import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     [2, 2, 6, 2] if "tiny" in model_name else [2, 2, 1_8, 2]
      SCREAMING_SNAKE_CASE_      =     True if "large" in model_name or "huge" in model_name else False
      SCREAMING_SNAKE_CASE_      =     True if "large" in model_name or "huge" in model_name else False
      SCREAMING_SNAKE_CASE_      =     True if "large" in model_name or "huge" in model_name else False

      if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
            if "fl3" in model_name:
                  SCREAMING_SNAKE_CASE_      =     [3, 3, 3, 3]
                  SCREAMING_SNAKE_CASE_      =     [5, 5, 5, 5]
            elif "fl4" in model_name:
                  SCREAMING_SNAKE_CASE_      =     [4, 4, 4, 4]
                  SCREAMING_SNAKE_CASE_      =     [3, 3, 3, 3]

      if "tiny" in model_name or "small" in model_name or "base" in model_name:
            SCREAMING_SNAKE_CASE_      =     [3, 3, 3, 3]
            if "lrf" in model_name:
                  SCREAMING_SNAKE_CASE_      =     [3, 3, 3, 3]
            else:
                  SCREAMING_SNAKE_CASE_      =     [2, 2, 2, 2]

      if "tiny" in model_name:
            SCREAMING_SNAKE_CASE_      =     9_6
      elif "small" in model_name:
            SCREAMING_SNAKE_CASE_      =     9_6
      elif "base" in model_name:
            SCREAMING_SNAKE_CASE_      =     1_2_8
      elif "large" in model_name:
            SCREAMING_SNAKE_CASE_      =     1_9_2
      elif "xlarge" in model_name:
            SCREAMING_SNAKE_CASE_      =     2_5_6
      elif "huge" in model_name:
            SCREAMING_SNAKE_CASE_      =     3_5_2

      # set label information
      SCREAMING_SNAKE_CASE_      =     "huggingface/label-files"
      if "large" in model_name or "huge" in model_name:
            SCREAMING_SNAKE_CASE_      =     "imagenet-22k-id2label.json"
      else:
            SCREAMING_SNAKE_CASE_      =     "imagenet-1k-id2label.json"

      SCREAMING_SNAKE_CASE_      =     json.load(open(hf_hub_download(__UpperCamelCase   ,					__UpperCamelCase   ,					repo_type="dataset" )   ,					"r" ) )
      SCREAMING_SNAKE_CASE_      =     {int(__UpperCamelCase ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_      =     {v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE_      =     FocalNetConfig(
          embed_dim=__UpperCamelCase   ,					depths=__UpperCamelCase   ,					focal_levels=__UpperCamelCase   ,					focal_windows=__UpperCamelCase   ,					use_conv_embed=__UpperCamelCase   ,					idalabel=__UpperCamelCase   ,					labelaid=__UpperCamelCase   ,					use_post_layernorm=__UpperCamelCase   ,					use_layerscale=__UpperCamelCase   ,					)

      return config


def    a__       (						__UpperCamelCase ):
      if "patch_embed.proj" in name:
            SCREAMING_SNAKE_CASE_      =     name.replace("patch_embed.proj"   ,					"embeddings.patch_embeddings.projection" )
      if "patch_embed.norm" in name:
            SCREAMING_SNAKE_CASE_      =     name.replace("patch_embed.norm"   ,					"embeddings.norm" )
      if "layers" in name:
            SCREAMING_SNAKE_CASE_      =     "encoder." + name
      if "encoder.layers" in name:
            SCREAMING_SNAKE_CASE_      =     name.replace("encoder.layers"   ,					"encoder.stages" )
      if "downsample.proj" in name:
            SCREAMING_SNAKE_CASE_      =     name.replace("downsample.proj"   ,					"downsample.projection" )
      if "blocks" in name:
            SCREAMING_SNAKE_CASE_      =     name.replace("blocks"   ,					"layers" )
      if "modulation.f.weight" in name or "modulation.f.bias" in name:
            SCREAMING_SNAKE_CASE_      =     name.replace("modulation.f"   ,					"modulation.projection_in" )
      if "modulation.h.weight" in name or "modulation.h.bias" in name:
            SCREAMING_SNAKE_CASE_      =     name.replace("modulation.h"   ,					"modulation.projection_context" )
      if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
            SCREAMING_SNAKE_CASE_      =     name.replace("modulation.proj"   ,					"modulation.projection_out" )

      if name == "norm.weight":
            SCREAMING_SNAKE_CASE_      =     "layernorm.weight"
      if name == "norm.bias":
            SCREAMING_SNAKE_CASE_      =     "layernorm.bias"

      if "head" in name:
            SCREAMING_SNAKE_CASE_      =     name.replace("head"   ,					"classifier" )
      else:
            SCREAMING_SNAKE_CASE_      =     "focalnet." + name

      return name


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase=False ):
      # fmt: off
      SCREAMING_SNAKE_CASE_      =     {
          "focalnet-tiny": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth",
          "focalnet-tiny-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth",
          "focalnet-small": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth",
          "focalnet-small-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth",
          "focalnet-base": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth",
          "focalnet-base-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth",
          "focalnet-large-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth",
          "focalnet-large-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth",
          "focalnet-xlarge-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth",
          "focalnet-xlarge-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth",
      }
      # fmt: on

      SCREAMING_SNAKE_CASE_      =     model_name_to_url[model_name]
      print("Checkpoint URL: "   ,					__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     torch.hub.load_state_dict_from_url(__UpperCamelCase   ,					map_location="cpu" )["model"]

      # rename keys
      for key in state_dict.copy().keys():
            SCREAMING_SNAKE_CASE_      =     state_dict.pop(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     val

      SCREAMING_SNAKE_CASE_      =     get_focalnet_config(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     FocalNetForImageClassification(__UpperCamelCase )
      model.eval()

      # load state dict
      model.load_state_dict(__UpperCamelCase )

      # verify conversion
      SCREAMING_SNAKE_CASE_      =     "http://images.cocodataset.org/val2017/000000039769.jpg"

      SCREAMING_SNAKE_CASE_      =     BitImageProcessor(
          do_resize=__UpperCamelCase   ,					size={"shortest_edge": 2_5_6}   ,					resample=PILImageResampling.BILINEAR   ,					do_center_crop=__UpperCamelCase   ,					crop_size=2_2_4   ,					do_normalize=__UpperCamelCase   ,					image_mean=__UpperCamelCase   ,					image_std=__UpperCamelCase   ,					)
      SCREAMING_SNAKE_CASE_      =     Image.open(requests.get(__UpperCamelCase   ,					stream=__UpperCamelCase ).raw )
      SCREAMING_SNAKE_CASE_      =     processor(images=__UpperCamelCase   ,					return_tensors="pt" )

      SCREAMING_SNAKE_CASE_      =     transforms.Compose(
          [
              transforms.Resize(2_5_6 ),
              transforms.CenterCrop(2_2_4 ),
              transforms.ToTensor(),
              transforms.Normalize(mean=[0.485, 0.456, 0.406]   ,					std=[0.229, 0.224, 0.225] ),
          ] )

      SCREAMING_SNAKE_CASE_      =     image_transforms(__UpperCamelCase ).unsqueeze(0 )

      # verify pixel_values
      assert torch.allclose(inputs.pixel_values   ,					__UpperCamelCase   ,					atol=1E-4 )

      SCREAMING_SNAKE_CASE_      =     model(**__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     outputs.logits.argmax(-1 ).item()
      print("Predicted class:"   ,					model.config.idalabel[predicted_class_idx] )

      print("First values of logits:"   ,					outputs.logits[0, :3] )

      if model_name == "focalnet-tiny":
            SCREAMING_SNAKE_CASE_      =     torch.tensor([0.2166, -0.4368, 0.2191] )
      elif model_name == "focalnet-tiny-lrf":
            SCREAMING_SNAKE_CASE_      =     torch.tensor([1.1669, 0.0125, -0.1695] )
      elif model_name == "focalnet-small":
            SCREAMING_SNAKE_CASE_      =     torch.tensor([0.4917, -0.0430, 0.1341] )
      elif model_name == "focalnet-small-lrf":
            SCREAMING_SNAKE_CASE_      =     torch.tensor([-0.2588, -0.5342, -0.2331] )
      elif model_name == "focalnet-base":
            SCREAMING_SNAKE_CASE_      =     torch.tensor([-0.1655, -0.4090, -0.1730] )
      elif model_name == "focalnet-base-lrf":
            SCREAMING_SNAKE_CASE_      =     torch.tensor([0.5306, -0.0483, -0.3928] )
      assert torch.allclose(outputs.logits[0, :3]   ,					__UpperCamelCase   ,					atol=1E-4 )
      print("Looks ok!" )

      if pytorch_dump_folder_path is not None:
            print(F'''Saving model and processor of {model_name} to {pytorch_dump_folder_path}''' )
            model.save_pretrained(__UpperCamelCase )
            processor.save_pretrained(__UpperCamelCase )

      if push_to_hub:
            print(F'''Pushing model and processor of {model_name} to the hub...''' )
            model.push_to_hub(F'''{model_name}''' )
            processor.push_to_hub(F'''{model_name}''' )


if __name__ == "__main__":
      A   :     Tuple       =				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default="focalnet-tiny",
          type=str,
          help="Name of the FocalNet 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 push the model and processor to the hub.",
      )

      A   :     List[str]       =				parser.parse_args()
      convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A   :     str       =				logging.get_logger(__name__)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase=False ):
      SCREAMING_SNAKE_CASE_      =     []
      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'''deit.encoder.layer.{i}.layernorm_before.weight''') )
            rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''') )
            rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''') )
            rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''') )
            rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''') )
            rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''') )
            rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''') )
            rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''') )
            rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''') )
            rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''') )

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("cls_token", "deit.embeddings.cls_token"),
              ("dist_token", "deit.embeddings.distillation_token"),
              ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
              ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
              ("pos_embed", "deit.embeddings.position_embeddings"),
          ] )

      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 "deit" from all keys that start with "deit"
            SCREAMING_SNAKE_CASE_      =     [(pair[0], pair[1][4:]) if pair[1].startswith("deit" ) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ("norm.weight", "deit.layernorm.weight"),
                    ("norm.bias", "deit.layernorm.bias"),
                    ("head.weight", "cls_classifier.weight"),
                    ("head.bias", "cls_classifier.bias"),
                    ("head_dist.weight", "distillation_classifier.weight"),
                    ("head_dist.bias", "distillation_classifier.bias"),
                ] )

      return rename_keys


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


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     dct.pop(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     val


def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     "http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE_      =     Image.open(requests.get(__UpperCamelCase   ,					stream=__UpperCamelCase ).raw )
      return im


@torch.no_grad()
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     DeiTConfig()
      # all deit models have fine-tuned heads
      SCREAMING_SNAKE_CASE_      =     False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE_      =     1_0_0_0
      SCREAMING_SNAKE_CASE_      =     "huggingface/label-files"
      SCREAMING_SNAKE_CASE_      =     "imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_      =     json.load(open(hf_hub_download(__UpperCamelCase   ,					__UpperCamelCase   ,					repo_type="dataset" )   ,					"r" ) )
      SCREAMING_SNAKE_CASE_      =     {int(__UpperCamelCase ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_      =     idalabel
      SCREAMING_SNAKE_CASE_      =     {v: k for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_      =     int(deit_name[-6:-4] )
      SCREAMING_SNAKE_CASE_      =     int(deit_name[-3:] )
      # size of the architecture
      if deit_name[9:].startswith("tiny" ):
            SCREAMING_SNAKE_CASE_      =     1_9_2
            SCREAMING_SNAKE_CASE_      =     7_6_8
            SCREAMING_SNAKE_CASE_      =     1_2
            SCREAMING_SNAKE_CASE_      =     3
      elif deit_name[9:].startswith("small" ):
            SCREAMING_SNAKE_CASE_      =     3_8_4
            SCREAMING_SNAKE_CASE_      =     1_5_3_6
            SCREAMING_SNAKE_CASE_      =     1_2
            SCREAMING_SNAKE_CASE_      =     6
      if deit_name[9:].startswith("base" ):
            pass
      elif deit_name[4:].startswith("large" ):
            SCREAMING_SNAKE_CASE_      =     1_0_2_4
            SCREAMING_SNAKE_CASE_      =     4_0_9_6
            SCREAMING_SNAKE_CASE_      =     2_4
            SCREAMING_SNAKE_CASE_      =     1_6

      # load original model from timm
      SCREAMING_SNAKE_CASE_      =     timm.create_model(__UpperCamelCase   ,					pretrained=__UpperCamelCase )
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      SCREAMING_SNAKE_CASE_      =     timm_model.state_dict()
      SCREAMING_SNAKE_CASE_      =     create_rename_keys(__UpperCamelCase   ,					__UpperCamelCase )
      for src, dest in rename_keys:
            rename_key(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )
      read_in_q_k_v(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )

      # load HuggingFace model
      SCREAMING_SNAKE_CASE_      =     DeiTForImageClassificationWithTeacher(__UpperCamelCase ).eval()
      model.load_state_dict(__UpperCamelCase )

      # Check outputs on an image, prepared by DeiTImageProcessor
      SCREAMING_SNAKE_CASE_      =     int(
          (2_5_6 / 2_2_4) * config.image_size )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      SCREAMING_SNAKE_CASE_      =     DeiTImageProcessor(size=__UpperCamelCase   ,					crop_size=config.image_size )
      SCREAMING_SNAKE_CASE_      =     image_processor(images=prepare_img()   ,					return_tensors="pt" )
      SCREAMING_SNAKE_CASE_      =     encoding["pixel_values"]
      SCREAMING_SNAKE_CASE_      =     model(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     timm_model(__UpperCamelCase )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__UpperCamelCase   ,					outputs.logits   ,					atol=1E-3 )

      Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
      print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}''' )
      model.save_pretrained(__UpperCamelCase )
      print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
      image_processor.save_pretrained(__UpperCamelCase )


if __name__ == "__main__":
      A   :     Tuple       =				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--deit_name",
          default="vit_deit_base_distilled_patch16_224",
          type=str,
          help="Name of the DeiT 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."
      )

      A   :     Dict       =				parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


