






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ImageGPTImageProcessor
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def __init__(      self		:			List[Any]       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Tuple=7       ,				lowerCamelCase		:			Any=3       ,				lowerCamelCase		:			Tuple=18       ,				lowerCamelCase		:			Dict=30       ,				lowerCamelCase		:			Union[str, Any]=400       ,				lowerCamelCase		:			Union[str, Any]=True       ,				lowerCamelCase		:			Optional[Any]=None       ,				lowerCamelCase		:			Any=True       ,				)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    size if size is not None else {"""height""": 18, """width""": 18}
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    min_resolution
         _UpperCAmelCase							=    max_resolution
         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    size
         _UpperCAmelCase							=    do_normalize





  def  lowerCamelCase       (      self		:			Any							)						->	str:


         """simple docstring"""
         return {
             # here we create 2 clusters for the sake of simplicity
             "clusters": np.asarray(
                 [
                     [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                     [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
                 ]							),
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
         }



@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  ImageGPTImageProcessor if is_vision_available() else None


  def  lowerCamelCase       (      self		:			List[str]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    ImageGPTImageProcessingTester(self							)


  @property
  def  lowerCamelCase       (      self		:			List[str]							)						->	Optional[Any]:


         """simple docstring"""
         return self.image_processor_tester.prepare_image_processor_dict()


  def  lowerCamelCase       (      self		:			str							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""clusters"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_resize"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""size"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_normalize"""							)							)


  def  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size       ,				{"""height""": 18, """width""": 18}							)

         _UpperCAmelCase							=    self.image_processing_class.from_dict(self.image_processor_dict       ,				size=42							)
         self.assertEqual(image_processor.size       ,				{"""height""": 42, """width""": 42}							)


  def  lowerCamelCase       (      self		:			int							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         _UpperCAmelCase							=    json.loads(image_processor.to_json_string()							)
         for key, value in self.image_processor_dict.items():
                if key == "clusters":
                       self.assertTrue(np.array_equal(lowerCamelCase       ,				obj[key]							)							)
                else:
                       self.assertEqual(obj[key]       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)

         with tempfile.TemporaryDirectory() as tmpdirname:
                _UpperCAmelCase							=    os.path.join(lowerCamelCase       ,				"""image_processor.json"""							)
                image_processor_first.to_json_file(lowerCamelCase							)
                _UpperCAmelCase							=    self.image_processing_class.from_json_file(lowerCamelCase							).to_dict()

         _UpperCAmelCase							=    image_processor_first.to_dict()
         for key, value in image_processor_first.items():
                if key == "clusters":
                       self.assertTrue(np.array_equal(lowerCamelCase       ,				image_processor_second[key]							)							)
                else:
                       self.assertEqual(image_processor_first[key]       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)

         with tempfile.TemporaryDirectory() as tmpdirname:
                image_processor_first.save_pretrained(lowerCamelCase							)
                _UpperCAmelCase							=    self.image_processing_class.from_pretrained(lowerCamelCase							).to_dict()

         _UpperCAmelCase							=    image_processor_first.to_dict()
         for key, value in image_processor_first.items():
                if key == "clusters":
                       self.assertTrue(np.array_equal(lowerCamelCase       ,				image_processor_second[key]							)							)
                else:
                       self.assertEqual(image_processor_first[key]       ,				lowerCamelCase							)





  @unittest.skip("""ImageGPT requires clusters at initialization"""							)
  def  lowerCamelCase       (      self		:			int							)						->	List[Any]:


         """simple docstring"""
         pass




def       _SCREAMING_SNAKE_CASE (							)				->						Dict:
       _UpperCAmelCase							=    load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,      split="""test""" )

       _UpperCAmelCase							=    Image.open(dataset[4]["""file"""] )
       _UpperCAmelCase							=    Image.open(dataset[5]["""file"""] )

       _UpperCAmelCase							=    [imagea, imagea]

       return images





@require_vision
@require_torch
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @slow
  def  lowerCamelCase       (      self		:			Any							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""							)

         _UpperCAmelCase							=    prepare_images()

         # test non-batched
         _UpperCAmelCase							=    image_processing(images[0]       ,				return_tensors="""pt"""							)

         self.assertIsInstance(encoding.input_ids       ,				torch.LongTensor							)
         self.assertEqual(encoding.input_ids.shape       ,				(1, 1024)							)

         _UpperCAmelCase							=    [306, 191, 191]
         self.assertEqual(encoding.input_ids[0, :3].tolist()       ,				lowerCamelCase							)

         # test batched
         _UpperCAmelCase							=    image_processing(lowerCamelCase       ,				return_tensors="""pt"""							)

         self.assertIsInstance(encoding.input_ids       ,				torch.LongTensor							)
         self.assertEqual(encoding.input_ids.shape       ,				(2, 1024)							)

         _UpperCAmelCase							=    [303, 13, 13]
         self.assertEqual(encoding.input_ids[1, -3:].tolist()       ,				lowerCamelCase							)

'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

		lowercase__											=parser.parse_args()

		lowercase__											=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

		lowercase__											=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)
