




'''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 						lowercase     (   unittest.TestCase):






      def    A__(self					)       ->   Any:
         SCREAMING_SNAKE_CASE__			:				int       						= tempfile.mkdtemp()

         SCREAMING_SNAKE_CASE__			:				Any       						= CLIPImageProcessor()
         SCREAMING_SNAKE_CASE__			:				List[str]       						= BertTokenizer.from_pretrained(
             '''hf-internal-testing/tiny-random-BertModel''' ,  model_input_names=['''input_ids''', '''attention_mask''']					)

         SCREAMING_SNAKE_CASE__			:				List[str]       						= GitProcessor(a__ ,  a__					)

         processor.save_pretrained(self.tmpdirname					)






      def    A__(self ,  **a__					)       ->   int:
         return AutoProcessor.from_pretrained(self.tmpdirname ,  **a__					).tokenizer






      def    A__(self ,  **a__					)       ->   Optional[int]:
         return AutoProcessor.from_pretrained(self.tmpdirname ,  **a__					).image_processor






      def    A__(self					)       ->   Tuple:
         shutil.rmtree(self.tmpdirname					)






      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				str       						= [np.random.randint(2_5_5 ,  size=(3, 3_0, 4_0_0) ,  dtype=np.uinta					)]

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [Image.fromarray(np.moveaxis(a__ ,  0 ,  -1					)					) for x in image_inputs]

         return image_inputs






      def    A__(self					)       ->   str:
         SCREAMING_SNAKE_CASE__			:				Tuple       						= GitProcessor(tokenizer=self.get_tokenizer() ,  image_processor=self.get_image_processor()					)
         processor.save_pretrained(self.tmpdirname					)

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.get_tokenizer(bos_token='''(BOS)''' ,  eos_token='''(EOS)'''					)
         SCREAMING_SNAKE_CASE__			:				Any       						= self.get_image_processor(do_normalize=a__ ,  padding_value=1.0					)

         SCREAMING_SNAKE_CASE__			:				List[str]       						= GitProcessor.from_pretrained(
             self.tmpdirname ,  bos_token='''(BOS)''' ,  eos_token='''(EOS)''' ,  do_normalize=a__ ,  padding_value=1.0					)

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

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






      def    A__(self					)       ->   Optional[Any]:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.get_image_processor()
         SCREAMING_SNAKE_CASE__			:				Any       						= self.get_tokenizer()

         SCREAMING_SNAKE_CASE__			:				List[str]       						= GitProcessor(tokenizer=a__ ,  image_processor=a__					)

         SCREAMING_SNAKE_CASE__			:				Any       						= self.prepare_image_inputs()

         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= image_processor(a__ ,  return_tensors='''np'''					)
         SCREAMING_SNAKE_CASE__			:				int       						= processor(images=a__ ,  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    A__(self					)       ->   str:
         SCREAMING_SNAKE_CASE__			:				Dict       						= self.get_image_processor()
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.get_tokenizer()

         SCREAMING_SNAKE_CASE__			:				List[str]       						= GitProcessor(tokenizer=a__ ,  image_processor=a__					)

         SCREAMING_SNAKE_CASE__			:				str       						= '''lower newer'''

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= processor(text=a__					)

         SCREAMING_SNAKE_CASE__			:				Dict       						= tokenizer(a__ ,  return_token_type_ids=a__					)

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






      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.get_image_processor()
         SCREAMING_SNAKE_CASE__			:				List[str]       						= self.get_tokenizer()

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= GitProcessor(tokenizer=a__ ,  image_processor=a__					)

         SCREAMING_SNAKE_CASE__			:				List[Any]       						= '''lower newer'''
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.prepare_image_inputs()

         SCREAMING_SNAKE_CASE__			:				List[str]       						= processor(text=a__ ,  images=a__					)

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

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






      def    A__(self					)       ->   str:
         SCREAMING_SNAKE_CASE__			:				int       						= self.get_image_processor()
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.get_tokenizer()

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= GitProcessor(tokenizer=a__ ,  image_processor=a__					)

         SCREAMING_SNAKE_CASE__			:				Any       						= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         SCREAMING_SNAKE_CASE__			:				str       						= processor.batch_decode(a__					)
         SCREAMING_SNAKE_CASE__			:				str       						= tokenizer.batch_decode(a__					)

         self.assertListEqual(a__ ,  a__					)






      def    A__(self					)       ->   List[Any]:
         SCREAMING_SNAKE_CASE__			:				Any       						= self.get_image_processor()
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.get_tokenizer()

         SCREAMING_SNAKE_CASE__			:				int       						= GitProcessor(tokenizer=a__ ,  image_processor=a__					)

         SCREAMING_SNAKE_CASE__			:				List[Any]       						= '''lower newer'''
         SCREAMING_SNAKE_CASE__			:				Any       						= self.prepare_image_inputs()

         SCREAMING_SNAKE_CASE__			:				int       						= processor(text=a__ ,  images=a__					)

         # 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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


lowercase__			=	logging.get_logger(__name__)

lowercase__			=	{'''vocab_file''': '''vocab.txt'''}

lowercase__			=	{
    '''vocab_file''': {
        '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
        '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
        '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
        '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
    }
}

lowercase__			=	{
    '''tau/splinter-base''': 5_1_2,
    '''tau/splinter-base-qass''': 5_1_2,
    '''tau/splinter-large''': 5_1_2,
    '''tau/splinter-large-qass''': 5_1_2,
}

lowercase__			=	{
    '''tau/splinter-base''': {'''do_lower_case''': False},
    '''tau/splinter-base-qass''': {'''do_lower_case''': False},
    '''tau/splinter-large''': {'''do_lower_case''': False},
    '''tau/splinter-large-qass''': {'''do_lower_case''': False},
}






class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  VOCAB_FILES_NAMES
      __lowercase 		=  PRETRAINED_VOCAB_FILES_MAP
      __lowercase 		=  PRETRAINED_INIT_CONFIGURATION
      __lowercase 		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __lowercase 		=  SplinterTokenizer






      def __init__(self ,  a__=None ,  a__=None ,  a__=True ,  a__="[UNK]" ,  a__="[SEP]" ,  a__="[PAD]" ,  a__="[CLS]" ,  a__="[MASK]" ,  a__="[QUESTION]" ,  a__=True ,  a__=None ,  **a__ ,  )       ->   Any:
         super().__init__(
             a__ ,  tokenizer_file=a__ ,  do_lower_case=a__ ,  unk_token=a__ ,  sep_token=a__ ,  pad_token=a__ ,  cls_token=a__ ,  mask_token=a__ ,  tokenize_chinese_chars=a__ ,  strip_accents=a__ ,  additional_special_tokens=(question_token,) ,  **a__ ,  )

         SCREAMING_SNAKE_CASE__			:				Tuple       						= json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
         if (
             pre_tok_state.get('''lowercase''' ,  a__					) != do_lower_case
             or pre_tok_state.get('''strip_accents''' ,  a__					) != strip_accents
         ):
            SCREAMING_SNAKE_CASE__			:				List[Any]       						= getattr(a__ ,  pre_tok_state.pop('''type'''					)					)
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= do_lower_case
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= strip_accents
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= pre_tok_class(**a__					)

         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= do_lower_case






      @property
      def    A__(self					)       ->   List[str]:
         return self.convert_tokens_to_ids(self.question_token					)






      def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         SCREAMING_SNAKE_CASE__			:				Dict       						= [self.cls_token_id]
         SCREAMING_SNAKE_CASE__			:				Tuple       						= [self.sep_token_id]
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= [self.question_token_id] + [self.convert_tokens_to_ids('''.'''					)]
         if self.padding_side == "right":
            # Input is question-then-context
            return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
         else:
            # Input is context-then-question
            return cls + token_ids_a + sep + token_ids_a + question_suffix + sep






      def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [self.sep_token_id]
         SCREAMING_SNAKE_CASE__			:				Any       						= [self.cls_token_id]
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [self.question_token_id] + [self.convert_tokens_to_ids('''.'''					)]
         if token_ids_a is None:
            return len(cls + token_ids_a + sep					) * [0]

         if self.padding_side == "right":
            # Input is question-then-context
            return len(cls + token_ids_a + question_suffix + sep					) * [0] + len(token_ids_a + sep					) * [1]
         else:
            # Input is context-then-question
            return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + question_suffix + sep					) * [1]






      def    A__(self ,  a__ ,  a__ = None					)       ->   Tuple[str]:
         SCREAMING_SNAKE_CASE__			:				int       						= self._tokenizer.model.save(a__ ,  name=a__					)
         return tuple(a__					)

