


"""simple docstring"""







import os
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 logging


a__       :							int        =  logging.get_logger(__name__)

a__       :							int        =  '''▁'''

a__       :							str        =  {'''vocab_file''': '''sentencepiece.bpe.model'''}

a__       :							int        =  {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

a__       :							int        =  {
    '''xlm-roberta-base''': 5_1_2,
    '''xlm-roberta-large''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_1_2,
}




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Any					      =    VOCAB_FILES_NAMES
	snake_case__       :						List[Any]					      =    PRETRAINED_VOCAB_FILES_MAP
	snake_case__       :						List[str]					      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__       :						Tuple					      =    ["input_ids", "attention_mask"]




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

						__SCREAMING_SNAKE_CASE		     =       {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=UpperCAmelCase__    ,    eos_token=UpperCAmelCase__    ,    unk_token=UpperCAmelCase__    ,    sep_token=UpperCAmelCase__    ,    cls_token=UpperCAmelCase__    ,    pad_token=UpperCAmelCase__    ,    mask_token=UpperCAmelCase__    ,    sp_model_kwargs=self.sp_model_kwargs    ,    **UpperCAmelCase__    ,    )

						__SCREAMING_SNAKE_CASE		     =       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(str(UpperCAmelCase__     )     )
						__SCREAMING_SNAKE_CASE		     =       vocab_file

						# Original fairseq vocab and spm vocab must be "aligned":
						# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
						# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
						# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
						# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

						# Mimic fairseq token-to-id alignment for the first 4 token
						__SCREAMING_SNAKE_CASE		     =       {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

						# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
						__SCREAMING_SNAKE_CASE		     =       1

						__SCREAMING_SNAKE_CASE		     =       len(self.sp_model     ) + self.fairseq_offset
						__SCREAMING_SNAKE_CASE		     =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}




	def __getstate__(				self    :       Optional[Any]     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       self.__dict__.copy()
						__SCREAMING_SNAKE_CASE		     =       None
						__SCREAMING_SNAKE_CASE		     =       self.sp_model.serialized_model_proto()
						return state




	def __setstate__(				self    :       Optional[Any]    ,    UpperCAmelCase__    :       Optional[Any]     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       d

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

						__SCREAMING_SNAKE_CASE		     =       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       List[int]    ,    UpperCAmelCase__    :       Optional[List[int]] = None     ) ->			List[int]:

						if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						__SCREAMING_SNAKE_CASE		     =       [self.cls_token_id]
						__SCREAMING_SNAKE_CASE		     =       [self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep




	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       List[int]    ,    UpperCAmelCase__    :       Optional[List[int]] = None    ,    UpperCAmelCase__    :       bool = False     ) ->			List[int]:

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

						if token_ids_a is None:
											return [1] + ([0] * len(UpperCAmelCase__     )) + [1]
						return [1] + ([0] * len(UpperCAmelCase__     )) + [1, 1] + ([0] * len(UpperCAmelCase__     )) + [1]




	def 						UpperCAmelCase_   (				self    :       List[str]    ,    UpperCAmelCase__    :       List[int]    ,    UpperCAmelCase__    :       Optional[List[int]] = None     ) ->			List[int]:
						__SCREAMING_SNAKE_CASE		     =       [self.sep_token_id]
						__SCREAMING_SNAKE_CASE		     =       [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 + sep + token_ids_a + sep     ) * [0]




	@property
	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[Any]:
						return len(self.sp_model     ) + self.fairseq_offset + 1  # Add the <mask> token




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       {self.convert_ids_to_tokens(UpperCAmelCase__     ): i for i in range(self.vocab_size     )}
						vocab.update(self.added_tokens_encoder     )
						return vocab




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       str     ) ->			List[str]:
						return self.sp_model.encode(UpperCAmelCase__    ,    out_type=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       Any     ) ->			Dict:
						if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
						__SCREAMING_SNAKE_CASE		     =       self.sp_model.PieceToId(UpperCAmelCase__     )

						# Need to return unknown token if the SP model returned 0
						return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




	def 						UpperCAmelCase_   (				self    :       List[str]    ,    UpperCAmelCase__    :       Optional[Any]     ) ->			Optional[int]:
						if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(index - self.fairseq_offset     )




	def 						UpperCAmelCase_   (				self    :       List[str]    ,    UpperCAmelCase__    :       Any     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       "".join(UpperCAmelCase__     ).replace(UpperCAmelCase__    ,    " "     ).strip()
						return out_string




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Optional[str] = None     ) ->			Tuple[str]:
						if not os.path.isdir(UpperCAmelCase__     ):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
											return
						__SCREAMING_SNAKE_CASE		     =       os.path.join(
						    UpperCAmelCase__    ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

						return (out_vocab_file,)




"""simple docstring"""







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


a__       :							Dict        =  logging.get_logger(__name__)

a__       :							str        =  {
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Optional[Any]					      =    "dpt"




	def __init__(				self    :       Tuple    ,    UpperCAmelCase__    :       int=7_6_8    ,    UpperCAmelCase__    :       List[str]=1_2    ,    UpperCAmelCase__    :       Any=1_2    ,    UpperCAmelCase__    :       int=3_0_7_2    ,    UpperCAmelCase__    :       List[str]="gelu"    ,    UpperCAmelCase__    :       Union[str, Any]=0.0    ,    UpperCAmelCase__    :       Any=0.0    ,    UpperCAmelCase__    :       List[Any]=0.02    ,    UpperCAmelCase__    :       List[Any]=1E-12    ,    UpperCAmelCase__    :       List[str]=3_8_4    ,    UpperCAmelCase__    :       Tuple=1_6    ,    UpperCAmelCase__    :       Optional[Any]=3    ,    UpperCAmelCase__    :       str=False    ,    UpperCAmelCase__    :       Any=True    ,    UpperCAmelCase__    :       Optional[Any]=[2, 5, 8, 1_1]    ,    UpperCAmelCase__    :       Union[str, Any]="project"    ,    UpperCAmelCase__    :       Dict=[4, 2, 1, 0.5]    ,    UpperCAmelCase__    :       Optional[Any]=[9_6, 1_9_2, 3_8_4, 7_6_8]    ,    UpperCAmelCase__    :       List[str]=2_5_6    ,    UpperCAmelCase__    :       Optional[Any]=-1    ,    UpperCAmelCase__    :       Union[str, Any]=False    ,    UpperCAmelCase__    :       Any=True    ,    UpperCAmelCase__    :       Dict=0.4    ,    UpperCAmelCase__    :       Union[str, Any]=2_5_5    ,    UpperCAmelCase__    :       List[Any]=0.1    ,    UpperCAmelCase__    :       Optional[int]=[1, 1_0_2_4, 2_4, 2_4]    ,    UpperCAmelCase__    :       List[Any]=[0, 1]    ,    UpperCAmelCase__    :       List[str]=None    ,    **UpperCAmelCase__    :       List[Any]    ,    ) ->			Dict:
						super().__init__(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       hidden_size
						__SCREAMING_SNAKE_CASE		     =       is_hybrid

						if self.is_hybrid:
											if backbone_config is None:
																logger.info("Initializing the config with a `BiT` backbone."     )
																__SCREAMING_SNAKE_CASE		     =       {
																    "global_padding": "same",
																    "layer_type": "bottleneck",
																    "depths": [3, 4, 9],
																    "out_features": ["stage1", "stage2", "stage3"],
																    "embedding_dynamic_padding": True,
																}
																__SCREAMING_SNAKE_CASE		     =       BitConfig(**UpperCAmelCase__     )
											elif isinstance(UpperCAmelCase__    ,    UpperCAmelCase__     ):
																logger.info("Initializing the config with a `BiT` backbone."     )
																__SCREAMING_SNAKE_CASE		     =       BitConfig(**UpperCAmelCase__     )
											elif isinstance(UpperCAmelCase__    ,    UpperCAmelCase__     ):
																__SCREAMING_SNAKE_CASE		     =       backbone_config
											else:
																raise ValueError(
																    F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""     )

											__SCREAMING_SNAKE_CASE		     =       backbone_featmap_shape
											__SCREAMING_SNAKE_CASE		     =       neck_ignore_stages

											if readout_type != "project":
																raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."     )
						else:
											__SCREAMING_SNAKE_CASE		     =       None
											__SCREAMING_SNAKE_CASE		     =       None
											__SCREAMING_SNAKE_CASE		     =       []

						__SCREAMING_SNAKE_CASE		     =       num_hidden_layers
						__SCREAMING_SNAKE_CASE		     =       num_attention_heads
						__SCREAMING_SNAKE_CASE		     =       intermediate_size
						__SCREAMING_SNAKE_CASE		     =       hidden_act
						__SCREAMING_SNAKE_CASE		     =       hidden_dropout_prob
						__SCREAMING_SNAKE_CASE		     =       attention_probs_dropout_prob
						__SCREAMING_SNAKE_CASE		     =       initializer_range
						__SCREAMING_SNAKE_CASE		     =       layer_norm_eps
						__SCREAMING_SNAKE_CASE		     =       image_size
						__SCREAMING_SNAKE_CASE		     =       patch_size
						__SCREAMING_SNAKE_CASE		     =       num_channels
						__SCREAMING_SNAKE_CASE		     =       qkv_bias
						__SCREAMING_SNAKE_CASE		     =       backbone_out_indices
						if readout_type not in ["ignore", "add", "project"]:
											raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"     )
						__SCREAMING_SNAKE_CASE		     =       readout_type
						__SCREAMING_SNAKE_CASE		     =       reassemble_factors
						__SCREAMING_SNAKE_CASE		     =       neck_hidden_sizes
						__SCREAMING_SNAKE_CASE		     =       fusion_hidden_size
						__SCREAMING_SNAKE_CASE		     =       head_in_index
						__SCREAMING_SNAKE_CASE		     =       use_batch_norm_in_fusion_residual
						# auxiliary head attributes (semantic segmentation)
						__SCREAMING_SNAKE_CASE		     =       use_auxiliary_head
						__SCREAMING_SNAKE_CASE		     =       auxiliary_loss_weight
						__SCREAMING_SNAKE_CASE		     =       semantic_loss_ignore_index
						__SCREAMING_SNAKE_CASE		     =       semantic_classifier_dropout




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       copy.deepcopy(self.__dict__     )

						if output["backbone_config"] is not None:
											__SCREAMING_SNAKE_CASE		     =       self.backbone_config.to_dict()

						__SCREAMING_SNAKE_CASE		     =       self.__class__.model_type
						return output
