




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     ):



		def        SCREAMING_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''': [0.48_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,
								}
								lowerCAmelCase_      			=  os.path.join(self.tmpdirname,	UpperCamelCase__   )
								with open(self.image_processor_file,	'''w''',	encoding='''utf-8'''   ) as fp:
														json.dump(UpperCamelCase__,	UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



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



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



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



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								shutil.rmtree(self.tmpdirname   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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

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

								return image_inputs



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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

								lowerCAmelCase_      			=  ChineseCLIPProcessor(tokenizer=UpperCamelCase__,	image_processor=UpperCamelCase__   )
								processor_slow.save_pretrained(self.tmpdirname   )
								lowerCAmelCase_      			=  ChineseCLIPProcessor.from_pretrained(self.tmpdirname,	use_fast=UpperCamelCase__   )

								lowerCAmelCase_      			=  ChineseCLIPProcessor(tokenizer=UpperCamelCase__,	image_processor=UpperCamelCase__   )
								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,	UpperCamelCase__   )
								self.assertIsInstance(processor_fast.tokenizer,	UpperCamelCase__   )

								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,	UpperCamelCase__   )
								self.assertIsInstance(processor_fast.image_processor,	UpperCamelCase__   )



		def        SCREAMING_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=UpperCamelCase__   )

								lowerCAmelCase_      			=  ChineseCLIPProcessor.from_pretrained(
								    self.tmpdirname,	cls_token='''(CLS)''',	sep_token='''(SEP)''',	do_normalize=UpperCamelCase__   )

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

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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.get_image_processor()
								lowerCAmelCase_      			=  self.get_tokenizer()

								lowerCAmelCase_      			=  ChineseCLIPProcessor(tokenizer=UpperCamelCase__,	image_processor=UpperCamelCase__   )

								lowerCAmelCase_      			=  self.prepare_image_inputs()

								lowerCAmelCase_      			=  image_processor(UpperCamelCase__,	return_tensors='''np'''   )
								lowerCAmelCase_      			=  processor(images=UpperCamelCase__,	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   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.get_image_processor()
								lowerCAmelCase_      			=  self.get_tokenizer()

								lowerCAmelCase_      			=  ChineseCLIPProcessor(tokenizer=UpperCamelCase__,	image_processor=UpperCamelCase__   )

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

								lowerCAmelCase_      			=  processor(text=UpperCamelCase__   )

								lowerCAmelCase_      			=  tokenizer(UpperCamelCase__   )

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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.get_image_processor()
								lowerCAmelCase_      			=  self.get_tokenizer()

								lowerCAmelCase_      			=  ChineseCLIPProcessor(tokenizer=UpperCamelCase__,	image_processor=UpperCamelCase__   )

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

								lowerCAmelCase_      			=  processor(text=UpperCamelCase__,	images=UpperCamelCase__   )

								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(UpperCamelCase__   ):
														processor()



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.get_image_processor()
								lowerCAmelCase_      			=  self.get_tokenizer()

								lowerCAmelCase_      			=  ChineseCLIPProcessor(tokenizer=UpperCamelCase__,	image_processor=UpperCamelCase__   )

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

								lowerCAmelCase_      			=  processor.batch_decode(UpperCamelCase__   )
								lowerCAmelCase_      			=  tokenizer.batch_decode(UpperCamelCase__   )

								self.assertListEqual(UpperCamelCase__,	UpperCamelCase__   )







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.get_image_processor()
								lowerCAmelCase_      			=  self.get_tokenizer()

								lowerCAmelCase_      			=  ChineseCLIPProcessor(tokenizer=UpperCamelCase__,	image_processor=UpperCamelCase__   )

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

								lowerCAmelCase_      			=  processor(text=UpperCamelCase__,	images=UpperCamelCase__   )

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










from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel




