'''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 	__A				(			UpperCamelCase__		,     unittest.TestCase     ):
				a__	:						Dict	       =  KandinskyInpaintPipeline
				a__	:						List[str]	       =  ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
				a__	:						Tuple	       =  [
				    '''prompt''',
				    '''negative_prompt''',
				    '''image_embeds''',
				    '''negative_image_embeds''',
				    '''image''',
				    '''mask_image''',
				]
				a__	:						Optional[int]	       =  [
				    '''generator''',
				    '''height''',
				    '''width''',
				    '''latents''',
				    '''guidance_scale''',
				    '''negative_prompt''',
				    '''num_inference_steps''',
				    '''return_dict''',
				    '''guidance_scale''',
				    '''num_images_per_prompt''',
				    '''output_type''',
				    '''return_dict''',
				]
				a__	:						str	       =  False



				@property
				def  _lowercase						(self   :   str			):
									return 32



				@property
				def  _lowercase						(self   :   int			):
									return 32



				@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   :   int			):
									return 100



				@property
				def  _lowercase						(self   :   int			):
									UpperCAmelCase_	   =       XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"			)
									return tokenizer



				@property
				def  _lowercase						(self   :   List[str]			):
									torch.manual_seed(0			)
									UpperCAmelCase_	   =       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       ,      )

									UpperCAmelCase_	   =       MultilingualCLIP(UpperCamelCase_			)
									UpperCAmelCase_	   =       text_encoder.eval()

									return text_encoder



				@property
				def  _lowercase						(self   :   Any			):
									torch.manual_seed(0			)

									UpperCAmelCase_	   =       {
									    "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,
									}

									UpperCAmelCase_	   =       UNetaDConditionModel(**UpperCamelCase_			)
									return model



				@property
				def  _lowercase						(self   :   str			):
									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  _lowercase						(self   :   Union[str, Any]			):
									torch.manual_seed(0			)
									UpperCAmelCase_	   =       VQModel(**self.dummy_movq_kwargs			)
									return model



				def  _lowercase						(self   :   List[str]			):
									UpperCAmelCase_	   =       self.dummy_text_encoder
									UpperCAmelCase_	   =       self.dummy_tokenizer
									UpperCAmelCase_	   =       self.dummy_unet
									UpperCAmelCase_	   =       self.dummy_movq

									UpperCAmelCase_	   =       DDIMScheduler(
									    num_train_timesteps=1000       ,      beta_schedule="linear"       ,      beta_start=0.0_00_85       ,      beta_end=0.0_12       ,      clip_sample=UpperCamelCase_       ,      set_alpha_to_one=UpperCamelCase_       ,      steps_offset=1       ,      prediction_type="epsilon"       ,      thresholding=UpperCamelCase_       ,      )

									UpperCAmelCase_	   =       {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "movq": movq,
									}

									return components



				def  _lowercase						(self   :   List[str]       ,      __a   :   str       ,      __a   :   Union[str, Any]=0			):
									UpperCAmelCase_	   =       floats_tensor((1, self.cross_attention_dim)       ,      rng=random.Random(UpperCamelCase_			)			).to(UpperCamelCase_			)
									UpperCAmelCase_	   =       floats_tensor((1, self.cross_attention_dim)       ,      rng=random.Random(seed + 1			)			).to(UpperCamelCase_			)
									# create init_image
									UpperCAmelCase_	   =       floats_tensor((1, 3, 64, 64)       ,      rng=random.Random(UpperCamelCase_			)			).to(UpperCamelCase_			)
									UpperCAmelCase_	   =       image.cpu().permute(0       ,      2       ,      3       ,      1			)[0]
									UpperCAmelCase_	   =       Image.fromarray(np.uinta(UpperCamelCase_			)			).convert("RGB"			).resize((256, 256)			)
									# create mask
									UpperCAmelCase_	   =       np.ones((64, 64)       ,      dtype=np.floataa			)
									UpperCAmelCase_	   =       0

									if str(UpperCamelCase_			).startswith("mps"			):
														UpperCAmelCase_	   =       torch.manual_seed(UpperCamelCase_			)
									else:
														UpperCAmelCase_	   =       torch.Generator(device=UpperCamelCase_			).manual_seed(UpperCamelCase_			)
									UpperCAmelCase_	   =       {
									    "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  _lowercase						(self   :   Optional[Any]			):
									UpperCAmelCase_	   =       "cpu"

									UpperCAmelCase_	   =       self.get_dummy_components()

									UpperCAmelCase_	   =       self.pipeline_class(**UpperCamelCase_			)
									UpperCAmelCase_	   =       pipe.to(UpperCamelCase_			)

									pipe.set_progress_bar_config(disable=UpperCamelCase_			)

									UpperCAmelCase_	   =       pipe(**self.get_dummy_inputs(UpperCamelCase_			)			)
									UpperCAmelCase_	   =       output.images

									UpperCAmelCase_	   =       pipe(
									    **self.get_dummy_inputs(UpperCamelCase_			)       ,      return_dict=UpperCamelCase_       ,      )[0]

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

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

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

									UpperCAmelCase_	   =       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  _lowercase						(self   :   Optional[int]			):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3			)




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



				def  _lowercase						(self   :   Dict			):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






				def  _lowercase						(self   :   Optional[int]			):
									UpperCAmelCase_	   =       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"			)

									UpperCAmelCase_	   =       load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"			)
									UpperCAmelCase_	   =       np.ones((768, 768)       ,      dtype=np.floataa			)
									UpperCAmelCase_	   =       0

									UpperCAmelCase_	   =       "a hat"

									UpperCAmelCase_	   =       KandinskyPriorPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1-prior"       ,      torch_dtype=torch.floataa			)
									pipe_prior.to(UpperCamelCase_			)

									UpperCAmelCase_	   =       KandinskyInpaintPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1-inpaint"       ,      torch_dtype=torch.floataa			)
									UpperCAmelCase_	   =       pipeline.to(UpperCamelCase_			)
									pipeline.set_progress_bar_config(disable=UpperCamelCase_			)

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

									UpperCAmelCase_	   =       pipeline(
									    UpperCamelCase_       ,      image=UpperCamelCase_       ,      mask_image=UpperCamelCase_       ,      image_embeds=UpperCamelCase_       ,      negative_image_embeds=UpperCamelCase_       ,      generator=UpperCamelCase_       ,      num_inference_steps=100       ,      height=768       ,      width=768       ,      output_type="np"       ,      )

									UpperCAmelCase_	   =       output.images[0]

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

									assert_mean_pixel_difference(UpperCamelCase_       ,      UpperCamelCase_			)





'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Tuple  ={
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}




class 	__A				(			UpperCamelCase__		,     UpperCamelCase__     ):
    a__	:						Optional[Any]	       =  """resnet"""
    a__	:						Tuple	       =  ["""basic""", """bottleneck"""]



    def __init__(self   :   List[Any]       ,      __a   :   Any=3       ,      __a   :   Dict=64       ,      __a   :   Union[str, Any]=[256, 512, 1024, 2048]       ,      __a   :   str=[3, 4, 6, 3]       ,      __a   :   Optional[Any]="bottleneck"       ,      __a   :   Tuple="relu"       ,      __a   :   int=False       ,      __a   :   Optional[int]=None       ,      __a   :   str=None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         if layer_type not in self.layer_types:
              raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types			)}"""			)
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       embedding_size
         UpperCAmelCase_	   =       hidden_sizes
         UpperCAmelCase_	   =       depths
         UpperCAmelCase_	   =       layer_type
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       downsample_in_first_stage
         UpperCAmelCase_	   =       ["stem"] + [f"""stage{idx}""" for idx in range(1       ,      len(__a			) + 1			)]
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       get_aligned_output_features_output_indices(
             out_features=__a       ,      out_indices=__a       ,      stage_names=self.stage_names			)




class 	__A				(			UpperCamelCase__     ):
    a__	:						int	       =  version.parse("""1.11"""     )



    @property
    def  _lowercase						(self   :   Optional[int]			):
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]			)






    @property
    def  _lowercase						(self   :   str			):
         return 1E-3




