






import copy
import random

from transformers import CLIPTokenizer

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	Any		,     *__a				:	Optional[Any]		,     **__a				:	Any							)			->  Optional[int]:


												"""simple docstring"""

												super().__init__(*__a		,     **__a							)
												__lowercase      :							Union[str, Any]				=     {}



					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	int		,     *__a				:	Union[str, Any]		,     **__a				:	Optional[int]							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Dict				=     super().add_tokens(__a		,     *__a		,     **__a							)
												if num_added_tokens == 0:
																			raise ValueError(
																			    F"The tokenizer already contains the token {placeholder_token}. Please pass a different"
																			    """ `placeholder_token` that is not already in the tokenizer."""							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	List[Any]		,     *__a				:	int		,     __a				:	Dict=1		,     **__a				:	Any							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							str				=     []
												if num_vec_per_token == 1:
																			self.try_adding_tokens(__a		,     *__a		,     **__a							)
																			output.append(__a							)
												else:
																			__lowercase      :							int				=     []
																			for i in range(__a							):
																										__lowercase      :							Any				=     placeholder_token + F"_{i}"
																										self.try_adding_tokens(__a		,     *__a		,     **__a							)
																										output.append(__a							)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
												for token in self.token_map:
																			if token in placeholder_token:
																										raise ValueError(
																										    F"The tokenizer already has placeholder token {token} that can get confused with"
																										    F" {placeholder_token}keep placeholder tokens independent"							)
												__lowercase      :							int				=     output



					def       lowerCAmelCase				(  self				:	str		,     __a				:	List[str]		,     __a				:	str=False		,     __a				:	Optional[Any]=1.0							)			->  List[str]:


												"""simple docstring"""

												if isinstance(__a		,     __a							):
																			__lowercase      :							int				=     []
																			for i in range(len(__a							)							):
																										output.append(self.replace_placeholder_tokens_in_text(text[i]		,     vector_shuffle=__a							)							)
																			return output
												for placeholder_token in self.token_map:
																			if placeholder_token in text:
																										__lowercase      :							List[Any]				=     self.token_map[placeholder_token]
																										__lowercase      :							Union[str, Any]				=     tokens[: 1 + int(len(__a							) * prop_tokens_to_load							)]
																										if vector_shuffle:
																																	__lowercase      :							Union[str, Any]				=     copy.copy(__a							)
																																	random.shuffle(__a							)
																										__lowercase      :							Optional[Any]				=     text.replace(__a		,     """ """.join(__a							)							)
												return text



					def __call__(  self				:	List[str]		,     __a				:	Optional[Any]		,     *__a				:	Tuple		,     __a				:	Optional[Any]=False		,     __a				:	Tuple=1.0		,     **__a				:	Optional[int]							)			->  List[str]:


												"""simple docstring"""

												return super().__call__(
												    self.replace_placeholder_tokens_in_text(
												        __a		,     vector_shuffle=__a		,     prop_tokens_to_load=__a							)		,     *__a		,     **__a		,     )



					def       lowerCAmelCase				(  self				:	int		,     __a				:	int		,     *__a				:	List[str]		,     __a				:	Union[str, Any]=False		,     __a				:	Optional[int]=1.0		,     **__a				:	Optional[Any]							)			->  Any:


												"""simple docstring"""

												return super().encode(
												    self.replace_placeholder_tokens_in_text(
												        __a		,     vector_shuffle=__a		,     prop_tokens_to_load=__a							)		,     *__a		,     **__a		,     )







from argparse import ArgumentParser

from . import BaseTransformersCLICommand
def       snake_case_		(     lowerCAmelCase_       :						Dict      ):
							return DownloadCommand(args.model       ,       args.cache_dir       ,       args.force       ,       args.trust_remote_code      )



class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					@staticmethod
					def       lowerCAmelCase				(  __a				:	ArgumentParser							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     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				:	Dict		,     __a				:	str		,     __a				:	str		,     __a				:	bool		,     __a				:	bool							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Dict				=     model
												__lowercase      :							List[Any]				=     cache
												__lowercase      :							Any				=     force
												__lowercase      :							Optional[int]				=     trust_remote_code



					def       lowerCAmelCase				(  self				:	str							)			->  List[str]:


												"""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							)