



"""simple docstring"""






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
		from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase			:			Tuple										=						get_tests_dir("fixtures/test_sentencepiece.model")

_lowercase			:			Optional[int]										=						{"target_lang": "fi", "source_lang": "en"}
_lowercase			:			Optional[Any]										=						">>zh<<"
_lowercase			:			List[Any]										=						"Helsinki-NLP/"

if is_torch_available():
		_lowercase			:			Union[str, Any]										=						"pt"
elif is_tf_available():
		_lowercase			:			Optional[Any]										=						"tf"
else:
		_lowercase			:			Union[str, Any]										=						"jax"

@require_sentencepiece
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       unittest.TestCase       ):


					'''simple docstring'''





					_a             =							MarianTokenizer
					_a             =							False
					_a             =							True



					def 		snake_case	(	self							:  Tuple					)-> Dict:
								super().setUp()
								lowerCamelCase__		:		str		     =['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
								lowerCamelCase__		:		Dict		     =dict(zip(lowerCamelCase,  range(len(lowerCamelCase					)					)					)					)
								lowerCamelCase__		:		int		     =Path(self.tmpdirname					)
								save_json(lowerCamelCase,  save_dir / VOCAB_FILES_NAMES['''vocab''']					)
								save_json(lowerCamelCase,  save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file''']					)
								if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
											copyfile(lowerCamelCase,  save_dir / VOCAB_FILES_NAMES['''source_spm''']					)
											copyfile(lowerCamelCase,  save_dir / VOCAB_FILES_NAMES['''target_spm''']					)

								lowerCamelCase__		:		Union[str, Any]		     =MarianTokenizer.from_pretrained(self.tmpdirname					)
								tokenizer.save_pretrained(self.tmpdirname					)



					def 		snake_case	(	self							:  str,  **lowerCamelCase							:  Optional[int]					)-> MarianTokenizer:
								return MarianTokenizer.from_pretrained(self.tmpdirname,  **lowerCamelCase					)



					def 		snake_case	(	self							:  Tuple,  lowerCamelCase							:  Dict					)-> Union[str, Any]:
								return (
								    "This is a test",
								    "This is a test",
								)



					def 		snake_case	(	self							:  int					)-> Any:
								lowerCamelCase__		:		int		     ='''</s>'''
								lowerCamelCase__		:		List[Any]		     =0

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



					def 		snake_case	(	self							:  Union[str, Any]					)-> Optional[int]:
								lowerCamelCase__		:		List[str]		     =list(self.get_tokenizer().get_vocab().keys()					)

								self.assertEqual(vocab_keys[0],  '''</s>'''					)
								self.assertEqual(vocab_keys[1],  '''<unk>'''					)
								self.assertEqual(vocab_keys[-1],  '''<pad>'''					)
								self.assertEqual(len(lowerCamelCase					),  9					)



					def 		snake_case	(	self							:  List[Any]					)-> List[Any]:
								self.assertEqual(self.get_tokenizer().vocab_size,  9					)



					def 		snake_case	(	self							:  Union[str, Any]					)-> Tuple:
								lowerCamelCase__		:		Optional[int]		     =MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''					)
								lowerCamelCase__		:		Dict		     =en_de_tokenizer(['''I am a small frog'''],  return_tensors=lowerCamelCase					)
								self.assertIsInstance(lowerCamelCase,  lowerCamelCase					)
								lowerCamelCase__		:		Union[str, Any]		     =[38, 121, 14, 697, 3_8848, 0]
								self.assertListEqual(lowerCamelCase,  batch.input_ids[0]					)

								lowerCamelCase__		:		int		     =tempfile.mkdtemp()
								en_de_tokenizer.save_pretrained(lowerCamelCase					)
								lowerCamelCase__		:		int		     =[x.name for x in Path(lowerCamelCase					).glob('''*'''					)]
								self.assertIn('''source.spm''',  lowerCamelCase					)
								MarianTokenizer.from_pretrained(lowerCamelCase					)



					def 		snake_case	(	self							:  int					)-> Dict:
								lowerCamelCase__		:		List[str]		     =self.get_tokenizer()

								lowerCamelCase__		:		Optional[Any]		     =tok(
								    ['''I am a small frog''' * 1000, '''I am a small frog'''],  padding=lowerCamelCase,  truncation=lowerCamelCase,  return_tensors=lowerCamelCase					)
								self.assertIsInstance(lowerCamelCase,  lowerCamelCase					)
								self.assertEqual(batch.input_ids.shape,  (2, 512)					)



					def 		snake_case	(	self							:  str					)-> str:
								lowerCamelCase__		:		List[Any]		     =self.get_tokenizer()
								lowerCamelCase__		:		Dict		     =tok(['''I am a tiny frog''', '''I am a small frog'''],  padding=lowerCamelCase,  return_tensors=lowerCamelCase					)
								self.assertIsInstance(lowerCamelCase,  lowerCamelCase					)
								self.assertEqual(batch_smaller.input_ids.shape,  (2, 10)					)



					@slow
					def 		snake_case	(	self							:  Dict					)-> int:
								# fmt: off
								lowerCamelCase__		:		str		     ={'''input_ids''': [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], '''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, 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], [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, 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=lowerCamelCase,  model_name='''Helsinki-NLP/opus-mt-en-de''',  revision='''1a8c2263da11e68e50938f97e10cd57820bd504c''',  decode_kwargs={'''use_source_tokenizer''': True},  )




					def 		snake_case	(	self							:  Optional[int]					)-> Dict:
								lowerCamelCase__		:		Dict		     =MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''					)

								lowerCamelCase__		:		List[str]		     ='''Tämä on testi'''
								lowerCamelCase__		:		List[Any]		     ='''This is a test'''

								lowerCamelCase__		:		List[Any]		     =[76, 7, 2047, 2]
								lowerCamelCase__		:		Optional[int]		     =[69, 12, 11, 940, 2]

								lowerCamelCase__		:		int		     =tokenizer(lowerCamelCase					).input_ids
								self.assertListEqual(lowerCamelCase,  lowerCamelCase					)

								lowerCamelCase__		:		Optional[int]		     =tokenizer(text_target=lowerCamelCase					).input_ids
								self.assertListEqual(lowerCamelCase,  lowerCamelCase					)

								lowerCamelCase__		:		Any		     =tokenizer.decode(lowerCamelCase,  skip_special_tokens=lowerCamelCase					)
								self.assertEqual(lowerCamelCase,  lowerCamelCase					)









"""simple docstring"""






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

from ...utils import logging


_lowercase			:			int										=						logging.get_logger(__name__)

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							CLIPConfig

					_a             =							['CLIPEncoderLayer']



					def __init__(	self							:  int,  lowerCamelCase							:  CLIPConfig					)-> List[Any]:
								super().__init__(lowerCamelCase					)

								lowerCamelCase__		:		Dict		     =CLIPVisionModelWithProjection(config.vision_config					)

								lowerCamelCase__		:		Dict		     =nn.Linear(config.vision_config.projection_dim,  1					)
								lowerCamelCase__		:		Union[str, Any]		     =nn.Linear(config.vision_config.projection_dim,  1					)




					@torch.no_grad()
					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  List[Any]=0.5,  lowerCamelCase							:  Optional[Any]=0.5					)-> Optional[int]:
								lowerCamelCase__		:		Dict		     =self.vision_model(lowerCamelCase					)[0]

								lowerCamelCase__		:		List[Any]		     =self.p_head(lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =nsfw_detected.flatten()
								lowerCamelCase__		:		Any		     =nsfw_detected > p_threshold
								lowerCamelCase__		:		Optional[Any]		     =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_:
														lowerCamelCase__		:		Tuple		     =np.zeros(images[idx].shape					)

								lowerCamelCase__		:		str		     =self.w_head(lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =watermark_detected.flatten()
								lowerCamelCase__		:		Optional[Any]		     =watermark_detected > w_threshold
								lowerCamelCase__		:		int		     =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_:
														lowerCamelCase__		:		Optional[int]		     =np.zeros(images[idx].shape					)

								return images, nsfw_detected, watermark_detected




