


'''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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase												=						logging.get_logger(__name__)


def        a__     (					UpperCamelCase_						:      List[Any],   UpperCamelCase_						:      int=False						):
   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'''vit.encoder.layer.{i}.layernorm_before.weight''')						)
      rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')						)
      rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')						)
      rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')						)
      rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')						)
      rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')						)
      rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')						)
      rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')						)
      rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')						)
      rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')						)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ('''cls_token''', '''vit.embeddings.cls_token'''),
           ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
           ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
           ('''pos_embed''', '''vit.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 "vit" from all keys that start with "vit"
      UpperCAmelCase__	:Tuple      =      [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''						) else pair for pair in rename_keys]
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ('''norm.weight''', '''vit.layernorm.weight'''),
              ('''norm.bias''', '''vit.layernorm.bias'''),
              ('''head.weight''', '''classifier.weight'''),
              ('''head.bias''', '''classifier.bias'''),
          ]						)

   return rename_keys


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


def        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   UpperCAmelCase__	:Optional[int]      =      ['''head.weight''', '''head.bias''']
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,   UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      int,   UpperCamelCase_						:      Dict,   UpperCamelCase_						:      Any						):
   UpperCAmelCase__	:List[str]      =      dct.pop(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      val


def        a__     (					):
   UpperCAmelCase__	:List[Any]      =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCAmelCase__	:Any      =      Image.open(requests.get(UpperCamelCase_,   stream=UpperCamelCase_						).raw						)
   return im


@torch.no_grad()
def        a__     (					UpperCamelCase_						:      List[str],   UpperCamelCase_						:      Optional[int]						):
   UpperCAmelCase__	:Any      =      ViTConfig()
   UpperCAmelCase__	:Union[str, Any]      =      False
   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   if vit_name[-5:] == "in21k":
      UpperCAmelCase__	:List[str]      =      True
      UpperCAmelCase__	:Optional[Any]      =      int(vit_name[-12:-10]						)
      UpperCAmelCase__	:List[Any]      =      int(vit_name[-9:-6]						)
   else:
      UpperCAmelCase__	:Any      =      1_000
      UpperCAmelCase__	:Any      =      '''huggingface/label-files'''
      UpperCAmelCase__	:Optional[int]      =      '''imagenet-1k-id2label.json'''
      UpperCAmelCase__	:Tuple      =      json.load(open(hf_hub_download(UpperCamelCase_,   UpperCamelCase_,   repo_type='''dataset'''						),   '''r'''						)						)
      UpperCAmelCase__	:Dict      =      {int(UpperCamelCase_						): v for k, v in idalabel.items()}
      UpperCAmelCase__	:Any      =      idalabel
      UpperCAmelCase__	:str      =      {v: k for k, v in idalabel.items()}
      UpperCAmelCase__	:Dict      =      int(vit_name[-6:-4]						)
      UpperCAmelCase__	:Tuple      =      int(vit_name[-3:]						)
   # size of the architecture
   if "deit" in vit_name:
      if vit_name[9:].startswith('''tiny'''						):
         UpperCAmelCase__	:Optional[int]      =      192
         UpperCAmelCase__	:int      =      768
         UpperCAmelCase__	:Tuple      =      12
         UpperCAmelCase__	:int      =      3
      elif vit_name[9:].startswith('''small'''						):
         UpperCAmelCase__	:Optional[int]      =      384
         UpperCAmelCase__	:Any      =      1_536
         UpperCAmelCase__	:Any      =      12
         UpperCAmelCase__	:Any      =      6
      else:
         pass
   else:
      if vit_name[4:].startswith('''small'''						):
         UpperCAmelCase__	:Optional[Any]      =      768
         UpperCAmelCase__	:Tuple      =      2_304
         UpperCAmelCase__	:int      =      8
         UpperCAmelCase__	:int      =      8
      elif vit_name[4:].startswith('''base'''						):
         pass
      elif vit_name[4:].startswith('''large'''						):
         UpperCAmelCase__	:Tuple      =      1_024
         UpperCAmelCase__	:int      =      4_096
         UpperCAmelCase__	:List[str]      =      24
         UpperCAmelCase__	:str      =      16
      elif vit_name[4:].startswith('''huge'''						):
         UpperCAmelCase__	:Union[str, Any]      =      1_280
         UpperCAmelCase__	:Optional[Any]      =      5_120
         UpperCAmelCase__	:Optional[int]      =      32
         UpperCAmelCase__	:Any      =      16

    # load original model from timm
   UpperCAmelCase__	:Optional[int]      =      timm.create_model(UpperCamelCase_,   pretrained=UpperCamelCase_						)
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   UpperCAmelCase__	:Dict      =      timm_model.state_dict()
   if base_model:
      remove_classification_head_(UpperCamelCase_						)
   UpperCAmelCase__	:int      =      create_rename_keys(UpperCamelCase_,   UpperCamelCase_						)
   for src, dest in rename_keys:
      rename_key(UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_						)
   read_in_q_k_v(UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_						)

   # load HuggingFace model
   if vit_name[-5:] == "in21k":
      UpperCAmelCase__	:List[str]      =      ViTModel(UpperCamelCase_						).eval()
   else:
      UpperCAmelCase__	:Dict      =      ViTForImageClassification(UpperCamelCase_						).eval()
   model.load_state_dict(UpperCamelCase_						)

   # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
   if "deit" in vit_name:
      UpperCAmelCase__	:Tuple      =      DeiTImageProcessor(size=config.image_size						)
   else:
      UpperCAmelCase__	:Tuple      =      ViTImageProcessor(size=config.image_size						)
   UpperCAmelCase__	:Optional[Any]      =      image_processor(images=prepare_img(),   return_tensors='''pt'''						)
   UpperCAmelCase__	:Optional[int]      =      encoding['''pixel_values''']
   UpperCAmelCase__	:str      =      model(UpperCamelCase_						)

   if base_model:
      UpperCAmelCase__	:Optional[Any]      =      timm_model.forward_features(UpperCamelCase_						)
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(UpperCamelCase_,   outputs.pooler_output,   atol=1e-3						)
   else:
      UpperCAmelCase__	:Tuple      =      timm_model(UpperCamelCase_						)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(UpperCamelCase_,   outputs.logits,   atol=1e-3						)

   Path(UpperCamelCase_						).mkdir(exist_ok=UpperCamelCase_						)
   print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''						)
   model.save_pretrained(UpperCamelCase_						)
   print(F'''Saving image processor to {pytorch_dump_folder_path}'''						)
   image_processor.save_pretrained(UpperCamelCase_						)


if __name__ == "__main__":
    __lowerCamelCase												=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--vit_name''',
        default='''vit_base_patch16_224''',
        type=str,
        help='''Name of the ViT 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_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







'''simple docstring'''





from typing import List, Optional

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


__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"autoformer"
 UpperCAmelCase				=					{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "student_t"			,       __lowerCamelCase							:	str = "nll"			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	List[int] = [1, 2, 3, 4, 5, 6, 7]			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	int = 6_4			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	str = "gelu"			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	int = 1_0_0			,       __lowerCamelCase							:	float = 0.02			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Optional[int]=True			,       __lowerCamelCase							:	int = 1_0			,       __lowerCamelCase							:	int = 2_5			,       __lowerCamelCase							:	int = 3			,       **__lowerCamelCase							:	int			,       ):
    # time series specific configuration
    UpperCAmelCase__	:Any      =      prediction_length
    UpperCAmelCase__	:Optional[int]      =      context_length if context_length is not None else prediction_length
    UpperCAmelCase__	:int      =      distribution_output
    UpperCAmelCase__	:str      =      loss
    UpperCAmelCase__	:Tuple      =      input_size
    UpperCAmelCase__	:Optional[int]      =      num_time_features
    UpperCAmelCase__	:List[Any]      =      lags_sequence
    UpperCAmelCase__	:Any      =      scaling
    UpperCAmelCase__	:Union[str, Any]      =      num_dynamic_real_features
    UpperCAmelCase__	:Dict      =      num_static_real_features
    UpperCAmelCase__	:Optional[int]      =      num_static_categorical_features
    if cardinality is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != num_static_categorical_features:
          raise ValueError(
              '''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
       UpperCAmelCase__	:Any      =      cardinality
    else:
       UpperCAmelCase__	:Dict      =      [0]
    if embedding_dimension is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != num_static_categorical_features:
          raise ValueError(
              '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
       UpperCAmelCase__	:List[str]      =      embedding_dimension
    else:
       UpperCAmelCase__	:int      =      [min(5_0			,       (cat + 1) // 2 ) for cat in self.cardinality]
    UpperCAmelCase__	:Optional[Any]      =      num_parallel_samples

    # Transformer architecture configuration
    UpperCAmelCase__	:Optional[Any]      =      input_size * len(self.lags_sequence ) + self._number_of_features
    UpperCAmelCase__	:Optional[Any]      =      d_model
    UpperCAmelCase__	:Any      =      encoder_attention_heads
    UpperCAmelCase__	:Any      =      decoder_attention_heads
    UpperCAmelCase__	:int      =      encoder_ffn_dim
    UpperCAmelCase__	:Union[str, Any]      =      decoder_ffn_dim
    UpperCAmelCase__	:Optional[Any]      =      encoder_layers
    UpperCAmelCase__	:str      =      decoder_layers

    UpperCAmelCase__	:Optional[Any]      =      dropout
    UpperCAmelCase__	:str      =      attention_dropout
    UpperCAmelCase__	:int      =      activation_dropout
    UpperCAmelCase__	:Optional[Any]      =      encoder_layerdrop
    UpperCAmelCase__	:Dict      =      decoder_layerdrop

    UpperCAmelCase__	:Union[str, Any]      =      activation_function
    UpperCAmelCase__	:List[Any]      =      init_std

    UpperCAmelCase__	:List[str]      =      use_cache

    # Autoformer
    UpperCAmelCase__	:List[str]      =      label_length
    UpperCAmelCase__	:Dict      =      moving_average
    UpperCAmelCase__	:str      =      autocorrelation_factor

    super().__init__(is_encoder_decoder=__lowerCamelCase			,       **__lowerCamelCase )





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    return (
        sum(self.embedding_dimension )
        + self.num_dynamic_real_features
        + self.num_time_features
        + self.num_static_real_features
        + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
    )



