






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  SCREAMING_SNAKE_CASE			(			lowercase__					):
     '''simple docstring'''


     UpperCamelCase_							:		Union[str, Any]			=	'''ibert'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	Any=3_0522					, UpperCAmelCase_       :	Tuple=768					, UpperCAmelCase_       :	Dict=12					, UpperCAmelCase_       :	List[Any]=12					, UpperCAmelCase_       :	List[Any]=3072					, UpperCAmelCase_       :	List[Any]="gelu"					, UpperCAmelCase_       :	int=0.1					, UpperCAmelCase_       :	Union[str, Any]=0.1					, UpperCAmelCase_       :	Tuple=512					, UpperCAmelCase_       :	int=2					, UpperCAmelCase_       :	Optional[Any]=0.02					, UpperCAmelCase_       :	str=1E-12					, UpperCAmelCase_       :	Any=1					, UpperCAmelCase_       :	str=0					, UpperCAmelCase_       :	str=2					, UpperCAmelCase_       :	Union[str, Any]="absolute"					, UpperCAmelCase_       :	str=False					, UpperCAmelCase_       :	str="none"					, **UpperCAmelCase_       :	int					, ):
       super().__init__(pad_token_id=UpperCAmelCase__					, bos_token_id=UpperCAmelCase__					, eos_token_id=UpperCAmelCase__					, **UpperCAmelCase__      )

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





class  SCREAMING_SNAKE_CASE			(			lowercase__					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	Optional[int]      ):
       if self.task == "multiple-choice":
         SCREAMING_SNAKE_CASE       :      Dict							=    {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    {0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]      )














import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


@slow
@require_torch_gpu
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def 				_A       (				self       :	List[Any]      ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"      )
       SCREAMING_SNAKE_CASE       :      int							=    sd_pipe.to(UpperCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       sd_pipe.set_scheduler("sample_euler"      )

       SCREAMING_SNAKE_CASE       :      str							=    "A painting of a squirrel eating a burger"
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Any							=    sd_pipe([prompt]					, generator=UpperCAmelCase_					, guidance_scale=9.0					, num_inference_steps=20					, output_type="np"      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    output.images

       SCREAMING_SNAKE_CASE       :      str							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE       :      int							=    np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339]      )

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


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      str							=    StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
       SCREAMING_SNAKE_CASE       :      str							=    sd_pipe.to(UpperCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       sd_pipe.set_scheduler("sample_euler"      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    "A painting of a squirrel eating a burger"
       SCREAMING_SNAKE_CASE       :      Dict							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    sd_pipe([prompt]					, generator=UpperCAmelCase_					, guidance_scale=9.0					, num_inference_steps=20					, output_type="np"      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    output.images

       SCREAMING_SNAKE_CASE       :      Dict							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE       :      int							=    np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5E-1






     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
       SCREAMING_SNAKE_CASE       :      Any							=    sd_pipe.to(UpperCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       sd_pipe.set_scheduler("sample_dpmpp_2m"      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    "A painting of a squirrel eating a burger"
       SCREAMING_SNAKE_CASE       :      Any							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    sd_pipe(
           [prompt]					, generator=UpperCAmelCase_					, guidance_scale=7.5					, num_inference_steps=15					, output_type="np"					, use_karras_sigmas=UpperCAmelCase_					, )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    output.images

       SCREAMING_SNAKE_CASE       :      List[Any]							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    np.array(
           [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048]      )

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






