





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_						   =    logging.get_logger(__name__)


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


def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	Optional[int]    ,							lowerCAmelCase:	Any				)->		Tuple:
  _snake_case   :				Optional[int]														=	dct.pop(lowerCAmelCase				)
  _snake_case   :				Optional[int]														=	val


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


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

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

  # load state_dict of original model, remove and rename some keys
  _snake_case   :				Optional[int]														=	timm_model.state_dict()
  _snake_case   :				Union[str, Any]														=	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
  _snake_case   :				Dict														=	DeiTForImageClassificationWithTeacher(lowerCAmelCase				).eval()
  model.load_state_dict(lowerCAmelCase				)

  # Check outputs on an image, prepared by DeiTImageProcessor
  _snake_case   :				Union[str, Any]														=	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
  _snake_case   :				str														=	DeiTImageProcessor(size=lowerCAmelCase    ,							crop_size=config.image_size				)
  _snake_case   :				Optional[Any]														=	image_processor(images=prepare_img()    ,							return_tensors='pt'				)
  _snake_case   :				int														=	encoding['pixel_values']
  _snake_case   :				Union[str, Any]														=	model(lowerCAmelCase				)

  _snake_case   :				str														=	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_						   =    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_						   =    parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)











from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin



@dataclass
class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       torch.FloatTensor




class        _lowerCAmelCase    (			UpperCAmelCase_							,    UpperCAmelCase_							):


  '''simple docstring'''







  @register_to_config
  def __init__(      self      :					str      ,				UpperCamelCase      :					int = 32      ,				UpperCamelCase      :					int = 64      ,				UpperCamelCase      :					int = 20      ,				UpperCamelCase      :					int = 7_68      ,				UpperCamelCase      :					Optional[int]=77      ,				UpperCamelCase      :					int=4      ,				UpperCamelCase      :					float = 0.0      ,				UpperCamelCase      :					str = "silu"      ,				UpperCamelCase      :					Optional[str] = None      ,				UpperCamelCase      :					Optional[str] = None      ,				UpperCamelCase      :					Optional[str] = "linear"      ,				UpperCamelCase      :					Optional[str] = "prd"      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					Optional[int] = None      ,				):


    '''simple docstring'''





    super().__init__()
    _snake_case   :				str														=	num_attention_heads
    _snake_case   :				Optional[int]														=	attention_head_dim
    _snake_case   :				Any														=	num_attention_heads * attention_head_dim
    _snake_case   :				List[Any]														=	additional_embeddings

    _snake_case   :				List[str]														=	time_embed_dim or inner_dim
    _snake_case   :				int														=	embedding_proj_dim or embedding_dim
    _snake_case   :				List[Any]														=	clip_embed_dim or embedding_dim

    _snake_case   :				Optional[Any]														=	Timesteps(UpperCamelCase      ,				UpperCamelCase      ,				0							)
    _snake_case   :				List[Any]														=	TimestepEmbedding(UpperCamelCase      ,				UpperCamelCase      ,				out_dim=UpperCamelCase      ,				act_fn=UpperCamelCase							)

    _snake_case   :				Optional[int]														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)

    if embedding_proj_norm_type is None:
      _snake_case   :				str														=	None
    elif embedding_proj_norm_type == "layer":
      _snake_case   :				List[Any]														=	nn.LayerNorm(UpperCamelCase							)
    else:
      raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""							)

    _snake_case   :				str														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)

    if encoder_hid_proj_type is None:
      _snake_case   :				Any														=	None
    elif encoder_hid_proj_type == "linear":
      _snake_case   :				Optional[int]														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)
    else:
      raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""							)

    _snake_case   :				List[str]														=	nn.Parameter(torch.zeros(1      ,				num_embeddings + additional_embeddings      ,				UpperCamelCase							)							)

    if added_emb_type == "prd":
      _snake_case   :				str														=	nn.Parameter(torch.zeros(1      ,				1      ,				UpperCamelCase							)							)
    elif added_emb_type is None:
      _snake_case   :				Dict														=	None
    else:
      raise ValueError(
          f"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."""							)

    _snake_case   :				Optional[int]														=	nn.ModuleList(
        [
            BasicTransformerBlock(
                UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				dropout=UpperCamelCase      ,				activation_fn='gelu'      ,				attention_bias=UpperCamelCase      ,				)
            for d in range(UpperCamelCase							)
        ]							)

    if norm_in_type == "layer":
      _snake_case   :				Optional[int]														=	nn.LayerNorm(UpperCamelCase							)
    elif norm_in_type is None:
      _snake_case   :				Optional[Any]														=	None
    else:
      raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}."""							)

    _snake_case   :				Optional[Any]														=	nn.LayerNorm(UpperCamelCase							)

    _snake_case   :				Union[str, Any]														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)

    _snake_case   :				List[Any]														=	torch.full(
        [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]      ,				-1_00_00.0							)
    causal_attention_mask.triu_(1							)
    _snake_case   :				Optional[Any]														=	causal_attention_mask[None, ...]
    self.register_buffer('causal_attention_mask'      ,				UpperCamelCase      ,				persistent=UpperCamelCase							)

    _snake_case   :				str														=	nn.Parameter(torch.zeros(1      ,				UpperCamelCase							)							)
    _snake_case   :				List[str]														=	nn.Parameter(torch.zeros(1      ,				UpperCamelCase							)							)







  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				Optional[Any]														=	{}

    def fn_recursive_add_processors(UpperCamelCase      :					str      ,				UpperCamelCase      :					torch.nn.Module      ,				UpperCamelCase      :					Dict[str, AttentionProcessor]							):
      if hasattr(UpperCamelCase      ,				'set_processor'							):
        _snake_case   :				Tuple														=	module.processor

      for sub_name, child in module.named_children():
        fn_recursive_add_processors(f"""{name}.{sub_name}"""      ,				UpperCamelCase      ,				UpperCamelCase							)

      return processors

    for name, module in self.named_children():
      fn_recursive_add_processors(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							)

    return processors







  def UpperCamelCase_ (      self      :					List[Any]      ,				UpperCamelCase      :					Union[AttentionProcessor, Dict[str, AttentionProcessor]]							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	len(self.attn_processors.keys()							)

    if isinstance(UpperCamelCase      ,				UpperCamelCase							) and len(UpperCamelCase							) != count:
      raise ValueError(
          f"""A dict of processors was passed, but the number of processors {len(UpperCamelCase							)} does not match the"""
          f""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""							)

    def fn_recursive_attn_processor(UpperCamelCase      :					str      ,				UpperCamelCase      :					torch.nn.Module      ,				UpperCamelCase      :					Union[str, Any]							):
      if hasattr(UpperCamelCase      ,				'set_processor'							):
        if not isinstance(UpperCamelCase      ,				UpperCamelCase							):
          module.set_processor(UpperCamelCase							)
        else:
          module.set_processor(processor.pop(f"""{name}.processor"""							)							)

      for sub_name, child in module.named_children():
        fn_recursive_attn_processor(f"""{name}.{sub_name}"""      ,				UpperCamelCase      ,				UpperCamelCase							)

    for name, module in self.named_children():
      fn_recursive_attn_processor(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    self.set_attn_processor(AttnProcessor()							)







  def UpperCamelCase_ (      self      :					Optional[Any]      ,				UpperCamelCase      :					Any      ,				UpperCamelCase      :					Union[torch.Tensor, float, int]      ,				UpperCamelCase      :					torch.FloatTensor      ,				UpperCamelCase      :					Optional[torch.FloatTensor] = None      ,				UpperCamelCase      :					Optional[torch.BoolTensor] = None      ,				UpperCamelCase      :					bool = True      ,				):


    '''simple docstring'''





    _snake_case   :				Dict														=	hidden_states.shape[0]

    _snake_case   :				str														=	timestep
    if not torch.is_tensor(UpperCamelCase							):
      _snake_case   :				Dict														=	torch.tensor([timesteps]      ,				dtype=torch.long      ,				device=hidden_states.device							)
    elif torch.is_tensor(UpperCamelCase							) and len(timesteps.shape							) == 0:
      _snake_case   :				Tuple														=	timesteps[None].to(hidden_states.device							)

    # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
    _snake_case   :				Optional[int]														=	timesteps * torch.ones(UpperCamelCase      ,				dtype=timesteps.dtype      ,				device=timesteps.device							)

    _snake_case   :				Union[str, Any]														=	self.time_proj(UpperCamelCase							)

    # timesteps does not contain any weights and will always return f32 tensors
    # but time_embedding might be fp16, so we need to cast here.
    _snake_case   :				Tuple														=	timesteps_projected.to(dtype=self.dtype							)
    _snake_case   :				List[Any]														=	self.time_embedding(UpperCamelCase							)

    if self.embedding_proj_norm is not None:
      _snake_case   :				Optional[Any]														=	self.embedding_proj_norm(UpperCamelCase							)

    _snake_case   :				Union[str, Any]														=	self.embedding_proj(UpperCamelCase							)
    if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
      _snake_case   :				Dict														=	self.encoder_hidden_states_proj(UpperCamelCase							)
    elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
      raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'							)

    _snake_case   :				str														=	self.proj_in(UpperCamelCase							)

    _snake_case   :				int														=	self.positional_embedding.to(hidden_states.dtype							)

    _snake_case   :				Optional[int]														=	[]
    _snake_case   :				List[Any]														=	0

    if encoder_hidden_states is not None:
      additional_embeds.append(UpperCamelCase							)
      additional_embeddings_len += encoder_hidden_states.shape[1]

    if len(proj_embeddings.shape							) == 2:
      _snake_case   :				str														=	proj_embeddings[:, None, :]

    if len(hidden_states.shape							) == 2:
      _snake_case   :				str														=	hidden_states[:, None, :]

    _snake_case   :				str														=	additional_embeds + [
        proj_embeddings,
        time_embeddings[:, None, :],
        hidden_states,
    ]

    if self.prd_embedding is not None:
      _snake_case   :				int														=	self.prd_embedding.to(hidden_states.dtype							).expand(UpperCamelCase      ,				-1      ,				-1							)
      additional_embeds.append(UpperCamelCase							)

    _snake_case   :				Optional[int]														=	torch.cat(
        UpperCamelCase      ,				dim=1      ,				)

    # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
    _snake_case   :				Tuple														=	additional_embeddings_len + proj_embeddings.shape[1] + 1
    if positional_embeddings.shape[1] < hidden_states.shape[1]:
      _snake_case   :				Optional[Any]														=	F.pad(
          UpperCamelCase      ,				(
              0,
              0,
              additional_embeddings_len,
              self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
          )      ,				value=0.0      ,				)

    _snake_case   :				Optional[Any]														=	hidden_states + positional_embeddings

    if attention_mask is not None:
      _snake_case   :				Any														=	(1 - attention_mask.to(hidden_states.dtype							)) * -1_00_00.0
      _snake_case   :				Tuple														=	F.pad(UpperCamelCase      ,				(0, self.additional_embeddings)      ,				value=0.0							)
      _snake_case   :				int														=	(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype							)
      _snake_case   :				str														=	attention_mask.repeat_interleave(self.config.num_attention_heads      ,				dim=0							)

    if self.norm_in is not None:
      _snake_case   :				Tuple														=	self.norm_in(UpperCamelCase							)

    for block in self.transformer_blocks:
      _snake_case   :				Any														=	block(UpperCamelCase      ,				attention_mask=UpperCamelCase							)

    _snake_case   :				Dict														=	self.norm_out(UpperCamelCase							)

    if self.prd_embedding is not None:
      _snake_case   :				str														=	hidden_states[:, -1]
    else:
      _snake_case   :				Any														=	hidden_states[:, additional_embeddings_len:]

    _snake_case   :				List[Any]														=	self.proj_to_clip_embeddings(UpperCamelCase							)

    if not return_dict:
      return (predicted_image_embedding,)

    return PriorTransformerOutput(predicted_image_embedding=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	(prior_latents * self.clip_std) + self.clip_mean
    return prior_latents




