






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()
lowerCamelCase     :							Union[str, Any]    =logging.get_logger(__name__)







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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     Any:
     UpperCamelCase__      :       List[Any]	    =  dct.pop(__lowerCAmelCase      )
     UpperCamelCase__      :       Any	    =  val







def 		SCREAMING_SNAKE_CASE							( )			->     Any:
     UpperCamelCase__      :       Union[str, Any]	    =  "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCamelCase__      :       Tuple	    =  Image.open(requests.get(__lowerCAmelCase ,   stream=__lowerCAmelCase      ).raw      )
     return im







@torch.no_grad()
def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     int:
     UpperCamelCase__      :       List[Any]	    =  DeiTConfig()
     # all deit models have fine-tuned heads
     UpperCamelCase__      :       Optional[int]	    =  False
     # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
     UpperCamelCase__      :       List[Any]	    =  1000
     UpperCamelCase__      :       int	    =  "huggingface/label-files"
     UpperCamelCase__      :       List[str]	    =  "imagenet-1k-id2label.json"
     UpperCamelCase__      :       List[str]	    =  json.load(open(hf_hub_download(__lowerCAmelCase ,   __lowerCAmelCase ,   repo_type="dataset"      ) ,   "r"      )      )
     UpperCamelCase__      :       Optional[int]	    =  {int(__lowerCAmelCase      ): v for k, v in idalabel.items()}
     UpperCamelCase__      :       str	    =  idalabel
     UpperCamelCase__      :       Dict	    =  {v: k for k, v in idalabel.items()}
     UpperCamelCase__      :       Optional[Any]	    =  int(deit_name[-6:-4]      )
     UpperCamelCase__      :       Dict	    =  int(deit_name[-3:]      )
     # size of the architecture
     if deit_name[9:].startswith("tiny"      ):
          UpperCamelCase__      :       Any	    =  192
          UpperCamelCase__      :       Optional[Any]	    =  768
          UpperCamelCase__      :       List[Any]	    =  12
          UpperCamelCase__      :       Any	    =  3
     elif deit_name[9:].startswith("small"      ):
          UpperCamelCase__      :       Union[str, Any]	    =  384
          UpperCamelCase__      :       Optional[int]	    =  1536
          UpperCamelCase__      :       List[Any]	    =  12
          UpperCamelCase__      :       Any	    =  6
     if deit_name[9:].startswith("base"      ):
          pass
     elif deit_name[4:].startswith("large"      ):
          UpperCamelCase__      :       Optional[Any]	    =  1024
          UpperCamelCase__      :       int	    =  4096
          UpperCamelCase__      :       int	    =  24
          UpperCamelCase__      :       List[Any]	    =  16

     # load original model from timm
     UpperCamelCase__      :       List[Any]	    =  timm.create_model(__lowerCAmelCase ,   pretrained=__lowerCAmelCase      )
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     UpperCamelCase__      :       int	    =  timm_model.state_dict()
     UpperCamelCase__      :       Optional[int]	    =  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
     UpperCamelCase__      :       Tuple	    =  DeiTForImageClassificationWithTeacher(__lowerCAmelCase      ).eval()
     model.load_state_dict(__lowerCAmelCase      )

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

     UpperCamelCase__      :       Optional[int]	    =  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 {deit_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__":
 lowerCamelCase     :							int    =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.'''
 )

 lowerCamelCase     :							List[str]    =parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)








from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

lowerCamelCase     :							str    =logging.get_logger(__name__)





@add_end_docstrings(A__	)
class       __a  (   A__	):







 def __init__(		self :       List[str]							,       **SCREAMING_SNAKE_CASE :       Tuple						):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)

      requires_backends(self							,       "vision"						)
      self.check_model_type(
          TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
          if self.framework == "tf"
          else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING						)







 def __call__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       Union[str, List[str], "Image", List["Image"]]							,       **SCREAMING_SNAKE_CASE :       str						):


      '''simple docstring'''






      return super().__call__(SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       List[str]							,       **SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  {}
      if "candidate_labels" in kwargs:
           UpperCamelCase__      :       Optional[Any]	    =  kwargs["candidate_labels"]
      if "hypothesis_template" in kwargs:
           UpperCamelCase__      :       int	    =  kwargs["hypothesis_template"]

      return preprocess_params, {}, {}







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       List[Any]							,       SCREAMING_SNAKE_CASE :       Tuple=None							,       SCREAMING_SNAKE_CASE :       Optional[int]="This is a photo of {}."						):


      '''simple docstring'''






      UpperCamelCase__      :       Dict	    =  load_image(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[Any]	    =  self.image_processor(images=[image]							,       return_tensors=self.framework						)
      UpperCamelCase__      :       Any	    =  candidate_labels
      UpperCamelCase__      :       Dict	    =  [hypothesis_template.format(SCREAMING_SNAKE_CASE						) for x in candidate_labels]
      UpperCamelCase__      :       Optional[Any]	    =  self.tokenizer(SCREAMING_SNAKE_CASE							,       return_tensors=self.framework							,       padding=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[Any]	    =  [text_inputs]
      return inputs







 def  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  model_inputs.pop("candidate_labels"						)
      UpperCamelCase__      :       List[str]	    =  model_inputs.pop("text_inputs"						)
      if isinstance(text_inputs[0]							,       SCREAMING_SNAKE_CASE						):
           UpperCamelCase__      :       Dict	    =  text_inputs[0]
      else:
           # Batching case.
           UpperCamelCase__      :       Union[str, Any]	    =  text_inputs[0][0]

      UpperCamelCase__      :       Any	    =  self.model(**SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Dict	    =  {
          "candidate_labels": candidate_labels,
          "logits": outputs.logits_per_image,
      }
      return model_outputs







 def  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  model_outputs.pop("candidate_labels"						)
      UpperCamelCase__      :       int	    =  model_outputs["logits"][0]
      if self.framework == "pt":
           UpperCamelCase__      :       Dict	    =  logits.softmax(dim=-1						).squeeze(-1						)
           UpperCamelCase__      :       Optional[Any]	    =  probs.tolist()
           if not isinstance(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						):
                UpperCamelCase__      :       List[Any]	    =  [scores]
      elif self.framework == "tf":
           UpperCamelCase__      :       Optional[Any]	    =  stable_softmax(SCREAMING_SNAKE_CASE							,       axis=-1						)
           UpperCamelCase__      :       Optional[int]	    =  probs.numpy().tolist()
      else:
           raise ValueError(F'Unsupported framework: {self.framework}'						)

      UpperCamelCase__      :       Optional[int]	    =  [
          {"score": score, "label": candidate_label}
          for score, candidate_label in sorted(zip(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)							,       key=lambda SCREAMING_SNAKE_CASE						: -x[0]						)
      ]
      return result