


'''simple docstring'''

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 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






					def  a_					(			self		)       ->    str:
										_a						=		tempfile.mkdtemp()

										_a						=		BlipImageProcessor()
										_a						=		GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"		)

										_a						=		BlipaProcessor(__UpperCamelCase     ,							__UpperCamelCase		)

										processor.save_pretrained(self.tmpdirname		)






					def  a_					(			self     ,							**__UpperCamelCase		)       ->    Dict:
										return AutoProcessor.from_pretrained(self.tmpdirname     ,							**__UpperCamelCase		).tokenizer






					def  a_					(			self     ,							**__UpperCamelCase		)       ->    Dict:
										return AutoProcessor.from_pretrained(self.tmpdirname     ,							**__UpperCamelCase		).image_processor






					def  a_					(			self		)       ->    Tuple:
										shutil.rmtree(self.tmpdirname		)






					def  a_					(			self		)       ->    Union[str, Any]:
										_a						=		[np.random.randint(255     ,							size=(3, 30, 400)     ,							dtype=np.uinta		)]

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

										return image_inputs






					def  a_					(			self		)       ->    Any:
										_a						=		BlipaProcessor(tokenizer=self.get_tokenizer()     ,							image_processor=self.get_image_processor()		)
										processor.save_pretrained(self.tmpdirname		)

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

										_a						=		BlipaProcessor.from_pretrained(
										    self.tmpdirname     ,							bos_token="(BOS)"     ,							eos_token="(EOS)"     ,							do_normalize=__UpperCamelCase     ,							padding_value=1.0		)

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

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






					def  a_					(			self		)       ->    Tuple:
										_a						=		self.get_image_processor()
										_a						=		self.get_tokenizer()

										_a						=		BlipaProcessor(tokenizer=__UpperCamelCase     ,							image_processor=__UpperCamelCase		)

										_a						=		self.prepare_image_inputs()

										_a						=		image_processor(__UpperCamelCase     ,							return_tensors="np"		)
										_a						=		processor(images=__UpperCamelCase     ,							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		)       ->    int:
										_a						=		self.get_image_processor()
										_a						=		self.get_tokenizer()

										_a						=		BlipaProcessor(tokenizer=__UpperCamelCase     ,							image_processor=__UpperCamelCase		)

										_a						=		"lower newer"

										_a						=		processor(text=__UpperCamelCase		)

										_a						=		tokenizer(__UpperCamelCase     ,							return_token_type_ids=__UpperCamelCase		)

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






					def  a_					(			self		)       ->    List[Any]:
										_a						=		self.get_image_processor()
										_a						=		self.get_tokenizer()

										_a						=		BlipaProcessor(tokenizer=__UpperCamelCase     ,							image_processor=__UpperCamelCase		)

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

										_a						=		processor(text=__UpperCamelCase     ,							images=__UpperCamelCase		)

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

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






					def  a_					(			self		)       ->    Tuple:
										_a						=		self.get_image_processor()
										_a						=		self.get_tokenizer()

										_a						=		BlipaProcessor(tokenizer=__UpperCamelCase     ,							image_processor=__UpperCamelCase		)

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

										_a						=		processor.batch_decode(__UpperCamelCase		)
										_a						=		tokenizer.batch_decode(__UpperCamelCase		)

										self.assertListEqual(__UpperCamelCase     ,							__UpperCamelCase		)






					def  a_					(			self		)       ->    int:
										_a						=		self.get_image_processor()
										_a						=		self.get_tokenizer()

										_a						=		BlipaProcessor(tokenizer=__UpperCamelCase     ,							image_processor=__UpperCamelCase		)

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

										_a						=		processor(text=__UpperCamelCase     ,							images=__UpperCamelCase		)

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





'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowercase__				     =		False





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):
     pass







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






     def  a_					(			self		)       ->    Dict:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def  a_					(			self		)       ->    List[str]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=2     ,							output_type="numpy"		).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(__UpperCamelCase		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		generator.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							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  a_					(			self		)       ->    List[Any]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(
              "shi-labs/versatile-diffusion"     ,							torch_dtype=torch.floataa		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							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([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]		)

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

