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       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Dict		):





												'''simple docstring'''





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


						@property
						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							1
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							3
												SCREAMING_SNAKE_CASE							:					Tuple           =							(32, 32)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							floats_tensor((batch_size, num_channels) + sizes					,	rng=random.Random(0		)		).to(UpperCamelCase__		)
												return image


						@property
						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					str           =							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  __A ( self			:       str		):





												'''simple docstring'''





												torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							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  __A ( self			:       Any		):





												'''simple docstring'''





												torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							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=5006					,	)
												return RobertaSeriesModelWithTransformation(UpperCamelCase__		)


						@property
						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												def extract(*UpperCamelCase__			:       str					,	**UpperCamelCase__			:       str		):





																		class       lowercase__		:


																								def __init__( self			:       Any		):





																														'''simple docstring'''





																														SCREAMING_SNAKE_CASE							:					str           =							torch.ones([0]		)



																								def  __A ( self			:       int					,	UpperCamelCase__			:       int		):





																														'''simple docstring'''





																														self.pixel_values.to(UpperCamelCase__		)
																														return self






																		return Out()

												return extract


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.dummy_image.to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							init_image / 2 + 0.5

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

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

												SCREAMING_SNAKE_CASE							:					Dict           =							output.images

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

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

												assert image.shape == (1, 32, 32, 3)
												SCREAMING_SNAKE_CASE							:					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  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.dummy_cond_unet
												SCREAMING_SNAKE_CASE							:					str           =							PNDMScheduler(skip_prk_steps=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					int           =							self.dummy_vae
												SCREAMING_SNAKE_CASE							:					Dict           =							self.dummy_text_encoder
												SCREAMING_SNAKE_CASE							:					Any           =							XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							77

												SCREAMING_SNAKE_CASE							:					Dict           =							self.dummy_image.to(UpperCamelCase__		)

												# put models in fp16
												SCREAMING_SNAKE_CASE							:					int           =							unet.half()
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							vae.half()
												SCREAMING_SNAKE_CASE							:					Tuple           =							bert.half()

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

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

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



						@unittest.skipIf(torch_device != '''cuda'''					,	'''This test requires a GPU'''		)
						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							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
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							init_image.resize((760, 504)		)

												SCREAMING_SNAKE_CASE							:					Any           =							'''BAAI/AltDiffusion'''
												SCREAMING_SNAKE_CASE							:					int           =							AltDiffusionImgaImgPipeline.from_pretrained(
												    UpperCamelCase__					,	safety_checker=UpperCamelCase__					,	)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)
												pipe.enable_attention_slicing()

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

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

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							image[255:258, 383:386, -1]

												assert image.shape == (504, 760, 3)
												SCREAMING_SNAKE_CASE							:					List[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       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





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



						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/img2img/sketch-mountains-input.jpg'''		)
												SCREAMING_SNAKE_CASE							:					int           =							init_image.resize((768, 512)		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''		)

												SCREAMING_SNAKE_CASE							:					Any           =							'''BAAI/AltDiffusion'''
												SCREAMING_SNAKE_CASE							:					Tuple           =							AltDiffusionImgaImgPipeline.from_pretrained(
												    UpperCamelCase__					,	safety_checker=UpperCamelCase__					,	)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)
												pipe.enable_attention_slicing()

												SCREAMING_SNAKE_CASE							:					int           =							'''A fantasy landscape, trending on artstation'''

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							pipe(
												    prompt=UpperCamelCase__					,	image=UpperCamelCase__					,	strength=0.75					,	guidance_scale=7.5					,	generator=UpperCamelCase__					,	output_type='''np'''					,	)
												SCREAMING_SNAKE_CASE							:					Dict           =							output.images[0]

												assert image.shape == (512, 768, 3)
												# img2img is flaky across GPUs even in fp32, so using MAE here
												assert np.abs(expected_image - image		).max() < 1E-2






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__UpperCamelCase	: Optional[Any]    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[Any]    		=  {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""deberta-v2"""


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Any=12_8100					,	UpperCamelCase__			:       Optional[int]=1536					,	UpperCamelCase__			:       Dict=24					,	UpperCamelCase__			:       List[str]=24					,	UpperCamelCase__			:       Tuple=6144					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       Optional[int]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Optional[int]=512					,	UpperCamelCase__			:       Optional[Any]=0					,	UpperCamelCase__			:       Union[str, Any]=0.02					,	UpperCamelCase__			:       List[Any]=1E-7					,	UpperCamelCase__			:       List[str]=False					,	UpperCamelCase__			:       str=-1					,	UpperCamelCase__			:       List[Any]=0					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       Tuple=0					,	UpperCamelCase__			:       str="gelu"					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							hidden_size
												SCREAMING_SNAKE_CASE							:					int           =							num_hidden_layers
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							num_attention_heads
												SCREAMING_SNAKE_CASE							:					List[Any]           =							intermediate_size
												SCREAMING_SNAKE_CASE							:					Any           =							hidden_act
												SCREAMING_SNAKE_CASE							:					Dict           =							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:					str           =							max_position_embeddings
												SCREAMING_SNAKE_CASE							:					Dict           =							type_vocab_size
												SCREAMING_SNAKE_CASE							:					str           =							initializer_range
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							relative_attention
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							max_relative_positions
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							pad_token_id
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							position_biased_input

												# Backwards compatibility
												if type(UpperCamelCase__		) == str:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[x.strip() for x in pos_att_type.lower().split('''|'''		)]

												SCREAMING_SNAKE_CASE							:					Any           =							pos_att_type
												SCREAMING_SNAKE_CASE							:					int           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Dict           =							layer_norm_eps

												SCREAMING_SNAKE_CASE							:					Tuple           =							kwargs.get('''pooler_hidden_size'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							pooler_dropout
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							pooler_hidden_act






class       lowercase__		(     UpperCamelCase_):


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{0: '''batch''', 1: '''sequence'''}
												if self._config.type_vocab_size > 0:
																		return OrderedDict(
																		    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]		)
												else:
																		return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]		)


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       "PreTrainedTokenizerBase" = None					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							super().generate_dummy_inputs(preprocessor=UpperCamelCase__					,	framework=UpperCamelCase__		)
												if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																		del dummy_inputs["token_type_ids"]
												return dummy_inputs





