





from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	{
    """snap-research/efficientformer-l1-300""": (
        """https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
    ),
}
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Any				      = 'efficientformer'


   def __init__(					self    :       Any    ,  a    :       List[int] = [3, 2, 6, 4]    ,  a    :       List[int] = [48, 96, 224, 448]    ,  a    :       List[bool] = [True, True, True, True]    ,  a    :       int = 448    ,  a    :       int = 32    ,  a    :       int = 4    ,  a    :       int = 7    ,  a    :       int = 5    ,  a    :       int = 8    ,  a    :       int = 4    ,  a    :       float = 0.0    ,  a    :       int = 16    ,  a    :       int = 3    ,  a    :       int = 3    ,  a    :       int = 3    ,  a    :       int = 2    ,  a    :       int = 1    ,  a    :       float = 0.0    ,  a    :       int = 1    ,  a    :       bool = True    ,  a    :       bool = True    ,  a    :       float = 1E-5    ,  a    :       str = "gelu"    ,  a    :       float = 0.02    ,  a    :       float = 1E-1_2    ,  a    :       int = 224    ,  a    :       float = 1E-0_5    ,  **a    :       Tuple    ,  )->     None:





     """simple docstring"""







     super().__init__(**a )

     lowercase__			    =						hidden_act
     lowercase__			    =						hidden_dropout_prob
     lowercase__			    =						hidden_sizes
     lowercase__			    =						num_hidden_layers
     lowercase__			    =						num_attention_heads
     lowercase__			    =						initializer_range
     lowercase__			    =						layer_norm_eps
     lowercase__			    =						patch_size
     lowercase__			    =						num_channels
     lowercase__			    =						depths
     lowercase__			    =						mlp_expansion_ratio
     lowercase__			    =						downsamples
     lowercase__			    =						dim
     lowercase__			    =						key_dim
     lowercase__			    =						attention_ratio
     lowercase__			    =						resolution
     lowercase__			    =						pool_size
     lowercase__			    =						downsample_patch_size
     lowercase__			    =						downsample_stride
     lowercase__			    =						downsample_pad
     lowercase__			    =						drop_path_rate
     lowercase__			    =						num_metaad_blocks
     lowercase__			    =						distillation
     lowercase__			    =						use_layer_scale
     lowercase__			    =						layer_scale_init_value
     lowercase__			    =						image_size
     lowercase__			    =						batch_norm_eps









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_					     =	False
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):
   pass






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


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Optional[int]:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,  torch_dtype=torch.floataa )
     pipe.to(a )
     pipe.set_progress_bar_config(disable=a )

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

     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						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 )
       lowercase__			    =						VersatileDiffusionPipeline.from_pretrained(a    ,  torch_dtype=torch.floataa )
     pipe.to(a )
     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						generator.manual_seed(0 )
     lowercase__			    =						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  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     List[str]:





     """simple docstring"""







     lowercase__			    =						VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,  torch_dtype=torch.floataa )
     pipe.to(a )
     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						'cyberpunk 2077'
     lowercase__			    =						load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						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

     lowercase__			    =						image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001] )

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

     lowercase__			    =						'A painting of a squirrel eating a burger '
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						pipe.text_to_image(
         prompt=a    ,  generator=a    ,  guidance_scale=7.5    ,  num_inference_steps=50    ,  output_type='numpy' ).images

     lowercase__			    =						image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

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

     lowercase__			    =						pipe.image_variation(a    ,  generator=a    ,  output_type='numpy' ).images

     lowercase__			    =						image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456] )

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


