





'''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__    :       Dict              =logging.get_logger(__name__)

A__    :       Optional[int]              ="""▁"""

A__    :       Any              ={"""vocab_file""": """sentencepiece.bpe.model"""}

A__    :       List[Any]              ={
    """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__    :       Tuple              ={
    """xlm-roberta-base""": 5_12,
    """xlm-roberta-large""": 5_12,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_12,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_12,
    """xlm-roberta-large-finetuned-conll03-english""": 5_12,
    """xlm-roberta-large-finetuned-conll03-german""": 5_12,
}




class 			UpperCAmelCase					(      lowerCAmelCase_       ):
      _lowercase:      List[Any]											=       VOCAB_FILES_NAMES
      _lowercase:      str											=       PRETRAINED_VOCAB_FILES_MAP
      _lowercase:      Any											=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase:      Optional[Any]											=       ["""input_ids""", """attention_mask"""]




      def __init__(      self      :			int      ,      __snake_case      :			List[str]      ,      __snake_case      :			int="<s>"      ,      __snake_case      :			List[str]="</s>"      ,      __snake_case      :			Union[str, Any]="</s>"      ,      __snake_case      :			List[str]="<s>"      ,      __snake_case      :			Optional[Any]="<unk>"      ,      __snake_case      :			Tuple="<pad>"      ,      __snake_case      :			Optional[Any]="<mask>"      ,      __snake_case      :			Optional[Any] = None      ,      **__snake_case      :			List[str]      ,      )    ->				str:
            _lowerCAmelCase												=							AddedToken(__SCREAMING_SNAKE_CASE      ,      lstrip=__SCREAMING_SNAKE_CASE      ,      rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE  ) else mask_token

            _lowerCAmelCase												=							{} if sp_model_kwargs is None else sp_model_kwargs

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

            _lowerCAmelCase												=							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
            self.sp_model.Load(str(__SCREAMING_SNAKE_CASE  )  )
            _lowerCAmelCase												=							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
            _lowerCAmelCase												=							{"""<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
            _lowerCAmelCase												=							1

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




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




      def __setstate__(      self      :			Optional[int]      ,      __snake_case      :			Union[str, Any]  )    ->				Dict:
            _lowerCAmelCase												=							d

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

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




      def     lowercase__   (      self      :			Tuple      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			List[str] = None  )    ->				List[Any]:

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




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			Dict      ,      __snake_case      :			Any = None      ,      __snake_case      :			List[str] = False  )    ->				Tuple:

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

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




      def     lowercase__   (      self      :			Tuple      ,      __snake_case      :			str      ,      __snake_case      :			str = None  )    ->				str:
            _lowerCAmelCase												=							[self.sep_token_id]
            _lowerCAmelCase												=							[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     lowercase__   (      self      :			str  )    ->				Optional[int]:
            return len(self.sp_model  ) + self.fairseq_offset + 1  # Add the <mask> token




      def     lowercase__   (      self      :			str  )    ->				Optional[int]:
            _lowerCAmelCase												=							{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE  ): i for i in range(self.vocab_size  )}
            vocab.update(self.added_tokens_encoder  )
            return vocab




      def     lowercase__   (      self      :			Optional[int]      ,      __snake_case      :			List[Any]  )    ->				str:
            return self.sp_model.encode(__SCREAMING_SNAKE_CASE      ,      out_type=__SCREAMING_SNAKE_CASE  )




      def     lowercase__   (      self      :			Optional[Any]      ,      __snake_case      :			Any  )    ->				Dict:

            if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
            _lowerCAmelCase												=							self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE  )

            # 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     lowercase__   (      self      :			List[str]      ,      __snake_case      :			Tuple  )    ->				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     lowercase__   (      self      :			Tuple      ,      __snake_case      :			List[str]  )    ->				Optional[Any]:
            _lowerCAmelCase												=							"""""".join(__SCREAMING_SNAKE_CASE  ).replace(__SCREAMING_SNAKE_CASE      ,      """ """  ).strip()
            return out_string




      def     lowercase__   (      self      :			List[Any]      ,      __snake_case      :			Tuple      ,      __snake_case      :			List[Any] = None  )    ->				Optional[int]:

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

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

            return (out_vocab_file,)













'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import CLIPSegProcessor, ViTImageProcessor




@require_vision
class 			UpperCAmelCase					(      unittest.TestCase       ):




      def     lowercase__   (      self      :			List[str]  )    ->				Dict:
            _lowerCAmelCase												=							tempfile.mkdtemp()

            # fmt: off
            _lowerCAmelCase												=							["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
            # fmt: on
            _lowerCAmelCase												=							dict(zip(__snake_case      ,      range(len(__snake_case  )  )  )  )
            _lowerCAmelCase												=							["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
            _lowerCAmelCase												=							{"""unk_token""": """<unk>"""}

            _lowerCAmelCase												=							os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["""vocab_file"""]  )
            _lowerCAmelCase												=							os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["""merges_file"""]  )
            with open(self.vocab_file      ,      """w"""      ,      encoding="""utf-8"""  ) as fp:
                  fp.write(json.dumps(__snake_case  ) + """\n"""  )
            with open(self.merges_file      ,      """w"""      ,      encoding="""utf-8"""  ) as fp:
                  fp.write("""\n""".join(__snake_case  )  )

            _lowerCAmelCase												=							{
                """do_resize""": True,
                """size""": 20,
                """do_center_crop""": True,
                """crop_size""": 18,
                """do_normalize""": True,
                """image_mean""": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
                """image_std""": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
            }
            _lowerCAmelCase												=							os.path.join(self.tmpdirname      ,      __snake_case  )
            with open(self.image_processor_file      ,      """w"""      ,      encoding="""utf-8"""  ) as fp:
                  json.dump(__snake_case      ,      __snake_case  )




      def     lowercase__   (      self      :			Optional[int]      ,      **__snake_case      :			Dict  )    ->				List[str]:
            return CLIPTokenizer.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			List[str]      ,      **__snake_case      :			Any  )    ->				List[Any]:
            return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			Tuple      ,      **__snake_case      :			List[str]  )    ->				int:
            return ViTImageProcessor.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			str  )    ->				Optional[Any]:
            shutil.rmtree(self.tmpdirname  )




      def     lowercase__   (      self      :			str  )    ->				List[Any]:
            _lowerCAmelCase												=							[np.random.randint(2_55      ,      size=(3, 30, 4_00)      ,      dtype=np.uinta  )]

            _lowerCAmelCase												=							[Image.fromarray(np.moveaxis(__snake_case      ,      0      ,      -1  )  ) for x in image_inputs]

            return image_inputs




      def     lowercase__   (      self      :			Optional[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							self.get_tokenizer()
            _lowerCAmelCase												=							self.get_rust_tokenizer()
            _lowerCAmelCase												=							self.get_image_processor()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )
            processor_slow.save_pretrained(self.tmpdirname  )
            _lowerCAmelCase												=							CLIPSegProcessor.from_pretrained(self.tmpdirname      ,      use_fast=__snake_case  )

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )
            processor_fast.save_pretrained(self.tmpdirname  )
            _lowerCAmelCase												=							CLIPSegProcessor.from_pretrained(self.tmpdirname  )

            self.assertEqual(processor_slow.tokenizer.get_vocab()      ,      tokenizer_slow.get_vocab()  )
            self.assertEqual(processor_fast.tokenizer.get_vocab()      ,      tokenizer_fast.get_vocab()  )
            self.assertEqual(tokenizer_slow.get_vocab()      ,      tokenizer_fast.get_vocab()  )
            self.assertIsInstance(processor_slow.tokenizer      ,      __snake_case  )
            self.assertIsInstance(processor_fast.tokenizer      ,      __snake_case  )

            self.assertEqual(processor_slow.image_processor.to_json_string()      ,      image_processor.to_json_string()  )
            self.assertEqual(processor_fast.image_processor.to_json_string()      ,      image_processor.to_json_string()  )
            self.assertIsInstance(processor_slow.image_processor      ,      __snake_case  )
            self.assertIsInstance(processor_fast.image_processor      ,      __snake_case  )




      def     lowercase__   (      self      :			Optional[int]  )    ->				Dict:
            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()  )
            processor.save_pretrained(self.tmpdirname  )

            _lowerCAmelCase												=							self.get_tokenizer(bos_token="""(BOS)"""      ,      eos_token="""(EOS)"""  )
            _lowerCAmelCase												=							self.get_image_processor(do_normalize=__snake_case      ,      padding_value=1.0  )

            _lowerCAmelCase												=							CLIPSegProcessor.from_pretrained(
                self.tmpdirname      ,      bos_token="""(BOS)"""      ,      eos_token="""(EOS)"""      ,      do_normalize=__snake_case      ,      padding_value=1.0  )

            self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()  )
            self.assertIsInstance(processor.tokenizer      ,      __snake_case  )

            self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()  )
            self.assertIsInstance(processor.image_processor      ,      __snake_case  )




      def     lowercase__   (      self      :			Tuple  )    ->				List[Any]:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							image_processor(__snake_case      ,      return_tensors="""np"""  )
            _lowerCAmelCase												=							processor(images=__snake_case      ,      return_tensors="""np"""  )

            for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum()      ,      input_processor[key].sum()      ,      delta=1E-2  )




      def     lowercase__   (      self      :			Any  )    ->				str:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							"""lower newer"""

            _lowerCAmelCase												=							processor(text=__snake_case  )

            _lowerCAmelCase												=							tokenizer(__snake_case  )

            for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]  )




      def     lowercase__   (      self      :			str  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							"""lower newer"""
            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							processor(text=__snake_case      ,      images=__snake_case  )

            self.assertListEqual(list(inputs.keys()  )      ,      ["""input_ids""", """attention_mask""", """pixel_values"""]  )

            # test if it raises when no input is passed
            with pytest.raises(__snake_case  ):
                  processor()




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Dict:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							self.prepare_image_inputs()
            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							processor(images=__snake_case      ,      visual_prompt=__snake_case  )

            self.assertListEqual(list(inputs.keys()  )      ,      ["""pixel_values""", """conditional_pixel_values"""]  )

            # test if it raises when no input is passed
            with pytest.raises(__snake_case  ):
                  processor()




      def     lowercase__   (      self      :			int  )    ->				Tuple:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _lowerCAmelCase												=							processor.batch_decode(__snake_case  )
            _lowerCAmelCase												=							tokenizer.batch_decode(__snake_case  )

            self.assertListEqual(__snake_case      ,      __snake_case  )






