




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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__  :  int					=       logging.get_logger(__name__)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase=False				):
  lowerCAmelCase__										=      []
  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""")				)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('''cls_token''', '''vit.embeddings.cls_token'''),
          ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
          ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
          ('''pos_embed''', '''vit.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 "vit" from all keys that start with "vit"
    lowerCAmelCase__										=      [(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'''),
        ]				)

  return rename_keys
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase=False				):
  for i in range(config.num_hidden_layers				):
    if base_model:
      lowerCAmelCase__										=      ''''''
    else:
      lowerCAmelCase__										=      '''vit.'''
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    lowerCAmelCase__										=      state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""				)
    lowerCAmelCase__										=      state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""				)
    # next, add query, keys and values (in that order) to the state dict
    lowerCAmelCase__										=      in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCAmelCase__										=      in_proj_bias[: config.hidden_size]
    lowerCAmelCase__										=      in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCAmelCase__										=      in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowerCAmelCase__										=      in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCAmelCase__										=      in_proj_bias[-config.hidden_size :]
def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      ['''head.weight''', '''head.bias''']
  for k in ignore_keys:
    state_dict.pop(__lowerCAmelCase   , __lowerCAmelCase				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      dct.pop(__lowerCAmelCase				)
  lowerCAmelCase__										=      val
def 		a_							( ):
  lowerCAmelCase__										=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  lowerCAmelCase__										=      Image.open(requests.get(__lowerCAmelCase   , stream=__lowerCAmelCase				).raw				)
  return im
@torch.no_grad()
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      ViTConfig()
  lowerCAmelCase__										=      False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    lowerCAmelCase__										=      True
    lowerCAmelCase__										=      int(vit_name[-12:-10]				)
    lowerCAmelCase__										=      int(vit_name[-9:-6]				)
  else:
    lowerCAmelCase__										=      10_00
    lowerCAmelCase__										=      '''huggingface/label-files'''
    lowerCAmelCase__										=      '''imagenet-1k-id2label.json'''
    lowerCAmelCase__										=      json.load(open(hf_hub_download(__lowerCAmelCase   , __lowerCAmelCase   , repo_type='''dataset'''				)   , '''r'''				)				)
    lowerCAmelCase__										=      {int(__lowerCAmelCase				): v for k, v in idalabel.items()}
    lowerCAmelCase__										=      idalabel
    lowerCAmelCase__										=      {v: k for k, v in idalabel.items()}
    lowerCAmelCase__										=      int(vit_name[-6:-4]				)
    lowerCAmelCase__										=      int(vit_name[-3:]				)
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith('''tiny'''				):
      lowerCAmelCase__										=      1_92
      lowerCAmelCase__										=      7_68
      lowerCAmelCase__										=      12
      lowerCAmelCase__										=      3
    elif vit_name[9:].startswith('''small'''				):
      lowerCAmelCase__										=      3_84
      lowerCAmelCase__										=      15_36
      lowerCAmelCase__										=      12
      lowerCAmelCase__										=      6
    else:
      pass
  else:
    if vit_name[4:].startswith('''small'''				):
      lowerCAmelCase__										=      7_68
      lowerCAmelCase__										=      23_04
      lowerCAmelCase__										=      8
      lowerCAmelCase__										=      8
    elif vit_name[4:].startswith('''base'''				):
      pass
    elif vit_name[4:].startswith('''large'''				):
      lowerCAmelCase__										=      10_24
      lowerCAmelCase__										=      40_96
      lowerCAmelCase__										=      24
      lowerCAmelCase__										=      16
    elif vit_name[4:].startswith('''huge'''				):
      lowerCAmelCase__										=      12_80
      lowerCAmelCase__										=      51_20
      lowerCAmelCase__										=      32
      lowerCAmelCase__										=      16

    # load original model from timm
  lowerCAmelCase__										=      timm.create_model(__lowerCAmelCase   , pretrained=__lowerCAmelCase				)
  timm_model.eval()

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

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    lowerCAmelCase__										=      ViTModel(__lowerCAmelCase				).eval()
  else:
    lowerCAmelCase__										=      ViTForImageClassification(__lowerCAmelCase				).eval()
  model.load_state_dict(__lowerCAmelCase				)

  # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
  if "deit" in vit_name:
    lowerCAmelCase__										=      DeiTImageProcessor(size=config.image_size				)
  else:
    lowerCAmelCase__										=      ViTImageProcessor(size=config.image_size				)
  lowerCAmelCase__										=      image_processor(images=prepare_img()   , return_tensors='''pt'''				)
  lowerCAmelCase__										=      encoding['''pixel_values''']
  lowerCAmelCase__										=      model(__lowerCAmelCase				)

  if base_model:
    lowerCAmelCase__										=      timm_model.forward_features(__lowerCAmelCase				)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(__lowerCAmelCase   , outputs.pooler_output   , atol=1E-3				)
  else:
    lowerCAmelCase__										=      timm_model(__lowerCAmelCase				)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(__lowerCAmelCase   , outputs.logits   , atol=1E-3				)

  Path(__lowerCAmelCase				).mkdir(exist_ok=__lowerCAmelCase				)
  print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""				)
  model.save_pretrained(__lowerCAmelCase				)
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
  image_processor.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
      __magic_name__  :  Tuple					=       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--vit_name""",
          default="""vit_base_patch16_224""",
          type=str,
          help="""Name of the 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."""
      )

      __magic_name__  :  Optional[int]					=       parser.parse_args()
      convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__magic_name__  :  Optional[Any]					=       {
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """InformerForPrediction""",
          """InformerModel""",
          """InformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_informer import (
                INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                InformerForPrediction,
                InformerModel,
                InformerPreTrainedModel,
            )

else:
      import sys

      __magic_name__  :  Any					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

