


'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase     :  str											=    logging.get_logger(__name__)

lowerCAmelCase     :  str											=    {
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     "bit"
     __magic_name__           =     ["preactivation", "bottleneck"]
     __magic_name__           =     ["SAME", "VALID"]







     def __init__(			self   ,						snake_case__=3   ,						snake_case__=64   ,						snake_case__=[256, 512, 1024, 2048]   ,						snake_case__=[3, 4, 6, 3]   ,						snake_case__="preactivation"   ,						snake_case__="relu"   ,						snake_case__=None   ,						snake_case__=32   ,						snake_case__=0.0   ,						snake_case__=False   ,						snake_case__=32   ,						snake_case__=1   ,						snake_case__=None   ,						snake_case__=None   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(**snake_case__ )
            if layer_type not in self.layer_types:
                   raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types )}' )
            if global_padding is not None:
                   if global_padding.upper() in self.supported_padding:
                          _lowerCAmelCase     :				Optional[int]       =							global_padding.upper()
                   else:
                          raise ValueError(F'Padding strategy {global_padding} not supported' )
            _lowerCAmelCase     :				List[str]       =							num_channels
            _lowerCAmelCase     :				Optional[int]       =							embedding_size
            _lowerCAmelCase     :				List[Any]       =							hidden_sizes
            _lowerCAmelCase     :				int       =							depths
            _lowerCAmelCase     :				int       =							layer_type
            _lowerCAmelCase     :				Tuple       =							hidden_act
            _lowerCAmelCase     :				Optional[Any]       =							global_padding
            _lowerCAmelCase     :				Any       =							num_groups
            _lowerCAmelCase     :				int       =							drop_path_rate
            _lowerCAmelCase     :				int       =							embedding_dynamic_padding
            _lowerCAmelCase     :				int       =							output_stride
            _lowerCAmelCase     :				int       =							width_factor

            _lowerCAmelCase     :				List[Any]       =							['stem'] + [F'stage{idx}' for idx in range(1   ,						len(snake_case__ ) + 1 )]
            _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[int]       =							get_aligned_output_features_output_indices(
                out_features=snake_case__   ,						out_indices=snake_case__   ,						stage_names=self.stage_names )





'''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


lowerCAmelCase     :  str											=    False
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     pass
@nightly
@require_torch_gpu
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

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







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
            # remove text_unet
            pipe.remove_unused_weights()
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Any       =							'A painting of a squirrel eating a burger '
            _lowerCAmelCase     :				List[Any]       =							torch.manual_seed(0 )
            _lowerCAmelCase     :				Any       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						guidance_scale=7.5   ,						num_inference_steps=2   ,						output_type='numpy' ).images

            with tempfile.TemporaryDirectory() as tmpdirname:
                   pipe.save_pretrained(snake_case__ )
                   _lowerCAmelCase     :				Any       =							VersatileDiffusionTextToImagePipeline.from_pretrained(snake_case__ )
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Optional[int]       =							generator.manual_seed(0 )
            _lowerCAmelCase     :				Tuple       =							pipe(
                prompt=snake_case__   ,						generator=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 ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							VersatileDiffusionTextToImagePipeline.from_pretrained(
                'shi-labs/versatile-diffusion'   ,						torch_dtype=torch.floataa )
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Dict       =							'A painting of a squirrel eating a burger '
            _lowerCAmelCase     :				Any       =							torch.manual_seed(0 )
            _lowerCAmelCase     :				int       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						guidance_scale=7.5   ,						num_inference_steps=50   ,						output_type='numpy' ).images

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

            assert image.shape == (1, 512, 512, 3)
            _lowerCAmelCase     :				Dict       =							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-2

