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_							=      logging.get_logger(__name__)




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




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : Optional[int]						,	A_   : List[Any]					)    ->  Optional[Any]:
  __UpperCAmelCase						    =       dct.pop(A_					)
  __UpperCAmelCase						    =       val




def       __lowerCAmelCase      (						)    ->  Optional[int]:
  __UpperCAmelCase						    =       "http://images.cocodataset.org/val2017/000000039769.jpg"
  __UpperCAmelCase						    =       Image.open(requests.get(A_						,	stream=A_					).raw					)
  return im




@torch.no_grad()
def       __lowerCAmelCase      (						A_   : Dict						,	A_   : Optional[int]					)    ->  List[str]:
  __UpperCAmelCase						    =       DeiTConfig()
  # all deit models have fine-tuned heads
  __UpperCAmelCase						    =       False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  __UpperCAmelCase						    =       10_00
  __UpperCAmelCase						    =       "huggingface/label-files"
  __UpperCAmelCase						    =       "imagenet-1k-id2label.json"
  __UpperCAmelCase						    =       json.load(open(hf_hub_download(A_						,	A_						,	repo_type="dataset"					)						,	"r"					)					)
  __UpperCAmelCase						    =       {int(A_					): v for k, v in idalabel.items()}
  __UpperCAmelCase						    =       idalabel
  __UpperCAmelCase						    =       {v: k for k, v in idalabel.items()}
  __UpperCAmelCase						    =       int(deit_name[-6:-4]					)
  __UpperCAmelCase						    =       int(deit_name[-3:]					)
  # size of the architecture
  if deit_name[9:].startswith("tiny"					):
    __UpperCAmelCase						    =       1_92
    __UpperCAmelCase						    =       7_68
    __UpperCAmelCase						    =       12
    __UpperCAmelCase						    =       3
  elif deit_name[9:].startswith("small"					):
    __UpperCAmelCase						    =       3_84
    __UpperCAmelCase						    =       15_36
    __UpperCAmelCase						    =       12
    __UpperCAmelCase						    =       6
  if deit_name[9:].startswith("base"					):
    pass
  elif deit_name[4:].startswith("large"					):
    __UpperCAmelCase						    =       10_24
    __UpperCAmelCase						    =       40_96
    __UpperCAmelCase						    =       24
    __UpperCAmelCase						    =       16

  # load original model from timm
  __UpperCAmelCase						    =       timm.create_model(A_						,	pretrained=A_					)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  __UpperCAmelCase						    =       timm_model.state_dict()
  __UpperCAmelCase						    =       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
  __UpperCAmelCase						    =       DeiTForImageClassificationWithTeacher(A_					).eval()
  model.load_state_dict(A_					)

  # Check outputs on an image, prepared by DeiTImageProcessor
  __UpperCAmelCase						    =       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
  __UpperCAmelCase						    =       DeiTImageProcessor(size=A_						,	crop_size=config.image_size					)
  __UpperCAmelCase						    =       image_processor(images=prepare_img()						,	return_tensors="pt"					)
  __UpperCAmelCase						    =       encoding["pixel_values"]
  __UpperCAmelCase						    =       model(A_					)

  __UpperCAmelCase						    =       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__":
    a_							=      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_							=      parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)




from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_							=      logging.get_logger(__name__)

a_							=      {
    """facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	Dict    		= 'vit_mae'






  def __init__(				self:			List[Any]				,				__lowerCAmelCase:			Any=768				,				__lowerCAmelCase:			List[str]=12				,				__lowerCAmelCase:			Optional[int]=12				,				__lowerCAmelCase:			Tuple=3_072				,				__lowerCAmelCase:			List[Any]="gelu"				,				__lowerCAmelCase:			Dict=0.0				,				__lowerCAmelCase:			Tuple=0.0				,				__lowerCAmelCase:			Any=0.02				,				__lowerCAmelCase:			List[Any]=1E-12				,				__lowerCAmelCase:			List[str]=224				,				__lowerCAmelCase:			Optional[Any]=16				,				__lowerCAmelCase:			Union[str, Any]=3				,				__lowerCAmelCase:			Tuple=True				,				__lowerCAmelCase:			Union[str, Any]=16				,				__lowerCAmelCase:			Optional[int]=512				,				__lowerCAmelCase:			int=8				,				__lowerCAmelCase:			int=2_048				,				__lowerCAmelCase:			str=0.75				,				__lowerCAmelCase:			Union[str, Any]=False				,				**__lowerCAmelCase:			List[Any]				,				) -> Optional[int]:




    '''simple docstring'''




    super().__init__(**__lowerCAmelCase   )

    __UpperCAmelCase						    =       hidden_size
    __UpperCAmelCase						    =       num_hidden_layers
    __UpperCAmelCase						    =       num_attention_heads
    __UpperCAmelCase						    =       intermediate_size
    __UpperCAmelCase						    =       hidden_act
    __UpperCAmelCase						    =       hidden_dropout_prob
    __UpperCAmelCase						    =       attention_probs_dropout_prob
    __UpperCAmelCase						    =       initializer_range
    __UpperCAmelCase						    =       layer_norm_eps
    __UpperCAmelCase						    =       image_size
    __UpperCAmelCase						    =       patch_size
    __UpperCAmelCase						    =       num_channels
    __UpperCAmelCase						    =       qkv_bias
    __UpperCAmelCase						    =       decoder_num_attention_heads
    __UpperCAmelCase						    =       decoder_hidden_size
    __UpperCAmelCase						    =       decoder_num_hidden_layers
    __UpperCAmelCase						    =       decoder_intermediate_size
    __UpperCAmelCase						    =       mask_ratio
    __UpperCAmelCase						    =       norm_pix_loss



