



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()
snake_case    =	logging.get_logger(__name__)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	List[str]=False				)  ->     Dict:
 _snake_case    :  Optional[int]					=					[]
 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"
  _snake_case    :  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"""),
      ]				)

 return rename_keys



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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				)  ->     Dict:
 _snake_case    :  Dict					=					["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(_snake_case				,       _snake_case				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Dict				)  ->     List[Any]:
 _snake_case    :  Optional[int]					=					dct.pop(_snake_case				)
 _snake_case    :  int					=					val



def       lowercase       (   )  ->     Union[str, Any]:
 _snake_case    :  List[str]					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 _snake_case    :  Optional[int]					=					Image.open(requests.get(_snake_case				,       stream=_snake_case				).raw				)
 return im



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Dict:
 _snake_case    :  Any					=					ViTConfig()
 _snake_case    :  Optional[int]					=					False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  _snake_case    :  List[str]					=					True
  _snake_case    :  Union[str, Any]					=					int(vit_name[-12:-10]				)
  _snake_case    :  Dict					=					int(vit_name[-9:-6]				)
 else:
  _snake_case    :  int					=					1_000
  _snake_case    :  Optional[int]					=					"""huggingface/label-files"""
  _snake_case    :  Optional[int]					=					"""imagenet-1k-id2label.json"""
  _snake_case    :  Union[str, Any]					=					json.load(open(hf_hub_download(_snake_case				,       _snake_case				,       repo_type="""dataset"""				)				,       """r"""				)				)
  _snake_case    :  List[Any]					=					{int(_snake_case				): v for k, v in idalabel.items()}
  _snake_case    :  Union[str, Any]					=					idalabel
  _snake_case    :  Tuple					=					{v: k for k, v in idalabel.items()}
  _snake_case    :  Tuple					=					int(vit_name[-6:-4]				)
  _snake_case    :  Any					=					int(vit_name[-3:]				)
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith("""tiny"""				):
   _snake_case    :  List[Any]					=					192
   _snake_case    :  List[str]					=					768
   _snake_case    :  List[str]					=					12
   _snake_case    :  Any					=					3
  elif vit_name[9:].startswith("""small"""				):
   _snake_case    :  Optional[int]					=					384
   _snake_case    :  Optional[Any]					=					1_536
   _snake_case    :  Optional[int]					=					12
   _snake_case    :  Union[str, Any]					=					6
  else:
   pass
 else:
  if vit_name[4:].startswith("""small"""				):
   _snake_case    :  Optional[Any]					=					768
   _snake_case    :  Dict					=					2_304
   _snake_case    :  Dict					=					8
   _snake_case    :  Optional[Any]					=					8
  elif vit_name[4:].startswith("""base"""				):
   pass
  elif vit_name[4:].startswith("""large"""				):
   _snake_case    :  str					=					1_024
   _snake_case    :  str					=					4_096
   _snake_case    :  List[str]					=					24
   _snake_case    :  Tuple					=					16
  elif vit_name[4:].startswith("""huge"""				):
   _snake_case    :  Union[str, Any]					=					1_280
   _snake_case    :  Optional[int]					=					5_120
   _snake_case    :  int					=					32
   _snake_case    :  Tuple					=					16

    # load original model from timm
 _snake_case    :  Dict					=					timm.create_model(_snake_case				,       pretrained=_snake_case				)
 timm_model.eval()

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

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  _snake_case    :  Optional[Any]					=					ViTModel(_snake_case				).eval()
 else:
  _snake_case    :  str					=					ViTForImageClassification(_snake_case				).eval()
 model.load_state_dict(_snake_case				)

 # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
 if "deit" in vit_name:
  _snake_case    :  Optional[int]					=					DeiTImageProcessor(size=config.image_size				)
 else:
  _snake_case    :  Dict					=					ViTImageProcessor(size=config.image_size				)
 _snake_case    :  Any					=					image_processor(images=prepare_img()				,       return_tensors="""pt"""				)
 _snake_case    :  List[str]					=					encoding["""pixel_values"""]
 _snake_case    :  Optional[int]					=					model(_snake_case				)

 if base_model:
  _snake_case    :  Dict					=					timm_model.forward_features(_snake_case				)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(_snake_case				,       outputs.pooler_output				,       atol=1e-3				)
 else:
  _snake_case    :  Tuple					=					timm_model(_snake_case				)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(_snake_case				,       outputs.logits				,       atol=1e-3				)

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


if __name__ == "__main__":
  snake_case    =	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."""
  )

  snake_case    =	parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__    =	{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    =	[
      """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CanineForMultipleChoice""",
      """CanineForQuestionAnswering""",
      """CanineForSequenceClassification""",
      """CanineForTokenClassification""",
      """CanineLayer""",
      """CanineModel""",
      """CaninePreTrainedModel""",
      """load_tf_weights_in_canine""",
  ]


if TYPE_CHECKING:
  from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
  from .tokenization_canine import CanineTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_canine import (
        CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
        CanineForMultipleChoice,
        CanineForQuestionAnswering,
        CanineForSequenceClassification,
        CanineForTokenClassification,
        CanineLayer,
        CanineModel,
        CaninePreTrainedModel,
        load_tf_weights_in_canine,
    )


else:
  import sys

  a__    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
