
from dataclasses import dataclass, field
from typing import Optional







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







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







@dataclass
class 				UpperCAmelCase						:
					a__:			Optional[str]      	=					field(
					    default="""codeparrot/codeparrot"""    ,			metadata={"""help""": """Model name or path of model to be evaluated."""}       )
					a__:			Optional[int]      	=					field(default=_UpperCamelCase    ,			metadata={"""help""": """Number of workers used for code evaluation."""}       )
					a__:			Optional[int]      	=					field(
					    default=_UpperCamelCase    ,			metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}    ,			)
					a__:			Optional[bool]      	=					field(
					    default=_UpperCamelCase    ,			metadata={"""help""": """Sample from the language model\'s output distribution."""}       )
					a__:			Optional[float]      	=					field(default=0.2    ,			metadata={"""help""": """Sampling temperature used for generation."""}       )
					a__:			Optional[int]      	=					field(default=2_56    ,			metadata={"""help""": """Maximum number of newly generated tokens."""}       )
					a__:			Optional[int]      	=					field(default=0    ,			metadata={"""help""": """Top-k parameter used for generation."""}       )
					a__:			Optional[float]      	=					field(default=0.95    ,			metadata={"""help""": """Top-p parameter used for nucleus sampling."""}       )
					a__:			Optional[int]      	=					field(default=10    ,			metadata={"""help""": """Number of generations to run in parallel."""}       )
					a__:			Optional[int]      	=					field(
					    default=2_00    ,			metadata={"""help""": """Number of completions to generate for each sample."""}       )
					a__:			Optional[int]      	=					field(default=1    ,			metadata={"""help""": """Random seed used for evaluation."""}       )
					a__:			Optional[str]      	=					field(
					    default="""eval_results.json"""    ,			metadata={"""help""": """Random seed used for evaluation."""}       )
					a__:			Optional[str]      	=					field(
					    default="""0"""    ,			metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}       )
					a__:			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 				UpperCAmelCase						:
					a__:			Optional[int]      	=					field(
					    default=_UpperCamelCase    ,			metadata={
					        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
					    }    ,			)
					a__:			Optional[str]      	=					field(
					    default="""transformersbook/codeparrot"""    ,			metadata={"""help""": """Folder or name of dataset to process."""}       )
					a__:			Optional[str]      	=					field(
					    default="""codeparrot-clean"""    ,			metadata={"""help""": """Folder to save processed processed dataset."""}       )
					a__:			Optional[int]      	=					field(
					    default=10_00_00    ,			metadata={"""help""": """Number of files to save per JSON output file."""}       )
					a__:			Optional[str]      	=					field(default="""content"""    ,			metadata={"""help""": """Column containing text data to process."""}       )
					a__:			Optional[float]      	=					field(
					    default=10_00    ,			metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}       )
					a__:			Optional[float]      	=					field(
					    default=1_00    ,			metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}       )
					a__:			Optional[float]      	=					field(
					    default=0.25    ,			metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}       )
					a__:			Optional[float]      	=					field(
					    default=1.5    ,			metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}       )
					a__:			Optional[float]      	=					field(
					    default=0.7    ,			metadata={"""help""": """Probability for filtering config, test and uncommon files."""}       )
					a__:			Optional[str]      	=					field(
					    default="""codeparrot/codeparrot"""    ,			metadata={"""help""": """Name or path to the tokenizer."""}    ,			)
					a__:			Optional[bool]      	=					field(
					    default=_UpperCamelCase    ,			metadata={"""help""": """If True, near-duplicate samples are removed."""}       )
					a__:			Optional[float]      	=					field(
					    default=0.85    ,			metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}       )







@dataclass
class 				UpperCAmelCase						:
					a__:			Optional[str]      	=					field(
					    default="""gpt2"""    ,			metadata={"""help""": """Base tokenizer to build new tokenizer from."""}       )
					a__:			Optional[str]      	=					field(
					    default="""transformersbook/codeparrot-train"""    ,			metadata={"""help""": """Dataset to train tokenizer on."""}       )
					a__:			Optional[str]      	=					field(default="""content"""    ,			metadata={"""help""": """Column containing text data to process."""}       )
					a__:			Optional[int]      	=					field(default=20_00_00    ,			metadata={"""help""": """Number of examples to train tokenizer on."""}       )
					a__:			Optional[int]      	=					field(
					    default=3_27_68    ,			metadata={"""help""": """Number of examples to train the tokenizer on."""}       )
					a__:			Optional[str]      	=					field(default="""codeparrot"""    ,			metadata={"""help""": """Name of new tokenizer."""}       )
					a__:			Optional[bool]      	=					field(default=_UpperCamelCase    ,			metadata={"""help""": """Push saved tokenizer to the hub."""}       )







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







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







'''simple docstring'''

import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Any:
		# Initialise PyTorch model
		_a	: int								=				BigBirdConfig.from_json_file(lowerCAmelCase_    )
		print(f"""Building PyTorch model from configuration: {config}"""    )

		if is_trivia_qa:
				_a	: Union[str, Any]								=				BigBirdForQuestionAnswering(lowerCAmelCase_    )
		else:
				_a	: Any								=				BigBirdForPreTraining(lowerCAmelCase_    )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(lowerCAmelCase_		,     lowerCAmelCase_		,     is_trivia_qa=lowerCAmelCase_    )

		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
		model.save_pretrained(lowerCAmelCase_    )


if __name__ == "__main__":
							__lowerCAmelCase		     =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
							)
							parser.add_argument(
							    '''--big_bird_config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        '''The config json file corresponding to the pre-trained BERT model. \n'''
							        '''This specifies the model architecture.'''
							    ),
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							parser.add_argument(
							    '''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
							)
							__lowerCAmelCase		     =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(
							    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
							)




