






"""simple docstring"""







import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_						:     str							  =   logging.get_logger(__name__)






def    _lowerCAmelCase							(   lowerCAmelCase		):







       '''simple docstring'''
       print("""Loading config file..."""		)

       def flatten_yaml_as_dict(lowerCAmelCase						,   lowerCAmelCase=""						,   lowerCAmelCase="."		):
              UpperCAmelCase						     =      []
              for k, v in d.items():
                     UpperCAmelCase						     =      parent_key + sep + k if parent_key else k
                     if isinstance(lowerCAmelCase						,   collections.abc.MutableMapping		):
                            items.extend(flatten_yaml_as_dict(lowerCAmelCase						,   lowerCAmelCase						,   sep=lowerCAmelCase		).items()		)
                     else:
                            items.append((new_key, v)		)
              return dict(lowerCAmelCase		)

       UpperCAmelCase						     =      argparse.Namespace()
       with open(lowerCAmelCase						,   """r"""		) as yaml_file:
              try:
                     UpperCAmelCase						     =      yaml.load(lowerCAmelCase						,   Loader=yaml.FullLoader		)

                     UpperCAmelCase						     =      flatten_yaml_as_dict(lowerCAmelCase		)
                     for k, v in flat_cfg.items():
                            setattr(lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase		)
              except yaml.YAMLError as exc:
                     logger.error("""Error while loading config file: {}. Error message: {}""".format(lowerCAmelCase						,   str(lowerCAmelCase		)		)		)
       return config






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      MobileViTVaConfig()

       UpperCAmelCase						     =      False

       # dataset
       if task_name.startswith("""imagenet1k_"""		):
              UpperCAmelCase						     =      1000
              if int(task_name.strip().split("""_"""		)[-1]		) == 384:
                     UpperCAmelCase						     =      384
              else:
                     UpperCAmelCase						     =      256
              UpperCAmelCase						     =      """imagenet-1k-id2label.json"""
       elif task_name.startswith("""imagenet21k_to_1k_"""		):
              UpperCAmelCase						     =      21000
              if int(task_name.strip().split("""_"""		)[-1]		) == 384:
                     UpperCAmelCase						     =      384
              else:
                     UpperCAmelCase						     =      256
              UpperCAmelCase						     =      """imagenet-22k-id2label.json"""
       elif task_name.startswith("""ade20k_"""		):
              UpperCAmelCase						     =      151
              UpperCAmelCase						     =      512
              UpperCAmelCase						     =      """ade20k-id2label.json"""
              UpperCAmelCase						     =      True
       elif task_name.startswith("""voc_"""		):
              UpperCAmelCase						     =      21
              UpperCAmelCase						     =      512
              UpperCAmelCase						     =      """pascal-voc-id2label.json"""
              UpperCAmelCase						     =      True

       # orig_config
       UpperCAmelCase						     =      load_orig_config_file(lowerCAmelCase		)
       assert getattr(lowerCAmelCase						,   """model.classification.name"""						,   -1		) == "mobilevit_v2", "Invalid model"
       UpperCAmelCase						     =      getattr(lowerCAmelCase						,   """model.classification.mitv2.width_multiplier"""						,   1.0		)
       assert (
           getattr(lowerCAmelCase						,   """model.classification.mitv2.attn_norm_layer"""						,   -1		) == "layer_norm_2d"
       ), "Norm layers other than layer_norm_2d is not supported"
       UpperCAmelCase						     =      getattr(lowerCAmelCase						,   """model.classification.activation.name"""						,   """swish"""		)
       # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

       if is_segmentation_model:
              UpperCAmelCase						     =      getattr(lowerCAmelCase						,   """model.segmentation.output_stride"""						,   16		)
              if "_deeplabv3" in task_name:
                     UpperCAmelCase						     =      getattr(lowerCAmelCase						,   """model.segmentation.deeplabv3.aspp_rates"""						,   [12, 24, 36]		)
                     UpperCAmelCase						     =      getattr(lowerCAmelCase						,   """model.segmentation.deeplabv3.aspp_out_channels"""						,   512		)
                     UpperCAmelCase						     =      getattr(lowerCAmelCase						,   """model.segmentation.deeplabv3.aspp_dropout"""						,   0.1		)

    # id2label
       UpperCAmelCase						     =      """huggingface/label-files"""
       UpperCAmelCase						     =      json.load(open(hf_hub_download(lowerCAmelCase						,   lowerCAmelCase						,   repo_type="""dataset"""		)						,   """r"""		)		)
       UpperCAmelCase						     =      {int(lowerCAmelCase		): v for k, v in idalabel.items()}
       UpperCAmelCase						     =      idalabel
       UpperCAmelCase						     =      {v: k for k, v in idalabel.items()}

       return config






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      dct.pop(lowerCAmelCase		)
       UpperCAmelCase						     =      val






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase=False		):







       '''simple docstring'''
       if base_model:
              UpperCAmelCase						     =      """"""
       else:
              UpperCAmelCase						     =      """mobilevitv2."""

       UpperCAmelCase						     =      []
       for k in state_dict.keys():
              if k[:8] == "encoder.":
                     UpperCAmelCase						     =      k[8:]
              else:
                     UpperCAmelCase						     =      k

              if ".block." in k:
                     UpperCAmelCase						     =      k_new.replace(""".block."""						,   """."""		)
              if ".conv." in k:
                     UpperCAmelCase						     =      k_new.replace(""".conv."""						,   """.convolution."""		)
              if ".norm." in k:
                     UpperCAmelCase						     =      k_new.replace(""".norm."""						,   """.normalization."""		)

              if "conv_1." in k:
                     UpperCAmelCase						     =      k_new.replace("""conv_1."""						,   F'''{model_prefix}conv_stem.'''		)
              for i in [1, 2]:
                     if F'''layer_{i}.''' in k:
                            UpperCAmelCase						     =      k_new.replace(F'''layer_{i}.'''						,   F'''{model_prefix}encoder.layer.{i-1}.layer.'''		)
              if ".exp_1x1." in k:
                     UpperCAmelCase						     =      k_new.replace(""".exp_1x1."""						,   """.expand_1x1."""		)
              if ".red_1x1." in k:
                     UpperCAmelCase						     =      k_new.replace(""".red_1x1."""						,   """.reduce_1x1."""		)

              for i in [3, 4, 5]:
                     if F'''layer_{i}.0.''' in k:
                            UpperCAmelCase						     =      k_new.replace(F'''layer_{i}.0.'''						,   F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''		)
                     if F'''layer_{i}.1.local_rep.0.''' in k:
                            UpperCAmelCase						     =      k_new.replace(F'''layer_{i}.1.local_rep.0.'''						,   F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''		)
                     if F'''layer_{i}.1.local_rep.1.''' in k:
                            UpperCAmelCase						     =      k_new.replace(F'''layer_{i}.1.local_rep.1.'''						,   F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''		)

              for i in [3, 4, 5]:
                     if i == 3:
                            UpperCAmelCase						     =      [0, 1]
                     elif i == 4:
                            UpperCAmelCase						     =      [0, 1, 2, 3]
                     elif i == 5:
                            UpperCAmelCase						     =      [0, 1, 2]

                     for j in j_in:
                            if F'''layer_{i}.1.global_rep.{j}.''' in k:
                                   UpperCAmelCase						     =      k_new.replace(
                                       F'''layer_{i}.1.global_rep.{j}.'''						,   F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''		)
                     if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
                            UpperCAmelCase						     =      k_new.replace(
                                F'''layer_{i}.1.global_rep.{j+1}.'''						,   F'''{model_prefix}encoder.layer.{i-1}.layernorm.'''		)

                     if F'''layer_{i}.1.conv_proj.''' in k:
                            UpperCAmelCase						     =      k_new.replace(F'''layer_{i}.1.conv_proj.'''						,   F'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''		)

              if "pre_norm_attn.0." in k:
                     UpperCAmelCase						     =      k_new.replace("""pre_norm_attn.0."""						,   """layernorm_before."""		)
              if "pre_norm_attn.1." in k:
                     UpperCAmelCase						     =      k_new.replace("""pre_norm_attn.1."""						,   """attention."""		)
              if "pre_norm_ffn.0." in k:
                     UpperCAmelCase						     =      k_new.replace("""pre_norm_ffn.0."""						,   """layernorm_after."""		)
              if "pre_norm_ffn.1." in k:
                     UpperCAmelCase						     =      k_new.replace("""pre_norm_ffn.1."""						,   """ffn.conv1."""		)
              if "pre_norm_ffn.3." in k:
                     UpperCAmelCase						     =      k_new.replace("""pre_norm_ffn.3."""						,   """ffn.conv2."""		)

              if "classifier.1." in k:
                     UpperCAmelCase						     =      k_new.replace("""classifier.1."""						,   """classifier."""		)

              if "seg_head." in k:
                     UpperCAmelCase						     =      k_new.replace("""seg_head."""						,   """segmentation_head."""		)
              if ".aspp_layer." in k:
                     UpperCAmelCase						     =      k_new.replace(""".aspp_layer."""						,   """."""		)
              if ".aspp_pool." in k:
                     UpperCAmelCase						     =      k_new.replace(""".aspp_pool."""						,   """."""		)

              rename_keys.append((k, k_new)		)
       return rename_keys






def    _lowerCAmelCase							(   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      []
       for k in state_dict.keys():
              if k.startswith("""seg_head.aux_head."""		):
                     keys_to_ignore.append(lowerCAmelCase		)
       for k in keys_to_ignore:
              state_dict.pop(lowerCAmelCase						,   lowerCAmelCase		)






def    _lowerCAmelCase							(   ):







       '''simple docstring'''
       UpperCAmelCase						     =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
       # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
       UpperCAmelCase						     =      Image.open(requests.get(lowerCAmelCase						,   stream=lowerCAmelCase		).raw		)
       return im






@torch.no_grad()
def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      get_mobilevitva_config(lowerCAmelCase						,   lowerCAmelCase		)

       # load original state_dict
       UpperCAmelCase						     =      torch.load(lowerCAmelCase						,   map_location="""cpu"""		)

       # load huggingface model
       if task_name.startswith("""ade20k_"""		) or task_name.startswith("""voc_"""		):
              UpperCAmelCase						     =      MobileViTVaForSemanticSegmentation(lowerCAmelCase		).eval()
              UpperCAmelCase						     =      False
       else:
              UpperCAmelCase						     =      MobileViTVaForImageClassification(lowerCAmelCase		).eval()
              UpperCAmelCase						     =      False

       # remove and rename some keys of load the original model
       UpperCAmelCase						     =      checkpoint
       remove_unused_keys(lowerCAmelCase		)
       UpperCAmelCase						     =      create_rename_keys(lowerCAmelCase						,   base_model=lowerCAmelCase		)
       for rename_key_src, rename_key_dest in rename_keys:
              rename_key(lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase		)

       # load modified state_dict
       model.load_state_dict(lowerCAmelCase		)

       # Check outputs on an image, prepared by MobileViTImageProcessor
       UpperCAmelCase						     =      MobileViTImageProcessor(crop_size=config.image_size						,   size=config.image_size + 32		)
       UpperCAmelCase						     =      image_processor(images=prepare_img()						,   return_tensors="""pt"""		)
       UpperCAmelCase						     =      model(**lowerCAmelCase		)

       # verify classification model
       if task_name.startswith("""imagenet"""		):
              UpperCAmelCase						     =      outputs.logits
              UpperCAmelCase						     =      logits.argmax(-1		).item()
              print("""Predicted class:"""						,   model.config.idalabel[predicted_class_idx]		)
              if task_name.startswith("""imagenet1k_256"""		) and config.width_multiplier == 1.0:
                     # expected_logits for base variant
                     UpperCAmelCase						     =      torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]		)
                     assert torch.allclose(logits[0, :3]						,   lowerCAmelCase						,   atol=1e-4		)

       Path(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)
       print(F'''Saving model {task_name} to {pytorch_dump_folder_path}'''		)
       model.save_pretrained(lowerCAmelCase		)
       print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
       image_processor.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_						:     Any							  =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--task''',
     default='''imagenet1k_256''',
     type=str,
     help=(
         '''Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '''
         '''\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            '''
     ),
     choices=[
         '''imagenet1k_256''',
         '''imagenet1k_384''',
         '''imagenet21k_to_1k_256''',
         '''imagenet21k_to_1k_384''',
         '''ade20k_deeplabv3''',
         '''voc_deeplabv3''',
     ],
 )

 parser.add_argument(
     '''--orig_checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
 )
 parser.add_argument('''--orig_config_path''', required=True, type=str, help='''Path to the original config file.''')
 parser.add_argument(
     '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
 )

 lowerCAmelCase_						:     Optional[int]							  =   parser.parse_args()
 convert_mobilevitva_checkpoint(
     args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
 )






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      ):
						UpperCamelCase      =       KandinskyInpaintPipeline
						UpperCamelCase      =       ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						UpperCamelCase      =       [
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						UpperCamelCase      =       [
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						UpperCamelCase      =       False

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return self.time_input_dim

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return self.time_input_dim * 4

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return 1_00

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Dict										=XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""			)
										return tokenizer

						@property
						def      A__    (		self :str			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						str										=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=10_05     ,			)

										__magic_name__						:						Tuple										=MultilingualCLIP(__snake_case			)
										__magic_name__						:						Optional[int]										=text_encoder.eval()

										return text_encoder

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										torch.manual_seed(0			)

										__magic_name__						:						Optional[Any]										={
										    """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,
										}

										__magic_name__						:						Union[str, Any]										=UNetaDConditionModel(**__snake_case			)
										return model

						@property
						def      A__    (		self :List[str]			):
										'''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 :Tuple			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						Dict										=VQModel(**self.dummy_movq_kwargs			)
										return model

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=self.dummy_text_encoder
										__magic_name__						:						Optional[Any]										=self.dummy_tokenizer
										__magic_name__						:						Optional[Any]										=self.dummy_unet
										__magic_name__						:						Tuple										=self.dummy_movq

										__magic_name__						:						List[str]										=DDIMScheduler(
										    num_train_timesteps=10_00     ,			beta_schedule="""linear"""     ,			beta_start=0.00085     ,			beta_end=0.012     ,			clip_sample=__snake_case     ,			set_alpha_to_one=__snake_case     ,			steps_offset=1     ,			prediction_type="""epsilon"""     ,			thresholding=__snake_case     ,			)

										__magic_name__						:						str										={
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}

										return components

						def      A__    (		self :str     ,			__snake_case :Optional[Any]     ,			__snake_case :int=0			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						Dict										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(seed + 1			)			).to(__snake_case			)
										# create init_image
										__magic_name__						:						str										=floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						int										=image.cpu().permute(0     ,			2     ,			3     ,			1			)[0]
										__magic_name__						:						str										=Image.fromarray(np.uinta(__snake_case			)			).convert("""RGB"""			).resize((2_56, 2_56)			)
										# create mask
										__magic_name__						:						Dict										=np.ones((64, 64)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										if str(__snake_case			).startswith("""mps"""			):
														__magic_name__						:						Dict										=torch.manual_seed(__snake_case			)
										else:
														__magic_name__						:						Tuple										=torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
										__magic_name__						:						List[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 :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										="""cpu"""

										__magic_name__						:						List[Any]										=self.get_dummy_components()

										__magic_name__						:						Union[str, Any]										=self.pipeline_class(**__snake_case			)
										__magic_name__						:						Tuple										=pipe.to(__snake_case			)

										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Tuple										=pipe(**self.get_dummy_inputs(__snake_case			)			)
										__magic_name__						:						List[Any]										=output.images

										__magic_name__						:						Any										=pipe(
										    **self.get_dummy_inputs(__snake_case			)     ,			return_dict=__snake_case     ,			)[0]

										__magic_name__						:						int										=image[0, -3:, -3:, -1]
										__magic_name__						:						str										=image_from_tuple[0, -3:, -3:, -1]

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

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

										__magic_name__						:						Optional[Any]										=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 :Dict			):
										'''simple docstring'''

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




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

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

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


						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

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

										__magic_name__						:						int										=load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""			)
										__magic_name__						:						List[Any]										=np.ones((7_68, 7_68)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										__magic_name__						:						int										="""a hat"""

										__magic_name__						:						int										=KandinskyPriorPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-prior"""     ,			torch_dtype=torch.floataa			)
										pipe_prior.to(__snake_case			)

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

										__magic_name__						:						Union[str, Any]										=torch.Generator(device="""cpu"""			).manual_seed(0			)
										__magic_name__					,			__magic_name__						:						Dict										=pipe_prior(
										    __snake_case     ,			generator=__snake_case     ,			num_inference_steps=5     ,			negative_prompt=""""""     ,			).to_tuple()

										__magic_name__						:						Optional[Any]										=pipeline(
										    __snake_case     ,			image=__snake_case     ,			mask_image=__snake_case     ,			image_embeds=__snake_case     ,			negative_image_embeds=__snake_case     ,			generator=__snake_case     ,			num_inference_steps=1_00     ,			height=7_68     ,			width=7_68     ,			output_type="""np"""     ,			)

										__magic_name__						:						Optional[int]										=output.images[0]

										assert image.shape == (7_68, 7_68, 3)

										assert_mean_pixel_difference(__snake_case     ,			__snake_case			)




