




"""simple docstring"""






from dataclasses import dataclass, field
from typing import Optional






@dataclass
class lowerCAmelCase__      :






			'''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=lowercase,      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=lowercase,      metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}		)
			lowerCamelCase__ =     field(default=lowercase,      metadata={"""help""": """If True the data is pretokenized."""}		)






@dataclass
class lowerCAmelCase__      :






			'''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 lowerCAmelCase__      :






			'''simple docstring'''



			lowerCamelCase__ =     field(
			    default="""codeparrot/codeparrot""",      metadata={"""help""": """Model name or path of model to be evaluated."""}		)
			lowerCamelCase__ =     field(default=lowercase,      metadata={"""help""": """Number of workers used for code evaluation."""}		)
			lowerCamelCase__ =     field(
			    default=lowercase,      metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""},      )
			lowerCamelCase__ =     field(
			    default=lowercase,      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 lowerCAmelCase__      :






			'''simple docstring'''



			lowerCamelCase__ =     field(
			    default=lowercase,      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=lowercase,      metadata={"""help""": """If True, near-duplicate samples are removed."""}		)
			lowerCamelCase__ =     field(
			    default=0.85,      metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}		)






@dataclass
class lowerCAmelCase__      :






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






@dataclass
class lowerCAmelCase__      :






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






@dataclass
class lowerCAmelCase__      :






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





"""simple docstring"""






import operator as op







def 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     Dict       =       []
				_lowerCamelCase	:     List[str]       =       lambda lowercase__				,       lowercase__						: int(x / y						)  # noqa: E731 integer division operation
				_lowerCamelCase	:     Optional[int]       =       {
				    '^': op.pow,
				    '*': op.mul,
				    '/': div,
				    '+': op.add,
				    '-': op.sub,
				}  # operators & their respective operation

				# print table header
				print('Symbol'.center(8						)				,       'Action'.center(12						)				,       'Stack'				,       sep=' | '						)
				print('-' * (30 + len(lowercase__						))						)

				for x in post_fix:
								if x.isdigit():  # if x in digit
												stack.append(lowercase__						)  # append x to stack
												# output in tabular format
												print(x.rjust(8						)				,       ('push(' + x + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '						)
								else:
												_lowerCamelCase	:     int       =       stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8						)				,       ('pop(' + b + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '						)

												_lowerCamelCase	:     Tuple       =       stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8						)				,       ('pop(' + a + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '						)

												stack.append(
												    str(opr[x](int(lowercase__						)				,       int(lowercase__						)						)						)						)  # evaluate the 2 values popped from stack & push result to stack
												# output in tabular format
												print(
												    x.rjust(8						)				,       ('push(' + a + x + b + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '				,       )

				return int(stack[0]						)


if __name__ == "__main__":
			lowercase__			=		input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
			print("""\n\tResult = """, solve(Postfix))