



from ...configuration_utils import PretrainedConfig
from ...utils import logging


a      :  Optional[Any]			     =			logging.get_logger(__name__)

a      :  List[str]			     =			{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}







class 				_a				(   _a					):
			A      		=	"""mra"""

			def __init__(self,   SCREAMING_SNAKE_CASE_=50265,   SCREAMING_SNAKE_CASE_=768,   SCREAMING_SNAKE_CASE_=12,   SCREAMING_SNAKE_CASE_=12,   SCREAMING_SNAKE_CASE_=3072,   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=512,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=0.0_2,   SCREAMING_SNAKE_CASE_=1E-5,   SCREAMING_SNAKE_CASE_="absolute",   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_="full",   SCREAMING_SNAKE_CASE_=0,   SCREAMING_SNAKE_CASE_=0,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=0,   SCREAMING_SNAKE_CASE_=2,   **SCREAMING_SNAKE_CASE_,   )       ->   Union[str, Any]:
						super().__init__(pad_token_id=__lowerCAmelCase,   bos_token_id=__lowerCAmelCase,   eos_token_id=__lowerCAmelCase,   **__lowerCAmelCase					)

						UpperCAmelCase_:		Any							      =     vocab_size
						UpperCAmelCase_:		Tuple							      =     max_position_embeddings
						UpperCAmelCase_:		Optional[int]							      =     hidden_size
						UpperCAmelCase_:		int							      =     num_hidden_layers
						UpperCAmelCase_:		Tuple							      =     num_attention_heads
						UpperCAmelCase_:		str							      =     intermediate_size
						UpperCAmelCase_:		Union[str, Any]							      =     hidden_act
						UpperCAmelCase_:		Optional[int]							      =     hidden_dropout_prob
						UpperCAmelCase_:		Dict							      =     attention_probs_dropout_prob
						UpperCAmelCase_:		Dict							      =     initializer_range
						UpperCAmelCase_:		int							      =     type_vocab_size
						UpperCAmelCase_:		Any							      =     layer_norm_eps
						UpperCAmelCase_:		Dict							      =     position_embedding_type
						UpperCAmelCase_:		Any							      =     block_per_row
						UpperCAmelCase_:		Any							      =     approx_mode
						UpperCAmelCase_:		List[Any]							      =     initial_prior_first_n_blocks
						UpperCAmelCase_:		int							      =     initial_prior_diagonal_n_blocks













import copy
import random

from transformers import CLIPTokenizer

class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def __init__(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														super().__init__(*__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      {}






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      super().add_tokens(__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	)
														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	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			__lowerCAmelCase=1	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      []
														if num_vec_per_token == 1:
																					self.try_adding_tokens(__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	)
																					output.append(__lowerCAmelCase	)
														else:
																					UpperCamelCase__					  =      []
																					for i in range(__lowerCAmelCase	):
																												UpperCamelCase__					  =      placeholder_token + f"""_{i}"""
																												self.try_adding_tokens(__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	)
																												output.append(__lowerCAmelCase	)
        # 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"""	)
														UpperCamelCase__					  =      output






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=False	,			__lowerCAmelCase=1.0	):
														if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	):
																					UpperCamelCase__					  =      []
																					for i in range(len(__lowerCAmelCase	)	):
																												output.append(self.replace_placeholder_tokens_in_text(text[i]	,			vector_shuffle=__lowerCAmelCase	)	)
																					return output
														for placeholder_token in self.token_map:
																					if placeholder_token in text:
																												UpperCamelCase__					  =      self.token_map[placeholder_token]
																												UpperCamelCase__					  =      tokens[: 1 + int(len(__lowerCAmelCase	) * prop_tokens_to_load	)]
																												if vector_shuffle:
																																			UpperCamelCase__					  =      copy.copy(__lowerCAmelCase	)
																																			random.shuffle(__lowerCAmelCase	)
																												UpperCamelCase__					  =      text.replace(__lowerCAmelCase	,			""" """.join(__lowerCAmelCase	)	)
														return text






							def __call__(    self	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			__lowerCAmelCase=False	,			__lowerCAmelCase=1.0	,			**__lowerCAmelCase	):
														return super().__call__(
														    self.replace_placeholder_tokens_in_text(
														        __lowerCAmelCase	,			vector_shuffle=__lowerCAmelCase	,			prop_tokens_to_load=__lowerCAmelCase	)	,			*__lowerCAmelCase	,			**__lowerCAmelCase	,			)







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			__lowerCAmelCase=False	,			__lowerCAmelCase=1.0	,			**__lowerCAmelCase	):
														return super().encode(
														    self.replace_placeholder_tokens_in_text(
														        __lowerCAmelCase	,			vector_shuffle=__lowerCAmelCase	,			prop_tokens_to_load=__lowerCAmelCase	)	,			*__lowerCAmelCase	,			**__lowerCAmelCase	,			)




