
import os
import unicodedata
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 SPIECE_UNDERLINE, logging


SCREAMING_SNAKE_CASE__   :			int												=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :			int												=    {"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    }
}

SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

# Segments (not really needed)
SCREAMING_SNAKE_CASE__   :			Optional[int]												=    0
SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    1
SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    2
SCREAMING_SNAKE_CASE__   :			Dict												=    3
SCREAMING_SNAKE_CASE__   :			List[Any]												=    4

class    lowerCAmelCase__     (	__lowercase			):
		a__       :						List[str]        =		VOCAB_FILES_NAMES
		a__       :						Optional[int]        =		PRETRAINED_VOCAB_FILES_MAP
		a__       :						Dict        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a__       :						int        =		"""left"""






		def __init__(		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=False							,			SCREAMING_SNAKE_CASE__		:							Tuple=True							,			SCREAMING_SNAKE_CASE__		:							str=False							,			SCREAMING_SNAKE_CASE__		:							Dict="<s>"							,			SCREAMING_SNAKE_CASE__		:							int="</s>"							,			SCREAMING_SNAKE_CASE__		:							Dict="<unk>"							,			SCREAMING_SNAKE_CASE__		:							int="<sep>"							,			SCREAMING_SNAKE_CASE__		:							Tuple="<pad>"							,			SCREAMING_SNAKE_CASE__		:							List[str]="<cls>"							,			SCREAMING_SNAKE_CASE__		:							int="<mask>"							,			SCREAMING_SNAKE_CASE__		:							Dict=["<eop>", "<eod>"]							,			SCREAMING_SNAKE_CASE__		:							Optional[Dict[str, Any]] = None							,			**SCREAMING_SNAKE_CASE__		:							Optional[int]							,			)     ->							None:
						# Mask token behave like a normal word, i.e. include the space before it
						__lowerCamelCase    					=	AddedToken(SCREAMING_SNAKE_CASE__							,			lstrip=SCREAMING_SNAKE_CASE__							,			rstrip=SCREAMING_SNAKE_CASE__  ) if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else mask_token

						__lowerCamelCase    					=	{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    do_lower_case=SCREAMING_SNAKE_CASE__							,			remove_space=SCREAMING_SNAKE_CASE__							,			keep_accents=SCREAMING_SNAKE_CASE__							,			bos_token=SCREAMING_SNAKE_CASE__							,			eos_token=SCREAMING_SNAKE_CASE__							,			unk_token=SCREAMING_SNAKE_CASE__							,			sep_token=SCREAMING_SNAKE_CASE__							,			pad_token=SCREAMING_SNAKE_CASE__							,			cls_token=SCREAMING_SNAKE_CASE__							,			mask_token=SCREAMING_SNAKE_CASE__							,			additional_special_tokens=SCREAMING_SNAKE_CASE__							,			sp_model_kwargs=self.sp_model_kwargs							,			**SCREAMING_SNAKE_CASE__							,			)

						__lowerCamelCase    					=	3

						__lowerCamelCase    					=	do_lower_case
						__lowerCamelCase    					=	remove_space
						__lowerCamelCase    					=	keep_accents
						__lowerCamelCase    					=	vocab_file

						__lowerCamelCase    					=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
						self.sp_model.Load(SCREAMING_SNAKE_CASE__  )






		@property
		def  __A       (		self		:							Optional[int]  )     ->							Dict:
						return len(self.sp_model  )






		def  __A       (		self		:							Tuple  )     ->							Tuple:
						__lowerCamelCase    					=	{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__  ): i for i in range(self.vocab_size  )}
						vocab.update(self.added_tokens_encoder  )
						return vocab






		def __getstate__(		self		:							int  )     ->							List[str]:
						__lowerCamelCase    					=	self.__dict__.copy()
						__lowerCamelCase    					=	None
						return state






		def __setstate__(		self		:							str							,			SCREAMING_SNAKE_CASE__		:							Tuple  )     ->							int:
						__lowerCamelCase    					=	d

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

						__lowerCamelCase    					=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
						self.sp_model.Load(self.vocab_file  )






		def  __A       (		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							List[Any]:
						if self.remove_space:
										__lowerCamelCase    					=	''' '''.join(inputs.strip().split()  )
						else:
										__lowerCamelCase    					=	inputs
						__lowerCamelCase    					=	outputs.replace('''``'''							,			'''"'''  ).replace('''\'\''''							,			'''"'''  )

						if not self.keep_accents:
										__lowerCamelCase    					=	unicodedata.normalize('''NFKD'''							,			SCREAMING_SNAKE_CASE__  )
										__lowerCamelCase    					=	''''''.join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE__  )]  )
						if self.do_lower_case:
										__lowerCamelCase    					=	outputs.lower()

						return outputs






		def  __A       (		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							str  )     ->							List[str]:
						__lowerCamelCase    					=	self.preprocess_text(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	self.sp_model.encode(SCREAMING_SNAKE_CASE__							,			out_type=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	[]
						for piece in pieces:
										if len(SCREAMING_SNAKE_CASE__  ) > 1 and piece[-1] == str(''','''  ) and piece[-2].isdigit():
														__lowerCamelCase    					=	self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE__							,			''''''  )  )
														if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																		if len(cur_pieces[0]  ) == 1:
																						__lowerCamelCase    					=	cur_pieces[1:]
																		else:
																						__lowerCamelCase    					=	cur_pieces[0][1:]
														cur_pieces.append(piece[-1]  )
														new_pieces.extend(SCREAMING_SNAKE_CASE__  )
										else:
														new_pieces.append(SCREAMING_SNAKE_CASE__  )

						return new_pieces






		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							str  )     ->							Optional[int]:
						return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Tuple  )     ->							str:
						return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							str  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	''''''.join(SCREAMING_SNAKE_CASE__  ).replace(SCREAMING_SNAKE_CASE__							,			''' '''  ).strip()
						return out_string






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							List[int]							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = None							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			**SCREAMING_SNAKE_CASE__		:							List[Any]							,			)     ->							str:
						__lowerCamelCase    					=	kwargs.pop('''use_source_tokenizer'''							,			SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__							,			skip_special_tokens=SCREAMING_SNAKE_CASE__  )

						# To avoid mixing byte-level and unicode for byte-level BPT
						# we need to build string separately for added tokens and byte-level tokens
						# cf. https://github.com/huggingface/transformers/issues/1133
						__lowerCamelCase    					=	[]
						__lowerCamelCase    					=	[]
						for token in filtered_tokens:
										if skip_special_tokens and token in self.all_special_ids:
														continue
										if token in self.added_tokens_encoder:
														if current_sub_text:
																		sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE__  )  )
																		__lowerCamelCase    					=	[]
														sub_texts.append(SCREAMING_SNAKE_CASE__  )
										else:
														current_sub_text.append(SCREAMING_SNAKE_CASE__  )
						if current_sub_text:
										sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE__  )  )

						# Mimic the behavior of the Rust tokenizer:
						# By default, there are no spaces between special tokens
						__lowerCamelCase    					=	''''''.join(SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	(
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
										__lowerCamelCase    					=	self.clean_up_tokenization(SCREAMING_SNAKE_CASE__  )
										return clean_text
						else:
										return text






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							List[int]							,			SCREAMING_SNAKE_CASE__		:							Optional[List[int]] = None  )     ->							List[int]:
						__lowerCamelCase    					=	[self.sep_token_id]
						__lowerCamelCase    					=	[self.cls_token_id]
						if token_ids_a is None:
										return token_ids_a + sep + cls
						return token_ids_a + sep + token_ids_a + sep + cls






		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							List[int]							,			SCREAMING_SNAKE_CASE__		:							Optional[List[int]] = None							,			SCREAMING_SNAKE_CASE__		:							bool = False  )     ->							List[int]:

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

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






		def  __A       (		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							List[int]							,			SCREAMING_SNAKE_CASE__		:							Optional[List[int]] = None  )     ->							List[int]:
						__lowerCamelCase    					=	[self.sep_token_id]
						__lowerCamelCase    					=	[2]

						if token_ids_a is None:
										return len(token_ids_a + sep  ) * [0] + cls_segment_id
						return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1] + cls_segment_id







		def  __A       (		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Optional[str] = None  )     ->							Tuple[str]:
						if not os.path.isdir(SCREAMING_SNAKE_CASE__  ):
										logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
										return
						__lowerCamelCase    					=	os.path.join(
						    SCREAMING_SNAKE_CASE__							,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

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

						return (out_vocab_file,)





import copy
from typing import Dict, List, Optional

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


SCREAMING_SNAKE_CASE__   :			Dict												=    {
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

SCREAMING_SNAKE_CASE__   :			int												=    logging.get_logger(__name__)

class    lowerCAmelCase__     (	__lowercase			):
		a__       :						Any        =		"""mask2former"""
		a__       :						Dict        =		["""swin"""]
		a__       :						Any        =		{"""hidden_size""": """hidden_dim"""}






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Optional[Dict] = None							,			SCREAMING_SNAKE_CASE__		:							int = 2_56							,			SCREAMING_SNAKE_CASE__		:							int = 2_56							,			SCREAMING_SNAKE_CASE__		:							int = 2_56							,			SCREAMING_SNAKE_CASE__		:							int = 10_24							,			SCREAMING_SNAKE_CASE__		:							str = "relu"							,			SCREAMING_SNAKE_CASE__		:							int = 6							,			SCREAMING_SNAKE_CASE__		:							int = 10							,			SCREAMING_SNAKE_CASE__		:							int = 8							,			SCREAMING_SNAKE_CASE__		:							float = 0.0							,			SCREAMING_SNAKE_CASE__		:							int = 20_48							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							int = 4							,			SCREAMING_SNAKE_CASE__		:							int = 2_55							,			SCREAMING_SNAKE_CASE__		:							int = 1_00							,			SCREAMING_SNAKE_CASE__		:							float = 0.1							,			SCREAMING_SNAKE_CASE__		:							float = 2.0							,			SCREAMING_SNAKE_CASE__		:							float = 5.0							,			SCREAMING_SNAKE_CASE__		:							float = 5.0							,			SCREAMING_SNAKE_CASE__		:							int = 1_25_44							,			SCREAMING_SNAKE_CASE__		:							float = 3.0							,			SCREAMING_SNAKE_CASE__		:							float = 0.75							,			SCREAMING_SNAKE_CASE__		:							float = 0.02							,			SCREAMING_SNAKE_CASE__		:							float = 1.0							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							List[int] = [4, 8, 16, 32]							,			SCREAMING_SNAKE_CASE__		:							bool = None							,			**SCREAMING_SNAKE_CASE__		:							Tuple							,			)     ->							str:
						if backbone_config is None:
										logger.info('''`backbone_config` is `None`. 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, 18, 2]							,			num_heads=[3, 6, 12, 24]							,			window_size=7							,			drop_path_rate=0.3							,			use_absolute_embeddings=SCREAMING_SNAKE_CASE__							,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							,			)

						if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
										__lowerCamelCase    					=	backbone_config.pop('''model_type'''  )
										__lowerCamelCase    					=	CONFIG_MAPPING[backbone_model_type]
										__lowerCamelCase    					=	config_class.from_dict(SCREAMING_SNAKE_CASE__  )

						# verify that the backbone is supported
						if backbone_config.model_type not in self.backbones_supported:
										logger.warning_once(
										    f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
										    f'''Supported model types: {','.join(self.backbones_supported  )}'''  )

						__lowerCamelCase    					=	backbone_config
						__lowerCamelCase    					=	feature_size
						__lowerCamelCase    					=	mask_feature_size
						__lowerCamelCase    					=	hidden_dim
						__lowerCamelCase    					=	encoder_feedforward_dim
						__lowerCamelCase    					=	activation_function
						__lowerCamelCase    					=	encoder_layers
						__lowerCamelCase    					=	decoder_layers
						__lowerCamelCase    					=	num_attention_heads
						__lowerCamelCase    					=	dropout
						__lowerCamelCase    					=	dim_feedforward
						__lowerCamelCase    					=	pre_norm
						__lowerCamelCase    					=	enforce_input_projection
						__lowerCamelCase    					=	common_stride
						__lowerCamelCase    					=	ignore_value
						__lowerCamelCase    					=	num_queries
						__lowerCamelCase    					=	no_object_weight
						__lowerCamelCase    					=	class_weight
						__lowerCamelCase    					=	mask_weight
						__lowerCamelCase    					=	dice_weight
						__lowerCamelCase    					=	train_num_points
						__lowerCamelCase    					=	oversample_ratio
						__lowerCamelCase    					=	importance_sample_ratio
						__lowerCamelCase    					=	init_std
						__lowerCamelCase    					=	init_xavier_std
						__lowerCamelCase    					=	use_auxiliary_loss
						__lowerCamelCase    					=	feature_strides
						__lowerCamelCase    					=	output_auxiliary_logits
						__lowerCamelCase    					=	decoder_layers

						super().__init__(**SCREAMING_SNAKE_CASE__  )






		@classmethod
		def  __A       (		cls		:							Any							,			SCREAMING_SNAKE_CASE__		:							PretrainedConfig							,			**SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							List[Any]:
						return cls(
						    backbone_config=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)







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



