





'''simple docstring'''

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





def lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]    )						->Dict:
    _SCREAMING_SNAKE_CASE									=			["""head.weight""", """head.bias"""]
    for k in ignore_keys:
        state_dict.pop(__lowerCamelCase					,       __lowerCamelCase    )





def lowerCamelCase						(     __lowerCamelCase :    List[str]    )						->str:
    # projection head is used in the self-supervised pre-training in MSN,
    # for downstream task it's not needed.
    _SCREAMING_SNAKE_CASE									=			[
        """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(__lowerCamelCase					,       __lowerCamelCase    )





def lowerCamelCase						(     __lowerCamelCase :    List[str]					,       __lowerCamelCase :    Dict					,       __lowerCamelCase :    Any    )						->int:
    _SCREAMING_SNAKE_CASE									=			dct.pop(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			val





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Optional[Any]    )						->Any:
    _SCREAMING_SNAKE_CASE									=			ViTMSNConfig()
    _SCREAMING_SNAKE_CASE									=			1000

    _SCREAMING_SNAKE_CASE									=			"""datasets/huggingface/label-files"""
    _SCREAMING_SNAKE_CASE									=			"""imagenet-1k-id2label.json"""
    _SCREAMING_SNAKE_CASE									=			json.load(open(hf_hub_download(__lowerCamelCase					,       __lowerCamelCase    )					,       """r"""    )    )
    _SCREAMING_SNAKE_CASE									=			{int(__lowerCamelCase    ): v for k, v in idalabel.items()}
    _SCREAMING_SNAKE_CASE									=			idalabel
    _SCREAMING_SNAKE_CASE									=			{v: k for k, v in idalabel.items()}

    if "s16" in checkpoint_url:
        _SCREAMING_SNAKE_CASE									=			384
        _SCREAMING_SNAKE_CASE									=			1536
        _SCREAMING_SNAKE_CASE									=			6
    elif "l16" in checkpoint_url:
        _SCREAMING_SNAKE_CASE									=			1024
        _SCREAMING_SNAKE_CASE									=			4096
        _SCREAMING_SNAKE_CASE									=			24
        _SCREAMING_SNAKE_CASE									=			16
        _SCREAMING_SNAKE_CASE									=			0.1
    elif "b4" in checkpoint_url:
        _SCREAMING_SNAKE_CASE									=			4
    elif "l7" in checkpoint_url:
        _SCREAMING_SNAKE_CASE									=			7
        _SCREAMING_SNAKE_CASE									=			1024
        _SCREAMING_SNAKE_CASE									=			4096
        _SCREAMING_SNAKE_CASE									=			24
        _SCREAMING_SNAKE_CASE									=			16
        _SCREAMING_SNAKE_CASE									=			0.1

    _SCREAMING_SNAKE_CASE									=			ViTMSNModel(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			torch.hub.load_state_dict_from_url(__lowerCamelCase					,       map_location="""cpu"""    )["""target_encoder"""]

    _SCREAMING_SNAKE_CASE									=			ViTImageProcessor(size=config.image_size    )

    remove_projection_head(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			create_rename_keys(__lowerCamelCase					,       base_model=__lowerCamelCase    )

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

    model.load_state_dict(__lowerCamelCase    )
    model.eval()

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

    _SCREAMING_SNAKE_CASE									=			Image.open(requests.get(__lowerCamelCase					,       stream=__lowerCamelCase    ).raw    )
    _SCREAMING_SNAKE_CASE									=			ViTImageProcessor(
        size=config.image_size					,       image_mean=__lowerCamelCase					,       image_std=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			image_processor(images=__lowerCamelCase					,       return_tensors="""pt"""    )

    # forward pass
    torch.manual_seed(2    )
    _SCREAMING_SNAKE_CASE									=			model(**__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			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:
        _SCREAMING_SNAKE_CASE									=			torch.tensor([[-1.0915, -1.4876, -1.1809]]    )
    elif "b16" in checkpoint_url:
        _SCREAMING_SNAKE_CASE									=			torch.tensor([[14.2889, -18.9045, 11.7281]]    )
    elif "l16" in checkpoint_url:
        _SCREAMING_SNAKE_CASE									=			torch.tensor([[41.5028, -22.8681, 45.6475]]    )
    elif "b4" in checkpoint_url:
        _SCREAMING_SNAKE_CASE									=			torch.tensor([[-4.3868, 5.2932, -0.4137]]    )
    else:
        _SCREAMING_SNAKE_CASE									=			torch.tensor([[-0.1792, -0.6465, 2.4263]]    )

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

    print(F'Saving model 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__":
      lowercase_					= 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."""
      )

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










'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline



