


from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 						lowerCamelCase(   a__):
				return DownloadCommand(args.model	,args.cache_dir	,args.force	,args.trust_remote_code)


class 						A__							(		UpperCamelCase__  ):




						@staticmethod
						def 							__UpperCamelCase				(       _a					:      ArgumentParser  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =parser.add_parser('''download'''  )
										download_parser.add_argument(
										    '''--cache-dir'''				,    type=_a				,    default=_a				,    help='''Path to location to store the models'''  )
										download_parser.add_argument(
										    '''--force'''				,    action='''store_true'''				,    help='''Force the model to be download even if already in cache-dir'''  )
										download_parser.add_argument(
										    '''--trust-remote-code'''				,    action='''store_true'''				,    help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''				,    )
										download_parser.add_argument('''model'''				,    type=_a				,    help='''Name of the model to download'''  )
										download_parser.set_defaults(func=_a  )




						def __init__(       self					:      Optional[Any]				,    _a					:      str				,    _a					:      str				,    _a					:      bool				,    _a					:      bool  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =model
										_SCREAMING_SNAKE_CASE			  =cache
										_SCREAMING_SNAKE_CASE			  =force
										_SCREAMING_SNAKE_CASE			  =trust_remote_code




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Optional[int]:


										"""simple docstring"""





										from ..models.auto import AutoModel, AutoTokenizer

										AutoModel.from_pretrained(
										    self._model				,    cache_dir=self._cache				,    force_download=self._force				,    trust_remote_code=self._trust_remote_code  )
										AutoTokenizer.from_pretrained(
										    self._model				,    cache_dir=self._cache				,    force_download=self._force				,    trust_remote_code=self._trust_remote_code  )



import string
from math import logaa







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =document.translate(
				    str.maketrans(''''''	,''''''	,string.punctuation)).replace('''\n'''	,'''''')
				_SCREAMING_SNAKE_CASE			  =document_without_punctuation.split(''' ''')  # word tokenization
				return len([word for word in tokenize_document if word.lower() == term.lower()])







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =corpus.lower().translate(
				    str.maketrans(''''''	,''''''	,string.punctuation))  # strip all punctuation and replace it with ''
				_SCREAMING_SNAKE_CASE			  =corpus_without_punctuation.split('''\n''')
				_SCREAMING_SNAKE_CASE			  =term.lower()
				return (len([doc for doc in docs if term in doc]), len(a__))







def 						lowerCamelCase(   a__	,a__	,a__=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(   a__	,a__):
				return round(tf * idf	,3)