


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast



@require_vision
class        a					(			unittest.TestCase   ):





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				str     =	tempfile.mkdtemp()

        __lowerCamelCase:				Optional[Any]     =	BlipImageProcessor()
        __lowerCamelCase:				List[str]     =	GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)

        __lowerCamelCase:				Tuple     =	BlipaProcessor(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

        processor.save_pretrained(self.tmpdirname							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	**SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        return AutoProcessor.from_pretrained(self.tmpdirname    ,	**SCREAMING_SNAKE_CASE_							).tokenizer





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	**SCREAMING_SNAKE_CASE_					:				Dict							):
        return AutoProcessor.from_pretrained(self.tmpdirname    ,	**SCREAMING_SNAKE_CASE_							).image_processor





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        shutil.rmtree(self.tmpdirname							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Dict     =	[np.random.randint(255    ,	size=(3, 30, 400)    ,	dtype=np.uinta							)]

        __lowerCamelCase:				List[str]     =	[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_    ,	0    ,	-1							)							) for x in image_inputs]

        return image_inputs





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				Union[str, Any]     =	BlipaProcessor(tokenizer=self.get_tokenizer()    ,	image_processor=self.get_image_processor()							)
        processor.save_pretrained(self.tmpdirname							)

        __lowerCamelCase:				Optional[Any]     =	self.get_tokenizer(bos_token="""(BOS)"""    ,	eos_token="""(EOS)"""							)
        __lowerCamelCase:				List[str]     =	self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_    ,	padding_value=1.0							)

        __lowerCamelCase:				List[Any]     =	BlipaProcessor.from_pretrained(
            self.tmpdirname    ,	bos_token="""(BOS)"""    ,	eos_token="""(EOS)"""    ,	do_normalize=SCREAMING_SNAKE_CASE_    ,	padding_value=1.0							)

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

        __lowerCamelCase:				Any     =	BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,	image_processor=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				int     =	self.prepare_image_inputs()

        __lowerCamelCase:				Any     =	image_processor(SCREAMING_SNAKE_CASE_    ,	return_tensors="""np"""							)
        __lowerCamelCase:				Any     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				List[str]     =	self.get_image_processor()
        __lowerCamelCase:				Dict     =	self.get_tokenizer()

        __lowerCamelCase:				Dict     =	BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,	image_processor=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				int     =	"""lower newer"""

        __lowerCamelCase:				Dict     =	processor(text=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				str     =	tokenizer(SCREAMING_SNAKE_CASE_    ,	return_token_type_ids=SCREAMING_SNAKE_CASE_							)

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase:				Dict     =	self.get_image_processor()
        __lowerCamelCase:				List[Any]     =	self.get_tokenizer()

        __lowerCamelCase:				str     =	BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,	image_processor=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	"""lower newer"""
        __lowerCamelCase:				Union[str, Any]     =	self.prepare_image_inputs()

        __lowerCamelCase:				Optional[int]     =	processor(text=SCREAMING_SNAKE_CASE_    ,	images=SCREAMING_SNAKE_CASE_							)

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

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





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

        __lowerCamelCase:				int     =	BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,	image_processor=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __lowerCamelCase:				Dict     =	processor.batch_decode(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_							)

        self.assertListEqual(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				List[str]     =	self.get_image_processor()
        __lowerCamelCase:				Tuple     =	self.get_tokenizer()

        __lowerCamelCase:				Tuple     =	BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,	image_processor=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	"""lower newer"""
        __lowerCamelCase:				Tuple     =	self.prepare_image_inputs()

        __lowerCamelCase:				Optional[int]     =	processor(text=SCREAMING_SNAKE_CASE_    ,	images=SCREAMING_SNAKE_CASE_							)

        # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
        self.assertListEqual(list(inputs.keys()							)    ,	["""pixel_values""", """input_ids""", """attention_mask"""]							)






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
   _A		:				str									=       argparse.ArgumentParser()

   parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

   parser.add_argument(
       '''--txt2img_unclip''',
       default='''kakaobrain/karlo-v1-alpha''',
       type=str,
       required=False,
       help='''The pretrained txt2img unclip.''',
   )

   _A		:				Optional[Any]									=       parser.parse_args()

   _A		:				str									=       UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

   _A		:				Optional[Any]									=       CLIPImageProcessor()
   _A		:				List[Any]									=       CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

   _A		:				str									=       UnCLIPImageVariationPipeline(
       decoder=txtaimg.decoder,
       text_encoder=txtaimg.text_encoder,
       tokenizer=txtaimg.tokenizer,
       text_proj=txtaimg.text_proj,
       feature_extractor=feature_extractor,
       image_encoder=image_encoder,
       super_res_first=txtaimg.super_res_first,
       super_res_last=txtaimg.super_res_last,
       decoder_scheduler=txtaimg.decoder_scheduler,
       super_res_scheduler=txtaimg.super_res_scheduler,
   )

   imgaimg.save_pretrained(args.dump_path)


