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


logging.set_verbosity_info()
lowerCamelCase			:				List[str] 			=					logging.get_logger(__name__)


def 	_lowerCAmelCase (						_UpperCamelCase						:					Any							,			_UpperCamelCase						:					List[str]=False    )				->      Optional[Any]:
     """simple docstring"""




     _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 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					Tuple							,			_UpperCamelCase						:					Optional[int]=False    )				->      Tuple:
     """simple docstring"""




     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 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					Union[str, Any]    )				->      Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =dct.pop(A__    )
     _SCREAMING_SNAKE_CASE							   =val


def 	_lowerCAmelCase (						)				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   ='http://images.cocodataset.org/val2017/000000039769.jpg'
     _SCREAMING_SNAKE_CASE							   =Image.open(requests.get(A__							,			stream=A__    ).raw    )
     return im


@torch.no_grad()
def 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					Dict    )				->      Union[str, Any]:
     """simple docstring"""




     _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							   =10_00
     _SCREAMING_SNAKE_CASE							   ='huggingface/label-files'
     _SCREAMING_SNAKE_CASE							   ='imagenet-1k-id2label.json'
     _SCREAMING_SNAKE_CASE							   =json.load(open(hf_hub_download(A__							,			A__							,			repo_type='dataset'    )							,			'r'    )    )
     _SCREAMING_SNAKE_CASE							   ={int(A__    ): 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_92
          _SCREAMING_SNAKE_CASE							   =7_68
          _SCREAMING_SNAKE_CASE							   =12
          _SCREAMING_SNAKE_CASE							   =3
     elif deit_name[9:].startswith('small'    ):
          _SCREAMING_SNAKE_CASE							   =3_84
          _SCREAMING_SNAKE_CASE							   =15_36
          _SCREAMING_SNAKE_CASE							   =12
          _SCREAMING_SNAKE_CASE							   =6
     if deit_name[9:].startswith('base'    ):
          pass
     elif deit_name[4:].startswith('large'    ):
          _SCREAMING_SNAKE_CASE							   =10_24
          _SCREAMING_SNAKE_CASE							   =40_96
          _SCREAMING_SNAKE_CASE							   =24
          _SCREAMING_SNAKE_CASE							   =16

     # load original model from timm
     _SCREAMING_SNAKE_CASE							   =timm.create_model(A__							,			pretrained=A__    )
     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(A__							,			A__    )
     for src, dest in rename_keys:
          rename_key(A__							,			A__							,			A__    )
     read_in_q_k_v(A__							,			A__							,			A__    )

     # load HuggingFace model
     _SCREAMING_SNAKE_CASE							   =DeiTForImageClassificationWithTeacher(A__    ).eval()
     model.load_state_dict(A__    )

     # Check outputs on an image, prepared by DeiTImageProcessor
     _SCREAMING_SNAKE_CASE							   =int(
         (2_56 / 2_24) * 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=A__							,			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(A__    )

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

     Path(A__    ).mkdir(exist_ok=A__    )
     print(f"Saving model {deit_name} to {pytorch_dump_folder_path}"    )
     model.save_pretrained(A__    )
     print(f"Saving image processor to {pytorch_dump_folder_path}"    )
     image_processor.save_pretrained(A__    )


if __name__ == "__main__":
     lowerCamelCase			:				str 			=					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			:				Optional[Any] 			=					parser.parse_args()
     convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


lowerCamelCase			:				Optional[int] 			=					{
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					["EfficientNetImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Tuple 			=					[
         "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "EfficientNetForImageClassification",
         "EfficientNetModel",
         "EfficientNetPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_efficientnet import (
         EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EfficientNetConfig,
         EfficientNetOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_efficientnet import EfficientNetImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_efficientnet import (
              EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              EfficientNetForImageClassification,
              EfficientNetModel,
              EfficientNetPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				int 			=					_LazyModule(__name__, globals()["__file__"], _import_structure)
