



"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class        _UpperCAmelCase       (			lowerCAmelCase__    ,     unittest.TestCase):
 _lowerCAmelCase   :			str							=		KandinskyVaaImgaImgPipeline
 _lowerCAmelCase   :			Dict							=		["""image_embeds""", """negative_image_embeds""", """image"""]
 _lowerCAmelCase   :			int							=		[
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
 ]
 _lowerCAmelCase   :			str							=		[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 _lowerCAmelCase   :			int							=		False

 @property
 def 						_snake_case					(     self		:       Any				):
     return 32

 @property
 def 						_snake_case					(     self		:       Union[str, Any]				):
     return 32

 @property
 def 						_snake_case					(     self		:       Dict				):
     return self.time_input_dim

 @property
 def 						_snake_case					(     self		:       Dict				):
     return self.time_input_dim * 4

 @property
 def 						_snake_case					(     self		:       Tuple				):
     return 100

 @property
 def 						_snake_case					(     self		:       int				):
     torch.manual_seed(0				)

     snake_case_ :     List[str]				=	{
         '''in_channels''': 4,
         # Out channels is double in channels because predicts mean and variance
         '''out_channels''': 8,
         '''addition_embed_type''': '''image''',
         '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
         '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
         '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
         '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
         '''layers_per_block''': 1,
         '''encoder_hid_dim''': self.text_embedder_hidden_size,
         '''encoder_hid_dim_type''': '''image_proj''',
         '''cross_attention_dim''': self.cross_attention_dim,
         '''attention_head_dim''': 4,
         '''resnet_time_scale_shift''': '''scale_shift''',
         '''class_embed_type''': None,
     }

     snake_case_ :     Any				=	UNetaDConditionModel(**lowercase_				)
     return model

 @property
 def 						_snake_case					(     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 						_snake_case					(     self		:       Dict				):
     torch.manual_seed(0				)
     snake_case_ :     Optional[Any]				=	VQModel(**self.dummy_movq_kwargs				)
     return model

 def 						_snake_case					(     self		:       Dict				):
     snake_case_ :     Any				=	self.dummy_unet
     snake_case_ :     Optional[int]				=	self.dummy_movq

     snake_case_ :     str				=	{
         '''num_train_timesteps''': 1000,
         '''beta_schedule''': '''linear''',
         '''beta_start''': 0.0_00_85,
         '''beta_end''': 0.0_12,
         '''clip_sample''': False,
         '''set_alpha_to_one''': False,
         '''steps_offset''': 0,
         '''prediction_type''': '''epsilon''',
         '''thresholding''': False,
     }

     snake_case_ :     str				=	DDIMScheduler(**lowercase_				)

     snake_case_ :     Dict				=	{
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''movq''': movq,
     }

     return components

 def 						_snake_case					(     self		:       Union[str, Any]  ,     lowercase_		:       Dict  ,     lowercase_		:       Dict=0				):
     snake_case_ :     List[str]				=	floats_tensor((1, self.text_embedder_hidden_size)  ,     rng=random.Random(lowercase_				)				).to(lowercase_				)
     snake_case_ :     Optional[int]				=	floats_tensor((1, self.text_embedder_hidden_size)  ,     rng=random.Random(seed + 1				)				).to(
         lowercase_				)
     # create init_image
     snake_case_ :     Union[str, Any]				=	floats_tensor((1, 3, 64, 64)  ,     rng=random.Random(lowercase_				)				).to(lowercase_				)
     snake_case_ :     Union[str, Any]				=	image.cpu().permute(0  ,     2  ,     3  ,     1				)[0]
     snake_case_ :     int				=	Image.fromarray(np.uinta(lowercase_				)				).convert('''RGB'''				).resize((256, 256)				)

     if str(lowercase_				).startswith('''mps'''				):
         snake_case_ :     List[str]				=	torch.manual_seed(lowercase_				)
     else:
         snake_case_ :     Tuple				=	torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
     snake_case_ :     Optional[int]				=	{
         '''image''': init_image,
         '''image_embeds''': image_embeds,
         '''negative_image_embeds''': negative_image_embeds,
         '''generator''': generator,
         '''height''': 64,
         '''width''': 64,
         '''num_inference_steps''': 10,
         '''guidance_scale''': 7.0,
         '''strength''': 0.2,
         '''output_type''': '''np''',
     }
     return inputs






 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Dict				=	'''cpu'''

     snake_case_ :     str				=	self.get_dummy_components()

     snake_case_ :     List[Any]				=	self.pipeline_class(**lowercase_				)
     snake_case_ :     List[Any]				=	pipe.to(lowercase_				)

     pipe.set_progress_bar_config(disable=lowercase_				)

     snake_case_ :     str				=	pipe(**self.get_dummy_inputs(lowercase_				)				)
     snake_case_ :     Tuple				=	output.images

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

     snake_case_ :     str				=	image[0, -3:, -3:, -1]
     snake_case_ :     List[str]				=	image_from_tuple[0, -3:, -3:, -1]

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

     snake_case_ :     Dict				=	np.array(
         [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]				)
     assert (
         np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
     ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
     ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"





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

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






 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_ :     int				=	load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''				)

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

     snake_case_ :     Any				=	KandinskyVaaPriorPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-prior'''  ,     torch_dtype=torch.floataa				)
     pipe_prior.to(lowercase_				)

     snake_case_ :     Any				=	KandinskyVaaImgaImgPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-decoder'''  ,     torch_dtype=torch.floataa				)
     snake_case_ :     Any				=	pipeline.to(lowercase_				)

     pipeline.set_progress_bar_config(disable=lowercase_				)

     snake_case_ :     Tuple				=	torch.Generator(device='''cpu'''				).manual_seed(0				)
     snake_case_,	snake_case_ :     List[Any]				=	pipe_prior(
         lowercase_  ,     generator=lowercase_  ,     num_inference_steps=5  ,     negative_prompt=''''''  ,     ).to_tuple()

     snake_case_ :     Dict				=	pipeline(
         image=lowercase_  ,     image_embeds=lowercase_  ,     negative_image_embeds=lowercase_  ,     generator=lowercase_  ,     num_inference_steps=100  ,     height=768  ,     width=768  ,     strength=0.2  ,     output_type='''np'''  ,     )

     snake_case_ :     Optional[Any]				=	output.images[0]

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

     assert_mean_pixel_difference(lowercase_  ,     lowercase_				)










"""simple docstring"""

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__    :  Dict							    =	logging.get_logger(__name__)




def __lowercase			(							_a		,			_a=False    ):
    snake_case_ :     List[str]				=	[]

    # fmt: off
    # stem:
    rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')    )
    rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')    )

    rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')    )
    rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')    )

    # backbone
    rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')    )
    rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')    )
    rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')    )

    for stage_idx in range(len(config.backbone_config.depths    )    ):
        for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")    )

        rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")    )
        rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")    )
        rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")    )

    # transformer encoder
    for i in range(config.num_hidden_layers    ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")    )
        rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")    )
        rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")    )
        rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")    )
        rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")    )
        rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")    )
        rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")    )
        rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")    )
        rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")    )
        rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")    )

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ('''norm.weight''', '''layernorm.weight'''),
                ('''norm.bias''', '''layernorm.bias'''),
                ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
            ]    )

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        snake_case_ :     Optional[int]				=	[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''    ) else pair for pair in rename_keys]
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ('''norm.weight''', '''vit.layernorm.weight'''),
                ('''norm.bias''', '''vit.layernorm.bias'''),
                ('''head.weight''', '''classifier.weight'''),
                ('''head.bias''', '''classifier.bias'''),
            ]    )
    # fmt: on

    return rename_keys




def __lowercase			(							_a		,			_a		,			_a=False    ):
    for i in range(config.num_hidden_layers    ):
        if base_model:
            snake_case_ :     List[str]				=	''''''
        else:
            snake_case_ :     Dict				=	'''vit.'''
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        snake_case_ :     List[str]				=	state_dict.pop(f"blocks.{i}.attn.qkv.weight"    )
        snake_case_ :     Optional[int]				=	state_dict.pop(f"blocks.{i}.attn.qkv.bias"    )
        # next, add query, keys and values (in that order) to the state dict
        snake_case_ :     Any				=	in_proj_weight[
            : config.hidden_size, :
        ]
        snake_case_ :     Dict				=	in_proj_bias[: config.hidden_size]
        snake_case_ :     str				=	in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        snake_case_ :     Optional[int]				=	in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        snake_case_ :     Dict				=	in_proj_weight[
            -config.hidden_size :, :
        ]
        snake_case_ :     str				=	in_proj_bias[-config.hidden_size :]




def __lowercase			(							_a    ):
    snake_case_ :     Dict				=	['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(_a		,			_a    )




def __lowercase			(							_a		,			_a		,			_a    ):
    snake_case_ :     Union[str, Any]				=	dct.pop(_a    )
    snake_case_ :     Union[str, Any]				=	val




def __lowercase			(							):
    snake_case_ :     Any				=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    snake_case_ :     Tuple				=	Image.open(requests.get(_a		,			stream=_a    ).raw    )
    return im




@torch.no_grad()
def __lowercase			(							_a		,			_a		,			_a=False    ):
    snake_case_ :     str				=	BitConfig(
        global_padding='''same'''		,			layer_type='''bottleneck'''		,			depths=(3, 4, 9)		,			out_features=['''stage3''']		,			embedding_dynamic_padding=_a		,			)
    snake_case_ :     Tuple				=	ViTHybridConfig(backbone_config=_a		,			image_size=384		,			num_labels=1_000    )
    snake_case_ :     int				=	False

    # load original model from timm
    snake_case_ :     str				=	timm.create_model(_a		,			pretrained=_a    )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    snake_case_ :     Any				=	timm_model.state_dict()
    if base_model:
        remove_classification_head_(_a    )
    snake_case_ :     int				=	create_rename_keys(_a		,			_a    )
    for src, dest in rename_keys:
        rename_key(_a		,			_a		,			_a    )
    read_in_q_k_v(_a		,			_a		,			_a    )

    snake_case_ :     Optional[Any]				=	'''huggingface/label-files'''
    snake_case_ :     Any				=	'''imagenet-1k-id2label.json'''
    snake_case_ :     Dict				=	json.load(open(hf_hub_download(_a		,			_a		,			repo_type='''dataset'''    )		,			'''r'''    )    )
    snake_case_ :     Dict				=	{int(_a    ): v for k, v in idalabel.items()}
    snake_case_ :     Optional[int]				=	idalabel
    snake_case_ :     Optional[Any]				=	{v: k for k, v in idalabel.items()}

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        snake_case_ :     Optional[Any]				=	ViTHybridModel(_a    ).eval()
    else:
        snake_case_ :     Any				=	ViTHybridForImageClassification(_a    ).eval()
    model.load_state_dict(_a    )

    # create image processor
    snake_case_ :     Optional[Any]				=	create_transform(**resolve_data_config({}		,			model=_a    )    )
    snake_case_ :     List[Any]				=	transform.transforms

    snake_case_ :     Optional[Any]				=	{
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    snake_case_ :     List[Any]				=	ViTHybridImageProcessor(
        do_resize=_a		,			size={'''shortest_edge''': timm_transforms[0].size}		,			resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,			do_center_crop=_a		,			crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}		,			do_normalize=_a		,			image_mean=timm_transforms[-1].mean.tolist()		,			image_std=timm_transforms[-1].std.tolist()		,			)

    snake_case_ :     Optional[int]				=	prepare_img()
    snake_case_ :     Optional[int]				=	transform(_a    ).unsqueeze(0    )
    snake_case_ :     int				=	processor(_a		,			return_tensors='''pt'''    ).pixel_values

    # verify pixel values
    assert torch.allclose(_a		,			_a    )

    # verify logits
    with torch.no_grad():
        snake_case_ :     List[str]				=	model(_a    )
        snake_case_ :     Any				=	outputs.logits

    print('''Predicted class:'''		,			logits.argmax(-1    ).item()    )
    if base_model:
        snake_case_ :     Optional[Any]				=	timm_model.forward_features(_a    )
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(_a		,			outputs.pooler_output		,			atol=1E-3    )
    else:
        snake_case_ :     int				=	timm_model(_a    )
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(_a		,			outputs.logits		,			atol=1E-3    )
    print('''Looks ok!'''    )

    if pytorch_dump_folder_path is not None:
        Path(_a    ).mkdir(exist_ok=_a    )
        print(f"Saving model {vit_name} to {pytorch_dump_folder_path}"    )
        model.save_pretrained(_a    )
        print(f"Saving processor to {pytorch_dump_folder_path}"    )
        processor.save_pretrained(_a    )

    if push_to_hub:
        print(f"Pushing model and processor to the hub {vit_name}"    )
        model.push_to_hub(f"ybelkada/{vit_name}"    )
        processor.push_to_hub(f"ybelkada/{vit_name}"    )


if __name__ == "__main__":
     lowercase__    :  int							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--vit_name''',
         default='''vit_base_r50_s16_384''',
         type=str,
         help='''Name of the hybrid ViT timm model you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
     )
     parser.add_argument(
         '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
     )

     lowercase__    :  Any							    =	parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





