



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       ):




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    tempfile.mkdtemp()

									_lowerCAmelCase						=    [
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """[PAD]""",
									    """[MASK]""",
									    """的""",
									    """价""",
									    """格""",
									    """是""",
									    """15""",
									    """便""",
									    """alex""",
									    """##andra""",
									    """，""",
									    """。""",
									    """-""",
									    """t""",
									    """shirt""",
									]
									_lowerCAmelCase						=    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]		)		)

									_lowerCAmelCase						=    {
									    """do_resize""": True,
									    """size""": {"""height""": 224, """width""": 224},
									    """do_center_crop""": True,
									    """crop_size""": {"""height""": 18, """width""": 18},
									    """do_normalize""": True,
									    """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
									    """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
									    """do_convert_rgb""": True,
									}
									_lowerCAmelCase						=    os.path.join(self.tmpdirname						,				_snake_case		)
									with open(self.image_processor_file						,				"""w"""						,				encoding="""utf-8"""		) as fp:
														json.dump(_snake_case						,				_snake_case		)




				def 							snake_case   (							self						,				**_snake_case		):



									"""simple docstring"""





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




				def 							snake_case   (							self						,				**_snake_case		):



									"""simple docstring"""





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




				def 							snake_case   (							self						,				**_snake_case		):



									"""simple docstring"""





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




				def 							snake_case   (							self		):



									"""simple docstring"""





									shutil.rmtree(self.tmpdirname		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    [np.random.randint(255						,				size=(3, 30, 400)						,				dtype=np.uinta		)]

									_lowerCAmelCase						=    [Image.fromarray(np.moveaxis(_snake_case						,				0						,				-1		)		) for x in image_inputs]

									return image_inputs




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.get_tokenizer()
									_lowerCAmelCase						=    self.get_rust_tokenizer()
									_lowerCAmelCase						=    self.get_image_processor()

									_lowerCAmelCase						=    ChineseCLIPProcessor(tokenizer=_snake_case						,				image_processor=_snake_case		)
									processor_slow.save_pretrained(self.tmpdirname		)
									_lowerCAmelCase						=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname						,				use_fast=_snake_case		)

									_lowerCAmelCase						=    ChineseCLIPProcessor(tokenizer=_snake_case						,				image_processor=_snake_case		)
									processor_fast.save_pretrained(self.tmpdirname		)
									_lowerCAmelCase						=    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						,				_snake_case		)
									self.assertIsInstance(processor_fast.tokenizer						,				_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						,				_snake_case		)
									self.assertIsInstance(processor_fast.image_processor						,				_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()						,				image_processor=self.get_image_processor()		)
									processor.save_pretrained(self.tmpdirname		)

									_lowerCAmelCase						=    self.get_tokenizer(cls_token="""(CLS)"""						,				sep_token="""(SEP)"""		)
									_lowerCAmelCase						=    self.get_image_processor(do_normalize=_snake_case		)

									_lowerCAmelCase						=    ChineseCLIPProcessor.from_pretrained(
									    self.tmpdirname						,				cls_token="""(CLS)"""						,				sep_token="""(SEP)"""						,				do_normalize=_snake_case		)

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

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.get_image_processor()
									_lowerCAmelCase						=    self.get_tokenizer()

									_lowerCAmelCase						=    ChineseCLIPProcessor(tokenizer=_snake_case						,				image_processor=_snake_case		)

									_lowerCAmelCase						=    self.prepare_image_inputs()

									_lowerCAmelCase						=    image_processor(_snake_case						,				return_tensors="""np"""		)
									_lowerCAmelCase						=    processor(images=_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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.get_image_processor()
									_lowerCAmelCase						=    self.get_tokenizer()

									_lowerCAmelCase						=    ChineseCLIPProcessor(tokenizer=_snake_case						,				image_processor=_snake_case		)

									_lowerCAmelCase						=    """Alexandra，T-shirt的价格是15便士。"""

									_lowerCAmelCase						=    processor(text=_snake_case		)

									_lowerCAmelCase						=    tokenizer(_snake_case		)

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.get_image_processor()
									_lowerCAmelCase						=    self.get_tokenizer()

									_lowerCAmelCase						=    ChineseCLIPProcessor(tokenizer=_snake_case						,				image_processor=_snake_case		)

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

									_lowerCAmelCase						=    processor(text=_snake_case						,				images=_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(_snake_case		):
														processor()




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.get_image_processor()
									_lowerCAmelCase						=    self.get_tokenizer()

									_lowerCAmelCase						=    ChineseCLIPProcessor(tokenizer=_snake_case						,				image_processor=_snake_case		)

									_lowerCAmelCase						=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									_lowerCAmelCase						=    processor.batch_decode(_snake_case		)
									_lowerCAmelCase						=    tokenizer.batch_decode(_snake_case		)

									self.assertListEqual(_snake_case						,				_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.get_image_processor()
									_lowerCAmelCase						=    self.get_tokenizer()

									_lowerCAmelCase						=    ChineseCLIPProcessor(tokenizer=_snake_case						,				image_processor=_snake_case		)

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

									_lowerCAmelCase						=    processor(text=_snake_case						,				images=_snake_case		)

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









from __future__ import annotations

import numpy as np







def 				lowerCAmelCase_	(     snake_case_     ):
      return np.maximum(0,snake_case_     )


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]

