'''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__  :      List[str]        =					logging.get_logger(__name__)



def  __lowerCamelCase       (      _UpperCamelCase  :  int							,    _UpperCamelCase  :  int=False					):


      '''simple docstring'''


      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  __lowerCamelCase       (      _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Tuple=False					):


      '''simple docstring'''


      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  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  ['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(_UpperCamelCase							,    _UpperCamelCase					)



def  __lowerCamelCase       (      _UpperCamelCase  :  Union[str, Any]							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Any					):


      '''simple docstring'''


      UpperCAmelCase_            =  dct.pop(_UpperCamelCase					)
      UpperCAmelCase_            =  val



def  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCAmelCase_            =  Image.open(requests.get(_UpperCamelCase							,    stream=_UpperCamelCase					).raw					)
      return im



@torch.no_grad()
def  __lowerCamelCase       (      _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  int=False					):


      '''simple docstring'''


      UpperCAmelCase_            =  BitConfig(
          global_padding='''same'''							,    layer_type='''bottleneck'''							,    depths=(3, 4, 9)							,    out_features=['''stage3''']							,    embedding_dynamic_padding=_UpperCamelCase							,    )
      UpperCAmelCase_            =  ViTHybridConfig(backbone_config=_UpperCamelCase							,    image_size=384							,    num_labels=1000					)
      UpperCAmelCase_            =  False

      # load original model from timm
      UpperCAmelCase_            =  timm.create_model(_UpperCamelCase							,    pretrained=_UpperCamelCase					)
      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_(_UpperCamelCase					)
      UpperCAmelCase_            =  create_rename_keys(_UpperCamelCase							,    _UpperCamelCase					)
      for src, dest in rename_keys:
            rename_key(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)
      read_in_q_k_v(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)

      UpperCAmelCase_            =  '''huggingface/label-files'''
      UpperCAmelCase_            =  '''imagenet-1k-id2label.json'''
      UpperCAmelCase_            =  json.load(open(hf_hub_download(_UpperCamelCase							,    _UpperCamelCase							,    repo_type='''dataset'''					)							,    '''r'''					)					)
      UpperCAmelCase_            =  {int(_UpperCamelCase					): 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(_UpperCamelCase					).eval()
      else:
            UpperCAmelCase_            =  ViTHybridForImageClassification(_UpperCamelCase					).eval()
      model.load_state_dict(_UpperCamelCase					)

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

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

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

      UpperCAmelCase_            =  prepare_img()
      UpperCAmelCase_            =  transform(_UpperCamelCase					).unsqueeze(0					)
      UpperCAmelCase_            =  processor(_UpperCamelCase							,    return_tensors='''pt'''					).pixel_values

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

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

      print('''Predicted class:'''							,    logits.argmax(-1					).item()					)
      if base_model:
            UpperCAmelCase_            =  timm_model.forward_features(_UpperCamelCase					)
            assert timm_pooled_output.shape == outputs.pooler_output.shape
            assert torch.allclose(_UpperCamelCase							,    outputs.pooler_output							,    atol=1E-3					)
      else:
            UpperCAmelCase_            =  timm_model(_UpperCamelCase					)
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(_UpperCamelCase							,    outputs.logits							,    atol=1E-3					)
      print('''Looks ok!'''					)

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

      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__  :      Dict        =					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__  :      List[Any]        =					parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase__  :      Optional[Any]        =					{
    "configuration_encodec": [
        "ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EncodecConfig",
    ],
    "feature_extraction_encodec": ["EncodecFeatureExtractor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      List[str]        =					[
          "ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
          "EncodecModel",
          "EncodecPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_encodec import (
          ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EncodecConfig,
      )
      from .feature_extraction_encodec import EncodecFeatureExtractor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_encodec import (
                ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
                EncodecModel,
                EncodecPreTrainedModel,
            )

else:
      import sys

      lowercase__  :      str        =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





