

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 				UpperCAmelCase_		(			__lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[int]	    =    KandinskyInpaintPipeline
							UpperCAmelCase__       :						Tuple	    =    ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
							UpperCAmelCase__       :						Optional[Any]	    =    [
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							    "mask_image",
							]
							UpperCAmelCase__       :						Dict	    =    [
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase__       :						Optional[Any]	    =    False


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


							@property
							def 				__lowercase				(			self       )    ->    Any:
												return 3_2


							@property
							def 				__lowercase				(			self       )    ->    Dict:
												return self.time_input_dim


							@property
							def 				__lowercase				(			self       )    ->    int:
												return self.time_input_dim * 4


							@property
							def 				__lowercase				(			self       )    ->    Tuple:
												return 1_0_0


							@property
							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Optional[Any]         =		XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''       )
												return tokenizer


							@property
							def 				__lowercase				(			self       )    ->    str:
												torch.manual_seed(0       )
												_a	:    List[str]         =		MCLIPConfig(
												    numDims=self.cross_attention_dim       ,							transformerDimensions=self.text_embedder_hidden_size       ,							hidden_size=self.text_embedder_hidden_size       ,							intermediate_size=3_7       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							vocab_size=1_0_0_5       ,							)

												_a	:    List[Any]         =		MultilingualCLIP(_a       )
												_a	:    str         =		text_encoder.eval()

												return text_encoder


							@property
							def 				__lowercase				(			self       )    ->    str:
												torch.manual_seed(0       )

												_a	:    Any         =		{
												    '''in_channels''': 9,
												    # Out channels is double in channels because predicts mean and variance
												    '''out_channels''': 8,
												    '''addition_embed_type''': '''text_image''',
												    '''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''': '''text_image_proj''',
												    '''cross_attention_dim''': self.cross_attention_dim,
												    '''attention_head_dim''': 4,
												    '''resnet_time_scale_shift''': '''scale_shift''',
												    '''class_embed_type''': None,
												}

												_a	:    List[Any]         =		UNetaDConditionModel(**_a       )
												return model


							@property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												return {
												    "block_out_channels": [3_2, 6_4],
												    "down_block_types": ["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",
												    ],
												    "vq_embed_dim": 4,
												}


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


							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    str         =		self.dummy_text_encoder
												_a	:    Optional[int]         =		self.dummy_tokenizer
												_a	:    Tuple         =		self.dummy_unet
												_a	:    Union[str, Any]         =		self.dummy_movq

												_a	:    Tuple         =		DDIMScheduler(
												    num_train_timesteps=1_0_0_0       ,							beta_schedule='''linear'''       ,							beta_start=0.0_0085       ,							beta_end=0.012       ,							clip_sample=_a       ,							set_alpha_to_one=_a       ,							steps_offset=1       ,							prediction_type='''epsilon'''       ,							thresholding=_a       ,							)

												_a	:    Union[str, Any]         =		{
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}

												return components


							def 				__lowercase				(			self       ,							_a       ,							_a=0       )    ->    List[str]:
												_a	:    str         =		floats_tensor((1, self.cross_attention_dim)       ,							rng=random.Random(_a       )       ).to(_a       )
												_a	:    List[str]         =		floats_tensor((1, self.cross_attention_dim)       ,							rng=random.Random(seed + 1       )       ).to(_a       )
												# create init_image
												_a	:    int         =		floats_tensor((1, 3, 6_4, 6_4)       ,							rng=random.Random(_a       )       ).to(_a       )
												_a	:    Optional[Any]         =		image.cpu().permute(0       ,							2       ,							3       ,							1       )[0]
												_a	:    Union[str, Any]         =		Image.fromarray(np.uinta(_a       )       ).convert('''RGB'''       ).resize((2_5_6, 2_5_6)       )
												# create mask
												_a	:    str         =		np.ones((6_4, 6_4)       ,							dtype=np.floataa       )
												_a	:    Tuple         =		0

												if str(_a       ).startswith('''mps'''       ):
																	_a	:    Union[str, Any]         =		torch.manual_seed(_a       )
												else:
																	_a	:    List[str]         =		torch.Generator(device=_a       ).manual_seed(_a       )
												_a	:    Any         =		{
												    '''prompt''': '''horse''',
												    '''image''': init_image,
												    '''mask_image''': mask,
												    '''image_embeds''': image_embeds,
												    '''negative_image_embeds''': negative_image_embeds,
												    '''generator''': generator,
												    '''height''': 6_4,
												    '''width''': 6_4,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 4.0,
												    '''output_type''': '''np''',
												}
												return inputs


							def 				__lowercase				(			self       )    ->    int:
												_a	:    List[str]         =		'''cpu'''

												_a	:    Tuple         =		self.get_dummy_components()

												_a	:    Tuple         =		self.pipeline_class(**_a       )
												_a	:    Dict         =		pipe.to(_a       )

												pipe.set_progress_bar_config(disable=_a       )

												_a	:    Any         =		pipe(**self.get_dummy_inputs(_a       )       )
												_a	:    Tuple         =		output.images

												_a	:    Optional[Any]         =		pipe(
												    **self.get_dummy_inputs(_a       )       ,							return_dict=_a       ,							)[0]

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

												print(F"""image.shape {image.shape}"""       )

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

												_a	:    Any         =		np.array(
												    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]       )

												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()}"""




							def 				__lowercase				(			self       )    ->    Any:
												super().test_inference_batch_single_identical(expected_max_diff=3e-3       )



@slow
@require_torch_gpu
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







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




							def 				__lowercase				(			self       )    ->    List[str]:
												_a	:    Any         =		load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''       )

												_a	:    Union[str, Any]         =		load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''       )
												_a	:    Dict         =		np.ones((7_6_8, 7_6_8)       ,							dtype=np.floataa       )
												_a	:    Optional[int]         =		0

												_a	:    Optional[Any]         =		'''a hat'''

												_a	:    Optional[int]         =		KandinskyPriorPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-prior'''       ,							torch_dtype=torch.floataa       )
												pipe_prior.to(_a       )

												_a	:    str         =		KandinskyInpaintPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-inpaint'''       ,							torch_dtype=torch.floataa       )
												_a	:    Optional[Any]         =		pipeline.to(_a       )
												pipeline.set_progress_bar_config(disable=_a       )

												_a	:    Any         =		torch.Generator(device='''cpu'''       ).manual_seed(0       )
												_a      ,					_a	:    Optional[Any]         =		pipe_prior(
												    _a       ,							generator=_a       ,							num_inference_steps=5       ,							negative_prompt=''''''       ,							).to_tuple()

												_a	:    Tuple         =		pipeline(
												    _a       ,							image=_a       ,							mask_image=_a       ,							image_embeds=_a       ,							negative_image_embeds=_a       ,							generator=_a       ,							num_inference_steps=1_0_0       ,							height=7_6_8       ,							width=7_6_8       ,							output_type='''np'''       ,							)

												_a	:    str         =		output.images[0]

												assert image.shape == (7_6_8, 7_6_8, 3)

												assert_mean_pixel_difference(_a       ,							_a       )






import logging

from transformers import PretrainedConfig


a__				=		logging.getLogger(__name__)


a__				=		{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bertabs"


							def __init__(			self       ,							_a=3_0_5_2_2       ,							_a=5_1_2       ,							_a=6       ,							_a=5_1_2       ,							_a=8       ,							_a=5_1_2       ,							_a=0.2       ,							_a=6       ,							_a=7_6_8       ,							_a=8       ,							_a=2_0_4_8       ,							_a=0.2       ,							**_a       ,							)    ->    Any:
												super().__init__(**_a       )

												_a	:    int         =		vocab_size
												_a	:    List[str]         =		max_pos

												_a	:    Tuple         =		enc_layers
												_a	:    Optional[Any]         =		enc_hidden_size
												_a	:    int         =		enc_heads
												_a	:    Optional[Any]         =		enc_ff_size
												_a	:    List[str]         =		enc_dropout

												_a	:    Tuple         =		dec_layers
												_a	:    Optional[Any]         =		dec_hidden_size
												_a	:    Optional[Any]         =		dec_heads
												_a	:    Optional[Any]         =		dec_ff_size
												_a	:    List[Any]         =		dec_dropout



