"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





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



      @property
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     List[Any]										=	1
        a__:     Optional[int]										=	3
        a__:     Optional[Any]										=	(32, 32)

        a__:     Optional[int]										=	floats_tensor((batch_size, num_channels) + sizes    ,			rng=random.Random(0)).to(lowercase)
        return image



      @property
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     int										=	UNetaDConditionModel(
            block_out_channels=(32, 64)    ,			layers_per_block=2    ,			sample_size=32    ,			in_channels=4    ,			out_channels=4    ,			down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,			up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,			cross_attention_dim=32    ,			)
        return model



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Tuple										=	AutoencoderKL(
            block_out_channels=[32, 64]    ,			in_channels=3    ,			out_channels=3    ,			down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,			up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,			latent_channels=4    ,			)
        return model



      @property
      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Optional[Any]										=	RobertaSeriesConfig(
            hidden_size=32    ,			project_dim=32    ,			intermediate_size=37    ,			layer_norm_eps=1e-05    ,			num_attention_heads=4    ,			num_hidden_layers=5    ,			pad_token_id=1    ,			vocab_size=50_06    ,			)
        return RobertaSeriesModelWithTransformation(lowercase)



      @property
      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        def extract(*lowercase    ,			**lowercase):




          class    __snake_case       :



                def __init__(						self)	->			List[str]:





                  '''simple docstring'''





                  a__:     List[Any]										=	torch.ones([0])



                def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





                  '''simple docstring'''





                  self.pixel_values.to(lowercase)
                  return self







          return Out()

        return extract



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Dict										=	'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:     int										=	self.dummy_cond_unet
        a__:     List[Any]										=	PNDMScheduler(skip_prk_steps=lowercase)
        a__:     Optional[int]										=	self.dummy_vae
        a__:     str										=	self.dummy_text_encoder
        a__:     List[Any]										=	XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
        a__:     Any										=	77

        a__:     str										=	self.dummy_image.to(lowercase)
        a__:     str										=	init_image / 2 + 0.5

        # make sure here that pndm scheduler skips prk
        a__:     Tuple										=	AltDiffusionImgaImgPipeline(
            unet=lowercase    ,			scheduler=lowercase    ,			vae=lowercase    ,			text_encoder=lowercase    ,			tokenizer=lowercase    ,			safety_checker=lowercase    ,			feature_extractor=self.dummy_extractor    ,			)
        a__:     List[str]										=	VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor    ,			do_normalize=lowercase)
        a__:     str										=	alt_pipe.to(lowercase)
        alt_pipe.set_progress_bar_config(disable=lowercase)

        a__:     Any										=	'A painting of a squirrel eating a burger'
        a__:     Dict										=	torch.Generator(device=lowercase).manual_seed(0)
        a__:     str										=	alt_pipe(
            [prompt]    ,			generator=lowercase    ,			guidance_scale=6.0    ,			num_inference_steps=2    ,			output_type='np'    ,			image=lowercase    ,			)

        a__:     Any										=	output.images

        a__:     Dict										=	torch.Generator(device=lowercase).manual_seed(0)
        a__:     List[str]										=	alt_pipe(
            [prompt]    ,			generator=lowercase    ,			guidance_scale=6.0    ,			num_inference_steps=2    ,			output_type='np'    ,			image=lowercase    ,			return_dict=lowercase    ,			)[0]

        a__:     Union[str, Any]										=	image[0, -3:, -3:, -1]
        a__:     List[str]										=	image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        a__:     List[Any]										=	np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5e-3



      @unittest.skipIf(torch_device != 'cuda'    ,			'This test requires a GPU')
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Any										=	self.dummy_cond_unet
        a__:     str										=	PNDMScheduler(skip_prk_steps=lowercase)
        a__:     List[str]										=	self.dummy_vae
        a__:     Tuple										=	self.dummy_text_encoder
        a__:     Union[str, Any]										=	XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
        a__:     Union[str, Any]										=	77

        a__:     Tuple										=	self.dummy_image.to(lowercase)

        # put models in fp16
        a__:     str										=	unet.half()
        a__:     Any										=	vae.half()
        a__:     int										=	bert.half()

        # make sure here that pndm scheduler skips prk
        a__:     Any										=	AltDiffusionImgaImgPipeline(
            unet=lowercase    ,			scheduler=lowercase    ,			vae=lowercase    ,			text_encoder=lowercase    ,			tokenizer=lowercase    ,			safety_checker=lowercase    ,			feature_extractor=self.dummy_extractor    ,			)
        a__:     Optional[Any]										=	VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor    ,			do_normalize=lowercase)
        a__:     Optional[int]										=	alt_pipe.to(lowercase)
        alt_pipe.set_progress_bar_config(disable=lowercase)

        a__:     List[Any]										=	'A painting of a squirrel eating a burger'
        a__:     int										=	torch.manual_seed(0)
        a__:     Any										=	alt_pipe(
            [prompt]    ,			generator=lowercase    ,			num_inference_steps=2    ,			output_type='np'    ,			image=lowercase    ,			).images

        assert image.shape == (1, 32, 32, 3)



      @unittest.skipIf(torch_device != 'cuda'    ,			'This test requires a GPU')
      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     List[Any]										=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/img2img/sketch-mountains-input.jpg')
        # resize to resolution that is divisible by 8 but not 16 or 32
        a__:     int										=	init_image.resize((7_60, 5_04))

        a__:     Any										=	'BAAI/AltDiffusion'
        a__:     List[str]										=	AltDiffusionImgaImgPipeline.from_pretrained(
            lowercase    ,			safety_checker=lowercase    ,			)
        pipe.to(lowercase)
        pipe.set_progress_bar_config(disable=lowercase)
        pipe.enable_attention_slicing()

        a__:     str										=	'A fantasy landscape, trending on artstation'

        a__:     int										=	torch.manual_seed(0)
        a__:     List[str]										=	pipe(
            prompt=lowercase    ,			image=lowercase    ,			strength=0.75    ,			guidance_scale=7.5    ,			generator=lowercase    ,			output_type='np'    ,			)
        a__:     str										=	output.images[0]

        a__:     Optional[int]										=	image[2_55:2_58, 3_83:3_86, -1]

        assert image.shape == (5_04, 7_60, 3)
        a__:     Union[str, Any]										=	np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000])

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







@slow
@require_torch_gpu
class    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/img2img/sketch-mountains-input.jpg')
        a__:     int										=	init_image.resize((7_68, 5_12))
        a__:     List[str]										=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy')

        a__:     Any										=	'BAAI/AltDiffusion'
        a__:     str										=	AltDiffusionImgaImgPipeline.from_pretrained(
            lowercase    ,			safety_checker=lowercase    ,			)
        pipe.to(lowercase)
        pipe.set_progress_bar_config(disable=lowercase)
        pipe.enable_attention_slicing()

        a__:     Optional[int]										=	'A fantasy landscape, trending on artstation'

        a__:     Union[str, Any]										=	torch.manual_seed(0)
        a__:     Tuple										=	pipe(
            prompt=lowercase    ,			image=lowercase    ,			strength=0.75    ,			guidance_scale=7.5    ,			generator=lowercase    ,			output_type='np'    ,			)
        a__:     Any										=	output.images[0]

        assert image.shape == (5_12, 7_68, 3)
        # img2img is flaky across GPUs even in fp32, so using MAE here
        assert np.abs(expected_image - image).max() < 1e-2



"""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__					       =	{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""roberta-prelayernorm"""



      def __init__(						self    ,			lowercase=5_02_65    ,			lowercase=7_68    ,			lowercase=12    ,			lowercase=12    ,			lowercase=30_72    ,			lowercase="gelu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=5_12    ,			lowercase=2    ,			lowercase=0.02    ,			lowercase=1e-12    ,			lowercase=1    ,			lowercase=0    ,			lowercase=2    ,			lowercase="absolute"    ,			lowercase=True    ,			lowercase=None    ,			**lowercase    ,			)	->			Any:





        '''simple docstring'''





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

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







class    __snake_case       (     __lowerCAmelCase				):



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





        '''simple docstring'''





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


