
'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase												=	{
    'vocab_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-german-cased': (
            'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
        ),
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCAmelCase												=	{
    'distilbert-base-uncased': 512,
    'distilbert-base-uncased-distilled-squad': 512,
    'distilbert-base-cased': 512,
    'distilbert-base-cased-distilled-squad': 512,
    'distilbert-base-german-cased': 512,
    'distilbert-base-multilingual-cased': 512,
}


__lowerCAmelCase												=	{
    'distilbert-base-uncased': {'do_lower_case': True},
    'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
    'distilbert-base-cased': {'do_lower_case': False},
    'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
    'distilbert-base-german-cased': {'do_lower_case': False},
    'distilbert-base-multilingual-cased': {'do_lower_case': False},
}


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

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

							def __init__(self  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=True  ,  UpperCAmelCase="[UNK]"  ,  UpperCAmelCase="[SEP]"  ,  UpperCAmelCase="[PAD]"  ,  UpperCAmelCase="[CLS]"  ,  UpperCAmelCase="[MASK]"  ,  UpperCAmelCase=True  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  ,  )					->       int:
														super().__init__(
														    UpperCAmelCase  ,  tokenizer_file=UpperCAmelCase  ,  do_lower_case=UpperCAmelCase  ,  unk_token=UpperCAmelCase  ,  sep_token=UpperCAmelCase  ,  pad_token=UpperCAmelCase  ,  cls_token=UpperCAmelCase  ,  mask_token=UpperCAmelCase  ,  tokenize_chinese_chars=UpperCAmelCase  ,  strip_accents=UpperCAmelCase  ,  **UpperCAmelCase  ,  )

														_snake_case       =  json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
														if (
														    normalizer_state.get("""lowercase"""  ,  UpperCAmelCase  ) != do_lower_case
														    or normalizer_state.get("""strip_accents"""  ,  UpperCAmelCase  ) != strip_accents
														    or normalizer_state.get("""handle_chinese_chars"""  ,  UpperCAmelCase  ) != tokenize_chinese_chars
														):
																					_snake_case       =  getattr(UpperCAmelCase  ,  normalizer_state.pop("""type"""  )  )
																					_snake_case       =  do_lower_case
																					_snake_case       =  strip_accents
																					_snake_case       =  tokenize_chinese_chars
																					_snake_case       =  normalizer_class(**UpperCAmelCase  )

														_snake_case       =  do_lower_case

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=None  )					->       Optional[int]:
														_snake_case       =  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output

							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]:
														_snake_case       =  self._tokenizer.model.save(UpperCAmelCase  ,  name=UpperCAmelCase  )
														return tuple(UpperCAmelCase  )

'''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  )