"""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 CLIPSegProcessor, ViTImageProcessor

@require_vision
class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Optional[Any])   ->     Dict:
					__snake_case      :							Dict						=			tempfile.mkdtemp()

					# fmt: off
					__snake_case      :							Dict						=			['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
					__snake_case      :							Dict						=			dict(zip(_A     ,  range(len(_A))))
					__snake_case      :							Optional[Any]						=			['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
					__snake_case      :							Optional[Any]						=			{'unk_token': '<unk>'}

					__snake_case      :							int						=			os.path.join(self.tmpdirname     ,  VOCAB_FILES_NAMES['vocab_file'])
					__snake_case      :							Optional[int]						=			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(_A) + '\n')
					with open(self.merges_file     ,  'w'     ,  encoding='utf-8') as fp:
						fp.write('\n'.join(_A))

					__snake_case      :							Dict						=			{
					    '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],
					}
					__snake_case      :							Any						=			os.path.join(self.tmpdirname     ,  _A)
					with open(self.image_processor_file     ,  'w'     ,  encoding='utf-8') as fp:
						json.dump(_A     ,  _A)



				def  _lowercase					(self    :				Dict     ,  **_A    :				Tuple)   ->     int:
					return CLIPTokenizer.from_pretrained(self.tmpdirname     ,  **_A)



				def  _lowercase					(self    :				Union[str, Any]     ,  **_A    :				List[Any])   ->     Optional[int]:
					return CLIPTokenizerFast.from_pretrained(self.tmpdirname     ,  **_A)



				def  _lowercase					(self    :				str     ,  **_A    :				Optional[int])   ->     int:
					return ViTImageProcessor.from_pretrained(self.tmpdirname     ,  **_A)



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



				def  _lowercase					(self    :				str)   ->     Union[str, Any]:
					__snake_case      :							int						=			[np.random.randint(2_55     ,  size=(3, 30, 4_00)     ,  dtype=np.uinta)]

					__snake_case      :							Union[str, Any]						=			[Image.fromarray(np.moveaxis(_A     ,  0     ,  -1)) for x in image_inputs]

					return image_inputs



				def  _lowercase					(self    :				Optional[int])   ->     Union[str, Any]:
					__snake_case      :							Tuple						=			self.get_tokenizer()
					__snake_case      :							Tuple						=			self.get_rust_tokenizer()
					__snake_case      :							Dict						=			self.get_image_processor()

					__snake_case      :							Optional[Any]						=			CLIPSegProcessor(tokenizer=_A     ,  image_processor=_A)
					processor_slow.save_pretrained(self.tmpdirname)
					__snake_case      :							Optional[Any]						=			CLIPSegProcessor.from_pretrained(self.tmpdirname     ,  use_fast=_A)

					__snake_case      :							List[str]						=			CLIPSegProcessor(tokenizer=_A     ,  image_processor=_A)
					processor_fast.save_pretrained(self.tmpdirname)
					__snake_case      :							Any						=			CLIPSegProcessor.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     ,  _A)
					self.assertIsInstance(processor_fast.tokenizer     ,  _A)

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



				def  _lowercase					(self    :				Optional[int])   ->     Dict:
					__snake_case      :							List[Any]						=			CLIPSegProcessor(tokenizer=self.get_tokenizer()     ,  image_processor=self.get_image_processor())
					processor.save_pretrained(self.tmpdirname)

					__snake_case      :							Dict						=			self.get_tokenizer(bos_token='(BOS)'     ,  eos_token='(EOS)')
					__snake_case      :							Optional[Any]						=			self.get_image_processor(do_normalize=_A     ,  padding_value=1.0)

					__snake_case      :							Optional[int]						=			CLIPSegProcessor.from_pretrained(
					    self.tmpdirname     ,  bos_token='(BOS)'     ,  eos_token='(EOS)'     ,  do_normalize=_A     ,  padding_value=1.0)

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

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



				def  _lowercase					(self    :				Tuple)   ->     Dict:
					__snake_case      :							Any						=			self.get_image_processor()
					__snake_case      :							Dict						=			self.get_tokenizer()

					__snake_case      :							Dict						=			CLIPSegProcessor(tokenizer=_A     ,  image_processor=_A)

					__snake_case      :							Any						=			self.prepare_image_inputs()

					__snake_case      :							Tuple						=			image_processor(_A     ,  return_tensors='np')
					__snake_case      :							Union[str, Any]						=			processor(images=_A     ,  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  _lowercase					(self    :				Any)   ->     int:
					__snake_case      :							Any						=			self.get_image_processor()
					__snake_case      :							List[Any]						=			self.get_tokenizer()

					__snake_case      :							List[Any]						=			CLIPSegProcessor(tokenizer=_A     ,  image_processor=_A)

					__snake_case      :							Optional[Any]						=			'lower newer'

					__snake_case      :							Optional[int]						=			processor(text=_A)

					__snake_case      :							List[str]						=			tokenizer(_A)

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



				def  _lowercase					(self    :				List[str])   ->     Any:
					__snake_case      :							int						=			self.get_image_processor()
					__snake_case      :							Union[str, Any]						=			self.get_tokenizer()

					__snake_case      :							Optional[Any]						=			CLIPSegProcessor(tokenizer=_A     ,  image_processor=_A)

					__snake_case      :							Optional[Any]						=			'lower newer'
					__snake_case      :							Dict						=			self.prepare_image_inputs()

					__snake_case      :							Union[str, Any]						=			processor(text=_A     ,  images=_A)

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

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



				def  _lowercase					(self    :				int)   ->     Union[str, Any]:
					__snake_case      :							int						=			self.get_image_processor()
					__snake_case      :							Tuple						=			self.get_tokenizer()

					__snake_case      :							Tuple						=			CLIPSegProcessor(tokenizer=_A     ,  image_processor=_A)

					__snake_case      :							Union[str, Any]						=			self.prepare_image_inputs()
					__snake_case      :							Any						=			self.prepare_image_inputs()

					__snake_case      :							Tuple						=			processor(images=_A     ,  visual_prompt=_A)

					self.assertListEqual(list(inputs.keys())     ,  ['pixel_values', 'conditional_pixel_values'])

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






				def  _lowercase					(self    :				List[str])   ->     List[Any]:
					__snake_case      :							List[Any]						=			self.get_image_processor()
					__snake_case      :							Union[str, Any]						=			self.get_tokenizer()

					__snake_case      :							Tuple						=			CLIPSegProcessor(tokenizer=_A     ,  image_processor=_A)

					__snake_case      :							Optional[Any]						=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__snake_case      :							Union[str, Any]						=			processor.batch_decode(_A)
					__snake_case      :							List[Any]						=			tokenizer.batch_decode(_A)

					self.assertListEqual(_A     ,  _A)






"""simple docstring"""







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_a					:					int=			False

class 		UpperCamelCase      (   unittest.TestCase						):
				pass







@nightly
@require_torch_gpu
class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Optional[Any])   ->     Tuple:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



				def  _lowercase					(self    :				int)   ->     Tuple:
					__snake_case      :							str						=			VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'     ,  torch_dtype=torch.floataa)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							int						=			load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg')

					__snake_case      :							List[Any]						=			torch.manual_seed(0)
					__snake_case      :							Optional[int]						=			pipe.dual_guided(
					    prompt='first prompt'     ,  image=_A     ,  text_to_image_strength=0.75     ,  generator=_A     ,  guidance_scale=7.5     ,  num_inference_steps=2     ,  output_type='numpy'     ,  ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
						pipe.save_pretrained(_A)
						__snake_case      :							Optional[Any]						=			VersatileDiffusionPipeline.from_pretrained(_A     ,  torch_dtype=torch.floataa)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Tuple						=			generator.manual_seed(0)
					__snake_case      :							int						=			pipe.dual_guided(
					    prompt='first prompt'     ,  image=_A     ,  text_to_image_strength=0.75     ,  generator=_A     ,  guidance_scale=7.5     ,  num_inference_steps=2     ,  output_type='numpy'     ,  ).images

					assert np.abs(image - new_image).sum() < 1E-5, "Models don't have the same forward pass"






				def  _lowercase					(self    :				Optional[Any])   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'     ,  torch_dtype=torch.floataa)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Tuple						=			'cyberpunk 2077'
					__snake_case      :							int						=			load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg')
					__snake_case      :							str						=			torch.manual_seed(0)
					__snake_case      :							Union[str, Any]						=			pipe.dual_guided(
					    prompt=_A     ,  image=_A     ,  text_to_image_strength=0.75     ,  generator=_A     ,  guidance_scale=7.5     ,  num_inference_steps=50     ,  output_type='numpy'     ,  ).images

					__snake_case      :							Optional[Any]						=			image[0, 2_53:2_56, 2_53:2_56, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case      :							Tuple						=			np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001])

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

					__snake_case      :							List[str]						=			'A painting of a squirrel eating a burger '
					__snake_case      :							str						=			torch.manual_seed(0)
					__snake_case      :							str						=			pipe.text_to_image(
					    prompt=_A     ,  generator=_A     ,  guidance_scale=7.5     ,  num_inference_steps=50     ,  output_type='numpy').images

					__snake_case      :							str						=			image[0, 2_53:2_56, 2_53:2_56, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case      :							Any						=			np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778])

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

					__snake_case      :							List[str]						=			pipe.image_variation(_A     ,  generator=_A     ,  output_type='numpy').images

					__snake_case      :							Dict						=			image[0, 2_53:2_56, 2_53:2_56, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case      :							List[Any]						=			np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456])

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





