
"""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()
_a      :			Any					=							logging.get_logger(__name__)
def 				a__   (   a   :				Optional[Any]       ,     a   :				int=False		):

  """simple docstring"""







  _snake_case     :						int						=   []

  # 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     :						Any						=   [(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 				a__   (   a   :				Any       ,     a   :				Tuple       ,     a   :				int=False		):

  """simple docstring"""







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

  """simple docstring"""







  _snake_case     :						Union[str, Any]						=   ["head.weight", "head.bias"]
  for k in ignore_keys:
    state_dict.pop(a       ,     a		)
def 				a__   (   a   :				List[Any]       ,     a   :				Tuple       ,     a   :				int		):

  """simple docstring"""







  _snake_case     :						int						=   dct.pop(a		)
  _snake_case     :						Optional[int]						=   val
def 				a__   (   ):

  """simple docstring"""







  _snake_case     :						Union[str, Any]						=   "http://images.cocodataset.org/val2017/000000039769.jpg"
  _snake_case     :						List[Any]						=   Image.open(requests.get(a       ,     stream=a		).raw		)
  return im
@torch.no_grad()
def 				a__   (   a   :				Any       ,     a   :				Any       ,     a   :				Optional[int]=False		):

  """simple docstring"""







  _snake_case     :						Optional[int]						=   BitConfig(
      global_padding="same"       ,     layer_type="bottleneck"       ,     depths=(3, 4, 9)       ,     out_features=["stage3"]       ,     embedding_dynamic_padding=a       ,     )
  _snake_case     :						int						=   ViTHybridConfig(backbone_config=a       ,     image_size=384       ,     num_labels=1_000		)
  _snake_case     :						int						=   False

  # load original model from timm
  _snake_case     :						Optional[int]						=   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     :						List[Any]						=   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     :						str						=   "huggingface/label-files"
  _snake_case     :						str						=   "imagenet-1k-id2label.json"
  _snake_case     :						str						=   json.load(open(hf_hub_download(a       ,     a       ,     repo_type="dataset"		)       ,     "r"		)		)
  _snake_case     :						Optional[int]						=   {int(a		): v for k, v in idalabel.items()}
  _snake_case     :						Dict						=   idalabel
  _snake_case     :						int						=   {v: k for k, v in idalabel.items()}

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    _snake_case     :						int						=   ViTHybridModel(a		).eval()
  else:
    _snake_case     :						Any						=   ViTHybridForImageClassification(a		).eval()
  model.load_state_dict(a		)

  # create image processor
  _snake_case     :						Dict						=   create_transform(**resolve_data_config({}       ,     model=a		)		)
  _snake_case     :						List[Any]						=   transform.transforms

  _snake_case     :						List[str]						=   {
      "bilinear": PILImageResampling.BILINEAR,
      "bicubic": PILImageResampling.BICUBIC,
      "nearest": PILImageResampling.NEAREST,
  }

  _snake_case     :						Tuple						=   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     :						Any						=   prepare_img()
  _snake_case     :						Tuple						=   transform(a		).unsqueeze(0		)
  _snake_case     :						List[Any]						=   processor(a       ,     return_tensors="pt"		).pixel_values

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

  # verify logits
  with torch.no_grad():
    _snake_case     :						Optional[int]						=   model(a		)
    _snake_case     :						Any						=   outputs.logits

  print("Predicted class:"       ,     logits.argmax(-1		).item()		)
  if base_model:
    _snake_case     :						List[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     :						Optional[Any]						=   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__":
    _a      :			str					=							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."""
    )

    _a      :			Tuple					=							parser.parse_args()
    convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""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() and is_transformers_version(""">=""", """4.25.0""")):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
    from .pipeline_unclip import UnCLIPPipeline
    from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
    from .text_proj import UnCLIPTextProjModel

