


import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_A		:				List[Any]									=       16
_A		:				Tuple									=       32



def     __lowerCAmelCase						(							snake_case	:    Accelerator						,      snake_case	:    int = 16						,      snake_case	:    str = "bert-base-cased" )       ->							Dict:
   __lowerCamelCase:				List[Any]     =	AutoTokenizer.from_pretrained(snake_case )
   __lowerCamelCase:				Dict     =	load_dataset("""glue"""						,      """mrpc""" )

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

   # Apply the method we just defined to all the examples in all the splits of the dataset
   __lowerCamelCase:				List[Any]     =	datasets.map(
       snake_case						,      batched=snake_case						,      remove_columns=["""idx""", """sentence1""", """sentence2"""]						,      load_from_cache_file=snake_case )

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

   def collate_fn(snake_case	:    Any ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(snake_case						,      padding="""max_length"""						,      max_length=128						,      return_tensors="""pt""" )
      return tokenizer.pad(snake_case						,      padding="""longest"""						,      return_tensors="""pt""" )

   # Instantiate dataloaders.
   __lowerCamelCase:				List[str]     =	DataLoader(
       tokenized_datasets["""train"""]						,      shuffle=snake_case						,      collate_fn=snake_case						,      batch_size=snake_case )
   __lowerCamelCase:				List[str]     =	DataLoader(
       tokenized_datasets["""validation"""]						,      shuffle=snake_case						,      collate_fn=snake_case						,      batch_size=snake_case )

   return train_dataloader, eval_dataloader



def     __lowerCAmelCase						(							snake_case	:    Union[str, Any]						,      snake_case	:    Tuple						,      snake_case	:    Optional[int]						,      snake_case	:    int )       ->							List[str]:
   model.eval()
   __lowerCamelCase:				List[Any]     =	0
   for step, batch in enumerate(snake_case ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device )
      with torch.no_grad():
         __lowerCamelCase:				int     =	model(**snake_case )
      __lowerCamelCase:				Any     =	outputs.logits.argmax(dim=-1 )
      # It is slightly faster to call this once, than multiple times
      __lowerCamelCase:				Dict     =	accelerator.gather(
          (predictions, batch["""labels"""]) )  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
         if step == len(snake_case ) - 1:
            __lowerCamelCase:				int     =	predictions[: len(eval_dataloader.dataset ) - samples_seen]
            __lowerCamelCase:				List[Any]     =	references[: len(eval_dataloader.dataset ) - samples_seen]
         else:
            samples_seen += references.shape[0]
      metric.add_batch(
          predictions=snake_case						,      references=snake_case						,      )

   __lowerCamelCase:				int     =	metric.compute()
   return eval_metric["accuracy"]



def     __lowerCAmelCase						(							snake_case	:    Optional[int]						,      snake_case	:    Optional[int] )       ->							Dict:
   # Initialize accelerator
   __lowerCamelCase:				Union[str, Any]     =	Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   __lowerCamelCase:				Optional[Any]     =	config["""lr"""]
   __lowerCamelCase:				Dict     =	int(config["""num_epochs"""] )
   __lowerCamelCase:				Union[str, Any]     =	int(config["""seed"""] )
   __lowerCamelCase:				List[str]     =	int(config["""batch_size"""] )
   __lowerCamelCase:				str     =	args.model_name_or_path

   set_seed(snake_case )
   __lowerCamelCase:				Optional[Any]     =	get_dataloaders(snake_case						,      snake_case						,      snake_case )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __lowerCamelCase:				Any     =	AutoModelForSequenceClassification.from_pretrained(snake_case						,      return_dict=snake_case )

   # Instantiate optimizer
   __lowerCamelCase:				List[str]     =	(
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   __lowerCamelCase:				int     =	optimizer_cls(params=model.parameters()						,      lr=snake_case )

   if accelerator.state.deepspeed_plugin is not None:
      __lowerCamelCase:				Optional[Any]     =	accelerator.state.deepspeed_plugin.deepspeed_config[
          """gradient_accumulation_steps"""
      ]
   else:
      __lowerCamelCase:				List[str]     =	1
   __lowerCamelCase:				List[str]     =	(len(snake_case ) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      __lowerCamelCase:				Optional[int]     =	get_linear_schedule_with_warmup(
          optimizer=snake_case						,      num_warmup_steps=0						,      num_training_steps=snake_case						,      )
   else:
      __lowerCamelCase:				str     =	DummyScheduler(snake_case						,      total_num_steps=snake_case						,      warmup_num_steps=0 )

   # 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.
   __lowerCamelCase:				str     =	accelerator.prepare(
       snake_case						,      snake_case						,      snake_case						,      snake_case						,      snake_case )

   # We need to keep track of how many total steps we have iterated over
   __lowerCamelCase:				int     =	0
   # We also need to keep track of the stating epoch so files are named properly
   __lowerCamelCase:				str     =	0
   __lowerCamelCase:				Dict     =	evaluate.load("""glue"""						,      """mrpc""" )
   __lowerCamelCase:				Optional[Any]     =	num_epochs

   if args.partial_train_epoch is not None:
      __lowerCamelCase:				List[str]     =	args.partial_train_epoch

   if args.resume_from_checkpoint:
      accelerator.load_state(args.resume_from_checkpoint )
      __lowerCamelCase:				str     =	args.resume_from_checkpoint.split("""epoch_""" )[1]
      __lowerCamelCase:				str     =	""""""
      for char in epoch_string:
         if char.isdigit():
            state_epoch_num += char
         else:
            break
      __lowerCamelCase:				List[str]     =	int(snake_case ) + 1
      __lowerCamelCase:				List[Any]     =	evaluation_loop(snake_case						,      snake_case						,      snake_case						,      snake_case )
      accelerator.print("""resumed checkpoint performance:"""						,      snake_case )
      accelerator.print("""resumed checkpoint's scheduler's lr:"""						,      lr_scheduler.get_lr()[0] )
      accelerator.print("""resumed optimizers's lr:"""						,      optimizer.param_groups[0]["""lr"""] )
      with open(os.path.join(args.output_dir						,      f'state_{starting_epoch-1}.json' )						,      """r""" ) as f:
         __lowerCamelCase:				Any     =	json.load(snake_case )
         assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
         assert (
             resumed_state["lr"] == lr_scheduler.get_lr()[0]
         ), "Scheduler learning rate mismatch, loading from checkpoint failed"
         assert (
             resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
         ), "Optimizer learning rate mismatch, loading from checkpoint failed"
         assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
         return

    # Now we train the model
   __lowerCamelCase:				Any     =	{}
   for epoch in range(snake_case						,      snake_case ):
      model.train()
      for step, batch in enumerate(snake_case ):
         __lowerCamelCase:				Optional[int]     =	model(**snake_case )
         __lowerCamelCase:				str     =	outputs.loss
         __lowerCamelCase:				Any     =	loss / gradient_accumulation_steps
         accelerator.backward(snake_case )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1
      __lowerCamelCase:				Union[str, Any]     =	f'epoch_{epoch}'
      __lowerCamelCase:				Tuple     =	os.path.join(args.output_dir						,      snake_case )
      accelerator.save_state(snake_case )
      __lowerCamelCase:				List[str]     =	evaluation_loop(snake_case						,      snake_case						,      snake_case						,      snake_case )
      __lowerCamelCase:				int     =	accuracy
      __lowerCamelCase:				Optional[Any]     =	lr_scheduler.get_lr()[0]
      __lowerCamelCase:				List[Any]     =	optimizer.param_groups[0]["""lr"""]
      __lowerCamelCase:				str     =	epoch
      __lowerCamelCase:				Optional[Any]     =	overall_step
      accelerator.print(f'epoch {epoch}:'						,      snake_case )

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
         with open(os.path.join(args.output_dir						,      f'state_{epoch}.json' )						,      """w""" ) as f:
            json.dump(snake_case						,      snake_case )



def     __lowerCAmelCase						(							)       ->							Optional[int]:
   __lowerCamelCase:				Tuple     =	argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
   parser.add_argument(
       """--model_name_or_path"""						,      type=snake_case						,      default="""bert-base-cased"""						,      help="""Path to pretrained model or model identifier from huggingface.co/models."""						,      required=snake_case						,      )
   parser.add_argument(
       """--output_dir"""						,      type=snake_case						,      default="""."""						,      help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""						,      )
   parser.add_argument(
       """--resume_from_checkpoint"""						,      type=snake_case						,      default=snake_case						,      help="""If the training should continue from a checkpoint folder."""						,      )
   parser.add_argument(
       """--partial_train_epoch"""						,      type=snake_case						,      default=snake_case						,      help="""If passed, the training will stop after this number of epochs."""						,      )
   parser.add_argument(
       """--num_epochs"""						,      type=snake_case						,      default=2						,      help="""Number of train epochs."""						,      )
   __lowerCamelCase:				Optional[int]     =	parser.parse_args()
   __lowerCamelCase:				List[str]     =	{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

   training_function(snake_case						,      snake_case )


if __name__ == "__main__":
   main()






import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A		:				List[Any]									=       logging.get_logger(__name__)



def     __lowerCAmelCase						(							snake_case	:    Any						,      snake_case	:    int )       ->							Dict:
   __lowerCamelCase:				Tuple     =	[]
   for i in range(encoder_config.num_hidden_layers ):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.norm1.weight', f'encoder.encoder.layer.{i}.layernorm_before.weight') )
      rename_keys.append((f'encoder.deit.blocks.{i}.norm1.bias', f'encoder.encoder.layer.{i}.layernorm_before.bias') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.attn.proj.weight', f'encoder.encoder.layer.{i}.attention.output.dense.weight') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.attn.proj.bias', f'encoder.encoder.layer.{i}.attention.output.dense.bias') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.norm2.weight', f'encoder.encoder.layer.{i}.layernorm_after.weight') )
      rename_keys.append((f'encoder.deit.blocks.{i}.norm2.bias', f'encoder.encoder.layer.{i}.layernorm_after.bias') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.mlp.fc1.weight', f'encoder.encoder.layer.{i}.intermediate.dense.weight') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.mlp.fc1.bias', f'encoder.encoder.layer.{i}.intermediate.dense.bias') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.mlp.fc2.weight', f'encoder.encoder.layer.{i}.output.dense.weight') )
      rename_keys.append((f'encoder.deit.blocks.{i}.mlp.fc2.bias', f'encoder.encoder.layer.{i}.output.dense.bias') )

   # cls token, position embeddings and patch embeddings of encoder
   rename_keys.extend(
       [
           ("""encoder.deit.cls_token""", """encoder.embeddings.cls_token"""),
           ("""encoder.deit.pos_embed""", """encoder.embeddings.position_embeddings"""),
           ("""encoder.deit.patch_embed.proj.weight""", """encoder.embeddings.patch_embeddings.projection.weight"""),
           ("""encoder.deit.patch_embed.proj.bias""", """encoder.embeddings.patch_embeddings.projection.bias"""),
           ("""encoder.deit.norm.weight""", """encoder.layernorm.weight"""),
           ("""encoder.deit.norm.bias""", """encoder.layernorm.bias"""),
       ] )

   return rename_keys



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    Union[str, Any] )       ->							Tuple:
   for i in range(encoder_config.num_hidden_layers ):
      # queries, keys and values (only weights, no biases)
      __lowerCamelCase:				Union[str, Any]     =	state_dict.pop(f'encoder.deit.blocks.{i}.attn.qkv.weight' )

      __lowerCamelCase:				Any     =	in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      __lowerCamelCase:				Dict     =	in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      __lowerCamelCase:				Any     =	in_proj_weight[
          -encoder_config.hidden_size :, :
      ]



def     __lowerCAmelCase						(							snake_case	:    Dict						,      snake_case	:    Union[str, Any]						,      snake_case	:    Optional[int] )       ->							int:
   __lowerCamelCase:				List[str]     =	dct.pop(snake_case )
   __lowerCamelCase:				Tuple     =	val



def     __lowerCAmelCase						(							snake_case	:    Dict )       ->							Any:
   if "handwritten" in checkpoint_url:
      __lowerCamelCase:				Tuple     =	"""https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"""  # industry
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
   elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
      __lowerCamelCase:				Union[str, Any]     =	"""https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
   __lowerCamelCase:				Union[str, Any]     =	Image.open(requests.get(snake_case						,      stream=snake_case ).raw ).convert("""RGB""" )
   return im



@torch.no_grad()
def     __lowerCAmelCase						(							snake_case	:    List[Any]						,      snake_case	:    Any )       ->							List[str]:
   __lowerCamelCase:				Union[str, Any]     =	ViTConfig(image_size=384						,      qkv_bias=snake_case )
   __lowerCamelCase:				Optional[int]     =	TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      __lowerCamelCase:				Optional[int]     =	768
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      __lowerCamelCase:				Optional[int]     =	1024
      __lowerCamelCase:				int     =	4096
      __lowerCamelCase:				Tuple     =	24
      __lowerCamelCase:				Optional[Any]     =	16
      __lowerCamelCase:				str     =	1024
   else:
      raise ValueError("""Should either find 'base' or 'large' in checkpoint URL""" )

   # the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
   if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
      __lowerCamelCase:				Any     =	False
      __lowerCamelCase:				Optional[Any]     =	"""relu"""
      __lowerCamelCase:				List[Any]     =	1024
      __lowerCamelCase:				List[str]     =	True
      __lowerCamelCase:				int     =	False
      __lowerCamelCase:				int     =	False

   # load HuggingFace model
   __lowerCamelCase:				Any     =	ViTModel(snake_case						,      add_pooling_layer=snake_case )
   __lowerCamelCase:				Dict     =	TrOCRForCausalLM(snake_case )
   __lowerCamelCase:				Any     =	VisionEncoderDecoderModel(encoder=snake_case						,      decoder=snake_case )
   model.eval()

   # load state_dict of original model, rename some keys
   __lowerCamelCase:				Any     =	torch.hub.load_state_dict_from_url(snake_case						,      map_location="""cpu"""						,      check_hash=snake_case )["""model"""]

   __lowerCamelCase:				Optional[Any]     =	create_rename_keys(snake_case						,      snake_case )
   for src, dest in rename_keys:
      rename_key(snake_case						,      snake_case						,      snake_case )
   read_in_q_k_v(snake_case						,      snake_case )

   # remove parameters we don't need
   del state_dict["encoder.deit.head.weight"]
   del state_dict["encoder.deit.head.bias"]
   del state_dict["decoder.version"]

   # add prefix to decoder keys
   for key, val in state_dict.copy().items():
      __lowerCamelCase:				int     =	state_dict.pop(snake_case )
      if key.startswith("""decoder""" ) and "output_projection" not in key:
         __lowerCamelCase:				Any     =	val
      else:
         __lowerCamelCase:				List[str]     =	val

    # load state dict
   model.load_state_dict(snake_case )

   # Check outputs on an image
   __lowerCamelCase:				Any     =	ViTImageProcessor(size=encoder_config.image_size )
   __lowerCamelCase:				Tuple     =	RobertaTokenizer.from_pretrained("""roberta-large""" )
   __lowerCamelCase:				List[Any]     =	TrOCRProcessor(snake_case						,      snake_case )

   __lowerCamelCase:				Optional[int]     =	processor(images=prepare_img(snake_case )						,      return_tensors="""pt""" ).pixel_values

   # verify logits
   __lowerCamelCase:				Tuple     =	torch.tensor([[model.config.decoder.decoder_start_token_id]] )
   __lowerCamelCase:				Dict     =	model(pixel_values=snake_case						,      decoder_input_ids=snake_case )
   __lowerCamelCase:				Any     =	outputs.logits

   __lowerCamelCase:				Dict     =	torch.Size([1, 1, 50265] )
   if "trocr-base-handwritten" in checkpoint_url:
      __lowerCamelCase:				Tuple     =	torch.tensor(
          [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311] )
   elif "trocr-large-handwritten" in checkpoint_url:
      __lowerCamelCase:				Optional[int]     =	torch.tensor(
          [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170] )
   elif "trocr-base-printed" in checkpoint_url:
      __lowerCamelCase:				Union[str, Any]     =	torch.tensor(
          [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210] )
   elif "trocr-large-printed" in checkpoint_url:
      __lowerCamelCase:				Union[str, Any]     =	torch.tensor(
          [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535] )

   if "stage1" not in checkpoint_url:
      assert logits.shape == expected_shape, "Shape of logits not as expected"
      assert torch.allclose(logits[0, 0, :10]						,      snake_case						,      atol=1e-3 ), "First elements of logits not as expected"

   Path(snake_case ).mkdir(exist_ok=snake_case )
   print(f'Saving model to {pytorch_dump_folder_path}' )
   model.save_pretrained(snake_case )
   print(f'Saving processor to {pytorch_dump_folder_path}' )
   processor.save_pretrained(snake_case )


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

   parser.add_argument(
       '''--checkpoint_url''',
       default='''https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt''',
       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		:				Optional[int]									=       parser.parse_args()
   convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


