


"""simple docstring"""





import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


__UpperCAmelCase    				=      logging.getLogger(__name__)





def     lowercase__	(  lowerCamelCase     :  List[str]				)					->     Dict:
  random.seed(lowerCamelCase				)
  np.random.seed(lowerCamelCase				)
  torch.manual_seed(lowerCamelCase				)
  torch.cuda.manual_seed_all(lowerCamelCase				)





def     lowercase__	(  lowerCamelCase     :  Any				,	lowerCamelCase     :  Optional[Any]				,	lowerCamelCase     :  str				)					->     List[Any]:
  model.eval()
  lowerCAmelCase__    :    Any								=					next(model.parameters()				).device
  lowerCAmelCase__    :    Any								=					1
  lowerCAmelCase__    :    Tuple								=					torch.zeros((eval_batch_size, context_len)				,	dtype=torch.long				,	device=lowerCamelCase				)
  lowerCAmelCase__    :    Optional[int]								=					DataLoader(lowerCamelCase				,	shuffle=lowerCamelCase				,	batch_size=lowerCamelCase				)
  lowerCAmelCase__    :    Union[str, Any]								=					torch.zeros(1				,	device=lowerCamelCase				)
  lowerCAmelCase__    :    List[str]								=					0
  for batch in eval_dataloader:
    batch.to(lowerCamelCase				)
    # pad
    context.zero_()
    for i in range(lowerCamelCase				):
      lowerCAmelCase__    :    List[str]								=					batch[i]
    lowerCAmelCase__    :    str								=					model(lowerCamelCase				,	labels=lowerCamelCase				)
    eval_loss += outputs[0].sum().item()
    nb_eval_examples += batch.size(0				)
  lowerCAmelCase__    :    Optional[Any]								=					eval_loss / nb_eval_examples
  lowerCAmelCase__    :    List[Any]								=					torch.exp(lowerCamelCase				)
  model.train()
  return perplexity





def     lowercase__	(  lowerCamelCase     :  Any="gpt2"				)					->     int:
  lowerCAmelCase__    :    List[Any]								=					GPTaLMHeadModel.from_pretrained(lowerCamelCase				,	output_hidden_states=lowerCamelCase				)
  torch.save(model.state_dict()				,	model_name + "local.pt"				)
  return model





def     lowercase__	(  lowerCamelCase     :  List[str]				,	lowerCamelCase     :  List[Any]				,	lowerCamelCase     :  str				)					->     int:
  lowerCAmelCase__    :    str								=					copy.deepcopy(lowerCamelCase				)
  model.to(lowerCamelCase				)

  lowerCAmelCase__    :    Optional[int]								=					["bias", "LayerNorm.weight"]
  lowerCAmelCase__    :    Union[str, Any]								=					[
      {
          "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay				)],
          "weight_decay": 0.0,
      },
      {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay				)], "weight_decay": 0.0},
  ]
  lowerCAmelCase__    :    Union[str, Any]								=					AdamW(lowerCamelCase				,	lr=5e-5				,	eps=1e-8				)
  lowerCAmelCase__    :    Optional[int]								=					get_linear_schedule_with_warmup(lowerCamelCase				,	0				,	lowerCamelCase				)
  torch.cuda.empty_cache()
  return model, lm_optimizer, lm_scheduler





def     lowercase__	(  lowerCamelCase     :  str				,	lowerCamelCase     :  Any				,	lowerCamelCase     :  Optional[Any]				,	lowerCamelCase     :  Any				)					->     Union[str, Any]:
  lowerCAmelCase__    :    List[Any]								=					np.array(lowerCamelCase				).mean()
  lowerCAmelCase__    :    Optional[Any]								=					np.array(lowerCamelCase				).std()
  lowerCAmelCase__    :    List[str]								=					(real_perps - avg) / std
  lowerCAmelCase__    :    Optional[int]								=					list(zip(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)				)
  joblib.dump(lowerCamelCase				,	lowerCamelCase				)





def     lowercase__	(  lowerCamelCase     :  Optional[Any]				,	lowerCamelCase     :  Dict				,	lowerCamelCase     :  Dict				,	lowerCamelCase     :  str				,	lowerCamelCase     :  int				,	lowerCamelCase     :  List[Any]				,	lowerCamelCase     :  Tuple				,	lowerCamelCase     :  Dict="dev.jbl"				,	lowerCamelCase     :  List[Any]=recopy_gpta				,	)					->     int:
  lowerCAmelCase__    :    Tuple								=					[]
  lowerCAmelCase__    :    Union[str, Any]								=					[]
  lowerCAmelCase__    :    List[Any]								=					[]

  # Initialize the transformer model
  lowerCAmelCase__    :    Optional[int]								=					copy.deepcopy(lowerCamelCase				)
  orig_model.to(device="cpu"				)
  torch.cuda.empty_cache()

  # Compute perplexity of initial transformer model for comparison
  model.train()
  lowerCAmelCase__				,		lowerCAmelCase__				,		lowerCAmelCase__    :    str								=					recopy_model(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)

  for step in tqdm(range(lowerCamelCase				)				):
    lowerCAmelCase__    :    Optional[Any]								=					torch.zeros((1, context_len)				,	dtype=torch.long				,	device=lowerCamelCase				)
    lowerCAmelCase__    :    Union[str, Any]								=					random.choice(lowerCamelCase				)
    lowerCAmelCase__    :    List[Any]								=					random.randint(0				,	len(story[0]				) - context_len - 1				)
    lowerCAmelCase__    :    Union[str, Any]								=					story[0][start : start + context_len]
    lm_optimizer.zero_grad()
    lowerCAmelCase__    :    List[Any]								=					model(lowerCamelCase				,	labels=lowerCamelCase				)
    lowerCAmelCase__    :    Optional[Any]								=					outputs[0]
    lowerCAmelCase__    :    Any								=					compute_perplexity(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)
    model.train()
    lm_loss.backward()
    # Do LM backprop
    torch.nn.utils.clip_grad_norm_(model.parameters()				,	3.0				)
    lm_optimizer.step()
    lm_scheduler.step()  # Update learning rate schedule

    # Compute perplexity after back-propagating on the selected context
    lowerCAmelCase__    :    str								=					compute_perplexity(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)

    # Periodically save the stored (X, IG(X)) pairs
    if step % 1_0_0_0 == 0 and step > 1:
      intermittent_save(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)

    # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
    lowerCAmelCase__				,		lowerCAmelCase__				,		lowerCAmelCase__    :    Any								=					recopy_model(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)

    past_perps.append(past_perp.item()				)
    real_perps.append(orig_perp - real_perp.item()				)
    contexts.append(np.array(context.cpu()				)				)

  intermittent_save(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)





def     lowercase__	(  lowerCamelCase     :  Tuple				,	lowerCamelCase     :  Tuple="data/tokenized_stories_train_wikitext103.jbl"				,	lowerCamelCase     :  Tuple=1_0_0				,	lowerCamelCase     :  Any=1_0_2_6				,	lowerCamelCase     :  List[Any]=True				)					->     Optional[int]:
  lowerCAmelCase__    :    Optional[int]								=					joblib.load(lowerCamelCase				)
  print("data loaded"				)
  lowerCAmelCase__    :    int								=					[]
  if trim:
    for i, example in enumerate(lowerCamelCase				):
      if len(example[0]				) > min_len:
        lowerCAmelCase__    :    Dict								=					random.randint(0				,	len(example[0]				) - context_len - 1				)
        objective_set.append(example[0, start : start + context_len]				)
      if len(lowerCamelCase				) >= number:
        break
    lowerCAmelCase__    :    Optional[int]								=					[]
    for j in range(i + 1				,	len(lowerCamelCase				)				):
      if len(data[j][0]				) > min_len:
        train_data.append(data[j]				)
  else:
    lowerCAmelCase__    :    str								=					data[0:number]
    lowerCAmelCase__    :    Optional[Any]								=					data[number:]

  joblib.dump(lowerCamelCase				,	"objective_set.jbl"				)
  print("objective set saved"				)
  return train_data, objective_set





def     lowercase__	(  lowerCamelCase     :  int				,	lowerCamelCase     :  Dict				,	lowerCamelCase     :  Tuple				,	lowerCamelCase     :  Any				,	lowerCamelCase     :  int=5_0				,	lowerCamelCase     :  Tuple="secondary_learner.pt"				)					->     List[str]:
  lowerCAmelCase__    :    Union[str, Any]								=					torch.device("cuda:0" if torch.cuda.is_available() else "cpu"				)
  # We will use the first 512 pairs from our dataset as a test set for
  # our secondary learner and the rest to train
  lowerCAmelCase__    :    int								=					train_dataset[:5_1_2]
  lowerCAmelCase__    :    Dict								=					train_dataset[5_1_2:]
  lowerCAmelCase__    :    Any								=					DataLoader(lowerCamelCase				,	shuffle=lowerCamelCase				,	batch_size=lowerCamelCase				)
  lowerCAmelCase__    :    str								=					DataLoader(lowerCamelCase				,	shuffle=lowerCamelCase				,	batch_size=lowerCamelCase				)

  # secondary learner model set up
  lowerCAmelCase__    :    List[Any]								=					nn.MSELoss()
  lowerCAmelCase__    :    List[Any]								=					nn.MSELoss(reduction="sum"				)
  secondary_learner.to(lowerCamelCase				)
  lowerCAmelCase__    :    Dict								=					torch.optim.Adam(secondary_learner.parameters()				,	lr=0.0_00_01				)
  secondary_learner.train()

  # TODO in original code this is written as number of actual batches seen
  # not number of items seen but other places it is number of items instead.
  # improve consistency! changed this to epochs for clarity
  lowerCAmelCase__    :    Optional[Any]								=					float("inf"				)
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(lowerCamelCase				)				):
    lowerCAmelCase__    :    Union[str, Any]								=					0.0
    secondary_learner.train()
    for step, batch in enumerate(lowerCamelCase				):
      lowerCAmelCase__    :    str								=					batch[0].to(lowerCamelCase				)
      lowerCAmelCase__    :    Dict								=					batch[1].to(lowerCamelCase				)
      lowerCAmelCase__    :    Dict								=					secondary_learner(lowerCamelCase				)
      q_optimizer.zero_grad()
      lowerCAmelCase__    :    Tuple								=					loss(lowerCamelCase				,	real_q.float()				)
      q_loss.backward()
      q_optimizer.step()
      tr_q_loss += q_loss.item()

      # model trains fairly quickly so we won't wait for a full epoch
      # eval is triggered at eval_freq and end of epochs
      if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(lowerCamelCase				)):
        lowerCAmelCase__    :    List[str]								=					tr_q_loss / (step + 1)

        secondary_learner.eval()
        lowerCAmelCase__    :    Tuple								=					0.0
        lowerCAmelCase__    :    Any								=					0.0
        lowerCAmelCase__    :    Any								=					[]
        lowerCAmelCase__    :    Tuple								=					[]
        # Compute performance of the secondary learner after this batch
        for stepa, batcha in enumerate(lowerCamelCase				):
          lowerCAmelCase__    :    int								=					batcha[0].to(lowerCamelCase				)
          lowerCAmelCase__    :    Any								=					batcha[1].to(lowerCamelCase				)
          lowerCAmelCase__    :    Union[str, Any]								=					secondary_learner(lowerCamelCase				)
          q_lossa += test_loss(lowerCamelCase				,	lowerCamelCase				).item()
          sum_qa += torch.sum(lowerCamelCase				).item()
          for ei, i in enumerate(predicted_qa.cpu().detach().numpy()				):
            predicted.append(i.item()				)
          for ei, i in enumerate(real_qa.cpu().detach().numpy()				):
            actual.append(i.item()				)

        q_lossa /= len(lowerCamelCase				)
        print(
            "Epoch: "				,	lowerCamelCase				,	"step: "				,	lowerCamelCase				,	"Avg. q:"				,	sum_qa / len(lowerCamelCase				)				,	"Train Loss: "				,	lowerCamelCase				,	"Test Loss: "				,	lowerCamelCase				,	)
        if q_lossa < best_test_loss:
          joblib.dump((predicted, actual)				,	"pred_vs_actual.jbl"				)
          torch.save(secondary_learner.state_dict()				,	lowerCamelCase				)
          lowerCAmelCase__    :    Tuple								=					q_lossa

      secondary_learner.train()
  return secondary_learner







class  lowercase_  ( nn.Module						):







      def __init__(      self :							Optional[Any]		,  _lowercase :							Dict    ):
        super(_lowercase		,  self    ).__init__()
        lowerCAmelCase__    :    Tuple								=					model.transformer.wte
        lowerCAmelCase__    :    List[Any]								=					copy.deepcopy(model.transformer.wte.weight    )
        lowerCAmelCase__    :    List[Any]								=					nn.Convad(self.embeddings.weight.size(1    )		,  2_5_6		,  3		,  padding=1    )
        lowerCAmelCase__    :    List[Any]								=					nn.Sequential(nn.Linear(2_5_6		,  3_2    )		,  nn.Dropout(p=0.1    )		,  nn.Linear(3_2		,  3_2    )		,  nn.Linear(3_2		,  1    )    )







      def 				_lowerCAmelCase       (      self :							Any		,  _lowercase :							Optional[Any]    ):
        lowerCAmelCase__    :    List[str]								=					torch.max(self.conv(self.embeddings(_lowercase    ).squeeze(1    ).transpose(1		,  2    )    )		,  2    )[0]
        lowerCAmelCase__    :    Tuple								=					self.fc(_lowercase    )
        return qs.squeeze(1    )







      @classmethod
      def 				_lowerCAmelCase       (      cls :							str		,  _lowercase :							Optional[int]		,  _lowercase :							Any    ):
        lowerCAmelCase__    :    Optional[int]								=					cls(_lowercase    )  # this calls __init__
        lowerCAmelCase__    :    Optional[Any]								=					torch.load(_lowercase    )
        secondary_learner.load_state_dict(_lowercase    )
        lowerCAmelCase__    :    List[str]								=					model.transformer.wte
        lowerCAmelCase__    :    Optional[Any]								=					copy.deepcopy(model.transformer.wte.weight    )
        return secondary_learner




"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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





def     lowercase__	(  lowerCamelCase     :  Optional[int]				,	lowerCamelCase     :  Union[str, Any]=False				,	lowerCamelCase     :  Optional[int]=False				)					->     Union[str, Any]:
  lowerCAmelCase__    :    str								=					"backbone." if is_semantic else ""

  lowerCAmelCase__    :    Optional[Any]								=					[]
  for i in range(config.num_hidden_layers				):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight")				)
    rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias")				)
    rename_keys.append(
        (F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight")				)
    rename_keys.append(
        (F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias")				)
    rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight")				)
    rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias")				)
    rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight")				)
    rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias")				)
    rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight")				)
    rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias")				)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          (F"{prefix}cls_token", "beit.embeddings.cls_token"),
          (F"{prefix}patch_embed.proj.weight", "beit.embeddings.patch_embeddings.projection.weight"),
          (F"{prefix}patch_embed.proj.bias", "beit.embeddings.patch_embeddings.projection.bias"),
      ]				)

  if has_lm_head:
    # mask token + shared relative position bias + layernorm
    rename_keys.extend(
        [
            ("mask_token", "beit.embeddings.mask_token"),
            (
                "rel_pos_bias.relative_position_bias_table",
                "beit.encoder.relative_position_bias.relative_position_bias_table",
            ),
            (
                "rel_pos_bias.relative_position_index",
                "beit.encoder.relative_position_bias.relative_position_index",
            ),
            ("norm.weight", "layernorm.weight"),
            ("norm.bias", "layernorm.bias"),
        ]				)
  elif is_semantic:
    # semantic segmentation classification heads
    rename_keys.extend(
        [
            ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
            ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
            ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
            ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
        ]				)
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("fc_norm.weight", "beit.pooler.layernorm.weight"),
            ("fc_norm.bias", "beit.pooler.layernorm.bias"),
            ("head.weight", "classifier.weight"),
            ("head.bias", "classifier.bias"),
        ]				)

  return rename_keys





def     lowercase__	(  lowerCamelCase     :  List[str]				,	lowerCamelCase     :  Any				,	lowerCamelCase     :  Dict=False				,	lowerCamelCase     :  Optional[int]=False				)					->     Tuple:
  for i in range(config.num_hidden_layers				):
    lowerCAmelCase__    :    Dict								=					"backbone." if is_semantic else ""
    # queries, keys and values
    lowerCAmelCase__    :    str								=					state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"				)
    lowerCAmelCase__    :    str								=					state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"				)
    lowerCAmelCase__    :    Optional[Any]								=					state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"				)

    lowerCAmelCase__    :    Union[str, Any]								=					in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCAmelCase__    :    Dict								=					q_bias
    lowerCAmelCase__    :    str								=					in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCAmelCase__    :    Optional[int]								=					in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCAmelCase__    :    Optional[Any]								=					v_bias

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    lowerCAmelCase__    :    Any								=					state_dict.pop(F"{prefix}blocks.{i}.gamma_1"				)
    lowerCAmelCase__    :    Optional[int]								=					state_dict.pop(F"{prefix}blocks.{i}.gamma_2"				)

    lowerCAmelCase__    :    Tuple								=					gamma_a
    lowerCAmelCase__    :    Any								=					gamma_a

    # relative_position bias table + index
    if not has_lm_head:
      # each layer has its own relative position bias
      lowerCAmelCase__    :    int								=					state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_bias_table"				)
      lowerCAmelCase__    :    Optional[int]								=					state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_index"				)

      lowerCAmelCase__    :    Any								=					table
      lowerCAmelCase__    :    int								=					index





def     lowercase__	(  lowerCamelCase     :  int				,	lowerCamelCase     :  Any				,	lowerCamelCase     :  Any				)					->     Dict:
  lowerCAmelCase__    :    str								=					dct.pop(lowerCamelCase				)
  lowerCAmelCase__    :    Union[str, Any]								=					val





def     lowercase__	(  )					->     Optional[int]:
  lowerCAmelCase__    :    Union[str, Any]								=					"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCAmelCase__    :    List[str]								=					Image.open(requests.get(lowerCamelCase				,	stream=lowerCamelCase				).raw				)
  return im





@torch.no_grad()
def     lowercase__	(  lowerCamelCase     :  Optional[int]				,	lowerCamelCase     :  Optional[int]				)					->     int:
  lowerCAmelCase__    :    str								=					BeitConfig()
  lowerCAmelCase__    :    Any								=					False
  lowerCAmelCase__    :    Optional[Any]								=					False
  lowerCAmelCase__    :    Optional[Any]								=					"huggingface/label-files"
  # set config parameters based on URL
  if checkpoint_url[-9:-4] == "pt22k":
    # masked image modeling
    lowerCAmelCase__    :    int								=					True
    lowerCAmelCase__    :    List[str]								=					True
    lowerCAmelCase__    :    Union[str, Any]								=					True
  elif checkpoint_url[-9:-4] == "ft22k":
    # intermediate fine-tuning on ImageNet-22k
    lowerCAmelCase__    :    List[Any]								=					True
    lowerCAmelCase__    :    Dict								=					2_1_8_4_1
    lowerCAmelCase__    :    str								=					"imagenet-22k-id2label.json"
    lowerCAmelCase__    :    Union[str, Any]								=					json.load(open(hf_hub_download(lowerCamelCase				,	lowerCamelCase				,	repo_type="dataset"				)				,	"r"				)				)
    lowerCAmelCase__    :    Tuple								=					{int(lowerCamelCase				): v for k, v in idalabel.items()}
    # this dataset contains 21843 labels but the model only has 21841
    # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
    del idalabel[9_2_0_5]
    del idalabel[1_5_0_2_7]
    lowerCAmelCase__    :    str								=					idalabel
    lowerCAmelCase__    :    str								=					{v: k for k, v in idalabel.items()}
  elif checkpoint_url[-8:-4] == "to1k":
    # fine-tuning on ImageNet-1k
    lowerCAmelCase__    :    int								=					True
    lowerCAmelCase__    :    Optional[int]								=					1_0_0_0
    lowerCAmelCase__    :    List[Any]								=					"imagenet-1k-id2label.json"
    lowerCAmelCase__    :    List[str]								=					json.load(open(hf_hub_download(lowerCamelCase				,	lowerCamelCase				,	repo_type="dataset"				)				,	"r"				)				)
    lowerCAmelCase__    :    Optional[Any]								=					{int(lowerCamelCase				): v for k, v in idalabel.items()}
    lowerCAmelCase__    :    List[Any]								=					idalabel
    lowerCAmelCase__    :    Union[str, Any]								=					{v: k for k, v in idalabel.items()}
    if "384" in checkpoint_url:
      lowerCAmelCase__    :    str								=					3_8_4
    if "512" in checkpoint_url:
      lowerCAmelCase__    :    Union[str, Any]								=					5_1_2
  elif "ade20k" in checkpoint_url:
    # fine-tuning
    lowerCAmelCase__    :    int								=					True
    lowerCAmelCase__    :    Tuple								=					1_5_0
    lowerCAmelCase__    :    Dict								=					"ade20k-id2label.json"
    lowerCAmelCase__    :    Union[str, Any]								=					json.load(open(hf_hub_download(lowerCamelCase				,	lowerCamelCase				,	repo_type="dataset"				)				,	"r"				)				)
    lowerCAmelCase__    :    str								=					{int(lowerCamelCase				): v for k, v in idalabel.items()}
    lowerCAmelCase__    :    List[Any]								=					idalabel
    lowerCAmelCase__    :    Any								=					{v: k for k, v in idalabel.items()}
    lowerCAmelCase__    :    Any								=					6_4_0
    lowerCAmelCase__    :    Dict								=					True
  else:
    raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"				)

  # size of the architecture
  if "base" in checkpoint_url:
    pass
  elif "large" in checkpoint_url:
    lowerCAmelCase__    :    Optional[int]								=					1_0_2_4
    lowerCAmelCase__    :    int								=					4_0_9_6
    lowerCAmelCase__    :    Optional[Any]								=					2_4
    lowerCAmelCase__    :    Optional[int]								=					1_6
    if "ade20k" in checkpoint_url:
      lowerCAmelCase__    :    Optional[int]								=					6_4_0
      lowerCAmelCase__    :    Tuple								=					[7, 1_1, 1_5, 2_3]
  else:
    raise ValueError("Should either find 'base' or 'large' in checkpoint URL"				)

  # load state_dict of original model, remove and rename some keys
  lowerCAmelCase__    :    List[str]								=					torch.hub.load_state_dict_from_url(lowerCamelCase				,	map_location="cpu"				,	check_hash=lowerCamelCase				)
  lowerCAmelCase__    :    Tuple								=					state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

  lowerCAmelCase__    :    Optional[Any]								=					create_rename_keys(lowerCamelCase				,	has_lm_head=lowerCamelCase				,	is_semantic=lowerCamelCase				)
  for src, dest in rename_keys:
    rename_key(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)
  read_in_q_k_v(lowerCamelCase				,	lowerCamelCase				,	has_lm_head=lowerCamelCase				,	is_semantic=lowerCamelCase				)
  if is_semantic:
    # add prefix to decoder keys
    for key, val in state_dict.copy().items():
      lowerCAmelCase__    :    Dict								=					state_dict.pop(lowerCamelCase				)
      if key.startswith("backbone.fpn"				):
        lowerCAmelCase__    :    Any								=					key.replace("backbone.fpn"				,	"fpn"				)
      lowerCAmelCase__    :    Any								=					val

    # load HuggingFace model
  if checkpoint_url[-9:-4] == "pt22k":
    lowerCAmelCase__    :    Union[str, Any]								=					BeitForMaskedImageModeling(lowerCamelCase				)
  elif "ade20k" in checkpoint_url:
    lowerCAmelCase__    :    List[str]								=					BeitForSemanticSegmentation(lowerCamelCase				)
  else:
    lowerCAmelCase__    :    int								=					BeitForImageClassification(lowerCamelCase				)
  model.eval()
  model.load_state_dict(lowerCamelCase				)

  # Check outputs on an image
  if is_semantic:
    lowerCAmelCase__    :    Optional[Any]								=					BeitImageProcessor(size=config.image_size				,	do_center_crop=lowerCamelCase				)
    lowerCAmelCase__    :    str								=					load_dataset("hf-internal-testing/fixtures_ade20k"				,	split="test"				)
    lowerCAmelCase__    :    Union[str, Any]								=					Image.open(ds[0]["file"]				)
  else:
    lowerCAmelCase__    :    Optional[int]								=					BeitImageProcessor(
        size=config.image_size				,	resample=PILImageResampling.BILINEAR				,	do_center_crop=lowerCamelCase				)
    lowerCAmelCase__    :    List[Any]								=					prepare_img()

  lowerCAmelCase__    :    Optional[int]								=					image_processor(images=lowerCamelCase				,	return_tensors="pt"				)
  lowerCAmelCase__    :    Union[str, Any]								=					encoding["pixel_values"]

  lowerCAmelCase__    :    Optional[Any]								=					model(lowerCamelCase				)
  lowerCAmelCase__    :    Optional[int]								=					outputs.logits

  # verify logits
  lowerCAmelCase__    :    Union[str, Any]								=					torch.Size([1, 1_0_0_0]				)
  if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"				):
    lowerCAmelCase__    :    Any								=					torch.Size([1, 1_9_6, 8_1_9_2]				)
  elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"				):
    lowerCAmelCase__    :    Any								=					torch.Size([1, 1_9_6, 8_1_9_2]				)
  elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"				):
    lowerCAmelCase__    :    Dict								=					torch.Size([1, 2_1_8_4_1]				)
    lowerCAmelCase__    :    Optional[int]								=					torch.tensor([2.22_88, 2.46_71, 0.73_95]				)
    lowerCAmelCase__    :    Any								=					2_3_9_7
  elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"				):
    lowerCAmelCase__    :    Tuple								=					torch.Size([1, 2_1_8_4_1]				)
    lowerCAmelCase__    :    List[str]								=					torch.tensor([1.68_81, -0.27_87, 0.59_01]				)
    lowerCAmelCase__    :    int								=					2_3_9_6
  elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"				):
    lowerCAmelCase__    :    Tuple								=					torch.tensor([0.12_41, 0.07_98, -0.65_69]				)
    lowerCAmelCase__    :    Tuple								=					2_8_5
  elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    List[Any]								=					torch.tensor([-1.23_85, -1.09_87, -1.01_08]				)
    lowerCAmelCase__    :    Union[str, Any]								=					2_8_1
  elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    Optional[Any]								=					torch.tensor([-1.53_03, -0.94_84, -0.31_47]				)
    lowerCAmelCase__    :    Optional[Any]								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"				):
    lowerCAmelCase__    :    Union[str, Any]								=					torch.tensor([0.46_10, -0.09_28, 0.20_86]				)
    lowerCAmelCase__    :    Any								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    str								=					torch.tensor([-0.48_04, 0.62_57, -0.18_37]				)
    lowerCAmelCase__    :    Any								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    int								=					torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]				)
    lowerCAmelCase__    :    Optional[int]								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    Union[str, Any]								=					torch.tensor([-0.30_62, 0.72_61, 0.48_52]				)
    lowerCAmelCase__    :    Optional[Any]								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"				):
    lowerCAmelCase__    :    Union[str, Any]								=					(1, 1_5_0, 1_6_0, 1_6_0)
    lowerCAmelCase__    :    Union[str, Any]								=					torch.tensor(
        [
            [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
            [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
            [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
        ]				)
  elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"				):
    lowerCAmelCase__    :    Dict								=					(1, 1_5_0, 1_6_0, 1_6_0)
    lowerCAmelCase__    :    List[Any]								=					torch.tensor(
        [
            [[-4.33_05, -2.30_49, -3.01_61], [-2.95_91, -1.53_05, -2.22_51], [-3.41_98, -1.80_04, -2.90_62]],
            [[-5.89_22, -3.74_35, -4.39_78], [-4.20_63, -2.78_72, -3.47_55], [-4.27_91, -3.18_74, -4.16_81]],
            [[0.98_95, 4.34_67, 4.76_63], [4.24_76, 5.68_30, 6.15_18], [4.55_50, 6.24_95, 6.51_54]],
        ]				)
  else:
    raise ValueError("Can't verify logits as model is not supported"				)

  assert logits.shape == expected_shape, "Shape of logits not as expected"
  if not has_lm_head:
    if is_semantic:
      assert torch.allclose(
          logits[0, :3, :3, :3]				,	lowerCamelCase				,	atol=1e-3				), "First elements of logits not as expected"
    else:
      print("Predicted class idx:"				,	logits.argmax(-1				).item()				)
      assert torch.allclose(
          logits[0, :3]				,	lowerCamelCase				,	atol=1e-3				), "First elements of logits not as expected"
      assert logits.argmax(-1				).item() == expected_class_idx, "Predicted class index not as expected"

  Path(lowerCamelCase				).mkdir(exist_ok=lowerCamelCase				)
  print(F"Saving model 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    				=      argparse.ArgumentParser()

   parser.add_argument(
       "--checkpoint_url",
       default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth",
       type=str,
       help="URL to the original PyTorch checkpoint (.pth file).",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
   )
   __UpperCAmelCase    				=      parser.parse_args()
   convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
