


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       :		str         =				logging.get_logger(__name__)




def     UpperCamelCase    (      _a       ,		_a=False		)					->							int:
     '''simple docstring'''





     lowercase_       :Optional[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"
          lowercase_       :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     UpperCamelCase    (      _a       ,		_a       ,		_a=False		)					->							str:
     '''simple docstring'''





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




def     UpperCamelCase    (      _a		)					->							Any:
     '''simple docstring'''





     lowercase_       :Dict     =       ['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(_a       ,		_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							List[Any]:
     '''simple docstring'''





     lowercase_       :Optional[Any]     =       dct.pop(_a		)
     lowercase_       :Dict     =       val




def     UpperCamelCase    (      )					->							Any:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     lowercase_       :Union[str, Any]     =       Image.open(requests.get(_a       ,		stream=_a		).raw		)
     return im




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





     lowercase_       :int     =       BitConfig(
         global_padding='''same'''       ,		layer_type='''bottleneck'''       ,		depths=(3, 4, 9)       ,		out_features=['''stage3''']       ,		embedding_dynamic_padding=_a       ,		)
     lowercase_       :Tuple     =       ViTHybridConfig(backbone_config=_a       ,		image_size=3_8_4       ,		num_labels=1_0_0_0		)
     lowercase_       :Optional[Any]     =       False

     # load original model from timm
     lowercase_       :Tuple     =       timm.create_model(_a       ,		pretrained=_a		)
     timm_model.eval()

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

     lowercase_       :List[Any]     =       '''huggingface/label-files'''
     lowercase_       :Any     =       '''imagenet-1k-id2label.json'''
     lowercase_       :List[Any]     =       json.load(open(hf_hub_download(_a       ,		_a       ,		repo_type='''dataset'''		)       ,		'''r'''		)		)
     lowercase_       :str     =       {int(_a		): v for k, v in idalabel.items()}
     lowercase_       :str     =       idalabel
     lowercase_       :List[str]     =       {v: k for k, v in idalabel.items()}

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          lowercase_       :Optional[Any]     =       ViTHybridModel(_a		).eval()
     else:
          lowercase_       :Dict     =       ViTHybridForImageClassification(_a		).eval()
     model.load_state_dict(_a		)

     # create image processor
     lowercase_       :str     =       create_transform(**resolve_data_config({}       ,		model=_a		)		)
     lowercase_       :Union[str, Any]     =       transform.transforms

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

     lowercase_       :Optional[Any]     =       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()       ,		)

     lowercase_       :Any     =       prepare_img()
     lowercase_       :List[Any]     =       transform(_a		).unsqueeze(0		)
     lowercase_       :Optional[Any]     =       processor(_a       ,		return_tensors='''pt'''		).pixel_values

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

     # verify logits
     with torch.no_grad():
          lowercase_       :List[Any]     =       model(_a		)
          lowercase_       :Optional[int]     =       outputs.logits

     print('''Predicted class:'''       ,		logits.argmax(-1		).item()		)
     if base_model:
          lowercase_       :Optional[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:
          lowercase_       :Union[str, 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__":
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				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       :		Tuple         =				parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE       :		List[str]         =				HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE       :		List[Any]         =				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE       :		Optional[Any]         =				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE       :		Any         =				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE       :		Dict         =				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)

