



import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a__      =		16
a__      =		32



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Dict			,    SCREAMING_SNAKE_CASE__    :		List[str]			)					->       str:
 # Initialize accelerator
 if args.with_tracking:
  _snake_case      :					Optional[int]					=   Accelerator(
      cpu=args.cpu			,    mixed_precision=args.mixed_precision			,    log_with="""all"""			,    project_dir=args.project_dir			)
 else:
  _snake_case      :					int					=   Accelerator(cpu=args.cpu			,    mixed_precision=args.mixed_precision			)

 if hasattr(args.checkpointing_steps			,    """isdigit"""			):
  if args.checkpointing_steps == "epoch":
   _snake_case      :					List[str]					=   args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   _snake_case      :					Optional[int]					=   int(args.checkpointing_steps			)
  else:
   raise ValueError(
       F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''			)
 else:
  _snake_case      :					str					=   None
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _snake_case      :					Tuple					=   config["""lr"""]
 _snake_case      :					List[str]					=   int(config["""num_epochs"""]			)
 _snake_case      :					int					=   int(config["""seed"""]			)
 _snake_case      :					Union[str, Any]					=   int(config["""batch_size"""]			)

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  _snake_case      :					int					=   os.path.split(SCREAMING_SNAKE_CASE__			)[-1].split("""."""			)[0]
  accelerator.init_trackers(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)

 _snake_case      :					Dict					=   AutoTokenizer.from_pretrained("""bert-base-cased"""			)
 _snake_case      :					Any					=   load_dataset("""glue"""			,    """mrpc"""			)
 _snake_case      :					Dict					=   evaluate.load("""glue"""			,    """mrpc"""			)

 def tokenize_function(SCREAMING_SNAKE_CASE__    :		List[str]			):
  # max_length=None => use the model max length (it's actually the default)
  _snake_case      :					Union[str, Any]					=   tokenizer(examples["""sentence1"""]			,    examples["""sentence2"""]			,    truncation=SCREAMING_SNAKE_CASE__			,    max_length=SCREAMING_SNAKE_CASE__			)
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 # starting with the main process first:
 with accelerator.main_process_first():
  _snake_case      :					Union[str, Any]					=   datasets.map(
      SCREAMING_SNAKE_CASE__			,    batched=SCREAMING_SNAKE_CASE__			,    remove_columns=["""idx""", """sentence1""", """sentence2"""]			,    )

 # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
 # transformers library
 _snake_case      :					str					=   tokenized_datasets.rename_column("""label"""			,    """labels"""			)

 # If the batch size is too big we use gradient accumulation
 _snake_case      :					Optional[Any]					=   1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  _snake_case      :					Any					=   batch_size // MAX_GPU_BATCH_SIZE
  _snake_case      :					List[str]					=   MAX_GPU_BATCH_SIZE

 def collate_fn(SCREAMING_SNAKE_CASE__    :		Dict			):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  _snake_case      :					List[Any]					=   128 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   _snake_case      :					Tuple					=   16
  elif accelerator.mixed_precision != "no":
   _snake_case      :					Dict					=   8
  else:
   _snake_case      :					Union[str, Any]					=   None

  return tokenizer.pad(
      SCREAMING_SNAKE_CASE__			,    padding="""longest"""			,    max_length=SCREAMING_SNAKE_CASE__			,    pad_to_multiple_of=SCREAMING_SNAKE_CASE__			,    return_tensors="""pt"""			,    )

 # Instantiate dataloaders.
 _snake_case      :					Optional[Any]					=   DataLoader(
     tokenized_datasets["""train"""]			,    shuffle=SCREAMING_SNAKE_CASE__			,    collate_fn=SCREAMING_SNAKE_CASE__			,    batch_size=SCREAMING_SNAKE_CASE__			)
 _snake_case      :					str					=   DataLoader(
     tokenized_datasets["""validation"""]			,    shuffle=SCREAMING_SNAKE_CASE__			,    collate_fn=SCREAMING_SNAKE_CASE__			,    batch_size=SCREAMING_SNAKE_CASE__			)

 set_seed(SCREAMING_SNAKE_CASE__			)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _snake_case      :					int					=   AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""			,    return_dict=SCREAMING_SNAKE_CASE__			)

 # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
 # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
 # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
 _snake_case      :					str					=   model.to(accelerator.device			)

 # Instantiate optimizer
 _snake_case      :					Optional[int]					=   AdamW(params=model.parameters()			,    lr=SCREAMING_SNAKE_CASE__			)

 # Instantiate scheduler
 _snake_case      :					List[Any]					=   get_linear_schedule_with_warmup(
     optimizer=SCREAMING_SNAKE_CASE__			,    num_warmup_steps=100			,    num_training_steps=(len(SCREAMING_SNAKE_CASE__			) * num_epochs) // gradient_accumulation_steps			,    )

 # Prepare everything
 # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
 # prepare method.
 _snake_case      :					List[str]					=   accelerator.prepare(
     SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)

 # We need to keep track of how many total steps we have iterated over
 _snake_case      :					Tuple					=   0
 # We also need to keep track of the stating epoch so files are named properly
 _snake_case      :					Tuple					=   0

 # Potentially load in the weights and states from a previous save
 if args.resume_from_checkpoint:
  if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
   accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''			)
   accelerator.load_state(args.resume_from_checkpoint			)
   _snake_case      :					Optional[int]					=   os.path.basename(args.resume_from_checkpoint			)
  else:
   # Get the most recent checkpoint
   _snake_case      :					List[Any]					=   [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
   dirs.sort(key=os.path.getctime			)
   _snake_case      :					Union[str, Any]					=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  _snake_case      :					Union[str, Any]					=   os.path.splitext(SCREAMING_SNAKE_CASE__			)[0]

  if "epoch" in training_difference:
   _snake_case      :					List[str]					=   int(training_difference.replace("""epoch_"""			,    """"""			)			) + 1
   _snake_case      :					Union[str, Any]					=   None
  else:
   _snake_case      :					Optional[Any]					=   int(training_difference.replace("""step_"""			,    """"""			)			)
   _snake_case      :					Any					=   resume_step // len(SCREAMING_SNAKE_CASE__			)
   resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE__			)

    # Now we train the model
 for epoch in range(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  model.train()
  if args.with_tracking:
   _snake_case      :					Any					=   0
  if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
   # We need to skip steps until we reach the resumed step
   _snake_case      :					Any					=   accelerator.skip_first_batches(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
   overall_step += resume_step
  else:
   # After the first iteration though, we need to go back to the original dataloader
   _snake_case      :					int					=   train_dataloader
  for step, batch in enumerate(SCREAMING_SNAKE_CASE__			):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device			)
   _snake_case      :					Optional[int]					=   model(**SCREAMING_SNAKE_CASE__			)
   _snake_case      :					Optional[Any]					=   outputs.loss
   _snake_case      :					Optional[int]					=   loss / gradient_accumulation_steps
   # We keep track of the loss at each epoch
   if args.with_tracking:
    total_loss += loss.detach().float()
   accelerator.backward(SCREAMING_SNAKE_CASE__			)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1

   if isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
    _snake_case      :					List[Any]					=   F'''step_{overall_step}'''
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      _snake_case      :					List[str]					=   os.path.join(args.output_dir			,    SCREAMING_SNAKE_CASE__			)
     accelerator.save_state(SCREAMING_SNAKE_CASE__			)

  model.eval()
  for step, batch in enumerate(SCREAMING_SNAKE_CASE__			):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device			)
   with torch.no_grad():
    _snake_case      :					List[str]					=   model(**SCREAMING_SNAKE_CASE__			)
   _snake_case      :					Optional[int]					=   outputs.logits.argmax(dim=-1			)
   _snake_case      :					Dict					=   accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
   metric.add_batch(
       predictions=SCREAMING_SNAKE_CASE__			,    references=SCREAMING_SNAKE_CASE__			,    )

  _snake_case      :					Tuple					=   metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F'''epoch {epoch}:'''			,    SCREAMING_SNAKE_CASE__			)
  if args.with_tracking:
   accelerator.log(
       {
           """accuracy""": eval_metric["""accuracy"""],
           """f1""": eval_metric["""f1"""],
           """train_loss""": total_loss.item() / len(SCREAMING_SNAKE_CASE__			),
           """epoch""": epoch,
       }			,    step=SCREAMING_SNAKE_CASE__			,    )

  if checkpointing_steps == "epoch":
   _snake_case      :					Union[str, Any]					=   F'''epoch_{epoch}'''
   if args.output_dir is not None:
    _snake_case      :					Union[str, Any]					=   os.path.join(args.output_dir			,    SCREAMING_SNAKE_CASE__			)
   accelerator.save_state(SCREAMING_SNAKE_CASE__			)

 if args.with_tracking:
  accelerator.end_training()



def  lowercase    (				)					->       str:
 _snake_case      :					Any					=   argparse.ArgumentParser(description="""Simple example of training script."""			)
 parser.add_argument(
     """--mixed_precision"""			,    type=SCREAMING_SNAKE_CASE__			,    default=SCREAMING_SNAKE_CASE__			,    choices=["""no""", """fp16""", """bf16""", """fp8"""]			,    help="""Whether to use mixed precision. Choose"""
     """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
     """and an Nvidia Ampere GPU."""			,    )
 parser.add_argument("""--cpu"""			,    action="""store_true"""			,    help="""If passed, will train on the CPU."""			)
 parser.add_argument(
     """--checkpointing_steps"""			,    type=SCREAMING_SNAKE_CASE__			,    default=SCREAMING_SNAKE_CASE__			,    help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""			,    )
 parser.add_argument(
     """--resume_from_checkpoint"""			,    type=SCREAMING_SNAKE_CASE__			,    default=SCREAMING_SNAKE_CASE__			,    help="""If the training should continue from a checkpoint folder."""			,    )
 parser.add_argument(
     """--with_tracking"""			,    action="""store_true"""			,    help="""Whether to load in all available experiment trackers from the environment and use them for logging."""			,    )
 parser.add_argument(
     """--output_dir"""			,    type=SCREAMING_SNAKE_CASE__			,    default="""."""			,    help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,    )
 parser.add_argument(
     """--project_dir"""			,    type=SCREAMING_SNAKE_CASE__			,    default="""logs"""			,    help="""Location on where to store experiment tracking logs` and relevent project information"""			,    )
 _snake_case      :					int					=   parser.parse_args()
 _snake_case      :					List[str]					=   {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
     main()





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()
a__      =		logging.get_logger(__name__)



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Optional[int]			,    SCREAMING_SNAKE_CASE__    :		Dict=False			,    SCREAMING_SNAKE_CASE__    :		Optional[Any]=False			)					->       Any:
 _snake_case      :					int					=   """backbone.""" if is_semantic else """"""

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

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

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  _snake_case      :					Tuple					=   state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''			)
  _snake_case      :					List[str]					=   state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''			)

  _snake_case      :					Dict					=   gamma_a
  _snake_case      :					Tuple					=   gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   _snake_case      :					Tuple					=   state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''			)
   _snake_case      :					Any					=   state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''			)

   _snake_case      :					Optional[int]					=   table
   _snake_case      :					Dict					=   index



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		List[str]			,    SCREAMING_SNAKE_CASE__    :		List[str]			,    SCREAMING_SNAKE_CASE__    :		Optional[Any]			)					->       Optional[int]:
 _snake_case      :					str					=   dct.pop(SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Tuple					=   val



def  lowercase    (				)					->       Any:
 _snake_case      :					List[Any]					=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
 _snake_case      :					List[str]					=   Image.open(requests.get(SCREAMING_SNAKE_CASE__			,    stream=SCREAMING_SNAKE_CASE__			).raw			)
 return im



@torch.no_grad()
def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Union[str, Any]			,    SCREAMING_SNAKE_CASE__    :		str			)					->       Dict:
 _snake_case      :					Any					=   BeitConfig()
 _snake_case      :					Union[str, Any]					=   False
 _snake_case      :					str					=   False
 _snake_case      :					Tuple					=   """huggingface/label-files"""
 # set config parameters based on URL
 if checkpoint_url[-9:-4] == "pt22k":
  # masked image modeling
  _snake_case      :					Optional[int]					=   True
  _snake_case      :					Union[str, Any]					=   True
  _snake_case      :					Dict					=   True
 elif checkpoint_url[-9:-4] == "ft22k":
  # intermediate fine-tuning on ImageNet-22k
  _snake_case      :					List[str]					=   True
  _snake_case      :					List[Any]					=   21_841
  _snake_case      :					Optional[Any]					=   """imagenet-22k-id2label.json"""
  _snake_case      :					Optional[int]					=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    repo_type="""dataset"""			)			,    """r"""			)			)
  _snake_case      :					str					=   {int(SCREAMING_SNAKE_CASE__			): 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_205]
  del idalabel[15_027]
  _snake_case      :					str					=   idalabel
  _snake_case      :					int					=   {v: k for k, v in idalabel.items()}
 elif checkpoint_url[-8:-4] == "to1k":
  # fine-tuning on ImageNet-1k
  _snake_case      :					Any					=   True
  _snake_case      :					List[Any]					=   1_000
  _snake_case      :					int					=   """imagenet-1k-id2label.json"""
  _snake_case      :					Dict					=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    repo_type="""dataset"""			)			,    """r"""			)			)
  _snake_case      :					List[str]					=   {int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
  _snake_case      :					Any					=   idalabel
  _snake_case      :					Dict					=   {v: k for k, v in idalabel.items()}
  if "384" in checkpoint_url:
   _snake_case      :					Any					=   384
  if "512" in checkpoint_url:
   _snake_case      :					List[str]					=   512
 elif "ade20k" in checkpoint_url:
  # fine-tuning
  _snake_case      :					Optional[Any]					=   True
  _snake_case      :					List[str]					=   150
  _snake_case      :					Any					=   """ade20k-id2label.json"""
  _snake_case      :					int					=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    repo_type="""dataset"""			)			,    """r"""			)			)
  _snake_case      :					Optional[Any]					=   {int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
  _snake_case      :					Any					=   idalabel
  _snake_case      :					Optional[Any]					=   {v: k for k, v in idalabel.items()}
  _snake_case      :					int					=   640
  _snake_case      :					List[Any]					=   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:
  _snake_case      :					List[Any]					=   1_024
  _snake_case      :					int					=   4_096
  _snake_case      :					Union[str, Any]					=   24
  _snake_case      :					Optional[int]					=   16
  if "ade20k" in checkpoint_url:
   _snake_case      :					Any					=   640
   _snake_case      :					Tuple					=   [7, 11, 15, 23]
 else:
  raise ValueError("""Should either find 'base' or 'large' in checkpoint URL"""			)

 # load state_dict of original model, remove and rename some keys
 _snake_case      :					Any					=   torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__			,    map_location="""cpu"""			,    check_hash=SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Optional[int]					=   state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

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

    # load HuggingFace model
 if checkpoint_url[-9:-4] == "pt22k":
  _snake_case      :					List[str]					=   BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE__			)
 elif "ade20k" in checkpoint_url:
  _snake_case      :					Tuple					=   BeitForSemanticSegmentation(SCREAMING_SNAKE_CASE__			)
 else:
  _snake_case      :					str					=   BeitForImageClassification(SCREAMING_SNAKE_CASE__			)
 model.eval()
 model.load_state_dict(SCREAMING_SNAKE_CASE__			)

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

 _snake_case      :					Tuple					=   image_processor(images=SCREAMING_SNAKE_CASE__			,    return_tensors="""pt"""			)
 _snake_case      :					int					=   encoding["""pixel_values"""]

 _snake_case      :					str					=   model(SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Tuple					=   outputs.logits

 # verify logits
 _snake_case      :					int					=   torch.Size([1, 1_000]			)
 if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""			):
  _snake_case      :					int					=   torch.Size([1, 196, 8_192]			)
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""			):
  _snake_case      :					Any					=   torch.Size([1, 196, 8_192]			)
 elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""			):
  _snake_case      :					Optional[int]					=   torch.Size([1, 21_841]			)
  _snake_case      :					Union[str, Any]					=   torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]			)
  _snake_case      :					List[str]					=   2_397
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""			):
  _snake_case      :					Union[str, Any]					=   torch.Size([1, 21_841]			)
  _snake_case      :					List[Any]					=   torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]			)
  _snake_case      :					Optional[int]					=   2_396
 elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""			):
  _snake_case      :					Optional[int]					=   torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]			)
  _snake_case      :					Any					=   285
 elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""			):
  _snake_case      :					Dict					=   torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]			)
  _snake_case      :					int					=   281
 elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""			):
  _snake_case      :					Optional[Any]					=   torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]			)
  _snake_case      :					int					=   761
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""			):
  _snake_case      :					Union[str, Any]					=   torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]			)
  _snake_case      :					Optional[int]					=   761
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""			):
  _snake_case      :					Dict					=   torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]			)
  _snake_case      :					Union[str, Any]					=   761
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""			):
  _snake_case      :					List[Any]					=   torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]			)
  _snake_case      :					int					=   761
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""			):
  _snake_case      :					List[str]					=   torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]			)
  _snake_case      :					Tuple					=   761
 elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""			):
  _snake_case      :					Dict					=   (1, 150, 160, 160)
  _snake_case      :					Dict					=   torch.tensor(
      [
          [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
          [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
          [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
      ]			)
 elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""			):
  _snake_case      :					Tuple					=   (1, 150, 160, 160)
  _snake_case      :					str					=   torch.tensor(
      [
          [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
          [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
          [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
      ]			)
 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]			,    SCREAMING_SNAKE_CASE__			,    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]			,    SCREAMING_SNAKE_CASE__			,    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(SCREAMING_SNAKE_CASE__			).mkdir(exist_ok=SCREAMING_SNAKE_CASE__			)
 print(F'''Saving model to {pytorch_dump_folder_path}'''			)
 model.save_pretrained(SCREAMING_SNAKE_CASE__			)
 print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
 image_processor.save_pretrained(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
  a__      =		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."""
  )
  a__      =		parser.parse_args()
  convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
