


"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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 			A__	(						UpperCamelCase_ ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					str													=KandinskyVaaInpaintPipeline
     snake_case__	:					Optional[int]													=['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
     snake_case__	:					int													=[
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
         '''mask_image''',
     ]
     snake_case__	:					List[str]													=[
         '''generator''',
         '''height''',
         '''width''',
         '''latents''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     snake_case__	:					Tuple													=False

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

     @property
     def 		a__						(	self:						str	)->    Optional[int]:
           return 32

     @property
     def 		a__						(	self:						str	)->    Dict:
           return self.time_input_dim

     @property
     def 		a__						(	self:						Any	)->    int:
           return self.time_input_dim * 4

     @property
     def 		a__						(	self:						Any	)->    Union[str, Any]:
           return 100

     @property
     def 		a__						(	self:						Any	)->    Optional[Any]:
           torch.manual_seed(0	)

           lowerCamelCase					:					Dict        =	{
               '''in_channels''': 9,
               # 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					:					List[Any]        =	UNetaDConditionModel(**__a	)
           return model

     @property
     def 		a__						(	self:						Optional[int]	)->    int:
           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:						List[str]	)->    Optional[int]:
           torch.manual_seed(0	)
           lowerCamelCase					:					Optional[Any]        =	VQModel(**self.dummy_movq_kwargs	)
           return model

     def 		a__						(	self:						Optional[int]	)->    Union[str, Any]:
           lowerCamelCase					:					Optional[int]        =	self.dummy_unet
           lowerCamelCase					:					str        =	self.dummy_movq

           lowerCamelCase					:					List[Any]        =	DDIMScheduler(
               num_train_timesteps=1_000     ,		beta_schedule="""linear"""     ,		beta_start=0.0_00_85     ,		beta_end=0.0_12     ,		clip_sample=__a     ,		set_alpha_to_one=__a     ,		steps_offset=1     ,		prediction_type="""epsilon"""     ,		thresholding=__a     ,		)

           lowerCamelCase					:					Any        =	{
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''movq''': movq,
           }

           return components

     def 		a__						(	self:						Any     ,		__a:						int     ,		__a:						List[Any]=0	)->    Union[str, Any]:
           lowerCamelCase					:					str        =	floats_tensor((1, self.text_embedder_hidden_size)     ,		rng=random.Random(__a	)	).to(__a	)
           lowerCamelCase					:					str        =	floats_tensor((1, self.text_embedder_hidden_size)     ,		rng=random.Random(seed + 1	)	).to(
               __a	)
           # create init_image
           lowerCamelCase					:					Dict        =	floats_tensor((1, 3, 64, 64)     ,		rng=random.Random(__a	)	).to(__a	)
           lowerCamelCase					:					List[str]        =	image.cpu().permute(0     ,		2     ,		3     ,		1	)[0]
           lowerCamelCase					:					List[str]        =	Image.fromarray(np.uinta(__a	)	).convert("""RGB"""	).resize((256, 256)	)
           # create mask
           lowerCamelCase					:					Tuple        =	np.ones((64, 64)     ,		dtype=np.floataa	)
           lowerCamelCase					:					int        =	0

           if str(__a	).startswith("""mps"""	):
                 lowerCamelCase					:					int        =	torch.manual_seed(__a	)
           else:
                 lowerCamelCase					:					Optional[int]        =	torch.Generator(device=__a	).manual_seed(__a	)
           lowerCamelCase					:					List[str]        =	{
               '''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:						int	)->    Optional[Any]:
           lowerCamelCase					:					Any        =	'''cpu'''

           lowerCamelCase					:					Dict        =	self.get_dummy_components()

           lowerCamelCase					:					Dict        =	self.pipeline_class(**__a	)
           lowerCamelCase					:					Union[str, Any]        =	pipe.to(__a	)

           pipe.set_progress_bar_config(disable=__a	)

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

           lowerCamelCase					:					List[str]        =	pipe(
               **self.get_dummy_inputs(__a	)     ,		return_dict=__a     ,		)[0]

           lowerCamelCase					:					Tuple        =	image[0, -3:, -3:, -1]
           lowerCamelCase					:					List[Any]        =	image_from_tuple[0, -3:, -3:, -1]

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

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

           lowerCamelCase					:					int        =	np.array(
               [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]	)

           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:						Optional[int]	)->    str:
           super().test_inference_batch_single_identical(expected_max_diff=3e-3	)


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


     """simple docstring"""

     def 		a__						(	self:						Union[str, Any]	)->    int:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







     def 		a__						(	self:						Union[str, Any]	)->    int:
           lowerCamelCase					:					Tuple        =	load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""	)

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

           lowerCamelCase					:					Optional[int]        =	'''a hat'''

           lowerCamelCase					:					Dict        =	KandinskyVaaPriorPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-prior"""     ,		torch_dtype=torch.floataa	)
           pipe_prior.to(__a	)

           lowerCamelCase					:					Optional[Any]        =	KandinskyVaaInpaintPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-decoder-inpaint"""     ,		torch_dtype=torch.floataa	)
           lowerCamelCase					:					int        =	pipeline.to(__a	)
           pipeline.set_progress_bar_config(disable=__a	)

           lowerCamelCase					:					Dict        =	torch.Generator(device="""cpu"""	).manual_seed(0	)
           lowerCamelCase					:					List[str]        =	pipe_prior(
               __a     ,		generator=__a     ,		num_inference_steps=5     ,		negative_prompt=""""""     ,		).to_tuple()

           lowerCamelCase					:					List[Any]        =	pipeline(
               image=__a     ,		mask_image=__a     ,		image_embeds=__a     ,		negative_image_embeds=__a     ,		generator=__a     ,		num_inference_steps=100     ,		height=768     ,		width=768     ,		output_type="""np"""     ,		)

           lowerCamelCase					:					Tuple        =	output.images[0]

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

           assert_mean_pixel_difference(__a     ,		__a	)








"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase      :Dict	    =		logging.get_logger()




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						LevitConfig			,				UpperCamelCase__       :						Path			,				UpperCamelCase__       :						bool = True    )   ->      Dict:
      print(F'Converting {name}...'    )

      with torch.no_grad():
            if hidden_sizes == 128:
                  if name[-1] == "S":
                        lowerCamelCase					:					Optional[Any]        =	timm.create_model("""levit_128s"""			,				pretrained=UpperCamelCase__    )
                  else:
                        lowerCamelCase					:					Dict        =	timm.create_model("""levit_128"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 192:
                  lowerCamelCase					:					Tuple        =	timm.create_model("""levit_192"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 256:
                  lowerCamelCase					:					Optional[int]        =	timm.create_model("""levit_256"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 384:
                  lowerCamelCase					:					Dict        =	timm.create_model("""levit_384"""			,				pretrained=UpperCamelCase__    )

            from_model.eval()
            lowerCamelCase					:					Optional[Any]        =	LevitForImageClassificationWithTeacher(UpperCamelCase__    ).eval()
            lowerCamelCase					:					Tuple        =	OrderedDict()

            lowerCamelCase					:					Optional[Any]        =	from_model.state_dict()
            lowerCamelCase					:					str        =	list(from_model.state_dict().keys()    )
            lowerCamelCase					:					List[Any]        =	list(our_model.state_dict().keys()    )
            print(len(UpperCamelCase__    )			,				len(UpperCamelCase__    )    )
            for i in range(len(UpperCamelCase__    )    ):
                  lowerCamelCase					:					str        =	weights[og_keys[i]]
            our_model.load_state_dict(UpperCamelCase__    )

            lowerCamelCase					:					int        =	torch.randn((2, 3, 224, 224)    )
            lowerCamelCase					:					Any        =	from_model(UpperCamelCase__    )
            lowerCamelCase					:					List[Any]        =	our_model(UpperCamelCase__    ).logits

      assert torch.allclose(UpperCamelCase__			,				UpperCamelCase__    ), "The model logits don't match the original one."

      lowerCamelCase					:					Dict        =	name
      print(UpperCamelCase__    )

      if push_to_hub:
            our_model.save_pretrained(save_directory / checkpoint_name    )
            lowerCamelCase					:					Optional[int]        =	LevitImageProcessor()
            image_processor.save_pretrained(save_directory / checkpoint_name    )

            print(F'Pushed {checkpoint_name}'    )




def   snake_case		(     UpperCamelCase__       :						Path			,				UpperCamelCase__       :						str = None			,				UpperCamelCase__       :						bool = True    )   ->      Optional[int]:
      lowerCamelCase					:					Optional[Any]        =	"""imagenet-1k-id2label.json"""
      lowerCamelCase					:					List[Any]        =	1000
      lowerCamelCase					:					Dict        =	(1, num_labels)

      lowerCamelCase					:					List[Any]        =	"""huggingface/label-files"""
      lowerCamelCase					:					Optional[int]        =	num_labels
      lowerCamelCase					:					List[str]        =	json.load(open(hf_hub_download(UpperCamelCase__			,				UpperCamelCase__			,				repo_type="""dataset"""    )			,				"""r"""    )    )
      lowerCamelCase					:					Any        =	{int(UpperCamelCase__    ): v for k, v in idalabel.items()}

      lowerCamelCase					:					List[Any]        =	idalabel
      lowerCamelCase					:					str        =	{v: k for k, v in idalabel.items()}

      lowerCamelCase					:					Tuple        =	partial(UpperCamelCase__			,				num_labels=UpperCamelCase__			,				idalabel=UpperCamelCase__			,				labelaid=UpperCamelCase__    )

      lowerCamelCase					:					Optional[int]        =	{
          """levit-128S""": 128,
          """levit-128""": 128,
          """levit-192""": 192,
          """levit-256""": 256,
          """levit-384""": 384,
      }

      lowerCamelCase					:					List[Any]        =	{
          """levit-128S""": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]			,				num_attention_heads=[4, 6, 8]			,				depths=[2, 3, 4]			,				key_dim=[16, 16, 16]			,				drop_path_rate=0			,				),
          """levit-128""": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]			,				num_attention_heads=[4, 8, 12]			,				depths=[4, 4, 4]			,				key_dim=[16, 16, 16]			,				drop_path_rate=0			,				),
          """levit-192""": ImageNetPreTrainedConfig(
              hidden_sizes=[192, 288, 384]			,				num_attention_heads=[3, 5, 6]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0			,				),
          """levit-256""": ImageNetPreTrainedConfig(
              hidden_sizes=[256, 384, 512]			,				num_attention_heads=[4, 6, 8]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0			,				),
          """levit-384""": ImageNetPreTrainedConfig(
              hidden_sizes=[384, 512, 768]			,				num_attention_heads=[6, 9, 12]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0.1			,				),
      }

      if model_name:
            convert_weight_and_push(
                names_to_hidden_sizes[model_name]			,				UpperCamelCase__			,				names_to_config[model_name]			,				UpperCamelCase__			,				UpperCamelCase__    )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(names_to_hidden_sizes[model_name]			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
      return config, expected_shape


if __name__ == "__main__":
    __lowerCamelCase      :Union[str, Any]	    =		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default=None,
        type=str,
        help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default='levit-dump-folder/',
        type=Path,
        required=False,
        help='Path to the output PyTorch model directory.',
    )
    parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
    parser.add_argument(
        '--no-push_to_hub',
        dest='push_to_hub',
        action='store_false',
        help='Do not push model and image processor to the hub',
    )

    __lowerCamelCase      :List[Any]	    =		parser.parse_args()
    __lowerCamelCase      :Path	    =		args.pytorch_dump_folder_path
    pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
    convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




