

"""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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_A							=  logging.get_logger(__name__)




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase=False    )	-> Tuple:
      UpperCAmelCase__			:							Optional[Any]							=    []
      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""")    )

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

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




def 					a__						(						lowerCAmelCase    )	-> Union[str, Any]:
      UpperCAmelCase__			:							Optional[Any]							=    ["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(lowerCAmelCase			,	lowerCAmelCase    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> Optional[int]:
      UpperCAmelCase__			:							List[Any]							=    dct.pop(lowerCAmelCase    )
      UpperCAmelCase__			:							Any							=    val




def 					a__						(						)	-> Union[str, Any]:
      UpperCAmelCase__			:							str							=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
      UpperCAmelCase__			:							str							=    Image.open(requests.get(lowerCAmelCase			,	stream=lowerCAmelCase    ).raw    )
      return im




@torch.no_grad()
def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      UpperCAmelCase__			:							int							=    ViTConfig()
      UpperCAmelCase__			:							List[str]							=    False
      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      if vit_name[-5:] == "in21k":
            UpperCAmelCase__			:							Tuple							=    True
            UpperCAmelCase__			:							List[str]							=    int(vit_name[-12:-10]    )
            UpperCAmelCase__			:							int							=    int(vit_name[-9:-6]    )
      else:
            UpperCAmelCase__			:							Optional[Any]							=    10_00
            UpperCAmelCase__			:							Any							=    """huggingface/label-files"""
            UpperCAmelCase__			:							Any							=    """imagenet-1k-id2label.json"""
            UpperCAmelCase__			:							Dict							=    json.load(open(hf_hub_download(lowerCAmelCase			,	lowerCAmelCase			,	repo_type="""dataset"""    )			,	"""r"""    )    )
            UpperCAmelCase__			:							List[Any]							=    {int(lowerCAmelCase    ): v for k, v in idalabel.items()}
            UpperCAmelCase__			:							str							=    idalabel
            UpperCAmelCase__			:							int							=    {v: k for k, v in idalabel.items()}
            UpperCAmelCase__			:							Any							=    int(vit_name[-6:-4]    )
            UpperCAmelCase__			:							Union[str, Any]							=    int(vit_name[-3:]    )
      # size of the architecture
      if "deit" in vit_name:
            if vit_name[9:].startswith("""tiny"""    ):
                  UpperCAmelCase__			:							Tuple							=    1_92
                  UpperCAmelCase__			:							Tuple							=    7_68
                  UpperCAmelCase__			:							str							=    12
                  UpperCAmelCase__			:							int							=    3
            elif vit_name[9:].startswith("""small"""    ):
                  UpperCAmelCase__			:							Any							=    3_84
                  UpperCAmelCase__			:							Union[str, Any]							=    15_36
                  UpperCAmelCase__			:							Tuple							=    12
                  UpperCAmelCase__			:							str							=    6
            else:
                  pass
      else:
            if vit_name[4:].startswith("""small"""    ):
                  UpperCAmelCase__			:							Optional[int]							=    7_68
                  UpperCAmelCase__			:							str							=    23_04
                  UpperCAmelCase__			:							Optional[int]							=    8
                  UpperCAmelCase__			:							List[Any]							=    8
            elif vit_name[4:].startswith("""base"""    ):
                  pass
            elif vit_name[4:].startswith("""large"""    ):
                  UpperCAmelCase__			:							List[str]							=    10_24
                  UpperCAmelCase__			:							List[Any]							=    40_96
                  UpperCAmelCase__			:							Dict							=    24
                  UpperCAmelCase__			:							Optional[int]							=    16
            elif vit_name[4:].startswith("""huge"""    ):
                  UpperCAmelCase__			:							Any							=    12_80
                  UpperCAmelCase__			:							List[str]							=    51_20
                  UpperCAmelCase__			:							Any							=    32
                  UpperCAmelCase__			:							List[Any]							=    16

    # load original model from timm
      UpperCAmelCase__			:							Union[str, Any]							=    timm.create_model(lowerCAmelCase			,	pretrained=lowerCAmelCase    )
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      UpperCAmelCase__			:							Tuple							=    timm_model.state_dict()
      if base_model:
            remove_classification_head_(lowerCAmelCase    )
      UpperCAmelCase__			:							str							=    create_rename_keys(lowerCAmelCase			,	lowerCAmelCase    )
      for src, dest in rename_keys:
            rename_key(lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )
      read_in_q_k_v(lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            UpperCAmelCase__			:							Optional[Any]							=    ViTModel(lowerCAmelCase    ).eval()
      else:
            UpperCAmelCase__			:							Optional[int]							=    ViTForImageClassification(lowerCAmelCase    ).eval()
      model.load_state_dict(lowerCAmelCase    )

      # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
      if "deit" in vit_name:
            UpperCAmelCase__			:							Any							=    DeiTImageProcessor(size=config.image_size    )
      else:
            UpperCAmelCase__			:							int							=    ViTImageProcessor(size=config.image_size    )
      UpperCAmelCase__			:							Dict							=    image_processor(images=prepare_img()			,	return_tensors="""pt"""    )
      UpperCAmelCase__			:							List[Any]							=    encoding["""pixel_values"""]
      UpperCAmelCase__			:							int							=    model(lowerCAmelCase    )

      if base_model:
            UpperCAmelCase__			:							Dict							=    timm_model.forward_features(lowerCAmelCase    )
            assert timm_pooled_output.shape == outputs.pooler_output.shape
            assert torch.allclose(lowerCAmelCase			,	outputs.pooler_output			,	atol=1E-3    )
      else:
            UpperCAmelCase__			:							int							=    timm_model(lowerCAmelCase    )
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(lowerCAmelCase			,	outputs.logits			,	atol=1E-3    )

      Path(lowerCAmelCase    ).mkdir(exist_ok=lowerCAmelCase    )
      print(F"""Saving model {vit_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__":
    _A							=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--vit_name""",
        default="""vit_base_patch16_224""",
        type=str,
        help="""Name of the 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."""
    )

    _A							=  parser.parse_args()
    convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




"""simple docstring"""
# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)

