

'''simple docstring'''

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


__lowerCamelCase : List[str]											=			logging.get_logger(__name__)

__lowerCamelCase : Optional[Any]											=			"▁"

__lowerCamelCase : List[str]											=			{"vocab_file": "sentencepiece.bpe.model"}

__lowerCamelCase : int											=			{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

__lowerCamelCase : int											=			{
    "facebook/xglm-564M": 2048,
}

class       UpperCAmelCase (						lowercase__  ):
		UpperCAmelCase					:	Optional[int] 			=		VOCAB_FILES_NAMES
		UpperCAmelCase					:	Optional[Any] 			=		PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase					:	Union[str, Any] 			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase					:	Union[str, Any] 			=		['''input_ids''', '''attention_mask''']




		def __init__(self      :	Dict							,			A__      :	Optional[int]							,			A__      :	List[Any]="<s>"							,			A__      :	int="</s>"							,			A__      :	Optional[Any]="</s>"							,			A__      :	Optional[Any]="<s>"							,			A__      :	Any="<unk>"							,			A__      :	Any="<pad>"							,			A__      :	Optional[Dict[str, Any]] = None							,			**A__      :	Optional[int]							,			)					-> int:
						lowercase         = {} if sp_model_kwargs is None else sp_model_kwargs

						# Compatibility with the original tokenizer
						lowercase         = 7
						lowercase         = [f'<madeupword{i}>' for i in range(self.num_madeup_words				)]

						lowercase         = kwargs.get("additional_special_tokens"							,			[]				)
						kwargs["additional_special_tokens"] += [
						    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
						]

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

						lowercase         = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
						self.sp_model.Load(str(UpperCAmelCase__				)				)
						lowercase         = 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'

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

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

						lowercase         = len(self.sp_model				)
						lowercase         = {f'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words				)}
						self.fairseq_tokens_to_ids.update(UpperCAmelCase__				)

						lowercase         = {v: k for k, v in self.fairseq_tokens_to_ids.items()}




		def __getstate__(self      :	int				)					-> int:
						lowercase         = self.__dict__.copy()
						lowercase         = None
						lowercase         = self.sp_model.serialized_model_proto()
						return state




		def __setstate__(self      :	Optional[Any]							,			A__      :	Any				)					-> List[Any]:
						lowercase         = d

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

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




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

						if token_ids_a is None:
										return [self.sep_token_id] + token_ids_a
						lowercase         = [self.sep_token_id]
						return sep + token_ids_a + sep + sep + token_ids_a




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

						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__				))
						return [1] + ([0] * len(UpperCAmelCase__				)) + [1, 1] + ([0] * len(UpperCAmelCase__				))




		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	List[int]							,			A__      :	Optional[List[int]] = None				)					-> Optional[Any]:
						lowercase         = [self.sep_token_id]

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




		@property
		def 				UpperCAmelCase__   (self      :	Dict				)					-> Union[str, Any]:
						return len(self.sp_model				) + self.fairseq_offset + self.num_madeup_words




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> List[Any]:
						lowercase         = {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      :	Tuple							,			A__      :	str				)					-> Dict:
						return self.sp_model.encode(UpperCAmelCase__							,			out_type=UpperCAmelCase__				)




		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	Optional[Any]				)					-> Optional[Any]:
						if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
						lowercase         = 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      :	Optional[Any]							,			A__      :	Any				)					-> Optional[Any]:
						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]							,			A__      :	Optional[Any]				)					-> Optional[Any]:
						lowercase         = ''''''.join(UpperCAmelCase__				).replace(UpperCAmelCase__							,			" "				).strip()
						return out_string





		def 				UpperCAmelCase__   (self      :	Optional[int]							,			A__      :	str							,			A__      :	Optional[str] = None				)					-> Tuple:
						if not os.path.isdir(UpperCAmelCase__				):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'				)
										return
						lowercase         = 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:
														lowercase         = self.sp_model.serialized_model_proto()
														fi.write(UpperCAmelCase__				)

						return (out_vocab_file,)











'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


__lowerCamelCase : Union[str, Any]											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Tuple							,			*A__      :	Union[str, Any]							,			**A__      :	Optional[Any]				)					-> None:
						warnings.warn(
						    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use CLIPImageProcessor instead."							,			A__							,			)
						super().__init__(*A__							,			**A__				)




