


"""simple docstring"""







import functools
import operator

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


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json""",
    # See all SEW models at https://huggingface.co/models?filter=sew
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Dict     =	"""sew"""






    def __init__( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   Any=32    ,			__UpperCAmelCase    :   Any=768    ,			__UpperCAmelCase    :   Union[str, Any]=12    ,			__UpperCAmelCase    :   Optional[int]=12    ,			__UpperCAmelCase    :   Optional[int]=3072    ,			__UpperCAmelCase    :   Dict=2    ,			__UpperCAmelCase    :   Any="gelu"    ,			__UpperCAmelCase    :   Dict=0.1    ,			__UpperCAmelCase    :   List[str]=0.1    ,			__UpperCAmelCase    :   int=0.1    ,			__UpperCAmelCase    :   Optional[Any]=0.0    ,			__UpperCAmelCase    :   str=0.1    ,			__UpperCAmelCase    :   Tuple=0.1    ,			__UpperCAmelCase    :   str=0.02    ,			__UpperCAmelCase    :   Optional[Any]=1e-5    ,			__UpperCAmelCase    :   Optional[Any]="group"    ,			__UpperCAmelCase    :   str="gelu"    ,			__UpperCAmelCase    :   Optional[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,			__UpperCAmelCase    :   int=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,			__UpperCAmelCase    :   List[str]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,			__UpperCAmelCase    :   List[Any]=False    ,			__UpperCAmelCase    :   Optional[Any]=128    ,			__UpperCAmelCase    :   Dict=16    ,			__UpperCAmelCase    :   List[str]=True    ,			__UpperCAmelCase    :   Any=0.05    ,			__UpperCAmelCase    :   int=10    ,			__UpperCAmelCase    :   int=2    ,			__UpperCAmelCase    :   Union[str, Any]=0.0    ,			__UpperCAmelCase    :   Tuple=10    ,			__UpperCAmelCase    :   Tuple=0    ,			__UpperCAmelCase    :   Optional[Any]="mean"    ,			__UpperCAmelCase    :   Dict=False    ,			__UpperCAmelCase    :   Union[str, Any]=False    ,			__UpperCAmelCase    :   List[str]=256    ,			__UpperCAmelCase    :   str=0    ,			__UpperCAmelCase    :   Optional[Any]=1    ,			__UpperCAmelCase    :   int=2    ,			**__UpperCAmelCase    :   int    ,			):
      super().__init__(**__UpperCAmelCase    ,			pad_token_id=__UpperCAmelCase    ,			bos_token_id=__UpperCAmelCase    ,			eos_token_id=__UpperCAmelCase)
      a						:		Optional[Any]											=      hidden_size
      a						:		int											=      feat_extract_norm
      a						:		Optional[Any]											=      feat_extract_activation
      a						:		Optional[int]											=      list(__UpperCAmelCase)
      a						:		Optional[int]											=      list(__UpperCAmelCase)
      a						:		Tuple											=      list(__UpperCAmelCase)
      a						:		Tuple											=      conv_bias
      a						:		List[str]											=      num_conv_pos_embeddings
      a						:		str											=      num_conv_pos_embedding_groups
      a						:		str											=      len(self.conv_dim)
      a						:		int											=      num_hidden_layers
      a						:		Dict											=      intermediate_size
      a						:		Union[str, Any]											=      squeeze_factor
      a						:		Optional[int]											=      hidden_act
      a						:		Optional[Any]											=      num_attention_heads
      a						:		Dict											=      hidden_dropout
      a						:		Tuple											=      attention_dropout
      a						:		Any											=      activation_dropout
      a						:		Union[str, Any]											=      feat_proj_dropout
      a						:		Optional[int]											=      final_dropout
      a						:		int											=      layerdrop
      a						:		Tuple											=      layer_norm_eps
      a						:		str											=      initializer_range
      a						:		Union[str, Any]											=      vocab_size

      if (
          (len(self.conv_stride) != self.num_feat_extract_layers)
          or (len(self.conv_kernel) != self.num_feat_extract_layers)
          or (len(self.conv_dim) != self.num_feat_extract_layers)
      ):
        raise ValueError(
            "Configuration for convolutional layers is incorrect."
            "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
            f'''but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)'''
            f'''= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      a						:		Optional[Any]											=      apply_spec_augment
      a						:		Any											=      mask_time_prob
      a						:		Optional[Any]											=      mask_time_length
      a						:		List[Any]											=      mask_time_min_masks
      a						:		Union[str, Any]											=      mask_feature_prob
      a						:		List[str]											=      mask_feature_length
      a						:		Dict											=      mask_feature_min_masks

      # ctc loss
      a						:		List[Any]											=      ctc_loss_reduction
      a						:		Union[str, Any]											=      ctc_zero_infinity

      # sequence classification
      a						:		Union[str, Any]											=      use_weighted_layer_sum
      a						:		Tuple											=      classifier_proj_size






    @property
    def      __snake_case							( self    :   Any):
      return functools.reduce(operator.mul    ,			self.conv_stride    ,			1)










"""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()
__lowercase    =				logging.get_logger(__name__)


def 					lowercase      (    A_  ,       A_=False	)->				int:
  '''simple docstring'''







  a						:		List[str]											=      []

  # 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"
    a						:		Tuple											=      [(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 					lowercase      (    A_  ,       A_  ,       A_=False	)->				Optional[Any]:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				Dict:
  '''simple docstring'''







  a						:		Dict											=      ["head.weight", "head.bias"]
  for k in ignore_keys:
    state_dict.pop(A_  ,       A_	)


def 					lowercase      (    A_  ,       A_  ,       A_	)->				List[Any]:
  '''simple docstring'''







  a						:		List[Any]											=      dct.pop(A_	)
  a						:		str											=      val


def 					lowercase      (    )->				List[Any]:
  '''simple docstring'''







  a						:		Any											=      "http://images.cocodataset.org/val2017/000000039769.jpg"
  a						:		Tuple											=      Image.open(requests.get(A_  ,       stream=A_	).raw	)
  return im


@torch.no_grad()
def 					lowercase      (    A_  ,       A_  ,       A_=False	)->				Union[str, Any]:
  '''simple docstring'''







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

  # load original model from timm
  a						:		Any											=      timm.create_model(A_  ,       pretrained=A_	)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  a						:		Optional[Any]											=      timm_model.state_dict()
  if base_model:
    remove_classification_head_(A_	)
  a						:		int											=      create_rename_keys(A_  ,       A_	)
  for src, dest in rename_keys:
    rename_key(A_  ,       A_  ,       A_	)
  read_in_q_k_v(A_  ,       A_  ,       A_	)

  a						:		Union[str, Any]											=      "huggingface/label-files"
  a						:		Optional[int]											=      "imagenet-1k-id2label.json"
  a						:		str											=      json.load(open(hf_hub_download(A_  ,       A_  ,       repo_type="dataset"	)  ,       "r"	)	)
  a						:		Optional[Any]											=      {int(A_	): v for k, v in idalabel.items()}
  a						:		str											=      idalabel
  a						:		Any											=      {v: k for k, v in idalabel.items()}

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

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

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

  a						:		str											=      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()  ,       )

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

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

  # verify logits
  with torch.no_grad():
    a						:		Dict											=      model(A_	)
    a						:		Tuple											=      outputs.logits

  print("Predicted class:"  ,       logits.argmax(-1	).item()	)
  if base_model:
    a						:		str											=      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:
    a						:		int											=      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__":
 __lowercase    =				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."""
 )

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






