

"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A							=  logging.get_logger(__name__)




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase=False    )	-> List[Any]:
      UpperCAmelCase__			:							Tuple							=    OrderedDict()
      for key, value in state_dict.items():
            if encoder_only and not key.startswith("""head"""    ):
                  UpperCAmelCase__			:							List[str]							=    """segformer.encoder.""" + key
            if key.startswith("""backbone"""    ):
                  UpperCAmelCase__			:							Any							=    key.replace("""backbone"""			,	"""segformer.encoder"""    )
            if "patch_embed" in key:
                  # replace for example patch_embed1 by patch_embeddings.0
                  UpperCAmelCase__			:							Tuple							=    key[key.find("""patch_embed"""    ) + len("""patch_embed"""    )]
                  UpperCAmelCase__			:							str							=    key.replace(F"""patch_embed{idx}"""			,	F"""patch_embeddings.{int(lowerCAmelCase    )-1}"""    )
            if "norm" in key:
                  UpperCAmelCase__			:							Dict							=    key.replace("""norm"""			,	"""layer_norm"""    )
            if "segformer.encoder.layer_norm" in key:
                  # replace for example layer_norm1 by layer_norm.0
                  UpperCAmelCase__			:							int							=    key[key.find("""segformer.encoder.layer_norm"""    ) + len("""segformer.encoder.layer_norm"""    )]
                  UpperCAmelCase__			:							Any							=    key.replace(F"""layer_norm{idx}"""			,	F"""layer_norm.{int(lowerCAmelCase    )-1}"""    )
            if "layer_norm1" in key:
                  UpperCAmelCase__			:							Union[str, Any]							=    key.replace("""layer_norm1"""			,	"""layer_norm_1"""    )
            if "layer_norm2" in key:
                  UpperCAmelCase__			:							List[Any]							=    key.replace("""layer_norm2"""			,	"""layer_norm_2"""    )
            if "block" in key:
                  # replace for example block1 by block.0
                  UpperCAmelCase__			:							Dict							=    key[key.find("""block"""    ) + len("""block"""    )]
                  UpperCAmelCase__			:							int							=    key.replace(F"""block{idx}"""			,	F"""block.{int(lowerCAmelCase    )-1}"""    )
            if "attn.q" in key:
                  UpperCAmelCase__			:							Tuple							=    key.replace("""attn.q"""			,	"""attention.self.query"""    )
            if "attn.proj" in key:
                  UpperCAmelCase__			:							Tuple							=    key.replace("""attn.proj"""			,	"""attention.output.dense"""    )
            if "attn" in key:
                  UpperCAmelCase__			:							Optional[int]							=    key.replace("""attn"""			,	"""attention.self"""    )
            if "fc1" in key:
                  UpperCAmelCase__			:							Dict							=    key.replace("""fc1"""			,	"""dense1"""    )
            if "fc2" in key:
                  UpperCAmelCase__			:							Optional[int]							=    key.replace("""fc2"""			,	"""dense2"""    )
            if "linear_pred" in key:
                  UpperCAmelCase__			:							Optional[int]							=    key.replace("""linear_pred"""			,	"""classifier"""    )
            if "linear_fuse" in key:
                  UpperCAmelCase__			:							Tuple							=    key.replace("""linear_fuse.conv"""			,	"""linear_fuse"""    )
                  UpperCAmelCase__			:							Tuple							=    key.replace("""linear_fuse.bn"""			,	"""batch_norm"""    )
            if "linear_c" in key:
                  # replace for example linear_c4 by linear_c.3
                  UpperCAmelCase__			:							str							=    key[key.find("""linear_c"""    ) + len("""linear_c"""    )]
                  UpperCAmelCase__			:							str							=    key.replace(F"""linear_c{idx}"""			,	F"""linear_c.{int(lowerCAmelCase    )-1}"""    )
            if key.startswith("""head"""    ):
                  UpperCAmelCase__			:							Optional[int]							=    key.replace("""head"""			,	"""classifier"""    )
            UpperCAmelCase__			:							List[str]							=    value

      return new_state_dict




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> Optional[int]:
      # for each of the encoder blocks:
      for i in range(config.num_encoder_blocks    ):
            for j in range(config.depths[i]    ):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  UpperCAmelCase__			:							Optional[Any]							=    state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""    )
                  UpperCAmelCase__			:							List[Any]							=    state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias"""    )
                  # next, add keys and values (in that order) to the state dict
                  UpperCAmelCase__			:							Any							=    kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  UpperCAmelCase__			:							List[Any]							=    kv_bias[: config.hidden_sizes[i]]
                  UpperCAmelCase__			:							List[Any]							=    kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  UpperCAmelCase__			:							int							=    kv_bias[
                      config.hidden_sizes[i] :
                  ]




def 					a__						(						)	-> str:
      UpperCAmelCase__			:							Optional[int]							=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
      UpperCAmelCase__			:							List[str]							=    Image.open(requests.get(lowerCAmelCase			,	stream=lowerCAmelCase    ).raw    )

      return image




@torch.no_grad()
def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> List[str]:
      UpperCAmelCase__			:							Any							=    SegformerConfig()
      UpperCAmelCase__			:							Tuple							=    False

      # set attributes based on model_name
      UpperCAmelCase__			:							Dict							=    """huggingface/label-files"""
      if "segformer" in model_name:
            UpperCAmelCase__			:							Union[str, Any]							=    model_name[len("""segformer."""    ) : len("""segformer."""    ) + 2]
            if "ade" in model_name:
                  UpperCAmelCase__			:							int							=    1_50
                  UpperCAmelCase__			:							Optional[int]							=    """ade20k-id2label.json"""
                  UpperCAmelCase__			:							List[str]							=    (1, 1_50, 1_28, 1_28)
            elif "city" in model_name:
                  UpperCAmelCase__			:							int							=    19
                  UpperCAmelCase__			:							Optional[Any]							=    """cityscapes-id2label.json"""
                  UpperCAmelCase__			:							List[str]							=    (1, 19, 1_28, 1_28)
            else:
                  raise ValueError(F"""Model {model_name} not supported"""    )
      elif "mit" in model_name:
            UpperCAmelCase__			:							List[str]							=    True
            UpperCAmelCase__			:							Optional[int]							=    model_name[4:6]
            UpperCAmelCase__			:							Dict							=    10_00
            UpperCAmelCase__			:							Dict							=    """imagenet-1k-id2label.json"""
            UpperCAmelCase__			:							List[str]							=    (1, 10_00)
      else:
            raise ValueError(F"""Model {model_name} not supported"""    )

      # set config attributes
      UpperCAmelCase__			:							Any							=    json.load(open(hf_hub_download(lowerCAmelCase			,	lowerCAmelCase			,	repo_type="""dataset"""    )			,	"""r"""    )    )
      UpperCAmelCase__			:							Union[str, Any]							=    {int(lowerCAmelCase    ): v for k, v in idalabel.items()}
      UpperCAmelCase__			:							int							=    idalabel
      UpperCAmelCase__			:							Tuple							=    {v: k for k, v in idalabel.items()}
      if size == "b0":
            pass
      elif size == "b1":
            UpperCAmelCase__			:							List[str]							=    [64, 1_28, 3_20, 5_12]
            UpperCAmelCase__			:							Dict							=    2_56
      elif size == "b2":
            UpperCAmelCase__			:							Dict							=    [64, 1_28, 3_20, 5_12]
            UpperCAmelCase__			:							Dict							=    7_68
            UpperCAmelCase__			:							Optional[int]							=    [3, 4, 6, 3]
      elif size == "b3":
            UpperCAmelCase__			:							str							=    [64, 1_28, 3_20, 5_12]
            UpperCAmelCase__			:							int							=    7_68
            UpperCAmelCase__			:							Optional[Any]							=    [3, 4, 18, 3]
      elif size == "b4":
            UpperCAmelCase__			:							List[Any]							=    [64, 1_28, 3_20, 5_12]
            UpperCAmelCase__			:							Optional[Any]							=    7_68
            UpperCAmelCase__			:							Tuple							=    [3, 8, 27, 3]
      elif size == "b5":
            UpperCAmelCase__			:							List[str]							=    [64, 1_28, 3_20, 5_12]
            UpperCAmelCase__			:							Tuple							=    7_68
            UpperCAmelCase__			:							List[str]							=    [3, 6, 40, 3]
      else:
            raise ValueError(F"""Size {size} not supported"""    )

      # load image processor (only resize + normalize)
      UpperCAmelCase__			:							List[Any]							=    SegformerImageProcessor(
          image_scale=(5_12, 5_12)			,	keep_ratio=lowerCAmelCase			,	align=lowerCAmelCase			,	do_random_crop=lowerCAmelCase    )

      # prepare image
      UpperCAmelCase__			:							Union[str, Any]							=    prepare_img()
      UpperCAmelCase__			:							Optional[Any]							=    image_processor(images=lowerCAmelCase			,	return_tensors="""pt"""    ).pixel_values

      logger.info(F"""Converting model {model_name}..."""    )

      # load original state dict
      if encoder_only:
            UpperCAmelCase__			:							Union[str, Any]							=    torch.load(lowerCAmelCase			,	map_location=torch.device("""cpu"""    )    )
      else:
            UpperCAmelCase__			:							Optional[Any]							=    torch.load(lowerCAmelCase			,	map_location=torch.device("""cpu"""    )    )["""state_dict"""]

      # rename keys
      UpperCAmelCase__			:							List[str]							=    rename_keys(lowerCAmelCase			,	encoder_only=lowerCAmelCase    )
      if not encoder_only:
            del state_dict["decode_head.conv_seg.weight"]
            del state_dict["decode_head.conv_seg.bias"]

      # key and value matrices need special treatment
      read_in_k_v(lowerCAmelCase			,	lowerCAmelCase    )

      # create HuggingFace model and load state dict
      if encoder_only:
            UpperCAmelCase__			:							List[str]							=    False
            UpperCAmelCase__			:							Any							=    SegformerForImageClassification(lowerCAmelCase    )
      else:
            UpperCAmelCase__			:							Optional[Any]							=    SegformerForSemanticSegmentation(lowerCAmelCase    )
      model.load_state_dict(lowerCAmelCase    )
      model.eval()

      # forward pass
      UpperCAmelCase__			:							int							=    model(lowerCAmelCase    )
      UpperCAmelCase__			:							Dict							=    outputs.logits

      # set expected_slice based on model name
      # ADE20k checkpoints
      if model_name == "segformer.b0.512x512.ade.160k":
            UpperCAmelCase__			:							Dict							=    torch.tensor(
                [
                    [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                    [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                    [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
                ]    )
      elif model_name == "segformer.b1.512x512.ade.160k":
            UpperCAmelCase__			:							Any							=    torch.tensor(
                [
                    [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
                    [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
                    [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
                ]    )
      elif model_name == "segformer.b2.512x512.ade.160k":
            UpperCAmelCase__			:							Optional[Any]							=    torch.tensor(
                [
                    [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
                    [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
                    [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
                ]    )
      elif model_name == "segformer.b3.512x512.ade.160k":
            UpperCAmelCase__			:							Optional[Any]							=    torch.tensor(
                [
                    [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
                    [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
                    [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
                ]    )
      elif model_name == "segformer.b4.512x512.ade.160k":
            UpperCAmelCase__			:							List[str]							=    torch.tensor(
                [
                    [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
                    [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
                    [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
                ]    )
      elif model_name == "segformer.b5.640x640.ade.160k":
            UpperCAmelCase__			:							List[str]							=    torch.tensor(
                [
                    [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
                    [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
                    [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
                ]    )
      # Cityscapes checkpoints
      elif model_name == "segformer.b0.1024x1024.city.160k":
            UpperCAmelCase__			:							int							=    torch.tensor(
                [
                    [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
                    [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
                    [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
                ]    )
      elif model_name == "segformer.b0.512x1024.city.160k":
            UpperCAmelCase__			:							int							=    torch.tensor(
                [
                    [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
                    [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
                    [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
                ]    )
      elif model_name == "segformer.b0.640x1280.city.160k":
            UpperCAmelCase__			:							int							=    torch.tensor(
                [
                    [
                        [-1.1372E01, -1.2787E01, -1.3477E01],
                        [-1.2536E01, -1.4194E01, -1.4409E01],
                        [-1.3217E01, -1.4888E01, -1.5327E01],
                    ],
                    [
                        [-1.4791E01, -1.7122E01, -1.8277E01],
                        [-1.7163E01, -1.9192E01, -1.9533E01],
                        [-1.7897E01, -1.9991E01, -2.0315E01],
                    ],
                    [
                        [7.6723E-01, 4.1921E-01, -7.7878E-02],
                        [4.7772E-01, 9.5557E-03, -2.8082E-01],
                        [3.6032E-01, -2.4826E-01, -5.1168E-01],
                    ],
                ]    )
      elif model_name == "segformer.b0.768x768.city.160k":
            UpperCAmelCase__			:							Optional[int]							=    torch.tensor(
                [
                    [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
                    [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
                    [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
                ]    )
      elif model_name == "segformer.b1.1024x1024.city.160k":
            UpperCAmelCase__			:							Optional[int]							=    torch.tensor(
                [
                    [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                    [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                    [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
                ]    )
      elif model_name == "segformer.b2.1024x1024.city.160k":
            UpperCAmelCase__			:							Optional[Any]							=    torch.tensor(
                [
                    [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
                    [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
                    [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
                ]    )
      elif model_name == "segformer.b3.1024x1024.city.160k":
            UpperCAmelCase__			:							Tuple							=    torch.tensor(
                [
                    [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
                    [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
                    [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
                ]    )
      elif model_name == "segformer.b4.1024x1024.city.160k":
            UpperCAmelCase__			:							str							=    torch.tensor(
                [
                    [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
                    [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
                    [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
                ]    )
      elif model_name == "segformer.b5.1024x1024.city.160k":
            UpperCAmelCase__			:							Dict							=    torch.tensor(
                [
                    [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
                    [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
                    [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
                ]    )
      else:
            UpperCAmelCase__			:							Tuple							=    logits.argmax(-1    ).item()
            print("""Predicted class:"""			,	model.config.idalabel[predicted_class_idx]    )

      # verify logits
      if not encoder_only:
            assert logits.shape == expected_shape
            assert torch.allclose(logits[0, :3, :3, :3]			,	lowerCAmelCase			,	atol=1E-2    )

      # finally, save model and image processor
      logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""    )
      Path(lowerCAmelCase    ).mkdir(exist_ok=lowerCAmelCase    )
      model.save_pretrained(lowerCAmelCase    )
      image_processor.save_pretrained(lowerCAmelCase    )


if __name__ == "__main__":
    _A							=  argparse.ArgumentParser()

    parser.add_argument(
        """--model_name""",
        default="""segformer.b0.512x512.ade.160k""",
        type=str,
        help="""Name of the model you'd like to convert.""",
    )
    parser.add_argument(
        """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
    )
    _A							=  parser.parse_args()
    convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)




"""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	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  KandinskyInpaintPipeline
  SCREAMING_SNAKE_CASE					      =  ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
  SCREAMING_SNAKE_CASE					      =  [
      'prompt',
      'negative_prompt',
      'image_embeds',
      'negative_image_embeds',
      'image',
      'mask_image',
  ]
  SCREAMING_SNAKE_CASE					      =  [
      'generator',
      'height',
      'width',
      'latents',
      'guidance_scale',
      'negative_prompt',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  SCREAMING_SNAKE_CASE					      =  False



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim * 4



  @property
  def  _a      (self	):


        """simple docstring"""

        return 100



  @property
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
        return tokenizer



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Optional[int]							=    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__			:							List[Any]							=    MultilingualCLIP(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    text_encoder.eval()

        return text_encoder



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)

        UpperCAmelCase__			:							Dict							=    {
            """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__			:							List[Any]							=    UNetaDConditionModel(**_lowerCamelCase	)
        return model



  @property
  def  _a      (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  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Any							=    VQModel(**self.dummy_movq_kwargs	)
        return model



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    self.dummy_text_encoder
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_tokenizer
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_unet
        UpperCAmelCase__			:							List[Any]							=    self.dummy_movq

        UpperCAmelCase__			:							Union[str, Any]							=    DDIMScheduler(
            num_train_timesteps=1000							,       beta_schedule="""linear"""							,       beta_start=0.00_085							,       beta_end=0.012							,       clip_sample=_lowerCamelCase							,       set_alpha_to_one=_lowerCamelCase							,       steps_offset=1							,       prediction_type="""epsilon"""							,       thresholding=_lowerCamelCase							,       )

        UpperCAmelCase__			:							Any							=    {
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase=0	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(_lowerCamelCase	)	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[int]							=    floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(seed + 1	)	).to(_lowerCamelCase	)
        # create init_image
        UpperCAmelCase__			:							Optional[int]							=    floats_tensor((1, 3, 64, 64)							,       rng=random.Random(_lowerCamelCase	)	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[int]							=    image.cpu().permute(0							,       2							,       3							,       1	)[0]
        UpperCAmelCase__			:							Optional[Any]							=    Image.fromarray(np.uinta(_lowerCamelCase	)	).convert("""RGB"""	).resize((256, 256)	)
        # create mask
        UpperCAmelCase__			:							List[Any]							=    np.ones((64, 64)							,       dtype=np.floataa	)
        UpperCAmelCase__			:							Optional[int]							=    0

        if str(_lowerCamelCase	).startswith("""mps"""	):
              UpperCAmelCase__			:							Tuple							=    torch.manual_seed(_lowerCamelCase	)
        else:
              UpperCAmelCase__			:							Optional[int]							=    torch.Generator(device=_lowerCamelCase	).manual_seed(_lowerCamelCase	)
        UpperCAmelCase__			:							Any							=    {
            """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	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    """cpu"""

        UpperCAmelCase__			:							Optional[Any]							=    self.get_dummy_components()

        UpperCAmelCase__			:							Optional[Any]							=    self.pipeline_class(**_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    pipe.to(_lowerCamelCase	)

        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							List[str]							=    pipe(**self.get_dummy_inputs(_lowerCamelCase	)	)
        UpperCAmelCase__			:							Optional[int]							=    output.images

        UpperCAmelCase__			:							Union[str, Any]							=    pipe(
            **self.get_dummy_inputs(_lowerCamelCase	)							,       return_dict=_lowerCamelCase							,       )[0]

        UpperCAmelCase__			:							Union[str, Any]							=    image[0, -3:, -3:, -1]
        UpperCAmelCase__			:							Optional[Any]							=    image_from_tuple[0, -3:, -3:, -1]

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

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

        UpperCAmelCase__			:							Any							=    np.array(
            [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]	)

        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	):


        """simple docstring"""

        super().test_inference_batch_single_identical(expected_max_diff=3e-3	)




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


  '''simple docstring'''







  def  _a      (self	):


        """simple docstring"""

        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""	)

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

        UpperCAmelCase__			:							int							=    """a hat"""

        UpperCAmelCase__			:							List[Any]							=    KandinskyPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-prior"""							,       torch_dtype=torch.floataa	)
        pipe_prior.to(_lowerCamelCase	)

        UpperCAmelCase__			:							str							=    KandinskyInpaintPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-inpaint"""							,       torch_dtype=torch.floataa	)
        UpperCAmelCase__			:							int							=    pipeline.to(_lowerCamelCase	)
        pipeline.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							int							=    torch.Generator(device="""cpu"""	).manual_seed(0	)
        UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    pipe_prior(
            _lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=5							,       negative_prompt=""""""							,       ).to_tuple()

        UpperCAmelCase__			:							Union[str, Any]							=    pipeline(
            _lowerCamelCase							,       image=_lowerCamelCase							,       mask_image=_lowerCamelCase							,       image_embeds=_lowerCamelCase							,       negative_image_embeds=_lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=100							,       height=768							,       width=768							,       output_type="""np"""							,       )

        UpperCAmelCase__			:							Any							=    output.images[0]

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

        assert_mean_pixel_difference(_lowerCamelCase							,       _lowerCamelCase	)

