





import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase :  List[Any]										=  get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
class       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:str							=     XLMProphetNetTokenizer
      _UpperCAmelCase			:List[Any]							=     False
      _UpperCAmelCase			:List[str]							=     True





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase			:      str 			=     XLMProphetNetTokenizer(A_ ,							keep_accents=A_     )
       tokenizer.save_pretrained(self.tmpdirname     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     "[PAD]"
       UpperCamelCase			:      List[Any] 			=     0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_     ) ,							A_     )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_     ) ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     list(self.get_tokenizer().get_vocab().keys()     )

       self.assertEqual(vocab_keys[0] ,							"[PAD]"     )
       self.assertEqual(vocab_keys[1] ,							"[CLS]"     )
       self.assertEqual(vocab_keys[-1] ,							"j"     )
       self.assertEqual(len(A_     ) ,							1012     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       self.assertEqual(self.get_tokenizer().vocab_size ,							1012     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     XLMProphetNetTokenizer(A_ ,							keep_accents=A_     )

       UpperCamelCase			:      str 			=     tokenizer.tokenize("This is a test"     )
       self.assertListEqual(A_ ,							["▁This", "▁is", "▁a", "▁t", "est"]     )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(A_     ) ,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,							)

       UpperCamelCase			:      Optional[Any] 			=     tokenizer.tokenize("I was born in 92000, and this is falsé."     )
       self.assertListEqual(
           A_ ,							[
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "9",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "é",
               ".",
           ] ,							)
       UpperCamelCase			:      Dict 			=     tokenizer.convert_tokens_to_ids(A_     )
       self.assertListEqual(
           A_ ,							[
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
           ] ,							)

       UpperCamelCase			:      Any 			=     tokenizer.convert_ids_to_tokens(A_     )
       self.assertListEqual(
           A_ ,							[
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "[UNK]",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "[UNK]",
               ".",
           ] ,							)





      @cached_property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"     )





      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     "Hello World!"
       UpperCamelCase			:      str 			=     [3_5389, 6672, 49, 2]
       self.assertListEqual(A_ ,							self.big_tokenizer.encode(A_     )     )





      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     {"input_ids": [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=A_ ,							model_name="microsoft/xprophetnet-large-wiki100-cased" ,							revision="1acad1643ddd54a44df6a1b797ada8373685d90e" ,							)







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Optional[int]							=     ['image_processor', 'tokenizer']
      _UpperCAmelCase			:Tuple							=     'BlipImageProcessor'
      _UpperCAmelCase			:Optional[int]							=     'AutoTokenizer'





      def __init__(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     False
       super().__init__(A_ ,							A_     )
       UpperCamelCase			:      str 			=     self.image_processor





      def __call__(				self ,							A_ = None ,							A_ = None ,							A_ = True ,							A_ = False ,							A_ = None ,							A_ = None ,							A_ = 0 ,							A_ = None ,							A_ = None ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = True ,							A_ = None ,							**A_ ,							):
       '''simple docstring'''




       if images is None and text is None:
        raise ValueError("You have to specify either images or text."     )

       # Get only text
       if images is None:
        UpperCamelCase			:      int 			=     self.tokenizer
        UpperCamelCase			:      Optional[int] 			=     self.tokenizer(
            text=A_ ,							add_special_tokens=A_ ,							padding=A_ ,							truncation=A_ ,							max_length=A_ ,							stride=A_ ,							pad_to_multiple_of=A_ ,							return_attention_mask=A_ ,							return_overflowing_tokens=A_ ,							return_special_tokens_mask=A_ ,							return_offsets_mapping=A_ ,							return_token_type_ids=A_ ,							return_length=A_ ,							verbose=A_ ,							return_tensors=A_ ,							**A_ ,							)
        return text_encoding

       # add pixel_values
       UpperCamelCase			:      int 			=     self.image_processor(A_ ,							return_tensors=A_     )

       if text is not None:
        UpperCamelCase			:      Dict 			=     self.tokenizer(
            text=A_ ,							add_special_tokens=A_ ,							padding=A_ ,							truncation=A_ ,							max_length=A_ ,							stride=A_ ,							pad_to_multiple_of=A_ ,							return_attention_mask=A_ ,							return_overflowing_tokens=A_ ,							return_special_tokens_mask=A_ ,							return_offsets_mapping=A_ ,							return_token_type_ids=A_ ,							return_length=A_ ,							verbose=A_ ,							return_tensors=A_ ,							**A_ ,							)
       else:
        UpperCamelCase			:      Dict 			=     None

       if text_encoding is not None:
        encoding_image_processor.update(A_     )

       return encoding_image_processor





      def  __UpperCamelCase(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




       return self.tokenizer.batch_decode(*A_ ,							**A_     )





      def  __UpperCamelCase(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




       return self.tokenizer.decode(*A_ ,							**A_     )





      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.tokenizer.model_input_names
       UpperCamelCase			:      int 			=     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
