






'''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()
SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase=False   )->			List[str]:
      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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=False   )->			List[str]:
      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    lowercase__			(				__UpperCamelCase   )->			str:
      UpperCamelCase      = ["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase		,						__UpperCamelCase   )







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[Any]:
      UpperCamelCase      = dct.pop(__UpperCamelCase   )
      UpperCamelCase      = val







def    lowercase__			(				)->			List[Any]:
      UpperCamelCase      = """http://images.cocodataset.org/val2017/000000039769.jpg"""
      UpperCamelCase      = Image.open(requests.get(__UpperCamelCase		,						stream=__UpperCamelCase   ).raw   )
      return im







@torch.no_grad()
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=False   )->			Optional[Any]:
      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__":
     SCREAMING_SNAKE_CASE__     =       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.'
     )

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








'''simple docstring'''

import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE__     =       'scheduler_config.json'






class     a_						(      lowerCamelCase    ):
  lowercase							=				1
  lowercase							=				2
  lowercase							=				3
  lowercase							=				4
  lowercase							=				5
  lowercase							=				6
  lowercase							=				7
  lowercase							=				8
  lowercase							=				9
  lowercase							=				10
  lowercase							=				11
  lowercase							=				12
  lowercase							=				13
  lowercase							=				14






@dataclass
class     a_						(      lowerCamelCase    ):
  lowercase							=				42






class     a_						:
  lowercase							=				SCHEDULER_CONFIG_NAME
  lowercase							=				[]
  lowercase							=				True

  @classmethod
  def   A__						(				cls      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE=False      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = cls.load_config(
            pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE      ,		subfolder=_SCREAMING_SNAKE_CASE      ,		return_unused_kwargs=_SCREAMING_SNAKE_CASE      ,		return_commit_hash=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)
        return cls.from_config(_SCREAMING_SNAKE_CASE      ,		return_unused_kwargs=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = False      ,		**_SCREAMING_SNAKE_CASE				)  ->				Dict:



        """simple docstring"""






        self.save_config(save_directory=_SCREAMING_SNAKE_CASE      ,		push_to_hub=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return self._get_compatibles()


  @classmethod
  def   A__						(				cls				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = list(set([cls.__name__] + cls._compatibles				)				)
        UpperCamelCase      = importlib.import_module(__name__.split("""."""				)[0]				)
        UpperCamelCase      = [
            getattr(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				) for c in compatible_classes_str if hasattr(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        ]
        return compatible_classes
