


import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case             =     get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class 	UpperCAmelCase_					(					UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		ReformerTokenizer
   __A :      Dict     		=		ReformerTokenizerFast
   __A :      Dict     		=		True
   __A :      Optional[int]     		=		False
   __A :      Tuple     		=		True






   def _snake_case    (		self ):






     """simple docstring"""





     super().setUp()

     lowerCamelCase :					Dict    =				ReformerTokenizer(__A						,				keep_accents=__A )
     tokenizer.save_pretrained(self.tmpdirname )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Any    =				"<s>"
     lowerCamelCase :					List[Any]    =				1

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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				list(self.get_tokenizer().get_vocab().keys() )

     self.assertEqual(vocab_keys[0]						,				"<unk>" )
     self.assertEqual(vocab_keys[1]						,				"<s>" )
     self.assertEqual(vocab_keys[-1]						,				"j" )
     self.assertEqual(len(__A )						,				1000 )






   def _snake_case    (		self ):






     """simple docstring"""





     self.assertEqual(self.get_tokenizer().vocab_size						,				1000 )






   def _snake_case    (		self ):






     """simple docstring"""





     if not self.test_rust_tokenizer:
       return

     lowerCamelCase :					List[str]    =				self.get_tokenizer()
     lowerCamelCase :					Dict    =				self.get_rust_tokenizer()

     lowerCamelCase :					str    =				"I was born in 92000, and this is falsé."

     lowerCamelCase :					Optional[Any]    =				tokenizer.tokenize(__A )
     lowerCamelCase :					Tuple    =				rust_tokenizer.tokenize(__A )
     self.assertListEqual(__A						,				__A )

     lowerCamelCase :					Union[str, Any]    =				tokenizer.encode(__A						,				add_special_tokens=__A )
     lowerCamelCase :					str    =				rust_tokenizer.encode(__A						,				add_special_tokens=__A )
     self.assertListEqual(__A						,				__A )

     lowerCamelCase :					Tuple    =				self.get_rust_tokenizer()
     lowerCamelCase :					Union[str, Any]    =				tokenizer.encode(__A )
     lowerCamelCase :					str    =				rust_tokenizer.encode(__A )
     self.assertListEqual(__A						,				__A )






   def _snake_case    (		self						,				__A=15 ):






     """simple docstring"""





     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
         lowerCamelCase :					Optional[int]    =				self.rust_tokenizer_class.from_pretrained(__A						,				**__A )

         # Simple input
         lowerCamelCase :					int    =				"This is a simple input"
         lowerCamelCase :					Optional[int]    =				["This is a simple input 1", "This is a simple input 2"]
         lowerCamelCase :					Any    =				("This is a simple input", "This is a pair")
         lowerCamelCase :					List[Any]    =				[
             ("This is a simple input 1", "This is a simple input 2"),
             ("This is a simple pair 1", "This is a simple pair 2"),
         ]

         # Simple input tests
         self.assertRaises(__A						,				tokenizer_r.encode						,				__A						,				max_length=__A						,				padding="max_length" )

         # Simple input
         self.assertRaises(__A						,				tokenizer_r.encode_plus						,				__A						,				max_length=__A						,				padding="max_length" )

         # Simple input
         self.assertRaises(
             __A						,				tokenizer_r.batch_encode_plus						,				__A						,				max_length=__A						,				padding="max_length"						,				)

         # Pair input
         self.assertRaises(__A						,				tokenizer_r.encode						,				__A						,				max_length=__A						,				padding="max_length" )

         # Pair input
         self.assertRaises(__A						,				tokenizer_r.encode_plus						,				__A						,				max_length=__A						,				padding="max_length" )

         # Pair input
         self.assertRaises(
             __A						,				tokenizer_r.batch_encode_plus						,				__A						,				max_length=__A						,				padding="max_length"						,				)






   def _snake_case    (		self ):






     """simple docstring"""





     pass






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				ReformerTokenizer(__A						,				keep_accents=__A )

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

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(__A )						,				[285, 46, 10, 170, 382]						,				)

     lowerCamelCase :					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",
             ".",
         ]						,				)
     lowerCamelCase :					str    =				tokenizer.convert_tokens_to_ids(__A )
     self.assertListEqual(
         __A						,				[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,				)

     lowerCamelCase :					Union[str, 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 _snake_case    (		self ):






     """simple docstring"""





     return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment" )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				"Hello World!"
     lowerCamelCase :					Dict    =				[126, 32, 262, 152, 38, 72, 287]

     self.assertListEqual(__A						,				self.big_tokenizer.encode(__A ) )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				(
         "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
         " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
     )
     lowerCamelCase :					Any    =				[
         108,
         265,
         24,
         111,
         4,
         258,
         156,
         35,
         28,
         275,
         3,
         259,
         297,
         260,
         84,
         4,
         35,
         110,
         44,
         8,
         259,
         91,
         268,
         21,
         11,
         209,
         274,
         109,
         266,
         277,
         117,
         86,
         93,
         315,
         258,
         278,
         258,
         277,
         258,
         0,
         258,
         288,
         258,
         319,
         258,
         0,
         258,
         0,
         258,
         0,
         258,
         0,
         258,
         287,
         258,
         315,
         258,
         289,
         258,
         278,
         99,
         269,
         266,
         262,
         8,
         259,
         241,
         4,
         217,
         230,
         268,
         266,
         55,
         168,
         106,
         75,
         193,
         266,
         223,
         27,
         49,
         26,
         282,
         25,
         264,
         299,
         19,
         26,
         0,
         258,
         277,
         117,
         86,
         93,
         176,
         183,
         270,
         11,
         262,
         42,
         61,
         265,
     ]

     self.assertListEqual(__A						,				self.big_tokenizer.encode(__A ) )






   @require_torch
   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     import torch

     from transformers import ReformerConfig, ReformerModel

     # Build sequence
     lowerCamelCase :					Dict    =				list(self.big_tokenizer.get_vocab().keys() )[:10]
     lowerCamelCase :					Tuple    =				" ".join(__A )
     lowerCamelCase :					List[str]    =				self.big_tokenizer.encode_plus(__A						,				return_tensors="pt" )
     lowerCamelCase :					Optional[int]    =				self.big_tokenizer.batch_encode_plus([sequence, sequence]						,				return_tensors="pt" )

     lowerCamelCase :					Any    =				ReformerConfig()
     # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
     lowerCamelCase :					Any    =				encoded_sequence["input_ids"].shape
     lowerCamelCase :					Dict    =				ReformerModel(__A )

     # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
     assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

     with torch.no_grad():
       model(**__A )
       model(**__A )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				{"input_ids": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], "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]]}  # noqa: E501
     # fmt: on

     # This tokenizer does not know some characters like ")".
     # That is the reason why we use very simple texts here.
     # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
     lowerCamelCase :					Optional[Any]    =				[
         "This is a very simple sentence.",
         "The quick brown fox jumps over the lazy dog.",
     ]

     self.tokenizer_integration_test_util(
         expected_encoding=__A						,				model_name="google/reformer-crime-and-punishment"						,				revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"						,				padding=__A						,				sequences=__A						,				)







from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		["image_processor", "tokenizer"]
   __A :      Dict     		=		"BridgeTowerImageProcessor"
   __A :      Optional[int]     		=		("RobertaTokenizer", "RobertaTokenizerFast")






   def __init__(		self						,				__A						,				__A ):






     """simple docstring"""





     super().__init__(__A						,				__A )






   def __call__(		self						,				__A						,				__A = None						,				__A = True						,				__A = False						,				__A = None						,				__A = None						,				__A = 0						,				__A = None						,				__A = None						,				__A = None						,				__A = False						,				__A = False						,				__A = False						,				__A = False						,				__A = True						,				__A = None						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					str    =				self.tokenizer(
         text=__A						,				add_special_tokens=__A						,				padding=__A						,				truncation=__A						,				max_length=__A						,				stride=__A						,				pad_to_multiple_of=__A						,				return_token_type_ids=__A						,				return_attention_mask=__A						,				return_overflowing_tokens=__A						,				return_special_tokens_mask=__A						,				return_offsets_mapping=__A						,				return_length=__A						,				verbose=__A						,				return_tensors=__A						,				**__A						,				)
     # add pixel_values + pixel_mask
     lowerCamelCase :					int    =				self.image_processor(
         __A						,				return_tensors=__A						,				do_normalize=__A						,				do_center_crop=__A						,				**__A )
     encoding.update(__A )

     return encoding






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





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






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





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






   @property
   def _snake_case    (		self ):






     """simple docstring"""





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



