
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


SCREAMING_SNAKE_CASE__     :    Dict      							=					False

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






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






		def 				__A (	self	:							Tuple						)  ->							List[str]:
						__lowerCamelCase					      =			VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(SCREAMING_SNAKE_CASE__						)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

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

						__lowerCamelCase					      =			torch.manual_seed(0						)
						__lowerCamelCase					      =			pipe(
						    prompt='''first prompt'''							,    image=SCREAMING_SNAKE_CASE__							,    text_to_image_strength=0.75							,    generator=SCREAMING_SNAKE_CASE__							,    guidance_scale=7.5							,    num_inference_steps=2							,    output_type='''numpy'''							,    ).images

						with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(SCREAMING_SNAKE_CASE__						)
										__lowerCamelCase					      =			VersatileDiffusionDualGuidedPipeline.from_pretrained(SCREAMING_SNAKE_CASE__						)

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

						__lowerCamelCase					      =			generator.manual_seed(0						)
						__lowerCamelCase					      =			pipe(
						    prompt='''first prompt'''							,    image=SCREAMING_SNAKE_CASE__							,    text_to_image_strength=0.75							,    generator=SCREAMING_SNAKE_CASE__							,    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]						)  ->							Any:
						__lowerCamelCase					      =			VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
						pipe.remove_unused_weights()
						pipe.to(SCREAMING_SNAKE_CASE__						)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			'''cyberpunk 2077'''
						__lowerCamelCase					      =			load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)
						__lowerCamelCase					      =			torch.manual_seed(0						)
						__lowerCamelCase					      =			pipe(
						    prompt=SCREAMING_SNAKE_CASE__							,    image=SCREAMING_SNAKE_CASE__							,    text_to_image_strength=0.75							,    generator=SCREAMING_SNAKE_CASE__							,    guidance_scale=7.5							,    num_inference_steps=50							,    output_type='''numpy'''							,    ).images

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

						assert image.shape == (1, 5_12, 5_12, 3)
						__lowerCamelCase					      =			np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]						)

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





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

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		Any   						=	SqueezeBertTokenizer
		a__ :		Union[str, Any]   						=	SqueezeBertTokenizerFast
		a__ :		int   						=	True






		def 				__A (	self	:							Optional[int]							,    **SCREAMING_SNAKE_CASE__	:							Union[str, Any]						)  ->							int:
						return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,    **SCREAMING_SNAKE_CASE__						)







		@slow
		def 				__A (	self	:							int						)  ->							Optional[int]:
						__lowerCamelCase					      =			SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''						)

						__lowerCamelCase					      =			tokenizer.encode('''sequence builders'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.encode('''multi-sequence build'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__							,    SCREAMING_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
						]



