"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    '''google/electra-small-generator''': '''https://huggingface.co/google/electra-small-generator/resolve/main/config.json''',
    '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/config.json''',
    '''google/electra-large-generator''': '''https://huggingface.co/google/electra-large-generator/resolve/main/config.json''',
    '''google/electra-small-discriminator''': (
        '''https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'''
    ),
    '''google/electra-base-discriminator''': (
        '''https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'''
    ),
    '''google/electra-large-discriminator''': (
        '''https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'''
    ),
}






class 		_lowercase (						__a      ):



						"""simple docstring"""




						lowercase__       =  '''electra'''





						def __init__(				self							:    int     , UpperCamelCase__							:    int=30522     , UpperCamelCase__							:    Union[str, Any]=128     , UpperCamelCase__							:    Tuple=256     , UpperCamelCase__							:    List[Any]=12     , UpperCamelCase__							:    List[str]=4     , UpperCamelCase__							:    Union[str, Any]=1024     , UpperCamelCase__							:    Tuple="gelu"     , UpperCamelCase__							:    List[Any]=0.1     , UpperCamelCase__							:    Optional[Any]=0.1     , UpperCamelCase__							:    List[str]=512     , UpperCamelCase__							:    Optional[Any]=2     , UpperCamelCase__							:    Any=0.02     , UpperCamelCase__							:    Optional[int]=1E-12     , UpperCamelCase__							:    Optional[int]="first"     , UpperCamelCase__							:    List[Any]=True     , UpperCamelCase__							:    List[Any]="gelu"     , UpperCamelCase__							:    List[Any]=0.1     , UpperCamelCase__							:    str=0     , UpperCamelCase__							:    Optional[int]="absolute"     , UpperCamelCase__							:    Optional[Any]=True     , UpperCamelCase__							:    List[str]=None     , **UpperCamelCase__							:    Optional[Any]     , )				->				Tuple:
													'''simple docstring'''



													super().__init__(pad_token_id=UpperCamelCase__     , **UpperCamelCase__				)

													__UpperCamelCase							=vocab_size
													__UpperCamelCase							=embedding_size
													__UpperCamelCase							=hidden_size
													__UpperCamelCase							=num_hidden_layers
													__UpperCamelCase							=num_attention_heads
													__UpperCamelCase							=intermediate_size
													__UpperCamelCase							=hidden_act
													__UpperCamelCase							=hidden_dropout_prob
													__UpperCamelCase							=attention_probs_dropout_prob
													__UpperCamelCase							=max_position_embeddings
													__UpperCamelCase							=type_vocab_size
													__UpperCamelCase							=initializer_range
													__UpperCamelCase							=layer_norm_eps

													__UpperCamelCase							=summary_type
													__UpperCamelCase							=summary_use_proj
													__UpperCamelCase							=summary_activation
													__UpperCamelCase							=summary_last_dropout
													__UpperCamelCase							=position_embedding_type
													__UpperCamelCase							=use_cache
													__UpperCamelCase							=classifier_dropout






class 		_lowercase (						__a      ):



						"""simple docstring"""




						@property
						def   UpperCAmelCase_ (				self							:    str				)				->				Mapping[str, Mapping[int, str]]:
													'''simple docstring'''



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




"""simple docstring"""

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowercase    =				False






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



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[Any]:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

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

													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=2     , output_type='''numpy'''     , ).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																				pipe.save_pretrained(UpperCamelCase__				)
																				__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCamelCase__				)

													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=generator.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , 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							:    Optional[int]				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							='''cyberpunk 2077'''
													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt=UpperCamelCase__     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=50     , output_type='''numpy'''     , ).images

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

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase							=np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]				)

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



