




"""simple docstring"""






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 lowerCAmelCase__	(     lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			KandinskyInpaintPipeline
			lowerCamelCase__      =			["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
			lowerCamelCase__      =			[
			    """prompt""",
			    """negative_prompt""",
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			    """mask_image""",
			]
			lowerCamelCase__      =			[
			    """generator""",
			    """height""",
			    """width""",
			    """latents""",
			    """guidance_scale""",
			    """negative_prompt""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			lowerCamelCase__      =			False
			@property
			def 					A_    (  self  ):
							return 32
			@property
			def 					A_    (  self  ):
							return 32
			@property
			def 					A_    (  self  ):
							return self.time_input_dim
			@property
			def 					A_    (  self  ):
							return self.time_input_dim * 4
			@property
			def 					A_    (  self  ):
							return 100
			@property
			def 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
							return tokenizer
			@property
			def 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			str    =    MCLIPConfig(
							    numDims=self.cross_attention_dim  ,      transformerDimensions=self.text_embedder_hidden_size  ,      hidden_size=self.text_embedder_hidden_size  ,      intermediate_size=37  ,      num_attention_heads=4  ,      num_hidden_layers=5  ,      vocab_size=1005  ,      )

							_lowerCamelCase     :			str    =    MultilingualCLIP(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    text_encoder.eval()

							return text_encoder
			@property
			def 					A_    (  self  ):
							torch.manual_seed(0  )

							_lowerCamelCase     :			List[str]    =    {
							    '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,
							}

							_lowerCamelCase     :			Union[str, Any]    =    UNetaDConditionModel(**lowercase  )
							return model
			@property
			def 					A_    (  self  ):
							return {
							    "block_out_channels": [32, 64],
							    "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": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}
			@property
			def 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			str    =    VQModel(**self.dummy_movq_kwargs  )
							return model
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    self.dummy_text_encoder
							_lowerCamelCase     :			Optional[Any]    =    self.dummy_tokenizer
							_lowerCamelCase     :			Optional[int]    =    self.dummy_unet
							_lowerCamelCase     :			Tuple    =    self.dummy_movq

							_lowerCamelCase     :			Dict    =    DDIMScheduler(
							    num_train_timesteps=1000  ,      beta_schedule='linear'  ,      beta_start=0.0_00_85  ,      beta_end=0.0_12  ,      clip_sample=lowercase  ,      set_alpha_to_one=lowercase  ,      steps_offset=1  ,      prediction_type='epsilon'  ,      thresholding=lowercase  ,      )

							_lowerCamelCase     :			List[str]    =    {
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'unet': unet,
							    'scheduler': scheduler,
							    'movq': movq,
							}

							return components
			def 					A_    (  self  ,      lowercase  ,      lowercase=0  ):
							_lowerCamelCase     :			List[Any]    =    floats_tensor((1, self.cross_attention_dim)  ,      rng=random.Random(lowercase  )  ).to(lowercase  )
							_lowerCamelCase     :			List[str]    =    floats_tensor((1, self.cross_attention_dim)  ,      rng=random.Random(seed + 1  )  ).to(lowercase  )
							# create init_image
							_lowerCamelCase     :			int    =    floats_tensor((1, 3, 64, 64)  ,      rng=random.Random(lowercase  )  ).to(lowercase  )
							_lowerCamelCase     :			Union[str, Any]    =    image.cpu().permute(0  ,      2  ,      3  ,      1  )[0]
							_lowerCamelCase     :			List[str]    =    Image.fromarray(np.uinta(lowercase  )  ).convert('RGB'  ).resize((256, 256)  )
							# create mask
							_lowerCamelCase     :			Tuple    =    np.ones((64, 64)  ,      dtype=np.floataa  )
							_lowerCamelCase     :			Optional[int]    =    0

							if str(lowercase  ).startswith('mps'  ):
											_lowerCamelCase     :			List[Any]    =    torch.manual_seed(lowercase  )
							else:
											_lowerCamelCase     :			List[Any]    =    torch.Generator(device=lowercase  ).manual_seed(lowercase  )
							_lowerCamelCase     :			str    =    {
							    'prompt': 'horse',
							    'image': init_image,
							    'mask_image': mask,
							    'image_embeds': image_embeds,
							    'negative_image_embeds': negative_image_embeds,
							    'generator': generator,
							    'height': 64,
							    'width': 64,
							    'num_inference_steps': 2,
							    'guidance_scale': 4.0,
							    'output_type': 'np',
							}
							return inputs
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    'cpu'

							_lowerCamelCase     :			Tuple    =    self.get_dummy_components()

							_lowerCamelCase     :			List[str]    =    self.pipeline_class(**lowercase  )
							_lowerCamelCase     :			Optional[int]    =    pipe.to(lowercase  )

							pipe.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			int    =    pipe(**self.get_dummy_inputs(lowercase  )  )
							_lowerCamelCase     :			List[Any]    =    output.images

							_lowerCamelCase     :			Dict    =    pipe(
							    **self.get_dummy_inputs(lowercase  )  ,      return_dict=lowercase  ,      )[0]

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

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

							assert image.shape == (1, 64, 64, 3)

							_lowerCamelCase     :			List[Any]    =    np.array(
							    [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]  )

							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 					A_    (  self  ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3  )






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






			'''simple docstring'''



			def 					A_    (  self  ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






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

							_lowerCamelCase     :			Optional[int]    =    load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
							_lowerCamelCase     :			str    =    np.ones((768, 768)  ,      dtype=np.floataa  )
							_lowerCamelCase     :			Any    =    0

							_lowerCamelCase     :			Any    =    'a hat'

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

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

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

							_lowerCamelCase     :			int    =    pipeline(
							    lowercase  ,      image=lowercase  ,      mask_image=lowercase  ,      image_embeds=lowercase  ,      negative_image_embeds=lowercase  ,      generator=lowercase  ,      num_inference_steps=100  ,      height=768  ,      width=768  ,      output_type='np'  ,      )

							_lowerCamelCase     :			Dict    =    output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(lowercase  ,      lowercase  )





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__     						=						{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """IBertForMaskedLM""",
			    """IBertForMultipleChoice""",
			    """IBertForQuestionAnswering""",
			    """IBertForSequenceClassification""",
			    """IBertForTokenClassification""",
			    """IBertModel""",
			    """IBertPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ibert import (
						    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    IBertForMaskedLM,
						    IBertForMultipleChoice,
						    IBertForQuestionAnswering,
						    IBertForSequenceClassification,
						    IBertForTokenClassification,
						    IBertModel,
						    IBertPreTrainedModel,
						)

else:
			import sys

			lowercase__     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)