
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        lowercase	(      SCREAMING_SNAKE_CASE__ ,						unittest.TestCase	):
							lowercase_ : Union[str, Any]       =KandinskyInpaintPipeline
							lowercase_ : int       =['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
							lowercase_ : Dict       =[
							    '''prompt''',
							    '''negative_prompt''',
							    '''image_embeds''',
							    '''negative_image_embeds''',
							    '''image''',
							    '''mask_image''',
							]
							lowercase_ : Tuple       =[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''latents''',
							    '''guidance_scale''',
							    '''negative_prompt''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_ : Optional[int]       =False




							@property
							def 				A__   (       self):
												return 3_2




							@property
							def 				A__   (       self):
												return 3_2




							@property
							def 				A__   (       self):
												return self.time_input_dim




							@property
							def 				A__   (       self):
												return self.time_input_dim * 4




							@property
							def 				A__   (       self):
												return 1_0_0




							@property
							def 				A__   (       self):
												lowercase					=		XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''')
												return tokenizer




							@property
							def 				A__   (       self):
												torch.manual_seed(0)
												lowercase					=		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     ,)

												lowercase					=		MultilingualCLIP(A__)
												lowercase					=		text_encoder.eval()

												return text_encoder




							@property
							def 				A__   (       self):
												torch.manual_seed(0)

												lowercase					=		{
												    '''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,
												}

												lowercase					=		UNetaDConditionModel(**A__)
												return model




							@property
							def 				A__   (       self):
												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 				A__   (       self):
												torch.manual_seed(0)
												lowercase					=		VQModel(**self.dummy_movq_kwargs)
												return model




							def 				A__   (       self):
												lowercase					=		self.dummy_text_encoder
												lowercase					=		self.dummy_tokenizer
												lowercase					=		self.dummy_unet
												lowercase					=		self.dummy_movq

												lowercase					=		DDIMScheduler(
												    num_train_timesteps=1_0_0_0     ,beta_schedule='''linear'''     ,beta_start=0.00085     ,beta_end=0.012     ,clip_sample=A__     ,set_alpha_to_one=A__     ,steps_offset=1     ,prediction_type='''epsilon'''     ,thresholding=A__     ,)

												lowercase					=		{
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}

												return components




							def 				A__   (       self     ,A__     ,A__=0):
												lowercase					=		floats_tensor((1, self.cross_attention_dim)     ,rng=random.Random(A__)).to(A__)
												lowercase					=		floats_tensor((1, self.cross_attention_dim)     ,rng=random.Random(seed + 1)).to(A__)
												# create init_image
												lowercase					=		floats_tensor((1, 3, 6_4, 6_4)     ,rng=random.Random(A__)).to(A__)
												lowercase					=		image.cpu().permute(0     ,2     ,3     ,1)[0]
												lowercase					=		Image.fromarray(np.uinta(A__)).convert('''RGB''').resize((2_5_6, 2_5_6))
												# create mask
												lowercase					=		np.ones((6_4, 6_4)     ,dtype=np.floataa)
												lowercase					=		0

												if str(A__).startswith('''mps'''):
																	lowercase					=		torch.manual_seed(A__)
												else:
																	lowercase					=		torch.Generator(device=A__).manual_seed(A__)
												lowercase					=		{
												    '''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 				A__   (       self):
												lowercase					=		'''cpu'''

												lowercase					=		self.get_dummy_components()

												lowercase					=		self.pipeline_class(**A__)
												lowercase					=		pipe.to(A__)

												pipe.set_progress_bar_config(disable=A__)

												lowercase					=		pipe(**self.get_dummy_inputs(A__))
												lowercase					=		output.images

												lowercase					=		pipe(
												    **self.get_dummy_inputs(A__)     ,return_dict=A__     ,)[0]

												lowercase					=		image[0, -3:, -3:, -1]
												lowercase					=		image_from_tuple[0, -3:, -3:, -1]

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

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

												lowercase					=		np.array(
												    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786])

												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        lowercase	(      unittest.TestCase	):




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





							def 				A__   (       self):
												lowercase					=		load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''')

												lowercase					=		load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''')
												lowercase					=		np.ones((7_6_8, 7_6_8)     ,dtype=np.floataa)
												lowercase					=		0

												lowercase					=		'''a hat'''

												lowercase					=		KandinskyPriorPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-prior'''     ,torch_dtype=torch.floataa)
												pipe_prior.to(A__)

												lowercase					=		KandinskyInpaintPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-inpaint'''     ,torch_dtype=torch.floataa)
												lowercase					=		pipeline.to(A__)
												pipeline.set_progress_bar_config(disable=A__)

												lowercase					=		torch.Generator(device='''cpu''').manual_seed(0)
												lowercase			,		lowercase					=		pipe_prior(
												    A__     ,generator=A__     ,num_inference_steps=5     ,negative_prompt=''''''     ,).to_tuple()

												lowercase					=		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'''     ,)

												lowercase					=		output.images[0]

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

												assert_mean_pixel_difference(A__     ,A__)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__	:Tuple										=					{
    "configuration_instructblip": [
        "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InstructBlipConfig",
        "InstructBlipQFormerConfig",
        "InstructBlipVisionConfig",
    ],
    "processing_instructblip": ["InstructBlipProcessor"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:List[str]										=					[
							    "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "InstructBlipQFormerModel",
							    "InstructBlipPreTrainedModel",
							    "InstructBlipForConditionalGeneration",
							    "InstructBlipVisionModel",
							]

if TYPE_CHECKING:
							from .configuration_instructblip import (
							    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    InstructBlipConfig,
							    InstructBlipQFormerConfig,
							    InstructBlipVisionConfig,
							)
							from .processing_instructblip import InstructBlipProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_instructblip import (
														    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    InstructBlipForConditionalGeneration,
														    InstructBlipPreTrainedModel,
														    InstructBlipQFormerModel,
														    InstructBlipVisionModel,
														)

else:
							import sys

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




