





import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42
  a_      =			jnp.floataa
  a_      =			True






  def     lowercase		(    self			:		Optional[int]					)							->						Optional[int]:
     super().setup()
     __lowerCAmelCase							     =     nn.Dense(5 ,  dtype=self.dtype					)






  def __call__(    self			:		Optional[Any] ,  *lowerCAmelCase_			:		List[Any] ,  **lowerCAmelCase_			:		List[Any]					)							->						List[Any]:
     __lowerCAmelCase							     =     super().__call__(*lowerCAmelCase_ ,  **lowerCAmelCase_					)
     __lowerCAmelCase							     =     self.cls(outputs[2]					)
     return outputs[:2] + (cls_out,)





class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			FlaxBigBirdForNaturalQuestionsModule





def   a_   (			lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Dict,     lowerCAmelCase_				:      Any     ):
   def cross_entropy(lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      str=None     ):
      __lowerCAmelCase							     =     logits.shape[-1]
      __lowerCAmelCase							     =     (labels[..., None] == jnp.arange(lowerCAmelCase_     )[None]).astype('f4'     )
      __lowerCAmelCase							     =     jax.nn.log_softmax(lowerCAmelCase_,     axis=-1     )
      __lowerCAmelCase							     =     -jnp.sum(labels * logits,     axis=-1     )
      if reduction is not None:
         __lowerCAmelCase							     =     reduction(lowerCAmelCase_     )
      return loss

   __lowerCAmelCase							     =     partial(lowerCAmelCase_,     reduction=jnp.mean     )
   __lowerCAmelCase							     =     cross_entropy(lowerCAmelCase_,     lowerCAmelCase_     )
   __lowerCAmelCase							     =     cross_entropy(lowerCAmelCase_,     lowerCAmelCase_     )
   __lowerCAmelCase							     =     cross_entropy(lowerCAmelCase_,     lowerCAmelCase_     )
   return (start_loss + end_loss + pooled_loss) / 3



@dataclass
class     _UpperCAmelCase   :

  """simple docstring"""


  a_      =			"""google/bigbird-roberta-base"""
  a_      =			30_00
  a_      =			1_05_00

  a_      =			1_28
  a_      =			3

  a_      =			1
  a_      =			5

  # tx_args
  a_      =			3e-5
  a_      =			0.0
  a_      =			2_00_00
  a_      =			0.0_095

  a_      =			"""bigbird-roberta-natural-questions"""
  a_      =			"""training-expt"""
  a_      =			"""data/nq-training.jsonl"""
  a_      =			"""data/nq-validation.jsonl"""






  def     lowercase		(    self			:		str					)							->						Any:
     os.makedirs(self.base_dir ,  exist_ok=lowerCAmelCase_					)
     __lowerCAmelCase							     =     os.path.join(self.base_dir ,  self.save_dir					)
     __lowerCAmelCase							     =     self.batch_size_per_device * jax.device_count()





@dataclass
class     _UpperCAmelCase   :

  """simple docstring"""


  a_      =			42
  a_      =			40_96  # no dynamic padding on TPUs






  def __call__(    self			:		Optional[int] ,  lowerCAmelCase_			:		Optional[Any]					)							->						List[str]:
     __lowerCAmelCase							     =     self.collate_fn(lowerCAmelCase_					)
     __lowerCAmelCase							     =     jax.tree_util.tree_map(lowerCAmelCase_ ,  lowerCAmelCase_					)
     return batch






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str					)							->						Dict:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.fetch_inputs(features['input_ids']					)
     __lowerCAmelCase							     =     {
         'input_ids': jnp.array(lowerCAmelCase_ ,  dtype=jnp.intaa					),
         'attention_mask': jnp.array(lowerCAmelCase_ ,  dtype=jnp.intaa					),
         'start_labels': jnp.array(features['start_token'] ,  dtype=jnp.intaa					),
         'end_labels': jnp.array(features['end_token'] ,  dtype=jnp.intaa					),
         'pooled_labels': jnp.array(features['category'] ,  dtype=jnp.intaa					),
     }
     return batch






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		list					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     [self._fetch_inputs(lowerCAmelCase_					) for ids in input_ids]
     return zip(*lowerCAmelCase_					)






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		list					)							->						List[Any]:
     __lowerCAmelCase							     =     [1 for _ in range(len(lowerCAmelCase_					)					)]
     while len(lowerCAmelCase_					) < self.max_length:
        input_ids.append(self.pad_id					)
        attention_mask.append(0					)
     return input_ids, attention_mask





def   a_   (			lowerCAmelCase_				:      Dict,     lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      str=None     ):
   if seed is not None:
      __lowerCAmelCase							     =     dataset.shuffle(seed=lowerCAmelCase_     )
   for i in range(len(lowerCAmelCase_     ) // batch_size     ):
      __lowerCAmelCase							     =     dataset[i * batch_size : (i + 1) * batch_size]
      yield dict(lowerCAmelCase_     )

@partial(jax.pmap,     axis_name='batch'     )
def   a_   (			lowerCAmelCase_				:      Union[str, Any],     lowerCAmelCase_				:      Optional[int],     **lowerCAmelCase_				:      Dict     ):
   def loss_fn(lowerCAmelCase_				:      List[str]     ):
      __lowerCAmelCase							     =     model_inputs.pop('start_labels'     )
      __lowerCAmelCase							     =     model_inputs.pop('end_labels'     )
      __lowerCAmelCase							     =     model_inputs.pop('pooled_labels'     )

      __lowerCAmelCase							     =     state.apply_fn(**lowerCAmelCase_,     params=lowerCAmelCase_,     dropout_rng=lowerCAmelCase_,     train=lowerCAmelCase_     )
      __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     outputs

      return state.loss_fn(
          lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_,     )

   __lowerCAmelCase     ,     __lowerCAmelCase							     =     jax.random.split(lowerCAmelCase_     )
   __lowerCAmelCase							     =     jax.value_and_grad(lowerCAmelCase_     )
   __lowerCAmelCase     ,     __lowerCAmelCase							     =     grad_fn(state.params     )
   __lowerCAmelCase							     =     jax.lax.pmean({'loss': loss},     axis_name='batch'     )
   __lowerCAmelCase							     =     jax.lax.pmean(lowerCAmelCase_,     'batch'     )

   __lowerCAmelCase							     =     state.apply_gradients(grads=lowerCAmelCase_     )
   return state, metrics, new_drp_rng

@partial(jax.pmap,     axis_name='batch'     )
def   a_   (			lowerCAmelCase_				:      Dict,     **lowerCAmelCase_				:      int     ):
   __lowerCAmelCase							     =     model_inputs.pop('start_labels'     )
   __lowerCAmelCase							     =     model_inputs.pop('end_labels'     )
   __lowerCAmelCase							     =     model_inputs.pop('pooled_labels'     )

   __lowerCAmelCase							     =     state.apply_fn(**lowerCAmelCase_,     params=state.params,     train=lowerCAmelCase_     )
   __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     outputs

   __lowerCAmelCase							     =     state.loss_fn(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )
   __lowerCAmelCase							     =     jax.lax.pmean({'loss': loss},     axis_name='batch'     )
   return metrics



class     _UpperCAmelCase   (   train_state.TrainState   ):

  """simple docstring"""


  a_      =			struct.field(pytree_node=_UpperCamelCase   )





@dataclass
class     _UpperCAmelCase   :

  """simple docstring"""


  a_      =			42
  a_      =			42
  a_      =			42
  a_      =			42
  a_      =			42
  a_      =			42
  a_      =			None






  def     lowercase		(    self			:		Optional[int] ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		int=None					)							->						int:
     __lowerCAmelCase							     =     model.params
     __lowerCAmelCase							     =     TrainState.create(
         apply_fn=model.__call__ ,  params=lowerCAmelCase_ ,  tx=lowerCAmelCase_ ,  loss_fn=lowerCAmelCase_ ,  )
     if ckpt_dir is not None:
        __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     restore_checkpoint(lowerCAmelCase_ ,  lowerCAmelCase_					)
        __lowerCAmelCase							     =     {
            'lr': args.lr,
            'init_lr': args.init_lr,
            'warmup_steps': args.warmup_steps,
            'num_train_steps': num_train_steps,
            'weight_decay': args.weight_decay,
        }
        __lowerCAmelCase     ,     __lowerCAmelCase							     =     build_tx(**lowerCAmelCase_					)
        __lowerCAmelCase							     =     train_state.TrainState(
            step=lowerCAmelCase_ ,  apply_fn=model.__call__ ,  params=lowerCAmelCase_ ,  tx=lowerCAmelCase_ ,  opt_state=lowerCAmelCase_ ,  )
        __lowerCAmelCase							     =     args
        __lowerCAmelCase							     =     data_collator
        __lowerCAmelCase							     =     lr
        __lowerCAmelCase							     =     params
     __lowerCAmelCase							     =     jax_utils.replicate(lowerCAmelCase_					)
     return state






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		Union[str, Any]					)							->						Optional[Any]:
     __lowerCAmelCase							     =     self.args
     __lowerCAmelCase							     =     len(lowerCAmelCase_					) // args.batch_size

     __lowerCAmelCase							     =     jax.random.PRNGKey(0					)
     __lowerCAmelCase							     =     jax.random.split(lowerCAmelCase_ ,  jax.device_count()					)
     for epoch in range(args.max_epochs					):
        __lowerCAmelCase							     =     jnp.array(0 ,  dtype=jnp.floataa					)
        __lowerCAmelCase							     =     get_batched_dataset(lowerCAmelCase_ ,  args.batch_size ,  seed=lowerCAmelCase_					)
        __lowerCAmelCase							     =     0
        for batch in tqdm(lowerCAmelCase_ ,  total=lowerCAmelCase_ ,  desc=f"""Running EPOCH-{epoch}"""					):
           __lowerCAmelCase							     =     self.data_collator(lowerCAmelCase_					)
           __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.train_step_fn(lowerCAmelCase_ ,  lowerCAmelCase_ ,  **lowerCAmelCase_					)
           running_loss += jax_utils.unreplicate(metrics['loss']					)
           i += 1
           if i % args.logging_steps == 0:
              __lowerCAmelCase							     =     jax_utils.unreplicate(state.step					)
              __lowerCAmelCase							     =     running_loss.item() / i
              __lowerCAmelCase							     =     self.scheduler_fn(state_step - 1					)

              __lowerCAmelCase							     =     self.evaluate(lowerCAmelCase_ ,  lowerCAmelCase_					)
              __lowerCAmelCase							     =     {
                  'step': state_step.item(),
                  'eval_loss': eval_loss.item(),
                  'tr_loss': tr_loss,
                  'lr': lr.item(),
              }
              tqdm.write(str(lowerCAmelCase_					)					)
              self.logger.log(lowerCAmelCase_ ,  commit=lowerCAmelCase_					)

           if i % args.save_steps == 0:
              self.save_checkpoint(args.save_dir + f"""-e{epoch}-s{i}""" ,  state=lowerCAmelCase_					)






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		List[str]					)							->						Optional[Any]:
     __lowerCAmelCase							     =     get_batched_dataset(lowerCAmelCase_ ,  self.args.batch_size					)
     __lowerCAmelCase							     =     len(lowerCAmelCase_					) // self.args.batch_size
     __lowerCAmelCase							     =     jnp.array(0 ,  dtype=jnp.floataa					)
     __lowerCAmelCase							     =     0
     for batch in tqdm(lowerCAmelCase_ ,  total=lowerCAmelCase_ ,  desc='Evaluating ... '					):
        __lowerCAmelCase							     =     self.data_collator(lowerCAmelCase_					)
        __lowerCAmelCase							     =     self.val_step_fn(lowerCAmelCase_ ,  **lowerCAmelCase_					)
        running_loss += jax_utils.unreplicate(metrics['loss']					)
        i += 1
     return running_loss / i






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Any					)							->						str:
     __lowerCAmelCase							     =     jax_utils.unreplicate(lowerCAmelCase_					)
     print(f"""SAVING CHECKPOINT IN {save_dir}""" ,  end=' ... '					)
     self.model_save_fn(lowerCAmelCase_ ,  params=state.params					)
     with open(os.path.join(lowerCAmelCase_ ,  'opt_state.msgpack'					) ,  'wb'					) as f:
        f.write(to_bytes(state.opt_state					)					)
     joblib.dump(self.args ,  os.path.join(lowerCAmelCase_ ,  'args.joblib'					)					)
     joblib.dump(self.data_collator ,  os.path.join(lowerCAmelCase_ ,  'data_collator.joblib'					)					)
     with open(os.path.join(lowerCAmelCase_ ,  'training_state.json'					) ,  'w'					) as f:
        json.dump({'step': state.step.item()} ,  lowerCAmelCase_					)
     print('DONE'					)





def   a_   (			lowerCAmelCase_				:      int,     lowerCAmelCase_				:      int     ):
   print(F"""RESTORING CHECKPOINT FROM {save_dir}""",     end=' ... '     )
   with open(os.path.join(lowerCAmelCase_,     'flax_model.msgpack'     ),     'rb'     ) as f:
      __lowerCAmelCase							     =     from_bytes(state.params,     f.read()     )

   with open(os.path.join(lowerCAmelCase_,     'opt_state.msgpack'     ),     'rb'     ) as f:
      __lowerCAmelCase							     =     from_bytes(state.opt_state,     f.read()     )

   __lowerCAmelCase							     =     joblib.load(os.path.join(lowerCAmelCase_,     'args.joblib'     )     )
   __lowerCAmelCase							     =     joblib.load(os.path.join(lowerCAmelCase_,     'data_collator.joblib'     )     )

   with open(os.path.join(lowerCAmelCase_,     'training_state.json'     ),     'r'     ) as f:
      __lowerCAmelCase							     =     json.load(lowerCAmelCase_     )
   __lowerCAmelCase							     =     training_state['step']

   print('DONE'     )
   return params, opt_state, step, args, data_collator

def   a_   (			lowerCAmelCase_				:      Dict,     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   __lowerCAmelCase							     =     num_train_steps - warmup_steps
   __lowerCAmelCase							     =     optax.linear_schedule(init_value=lowerCAmelCase_,     end_value=lowerCAmelCase_,     transition_steps=lowerCAmelCase_     )
   __lowerCAmelCase							     =     optax.linear_schedule(init_value=lowerCAmelCase_,     end_value=1E-7,     transition_steps=lowerCAmelCase_     )
   __lowerCAmelCase							     =     optax.join_schedules(schedules=[warmup_fn, decay_fn],     boundaries=[warmup_steps]     )
   return lr




def   a_   (			lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Tuple     ):
   def weight_decay_mask(lowerCAmelCase_				:      int     ):
      __lowerCAmelCase							     =     traverse_util.flatten_dict(lowerCAmelCase_     )
      __lowerCAmelCase							     =     {k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
      return traverse_util.unflatten_dict(lowerCAmelCase_     )

   __lowerCAmelCase							     =     scheduler_fn(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

   __lowerCAmelCase							     =     optax.adamw(learning_rate=lowerCAmelCase_,     weight_decay=lowerCAmelCase_,     mask=lowerCAmelCase_     )
   return tx, lr








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()
_snake_case  :   List[str]						   =  logging.get_logger(__name__)

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

def   a_   (			lowerCAmelCase_				:      Optional[int]     ):
   __lowerCAmelCase							     =     ['head.weight', 'head.bias']
   for k in ignore_keys:
      state_dict.pop(lowerCAmelCase_,     lowerCAmelCase_     )

def   a_   (			lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      Optional[int]     ):
   __lowerCAmelCase							     =     dct.pop(lowerCAmelCase_     )
   __lowerCAmelCase							     =     val

def   a_   (			):
   __lowerCAmelCase							     =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   __lowerCAmelCase							     =     Image.open(requests.get(lowerCAmelCase_,     stream=lowerCAmelCase_     ).raw     )
   return im

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

    # load original model from timm
   __lowerCAmelCase							     =     timm.create_model(lowerCAmelCase_,     pretrained=lowerCAmelCase_     )
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   __lowerCAmelCase							     =     timm_model.state_dict()
   if base_model:
      remove_classification_head_(lowerCAmelCase_     )
   __lowerCAmelCase							     =     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":
      __lowerCAmelCase							     =     ViTModel(lowerCAmelCase_     ).eval()
   else:
      __lowerCAmelCase							     =     ViTForImageClassification(lowerCAmelCase_     ).eval()
   model.load_state_dict(lowerCAmelCase_     )

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

   if base_model:
      __lowerCAmelCase							     =     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:
      __lowerCAmelCase							     =     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__":
     _snake_case  :   List[Any]						   =  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.'
     )

     _snake_case  :   Optional[int]						   =  parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)

