





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()
lowercase__						:      int	=    logging.get_logger(__name__)







def  a__    (    lowercase :					Optional[Any], lowercase :					str				)		->		Tuple:





     """simple docstring"""

     _UpperCamelCase       =	[]
     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  a__    (    lowercase :					Dict, lowercase :					Dict				)		->		Dict:





     """simple docstring"""

     for i in range(encoder_config.num_hidden_layers				):
          # queries, keys and values (only weights, no biases)
          _UpperCamelCase       =	state_dict.pop(F"""encoder.deit.blocks.{i}.attn.qkv.weight"""				)

          _UpperCamelCase       =	in_proj_weight[
              : encoder_config.hidden_size, :
          ]
          _UpperCamelCase       =	in_proj_weight[
              encoder_config.hidden_size : encoder_config.hidden_size * 2, :
          ]
          _UpperCamelCase       =	in_proj_weight[
              -encoder_config.hidden_size :, :
          ]







def  a__    (    lowercase :					Optional[int], lowercase :					Optional[int], lowercase :					Optional[Any]				)		->		str:





     """simple docstring"""

     _UpperCamelCase       =	dct.pop(__lowerCAmelCase				)
     _UpperCamelCase       =	val







def  a__    (    lowercase :					Optional[Any]				)		->		List[str]:





     """simple docstring"""

     if "handwritten" in checkpoint_url:
          _UpperCamelCase       =	'''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:
          _UpperCamelCase       =	'''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
     _UpperCamelCase       =	Image.open(requests.get(__lowerCAmelCase, stream=__lowerCAmelCase				).raw				).convert('''RGB'''				)
     return im







@torch.no_grad()
def  a__    (    lowercase :					Optional[int], lowercase :					int				)		->		List[str]:





     """simple docstring"""

     _UpperCamelCase       =	ViTConfig(image_size=384, qkv_bias=__lowerCAmelCase				)
     _UpperCamelCase       =	TrOCRConfig()

     # size of the architecture
     if "base" in checkpoint_url:
          _UpperCamelCase       =	768
     elif "large" in checkpoint_url:
          # use ViT-large encoder
          _UpperCamelCase       =	1024
          _UpperCamelCase       =	4096
          _UpperCamelCase       =	24
          _UpperCamelCase       =	16
          _UpperCamelCase       =	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:
          _UpperCamelCase       =	False
          _UpperCamelCase       =	'''relu'''
          _UpperCamelCase       =	1024
          _UpperCamelCase       =	True
          _UpperCamelCase       =	False
          _UpperCamelCase       =	False

     # load HuggingFace model
     _UpperCamelCase       =	ViTModel(__lowerCAmelCase, add_pooling_layer=__lowerCAmelCase				)
     _UpperCamelCase       =	TrOCRForCausalLM(__lowerCAmelCase				)
     _UpperCamelCase       =	VisionEncoderDecoderModel(encoder=__lowerCAmelCase, decoder=__lowerCAmelCase				)
     model.eval()

     # load state_dict of original model, rename some keys
     _UpperCamelCase       =	torch.hub.load_state_dict_from_url(__lowerCAmelCase, map_location='''cpu''', check_hash=__lowerCAmelCase				)['''model''']

     _UpperCamelCase       =	create_rename_keys(__lowerCAmelCase, __lowerCAmelCase				)
     for src, dest in rename_keys:
          rename_key(__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase				)
     read_in_q_k_v(__lowerCAmelCase, __lowerCAmelCase				)

     # 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():
          _UpperCamelCase       =	state_dict.pop(__lowerCAmelCase				)
          if key.startswith('''decoder'''				) and "output_projection" not in key:
               _UpperCamelCase       =	val
          else:
               _UpperCamelCase       =	val

    # load state dict
     model.load_state_dict(__lowerCAmelCase				)

     # Check outputs on an image
     _UpperCamelCase       =	ViTImageProcessor(size=encoder_config.image_size				)
     _UpperCamelCase       =	RobertaTokenizer.from_pretrained('''roberta-large'''				)
     _UpperCamelCase       =	TrOCRProcessor(__lowerCAmelCase, __lowerCAmelCase				)

     _UpperCamelCase       =	processor(images=prepare_img(__lowerCAmelCase				), return_tensors='''pt'''				).pixel_values

     # verify logits
     _UpperCamelCase       =	torch.tensor([[model.config.decoder.decoder_start_token_id]]				)
     _UpperCamelCase       =	model(pixel_values=__lowerCAmelCase, decoder_input_ids=__lowerCAmelCase				)
     _UpperCamelCase       =	outputs.logits

     _UpperCamelCase       =	torch.Size([1, 1, 50265]				)
     if "trocr-base-handwritten" in checkpoint_url:
          _UpperCamelCase       =	torch.tensor(
              [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1]				)
     elif "trocr-large-handwritten" in checkpoint_url:
          _UpperCamelCase       =	torch.tensor(
              [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0]				)
     elif "trocr-base-printed" in checkpoint_url:
          _UpperCamelCase       =	torch.tensor(
              [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0]				)
     elif "trocr-large-printed" in checkpoint_url:
          _UpperCamelCase       =	torch.tensor(
              [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5]				)

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


if __name__ == "__main__":
   lowercase__						:      Optional[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.'
   )
   lowercase__						:      int	=    parser.parse_args()
   convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










'''simple docstring'''

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase__						:      Any	=    logging.getLogger(__name__)







def  a__    (    lowercase :					Optional[Any], lowercase :					Tuple				)		->		Any:





     """simple docstring"""

     return (preds == labels).mean()
@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					str						    =   field(
           metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,						)

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					str						    =   field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()			)}			)
       _snake_case     :					str						    =   field(metadata={'help': 'Should contain the data files for the task.'}			)
       _snake_case     :					int						    =   field(
           default=1_2_8						,						metadata={
               'help': (
                   'The maximum total input sequence length after tokenization. Sequences longer '
                   'than this will be truncated, sequences shorter will be padded.'
               )
           }						,						)
       _snake_case     :					bool						    =   field(
           default=__magic_name__						,						metadata={'help': 'Overwrite the cached training and evaluation sets'}			)







def  a__    (    )		->		Optional[Any]:





     """simple docstring"""

     _UpperCamelCase       =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir				)
         and os.listdir(training_args.output_dir				)
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
              ''' --overwrite_output_dir to overcome.'''				)

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, )
     logger.warning(
         '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''', training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1				), training_args.fpaa, )
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank				):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('''Training/evaluation parameters %s''', lowercase				)

     # Set seed
     set_seed(training_args.seed				)

     try:
          _UpperCamelCase       =	processors[data_args.task_name]()
          _UpperCamelCase       =	processor.get_labels()
          _UpperCamelCase       =	len(lowercase				)
     except KeyError:
          raise ValueError('''Task not found: %s''' % (data_args.task_name)				)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     _UpperCamelCase       =	AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=lowercase, finetuning_task=data_args.task_name, cache_dir=model_args.cache_dir, )
     _UpperCamelCase       =	AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, )
     _UpperCamelCase       =	AutoModelForMultipleChoice.from_pretrained(
         model_args.model_name_or_path, from_tf=bool('''.ckpt''' in model_args.model_name_or_path				), config=lowercase, cache_dir=model_args.cache_dir, )

     # Get datasets
     _UpperCamelCase       =	(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir, tokenizer=lowercase, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.train, )
         if training_args.do_train
         else None
     )
     _UpperCamelCase       =	(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir, tokenizer=lowercase, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.dev, )
         if training_args.do_eval
         else None
     )

     def compute_metrics(lowercase :					EvalPrediction				) -> Dict:
          _UpperCamelCase       =	np.argmax(p.predictions, axis=1				)
          return {"acc": simple_accuracy(lowercase, p.label_ids				)}

     # Data collator
     _UpperCamelCase       =	DataCollatorWithPadding(lowercase, pad_to_multiple_of=8				) if training_args.fpaa else None

     # Initialize our Trainer
     _UpperCamelCase       =	Trainer(
         model=lowercase, args=lowercase, train_dataset=lowercase, eval_dataset=lowercase, compute_metrics=lowercase, data_collator=lowercase, )

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
               tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
     _UpperCamelCase       =	{}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''				)

          _UpperCamelCase       =	trainer.evaluate()

          _UpperCamelCase       =	os.path.join(training_args.output_dir, '''eval_results.txt'''				)
          if trainer.is_world_master():
               with open(lowercase, '''w'''				) as writer:
                    logger.info('''***** Eval results *****'''				)
                    for key, value in result.items():
                         logger.info('''  %s = %s''', lowercase, lowercase				)
                         writer.write('''%s = %s\n''' % (key, value)				)

                    results.update(lowercase				)

     return results







def  a__    (    lowercase :					Tuple				)		->		List[Any]:





     """simple docstring"""

     main()


if __name__ == "__main__":
   main()



