
"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor



@require_vision
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   tempfile.mkdtemp()

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [
											    """[UNK]""",
											    """[CLS]""",
											    """[SEP]""",
											    """[PAD]""",
											    """[MASK]""",
											    """的""",
											    """价""",
											    """格""",
											    """是""",
											    """15""",
											    """便""",
											    """alex""",
											    """##andra""",
											    """，""",
											    """。""",
											    """-""",
											    """t""",
											    """shirt""",
											]
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES["""vocab_file"""]    )
											with open(self.vocab_file   ,       """w"""   ,       encoding="""utf-8"""    ) as vocab_writer:
																		vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {
											    """do_resize""": True,
											    """size""": {"""height""": 2_24, """width""": 2_24},
											    """do_center_crop""": True,
											    """crop_size""": {"""height""": 18, """width""": 18},
											    """do_normalize""": True,
											    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
											    """image_std""": [0.26862954, 0.26130258, 0.27577711],
											    """do_convert_rgb""": True,
											}
											SCREAMING_SNAKE_CASE__						:       Tuple           =   os.path.join(self.tmpdirname   ,       SCREAMING_SNAKE_CASE__    )
											with open(self.image_processor_file   ,       """w"""   ,       encoding="""utf-8"""    ) as fp:
																		json.dump(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return BertTokenizer.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											return BertTokenizerFast.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											shutil.rmtree(self.tmpdirname    )



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   [np.random.randint(2_55   ,       size=(3, 30, 4_00)   ,       dtype=np.uinta    )]

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__   ,       0   ,       -1    )    ) for x in image_inputs]

											return image_inputs



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_rust_tokenizer()
											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.get_image_processor()

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )
											processor_slow.save_pretrained(self.tmpdirname    )
											SCREAMING_SNAKE_CASE__						:       str           =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname   ,       use_fast=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )
											processor_fast.save_pretrained(self.tmpdirname    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname    )

											self.assertEqual(processor_slow.tokenizer.get_vocab()   ,       tokenizer_slow.get_vocab()    )
											self.assertEqual(processor_fast.tokenizer.get_vocab()   ,       tokenizer_fast.get_vocab()    )
											self.assertEqual(tokenizer_slow.get_vocab()   ,       tokenizer_fast.get_vocab()    )
											self.assertIsInstance(processor_slow.tokenizer   ,       SCREAMING_SNAKE_CASE__    )
											self.assertIsInstance(processor_fast.tokenizer   ,       SCREAMING_SNAKE_CASE__    )

											self.assertEqual(processor_slow.image_processor.to_json_string()   ,       image_processor.to_json_string()    )
											self.assertEqual(processor_fast.image_processor.to_json_string()   ,       image_processor.to_json_string()    )
											self.assertIsInstance(processor_slow.image_processor   ,       SCREAMING_SNAKE_CASE__    )
											self.assertIsInstance(processor_fast.image_processor   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer()   ,       image_processor=self.get_image_processor()    )
											processor.save_pretrained(self.tmpdirname    )

											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_tokenizer(cls_token="""(CLS)"""   ,       sep_token="""(SEP)"""    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Tuple           =   ChineseCLIPProcessor.from_pretrained(
											    self.tmpdirname   ,       cls_token="""(CLS)"""   ,       sep_token="""(SEP)"""   ,       do_normalize=SCREAMING_SNAKE_CASE__    )

											self.assertEqual(processor.tokenizer.get_vocab()   ,       tokenizer_add_kwargs.get_vocab()    )
											self.assertIsInstance(processor.tokenizer   ,       SCREAMING_SNAKE_CASE__    )

											self.assertEqual(processor.image_processor.to_json_string()   ,       image_processor_add_kwargs.to_json_string()    )
											self.assertIsInstance(processor.image_processor   ,       SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       Tuple           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__						:       Dict           =   image_processor(SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    )

											for key in input_feat_extract.keys():
																		self.assertAlmostEqual(input_feat_extract[key].sum()   ,       input_processor[key].sum()   ,       delta=1E-2    )



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   """Alexandra，T-shirt的价格是15便士。"""

											SCREAMING_SNAKE_CASE__						:       Tuple           =   processor(text=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       str           =   tokenizer(SCREAMING_SNAKE_CASE__    )

											for key in encoded_tok.keys():
																		self.assertListEqual(encoded_tok[key]   ,       encoded_processor[key]    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       int           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """Alexandra，T-shirt的价格是15便士。"""
											SCREAMING_SNAKE_CASE__						:       Any           =   self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   processor(text=SCREAMING_SNAKE_CASE__   ,       images=SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(list(inputs.keys()    )   ,       ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]    )

											# test if it raises when no input is passed
											with pytest.raises(SCREAMING_SNAKE_CASE__    ):
																		processor()



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       str           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       int           =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											SCREAMING_SNAKE_CASE__						:       Any           =   processor.batch_decode(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   tokenizer.batch_decode(SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       str           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       str           =   """Alexandra，T-shirt的价格是15便士。"""
											SCREAMING_SNAKE_CASE__						:       str           =   self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   processor(text=SCREAMING_SNAKE_CASE__   ,       images=SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(list(inputs.keys()    )   ,       processor.model_input_names    )









A__  :  Any								=					'''Tobias Carryer'''

from time import time



class 				__snake_case						:




							def __init__(       self     :       Any				,				A_     :       Tuple				,				A_     :       Dict				,				A_     :       Tuple				,				A_     :       str=int(time())):  # noqa: B008
									lowerCAmelCase_			:     int									= multiplier
									lowerCAmelCase_			:     int									= increment
									lowerCAmelCase_			:     str									= modulo
									lowerCAmelCase_			:     str									= seed







							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Optional[int]									= (self.multiplier * self.seed + self.increment) % self.modulo
									return self.seed


if __name__ == "__main__":
						# Show the LCG in action.
						A__  :  Union[str, Any]								=					LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
						while True:
												print(lcg.next_number())





