


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


a : Tuple		=				logging.get_logger(__name__)


a : Optional[Any]		=				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
a : Optional[Any]		=				{
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

a : Dict		=				{
    '''RUCAIBox/mvp''': 1024,
}
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				VOCAB_FILES_NAMES
					__UpperCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
					__UpperCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__UpperCAmelCase            =				["input_ids", "attention_mask"]
					__UpperCAmelCase            =				MvpTokenizer






					def __init__(				self ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_="replace" ,       snake_case_="<s>" ,       snake_case_="</s>" ,       snake_case_="</s>" ,       snake_case_="<s>" ,       snake_case_="<unk>" ,       snake_case_="<pad>" ,       snake_case_="<mask>" ,       snake_case_=False ,       snake_case_=True ,       **snake_case_ ,       )   ->			Tuple:







								'''simple docstring'''

								super().__init__(
								    snake_case_ ,       snake_case_ ,       tokenizer_file=snake_case_ ,       errors=snake_case_ ,       bos_token=snake_case_ ,       eos_token=snake_case_ ,       sep_token=snake_case_ ,       cls_token=snake_case_ ,       unk_token=snake_case_ ,       pad_token=snake_case_ ,       mask_token=snake_case_ ,       add_prefix_space=snake_case_ ,       trim_offsets=snake_case_ ,       **snake_case_ ,       )

								__lowercase 					=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
								if pre_tok_state.get('''add_prefix_space''' ,       snake_case_						) != add_prefix_space:
											__lowercase 					=   getattr(snake_case_ ,       pre_tok_state.pop('''type'''						)						)
											__lowercase 					=   add_prefix_space
											__lowercase 					=   pre_tok_class(**snake_case_						)

								__lowercase 					=   add_prefix_space

								# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
								__lowercase 					=   '''post_processor'''
								__lowercase 					=   getattr(self.backend_tokenizer ,       snake_case_ ,       snake_case_						)
								if tokenizer_component_instance:
											__lowercase 					=   json.loads(tokenizer_component_instance.__getstate__()						)

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
														__lowercase 					=   tuple(state['''sep''']						)
											if "cls" in state:
														__lowercase 					=   tuple(state['''cls''']						)

											__lowercase 					=   False

											if state.get('''add_prefix_space''' ,       snake_case_						) != add_prefix_space:
														__lowercase 					=   add_prefix_space
														__lowercase 					=   True

											if state.get('''trim_offsets''' ,       snake_case_						) != trim_offsets:
														__lowercase 					=   trim_offsets
														__lowercase 					=   True

											if changes_to_apply:
														__lowercase 					=   getattr(snake_case_ ,       state.pop('''type'''						)						)
														__lowercase 					=   component_class(**snake_case_						)
														setattr(self.backend_tokenizer ,       snake_case_ ,       snake_case_						)






					@property
					def A      (				self						)   ->			str:







								'''simple docstring'''

								if self._mask_token is None:
											if self.verbose:
														logger.error('''Using mask_token, but it is not set yet.'''						)
											return None
								return str(self._mask_token						)






					@mask_token.setter
					def A      (				self ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else value
								__lowercase 					=   value






					def A      (				self ,       *snake_case_ ,       **snake_case_						)   ->			BatchEncoding:







								'''simple docstring'''

								__lowercase 					=   kwargs.get('''is_split_into_words''' ,       snake_case_						)

								if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
											    '''to use it with pretokenized inputs.'''						)

								return super()._batch_encode_plus(*snake_case_ ,       **snake_case_						)






					def A      (				self ,       *snake_case_ ,       **snake_case_						)   ->			BatchEncoding:







								'''simple docstring'''

								__lowercase 					=   kwargs.get('''is_split_into_words''' ,       snake_case_						)

								if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
											    '''to use it with pretokenized inputs.'''						)

								return super()._encode_plus(*snake_case_ ,       **snake_case_						)






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			Tuple[str]:







								'''simple docstring'''

								__lowercase 					=   self._tokenizer.model.save(snake_case_ ,       name=snake_case_						)
								return tuple(snake_case_						)






					def A      (				self ,       snake_case_ ,       snake_case_=None						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
											return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			List[int]:







								'''simple docstring'''

								__lowercase 					=   [self.sep_token_id]
								__lowercase 					=   [self.cls_token_id]

								if token_ids_a is None:
											return len(cls + token_ids_a + sep						) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






from typing import List

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


a : Any		=				logging.get_logger(__name__)

a : int		=				{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"efficientformer"






					def __init__(				self ,       snake_case_ = [3, 2, 6, 4] ,       snake_case_ = [4_8, 9_6, 2_2_4, 4_4_8] ,       snake_case_ = [True, True, True, True] ,       snake_case_ = 4_4_8 ,       snake_case_ = 3_2 ,       snake_case_ = 4 ,       snake_case_ = 7 ,       snake_case_ = 5 ,       snake_case_ = 8 ,       snake_case_ = 4 ,       snake_case_ = 0.0 ,       snake_case_ = 1_6 ,       snake_case_ = 3 ,       snake_case_ = 3 ,       snake_case_ = 3 ,       snake_case_ = 2 ,       snake_case_ = 1 ,       snake_case_ = 0.0 ,       snake_case_ = 1 ,       snake_case_ = True ,       snake_case_ = True ,       snake_case_ = 1e-5 ,       snake_case_ = "gelu" ,       snake_case_ = 0.0_2 ,       snake_case_ = 1e-1_2 ,       snake_case_ = 2_2_4 ,       snake_case_ = 1e-0_5 ,       **snake_case_ ,       )   ->			None:







								'''simple docstring'''

								super().__init__(**snake_case_						)

								__lowercase 					=   hidden_act
								__lowercase 					=   hidden_dropout_prob
								__lowercase 					=   hidden_sizes
								__lowercase 					=   num_hidden_layers
								__lowercase 					=   num_attention_heads
								__lowercase 					=   initializer_range
								__lowercase 					=   layer_norm_eps
								__lowercase 					=   patch_size
								__lowercase 					=   num_channels
								__lowercase 					=   depths
								__lowercase 					=   mlp_expansion_ratio
								__lowercase 					=   downsamples
								__lowercase 					=   dim
								__lowercase 					=   key_dim
								__lowercase 					=   attention_ratio
								__lowercase 					=   resolution
								__lowercase 					=   pool_size
								__lowercase 					=   downsample_patch_size
								__lowercase 					=   downsample_stride
								__lowercase 					=   downsample_pad
								__lowercase 					=   drop_path_rate
								__lowercase 					=   num_metaad_blocks
								__lowercase 					=   distillation
								__lowercase 					=   use_layer_scale
								__lowercase 					=   layer_scale_init_value
								__lowercase 					=   image_size
								__lowercase 					=   batch_norm_eps


