




'''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


lowercase      :   str      =       False







class 					_a (unittest.TestCase	):





     '''simple docstring'''



     pass







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





     '''simple docstring'''



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







     def 			snake_case_      (				self							)      ->				str:
          snake_case			:      int        =			VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,torch_dtype=torch.floataa							)
          pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      List[str]        =			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.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			:      Dict        =			VersatileDiffusionPipeline.from_pretrained(__a							,torch_dtype=torch.floataa							)
          pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      List[Any]        =			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 			snake_case_      (				self							)      ->				List[Any]:
          snake_case			:      Any        =			VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,torch_dtype=torch.floataa							)
          pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      Union[str, Any]        =			"""cyberpunk 2077"""
          snake_case			:      Any        =			load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)
          snake_case			:      List[str]        =			torch.manual_seed(0							)
          snake_case			:      Dict        =			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			:      Tuple        =			image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 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			:      Dict        =			"""A painting of a squirrel eating a burger """
          snake_case			:      int        =			torch.manual_seed(0							)
          snake_case			:      Tuple        =			pipe.text_to_image(
              prompt=__a							,generator=__a							,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			:      str        =			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			:      Optional[Any]        =			pipe.image_variation(__a							,generator=__a							,output_type="""numpy"""							).images

          snake_case			:      Tuple        =			image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          snake_case			:      Union[str, 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









'''simple docstring'''







import logging

from transformers.configuration_utils import PretrainedConfig


lowercase      :   int      =       logging.getLogger(__name__)







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					"""masked_bert"""







     def __init__(				self							,__a=30_522							,__a=768							,__a=12							,__a=12							,__a=3_072							,__a="gelu"							,__a=0.1							,__a=0.1							,__a=512							,__a=2							,__a=0.02							,__a=1E-12							,__a=0							,__a="topK"							,__a="constant"							,__a=0.0							,**__a							,)      ->				List[str]:
          super().__init__(pad_token_id=__a							,**__a							)

          snake_case			:      Dict        =			vocab_size
          snake_case			:      Optional[Any]        =			hidden_size
          snake_case			:      Dict        =			num_hidden_layers
          snake_case			:      List[Any]        =			num_attention_heads
          snake_case			:      Dict        =			hidden_act
          snake_case			:      Any        =			intermediate_size
          snake_case			:      Optional[int]        =			hidden_dropout_prob
          snake_case			:      Optional[Any]        =			attention_probs_dropout_prob
          snake_case			:      List[Any]        =			max_position_embeddings
          snake_case			:      int        =			type_vocab_size
          snake_case			:      int        =			initializer_range
          snake_case			:      int        =			layer_norm_eps
          snake_case			:      Any        =			pruning_method
          snake_case			:      Union[str, Any]        =			mask_init
          snake_case			:      int        =			mask_scale



