






'''simple docstring'''

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 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      tempfile.mkdtemp()

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

        UpperCamelCase       						=      {
            """do_resize""": True,
            """size""": {"""height""": 224, """width""": 224},
            """do_center_crop""": True,
            """crop_size""": {"""height""": 18, """width""": 18},
            """do_normalize""": True,
            """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
            """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
            """do_convert_rgb""": True,
        }
        UpperCamelCase       						=      os.path.join(self.tmpdirname    ,	_SCREAMING_SNAKE_CASE   )
        with open(self.image_processor_file    ,	"""w"""    ,	encoding="""utf-8"""   ) as fp:
              json.dump(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






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

  def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   str:



        """simple docstring"""






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

  def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   Optional[int]:



        """simple docstring"""






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

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        shutil.rmtree(self.tmpdirname   )

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






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

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

        return image_inputs

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






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

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )
        processor_slow.save_pretrained(self.tmpdirname   )
        UpperCamelCase       						=      ChineseCLIPProcessor.from_pretrained(self.tmpdirname    ,	use_fast=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )
        processor_fast.save_pretrained(self.tmpdirname   )
        UpperCamelCase       						=      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    ,	_SCREAMING_SNAKE_CASE   )
        self.assertIsInstance(processor_fast.tokenizer    ,	_SCREAMING_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    ,	_SCREAMING_SNAKE_CASE   )
        self.assertIsInstance(processor_fast.image_processor    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






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

        UpperCamelCase       						=      self.get_tokenizer(cls_token="""(CLS)"""    ,	sep_token="""(SEP)"""   )
        UpperCamelCase       						=      self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      ChineseCLIPProcessor.from_pretrained(
            self.tmpdirname    ,	cls_token="""(CLS)"""    ,	sep_token="""(SEP)"""    ,	do_normalize=_SCREAMING_SNAKE_CASE   )

        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 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_image_processor()
        UpperCamelCase       						=      self.get_tokenizer()

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.prepare_image_inputs()

        UpperCamelCase       						=      image_processor(_SCREAMING_SNAKE_CASE    ,	return_tensors="""np"""   )
        UpperCamelCase       						=      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 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.get_image_processor()
        UpperCamelCase       						=      self.get_tokenizer()

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )

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

        UpperCamelCase       						=      processor(text=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      tokenizer(_SCREAMING_SNAKE_CASE   )

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

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.get_image_processor()
        UpperCamelCase       						=      self.get_tokenizer()

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )

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

        UpperCamelCase       						=      processor(text=_SCREAMING_SNAKE_CASE    ,	images=_SCREAMING_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(_SCREAMING_SNAKE_CASE   ):
              processor()

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      self.get_image_processor()
        UpperCamelCase       						=      self.get_tokenizer()

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )

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

        UpperCamelCase       						=      processor.batch_decode(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   )

        self.assertListEqual(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )


  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.get_image_processor()
        UpperCamelCase       						=      self.get_tokenizer()

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )

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

        UpperCamelCase       						=      processor(text=_SCREAMING_SNAKE_CASE    ,	images=_SCREAMING_SNAKE_CASE   )

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








'''simple docstring'''

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     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.',
     )

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     SCREAMING_SNAKE_CASE__											=     CLIPImageProcessor()
     SCREAMING_SNAKE_CASE__											=     CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

     SCREAMING_SNAKE_CASE__											=     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)
