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

from ...utils import logging


_SCREAMING_SNAKE_CASE      						=	logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_						(	snake_case_						):
   __lowerCAmelCase      =							CLIPConfig

   __lowerCAmelCase      =							["""CLIPEncoderLayer"""]







   def __init__(			self		:		Tuple					,					lowerCamelCase_		:		CLIPConfig							):





    """simple docstring"""


    super().__init__(lowerCamelCase_							)

    UpperCamelCase          =	CLIPVisionModelWithProjection(config.vision_config							)

    UpperCamelCase          =	nn.Linear(config.vision_config.projection_dim					,					1							)
    UpperCamelCase          =	nn.Linear(config.vision_config.projection_dim					,					1							)







   @torch.no_grad()
   def 		lowerCamelCase_     (			self		:		int					,					lowerCamelCase_		:		Union[str, Any]					,					lowerCamelCase_		:		int					,					lowerCamelCase_		:		Any=0.5					,					lowerCamelCase_		:		str=0.5							):





    """simple docstring"""


    UpperCamelCase          =	self.vision_model(lowerCamelCase_							)[0]

    UpperCamelCase          =	self.p_head(lowerCamelCase_							)
    UpperCamelCase          =	nsfw_detected.flatten()
    UpperCamelCase          =	nsfw_detected > p_threshold
    UpperCamelCase          =	nsfw_detected.tolist()

    if any(lowerCamelCase_							):
     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(lowerCamelCase_							):
     if nsfw_detected_:
      UpperCamelCase          =	np.zeros(images[idx].shape							)

    UpperCamelCase          =	self.w_head(lowerCamelCase_							)
    UpperCamelCase          =	watermark_detected.flatten()
    UpperCamelCase          =	watermark_detected > w_threshold
    UpperCamelCase          =	watermark_detected.tolist()

    if any(lowerCamelCase_							):
     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(lowerCamelCase_							):
     if watermark_detected_:
      UpperCamelCase          =	np.zeros(images[idx].shape							)

    return images, nsfw_detected, watermark_detected

import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 		_lowercase						(					snake_case_						,      unittest.TestCase       ):
 lowercase				     =   XLMTokenizer
 lowercase				     =   False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  UpperCamelCase_					:						List[Any] =					[
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      'w</w>',
      'r</w>',
      't</w>',
      'lo',
      'low',
      'er</w>',
      'low</w>',
      'lowest</w>',
      'newer</w>',
      'wider</w>',
      '<unk>',
  ]
  UpperCamelCase_					:						str =					dict(zip(snake_case   ,      range(len(snake_case			)			)			)			)
  UpperCamelCase_					:						Tuple =					['l o 123', 'lo w 1456', 'e r</w> 1789', '']

  UpperCamelCase_					:						Tuple =					os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['vocab_file']			)
  UpperCamelCase_					:						Any =					os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['merges_file']			)
  with open(self.vocab_file   ,      'w'			) as fp:
   fp.write(json.dumps(snake_case			)			)
  with open(self.merges_file   ,      'w'			) as fp:
   fp.write('\n'.join(snake_case			)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'lower newer'
  UpperCamelCase_					:						Optional[int] =					'lower newer'
  return input_text, output_text

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer(self.vocab_file   ,      self.merges_file			)

  UpperCamelCase_					:						List[str] =					'lower'
  UpperCamelCase_					:						Optional[int] =					['low', 'er</w>']
  UpperCamelCase_					:						Optional[Any] =					tokenizer.tokenize(snake_case			)
  self.assertListEqual(snake_case   ,      snake_case			)

  UpperCamelCase_					:						List[Any] =					tokens + ['<unk>']
  UpperCamelCase_					:						int =					[1_4, 1_5, 2_0]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case			)   ,      snake_case			)


 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer.from_pretrained('xlm-mlm-en-2048'			)

  UpperCamelCase_					:						int =					tokenizer.encode('sequence builders'   ,      add_special_tokens=snake_case			)
  UpperCamelCase_					:						Optional[int] =					tokenizer.encode('multi-sequence build'   ,      add_special_tokens=snake_case			)

  UpperCamelCase_					:						Tuple =					tokenizer.build_inputs_with_special_tokens(snake_case			)
  UpperCamelCase_					:						Any =					tokenizer.build_inputs_with_special_tokens(snake_case   ,      snake_case			)

  assert encoded_sentence == [0] + text + [1]
  assert encoded_pair == [0] + text + [1] + text_a + [1]



