



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        __lowerCAmelCase   (		__lowercase     ,		unittest.TestCase      ):
 lowerCAmelCase__				    =     KandinskyVaaImgaImgPipeline
 lowerCAmelCase__				    =     ["image_embeds", "negative_image_embeds", "image"]
 lowerCAmelCase__				    =     [
     "image_embeds",
     "negative_image_embeds",
     "image",
 ]
 lowerCAmelCase__				    =     [
     "generator",
     "height",
     "width",
     "strength",
     "guidance_scale",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 lowerCAmelCase__				    =     False




 @property
 def 				lowerCamelCase					(  self ):



    '''simple docstring'''
    return 32




 @property
 def 				lowerCamelCase					(  self ):



    '''simple docstring'''
    return 32




 @property
 def 				lowerCamelCase					(  self ):



    '''simple docstring'''
    return self.time_input_dim




 @property
 def 				lowerCamelCase					(  self ):



    '''simple docstring'''
    return self.time_input_dim * 4




 @property
 def 				lowerCamelCase					(  self ):



    '''simple docstring'''
    return 100




 @property
 def 				lowerCamelCase					(  self ):



    '''simple docstring'''
    torch.manual_seed(0 )

    __lowerCamelCase  							=			{
        '''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,
    }

    __lowerCamelCase  							=			UNetaDConditionModel(**_a )
    return model




 @property
 def 				lowerCamelCase					(  self ):



    '''simple docstring'''
    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 				lowerCamelCase					(  self ):



    '''simple docstring'''
    torch.manual_seed(0 )
    __lowerCamelCase  							=			VQModel(**self.dummy_movq_kwargs )
    return model




 def 				lowerCamelCase					(  self ):



    '''simple docstring'''
    __lowerCamelCase  							=			self.dummy_unet
    __lowerCamelCase  							=			self.dummy_movq

    __lowerCamelCase  							=			{
        '''num_train_timesteps''': 1000,
        '''beta_schedule''': '''linear''',
        '''beta_start''': 0.00_085,
        '''beta_end''': 0.012,
        '''clip_sample''': False,
        '''set_alpha_to_one''': False,
        '''steps_offset''': 0,
        '''prediction_type''': '''epsilon''',
        '''thresholding''': False,
    }

    __lowerCamelCase  							=			DDIMScheduler(**_a )

    __lowerCamelCase  							=			{
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''movq''': movq,
    }

    return components




 def 				lowerCamelCase					(  self		, __UpperCAmelCase		, __UpperCAmelCase=0 ):



    '''simple docstring'''
    __lowerCamelCase  							=			floats_tensor((1, self.text_embedder_hidden_size)		, rng=random.Random(_a ) ).to(_a )
    __lowerCamelCase  							=			floats_tensor((1, self.text_embedder_hidden_size)		, rng=random.Random(seed + 1 ) ).to(
        _a )
    # create init_image
    __lowerCamelCase  							=			floats_tensor((1, 3, 64, 64)		, rng=random.Random(_a ) ).to(_a )
    __lowerCamelCase  							=			image.cpu().permute(0		, 2		, 3		, 1 )[0]
    __lowerCamelCase  							=			Image.fromarray(np.uinta(_a ) ).convert('''RGB''' ).resize((256, 256) )

    if str(_a ).startswith('''mps''' ):
       __lowerCamelCase  							=			torch.manual_seed(_a )
    else:
       __lowerCamelCase  							=			torch.Generator(device=_a ).manual_seed(_a )
    __lowerCamelCase  							=			{
        '''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 				lowerCamelCase					(  self ):



    '''simple docstring'''
    __lowerCamelCase  							=			'''cpu'''

    __lowerCamelCase  							=			self.get_dummy_components()

    __lowerCamelCase  							=			self.pipeline_class(**_a )
    __lowerCamelCase  							=			pipe.to(_a )

    pipe.set_progress_bar_config(disable=_a )

    __lowerCamelCase  							=			pipe(**self.get_dummy_inputs(_a ) )
    __lowerCamelCase  							=			output.images

    __lowerCamelCase  							=			pipe(
        **self.get_dummy_inputs(_a )		, return_dict=_a		, )[0]

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

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

    __lowerCamelCase  							=			np.array(
        [0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263] )
    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        __lowerCAmelCase   (		unittest.TestCase      ):




 def 				lowerCamelCase					(  self ):



    '''simple docstring'''
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 def 				lowerCamelCase					(  self ):



    '''simple docstring'''
    __lowerCamelCase  							=			load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )

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

    __lowerCamelCase  							=			KandinskyVaaPriorPipeline.from_pretrained(
        '''kandinsky-community/kandinsky-2-2-prior'''		, torch_dtype=torch.floataa )
    pipe_prior.to(_a )

    __lowerCamelCase  							=			KandinskyVaaImgaImgPipeline.from_pretrained(
        '''kandinsky-community/kandinsky-2-2-decoder'''		, torch_dtype=torch.floataa )
    __lowerCamelCase  							=			pipeline.to(_a )

    pipeline.set_progress_bar_config(disable=_a )

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

    __lowerCamelCase  							=			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'''		, )

    __lowerCamelCase  							=			output.images[0]

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

    assert_mean_pixel_difference(_a		, _a )









import operator as op


a__				=		'''scaler.pt'''
a__				=		'''pytorch_model'''
a__				=		'''random_states'''
a__				=		'''optimizer'''
a__				=		'''scheduler'''
a__				=		'''pytorch_model.bin'''
a__				=		'''pytorch_model.bin.index.json'''
a__				=		'''model.safetensors'''
a__				=		'''model.safetensors.index.json'''
a__				=		'''1.10.2'''
a__				=		'''py38'''
a__				=		'''4.17.0'''
a__				=		['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
a__				=		['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
a__				=		['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
a__				=		['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
a__				=		['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
a__				=		'''2.0.1'''
a__				=		['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
a__				=		['''default''', '''reduce-overhead''', '''max-autotune''']

a__				=		{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
a__				=		[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

a__				=		['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
a__				=		['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']



