
"""simple docstring"""







import string
from math import logaa
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						List[str]						      =				document.translate(
	    str.maketrans(''	,  ''	,  string.punctuation		)		).replace('\n'	,  ''		)
	__lowerCAmelCase      :						Dict						      =				document_without_punctuation.split(' '		)  # word tokenization
	return len([word for word in tokenize_document if word.lower() == term.lower()]		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Optional[Any]						      =				corpus.lower().translate(
	    str.maketrans(''	,  ''	,  string.punctuation		)		)  # strip all punctuation and replace it with ''
	__lowerCAmelCase      :						List[Any]						      =				corpus_without_punctuation.split('\n'		)
	__lowerCAmelCase      :						Optional[Any]						      =				term.lower()
	return (len([doc for doc in docs if term in doc]		), len(_UpperCamelCase		))
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase=False		):
	if smoothing:
		if n == 0:
			raise ValueError('log10(0) is undefined.'		)
		return round(1 + logaa(n / (1 + df)		)	,  3		)

	if df == 0:
		raise ZeroDivisionError('df must be > 0'		)
	elif n == 0:
		raise ValueError('log10(0) is undefined.'		)
	return round(logaa(n / df		)	,  3		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	return round(tf * idf	,  3		)

"""simple docstring"""







lowerCamelCase__        =		"""0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
							from .utils import rich