


from collections import OrderedDict
from typing import Mapping

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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'ibert'




						def __init__(	self						:   List[Any]   ,     __A						:   Optional[Any]=30522   ,     __A						:   Union[str, Any]=768   ,     __A						:   Tuple=12   ,     __A						:   Union[str, Any]=12   ,     __A						:   Dict=3072   ,     __A						:   List[str]="gelu"   ,     __A						:   Any=0.1   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Tuple=512   ,     __A						:   Optional[Any]=2   ,     __A						:   Union[str, Any]=0.02   ,     __A						:   Optional[int]=1E-12   ,     __A						:   str=1   ,     __A						:   Tuple=0   ,     __A						:   List[Any]=2   ,     __A						:   Any="absolute"   ,     __A						:   Dict=False   ,     __A						:   Dict="none"   ,     **__A						:   str   ,     )      ->Optional[Any]:






									"""simple docstring"""






									super().__init__(pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A   ,     **__A						)

									a__ :List[str]						  =    vocab_size
									a__ :Optional[Any]						  =    hidden_size
									a__ :Dict						  =    num_hidden_layers
									a__ :Optional[Any]						  =    num_attention_heads
									a__ :Optional[Any]						  =    hidden_act
									a__ :List[Any]						  =    intermediate_size
									a__ :List[str]						  =    hidden_dropout_prob
									a__ :Any						  =    attention_probs_dropout_prob
									a__ :Union[str, Any]						  =    max_position_embeddings
									a__ :Optional[Any]						  =    type_vocab_size
									a__ :str						  =    initializer_range
									a__ :List[Any]						  =    layer_norm_eps
									a__ :Optional[Any]						  =    position_embedding_type
									a__ :Dict						  =    quant_mode
									a__ :Any						  =    force_dequant






class 					lowerCAmelCase_ (       _a):




						@property
						def  _snake_case					(	self						:   Tuple						)      ->Mapping[str, Mapping[int, str]]:






									"""simple docstring"""






									if self.task == "multiple-choice":
												a__ :Optional[int]						  =    {0: "batch", 1: "choice", 2: "sequence"}
									else:
												a__ :str						  =    {0: "batch", 1: "sequence"}
									return OrderedDict(
									    [
									        ("input_ids", dynamic_axis),
									        ("attention_mask", dynamic_axis),
									    ]						)






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


snake_case__			 =						False


class 					lowerCAmelCase_ (       unittest.TestCase):
						pass






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




						def  _snake_case					(	self						:   Tuple						)      ->Optional[int]:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Dict						)      ->Any:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

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

									with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(__A						)
												a__ :List[Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(__A						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Optional[int]						  =    generator.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     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						:   Optional[Any]						)      ->List[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(
									    "shi-labs/versatile-diffusion"   ,     torch_dtype=torch.floataa						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Tuple						  =    "A painting of a squirrel eating a burger "
									a__ :Tuple						  =    torch.manual_seed(0						)
									a__ :Optional[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=50   ,     output_type="numpy"						).images

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

									assert image.shape == (1, 512, 512, 3)
									a__ :Tuple						  =    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-2


