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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	'▁'

__lowerCAmelCase												=	{'vocab_file': 'sentencepiece.bpe.model'}

__lowerCAmelCase												=	{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

__lowerCAmelCase												=	{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}


class       _lowerCAmelCase   (   __lowercase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						VOCAB_FILES_NAMES
							lowerCAmelCase_ 						=						PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase_ 						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase_ 						=						["input_ids", "attention_mask"]

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

														_snake_case       =  {} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    bos_token=snake_case_  ,  eos_token=snake_case_  ,  unk_token=snake_case_  ,  sep_token=snake_case_  ,  cls_token=snake_case_  ,  pad_token=snake_case_  ,  mask_token=snake_case_  ,  sp_model_kwargs=self.sp_model_kwargs  ,  **snake_case_  ,  )

														_snake_case       =  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
														self.sp_model.Load(str(snake_case_  )  )
														_snake_case       =  vocab_file

														# Original fairseq vocab and spm vocab must be "aligned":
														# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
														# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
														# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
														# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

														# Mimic fairseq token-to-id alignment for the first 4 token
														_snake_case       =  {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

														# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
														_snake_case       =  1

														_snake_case       =  len(self.sp_model  ) + self.fairseq_offset
														_snake_case       =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}

							def __getstate__(self  )					->       Any:
														_snake_case       =  self.__dict__.copy()
														_snake_case       =  None
														_snake_case       =  self.sp_model.serialized_model_proto()
														return state

							def __setstate__(self  ,  UpperCAmelCase  )					->       Dict:
														_snake_case       =  d

														# for backward compatibility
														if not hasattr(self  ,  """sp_model_kwargs"""  ):
																					_snake_case       =  {}

														_snake_case       =  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
														self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       Optional[Any]:

														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														_snake_case       =  [self.cls_token_id]
														_snake_case       =  [self.sep_token_id]
														return cls + token_ids_a + sep + sep + token_ids_a + sep

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  UpperCAmelCase = False  )					->       Any:

														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=snake_case_  ,  token_ids_a=snake_case_  ,  already_has_special_tokens=snake_case_  )

														if token_ids_a is None:
																					return [1] + ([0] * len(snake_case_  )) + [1]
														return [1] + ([0] * len(snake_case_  )) + [1, 1] + ([0] * len(snake_case_  )) + [1]

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       Union[str, Any]:
														_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 + sep + token_ids_a + sep  ) * [0]

							@property
							def 	lowercase							(self  )					->       Optional[int]:
														return len(self.sp_model  ) + self.fairseq_offset + 1  # Add the <mask> token

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  {self.convert_ids_to_tokens(snake_case_  ): i for i in range(self.vocab_size  )}
														vocab.update(self.added_tokens_encoder  )
														return vocab

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       Any:
														return self.sp_model.encode(snake_case_  ,  out_type=snake_case_  )

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       Optional[int]:
														if token in self.fairseq_tokens_to_ids:
																					return self.fairseq_tokens_to_ids[token]
														_snake_case       =  self.sp_model.PieceToId(snake_case_  )

														# Need to return unknown token if the SP model returned 0
														return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       str:
														if index in self.fairseq_ids_to_tokens:
																					return self.fairseq_ids_to_tokens[index]
														return self.sp_model.IdToPiece(index - self.fairseq_offset  )

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       Optional[int]:
														_snake_case       =  ''''''.join(snake_case_  ).replace(snake_case_  ,  """ """  ).strip()
														return out_string





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

														if os.path.abspath(self.vocab_file  ) != os.path.abspath(snake_case_  ) and os.path.isfile(self.vocab_file  ):
																					copyfile(self.vocab_file  ,  snake_case_  )
														elif not os.path.isfile(self.vocab_file  ):
																					with open(snake_case_  ,  """wb"""  ) as fi:
																												_snake_case       =  self.sp_model.serialized_model_proto()
																												fi.write(snake_case_  )

														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  )