
"""simple docstring"""


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__A       						=       logging.get_logger(__name__)

__A       						=       {
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''
			lowerCamelCase  					=		'''oneformer'''
			lowerCamelCase  					=		{'''hidden_size''': '''hidden_dim'''}



			def __init__(							self     ,     __UpperCAmelCase = None     ,     __UpperCAmelCase = 2_55     ,     __UpperCAmelCase = 1_50     ,     __UpperCAmelCase = 0.1     ,     __UpperCAmelCase = 2.0     ,     __UpperCAmelCase = 5.0     ,     __UpperCAmelCase = 5.0     ,     __UpperCAmelCase = 0.5     ,     __UpperCAmelCase = 0.0_7     ,     __UpperCAmelCase = 1_25_44     ,     __UpperCAmelCase = 3.0     ,     __UpperCAmelCase = 0.7_5     ,     __UpperCAmelCase = 0.0_2     ,     __UpperCAmelCase = 1.0     ,     __UpperCAmelCase = 1e-05     ,     __UpperCAmelCase = False     ,     __UpperCAmelCase = True     ,     __UpperCAmelCase = True     ,     __UpperCAmelCase = [4, 8, 16, 32]     ,     __UpperCAmelCase = 77     ,     __UpperCAmelCase = 2_56     ,     __UpperCAmelCase = 77     ,     __UpperCAmelCase = 6     ,     __UpperCAmelCase = 4_94_08     ,     __UpperCAmelCase = 2     ,     __UpperCAmelCase = 16     ,     __UpperCAmelCase = 2_56     ,     __UpperCAmelCase = 2_56     ,     __UpperCAmelCase = 2_56     ,     __UpperCAmelCase = 10_24     ,     __UpperCAmelCase = "GN"     ,     __UpperCAmelCase = 6     ,     __UpperCAmelCase = 10     ,     __UpperCAmelCase = True     ,     __UpperCAmelCase = 8     ,     __UpperCAmelCase = 0.1     ,     __UpperCAmelCase = 20_48     ,     __UpperCAmelCase = False     ,     __UpperCAmelCase = False     ,     __UpperCAmelCase = 2     ,     __UpperCAmelCase = 4     ,     **__UpperCAmelCase     ,     ) ->     Tuple:
				if backbone_config is None:
					logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""  )
					_lowerCAmelCase								=CONFIG_MAPPING["""swin"""](
					    image_size=2_24     ,     in_channels=3     ,     patch_size=4     ,     embed_dim=96     ,     depths=[2, 2, 6, 2]     ,     num_heads=[3, 6, 12, 24]     ,     window_size=7     ,     drop_path_rate=0.3     ,     use_absolute_embeddings=__UpperCAmelCase     ,     out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]     ,     )
				elif isinstance(__UpperCAmelCase     ,     __UpperCAmelCase  ):
					_lowerCAmelCase								=backbone_config.get("""model_type"""  )
					_lowerCAmelCase								=CONFIG_MAPPING[backbone_model_type]
					_lowerCAmelCase								=config_class.from_dict(__UpperCAmelCase  )

				_lowerCAmelCase								=backbone_config

				_lowerCAmelCase								=ignore_value
				_lowerCAmelCase								=num_queries
				_lowerCAmelCase								=no_object_weight
				_lowerCAmelCase								=class_weight
				_lowerCAmelCase								=mask_weight
				_lowerCAmelCase								=dice_weight
				_lowerCAmelCase								=contrastive_weight
				_lowerCAmelCase								=contrastive_temperature
				_lowerCAmelCase								=train_num_points
				_lowerCAmelCase								=oversample_ratio
				_lowerCAmelCase								=importance_sample_ratio
				_lowerCAmelCase								=init_std
				_lowerCAmelCase								=init_xavier_std
				_lowerCAmelCase								=layer_norm_eps
				_lowerCAmelCase								=is_training
				_lowerCAmelCase								=use_auxiliary_loss
				_lowerCAmelCase								=output_auxiliary_logits
				_lowerCAmelCase								=strides
				_lowerCAmelCase								=task_seq_len
				_lowerCAmelCase								=text_encoder_width
				_lowerCAmelCase								=text_encoder_context_length
				_lowerCAmelCase								=text_encoder_num_layers
				_lowerCAmelCase								=text_encoder_vocab_size
				_lowerCAmelCase								=text_encoder_proj_layers
				_lowerCAmelCase								=text_encoder_n_ctx
				_lowerCAmelCase								=conv_dim
				_lowerCAmelCase								=mask_dim
				_lowerCAmelCase								=hidden_dim
				_lowerCAmelCase								=encoder_feedforward_dim
				_lowerCAmelCase								=norm
				_lowerCAmelCase								=encoder_layers
				_lowerCAmelCase								=decoder_layers
				_lowerCAmelCase								=use_task_norm
				_lowerCAmelCase								=num_attention_heads
				_lowerCAmelCase								=dropout
				_lowerCAmelCase								=dim_feedforward
				_lowerCAmelCase								=pre_norm
				_lowerCAmelCase								=enforce_input_proj
				_lowerCAmelCase								=query_dec_layers
				_lowerCAmelCase								=common_stride
				_lowerCAmelCase								=decoder_layers

				super().__init__(**__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self  ) ->     Dict[str, any]:
				_lowerCAmelCase								=copy.deepcopy(self.__dict__  )
				_lowerCAmelCase								=self.backbone_config.to_dict()
				_lowerCAmelCase								=self.__class__.model_type
				return output




from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


lowerCAmelCase__							= logging.get_logger(__name__)

lowerCAmelCase__							= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__							= {
    'vocab_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json',
    },
    'merges_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'openai/clip-vit-base-patch32': (
            'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase__							= {
    'openai/clip-vit-base-patch32': 77,
}
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					VOCAB_FILES_NAMES
 UpperCAmelCase_	 =					PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_	 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_	 =					['input_ids', 'attention_mask']
 UpperCAmelCase_	 =					CLIPTokenizer




 def __init__(			self					:  Optional[int]			,    lowercase__					:  Optional[int]=None			,    lowercase__					:  Tuple=None			,    lowercase__					:  Tuple=None			,    lowercase__					:  str="<|endoftext|>"			,    lowercase__					:  List[str]="<|startoftext|>"			,    lowercase__					:  Union[str, Any]="<|endoftext|>"			,    lowercase__					:  Any="<|endoftext|>"			,    **lowercase__					:  Any			,    ):







        '''simple docstring'''



        super().__init__(
            lowercase__			,    lowercase__			,    tokenizer_file=lowercase__			,    unk_token=lowercase__			,    bos_token=lowercase__			,    eos_token=lowercase__			,    pad_token=lowercase__			,    **lowercase__			,    )

        if not isinstance(self.backend_tokenizer.pre_tokenizer			,    pre_tokenizers.Sequence):
               raise ValueError(
                   'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
                   ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
                   ' to be compatible with this version.The easiest way to do so is'
                   ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
                   ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
                   ' transformers.')

        self._wrap_decode_method_backend_tokenizer()




 def 		__snake_case						(			self					:  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.backend_tokenizer.decode

        def new_decode_method(*lowercase__					:  str			,    **lowercase__					:  int):
               lowerCAmelCase__     	=					orig_decode_method(*lowercase__			,    **lowercase__)
               lowerCAmelCase__     	=					text.replace(self.backend_tokenizer.model.end_of_word_suffix			,    ' ').strip()
               return text

        lowerCAmelCase__     	=					new_decode_method




 def 		__snake_case						(			self					:  Tuple			,    lowercase__					:  List[int]			,    lowercase__					:  Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.bos_token_id]
        lowerCAmelCase__     	=					[self.eos_token_id]

        if token_ids_a is None:
               return bos_token + token_ids_a + eos_token
        return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token




 def 		__snake_case						(			self					:  Any			,    lowercase__					:  List[int]			,    lowercase__					:  Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.bos_token_id]
        lowerCAmelCase__     	=					[self.eos_token_id]

        if token_ids_a is None:
               return len(bos_token + token_ids_a + eos_token) * [0]
        return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token) * [0]




 def 		__snake_case						(			self					:  Dict			,    lowercase__					:  str			,    lowercase__					:  Optional[str] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					self._tokenizer.model.save(lowercase__			,    name=lowercase__)
        return tuple(lowercase__)
