



from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							SqueezeBertTokenizer
				__lowerCamelCase					=							SqueezeBertTokenizerFast
				__lowerCamelCase					=							True




				def 				snake_case  (				self		,							**_snake_case						):



									"""simple docstring"""





									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname		,							**_snake_case						)




				@slow
				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       SqueezeBertTokenizer.from_pretrained("""squeezebert/squeezebert-mnli-headless"""						)

									_lowerCAmelCase         =       tokenizer.encode("""sequence builders"""		,							add_special_tokens=_snake_case						)
									_lowerCAmelCase         =       tokenizer.encode("""multi-sequence build"""		,							add_special_tokens=_snake_case						)

									_lowerCAmelCase         =       tokenizer.build_inputs_with_special_tokens(_snake_case						)
									_lowerCAmelCase         =       tokenizer.build_inputs_with_special_tokens(_snake_case		,							_snake_case						)

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]




import gc
import tempfile
import unittest

import numpy as np
import torch

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


lowerCAmelCase__	:  Union[str, Any]						=  False





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




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




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



									'''simple docstring'''







									snake_case__   :      str   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      Union[str, Any]   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)

									snake_case__   :      Dict   				= torch.manual_seed(0						)
									snake_case__   :      Tuple   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=2       ,			output_type='numpy'       ,			).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(__UpperCamelCase						)
													snake_case__   :      List[Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase						)

									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      str   				= generator.manual_seed(0						)
									snake_case__   :      List[str]   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			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						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      str   				= 'cyberpunk 2077'
									snake_case__   :      Dict   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)
									snake_case__   :      Union[str, Any]   				= torch.manual_seed(0						)
									snake_case__   :      Union[str, Any]   				= pipe(
									    prompt=__UpperCamelCase       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=50       ,			output_type='numpy'       ,			).images

									snake_case__   :      int   				= image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									snake_case__   :      List[str]   				= np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]						)

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


