import argparse

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)
#
# 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
#
########################################################################


SCREAMING_SNAKE_CASE :	str							     =		16
SCREAMING_SNAKE_CASE :	Any							     =		32



def   __A		(			_A							,				_A = 16					):



 """simple docstring"""


 __a       =  AutoTokenizer.from_pretrained("bert-base-cased"					)
 __a       =  load_dataset("glue"							,				"mrpc"					)

 def tokenize_function(_A					):
  # max_length=None => use the model max length (it's actually the default)
  __a       =  tokenizer(examples["sentence1"]							,				examples["sentence2"]							,				truncation=_A							,				max_length=_A					)
  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():
  __a       =  datasets.map(
      _A							,				batched=_A							,				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
 __a       =  tokenized_datasets.rename_column("label"							,				"labels"					)

 def collate_fn(_A					):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __a       =  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":
   __a       =  16
  elif accelerator.mixed_precision != "no":
   __a       =  8
  else:
   __a       =  None

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

 # Instantiate dataloaders.
 __a       =  DataLoader(
     tokenized_datasets["train"]							,				shuffle=_A							,				collate_fn=_A							,				batch_size=_A							,				drop_last=_A					)
 __a       =  DataLoader(
     tokenized_datasets["validation"]							,				shuffle=_A							,				collate_fn=_A							,				batch_size=_A							,				drop_last=(accelerator.mixed_precision == "fp8")							,				)

 return train_dataloader, eval_dataloader



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  Accelerator(cpu=args.cpu							,				mixed_precision=args.mixed_precision					)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __a       =  config["lr"]
 __a       =  int(config["num_epochs"]					)
 __a       =  int(config["seed"]					)
 __a       =  int(config["batch_size"]					)

 __a       =  evaluate.load("glue"							,				"mrpc"					)

 # If the batch size is too big we use gradient accumulation
 __a       =  1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  __a       =  batch_size // MAX_GPU_BATCH_SIZE
  __a       =  MAX_GPU_BATCH_SIZE

 set_seed(_A					)
 __a       ,     __a       =  get_dataloaders(_A							,				_A					)
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 __a       =  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"							,				return_dict=_A					)

 # 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).
 __a       =  model.to(accelerator.device					)
 # Instantiate optimizer
 __a       =  AdamW(params=model.parameters()							,				lr=_A					)

 # Instantiate scheduler
 __a       =  get_linear_schedule_with_warmup(
     optimizer=_A							,				num_warmup_steps=100							,				num_training_steps=(len(_A					) * 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.

 __a       ,     __a       ,     __a       ,     __a       ,     __a       =  accelerator.prepare(
     _A							,				_A							,				_A							,				_A							,				_A					)

 # Now we train the model
 for epoch in range(_A					):
  model.train()
  for step, batch in enumerate(_A					):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device					)
   __a       =  model(**_A					)
   __a       =  outputs.loss
   __a       =  loss / gradient_accumulation_steps
   accelerator.backward(_A					)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

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

  __a       =  metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f"""epoch {epoch}:"""							,				_A					)



def   __A		(			):



 """simple docstring"""


 __a       =  argparse.ArgumentParser(description="Simple example of training script."					)
 parser.add_argument(
     "--mixed_precision"							,				type=_A							,				default=_A							,				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."					)
 __a       =  parser.parse_args()
 __a       =  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(_A							,				_A					)


if __name__ == "__main__":
  main()



import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :	str							     =		logging.get_logger(__name__)



def   __A		(			_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 __a       =  "backbone." if is_semantic else ""

 __a       =  []
 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"),
         (f"""{prefix}pos_embed""", "beit.embeddings.position_embeddings"),
     ]					)

 if has_lm_head:
  # mask token + layernorm
  rename_keys.extend(
      [
          ("mask_token", "beit.embeddings.mask_token"),
          ("norm.weight", "layernorm.weight"),
          ("norm.bias", "layernorm.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   __A		(			_A							,				_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 for i in range(config.num_hidden_layers					):
  __a       =  "backbone." if is_semantic else ""
  # queries, keys and values
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""					)

  __a       =  in_proj_weight[
      : config.hidden_size, :
  ]
  __a       =  q_bias
  __a       =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __a       =  in_proj_weight[
      -config.hidden_size :, :
  ]
  __a       =  v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""					)

  __a       =  gamma_a
  __a       =  gamma_a



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  dct.pop(_A					)
 __a       =  val



def   __A		(			):



 """simple docstring"""


 __a       =  "http://images.cocodataset.org/val2017/000000039769.jpg"
 __a       =  Image.open(requests.get(_A							,				stream=_A					).raw					)
 return im



@torch.no_grad()
def   __A		(			_A							,				_A							,				_A=False					):



 """simple docstring"""


 __a       =  False if "rvlcdip" in checkpoint_url else True
 __a       =  BeitConfig(use_absolute_position_embeddings=_A							,				use_mask_token=_A					)

 # size of the architecture
 if "large" in checkpoint_url or "dit-l" in checkpoint_url:
  __a       =  1024
  __a       =  4096
  __a       =  24
  __a       =  16

 # labels
 if "rvlcdip" in checkpoint_url:
  __a       =  16
  __a       =  "huggingface/label-files"
  __a       =  "rvlcdip-id2label.json"
  __a       =  json.load(open(hf_hub_download(_A							,				_A							,				repo_type="dataset"					)							,				"r"					)					)
  __a       =  {int(_A					): v for k, v in idalabel.items()}
  __a       =  idalabel
  __a       =  {v: k for k, v in idalabel.items()}

 # load state_dict of original model, remove and rename some keys
 __a       =  torch.hub.load_state_dict_from_url(_A							,				map_location="cpu"					)["model"]

 __a       =  create_rename_keys(_A							,				has_lm_head=_A					)
 for src, dest in rename_keys:
  rename_key(_A							,				_A							,				_A					)
 read_in_q_k_v(_A							,				_A							,				has_lm_head=_A					)

 # load HuggingFace model
 __a       =  BeitForMaskedImageModeling(_A					) if has_lm_head else BeitForImageClassification(_A					)
 model.eval()
 model.load_state_dict(_A					)

 # Check outputs on an image
 __a       =  BeitImageProcessor(
     size=config.image_size							,				resample=PILImageResampling.BILINEAR							,				do_center_crop=_A					)
 __a       =  prepare_img()

 __a       =  image_processor(images=_A							,				return_tensors="pt"					)
 __a       =  encoding["pixel_values"]

 __a       =  model(_A					)
 __a       =  outputs.logits

 # verify logits
 __a       =  [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
 assert logits.shape == torch.Size(_A					), "Shape of logits not as expected"

 Path(_A					).mkdir(exist_ok=_A					)
 print(f"""Saving model to {pytorch_dump_folder_path}"""					)
 model.save_pretrained(_A					)
 print(f"""Saving image processor to {pytorch_dump_folder_path}"""					)
 image_processor.save_pretrained(_A					)

 if push_to_hub:
  if has_lm_head:
   __a       =  "dit-base" if "base" in checkpoint_url else "dit-large"
  else:
   __a       =  "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
  image_processor.push_to_hub(
      repo_path_or_name=Path(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add image processor"							,				use_temp_dir=_A							,				)
  model.push_to_hub(
      repo_path_or_name=Path(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add model"							,				use_temp_dir=_A							,				)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	int							     =		argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_url""",
      default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.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."""
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
  )
  SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


