






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

def 					__lowercase						(			__lowercase   ,  __lowercase=False       )       ->						int:

      '''simple docstring'''


      _A			     =	[]

      # 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			     =	[(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						(			__lowercase   ,  __lowercase   ,  __lowercase=False       )       ->						Tuple:

      '''simple docstring'''


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

def 					__lowercase						(			__lowercase       )       ->						List[str]:

      '''simple docstring'''


      _A			     =	["head.weight", "head.bias"]
      for k in ignore_keys:
            state_dict.pop(__lowercase   ,  __lowercase       )

def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase       )       ->						Tuple:

      '''simple docstring'''


      _A			     =	dct.pop(__lowercase       )
      _A			     =	val

def 					__lowercase						(			)       ->						List[str]:

      '''simple docstring'''


      _A			     =	"http://images.cocodataset.org/val2017/000000039769.jpg"
      _A			     =	Image.open(requests.get(__lowercase   ,  stream=__lowercase       ).raw       )
      return im

@torch.no_grad()
def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase=False       )       ->						Tuple:

      '''simple docstring'''


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

      # load original model from timm
      _A			     =	timm.create_model(__lowercase   ,  pretrained=__lowercase       )
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      _A			     =	timm_model.state_dict()
      if base_model:
            remove_classification_head_(__lowercase       )
      _A			     =	create_rename_keys(__lowercase   ,  __lowercase       )
      for src, dest in rename_keys:
            rename_key(__lowercase   ,  __lowercase   ,  __lowercase       )
      read_in_q_k_v(__lowercase   ,  __lowercase   ,  __lowercase       )

      _A			     =	"huggingface/label-files"
      _A			     =	"imagenet-1k-id2label.json"
      _A			     =	json.load(open(hf_hub_download(__lowercase   ,  __lowercase   ,  repo_type="dataset"       )   ,  "r"       )       )
      _A			     =	{int(__lowercase       ): v for k, v in idalabel.items()}
      _A			     =	idalabel
      _A			     =	{v: k for k, v in idalabel.items()}

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            _A			     =	ViTHybridModel(__lowercase       ).eval()
      else:
            _A			     =	ViTHybridForImageClassification(__lowercase       ).eval()
      model.load_state_dict(__lowercase       )

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

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

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

      _A			     =	prepare_img()
      _A			     =	transform(__lowercase       ).unsqueeze(0       )
      _A			     =	processor(__lowercase   ,  return_tensors="pt"       ).pixel_values

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

      # verify logits
      with torch.no_grad():
            _A			     =	model(__lowercase       )
            _A			     =	outputs.logits

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

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

      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__":
    lowerCamelCase_												=					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.'''
    )

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










import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
				# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
				# setup instructions, if using on-demand hardware

				# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
				# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
				# Throw an error if user passes both BYO and on-demand cluster args
				# Otherwise, use default values
				__A   			=argparse.ArgumentParser()
				parser.add_argument("--user", type=str, default="ubuntu")
				parser.add_argument("--host", type=str, default="localhost")
				parser.add_argument("--key_path", type=str, default=None)
				parser.add_argument("--instance", type=str, default="V100:1")
				parser.add_argument("--provider", type=str, default="cheapest")
				parser.add_argument("--use_spot", type=bool, default=False)
				parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
				__A			,			__A   			=parser.parse_known_args()
				if args.host != "localhost":
								if args.instance != "V100:1" or args.provider != "cheapest":
												raise ValueError("Cannot specify both BYO and on-demand cluster args")
								__A   			=rh.cluster(
								    name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
								)
				else:
								__A   			=rh.cluster(
								    name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
								)
				__A   			=args.example.rsplit("/", 1)[0]

				# Set up remote environment
				cluster.install_packages(["pip:./"])  # Installs transformers from local source
				# Note transformers is copied into the home directory on the remote machine, so we can install from there
				cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
				cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

				# Run example. You can bypass the CLI wrapper and paste your own code here.
				cluster.run([f'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])

				# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
				# from my_script... import train
				# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
				# launch_train_gpu = rh.function(fn=train,
				#                                system=gpu,
				#                                reqs=reqs,
				#                                name='train_bert_glue')
				#
				# We can pass in arguments just like we would to a function:
				# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
				#                  stream_logs=True)





