





"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import CLIPImageProcessor, CLIPProcessor

@require_vision
class 		lowercase					(					unittest.TestCase					):





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       tempfile.mkdtemp()

            # fmt: off
            lowerCAmelCase  						=       ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
            # fmt: on
            lowerCAmelCase  						=       dict(zip(lowercase      ,						range(len(lowercase ) ) ) )
            lowerCAmelCase  						=       ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
            lowerCAmelCase  						=       {"""unk_token""": """<unk>"""}

            lowerCAmelCase  						=       os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES["""vocab_file"""] )
            lowerCAmelCase  						=       os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES["""merges_file"""] )
            with open(self.vocab_file      ,						"""w"""      ,						encoding="""utf-8""" ) as fp:
                  fp.write(json.dumps(lowercase ) + """\n""" )
            with open(self.merges_file      ,						"""w"""      ,						encoding="""utf-8""" ) as fp:
                  fp.write("""\n""".join(lowercase ) )

            lowerCAmelCase  						=       {
                """do_resize""": True,
                """size""": 20,
                """do_center_crop""": True,
                """crop_size""": 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],
            }
            lowerCAmelCase  						=       os.path.join(self.tmpdirname      ,						lowercase )
            with open(self.image_processor_file      ,						"""w"""      ,						encoding="""utf-8""" ) as fp:
                  json.dump(lowercase      ,						lowercase )





      def     _snake_case			(       self      ,						**lowercase )		->     Dict:
            return CLIPTokenizer.from_pretrained(self.tmpdirname      ,						**lowercase )





      def     _snake_case			(       self      ,						**lowercase )		->     List[str]:
            return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,						**lowercase )





      def     _snake_case			(       self      ,						**lowercase )		->     int:
            return CLIPImageProcessor.from_pretrained(self.tmpdirname      ,						**lowercase )





      def     _snake_case			(       self )		->     Optional[int]:
            shutil.rmtree(self.tmpdirname )





      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       [np.random.randint(255      ,						size=(3, 30, 400)      ,						dtype=np.uinta )]

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

            return image_inputs





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       self.get_tokenizer()
            lowerCAmelCase  						=       self.get_rust_tokenizer()
            lowerCAmelCase  						=       self.get_image_processor()

            lowerCAmelCase  						=       CLIPProcessor(tokenizer=lowercase      ,						image_processor=lowercase )
            processor_slow.save_pretrained(self.tmpdirname )
            lowerCAmelCase  						=       CLIPProcessor.from_pretrained(self.tmpdirname      ,						use_fast=lowercase )

            lowerCAmelCase  						=       CLIPProcessor(tokenizer=lowercase      ,						image_processor=lowercase )
            processor_fast.save_pretrained(self.tmpdirname )
            lowerCAmelCase  						=       CLIPProcessor.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      ,						lowercase )
            self.assertIsInstance(processor_fast.tokenizer      ,						lowercase )

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





      def     _snake_case			(       self )		->     Any:
            lowerCAmelCase  						=       CLIPProcessor(tokenizer=self.get_tokenizer()      ,						image_processor=self.get_image_processor() )
            processor.save_pretrained(self.tmpdirname )

            lowerCAmelCase  						=       self.get_tokenizer(bos_token="""(BOS)"""      ,						eos_token="""(EOS)""" )
            lowerCAmelCase  						=       self.get_image_processor(do_normalize=lowercase      ,						padding_value=1.0 )

            lowerCAmelCase  						=       CLIPProcessor.from_pretrained(
                self.tmpdirname      ,						bos_token="""(BOS)"""      ,						eos_token="""(EOS)"""      ,						do_normalize=lowercase      ,						padding_value=1.0 )

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

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





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       self.get_image_processor()
            lowerCAmelCase  						=       self.get_tokenizer()

            lowerCAmelCase  						=       CLIPProcessor(tokenizer=lowercase      ,						image_processor=lowercase )

            lowerCAmelCase  						=       self.prepare_image_inputs()

            lowerCAmelCase  						=       image_processor(lowercase      ,						return_tensors="""np""" )
            lowerCAmelCase  						=       processor(images=lowercase      ,						return_tensors="""np""" )

            for key in input_image_proc.keys():
                  self.assertAlmostEqual(input_image_proc[key].sum()      ,						input_processor[key].sum()      ,						delta=1e-2 )





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       self.get_image_processor()
            lowerCAmelCase  						=       self.get_tokenizer()

            lowerCAmelCase  						=       CLIPProcessor(tokenizer=lowercase      ,						image_processor=lowercase )

            lowerCAmelCase  						=       """lower newer"""

            lowerCAmelCase  						=       processor(text=lowercase )

            lowerCAmelCase  						=       tokenizer(lowercase )

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





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       self.get_image_processor()
            lowerCAmelCase  						=       self.get_tokenizer()

            lowerCAmelCase  						=       CLIPProcessor(tokenizer=lowercase      ,						image_processor=lowercase )

            lowerCAmelCase  						=       """lower newer"""
            lowerCAmelCase  						=       self.prepare_image_inputs()

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

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

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





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       self.get_image_processor()
            lowerCAmelCase  						=       self.get_tokenizer()

            lowerCAmelCase  						=       CLIPProcessor(tokenizer=lowercase      ,						image_processor=lowercase )

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

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

            self.assertListEqual(lowercase      ,						lowercase )





      def     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       self.get_image_processor()
            lowerCAmelCase  						=       self.get_tokenizer()

            lowerCAmelCase  						=       CLIPProcessor(tokenizer=lowercase      ,						image_processor=lowercase )

            lowerCAmelCase  						=       """lower newer"""
            lowerCAmelCase  						=       self.prepare_image_inputs()

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

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









import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_lowerCAmelCase					:			Optional[Any]  				=			False






class 	__magic_name__    (			unittest.TestCase      ):
			pass






@slow
@require_torch_gpu
class 	__magic_name__    (			unittest.TestCase      ):





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
						pipe.to(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
						__a        =torch.manual_seed(0					)
						__a        =pipe(
						    image=__snake_case					,  generator=__snake_case					,  guidance_scale=7.5					,  num_inference_steps=50					,  output_type='numpy'					,  ).images

						__a        =image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						__a        =np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






