



import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case    				=		"""▁"""

snake_case    				=		get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		int			=	BertGenerationTokenizer
     UpperCamelCase_							:		Optional[int]			=	False
     UpperCamelCase_							:		Tuple			=	True


     def 				_A       (				self       :	List[Any]      ):
       super().setUp()

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    BertGenerationTokenizer(UpperCAmelCase_					, keep_accents=UpperCAmelCase_      )
       tokenizer.save_pretrained(self.tmpdirname      )


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      Any							=    "<s>"
       SCREAMING_SNAKE_CASE       :      List[str]							=    1

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


     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, 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]					, "<pad>"      )
       self.assertEqual(len(UpperCAmelCase_      )					, 1002      )


     def 				_A       (				self       :	Tuple      ):
       self.assertEqual(self.get_tokenizer().vocab_size					, 1000      )


     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    BertGenerationTokenizer(UpperCAmelCase_					, keep_accents=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tokenizer.tokenize("This is a test"      )
       self.assertListEqual(UpperCAmelCase_					, ["▁This", "▁is", "▁a", "▁t", "est"]      )

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

       SCREAMING_SNAKE_CASE       :      str							=    tokenizer.tokenize("I was born in 92000, and this is falsé."      )
       self.assertListEqual(
           UpperCAmelCase_					, [
               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",
               ".",
           ]					, )
       SCREAMING_SNAKE_CASE       :      str							=    tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )
       self.assertListEqual(
           UpperCAmelCase_					, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]					, )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tokenizer.convert_ids_to_tokens(UpperCAmelCase_      )
       self.assertListEqual(
           UpperCAmelCase_					, [
               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 				_A       (				self       :	Optional[int]      ):
       return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"      )


     @slow
     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    "Hello World!"
       SCREAMING_SNAKE_CASE       :      str							=    [1_8536, 2260, 101]

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


     @slow
     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      int							=    (
           "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"
       )
       SCREAMING_SNAKE_CASE       :      str							=    [
           871,
           419,
           358,
           946,
           991,
           2521,
           452,
           358,
           1357,
           387,
           7751,
           3536,
           112,
           985,
           456,
           126,
           865,
           938,
           5400,
           5734,
           458,
           1368,
           467,
           786,
           2462,
           5246,
           1159,
           633,
           865,
           4519,
           457,
           582,
           852,
           2557,
           427,
           916,
           508,
           405,
           3_4324,
           497,
           391,
           408,
           1_1342,
           1244,
           385,
           100,
           938,
           985,
           456,
           574,
           362,
           1_2597,
           3200,
           3129,
           1172,
       ]

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


     @require_torch
     @slow
     def 				_A       (				self       :	int      ):
       import torch

       from transformers import BertGenerationConfig, BertGenerationEncoder

       # Build sequence
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    list(self.big_tokenizer.get_vocab().keys()      )[:10]
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    " ".join(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      str							=    self.big_tokenizer.encode_plus(UpperCAmelCase_					, return_tensors="pt"					, return_token_type_ids=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      str							=    self.big_tokenizer.batch_encode_plus(
           [sequence + " " + sequence]					, return_tensors="pt"					, return_token_type_ids=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    BertGenerationConfig()
       SCREAMING_SNAKE_CASE       :      Tuple							=    BertGenerationEncoder(UpperCAmelCase_      )

       assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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






     @slow
     def 				_A       (				self       :	Any      ):
       # fmt: off
       SCREAMING_SNAKE_CASE       :      str							=    {"input_ids": [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=UpperCAmelCase_					, model_name="google/bert_for_seq_generation_L-24_bbc_encoder"					, revision="c817d1fd1be2ffa69431227a1fe320544943d4db"					, )














import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


snake_case    				=		logging.get_logger(__name__)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Union[str, Any]			=	CLIPConfig

     UpperCamelCase_							:		Optional[int]			=	['''CLIPEncoderLayer''']


     def __init__(				self       :	List[Any]					, UpperCAmelCase_       :	CLIPConfig      ):
       super().__init__(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Any							=    CLIPVisionModelWithProjection(config.vision_config      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    nn.Linear(config.vision_config.projection_dim					, 1      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    nn.Linear(config.vision_config.projection_dim					, 1      )






     @torch.no_grad()
     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	List[Any]=0.5					, UpperCAmelCase_       :	Optional[Any]=0.5      ):
       SCREAMING_SNAKE_CASE       :      str							=    self.vision_model(UpperCAmelCase_      )[0]

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.p_head(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    nsfw_detected.flatten()
       SCREAMING_SNAKE_CASE       :      List[str]							=    nsfw_detected > p_threshold
       SCREAMING_SNAKE_CASE       :      List[Any]							=    nsfw_detected.tolist()

       if any(UpperCAmelCase_      ):
         logger.warning(
             "Potential NSFW content was detected in one or more images. A black image will be returned instead."
             " Try again with a different prompt and/or seed."      )

       for idx, nsfw_detected_ in enumerate(UpperCAmelCase_      ):
         if nsfw_detected_:
           SCREAMING_SNAKE_CASE       :      Dict							=    np.zeros(images[idx].shape      )

       SCREAMING_SNAKE_CASE       :      Any							=    self.w_head(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    watermark_detected.flatten()
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    watermark_detected > w_threshold
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    watermark_detected.tolist()

       if any(UpperCAmelCase_      ):
         logger.warning(
             "Potential watermarked content was detected in one or more images. A black image will be returned instead."
             " Try again with a different prompt and/or seed."      )

       for idx, watermark_detected_ in enumerate(UpperCAmelCase_      ):
         if watermark_detected_:
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    np.zeros(images[idx].shape      )

       return images, nsfw_detected, watermark_detected






