

'''simple docstring'''






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
@require_vision
class      SCREAMING_SNAKE_CASE__      (    unittest.TestCase				):




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	tempfile.mkdtemp()

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	CLIPImageProcessor()
          SCREAMING_SNAKE_CASE_				:      str					=	BertTokenizer.from_pretrained(
              "hf-internal-testing/tiny-random-BertModel"    ,					model_input_names=["input_ids", "attention_mask"]      )

          SCREAMING_SNAKE_CASE_				:      Any					=	GitProcessor(lowercase__    ,					lowercase__      )

          processor.save_pretrained(self.tmpdirname      )




    def   __lowerCamelCase				( self    ,					**lowercase__      ):






          """simple docstring"""






          return AutoProcessor.from_pretrained(self.tmpdirname    ,					**lowercase__      ).tokenizer




    def   __lowerCamelCase				( self    ,					**lowercase__      ):






          """simple docstring"""






          return AutoProcessor.from_pretrained(self.tmpdirname    ,					**lowercase__      ).image_processor




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          shutil.rmtree(self.tmpdirname      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	[np.random.randint(255    ,					size=(3, 30, 400)    ,					dtype=np.uinta      )]

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	[Image.fromarray(np.moveaxis(lowercase__    ,					0    ,					-1      )      ) for x in image_inputs]

          return image_inputs




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	GitProcessor(tokenizer=self.get_tokenizer()    ,					image_processor=self.get_image_processor()      )
          processor.save_pretrained(self.tmpdirname      )

          SCREAMING_SNAKE_CASE_				:      Tuple					=	self.get_tokenizer(bos_token="(BOS)"    ,					eos_token="(EOS)"      )
          SCREAMING_SNAKE_CASE_				:      Tuple					=	self.get_image_processor(do_normalize=lowercase__    ,					padding_value=1.0      )

          SCREAMING_SNAKE_CASE_				:      str					=	GitProcessor.from_pretrained(
              self.tmpdirname    ,					bos_token="(BOS)"    ,					eos_token="(EOS)"    ,					do_normalize=lowercase__    ,					padding_value=1.0      )

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

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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[str]					=	self.get_image_processor()
          SCREAMING_SNAKE_CASE_				:      Dict					=	self.get_tokenizer()

          SCREAMING_SNAKE_CASE_				:      Dict					=	GitProcessor(tokenizer=lowercase__    ,					image_processor=lowercase__      )

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.prepare_image_inputs()

          SCREAMING_SNAKE_CASE_				:      Tuple					=	image_processor(lowercase__    ,					return_tensors="np"      )
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	processor(images=lowercase__    ,					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   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      int					=	self.get_image_processor()
          SCREAMING_SNAKE_CASE_				:      int					=	self.get_tokenizer()

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	GitProcessor(tokenizer=lowercase__    ,					image_processor=lowercase__      )

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	"lower newer"

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	processor(text=lowercase__      )

          SCREAMING_SNAKE_CASE_				:      str					=	tokenizer(lowercase__    ,					return_token_type_ids=lowercase__      )

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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	self.get_image_processor()
          SCREAMING_SNAKE_CASE_				:      Any					=	self.get_tokenizer()

          SCREAMING_SNAKE_CASE_				:      int					=	GitProcessor(tokenizer=lowercase__    ,					image_processor=lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Any					=	"lower newer"
          SCREAMING_SNAKE_CASE_				:      Any					=	self.prepare_image_inputs()

          SCREAMING_SNAKE_CASE_				:      int					=	processor(text=lowercase__    ,					images=lowercase__      )

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

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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.get_image_processor()
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	self.get_tokenizer()

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	GitProcessor(tokenizer=lowercase__    ,					image_processor=lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Tuple					=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          SCREAMING_SNAKE_CASE_				:      int					=	processor.batch_decode(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	tokenizer.batch_decode(lowercase__      )

          self.assertListEqual(lowercase__    ,					lowercase__      )







    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[str]					=	self.get_image_processor()
          SCREAMING_SNAKE_CASE_				:      List[str]					=	self.get_tokenizer()

          SCREAMING_SNAKE_CASE_				:      int					=	GitProcessor(tokenizer=lowercase__    ,					image_processor=lowercase__      )

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	"lower newer"
          SCREAMING_SNAKE_CASE_				:      int					=	self.prepare_image_inputs()

          SCREAMING_SNAKE_CASE_				:      List[str]					=	processor(text=lowercase__    ,					images=lowercase__      )

          # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
          self.assertListEqual(list(inputs.keys()      )    ,					["input_ids", "attention_mask", "pixel_values"]      )




'''simple docstring'''






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

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case_					  =	logging.get_logger(__name__)

snake_case_					  =	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case_					  =	{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):




    def __init__( self    ,					lowercase__=None    ,					lowercase__=None    ,					lowercase__=False    ,					lowercase__=True    ,					lowercase__=False    ,					lowercase__="<s>"    ,					lowercase__="</s>"    ,					lowercase__="<unk>"    ,					lowercase__="<sep>"    ,					lowercase__="<pad>"    ,					lowercase__="<cls>"    ,					lowercase__="<mask>"    ,					lowercase__=["<eop>", "<eod>"]    ,					**lowercase__    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	AddedToken(lowercase__    ,					lstrip=lowercase__    ,					rstrip=lowercase__      ) if isinstance(lowercase__    ,					lowercase__      ) else mask_token

          super().__init__(
              vocab_file=lowercase__    ,					tokenizer_file=lowercase__    ,					do_lower_case=lowercase__    ,					remove_space=lowercase__    ,					keep_accents=lowercase__    ,					bos_token=lowercase__    ,					eos_token=lowercase__    ,					unk_token=lowercase__    ,					sep_token=lowercase__    ,					pad_token=lowercase__    ,					cls_token=lowercase__    ,					mask_token=lowercase__    ,					additional_special_tokens=lowercase__    ,					**lowercase__    ,					)

          SCREAMING_SNAKE_CASE_				:      List[str]					=	3
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	do_lower_case
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	remove_space
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	keep_accents
          SCREAMING_SNAKE_CASE_				:      List[str]					=	vocab_file
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	False if not self.vocab_file else True

          try:
                import jieba
          except ModuleNotFoundError as error:
                raise error.__class__(
                    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
                    "See https://pypi.org/project/jieba/ for installation."      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	jieba
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	str.maketrans(" \n"    ,					"\u2582\u2583"      )




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__ = None      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	[self.sep_token_id]
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	[self.cls_token_id]
          if token_ids_a is None:
                return token_ids_a + sep + cls
          return token_ids_a + sep + token_ids_a + sep + cls




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__ = None      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[Any]					=	[self.sep_token_id]
          SCREAMING_SNAKE_CASE_				:      Dict					=	[2]

          if token_ids_a is None:
                return len(token_ids_a + sep      ) * [0] + cls_segment_id
          return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1] + cls_segment_id




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__ = None      ):






          """simple docstring"""






          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                    "tokenizer."      )

          if not os.path.isdir(lowercase__      ):
                logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
                return
          SCREAMING_SNAKE_CASE_				:      str					=	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__      ):
                copyfile(self.vocab_file    ,					lowercase__      )

          return (out_vocab_file,)




    def   __lowerCamelCase				( self    ,					lowercase__    ,					*lowercase__    ,					**lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      int					=	[
              " ".join([x.translate(self.translator      ) for x in self.jieba.cut(lowercase__    ,					cut_all=lowercase__      )]      )
              for text in batch_text_or_text_pairs
          ]
          return super()._batch_encode_plus(lowercase__    ,					*lowercase__    ,					**lowercase__      )







    def   __lowerCamelCase				( self    ,					*lowercase__    ,					**lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	super()._decode(*lowercase__    ,					**lowercase__      )
          SCREAMING_SNAKE_CASE_				:      str					=	text.replace(" "    ,					""      ).replace("\u2582"    ,					" "      ).replace("\u2583"    ,					"\n"      )
          return text

