



"""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()
lowerCamelCase       	=						logging.get_logger(__name__)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__=False				):
      UpperCAmelCase_      			=							[]

      # 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"
            UpperCAmelCase_      			=							[(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__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__=False				):
      for i in range(config.num_hidden_layers				):
            if base_model:
                  UpperCAmelCase_      			=							""
            else:
                  UpperCAmelCase_      			=							"vit."
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            UpperCAmelCase_      			=							state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""				)
            UpperCAmelCase_      			=							state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""				)
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_      			=							in_proj_weight[
                : config.hidden_size, :
            ]
            UpperCAmelCase_      			=							in_proj_bias[: config.hidden_size]
            UpperCAmelCase_      			=							in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            UpperCAmelCase_      			=							in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            UpperCAmelCase_      			=							in_proj_weight[
                -config.hidden_size :, :
            ]
            UpperCAmelCase_      			=							in_proj_bias[-config.hidden_size :]
def        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							["head.weight", "head.bias"]
      for k in ignore_keys:
            state_dict.pop(lowerCAmelCase__     ,      lowerCAmelCase__				)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							dct.pop(lowerCAmelCase__				)
      UpperCAmelCase_      			=							val
def        a__     (				):
      UpperCAmelCase_      			=							"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_      			=							Image.open(requests.get(lowerCAmelCase__     ,      stream=lowerCAmelCase__				).raw				)
      return im
@torch.no_grad()
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__=False				):
      UpperCAmelCase_      			=							BitConfig(
          global_padding="same"     ,      layer_type="bottleneck"     ,      depths=(3, 4, 9)     ,      out_features=["stage3"]     ,      embedding_dynamic_padding=lowerCAmelCase__     ,      )
      UpperCAmelCase_      			=							ViTHybridConfig(backbone_config=lowerCAmelCase__     ,      image_size=384     ,      num_labels=1000				)
      UpperCAmelCase_      			=							False

      # load original model from timm
      UpperCAmelCase_      			=							timm.create_model(lowerCAmelCase__     ,      pretrained=lowerCAmelCase__				)
      timm_model.eval()

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

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

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            UpperCAmelCase_      			=							ViTHybridModel(lowerCAmelCase__				).eval()
      else:
            UpperCAmelCase_      			=							ViTHybridForImageClassification(lowerCAmelCase__				).eval()
      model.load_state_dict(lowerCAmelCase__				)

      # create image processor
      UpperCAmelCase_      			=							create_transform(**resolve_data_config({}     ,      model=lowerCAmelCase__				)				)
      UpperCAmelCase_      			=							transform.transforms

      UpperCAmelCase_      			=							{
          "bilinear": PILImageResampling.BILINEAR,
          "bicubic": PILImageResampling.BICUBIC,
          "nearest": PILImageResampling.NEAREST,
      }

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

      UpperCAmelCase_      			=							prepare_img()
      UpperCAmelCase_      			=							transform(lowerCAmelCase__				).unsqueeze(0				)
      UpperCAmelCase_      			=							processor(lowerCAmelCase__     ,      return_tensors="pt"				).pixel_values

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

      # verify logits
      with torch.no_grad():
            UpperCAmelCase_      			=							model(lowerCAmelCase__				)
            UpperCAmelCase_      			=							outputs.logits

      print("Predicted class:"     ,      logits.argmax(-1				).item()				)
      if base_model:
            UpperCAmelCase_      			=							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_      			=							timm_model(lowerCAmelCase__				)
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(lowerCAmelCase__     ,      outputs.logits     ,      atol=1e-3				)
      print("Looks ok!"				)

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

      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__":
  lowerCamelCase       	=						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."""
  )

  lowerCamelCase       	=						parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase       	=						logging.get_logger(__name__)

lowerCamelCase       	=						{
    """RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
    """RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
    """RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
    """RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
    """RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					'''rwkv'''
       UpperCamelCase						=					{'''max_position_embeddings''': '''context_length'''}



       def __init__(							self      :  str		,    _UpperCAmelCase      :  int=50277		,    _UpperCAmelCase      :  Optional[Any]=1024		,    _UpperCAmelCase      :  str=4096		,    _UpperCAmelCase      :  Any=32		,    _UpperCAmelCase      :  Any=None		,    _UpperCAmelCase      :  Optional[int]=None		,    _UpperCAmelCase      :  Tuple=1e-5		,    _UpperCAmelCase      :  str=0		,    _UpperCAmelCase      :  Any=0		,    _UpperCAmelCase      :  Union[str, Any]=6		,    _UpperCAmelCase      :  Dict=False		,    _UpperCAmelCase      :  Any=True		,    **_UpperCAmelCase      :  Optional[Any]		,    )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							vocab_size
             UpperCAmelCase_      			=							context_length
             UpperCAmelCase_      			=							hidden_size
             UpperCAmelCase_      			=							num_hidden_layers
             UpperCAmelCase_      			=							attention_hidden_size if attention_hidden_size is not None else hidden_size
             UpperCAmelCase_      			=							intermediate_size if intermediate_size is not None else 4 * hidden_size
             UpperCAmelCase_      			=							layer_norm_epsilon
             UpperCAmelCase_      			=							rescale_every
             UpperCAmelCase_      			=							use_cache

             UpperCAmelCase_      			=							bos_token_id
             UpperCAmelCase_      			=							eos_token_id

             super().__init__(
                 tie_word_embeddings=_UpperCAmelCase		,    bos_token_id=_UpperCAmelCase		,    eos_token_id=_UpperCAmelCase		,    **_UpperCAmelCase   )




