'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase  :str  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Union[str, Any]  				=    {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[Any]							=						"beit"






    def __init__(       self    ,							lowercase__=8_192    ,							lowercase__=768    ,							lowercase__=12    ,							lowercase__=12    ,							lowercase__=3_072    ,							lowercase__="gelu"    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.0_2    ,							lowercase__=1E-12    ,							lowercase__=224    ,							lowercase__=16    ,							lowercase__=3    ,							lowercase__=False    ,							lowercase__=False    ,							lowercase__=False    ,							lowercase__=False    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=True    ,							lowercase__=[3, 5, 7, 11]    ,							lowercase__=[1, 2, 3, 6]    ,							lowercase__=True    ,							lowercase__=0.4    ,							lowercase__=256    ,							lowercase__=1    ,							lowercase__=False    ,							lowercase__=255    ,							**lowercase__    ,							)							-> Optional[Any]:
         super().__init__(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_size
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       num_hidden_layers
         SCREAMING_SNAKE_CASE					:			Dict    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       intermediate_size
         SCREAMING_SNAKE_CASE					:			Any    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Dict    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			Any    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			Any    	=       layer_norm_eps

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       image_size
         SCREAMING_SNAKE_CASE					:			Any    	=       patch_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       num_channels
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       use_mask_token
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       use_absolute_position_embeddings
         SCREAMING_SNAKE_CASE					:			int    	=       use_relative_position_bias
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       use_shared_relative_position_bias
         SCREAMING_SNAKE_CASE					:			str    	=       layer_scale_init_value
         SCREAMING_SNAKE_CASE					:			int    	=       drop_path_rate
         SCREAMING_SNAKE_CASE					:			Dict    	=       use_mean_pooling
         # decode head attributes (semantic segmentation)
         SCREAMING_SNAKE_CASE					:			str    	=       out_indices
         SCREAMING_SNAKE_CASE					:			List[str]    	=       pool_scales
         # auxiliary head attributes (semantic segmentation)
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       use_auxiliary_head
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       auxiliary_loss_weight
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       auxiliary_channels
         SCREAMING_SNAKE_CASE					:			Dict    	=       auxiliary_num_convs
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       auxiliary_concat_input
         SCREAMING_SNAKE_CASE					:			List[str]    	=       semantic_loss_ignore_index






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[int]							=						version.parse("1.11" )






    @property
    def       _UpperCamelCase    (       self )							-> Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ] )






    @property
    def       _UpperCamelCase    (       self )							-> float:
         return 1E-4




'''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  :List[Any]  				=    False






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    pass






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




    '''simple docstring'''







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






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Dict    	=       VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

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

         with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase__ )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       generator.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							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       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(
             'shi-labs/versatile-diffusion'    ,							torch_dtype=torch.floataa )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

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

         SCREAMING_SNAKE_CASE					:			Dict    	=       image[0, 253:256, 253:256, -1]

         assert image.shape == (1, 512, 512, 3)
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

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



