

'''simple docstring'''

import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer





class 							_snake_case       (				_a  ):
   _A				:   Any			      =    ['''image_processor''', '''tokenizer''']
   _A				:   Union[str, Any]			      =    '''BlipImageProcessor'''
   _A				:   Optional[Any]			      =    '''AutoTokenizer'''
   def __init__(       self	:					Optional[Any]   ,SCREAMING_SNAKE_CASE__	:					Union[str, Any]   ,SCREAMING_SNAKE_CASE__	:					List[Any]   ,SCREAMING_SNAKE_CASE__	:					List[Any]							):
     super().__init__(lowerCamelCase_   ,lowerCamelCase_							)

     # add QFormer tokenizer
     SCREAMING_SNAKE_CASE:List[str]  					=    qformer_tokenizer
   def __call__(       self	:					Tuple   ,SCREAMING_SNAKE_CASE__	:					Tuple = None   ,SCREAMING_SNAKE_CASE__	:					List[str] = None   ,SCREAMING_SNAKE_CASE__	:					Union[str, Any] = True   ,SCREAMING_SNAKE_CASE__	:					Any = False   ,SCREAMING_SNAKE_CASE__	:					Any = None   ,SCREAMING_SNAKE_CASE__	:					Optional[Any] = None   ,SCREAMING_SNAKE_CASE__	:					Optional[int] = 0   ,SCREAMING_SNAKE_CASE__	:					List[Any] = None   ,SCREAMING_SNAKE_CASE__	:					Union[str, Any] = None   ,SCREAMING_SNAKE_CASE__	:					Optional[int] = False   ,SCREAMING_SNAKE_CASE__	:					str = False   ,SCREAMING_SNAKE_CASE__	:					Tuple = False   ,SCREAMING_SNAKE_CASE__	:					Tuple = False   ,SCREAMING_SNAKE_CASE__	:					List[str] = False   ,SCREAMING_SNAKE_CASE__	:					Any = True   ,SCREAMING_SNAKE_CASE__	:					Tuple = None   ,**SCREAMING_SNAKE_CASE__	:					str   ,):

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

     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    BatchFeature()

     if text is not None:
       SCREAMING_SNAKE_CASE:Optional[int]  					=    self.tokenizer(
           text=lowerCamelCase_   ,add_special_tokens=lowerCamelCase_   ,padding=lowerCamelCase_   ,truncation=lowerCamelCase_   ,max_length=lowerCamelCase_   ,stride=lowerCamelCase_   ,pad_to_multiple_of=lowerCamelCase_   ,return_attention_mask=lowerCamelCase_   ,return_overflowing_tokens=lowerCamelCase_   ,return_special_tokens_mask=lowerCamelCase_   ,return_offsets_mapping=lowerCamelCase_   ,return_token_type_ids=lowerCamelCase_   ,return_length=lowerCamelCase_   ,verbose=lowerCamelCase_   ,return_tensors=lowerCamelCase_   ,**lowerCamelCase_   ,)
       encoding.update(lowerCamelCase_							)
       SCREAMING_SNAKE_CASE:str  					=    self.qformer_tokenizer(
           text=lowerCamelCase_   ,add_special_tokens=lowerCamelCase_   ,padding=lowerCamelCase_   ,truncation=lowerCamelCase_   ,max_length=lowerCamelCase_   ,stride=lowerCamelCase_   ,pad_to_multiple_of=lowerCamelCase_   ,return_attention_mask=lowerCamelCase_   ,return_overflowing_tokens=lowerCamelCase_   ,return_special_tokens_mask=lowerCamelCase_   ,return_offsets_mapping=lowerCamelCase_   ,return_token_type_ids=lowerCamelCase_   ,return_length=lowerCamelCase_   ,verbose=lowerCamelCase_   ,return_tensors=lowerCamelCase_   ,**lowerCamelCase_   ,)
       SCREAMING_SNAKE_CASE:Optional[Any]  					=    qformer_text_encoding.pop("input_ids"							)
       SCREAMING_SNAKE_CASE:List[str]  					=    qformer_text_encoding.pop("attention_mask"							)

     if images is not None:
       SCREAMING_SNAKE_CASE:Dict  					=    self.image_processor(lowerCamelCase_   ,return_tensors=lowerCamelCase_							)
       encoding.update(lowerCamelCase_							)

     return encoding
   def 			__UpperCamelCase  (       self	:					Tuple   ,*SCREAMING_SNAKE_CASE__	:					Dict   ,**SCREAMING_SNAKE_CASE__	:					Any							):
     return self.tokenizer.batch_decode(*lowerCamelCase_   ,**lowerCamelCase_							)
   def 			__UpperCamelCase  (       self	:					Union[str, Any]   ,*SCREAMING_SNAKE_CASE__	:					Union[str, Any]   ,**SCREAMING_SNAKE_CASE__	:					Any							):
     return self.tokenizer.decode(*lowerCamelCase_   ,**lowerCamelCase_							)
   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 			__UpperCamelCase  (       self	:					List[str]							):
     SCREAMING_SNAKE_CASE:Tuple  					=    self.tokenizer.model_input_names
     SCREAMING_SNAKE_CASE:Tuple  					=    self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
   def 			__UpperCamelCase  (       self	:					List[str]   ,SCREAMING_SNAKE_CASE__	:					str   ,**SCREAMING_SNAKE_CASE__	:					Tuple							):

     if os.path.isfile(lowerCamelCase_							):
       raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''							)
     os.makedirs(lowerCamelCase_   ,exist_ok=lowerCamelCase_							)
     SCREAMING_SNAKE_CASE:Tuple  					=    os.path.join(lowerCamelCase_   ,"qformer_tokenizer"							)
     self.qformer_tokenizer.save_pretrained(lowerCamelCase_							)
     return super().save_pretrained(lowerCamelCase_   ,**lowerCamelCase_							)







   @classmethod
   def 			__UpperCamelCase  (       cls	:					Dict   ,SCREAMING_SNAKE_CASE__	:					str   ,**SCREAMING_SNAKE_CASE__	:					List[str]							):
     SCREAMING_SNAKE_CASE:List[Any]  					=    AutoTokenizer.from_pretrained(lowerCamelCase_   ,subfolder="qformer_tokenizer"							)
     SCREAMING_SNAKE_CASE:str  					=    cls._get_arguments_from_pretrained(lowerCamelCase_   ,**lowerCamelCase_							)
     args.append(lowerCamelCase_							)
     return cls(*lowerCamelCase_							)











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


_lowercase: Optional[int]      			=		get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
class 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							XLMProphetNetTokenizer
	__A							=							False
	__A							=							True




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()

						# We have a SentencePiece fixture for testing
						a					     =   XLMProphetNetTokenizer(lowerCamelCase_		,       keep_accents=lowerCamelCase_		)
						tokenizer.save_pretrained(self.tmpdirname		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "[PAD]"
						a					     =   0

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   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(lowerCamelCase_		)		,       1012		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   XLMProphetNetTokenizer(lowerCamelCase_		,       keep_accents=lowerCamelCase_		)

						a					     =   tokenizer.tokenize("This is a test"		)
						self.assertListEqual(lowerCamelCase_		,       ["▁This", "▁is", "▁a", "▁t", "est"]		)

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

						a					     =   tokenizer.tokenize("I was born in 92000, and this is falsé."		)
						self.assertListEqual(
						    lowerCamelCase_		,       [
						        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",
						        ".",
						    ]		,       )
						a					     =   tokenizer.convert_tokens_to_ids(lowerCamelCase_		)
						self.assertListEqual(
						    lowerCamelCase_		,       [
						        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]
						    ]		,       )

						a					     =   tokenizer.convert_ids_to_tokens(lowerCamelCase_		)
						self.assertListEqual(
						    lowerCamelCase_		,       [
						        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"""

						a					     =   "Hello World!"
						a					     =   [35389, 6672, 49, 2]
						self.assertListEqual(lowerCamelCase_		,       self.big_tokenizer.encode(lowerCamelCase_		)		)




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   {"input_ids": [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 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, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 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=lowerCamelCase_		,       model_name="microsoft/xprophetnet-large-wiki100-cased"		,       revision="1acad1643ddd54a44df6a1b797ada8373685d90e"		,       )
