicate samples are removed."""}		)
				lowerCAmelCase__            =     field(
				    default=0.85   ,							metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}		)





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__            =     field(
				    default="""gpt2"""   ,							metadata={"""help""": """Base tokenizer to build new tokenizer from."""}		)
				lowerCAmelCase__            =     field(
				    default="""transformersbook/codeparrot-train"""   ,							metadata={"""help""": """Dataset to train tokenizer on."""}		)
				lowerCAmelCase__            =     field(default="""content"""   ,							metadata={"""help""": """Column containing text data to process."""}		)
				lowerCAmelCase__            =     field(default=20_00_00   ,							metadata={"""help""": """Number of examples to train tokenizer on."""}		)
				lowerCAmelCase__            =     field(
				    default=3_27_68   ,							metadata={"""help""": """Number of examples to train the tokenizer on."""}		)
				lowerCAmelCase__            =     field(default="""codeparrot"""   ,							metadata={"""help""": """Name of new tokenizer."""}		)
				lowerCAmelCase__            =     field(default=SCREAMING_SNAKE_CASE_   ,							metadata={"""help""": """Push saved tokenizer to the hub."""}		)





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





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





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





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








"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


a_							=    logging.get_logger(__name__)

a_							=    {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

a_							=    {
    """vocab_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
        ),
        """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
        ),
    },
}

a_							=    {
    """squeezebert/squeezebert-uncased""": 5_12,
    """squeezebert/squeezebert-mnli""": 5_12,
    """squeezebert/squeezebert-mnli-headless""": 5_12,
}


a_							=    {
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}







class 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			a_       :			Union[str, Any]												=			VOCAB_FILES_NAMES
			a_       :			List[str]												=			PRETRAINED_VOCAB_FILES_MAP
			a_       :			Optional[Any]												=			PRETRAINED_INIT_CONFIGURATION
			a_       :			Tuple												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_       :			Optional[int]												=			SqueezeBertTokenizer
			def __init__(	self				,   A=None				,   A=None				,   A=True				,   A="[UNK]"				,   A="[SEP]"				,   A="[PAD]"				,   A="[CLS]"				,   A="[MASK]"				,   A=True				,   A=None				,   **A				,   ):
						super().__init__(
						    A				,   tokenizer_file=A				,   do_lower_case=A				,   unk_token=A				,   sep_token=A				,   pad_token=A				,   cls_token=A				,   mask_token=A				,   tokenize_chinese_chars=A				,   strip_accents=A				,   **A				,   )

						_lowerCamelCase					:					Union[str, Any]	       =    json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
						if (
						    normalizer_state.get('lowercase'				,   A							) != do_lower_case
						    or normalizer_state.get('strip_accents'				,   A							) != strip_accents
						    or normalizer_state.get('handle_chinese_chars'				,   A							) != tokenize_chinese_chars
						):
									_lowerCamelCase					:					List[Any]	       =    getattr(A				,   normalizer_state.pop('type'							)							)
									_lowerCamelCase					:					Optional[Any]	       =    do_lower_case
									_lowerCamelCase					:					str	       =    strip_accents
									_lowerCamelCase					:					Any	       =    tokenize_chinese_chars
									_lowerCamelCase					:					int	       =    normalizer_class(**A							)

						_lowerCamelCase					:					Dict	       =    do_lower_case
			def    _lowerCAmelCase		(	self				,   A				,   A=None							):
						_lowerCamelCase					:					Dict	       =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

						return output
			def    _lowerCAmelCase		(	self				,   A				,   A = None							):
						_lowerCamelCase					:					Union[str, Any]	       =    [self.sep_token_id]
						_lowerCamelCase					:					Optional[Any]	       =    [self.cls_token_id]
						if token_ids_a is None:
									return len(cls + token_ids_a + sep							) * [0]
						return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]

			def    _lowerCAmelCase		(	self				,   A				,   A = None							):
						_lowerCamelCase					:					str	       =    self._tokenizer.model.save(A				,   name=A							)
						return tuple(A							)





