

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 				lowercase       ( unittest.TestCase    ):




				def 					a							(      self ):
											snake_case_           =					tempfile.mkdtemp()

											snake_case_           =					[
											    '[UNK]',
											    '[CLS]',
											    '[SEP]',
											    '[PAD]',
											    '[MASK]',
											    '的',
											    '价',
											    '格',
											    '是',
											    '15',
											    '便',
											    'alex',
											    '##andra',
											    '，',
											    '。',
											    '-',
											    't',
											    'shirt',
											]
											snake_case_           =					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] ) )

											snake_case_           =					{
											    'do_resize': True,
											    'size': {'height': 224, 'width': 224},
											    'do_center_crop': True,
											    'crop_size': {'height': 18, 'width': 18},
											    'do_normalize': True,
											    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
											    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
											    'do_convert_rgb': True,
											}
											snake_case_           =					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 					a							(      self   ,     **snake_case ):
											return BertTokenizer.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     **snake_case ):
											return BertTokenizerFast.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     **snake_case ):
											return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self ):
											shutil.rmtree(self.tmpdirname )




				def 					a							(      self ):
											snake_case_           =					[np.random.randint(255   ,     size=(3, 30, 400)   ,     dtype=np.uinta )]

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

											return image_inputs




				def 					a							(      self ):
											snake_case_           =					self.get_tokenizer()
											snake_case_           =					self.get_rust_tokenizer()
											snake_case_           =					self.get_image_processor()

											snake_case_           =					ChineseCLIPProcessor(tokenizer=snake_case   ,     image_processor=snake_case )
											processor_slow.save_pretrained(self.tmpdirname )
											snake_case_           =					ChineseCLIPProcessor.from_pretrained(self.tmpdirname   ,     use_fast=snake_case )

											snake_case_           =					ChineseCLIPProcessor(tokenizer=snake_case   ,     image_processor=snake_case )
											processor_fast.save_pretrained(self.tmpdirname )
											snake_case_           =					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 					a							(      self ):
											snake_case_           =					ChineseCLIPProcessor(tokenizer=self.get_tokenizer()   ,     image_processor=self.get_image_processor() )
											processor.save_pretrained(self.tmpdirname )

											snake_case_           =					self.get_tokenizer(cls_token='(CLS)'   ,     sep_token='(SEP)' )
											snake_case_           =					self.get_image_processor(do_normalize=snake_case )

											snake_case_           =					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 					a							(      self ):
											snake_case_           =					self.get_image_processor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					ChineseCLIPProcessor(tokenizer=snake_case   ,     image_processor=snake_case )

											snake_case_           =					self.prepare_image_inputs()

											snake_case_           =					image_processor(snake_case   ,     return_tensors='np' )
											snake_case_           =					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 					a							(      self ):
											snake_case_           =					self.get_image_processor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					ChineseCLIPProcessor(tokenizer=snake_case   ,     image_processor=snake_case )

											snake_case_           =					'Alexandra，T-shirt的价格是15便士。'

											snake_case_           =					processor(text=snake_case )

											snake_case_           =					tokenizer(snake_case )

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




				def 					a							(      self ):
											snake_case_           =					self.get_image_processor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					ChineseCLIPProcessor(tokenizer=snake_case   ,     image_processor=snake_case )

											snake_case_           =					'Alexandra，T-shirt的价格是15便士。'
											snake_case_           =					self.prepare_image_inputs()

											snake_case_           =					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 					a							(      self ):
											snake_case_           =					self.get_image_processor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					ChineseCLIPProcessor(tokenizer=snake_case   ,     image_processor=snake_case )

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

											snake_case_           =					processor.batch_decode(snake_case )
											snake_case_           =					tokenizer.batch_decode(snake_case )

											self.assertListEqual(snake_case   ,     snake_case )




				def 					a							(      self ):
											snake_case_           =					self.get_image_processor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					ChineseCLIPProcessor(tokenizer=snake_case   ,     image_processor=snake_case )

											snake_case_           =					'Alexandra，T-shirt的价格是15便士。'
											snake_case_           =					self.prepare_image_inputs()

											snake_case_           =					processor(text=snake_case   ,     images=snake_case )

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







def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					(
							    first_str_length if first_str_length > second_str_length else second_str_length
							)
							snake_case_           =					[]
							for char_count in range(UpperCamelCase__   ):
														if char_count < first_str_length:
																					output_list.append(first_str[char_count]   )
														if char_count < second_str_length:
																					output_list.append(second_str[char_count]   )
							return "".join(UpperCamelCase__   )


if __name__ == "__main__":
					print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)




