
'''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  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[int]=False							):



      """simple docstring"""





      lowercase_     :					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_     :					Dict													=	[(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  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Tuple=False							):



      """simple docstring"""





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



      """simple docstring"""





      lowercase_     :					Tuple													=	['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			List[str]				,				__SCREAMING_SNAKE_CASE :			List[str]							):



      """simple docstring"""





      lowercase_     :					Dict													=	dct.pop(__SCREAMING_SNAKE_CASE							)
      lowercase_     :					Tuple													=	val
def  snake_case_		(    ):



      """simple docstring"""





      lowercase_     :					Any													=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowercase_     :					Any													=	Image.open(requests.get(__SCREAMING_SNAKE_CASE				,				stream=__SCREAMING_SNAKE_CASE							).raw							)
      return im
@torch.no_grad()
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			int=False							):



      """simple docstring"""





      lowercase_     :					Optional[int]													=	BitConfig(
          global_padding='''same'''				,				layer_type='''bottleneck'''				,				depths=(3, 4, 9)				,				out_features=['''stage3''']				,				embedding_dynamic_padding=__SCREAMING_SNAKE_CASE				,				)
      lowercase_     :					Optional[int]													=	ViTHybridConfig(backbone_config=__SCREAMING_SNAKE_CASE				,				image_size=384				,				num_labels=1000							)
      lowercase_     :					Union[str, Any]													=	False

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

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

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

      # create image processor
      lowercase_     :					Dict													=	create_transform(**resolve_data_config({}				,				model=__SCREAMING_SNAKE_CASE							)							)
      lowercase_     :					Optional[int]													=	transform.transforms

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

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

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

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

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

      print('''Predicted class:'''				,				logits.argmax(-1							).item()							)
      if base_model:
            lowercase_     :					Optional[Any]													=	timm_model.forward_features(__SCREAMING_SNAKE_CASE							)
            assert timm_pooled_output.shape == outputs.pooler_output.shape
            assert torch.allclose(__SCREAMING_SNAKE_CASE				,				outputs.pooler_output				,				atol=1E-3							)
      else:
            lowercase_     :					int													=	timm_model(__SCREAMING_SNAKE_CASE							)
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(__SCREAMING_SNAKE_CASE				,				outputs.logits				,				atol=1E-3							)
      print('''Looks ok!'''							)

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

      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			:  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."
       )

       _lowercase			:  Dict   							=	parser.parse_args()
       convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''







import logging
import os

from .state import PartialState






class 			lowerCAmelCase__       (  logging.LoggerAdapter					):






					@staticmethod
					def      _snake_case   (       __SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	PartialState()
											return not main_process_only or (main_process_only and state.is_main_process)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if PartialState._shared_state == {}:
																	raise RuntimeError(
																	    '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''		)
											lowercase_     :					Tuple													=	kwargs.pop('''main_process_only'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[int]													=	kwargs.pop('''in_order'''    ,							__SCREAMING_SNAKE_CASE		)

											if self.isEnabledFor(__SCREAMING_SNAKE_CASE		):
																	if self._should_log(__SCREAMING_SNAKE_CASE		):
																							lowercase_						,			lowercase_     :					Optional[Any]													=	self.process(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
																							self.logger.log(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)

																	elif in_order:
																							lowercase_     :					Optional[Any]													=	PartialState()
																							for i in range(state.num_processes		):
																													if i == state.process_index:
																																			lowercase_						,			lowercase_     :					Optional[int]													=	self.process(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
																																			self.logger.log(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
																													state.wait_for_everyone()







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			str = None							):



						"""simple docstring"""





						if log_level is None:
												lowercase_     :					Any													=	os.environ.get('''ACCELERATE_LOG_LEVEL'''				,				__SCREAMING_SNAKE_CASE							)
						lowercase_     :					List[str]													=	logging.getLogger(__SCREAMING_SNAKE_CASE							)
						if log_level is not None:
												logger.setLevel(log_level.upper()							)
												logger.root.setLevel(log_level.upper()							)
						return MultiProcessAdapter(__SCREAMING_SNAKE_CASE				,				{}							)



