



import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase: int      			=		logging.get_logger(__name__)


_lowercase: str      			=		{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_lowercase: List[Any]      			=		{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

_lowercase: str      			=		{'facebook/blenderbot_small-90M': 512}






def     a(      A       :    List[Any]       )		->       List[str]:
					"""simple docstring"""





					a					     =   set()
					a					     =   word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)       )
										a					     =   char

					a					     =   set(A       )
					return pairs







class 	_lowercase							(   lowerCamelCase_	):





	"""simple docstring"""
	__A							=							VOCAB_FILES_NAMES
	__A							=							PRETRAINED_VOCAB_FILES_MAP
	__A							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A							=							["input_ids", "attention_mask"]




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_="__start__"		,       lowerCamelCase_="__end__"		,       lowerCamelCase_="__unk__"		,       lowerCamelCase_="__null__"		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

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

						with open(__a		,       encoding="utf-8"		) as vocab_handle:
											a					     =   json.load(__a		)
						a					     =   {v: k for k, v in self.encoder.items()}
						with open(__a		,       encoding="utf-8"		) as merges_handle:
											a					     =   merges_handle.read().split("\n"		)[1:-1]
						a					     =   [tuple(merge.split()		) for merge in merges]
						a					     =   dict(zip(__a		,       range(len(__a		)		)		)		)
						a					     =   {}




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return len(self.encoder		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return dict(self.encoder		,       **self.added_tokens_encoder		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						if token in self.cache:
											return self.cache[token]
						a					     =   re.sub("([.,!?()])"		,       R" \1"		,       __a		)
						a					     =   re.sub("(')"		,       R" \1 "		,       __a		)
						a					     =   re.sub(R"\s{2,}"		,       " "		,       __a		)
						if "\n" in token:
											a					     =   token.replace("\n"		,       " __newln__"		)

						a					     =   token.split(" "		)
						a					     =   []
						for token in tokens:
											if not len(__a		):
																continue

											a					     =   token.lower()
											a					     =   tuple(__a		)
											a					     =   tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
											a					     =   get_pairs(__a		)

											if not pairs:
																words.append(__a		)
																continue

											while True:
																a					     =   min(__a		,       key=lambda lowerCamelCase_		: self.bpe_ranks.get(__a		,       float("inf"		)		)		)
																if bigram not in self.bpe_ranks:
																					break
																a     ,				a					     =   bigram
																a					     =   []
																a					     =   0

																while i < len(__a		):
																					try:
																										a					     =   word.index(__a		,       __a		)
																										new_word.extend(word[i:j]		)
																										a					     =   j
																					except ValueError:
																										new_word.extend(word[i:]		)
																										break

																					if word[i] == first and i < len(__a		) - 1 and word[i + 1] == second:
																										new_word.append(first + second		)
																										i += 2
																					else:
																										new_word.append(word[i]		)
																										i += 1
																a					     =   tuple(__a		)
																a					     =   new_word
																if len(__a		) == 1:
																					break
																else:
																					a					     =   get_pairs(__a		)
											a					     =   "@@ ".join(__a		)
											a					     =   word[:-4]

											a					     =   word
											words.append(__a		)
						return " ".join(__a		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   []

						a					     =   re.findall(R"\S+\n?"		,       __a		)

						for token in words:
											split_tokens.extend(list(self.bpe(__a		).split(" "		)		)		)
						return split_tokens




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   token.lower()
						return self.encoder.get(__a		,       self.encoder.get(self.unk_token		)		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return self.decoder.get(__a		,       self.unk_token		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   " ".join(__a		).replace("@@ "		,       ""		).strip()
						return out_string




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						if not os.path.isdir(__a		):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
											return
						a					     =   os.path.join(
						    __a		,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
						a					     =   os.path.join(
						    __a		,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

						with open(__a		,       "w"		,       encoding="utf-8"		) as f:
											f.write(json.dumps(self.encoder		,       indent=2		,       sort_keys=__a		,       ensure_ascii=__a		) + "\n"		)

						a					     =   0
						with open(__a		,       "w"		,       encoding="utf-8"		) as writer:
											writer.write("#version: 0.2\n"		)
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,       key=lambda lowerCamelCase_		: kv[1]		):
																if index != token_index:
																					logger.warning(
																					    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																					    " Please check that the tokenizer is not corrupted!"		)
																					a					     =   token_index
																writer.write(" ".join(__a		) + "\n"		)
																index += 1

						return vocab_file, merge_file







'''simple docstring'''







import copy

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


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Optional[int]      	=			{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='deta'
				__a            ={
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}




				def __init__(  self     :					List[str]					,				__a     :					List[str]=None					,				__a     :					Dict=9_00					,				__a     :					str=20_48					,				__a     :					Tuple=6					,				__a     :					List[str]=20_48					,				__a     :					str=8					,				__a     :					Union[str, Any]=6					,				__a     :					int=10_24					,				__a     :					List[Any]=8					,				__a     :					Dict=0.0					,				__a     :					Tuple=True					,				__a     :					Optional[Any]="relu"					,				__a     :					Tuple=2_56					,				__a     :					Optional[Any]=0.1					,				__a     :					int=0.0					,				__a     :					List[Any]=0.0					,				__a     :					Optional[int]=0.02					,				__a     :					str=1.0					,				__a     :					Dict=True					,				__a     :					Dict=False					,				__a     :					Optional[int]="sine"					,				__a     :					Any=5					,				__a     :					List[str]=4					,				__a     :					Optional[int]=4					,				__a     :					List[str]=True					,				__a     :					str=3_00					,				__a     :					int=True					,				__a     :					int=True					,				__a     :					Tuple=1					,				__a     :					Optional[int]=5					,				__a     :					Tuple=2					,				__a     :					Dict=1					,				__a     :					Optional[int]=1					,				__a     :					Any=5					,				__a     :					Optional[int]=2					,				__a     :					Dict=0.1					,				__a     :					str=0.25					,				**__a     :					Tuple					,				):
								if backbone_config is None:
												logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."					)
												_a								= CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]					)
								else:
												if isinstance(__a					,				__a					):
																_a								= backbone_config.pop("model_type"					)
																_a								= CONFIG_MAPPING[backbone_model_type]
																_a								= config_class.from_dict(__a					)

								_a								= backbone_config
								_a								= num_queries
								_a								= max_position_embeddings
								_a								= d_model
								_a								= encoder_ffn_dim
								_a								= encoder_layers
								_a								= encoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= activation_function
								_a								= init_std
								_a								= init_xavier_std
								_a								= encoder_layerdrop
								_a								= auxiliary_loss
								_a								= position_embedding_type
								# deformable attributes
								_a								= num_feature_levels
								_a								= encoder_n_points
								_a								= decoder_n_points
								_a								= two_stage
								_a								= two_stage_num_proposals
								_a								= with_box_refine
								_a								= assign_first_stage
								if two_stage is True and with_box_refine is False:
												raise ValueError("If two_stage is True, with_box_refine must be True."					)
								# Hungarian matcher
								_a								= class_cost
								_a								= bbox_cost
								_a								= giou_cost
								# Loss coefficients
								_a								= mask_loss_coefficient
								_a								= dice_loss_coefficient
								_a								= bbox_loss_coefficient
								_a								= giou_loss_coefficient
								_a								= eos_coefficient
								_a								= focal_alpha
								super().__init__(is_encoder_decoder=__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								return self.encoder_attention_heads




				@property
				def   UpperCamelCase__   (  self     :					Dict					):
								return self.d_model




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= copy.deepcopy(self.__dict__					)
								_a								= self.backbone_config.to_dict()
								_a								= self.__class__.model_type
								return output
