
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()
lowercase						:      int									= logging.get_logger(__name__)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    str=False)      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     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"
														__UpperCamelCase			:     Tuple         =      [(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				:    int		,						_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Optional[int]=False)      ->					Optional[Any]:






							'''simple docstring'''





							for i in range(config.num_hidden_layers):
														if base_model:
																					__UpperCamelCase			:     Optional[int]         =      ""
														else:
																					__UpperCamelCase			:     str         =      "deit."
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														__UpperCamelCase			:     int         =      state_dict.pop(F'blocks.{i}.attn.qkv.weight')
														__UpperCamelCase			:     Union[str, 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			:     List[str]         =      in_proj_bias[: config.hidden_size]
														__UpperCamelCase			:     Union[str, Any]         =      in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__UpperCamelCase			:     List[str]         =      in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														__UpperCamelCase			:     Optional[Any]         =      in_proj_weight[
														    -config.hidden_size :, :
														]
														__UpperCamelCase			:     str         =      in_proj_bias[-config.hidden_size :]




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any]		,						_lowerCamelCase				:    Any		,						_lowerCamelCase				:    List[str])      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      dct.pop(_lowerCamelCase)
							__UpperCamelCase			:     Tuple         =      val




def 			_SCREAMING_SNAKE_CASE      (	)      ->					Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      "http://images.cocodataset.org/val2017/000000039769.jpg"
							__UpperCamelCase			:     Any         =      Image.open(requests.get(_lowerCamelCase		,						stream=_lowerCamelCase).raw)
							return im




@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Any		,						_lowerCamelCase				:    Dict)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      DeiTConfig()
							# all deit models have fine-tuned heads
							__UpperCamelCase			:     Union[str, Any]         =      False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							__UpperCamelCase			:     Dict         =      1_000
							__UpperCamelCase			:     List[str]         =      "huggingface/label-files"
							__UpperCamelCase			:     Tuple         =      "imagenet-1k-id2label.json"
							__UpperCamelCase			:     int         =      json.load(open(hf_hub_download(_lowerCamelCase		,						_lowerCamelCase		,						repo_type="dataset")		,						"r"))
							__UpperCamelCase			:     List[Any]         =      {int(_lowerCamelCase): v for k, v in idalabel.items()}
							__UpperCamelCase			:     Tuple         =      idalabel
							__UpperCamelCase			:     Dict         =      {v: k for k, v in idalabel.items()}
							__UpperCamelCase			:     Union[str, Any]         =      int(deit_name[-6:-4])
							__UpperCamelCase			:     Optional[int]         =      int(deit_name[-3:])
							# size of the architecture
							if deit_name[9:].startswith("tiny"):
														__UpperCamelCase			:     Union[str, Any]         =      192
														__UpperCamelCase			:     List[str]         =      768
														__UpperCamelCase			:     Optional[Any]         =      12
														__UpperCamelCase			:     str         =      3
							elif deit_name[9:].startswith("small"):
														__UpperCamelCase			:     str         =      384
														__UpperCamelCase			:     Union[str, Any]         =      1_536
														__UpperCamelCase			:     Union[str, Any]         =      12
														__UpperCamelCase			:     Optional[Any]         =      6
							if deit_name[9:].startswith("base"):
														pass
							elif deit_name[4:].startswith("large"):
														__UpperCamelCase			:     Union[str, Any]         =      1_024
														__UpperCamelCase			:     Tuple         =      4_096
														__UpperCamelCase			:     Optional[Any]         =      24
														__UpperCamelCase			:     Optional[int]         =      16

							# load original model from timm
							__UpperCamelCase			:     Tuple         =      timm.create_model(_lowerCamelCase		,						pretrained=_lowerCamelCase)
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							__UpperCamelCase			:     Optional[Any]         =      timm_model.state_dict()
							__UpperCamelCase			:     List[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
							__UpperCamelCase			:     List[Any]         =      DeiTForImageClassificationWithTeacher(_lowerCamelCase).eval()
							model.load_state_dict(_lowerCamelCase)

							# Check outputs on an image, prepared by DeiTImageProcessor
							__UpperCamelCase			:     Dict         =      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			:     str         =      DeiTImageProcessor(size=_lowerCamelCase		,						crop_size=config.image_size)
							__UpperCamelCase			:     List[str]         =      image_processor(images=prepare_img()		,						return_tensors="pt")
							__UpperCamelCase			:     List[Any]         =      encoding["pixel_values"]
							__UpperCamelCase			:     Tuple         =      model(_lowerCamelCase)

							__UpperCamelCase			:     List[Any]         =      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__":
		lowercase						:      Tuple									= 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.'
		)

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

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase						:      Optional[Any]									= logging.get_logger(__name__)

lowercase						:      List[str]									= {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}




class 				lowerCamelCase__							(       __lowercase  ,		__lowercase):




	'''simple docstring'''




	_A			=  'nat'

	_A			=  {
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}







	def __init__(      self	:Optional[Any]  ,     a	:Any=4  ,     a	:Any=3  ,     a	:int=6_4  ,     a	:Dict=[3, 4, 6, 5]  ,     a	:Dict=[2, 4, 8, 1_6]  ,     a	:Optional[Any]=7  ,     a	:Any=3.0  ,     a	:Optional[int]=True  ,     a	:int=0.0  ,     a	:Union[str, Any]=0.0  ,     a	:List[Any]=0.1  ,     a	:str="gelu"  ,     a	:Union[str, Any]=0.02  ,     a	:Tuple=1E-5  ,     a	:str=0.0  ,     a	:Optional[int]=None  ,     a	:Dict=None  ,     **a	:Optional[Any]  ,     )	->				int:
								super().__init__(**a		)

								__UpperCamelCase			:     Any         =      patch_size
								__UpperCamelCase			:     str         =      num_channels
								__UpperCamelCase			:     List[Any]         =      embed_dim
								__UpperCamelCase			:     str         =      depths
								__UpperCamelCase			:     str         =      len(a		)
								__UpperCamelCase			:     Optional[Any]         =      num_heads
								__UpperCamelCase			:     Dict         =      kernel_size
								__UpperCamelCase			:     Union[str, Any]         =      mlp_ratio
								__UpperCamelCase			:     Union[str, Any]         =      qkv_bias
								__UpperCamelCase			:     List[str]         =      hidden_dropout_prob
								__UpperCamelCase			:     Any         =      attention_probs_dropout_prob
								__UpperCamelCase			:     Any         =      drop_path_rate
								__UpperCamelCase			:     Any         =      hidden_act
								__UpperCamelCase			:     Tuple         =      layer_norm_eps
								__UpperCamelCase			:     Dict         =      initializer_range
								# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								__UpperCamelCase			:     int         =      int(embed_dim * 2 ** (len(a		) - 1)		)
								__UpperCamelCase			:     List[Any]         =      layer_scale_init_value
								__UpperCamelCase			:     Optional[Any]         =      ["stem"] + [f'stage{idx}' for idx in range(1  ,     len(a		) + 1		)]
								__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      get_aligned_output_features_output_indices(
								    out_features=a  ,     out_indices=a  ,     stage_names=self.stage_names		)