


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

def 	_a     ( SCREAMING_SNAKE_CASE		: List[str]			,       SCREAMING_SNAKE_CASE		: Any=False   ):







 """simple docstring"""



 UpperCamelCase__    :			Optional[int]      				=      []
 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__    :			Dict      				=      [(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     ( SCREAMING_SNAKE_CASE		: str			,       SCREAMING_SNAKE_CASE		: List[Any]			,       SCREAMING_SNAKE_CASE		: List[str]=False   ):







 """simple docstring"""



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

def 	_a     ( SCREAMING_SNAKE_CASE		: int   ):







 """simple docstring"""



 UpperCamelCase__    :			str      				=      ['''head.weight''', '''head.bias''']
 for k in ignore_keys:
  state_dict.pop(lowerCamelCase_			,       lowerCamelCase_   )

def 	_a     ( SCREAMING_SNAKE_CASE		: Dict			,       SCREAMING_SNAKE_CASE		: int			,       SCREAMING_SNAKE_CASE		: List[str]   ):







 """simple docstring"""



 UpperCamelCase__    :			str      				=      dct.pop(lowerCamelCase_   )
 UpperCamelCase__    :			Dict      				=      val

def 	_a     ( ):







 """simple docstring"""



 UpperCamelCase__    :			str      				=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 UpperCamelCase__    :			int      				=      Image.open(requests.get(lowerCamelCase_			,       stream=lowerCamelCase_   ).raw   )
 return im

@torch.no_grad()
def 	_a     ( SCREAMING_SNAKE_CASE		: List[Any]			,       SCREAMING_SNAKE_CASE		: Optional[int]   ):







 """simple docstring"""



 UpperCamelCase__    :			Union[str, Any]      				=      ViTConfig()
 UpperCamelCase__    :			List[str]      				=      False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  UpperCamelCase__    :			Any      				=      True
  UpperCamelCase__    :			Optional[Any]      				=      int(vit_name[-12:-10]   )
  UpperCamelCase__    :			int      				=      int(vit_name[-9:-6]   )
 else:
  UpperCamelCase__    :			Any      				=      1000
  UpperCamelCase__    :			int      				=      '''huggingface/label-files'''
  UpperCamelCase__    :			Optional[int]      				=      '''imagenet-1k-id2label.json'''
  UpperCamelCase__    :			List[str]      				=      json.load(open(hf_hub_download(lowerCamelCase_			,       lowerCamelCase_			,       repo_type='''dataset'''   )			,       '''r'''   )   )
  UpperCamelCase__    :			Union[str, Any]      				=      {int(lowerCamelCase_   ): v for k, v in idalabel.items()}
  UpperCamelCase__    :			Dict      				=      idalabel
  UpperCamelCase__    :			Optional[Any]      				=      {v: k for k, v in idalabel.items()}
  UpperCamelCase__    :			Optional[Any]      				=      int(vit_name[-6:-4]   )
  UpperCamelCase__    :			Union[str, Any]      				=      int(vit_name[-3:]   )
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith('''tiny'''   ):
   UpperCamelCase__    :			Union[str, Any]      				=      192
   UpperCamelCase__    :			str      				=      768
   UpperCamelCase__    :			Union[str, Any]      				=      12
   UpperCamelCase__    :			List[str]      				=      3
  elif vit_name[9:].startswith('''small'''   ):
   UpperCamelCase__    :			Optional[Any]      				=      384
   UpperCamelCase__    :			str      				=      1536
   UpperCamelCase__    :			Any      				=      12
   UpperCamelCase__    :			Optional[Any]      				=      6
  else:
   pass
 else:
  if vit_name[4:].startswith('''small'''   ):
   UpperCamelCase__    :			List[Any]      				=      768
   UpperCamelCase__    :			List[str]      				=      2304
   UpperCamelCase__    :			Union[str, Any]      				=      8
   UpperCamelCase__    :			List[str]      				=      8
  elif vit_name[4:].startswith('''base'''   ):
   pass
  elif vit_name[4:].startswith('''large'''   ):
   UpperCamelCase__    :			str      				=      1024
   UpperCamelCase__    :			List[str]      				=      4096
   UpperCamelCase__    :			Dict      				=      24
   UpperCamelCase__    :			Optional[Any]      				=      16
  elif vit_name[4:].startswith('''huge'''   ):
   UpperCamelCase__    :			Tuple      				=      1280
   UpperCamelCase__    :			str      				=      5120
   UpperCamelCase__    :			Any      				=      32
   UpperCamelCase__    :			Tuple      				=      16

    # load original model from timm
 UpperCamelCase__    :			Dict      				=      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()
 if base_model:
  remove_classification_head_(lowerCamelCase_   )
 UpperCamelCase__    :			str      				=      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
 if vit_name[-5:] == "in21k":
  UpperCamelCase__    :			Optional[int]      				=      ViTModel(lowerCamelCase_   ).eval()
 else:
  UpperCamelCase__    :			str      				=      ViTForImageClassification(lowerCamelCase_   ).eval()
 model.load_state_dict(lowerCamelCase_   )

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

 if base_model:
  UpperCamelCase__    :			List[Any]      				=      timm_model.forward_features(lowerCamelCase_   )
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(lowerCamelCase_			,       outputs.pooler_output			,       atol=1E-3   )
 else:
  UpperCamelCase__    :			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 {vit_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__":
       __UpperCamelCase				:				int  				= 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."
       )

       __UpperCamelCase				:				str  				= parser.parse_args()
       convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)










from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    Dict,  lowerCamelCase						:    pyspark.sql.DataFrame,  lowerCamelCase						:    Optional[NamedSplit] = None,  lowerCamelCase						:    Optional[Features] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = "arrow",  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(
								    split=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  keep_in_memory=lowerCamelCase,  streaming=lowerCamelCase,  **lowerCamelCase,  )
								lowercase__    =			load_from_cache_file
								lowercase__    =			file_format
								lowercase__    =			Spark(
								    df=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  working_dir=lowerCamelCase,  **lowerCamelCase,  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								if self.streaming:
												return self.builder.as_streaming_dataset(split=self.split  )
								lowercase__    =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
								self.builder.download_and_prepare(
								    download_mode=lowerCamelCase,  file_format=self._file_format,  )
								return self.builder.as_dataset(split=self.split  )




