



'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase	:		Dict				=   logging.get_logger(__name__)

lowerCAmelCase	:		Optional[int]				=   {
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}






class 					UpperCAmelCase__	(		UpperCamelCase__							,       UpperCamelCase__ ):
					a      :     List[Any]        =							"""dinat"""

					a      :     Dict        =							{
					    """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=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]			,				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			,				)     ->			List[str]:
						super().__init__(**UpperCamelCase     )

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




'''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_squeezebert import SqueezeBertTokenizer


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

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

lowerCAmelCase	:		str				=   {
    '''vocab_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
        ),
        '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase	:		Optional[Any]				=   {
    '''squeezebert/squeezebert-uncased''': 5_1_2,
    '''squeezebert/squeezebert-mnli''': 5_1_2,
    '''squeezebert/squeezebert-mnli-headless''': 5_1_2,
}


lowerCAmelCase	:		Tuple				=   {
    '''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Dict        =							VOCAB_FILES_NAMES
					a      :     Any        =							PRETRAINED_VOCAB_FILES_MAP
					a      :     Dict        =							PRETRAINED_INIT_CONFIGURATION
					a      :     str        =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a      :     Optional[Any]        =							SqueezeBertTokenizer



					def __init__(    self			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=True			,				UpperCamelCase="[UNK]"			,				UpperCamelCase="[SEP]"			,				UpperCamelCase="[PAD]"			,				UpperCamelCase="[CLS]"			,				UpperCamelCase="[MASK]"			,				UpperCamelCase=True			,				UpperCamelCase=None			,				**UpperCamelCase			,				)     ->			List[Any]:
						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			,				)

						__lowerCAmelCase												=  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
						):
							__lowerCAmelCase												=  getattr(UpperCamelCase			,				normalizer_state.pop("type"     )     )
							__lowerCAmelCase												=  do_lower_case
							__lowerCAmelCase												=  strip_accents
							__lowerCAmelCase												=  tokenize_chinese_chars
							__lowerCAmelCase												=  normalizer_class(**UpperCamelCase     )

						__lowerCAmelCase												=  do_lower_case



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=None     )     ->			str:
						__lowerCAmelCase												=  [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        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			List[int]:
						__lowerCAmelCase												=  [self.sep_token_id]
						__lowerCAmelCase												=  [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        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			Tuple[str]:
						__lowerCAmelCase												=  self._tokenizer.model.save(UpperCamelCase			,				name=UpperCamelCase     )
						return tuple(UpperCamelCase     )