





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    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     snake_case_		(   a,		unittest.TestCase							):






 '''simple docstring'''





 __UpperCamelCase       =      KandinskyVaaImgaImgPipeline
 __UpperCamelCase       =      ['image_embeds', 'negative_image_embeds', 'image']
 __UpperCamelCase       =      [
     'image_embeds',
     'negative_image_embeds',
     'image',
 ]
 __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  __UpperCAmelCase						(  self		)	->       Any:
    return 32

 @property
 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    return 32

 @property
 def  __UpperCAmelCase						(  self		)	->       List[str]:
    return self.time_input_dim

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

 @property
 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    return 100

 @property
 def  __UpperCAmelCase						(  self		)	->       Tuple:
    torch.manual_seed(0		)

    UpperCAmelCase__						={
        "in_channels": 4,
        # Out channels is double in channels because predicts mean and variance
        "out_channels": 8,
        "addition_embed_type": "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": "image_proj",
        "cross_attention_dim": self.cross_attention_dim,
        "attention_head_dim": 4,
        "resnet_time_scale_shift": "scale_shift",
        "class_embed_type": None,
    }

    UpperCAmelCase__						=UNetaDConditionModel(**A_		)
    return model

 @property
 def  __UpperCAmelCase						(  self		)	->       Tuple:
    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  __UpperCAmelCase						(  self		)	->       Dict:
    torch.manual_seed(0		)
    UpperCAmelCase__						=VQModel(**self.dummy_movq_kwargs		)
    return model

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.dummy_unet
    UpperCAmelCase__						=self.dummy_movq

    UpperCAmelCase__						={
        "num_train_timesteps": 1000,
        "beta_schedule": "linear",
        "beta_start": 0.0_00_85,
        "beta_end": 0.0_12,
        "clip_sample": False,
        "set_alpha_to_one": False,
        "steps_offset": 0,
        "prediction_type": "epsilon",
        "thresholding": False,
    }

    UpperCAmelCase__						=DDIMScheduler(**A_		)

    UpperCAmelCase__						={
        "unet": unet,
        "scheduler": scheduler,
        "movq": movq,
    }

    return components

 def  __UpperCAmelCase						(  self,		A_,		A_=0		)	->       Optional[int]:
    UpperCAmelCase__						=floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(A_		)		).to(A_		)
    UpperCAmelCase__						=floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(seed + 1		)		).to(
        A_		)
    # create init_image
    UpperCAmelCase__						=floats_tensor((1, 3, 64, 64),		rng=random.Random(A_		)		).to(A_		)
    UpperCAmelCase__						=image.cpu().permute(0,		2,		3,		1		)[0]
    UpperCAmelCase__						=Image.fromarray(np.uinta(A_		)		).convert("RGB"		).resize((256, 256)		)

    if str(A_		).startswith("mps"		):
       UpperCAmelCase__						=torch.manual_seed(A_		)
    else:
       UpperCAmelCase__						=torch.Generator(device=A_		).manual_seed(A_		)
    UpperCAmelCase__						={
        "image": init_image,
        "image_embeds": image_embeds,
        "negative_image_embeds": negative_image_embeds,
        "generator": generator,
        "height": 64,
        "width": 64,
        "num_inference_steps": 10,
        "guidance_scale": 7.0,
        "strength": 0.2,
        "output_type": "np",
    }
    return inputs






 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						="cpu"

    UpperCAmelCase__						=self.get_dummy_components()

    UpperCAmelCase__						=self.pipeline_class(**A_		)
    UpperCAmelCase__						=pipe.to(A_		)

    pipe.set_progress_bar_config(disable=A_		)

    UpperCAmelCase__						=pipe(**self.get_dummy_inputs(A_		)		)
    UpperCAmelCase__						=output.images

    UpperCAmelCase__						=pipe(
        **self.get_dummy_inputs(A_		),		return_dict=A_,		)[0]

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

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

    UpperCAmelCase__						=np.array(
        [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]		)
    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     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





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






 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/kandinskyv22/kandinskyv22_img2img_frog.npy"		)

    UpperCAmelCase__						=load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"		)
    UpperCAmelCase__						="A red cartoon frog, 4k"

    UpperCAmelCase__						=KandinskyVaaPriorPipeline.from_pretrained(
        "kandinsky-community/kandinsky-2-2-prior",		torch_dtype=torch.floataa		)
    pipe_prior.to(A_		)

    UpperCAmelCase__						=KandinskyVaaImgaImgPipeline.from_pretrained(
        "kandinsky-community/kandinsky-2-2-decoder",		torch_dtype=torch.floataa		)
    UpperCAmelCase__						=pipeline.to(A_		)

    pipeline.set_progress_bar_config(disable=A_		)

    UpperCAmelCase__						=torch.Generator(device="cpu"		).manual_seed(0		)
    UpperCAmelCase__			,							UpperCAmelCase__						=pipe_prior(
        A_,		generator=A_,		num_inference_steps=5,		negative_prompt="",		).to_tuple()

    UpperCAmelCase__						=pipeline(
        image=A_,		image_embeds=A_,		negative_image_embeds=A_,		generator=A_,		num_inference_steps=100,		height=768,		width=768,		strength=0.2,		output_type="np",		)

    UpperCAmelCase__						=output.images[0]

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

    assert_mean_pixel_difference(A_,		A_		)










import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      ['image_processor', 'tokenizer']
 __UpperCamelCase       =      'BlipImageProcessor'
 __UpperCamelCase       =      'AutoTokenizer'

 def __init__(  self,		A_,		A_,		A_		)	->       str:
    super().__init__(A_,		A_		)

    # add QFormer tokenizer
    UpperCAmelCase__						=qformer_tokenizer

 def __call__(  self,		A_ = None,		A_ = None,		A_ = True,		A_ = False,		A_ = None,		A_ = None,		A_ = 0,		A_ = None,		A_ = None,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = True,		A_ = None,		**A_,		)	->       BatchFeature:
    if images is None and text is None:
       raise ValueError("You have to specify at least images or text."		)

    UpperCAmelCase__						=BatchFeature()

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       encoding.update(A_		)
       UpperCAmelCase__						=self.qformer_tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       UpperCAmelCase__						=qformer_text_encoding.pop("input_ids"		)
       UpperCAmelCase__						=qformer_text_encoding.pop("attention_mask"		)

    if images is not None:
       UpperCAmelCase__						=self.image_processor(A_,		return_tensors=A_		)
       encoding.update(A_		)

    return encoding

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       List[Any]:
    return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)

 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.tokenizer.model_input_names
    UpperCAmelCase__						=self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)

 def  __UpperCAmelCase						(  self,		A_,		**A_		)	->       str:
    if os.path.isfile(A_		):
       raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
    os.makedirs(A_,		exist_ok=A_		)
    UpperCAmelCase__						=os.path.join(A_,		"qformer_tokenizer"		)
    self.qformer_tokenizer.save_pretrained(A_		)
    return super().save_pretrained(A_,		**A_		)






 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		**A_		)	->       int:
    UpperCAmelCase__						=AutoTokenizer.from_pretrained(A_,		subfolder="qformer_tokenizer"		)
    UpperCAmelCase__						=cls._get_arguments_from_pretrained(A_,		**A_		)
    args.append(A_		)
    return cls(*A_		)



