





'''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


UpperCAmelCase					:		Any		=							logging.get_logger(__name__)

UpperCAmelCase					:		List[Any]		=							'▁'

UpperCAmelCase					:		Tuple		=							{'vocab_file': 'sentencepiece.bpe.model'}

UpperCAmelCase					:		Optional[int]		=							{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

UpperCAmelCase					:		int		=							{
    'facebook/xglm-564M': 2_0_4_8,
}





class lowerCamelCase						(a__					):
  _lowercase  :	List[Any]       					=				VOCAB_FILES_NAMES
  _lowercase  :	Any       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	List[Any]       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	Optional[Any]       					=				["""input_ids""", """attention_mask"""]







  def __init__(		self     ,     lowercase__     ,     lowercase__="<s>"     ,     lowercase__="</s>"     ,     lowercase__="</s>"     ,     lowercase__="<s>"     ,     lowercase__="<unk>"     ,     lowercase__="<pad>"     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						None:






        """simple docstring"""





        _snake_case		:	Dict					     =     {} if sp_model_kwargs is None else sp_model_kwargs

        # Compatibility with the original tokenizer
        _snake_case		:	Union[str, Any]					     =     7
        _snake_case		:	Union[str, Any]					     =     [F'''<madeupword{i}>''' for i in range(self.num_madeup_words		)]

        _snake_case		:	List[str]					     =     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=lowercase__     ,     eos_token=lowercase__     ,     unk_token=lowercase__     ,     sep_token=lowercase__     ,     cls_token=lowercase__     ,     pad_token=lowercase__     ,     sp_model_kwargs=self.sp_model_kwargs     ,     **lowercase__     ,     )

        _snake_case		:	Tuple					     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(str(lowercase__		)		)
        _snake_case		:	Dict					     =     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
        _snake_case		:	Any					     =     1

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

        _snake_case		:	Dict					     =     len(self.sp_model		)
        _snake_case		:	str					     =     {F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words		)}
        self.fairseq_tokens_to_ids.update(lowercase__		)

        _snake_case		:	Optional[Any]					     =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}







  def __getstate__(		self		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     self.__dict__.copy()
        _snake_case		:	Any					     =     None
        _snake_case		:	List[str]					     =     self.sp_model.serialized_model_proto()
        return state







  def __setstate__(		self     ,     lowercase__		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Any					     =     d

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

        _snake_case		:	List[str]					     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						List[int]:






        """simple docstring"""





        if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
        _snake_case		:	List[str]					     =     [self.sep_token_id]
        return sep + token_ids_a + sep + sep + token_ids_a







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = False		)		->						List[int]:






        """simple docstring"""





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

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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						List[int]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     [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		)		->						List[str]:






        """simple docstring"""





        return len(self.sp_model		) + self.fairseq_offset + self.num_madeup_words







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     {self.convert_ids_to_tokens(lowercase__		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[str]:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[Any]:






        """simple docstring"""





        if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
        _snake_case		:	List[str]					     =     self.sp_model.PieceToId(lowercase__		)

        # 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     ,     lowercase__		)		->						Any:






        """simple docstring"""





        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     ,     lowercase__		)		->						str:






        """simple docstring"""





        _snake_case		:	str					     =     ''''''.join(lowercase__		).replace(lowercase__     ,     ''' '''		).strip()
        return out_string







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






        """simple docstring"""





        if not os.path.isdir(lowercase__		):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
        _snake_case		:	Any					     =     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:
                    _snake_case		:	int					     =     self.sp_model.serialized_model_proto()
                    fi.write(lowercase__		)

        return (out_vocab_file,)









'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase					:		Dict		=							logging.get_logger(__name__)





class lowerCamelCase						(a__					):
  _lowercase  :	int       					=				["""pixel_values"""]







  def __init__(		self     ,     lowercase__ = True     ,     lowercase__ = 32     ,     lowercase__=PILImageResampling.BILINEAR     ,     lowercase__ = True     ,     **lowercase__     ,     )		->						None:






        """simple docstring"""





        _snake_case		:	Any					     =     do_resize
        _snake_case		:	List[str]					     =     do_rescale
        _snake_case		:	Any					     =     size_divisor
        _snake_case		:	Optional[Any]					     =     resample
        super().__init__(**lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__		)		->						np.ndarray:






        """simple docstring"""





        _snake_case     ,							_snake_case		:	Dict					     =     get_image_size(lowercase__		)
        # Rounds the height and width down to the closest multiple of size_divisor
        _snake_case		:	Optional[int]					     =     height // size_divisor * size_divisor
        _snake_case		:	Dict					     =     width // size_divisor * size_divisor
        _snake_case		:	str					     =     resize(lowercase__     ,     (new_h, new_w)     ,     resample=lowercase__     ,     data_format=lowercase__     ,     **lowercase__		)
        return image







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__		)		->						np.ndarray:






        """simple docstring"""





        return rescale(image=lowercase__     ,     scale=lowercase__     ,     data_format=lowercase__     ,     **lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__=None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = ChannelDimension.FIRST     ,     **lowercase__     ,     )		->						BatchFeature:






        """simple docstring"""





        _snake_case		:	Any					     =     do_resize if do_resize is not None else self.do_resize
        _snake_case		:	List[Any]					     =     do_rescale if do_rescale is not None else self.do_rescale
        _snake_case		:	List[str]					     =     size_divisor if size_divisor is not None else self.size_divisor
        _snake_case		:	int					     =     resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
              raise ValueError('''size_divisor is required for resizing'''		)

        _snake_case		:	Tuple					     =     make_list_of_images(lowercase__		)

        if not valid_images(lowercase__		):
              raise ValueError('''Invalid image(s)'''		)

        # All transformations expect numpy arrays.
        _snake_case		:	Tuple					     =     [to_numpy_array(lowercase__		) for img in images]

        if do_resize:
              _snake_case		:	Optional[int]					     =     [self.resize(lowercase__     ,     size_divisor=lowercase__     ,     resample=lowercase__		) for image in images]

        if do_rescale:
              _snake_case		:	Union[str, Any]					     =     [self.rescale(lowercase__     ,     scale=1 / 255		) for image in images]

        _snake_case		:	Union[str, Any]					     =     [to_channel_dimension_format(lowercase__     ,     lowercase__		) for image in images]

        _snake_case		:	List[str]					     =     {'''pixel_values''': images}
        return BatchFeature(data=lowercase__     ,     tensor_type=lowercase__		)


