



import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


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


     UpperCamelCase_							:		Union[str, Any]			=	MvpTokenizer
     UpperCamelCase_							:		Optional[Any]			=	MvpTokenizerFast
     UpperCamelCase_							:		List[Any]			=	True
     UpperCamelCase_							:		Dict			=	filter_roberta_detectors


     def 				_A       (				self       :	str      ):
       super().setUp()
       SCREAMING_SNAKE_CASE       :      List[Any]							=    [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       SCREAMING_SNAKE_CASE       :      Tuple							=    dict(zip(UpperCAmelCase_					, range(len(UpperCAmelCase_      )      )      )      )
       SCREAMING_SNAKE_CASE       :      int							=    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       SCREAMING_SNAKE_CASE       :      List[Any]							=    {"unk_token": "<unk>"}

       SCREAMING_SNAKE_CASE       :      str							=    os.path.join(self.tmpdirname					, VOCAB_FILES_NAMES["vocab_file"]      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    os.path.join(self.tmpdirname					, VOCAB_FILES_NAMES["merges_file"]      )
       with open(self.vocab_file					, "w"					, encoding="utf-8"      ) as fp:
         fp.write(json.dumps(UpperCAmelCase_      ) + "\n"      )
       with open(self.merges_file					, "w"					, encoding="utf-8"      ) as fp:
         fp.write("\n".join(UpperCAmelCase_      )      )


     def 				_A       (				self       :	List[str]					, **UpperCAmelCase_       :	Optional[Any]      ):
       kwargs.update(self.special_tokens_map      )
       return self.tokenizer_class.from_pretrained(self.tmpdirname					, **UpperCAmelCase_      )


     def 				_A       (				self       :	Optional[Any]					, **UpperCAmelCase_       :	List[str]      ):
       kwargs.update(self.special_tokens_map      )
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					, **UpperCAmelCase_      )


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	Any      ):
       return "lower newer", "lower newer"


     @cached_property
     def 				_A       (				self       :	List[Any]      ):
       return MvpTokenizer.from_pretrained("RUCAIBox/mvp"      )


     @cached_property
     def 				_A       (				self       :	Optional[int]      ):
       return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"      )


     @require_torch
     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    ["A long paragraph for summarization.", "Another paragraph for summarization."]
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    tokenizer(UpperCAmelCase_					, max_length=len(UpperCAmelCase_      )					, padding=UpperCAmelCase_					, return_tensors="pt"      )
         self.assertIsInstance(UpperCAmelCase_					, UpperCAmelCase_      )

         self.assertEqual((2, 9)					, batch.input_ids.shape      )
         self.assertEqual((2, 9)					, batch.attention_mask.shape      )
         SCREAMING_SNAKE_CASE       :      List[str]							=    batch.input_ids.tolist()[0]
         self.assertListEqual(UpperCAmelCase_					, UpperCAmelCase_      )
         # Test that special tokens are reset


     @require_torch
     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    ["A long paragraph for summarization.", "Another paragraph for summarization."]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         SCREAMING_SNAKE_CASE       :      Tuple							=    tokenizer(UpperCAmelCase_					, padding=UpperCAmelCase_					, return_tensors="pt"      )
         # check if input_ids are returned and no labels
         self.assertIn("input_ids"					, UpperCAmelCase_      )
         self.assertIn("attention_mask"					, UpperCAmelCase_      )
         self.assertNotIn("labels"					, UpperCAmelCase_      )
         self.assertNotIn("decoder_attention_mask"					, UpperCAmelCase_      )


     @require_torch
     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    [
           "Summary of the text.",
           "Another summary.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tokenizer(text_target=UpperCAmelCase_					, max_length=32					, padding="max_length"					, return_tensors="pt"      )
         self.assertEqual(32					, targets["input_ids"].shape[1]      )


     @require_torch
     def 				_A       (				self       :	List[str]      ):
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tokenizer(
             ["I am a small frog" * 1024, "I am a small frog"]					, padding=UpperCAmelCase_					, truncation=UpperCAmelCase_					, return_tensors="pt"      )
         self.assertIsInstance(UpperCAmelCase_					, UpperCAmelCase_      )
         self.assertEqual(batch.input_ids.shape					, (2, 1024)      )


     @require_torch
     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    ["A long paragraph for summarization."]
       SCREAMING_SNAKE_CASE       :      Tuple							=    [
           "Summary of the text.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tokenizer(UpperCAmelCase_					, text_target=UpperCAmelCase_					, return_tensors="pt"      )
         SCREAMING_SNAKE_CASE       :      Tuple							=    inputs["input_ids"]
         SCREAMING_SNAKE_CASE       :      List[str]							=    inputs["labels"]
         self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
         self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
         self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
         self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )


     def 				_A       (				self       :	List[str]      ):
       pass






     def 				_A       (				self       :	Any      ):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
           SCREAMING_SNAKE_CASE       :      Tuple							=    self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_					, **UpperCAmelCase_      )
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.tokenizer_class.from_pretrained(UpperCAmelCase_					, **UpperCAmelCase_      )
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    "A, <mask> AllenNLP sentence."
           SCREAMING_SNAKE_CASE       :      List[str]							=    tokenizer_r.encode_plus(UpperCAmelCase_					, add_special_tokens=UpperCAmelCase_					, return_token_type_ids=UpperCAmelCase_      )
           SCREAMING_SNAKE_CASE       :      str							=    tokenizer_p.encode_plus(UpperCAmelCase_					, add_special_tokens=UpperCAmelCase_					, return_token_type_ids=UpperCAmelCase_      )

           # token_type_ids should put 0 everywhere
           self.assertEqual(sum(tokens_r["token_type_ids"]      )					, sum(tokens_p["token_type_ids"]      )      )

           # attention_mask should put 1 everywhere, so sum over length should be 1
           self.assertEqual(
               sum(tokens_r["attention_mask"]      ) / len(tokens_r["attention_mask"]      )					, sum(tokens_p["attention_mask"]      ) / len(tokens_p["attention_mask"]      )					, )

           SCREAMING_SNAKE_CASE       :      Any							=    tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]      )
           SCREAMING_SNAKE_CASE       :      str							=    tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]      )

           # Rust correctly handles the space before the mask while python doesnt
           self.assertSequenceEqual(tokens_p["input_ids"]					, [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )
           self.assertSequenceEqual(tokens_r["input_ids"]					, [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )

           self.assertSequenceEqual(
               UpperCAmelCase_					, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )
           self.assertSequenceEqual(
               UpperCAmelCase_					, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )














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






