





import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def 	lowerCAmelCase_					(			__a      ,       __a=False						)	->						Union[str, Any]:







  """simple docstring"""





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

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ("module.cls_token", "vit.embeddings.cls_token"),
          ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
          ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
          ("module.pos_embed", "vit.embeddings.position_embeddings"),
      ]						)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ("module.norm.weight", "layernorm.weight"),
            ("module.norm.bias", "layernorm.bias"),
        ]						)

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    lowerCamelCase__: 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"),
        ]						)

  return rename_keys





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a=False						)	->						Any:







  """simple docstring"""





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





def 	lowerCAmelCase_					(			__a						)	->						Optional[Any]:







  """simple docstring"""





  lowerCamelCase__: Tuple         =["head.weight", "head.bias"]
  for k in ignore_keys:
    state_dict.pop(__a      ,       __a						)





def 	lowerCAmelCase_					(			__a						)	->						Optional[int]:







  """simple docstring"""





  lowerCamelCase__: Any         =[
      "module.fc.fc1.weight",
      "module.fc.fc1.bias",
      "module.fc.bn1.weight",
      "module.fc.bn1.bias",
      "module.fc.bn1.running_mean",
      "module.fc.bn1.running_var",
      "module.fc.bn1.num_batches_tracked",
      "module.fc.fc2.weight",
      "module.fc.fc2.bias",
      "module.fc.bn2.weight",
      "module.fc.bn2.bias",
      "module.fc.bn2.running_mean",
      "module.fc.bn2.running_var",
      "module.fc.bn2.num_batches_tracked",
      "module.fc.fc3.weight",
      "module.fc.fc3.bias",
  ]
  for k in ignore_keys:
    state_dict.pop(__a      ,       __a						)





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__: int         =dct.pop(__a						)
  lowerCamelCase__: Tuple         =val





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						str:







  """simple docstring"""





  lowerCamelCase__: List[str]         =ViTMSNConfig()
  lowerCamelCase__: Optional[Any]         =1000

  lowerCamelCase__: Dict         ="datasets/huggingface/label-files"
  lowerCamelCase__: List[str]         ="imagenet-1k-id2label.json"
  lowerCamelCase__: List[str]         =json.load(open(hf_hub_download(__a      ,       __a						)      ,       "r"						)						)
  lowerCamelCase__: int         ={int(__a						): v for k, v in idalabel.items()}
  lowerCamelCase__: List[str]         =idalabel
  lowerCamelCase__: Union[str, Any]         ={v: k for k, v in idalabel.items()}

  if "s16" in checkpoint_url:
    lowerCamelCase__: Dict         =384
    lowerCamelCase__: Union[str, Any]         =1536
    lowerCamelCase__: Dict         =6
  elif "l16" in checkpoint_url:
    lowerCamelCase__: str         =1024
    lowerCamelCase__: List[Any]         =4096
    lowerCamelCase__: List[str]         =24
    lowerCamelCase__: List[Any]         =16
    lowerCamelCase__: Any         =0.1
  elif "b4" in checkpoint_url:
    lowerCamelCase__: List[str]         =4
  elif "l7" in checkpoint_url:
    lowerCamelCase__: Optional[Any]         =7
    lowerCamelCase__: int         =1024
    lowerCamelCase__: int         =4096
    lowerCamelCase__: Tuple         =24
    lowerCamelCase__: List[Any]         =16
    lowerCamelCase__: Tuple         =0.1

  lowerCamelCase__: Union[str, Any]         =ViTMSNModel(__a						)

  lowerCamelCase__: Tuple         =torch.hub.load_state_dict_from_url(__a      ,       map_location="cpu"						)["target_encoder"]

  lowerCamelCase__: Union[str, Any]         =ViTImageProcessor(size=config.image_size						)

  remove_projection_head(__a						)
  lowerCamelCase__: Optional[Any]         =create_rename_keys(__a      ,       base_model=__a						)

  for src, dest in rename_keys:
    rename_key(__a      ,       __a      ,       __a						)
  read_in_q_k_v(__a      ,       __a      ,       base_model=__a						)

  model.load_state_dict(__a						)
  model.eval()

  lowerCamelCase__: Dict         ="http://images.cocodataset.org/val2017/000000039769.jpg"

  lowerCamelCase__: Any         =Image.open(requests.get(__a      ,       stream=__a						).raw						)
  lowerCamelCase__: Union[str, Any]         =ViTImageProcessor(
      size=config.image_size      ,       image_mean=__a      ,       image_std=__a						)
  lowerCamelCase__: List[str]         =image_processor(images=__a      ,       return_tensors="pt"						)

  # forward pass
  torch.manual_seed(2						)
  lowerCamelCase__: Union[str, Any]         =model(**__a						)
  lowerCamelCase__: List[str]         =outputs.last_hidden_state

  # The following Colab Notebook was used to generate these outputs:
  # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
  if "s16" in checkpoint_url:
    lowerCamelCase__: str         =torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]]						)
  elif "b16" in checkpoint_url:
    lowerCamelCase__: Tuple         =torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]]						)
  elif "l16" in checkpoint_url:
    lowerCamelCase__: List[Any]         =torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]]						)
  elif "b4" in checkpoint_url:
    lowerCamelCase__: Union[str, Any]         =torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]]						)
  else:
    lowerCamelCase__: Dict         =torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]]						)

  # verify logits
  assert torch.allclose(last_hidden_state[:, 0, :3]      ,       __a      ,       atol=1e-4						)

  print(F"""Saving model to {pytorch_dump_folder_path}"""						)
  model.save_pretrained(__a						)

  print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
  image_processor.save_pretrained(__a						)


if __name__ == "__main__":
     __A							   =							argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--checkpoint_url",
         default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
         type=str,
         help="URL of the checkpoint you'd like to convert.",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
     )

     __A							   =							parser.parse_args()
     convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






"""simple docstring"""

A_						:    Optional[Any]				=      '0.18.2'

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
							if not is_onnx_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_onnx_objects import *  # noqa F403
else:
							from .pipelines import OnnxRuntimeModel

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_pt_objects import *  # noqa F403
else:
							from .models import (
							    AutoencoderKL,
							    ControlNetModel,
							    ModelMixin,
							    PriorTransformer,
							    TaFilmDecoder,
							    TransformeraDModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    VQModel,
							)
							from .optimization import (
							    get_constant_schedule,
							    get_constant_schedule_with_warmup,
							    get_cosine_schedule_with_warmup,
							    get_cosine_with_hard_restarts_schedule_with_warmup,
							    get_linear_schedule_with_warmup,
							    get_polynomial_decay_schedule_with_warmup,
							    get_scheduler,
							)
							from .pipelines import (
							    AudioPipelineOutput,
							    ConsistencyModelPipeline,
							    DanceDiffusionPipeline,
							    DDIMPipeline,
							    DDPMPipeline,
							    DiffusionPipeline,
							    DiTPipeline,
							    ImagePipelineOutput,
							    KarrasVePipeline,
							    LDMPipeline,
							    LDMSuperResolutionPipeline,
							    PNDMPipeline,
							    RePaintPipeline,
							    ScoreSdeVePipeline,
							)
							from .schedulers import (
							    CMStochasticIterativeScheduler,
							    DDIMInverseScheduler,
							    DDIMParallelScheduler,
							    DDIMScheduler,
							    DDPMParallelScheduler,
							    DDPMScheduler,
							    DEISMultistepScheduler,
							    DPMSolverMultistepInverseScheduler,
							    DPMSolverMultistepScheduler,
							    DPMSolverSinglestepScheduler,
							    EulerAncestralDiscreteScheduler,
							    EulerDiscreteScheduler,
							    HeunDiscreteScheduler,
							    IPNDMScheduler,
							    KarrasVeScheduler,
							    KDPMaAncestralDiscreteScheduler,
							    KDPMaDiscreteScheduler,
							    PNDMScheduler,
							    RePaintScheduler,
							    SchedulerMixin,
							    ScoreSdeVeScheduler,
							    UnCLIPScheduler,
							    UniPCMultistepScheduler,
							    VQDiffusionScheduler,
							)
							from .training_utils import EMAModel

try:
							if not (is_torch_available() and is_scipy_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
							from .schedulers import LMSDiscreteScheduler

try:
							if not (is_torch_available() and is_torchsde_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
							from .schedulers import DPMSolverSDEScheduler

try:
							if not (is_torch_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
							from .pipelines import (
							    AltDiffusionImgaImgPipeline,
							    AltDiffusionPipeline,
							    AudioLDMPipeline,
							    CycleDiffusionPipeline,
							    IFImgaImgPipeline,
							    IFImgaImgSuperResolutionPipeline,
							    IFInpaintingPipeline,
							    IFInpaintingSuperResolutionPipeline,
							    IFPipeline,
							    IFSuperResolutionPipeline,
							    ImageTextPipelineOutput,
							    KandinskyImgaImgPipeline,
							    KandinskyInpaintPipeline,
							    KandinskyPipeline,
							    KandinskyPriorPipeline,
							    KandinskyVaaControlnetImgaImgPipeline,
							    KandinskyVaaControlnetPipeline,
							    KandinskyVaaImgaImgPipeline,
							    KandinskyVaaInpaintPipeline,
							    KandinskyVaaPipeline,
							    KandinskyVaaPriorEmbaEmbPipeline,
							    KandinskyVaaPriorPipeline,
							    LDMTextToImagePipeline,
							    PaintByExamplePipeline,
							    SemanticStableDiffusionPipeline,
							    ShapEImgaImgPipeline,
							    ShapEPipeline,
							    StableDiffusionAttendAndExcitePipeline,
							    StableDiffusionControlNetImgaImgPipeline,
							    StableDiffusionControlNetInpaintPipeline,
							    StableDiffusionControlNetPipeline,
							    StableDiffusionDepthaImgPipeline,
							    StableDiffusionDiffEditPipeline,
							    StableDiffusionImageVariationPipeline,
							    StableDiffusionImgaImgPipeline,
							    StableDiffusionInpaintPipeline,
							    StableDiffusionInpaintPipelineLegacy,
							    StableDiffusionInstructPixaPixPipeline,
							    StableDiffusionLatentUpscalePipeline,
							    StableDiffusionLDMaDPipeline,
							    StableDiffusionModelEditingPipeline,
							    StableDiffusionPanoramaPipeline,
							    StableDiffusionParadigmsPipeline,
							    StableDiffusionPipeline,
							    StableDiffusionPipelineSafe,
							    StableDiffusionPixaPixZeroPipeline,
							    StableDiffusionSAGPipeline,
							    StableDiffusionUpscalePipeline,
							    StableUnCLIPImgaImgPipeline,
							    StableUnCLIPPipeline,
							    TextToVideoSDPipeline,
							    TextToVideoZeroPipeline,
							    UnCLIPImageVariationPipeline,
							    UnCLIPPipeline,
							    UniDiffuserModel,
							    UniDiffuserPipeline,
							    UniDiffuserTextDecoder,
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							    VideoToVideoSDPipeline,
							    VQDiffusionPipeline,
							)

try:
							if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
							from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
							if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
							from .pipelines import StableDiffusionKDiffusionPipeline

try:
							if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
							from .pipelines import (
							    OnnxStableDiffusionImgaImgPipeline,
							    OnnxStableDiffusionInpaintPipeline,
							    OnnxStableDiffusionInpaintPipelineLegacy,
							    OnnxStableDiffusionPipeline,
							    OnnxStableDiffusionUpscalePipeline,
							    StableDiffusionOnnxPipeline,
							)

try:
							if not (is_torch_available() and is_librosa_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
							from .pipelines import AudioDiffusionPipeline, Mel

try:
							if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
							from .pipelines import SpectrogramDiffusionPipeline

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_flax_objects import *  # noqa F403
else:
							from .models.controlnet_flax import FlaxControlNetModel
							from .models.modeling_flax_utils import FlaxModelMixin
							from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
							from .models.vae_flax import FlaxAutoencoderKL
							from .pipelines import FlaxDiffusionPipeline
							from .schedulers import (
							    FlaxDDIMScheduler,
							    FlaxDDPMScheduler,
							    FlaxDPMSolverMultistepScheduler,
							    FlaxKarrasVeScheduler,
							    FlaxLMSDiscreteScheduler,
							    FlaxPNDMScheduler,
							    FlaxSchedulerMixin,
							    FlaxScoreSdeVeScheduler,
							)


try:
							if not (is_flax_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
							from .pipelines import (
							    FlaxStableDiffusionControlNetPipeline,
							    FlaxStableDiffusionImgaImgPipeline,
							    FlaxStableDiffusionInpaintPipeline,
							    FlaxStableDiffusionPipeline,
							)

try:
							if not (is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_note_seq_objects import *  # noqa F403
else:
							from .pipelines import MidiProcessor





