



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


UpperCamelCase__ =						"▁"

UpperCamelCase__ =						{"vocab_file": "spiece.model"}

UpperCamelCase__ =						{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

UpperCamelCase__ =						{
    "google/pegasus-xsum": 5_12,
}


UpperCamelCase__ =						logging.get_logger(__name__)
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  str      =			VOCAB_FILES_NAMES

			lowercase__      :  Tuple      =			VOCAB_FILES_NAMES
			lowercase__      :  str      =			PRETRAINED_VOCAB_FILES_MAP
			lowercase__      :  Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase__      :  Optional[Any]      =			["""input_ids""", """attention_mask"""]





			def __init__(				self	:       List[str]			,     lowercase	:       str			,     lowercase	:       Union[str, Any]="<pad>"			,     lowercase	:       Tuple="</s>"			,     lowercase	:       Any="<unk>"			,     lowercase	:       int="<mask_2>"			,     lowercase	:       str="<mask_1>"			,     lowercase	:       Union[str, Any]=None			,     lowercase	:       str=103			,     lowercase	:       Optional[Dict[str, Any]] = None			,     **lowercase	:       List[Any]			,     )						->					None:
									"""simple docstring"""







									__lowercase					=				offset
									if additional_special_tokens is not None:
															if not isinstance(lowercase			,     lowercase   ):
																					raise TypeError(
																					    F"additional_special_tokens should be of type {type(lowercase   )}, but is"
																					    F" {type(lowercase   )}"   )

															__lowercase					=				(
															    ([mask_token_sent] + additional_special_tokens)
															    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
															    else additional_special_tokens
															)
															# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
															additional_special_tokens_extended += [
															    F"<unk_{i}>" for i in range(len(lowercase   )			,     self.offset - 1   )
															]

															if len(set(lowercase   )   ) != len(lowercase   ):
																					raise ValueError(
																					    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
																					    F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."   )
															__lowercase					=				additional_special_tokens_extended
									else:
															__lowercase					=				[mask_token_sent] if mask_token_sent is not None else []
															additional_special_tokens += [F"<unk_{i}>" for i in range(2			,     self.offset   )]

									__lowercase					=				{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    eos_token=lowercase			,     unk_token=lowercase			,     mask_token=lowercase			,     pad_token=lowercase			,     mask_token_sent=lowercase			,     offset=lowercase			,     additional_special_tokens=lowercase			,     sp_model_kwargs=self.sp_model_kwargs			,     **lowercase			,     )
									__lowercase					=				mask_token_sent
									__lowercase					=				vocab_file
									__lowercase					=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
									self.sp_model.Load(lowercase   )

									# add special tokens to encoder dict
									__lowercase					=				{
									    0: self.pad_token,
									    1: self.eos_token,
									}

									if self.mask_token_sent is not None:
															self.encoder.update(
															    {
															        2: self.mask_token_sent,
															        3: self.mask_token,
															    }   )

									if self.offset > 0:
															# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
															# mask_token_sent is already added to list -> so start at 1
															self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,     self.offset - 1   )}   )

									__lowercase					=				{v: k for k, v in self.encoder.items()}





			@property
			def 		snake_case__			(				self	:       Dict   )						->					int:
									"""simple docstring"""







									return len(self.sp_model   ) + self.offset





			def 		snake_case__			(				self	:       str   )						->					Dict[str, int]:
									"""simple docstring"""







									__lowercase					=				{self.convert_ids_to_tokens(lowercase   ): i for i in range(self.vocab_size   )}
									vocab.update(self.added_tokens_encoder   )
									return vocab





			def __getstate__(				self	:       Any   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				self.__dict__.copy()
									__lowercase					=				None
									return state





			def __setstate__(				self	:       int			,     lowercase	:       Optional[Any]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				d

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

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





			def 		snake_case__			(				self	:       Any			,     lowercase	:       str   )						->					List[str]:
									"""simple docstring"""







									return self.sp_model.encode(lowercase			,     out_type=lowercase   )





			def 		snake_case__			(				self	:       Dict			,     lowercase	:       str   )						->					int:
									"""simple docstring"""







									if token in self.decoder:
															return self.decoder[token]
									elif token in self.added_tokens_decoder:
															return self.added_tokens_decoder[token]
									__lowercase					=				self.sp_model.piece_to_id(lowercase   )
									return sp_id + self.offset





			def 		snake_case__			(				self	:       List[str]			,     lowercase	:       int   )						->					str:
									"""simple docstring"""







									if index in self.encoder:
															return self.encoder[index]
									elif index in self.added_tokens_encoder:
															return self.added_tokens_encoder[index]
									else:
															__lowercase					=				self.sp_model.IdToPiece(index - self.offset   )
									return token





			def 		snake_case__			(				self	:       List[Any]			,     lowercase	:       Optional[int]   )						->					Any:
									"""simple docstring"""







									__lowercase					=				[]
									__lowercase					=				""""""
									for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																					out_string += self.sp_model.decode(lowercase   ) + token
																					__lowercase					=				[]
															else:
																					current_sub_tokens.append(lowercase   )
									out_string += self.sp_model.decode(lowercase   )
									return out_string.strip()





			def 		snake_case__			(				self	:       str			,     lowercase	:       Tuple=False   )						->					List[str]:
									"""simple docstring"""







									return 1





			def 		snake_case__			(				self	:       Union[str, Any]			,     lowercase	:       Optional[Any]   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				set(self.all_special_ids   )  # call it once instead of inside list comp
									all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special

									return [1 if x in all_special_ids else 0 for x in seq]





			def 		snake_case__			(				self	:       Tuple			,     lowercase	:       List			,     lowercase	:       Optional[List] = None			,     lowercase	:       bool = False   )						->					List[int]:
									"""simple docstring"""







									if already_has_special_tokens:
															return self._special_token_mask(lowercase   )
									elif token_ids_a is None:
															return self._special_token_mask(lowercase   ) + [1]
									else:
															return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]





			def 		snake_case__			(				self	:       Tuple			,     lowercase	:       Dict			,     lowercase	:       Dict=None   )						->					List[int]:
									"""simple docstring"""







									if token_ids_a is None:
															return token_ids_a + [self.eos_token_id]
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return token_ids_a + token_ids_a + [self.eos_token_id]







			def 		snake_case__			(				self	:       Optional[Any]			,     lowercase	:       str			,     lowercase	:       Optional[str] = None   )						->					Tuple[str]:
									"""simple docstring"""







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

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

									return (out_vocab_file,)










from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__ =						logging.get_logger(__name__)

UpperCamelCase__ =						{
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Union[str, Any]      =			"""yolos"""





			def __init__(				self	:       Optional[int]			,     lowercase	:       Any=768			,     lowercase	:       Tuple=12			,     lowercase	:       Tuple=12			,     lowercase	:       str=3_072			,     lowercase	:       Optional[Any]="gelu"			,     lowercase	:       Union[str, Any]=0.0			,     lowercase	:       Dict=0.0			,     lowercase	:       Optional[int]=0.02			,     lowercase	:       Optional[Any]=1E-1_2			,     lowercase	:       Tuple=[512, 864]			,     lowercase	:       Optional[int]=16			,     lowercase	:       Dict=3			,     lowercase	:       Optional[Any]=True			,     lowercase	:       Optional[int]=100			,     lowercase	:       Optional[int]=True			,     lowercase	:       Any=False			,     lowercase	:       Any=1			,     lowercase	:       Any=5			,     lowercase	:       List[str]=2			,     lowercase	:       Union[str, Any]=5			,     lowercase	:       str=2			,     lowercase	:       Tuple=0.1			,     **lowercase	:       str			,     )						->					Optional[Any]:
									"""simple docstring"""







									super().__init__(**lowercase   )

									__lowercase					=				hidden_size
									__lowercase					=				num_hidden_layers
									__lowercase					=				num_attention_heads
									__lowercase					=				intermediate_size
									__lowercase					=				hidden_act
									__lowercase					=				hidden_dropout_prob
									__lowercase					=				attention_probs_dropout_prob
									__lowercase					=				initializer_range
									__lowercase					=				layer_norm_eps
									__lowercase					=				image_size
									__lowercase					=				patch_size
									__lowercase					=				num_channels
									__lowercase					=				qkv_bias
									__lowercase					=				num_detection_tokens
									__lowercase					=				use_mid_position_embeddings
									__lowercase					=				auxiliary_loss
									# Hungarian matcher
									__lowercase					=				class_cost
									__lowercase					=				bbox_cost
									__lowercase					=				giou_cost
									# Loss coefficients
									__lowercase					=				bbox_loss_coefficient
									__lowercase					=				giou_loss_coefficient
									__lowercase					=				eos_coefficient





class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Dict      =			version.parse("""1.11"""			)





			@property
			def 		snake_case__			(				self	:       List[str]   )						->					Mapping[str, Mapping[int, str]]:
									"""simple docstring"""







									return OrderedDict(
									    [
									        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
									    ]   )





			@property
			def 		snake_case__			(				self	:       int   )						->					float:
									"""simple docstring"""







									return 1E-4







			@property
			def 		snake_case__			(				self	:       Optional[Any]   )						->					int:
									"""simple docstring"""







									return 12





