

'''simple docstring'''

import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A_       						=							get_tests_dir("fixtures/spiece.model")





@require_sentencepiece
@require_tokenizers
class 							_snake_case					(       _a	,  unittest.TestCase						):
   _A       :     str       =					DebertaVaTokenizer
   _A       :     List[str]       =					DebertaVaTokenizerFast
   _A       :     Dict       =					True
   _A       :     Union[str, Any]       =					True
   def     __UpperCamelCase						( self			:      Optional[int]							):
     super().setUp()

     # We have a SentencePiece fixture for testing
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					DebertaVaTokenizer(SCREAMING_SNAKE_CASE__     ,unk_token="<unk>"							)
     tokenizer.save_pretrained(self.tmpdirname							)
   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Dict						      =					"this is a test"
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					"this is a test"
     return input_text, output_text
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:str						      =					"<pad>"
     SCREAMING_SNAKE_CASE:List[Any]						      =					0

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__							)     ,SCREAMING_SNAKE_CASE__							)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__							)     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Dict							):
     SCREAMING_SNAKE_CASE:Tuple						      =					list(self.get_tokenizer().get_vocab().keys()							)
     self.assertEqual(vocab_keys[0]     ,"<pad>"							)
     self.assertEqual(vocab_keys[1]     ,"<unk>"							)
     self.assertEqual(vocab_keys[-1]     ,"[PAD]"							)
     self.assertEqual(len(SCREAMING_SNAKE_CASE__							)     ,30_001							)
   def     __UpperCamelCase						( self			:      int							):
     self.assertEqual(self.get_tokenizer().vocab_size     ,30_000							)
   def     __UpperCamelCase						( self			:      Any							):
     # fmt: off
     SCREAMING_SNAKE_CASE:int						      =					" \tHeLLo!how  \n Are yoU?  "
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					["▁hello", "!", "how", "▁are", "▁you", "?"]
     # fmt: on

     SCREAMING_SNAKE_CASE:Optional[int]						      =					DebertaVaTokenizer(SCREAMING_SNAKE_CASE__     ,do_lower_case=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__     ,do_lower_case=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Dict						      =					rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."							)
   def     __UpperCamelCase						( self			:      List[str]							):
     pass
   @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."							)
   def     __UpperCamelCase						( self			:      str							):
     pass
   def     __UpperCamelCase						( self			:      str							):
     # fmt: off
     SCREAMING_SNAKE_CASE:Tuple						      =					"I was born in 92000, and this is falsé."
     SCREAMING_SNAKE_CASE:List[str]						      =					["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
     # fmt: on

     SCREAMING_SNAKE_CASE:Dict						      =					DebertaVaTokenizer(SCREAMING_SNAKE_CASE__     ,split_by_punct=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str						      =					tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__     ,split_by_punct=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Dict						      =					rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     # fmt: off
     SCREAMING_SNAKE_CASE:int						      =					"I was born in 92000, and this is falsé."
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
     # fmt: on

     SCREAMING_SNAKE_CASE:List[Any]						      =					DebertaVaTokenizer(SCREAMING_SNAKE_CASE__     ,do_lower_case=SCREAMING_SNAKE_CASE__     ,split_by_punct=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Dict						      =					tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__     ,do_lower_case=SCREAMING_SNAKE_CASE__     ,split_by_punct=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any						      =					rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      List[str]							):
     # fmt: off
     SCREAMING_SNAKE_CASE:Tuple						      =					"I was born in 92000, and this is falsé."
     SCREAMING_SNAKE_CASE:List[str]						      =					["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
     # fmt: on

     SCREAMING_SNAKE_CASE:int						      =					DebertaVaTokenizer(SCREAMING_SNAKE_CASE__     ,do_lower_case=SCREAMING_SNAKE_CASE__     ,split_by_punct=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Tuple						      =					DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__     ,do_lower_case=SCREAMING_SNAKE_CASE__     ,split_by_punct=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     # fmt: off
     SCREAMING_SNAKE_CASE:List[Any]						      =					"I was born in 92000, and this is falsé."
     SCREAMING_SNAKE_CASE:str						      =					["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
     # fmt: on

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					DebertaVaTokenizer(SCREAMING_SNAKE_CASE__     ,do_lower_case=SCREAMING_SNAKE_CASE__     ,split_by_punct=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[str]						      =					tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[str]						      =					DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__     ,do_lower_case=SCREAMING_SNAKE_CASE__     ,split_by_punct=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      List[str]							):
     # fmt: off
     SCREAMING_SNAKE_CASE:List[str]						      =					" \tHeLLo!how  \n Are yoU?  "
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
     # fmt: on

     SCREAMING_SNAKE_CASE:List[str]						      =					DebertaVaTokenizer(SCREAMING_SNAKE_CASE__     ,do_lower_case=SCREAMING_SNAKE_CASE__     ,split_by_punct=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__     ,do_lower_case=SCREAMING_SNAKE_CASE__     ,split_by_punct=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple						      =					rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      int							):
     SCREAMING_SNAKE_CASE:List[str]						      =					self.get_tokenizer()
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.get_rust_tokenizer()

     SCREAMING_SNAKE_CASE:Dict						      =					"I was born in 92000, and this is falsé."

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					self.get_rust_tokenizer()
     SCREAMING_SNAKE_CASE:str						      =					tokenizer.encode(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					rust_tokenizer.encode(SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:Tuple						      =					"This is a test"
     SCREAMING_SNAKE_CASE:List[Any]						      =					[13, 1, 4_398, 25, 21, 1_289]
     SCREAMING_SNAKE_CASE:Optional[int]						      =					["▁", "T", "his", "▁is", "▁a", "▁test"]
     SCREAMING_SNAKE_CASE:Dict						      =					["▁", "<unk>", "his", "▁is", "▁a", "▁test"]

     SCREAMING_SNAKE_CASE:List[Any]						      =					DebertaVaTokenizer(SCREAMING_SNAKE_CASE__     ,keep_accents=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__     ,keep_accents=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Any						      =					tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[str]						      =					tokenizer.tokenize(SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any						      =					tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[str]						      =					rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					rust_tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     # fmt: off
     SCREAMING_SNAKE_CASE:Tuple						      =					"I was born in 92000, and this is falsé."
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[13, 1, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]
     SCREAMING_SNAKE_CASE:Tuple						      =					["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
     SCREAMING_SNAKE_CASE:List[Any]						      =					["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
     # fmt: on

     SCREAMING_SNAKE_CASE:int						      =					tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					tokenizer.tokenize(SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any						      =					rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[str]						      =					rust_tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:Tuple						      =					DebertaVaTokenizer(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Any						      =					tokenizer.encode("sequence builders"							)
     SCREAMING_SNAKE_CASE:List[str]						      =					tokenizer.encode("multi-sequence build"							)

     SCREAMING_SNAKE_CASE:Any						      =					tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple						      =					tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]     ,SCREAMING_SNAKE_CASE__							)
     self.assertEqual(
         [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]     ,SCREAMING_SNAKE_CASE__     ,)







   @slow
   def     __UpperCamelCase						( self			:      Optional[int]							):
     # fmt: off
     SCREAMING_SNAKE_CASE:Any						      =					{"input_ids": [[1, 39_867, 36, 19_390, 486, 27, 35_052, 81_436, 18, 60_685, 1_225, 7, 35_052, 81_436, 18, 9_367, 16_899, 18, 15_937, 53, 594, 773, 18, 16_287, 30_465, 36, 15_937, 6, 41_139, 38, 36_979, 60_763, 191, 6, 34_132, 99, 6, 50_538, 390, 43_230, 6, 34_132, 2_779, 20_850, 14, 699, 1_072, 1_194, 36, 382, 10_901, 53, 7, 699, 1_072, 2_084, 36, 20_422, 630, 53, 19, 105, 3_049, 1_896, 1_053, 16_899, 1_506, 11, 37_978, 4_243, 7, 1_237, 31_869, 200, 16_566, 654, 6, 35_052, 81_436, 7, 55_630, 13_593, 4, 2], [1, 26, 15_011, 13, 667, 8, 1_053, 18, 23_611, 1_237, 72_356, 12_820, 34, 104_134, 1_209, 35, 13_313, 6_627, 21, 202, 347, 7, 164, 2_399, 11, 46, 4_485, 4, 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], [1, 5, 1_232, 2_864, 15_785, 14_951, 105, 5, 8_581, 1_250, 4, 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]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=SCREAMING_SNAKE_CASE__     ,model_name="microsoft/deberta-v2-xlarge"     ,revision="ad6e42c1532ddf3a15c39246b63f5559d558b670"     ,)









'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     Union[str, Any]       =					['''pixel_values''']
   def __init__( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BILINEAR     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Union[int, float] = 1 / 255     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,**SCREAMING_SNAKE_CASE__			:      List[str]     ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					size if size is not None else {"shortest_edge": 384}
     SCREAMING_SNAKE_CASE:List[str]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					do_resize
     SCREAMING_SNAKE_CASE:List[Any]						      =					size
     # Default value set here for backwards compatibility where the value in config is None
     SCREAMING_SNAKE_CASE:Dict						      =					crop_pct if crop_pct is not None else 224 / 256
     SCREAMING_SNAKE_CASE:Any						      =					resample
     SCREAMING_SNAKE_CASE:Dict						      =					do_rescale
     SCREAMING_SNAKE_CASE:Optional[int]						      =					rescale_factor
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					do_normalize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE:Tuple						      =					image_std if image_std is not None else IMAGENET_STANDARD_STD
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int]     ,SCREAMING_SNAKE_CASE__			:      float     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      int     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     if "shortest_edge" not in size:
       raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''							)
     SCREAMING_SNAKE_CASE:Any						      =					size["shortest_edge"]

     if shortest_edge < 384:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       SCREAMING_SNAKE_CASE:Any						      =					int(shortest_edge / crop_pct							)
       SCREAMING_SNAKE_CASE:Any						      =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[int, float]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     return rescale(SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     return normalize(SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      ImageInput     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, TensorType]] = None     ,SCREAMING_SNAKE_CASE__			:      ChannelDimension = ChannelDimension.FIRST     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE:List[str]						      =					crop_pct if crop_pct is not None else self.crop_pct
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE:List[str]						      =					do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE:str						      =					rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE:str						      =					do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE:Any						      =					image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE:Optional[int]						      =					size if size is not None else self.size
     SCREAMING_SNAKE_CASE:Tuple						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					make_list_of_images(SCREAMING_SNAKE_CASE__							)

     if not valid_images(SCREAMING_SNAKE_CASE__							):
       raise ValueError(
           "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
           "torch.Tensor, tf.Tensor or jax.ndarray."							)

     if do_resize and size is None or resample is None:
       raise ValueError("Size and resample must be specified if do_resize is True."							)

     if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
       raise ValueError("crop_pct must be specified if size < 384."							)

     if do_rescale and rescale_factor is None:
       raise ValueError("Rescale factor must be specified if do_rescale is True."							)

     if do_normalize and (image_mean is None or image_std is None):
       raise ValueError("Image mean and std must be specified if do_normalize is True."							)

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE:int						      =					[to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					[self.resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,crop_pct=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.rescale(image=SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					[self.normalize(image=SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:Any						      =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:int						      =					{"pixel_values": images}
     return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,tensor_type=SCREAMING_SNAKE_CASE__							)






