
import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch







class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Union[str, Any]							 =       JukeboxTokenizer
	_UpperCamelCase     :     Optional[int]							 =       {
	    """artist""": """Zac Brown Band""",
	    """genres""": """Country""",
	    """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
	}





	@require_torch
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		import torch

		lowercase       =    JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics'       )
		lowercase       =    tokenizer(**self.metas       )['input_ids']
		# fmt: off
		lowercase       =    [
		    torch.tensor([[
		        0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
		        76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
		        44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
		        47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
		        76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
		        30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
		        27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
		        45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
		        41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
		        19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
		        76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
		        76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
		        64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
		        30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
		        27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
		        34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
		        27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
		        41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
		        76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
		        44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
		        76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
		        32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
		        40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
		        20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
		        45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
		        31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
		        45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
		        76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
		        34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
		        31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
		        40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
		        38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
		        78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
		        76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
		        41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
		        27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
		        46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
		        76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
		        41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
		        46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
		        41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
		        78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
		        40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
		        27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
		        76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
		        76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
		        41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
		        76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
		        27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
		        78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
		        34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
		        44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
		        40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
		        78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
		        46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
		        38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
		        40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
		        27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
		        20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
		        76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
		        76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
		        76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
		        76, 76]]       ),
		    torch.tensor([[0, 0, 0, 1069, 11]]       ),
		    torch.tensor([[0, 0, 0, 1069, 11]]       ),
		]
		# fmt: on
		self.assertTrue(torch.allclose(tokens[0]		,    EXPECTED_OUTPUT[0]       )       )
		self.assertTrue(torch.allclose(tokens[1]		,    EXPECTED_OUTPUT[1]       )       )
		self.assertTrue(torch.allclose(tokens[2]		,    EXPECTED_OUTPUT[2]       )       )





	@require_torch
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		import torch

		lowercase       =    JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics'       )
		lowercase       =    tokenizer(**self.metas       )['input_ids']
		# fmt: off
		lowercase       =    [
		    torch.tensor([[
		        0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
		        31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
		        31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
		        40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
		        79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
		        77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
		        27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
		        37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
		        32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
		        77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
		        77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
		        77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
		        46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
		        77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
		        77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
		        77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
		        77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
		        64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
		        40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
		        40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
		        38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
		        31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
		        41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
		        77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
		        46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
		        41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
		        31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
		        31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
		        23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
		        44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
		        31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
		        38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
		        40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
		        77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
		        27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
		        31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
		        34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
		        31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
		        1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
		        31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
		        45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
		        31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
		        77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
		        15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
		        11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
		        45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
		        41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
		        44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
		        46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
		        27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
		        77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
		        35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
		        77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
		        31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
		        77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
		        41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
		        77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
		        40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
		        77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
		        77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
		        27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
		        77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
		        77, 77, 77, 77, 77, 77]]       ),
		    torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]       ),
		    torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]       ),
		]
		# fmt: on
		self.assertTrue(torch.allclose(tokens[0]		,    EXPECTED_OUTPUT[0]       )       )
		self.assertTrue(torch.allclose(tokens[1]		,    EXPECTED_OUTPUT[1]       )       )
		self.assertTrue(torch.allclose(tokens[2]		,    EXPECTED_OUTPUT[2]       )       )



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        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    tempfile.mkdtemp()

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

		lowercase       =    {
		    '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_145_466, 0.4_578_275, 0.40_821_073],
		    'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
		    'do_convert_rgb': True,
		}
		lowercase       =    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 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return BertTokenizer.from_pretrained(self.tmpdirname		,    **snake_case       )





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





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





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





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

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

		return image_inputs





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_tokenizer()
		lowercase       =    self.get_rust_tokenizer()
		lowercase       =    self.get_image_processor()

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

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

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

		lowercase       =    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 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

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

		lowercase       =    self.prepare_image_inputs()

		lowercase       =    image_processor(snake_case		,    return_tensors='np'       )
		lowercase       =    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 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

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

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

		lowercase       =    processor(text=snake_case       )

		lowercase       =    tokenizer(snake_case       )

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

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

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

		lowercase       =    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 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

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

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

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

		self.assertListEqual(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

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

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

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

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

