
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_rembert import RemBertTokenizer
else:
				__lowerCAmelCase												=	None

__lowerCAmelCase												=	logging.get_logger(__name__)
__lowerCAmelCase												=	{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase												=	{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase												=	{
    'google/rembert': 256,
}

__lowerCAmelCase												=	'▁'


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						VOCAB_FILES_NAMES
							lowerCAmelCase_ 						=						PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase_ 						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase_ 						=						RemBertTokenizer

							def __init__(self  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=False  ,  UpperCAmelCase="[CLS]"  ,  UpperCAmelCase="[SEP]"  ,  UpperCAmelCase="<unk>"  ,  UpperCAmelCase="[SEP]"  ,  UpperCAmelCase="<pad>"  ,  UpperCAmelCase="[CLS]"  ,  UpperCAmelCase="[MASK]"  ,  **UpperCAmelCase  ,  )					->       Optional[Any]:
														# Mask token behave like a normal word, i.e. include the space before it
														_snake_case       =  AddedToken(UpperCAmelCase  ,  lstrip=UpperCAmelCase  ,  rstrip=UpperCAmelCase  ) if isinstance(UpperCAmelCase  ,  UpperCAmelCase  ) else mask_token

														super().__init__(
														    UpperCAmelCase  ,  tokenizer_file=UpperCAmelCase  ,  do_lower_case=UpperCAmelCase  ,  remove_space=UpperCAmelCase  ,  keep_accents=UpperCAmelCase  ,  bos_token=UpperCAmelCase  ,  eos_token=UpperCAmelCase  ,  unk_token=UpperCAmelCase  ,  sep_token=UpperCAmelCase  ,  pad_token=UpperCAmelCase  ,  cls_token=UpperCAmelCase  ,  mask_token=UpperCAmelCase  ,  **UpperCAmelCase  ,  )

														_snake_case       =  do_lower_case
														_snake_case       =  remove_space
														_snake_case       =  keep_accents
														_snake_case       =  vocab_file
														_snake_case       =  False if not self.vocab_file else True

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       List[int]:
														_snake_case       =  [self.sep_token_id]
														_snake_case       =  [self.cls_token_id]
														if token_ids_a is None:
																					return cls + token_ids_a + sep
														return cls + token_ids_a + sep + token_ids_a + sep

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  UpperCAmelCase = False  )					->       List[int]:

														if already_has_special_tokens:
																					if token_ids_a is not None:
																												raise ValueError(
																												    """You should not supply a second sequence if the provided sequence of """
																												    """ids is already formatted with special tokens for the model."""  )
																					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

														if token_ids_a is not None:
																					return [1] + ([0] * len(UpperCAmelCase  )) + [1] + ([0] * len(UpperCAmelCase  )) + [1]
														return [1] + ([0] * len(UpperCAmelCase  )) + [1]

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       List[int]:
														_snake_case       =  [self.sep_token_id]
														_snake_case       =  [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  ) * [0] + len(token_ids_a + sep  ) * [1]





							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       Tuple[str]:
														if not os.path.isdir(UpperCAmelCase  ):
																					logger.error("""Vocabulary path ({}) should be a directory""".format(UpperCAmelCase  )  )
																					return
														_snake_case       =  os.path.join(
														    UpperCAmelCase  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

														if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCAmelCase  ):
																					copyfile(self.vocab_file  ,  UpperCAmelCase  )

														return (out_vocab_file,)

'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class       _lowerCAmelCase   (   __snake_case					,							__snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"nat"

							lowerCAmelCase_ 						=						{
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}

							def __init__(self  ,  UpperCAmelCase=4  ,  UpperCAmelCase=3  ,  UpperCAmelCase=64  ,  UpperCAmelCase=[3, 4, 6, 5]  ,  UpperCAmelCase=[2, 4, 8, 16]  ,  UpperCAmelCase=7  ,  UpperCAmelCase=3.0  ,  UpperCAmelCase=True  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=1e-5  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  ,  )					->       str:
														super().__init__(**UpperCAmelCase  )

														_snake_case       =  patch_size
														_snake_case       =  num_channels
														_snake_case       =  embed_dim
														_snake_case       =  depths
														_snake_case       =  len(UpperCAmelCase  )
														_snake_case       =  num_heads
														_snake_case       =  kernel_size
														_snake_case       =  mlp_ratio
														_snake_case       =  qkv_bias
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  drop_path_rate
														_snake_case       =  hidden_act
														_snake_case       =  layer_norm_eps
														_snake_case       =  initializer_range
														# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_snake_case       =  int(embed_dim * 2 ** (len(UpperCAmelCase  ) - 1)  )
														_snake_case       =  layer_scale_init_value
														_snake_case       =  ["""stem"""] + [f"""stage{idx}""" for idx in range(1  ,  len(UpperCAmelCase  ) + 1  )]
														_snake_case,							_snake_case       =  get_aligned_output_features_output_indices(
														    out_features=UpperCAmelCase  ,  out_indices=UpperCAmelCase  ,  stage_names=self.stage_names  )