






"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class       __A		(   UpperCAmelCase__     ,unittest.TestCase			):
					UpperCAmelCase__								=				KandinskyVaaControlnetImgaImgPipeline
					UpperCAmelCase__								=				["image_embeds", "negative_image_embeds", "image", "hint"]
					UpperCAmelCase__								=				["image_embeds", "negative_image_embeds", "image", "hint"]
					UpperCAmelCase__								=				[
					    "generator",
					    "height",
					    "width",
					    "strength",
					    "guidance_scale",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					UpperCAmelCase__								=				False

					@property
					def   lowerCamelCase__   (			self  :      Tuple					)			->							int:
											return 3_2

					@property
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							List[Any]:
											return 3_2

					@property
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							str:
											return self.time_input_dim

					@property
					def   lowerCamelCase__   (			self  :      Any					)			->							List[Any]:
											return self.time_input_dim * 4

					@property
					def   lowerCamelCase__   (			self  :      List[str]					)			->							Any:
											return 1_0_0

					@property
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							str:
											torch.manual_seed(0					)

											__magic_name__:	Any							=       {
											    """in_channels""": 8,
											    # Out channels is double in channels because predicts mean and variance
											    """out_channels""": 8,
											    """addition_embed_type""": """image_hint""",
											    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
											    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
											    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
											    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
											    """layers_per_block""": 1,
											    """encoder_hid_dim""": self.text_embedder_hidden_size,
											    """encoder_hid_dim_type""": """image_proj""",
											    """cross_attention_dim""": self.cross_attention_dim,
											    """attention_head_dim""": 4,
											    """resnet_time_scale_shift""": """scale_shift""",
											    """class_embed_type""": None,
											}

											__magic_name__:	Optional[Any]							=       UNetaDConditionModel(**_UpperCAmelCase					)
											return model

					@property
					def   lowerCamelCase__   (			self  :      Dict					)			->							List[str]:
											return {
											    "block_out_channels": [3_2, 3_2, 6_4, 6_4],
											    "down_block_types": [
											        "DownEncoderBlock2D",
											        "DownEncoderBlock2D",
											        "DownEncoderBlock2D",
											        "AttnDownEncoderBlock2D",
											    ],
											    "in_channels": 3,
											    "latent_channels": 4,
											    "layers_per_block": 1,
											    "norm_num_groups": 8,
											    "norm_type": "spatial",
											    "num_vq_embeddings": 1_2,
											    "out_channels": 3,
											    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
											    "vq_embed_dim": 4,
											}

					@property
					def   lowerCamelCase__   (			self  :      Any					)			->							Dict:
											torch.manual_seed(0					)
											__magic_name__:	List[str]							=       VQModel(**self.dummy_movq_kwargs					)
											return model

					def   lowerCamelCase__   (			self  :      Tuple					)			->							str:
											__magic_name__:	Union[str, Any]							=       self.dummy_unet
											__magic_name__:	Any							=       self.dummy_movq

											__magic_name__:	List[Any]							=       {
											    """num_train_timesteps""": 1_0_0_0,
											    """beta_schedule""": """linear""",
											    """beta_start""": 0.00085,
											    """beta_end""": 0.012,
											    """clip_sample""": False,
											    """set_alpha_to_one""": False,
											    """steps_offset""": 0,
											    """prediction_type""": """epsilon""",
											    """thresholding""": False,
											}

											__magic_name__:	int							=       DDIMScheduler(**_UpperCAmelCase					)

											__magic_name__:	Tuple							=       {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """movq""": movq,
											}

											return components

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      List[str]     ,		__snake_case  :      int=0					)			->							Dict:
											__magic_name__:	List[str]							=       floats_tensor((1, self.text_embedder_hidden_size)     ,		rng=random.Random(_UpperCAmelCase					)					).to(_UpperCAmelCase					)
											__magic_name__:	Any							=       floats_tensor((1, self.text_embedder_hidden_size)     ,		rng=random.Random(seed + 1					)					).to(
											    _UpperCAmelCase					)
											# create init_image
											__magic_name__:	List[Any]							=       floats_tensor((1, 3, 6_4, 6_4)     ,		rng=random.Random(_UpperCAmelCase					)					).to(_UpperCAmelCase					)
											__magic_name__:	Any							=       image.cpu().permute(0     ,		2     ,		3     ,		1					)[0]
											__magic_name__:	Dict							=       Image.fromarray(np.uinta(_UpperCAmelCase					)					).convert("""RGB"""					).resize((2_5_6, 2_5_6)					)
											# create hint
											__magic_name__:	str							=       floats_tensor((1, 3, 6_4, 6_4)     ,		rng=random.Random(_UpperCAmelCase					)					).to(_UpperCAmelCase					)

											if str(_UpperCAmelCase					).startswith("""mps"""					):
																	__magic_name__:	Optional[Any]							=       torch.manual_seed(_UpperCAmelCase					)
											else:
																	__magic_name__:	Tuple							=       torch.Generator(device=_UpperCAmelCase					).manual_seed(_UpperCAmelCase					)
											__magic_name__:	List[str]							=       {
											    """image""": init_image,
											    """image_embeds""": image_embeds,
											    """negative_image_embeds""": negative_image_embeds,
											    """hint""": hint,
											    """generator""": generator,
											    """height""": 6_4,
											    """width""": 6_4,
											    """num_inference_steps""": 1_0,
											    """guidance_scale""": 7.0,
											    """strength""": 0.2,
											    """output_type""": """np""",
											}
											return inputs






					def   lowerCamelCase__   (			self  :      int					)			->							List[str]:
											__magic_name__:	int							=       """cpu"""

											__magic_name__:	Optional[int]							=       self.get_dummy_components()

											__magic_name__:	Any							=       self.pipeline_class(**_UpperCAmelCase					)
											__magic_name__:	int							=       pipe.to(_UpperCAmelCase					)

											pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

											__magic_name__:	int							=       pipe(**self.get_dummy_inputs(_UpperCAmelCase					)					)
											__magic_name__:	Optional[int]							=       output.images

											__magic_name__:	str							=       pipe(
											    **self.get_dummy_inputs(_UpperCAmelCase					)     ,		return_dict=_UpperCAmelCase     ,		)[0]

											__magic_name__:	Optional[int]							=       image[0, -3:, -3:, -1]
											__magic_name__:	List[str]							=       image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)

											__magic_name__:	Any							=       np.array(
											    [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]					)
											assert (
											    np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
											), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
											assert (
											    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2
											), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'



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

					def   lowerCamelCase__   (			self  :      List[str]					)			->							Any:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






					def   lowerCamelCase__   (			self  :      List[Any]					)			->							List[Any]:
											__magic_name__:	Tuple							=       load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""					)

											__magic_name__:	Union[str, Any]							=       load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
											__magic_name__:	Dict							=       init_image.resize((5_1_2, 5_1_2)					)

											__magic_name__:	Optional[Any]							=       load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinskyv22/hint_image_cat.png"""					)
											__magic_name__:	Any							=       torch.from_numpy(np.array(_UpperCAmelCase					)					).float() / 255.0
											__magic_name__:	List[str]							=       hint.permute(2     ,		0     ,		1					).unsqueeze(0					)

											__magic_name__:	List[str]							=       """A robot, 4k photo"""

											__magic_name__:	Union[str, Any]							=       KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-prior"""     ,		torch_dtype=torch.floataa					)
											pipe_prior.to(_UpperCAmelCase					)

											__magic_name__:	Any							=       KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-controlnet-depth"""     ,		torch_dtype=torch.floataa					)
											__magic_name__:	Optional[Any]							=       pipeline.to(_UpperCAmelCase					)

											pipeline.set_progress_bar_config(disable=_UpperCAmelCase					)

											__magic_name__:	Optional[Any]							=       torch.Generator(device="""cpu"""					).manual_seed(0					)

											__magic_name__,			__magic_name__:	Optional[int]							=       pipe_prior(
											    _UpperCAmelCase     ,		image=_UpperCAmelCase     ,		strength=0.85     ,		generator=_UpperCAmelCase     ,		negative_prompt=""""""     ,		).to_tuple()

											__magic_name__:	Tuple							=       pipeline(
											    image=_UpperCAmelCase     ,		image_embeds=_UpperCAmelCase     ,		negative_image_embeds=_UpperCAmelCase     ,		hint=_UpperCAmelCase     ,		generator=_UpperCAmelCase     ,		num_inference_steps=1_0_0     ,		height=5_1_2     ,		width=5_1_2     ,		strength=0.5     ,		output_type="""np"""     ,		)

											__magic_name__:	Dict							=       output.images[0]

											assert image.shape == (5_1_2, 5_1_2, 3)

											assert_mean_pixel_difference(_UpperCAmelCase     ,		_UpperCAmelCase					)







from collections import OrderedDict
from typing import Mapping

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


A			:						List[str]        =  logging.get_logger(__name__)

A			:						Tuple        =  {
    '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 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''ibert'''

					def __init__(self       :		int ,   _UpperCAmelCase       :		Union[str, Any]=3_0522 ,   _UpperCAmelCase       :		Any=768 ,   _UpperCAmelCase       :		Optional[Any]=12 ,   _UpperCAmelCase       :		Union[str, Any]=12 ,   _UpperCAmelCase       :		List[Any]=3072 ,   _UpperCAmelCase       :		List[Any]="gelu" ,   _UpperCAmelCase       :		List[str]=0.1 ,   _UpperCAmelCase       :		Union[str, Any]=0.1 ,   _UpperCAmelCase       :		Optional[int]=512 ,   _UpperCAmelCase       :		str=2 ,   _UpperCAmelCase       :		Union[str, Any]=0.02 ,   _UpperCAmelCase       :		Dict=1E-1_2 ,   _UpperCAmelCase       :		int=1 ,   _UpperCAmelCase       :		str=0 ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		int="absolute" ,   _UpperCAmelCase       :		Any=False ,   _UpperCAmelCase       :		List[Any]="none" ,   **_UpperCAmelCase       :		List[Any] ,   )		->  Any:




							"""simple docstring"""


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

							lowercase__	     =						vocab_size
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						hidden_act
							lowercase__	     =						intermediate_size
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_size
							lowercase__	     =						initializer_range
							lowercase__	     =						layer_norm_eps
							lowercase__	     =						position_embedding_type
							lowercase__	     =						quant_mode
							lowercase__	     =						force_dequant






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






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




							"""simple docstring"""


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

