





'''simple docstring'''
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__							:							int =logging.get_logger(__name__)
def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Any     ) ->      List[Any]:



       """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 				lowercase__   (   __lowercase						:  Optional[Any]				,  __lowercase						:  int     ) ->      Tuple:



       """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 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  str				,  __lowercase						:  int     ) ->      List[Any]:



       """simple docstring"""






       __UpperCamelCase       =  dct.pop(__lowercase     )
       __UpperCamelCase       =  val
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      Any:



       """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(__lowercase				,  stream=__lowercase     ).raw     ).convert('RGB'     )
       return im
@torch.no_grad()
def 				lowercase__   (   __lowercase						:  Dict				,  __lowercase						:  str     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  ViTConfig(image_size=384				,  qkv_bias=__lowercase     )
       __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(__lowercase				,  add_pooling_layer=__lowercase     )
       __UpperCamelCase       =  TrOCRForCausalLM(__lowercase     )
       __UpperCamelCase       =  VisionEncoderDecoderModel(encoder=__lowercase				,  decoder=__lowercase     )
       model.eval()

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

       __UpperCamelCase       =  create_rename_keys(__lowercase				,  __lowercase     )
       for src, dest in rename_keys:
              rename_key(__lowercase				,  __lowercase				,  __lowercase     )
       read_in_q_k_v(__lowercase				,  __lowercase     )

       # 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(__lowercase     )
              if key.startswith('decoder'     ) and "output_projection" not in key:
                     __UpperCamelCase       =  val
              else:
                     __UpperCamelCase       =  val

    # load state dict
       model.load_state_dict(__lowercase     )

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

       __UpperCamelCase       =  processor(images=prepare_img(__lowercase     )				,  return_tensors='pt'     ).pixel_values

       # verify logits
       __UpperCamelCase       =  torch.tensor([[model.config.decoder.decoder_start_token_id]]     )
       __UpperCamelCase       =  model(pixel_values=__lowercase				,  decoder_input_ids=__lowercase     )
       __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]				,  __lowercase				,  atol=1e-3     ), "First elements of logits not as expected"

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


if __name__ == "__main__":
    a__							:							Any =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__							:							str =parser.parse_args()
    convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''
import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							Any							,	__A       :							Dict							,	__A       :							str							,	__A       :							List[Any]=1_0_2_4							,	__A       :							Tuple=1_0_2_4							,	__A       :							str=3.6	):
        __UpperCamelCase       =  tokenizer
        __UpperCamelCase       =  tokenizer.bos_token_id
        __UpperCamelCase       =  dataset
        __UpperCamelCase       =  seq_length
        __UpperCamelCase       =  seq_length * chars_per_token * num_of_sequences






 def __iter__(       self       :							Any	):
        __UpperCamelCase       =  iter(self.dataset	)
        __UpperCamelCase       =  True
        while more_examples:
               __UpperCamelCase       ,						__UpperCamelCase       =  [], 0
               while True:
                      if buffer_len >= self.input_characters:
                             break
                      try:
                             buffer.append(next(__A	)['content']	)
                             buffer_len += len(buffer[-1]	)
                      except StopIteration:
                             __UpperCamelCase       =  False
                             break
               __UpperCamelCase       =  tokenizer(__A							,	truncation=__A	)['input_ids']
               __UpperCamelCase       =  []
               for tokenized_input in tokenized_inputs:
                      all_token_ids.extend(tokenized_input + [self.concat_token_id]	)
               for i in range(0							,	len(__A	)							,	self.seq_length	):
                      __UpperCamelCase       =  all_token_ids[i : i + self.seq_length]
                      if len(__A	) == self.seq_length:
                             yield torch.tensor(__A	)






def 				lowercase__   (   __lowercase						:  Optional[Any]     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  {'streaming': True}
       __UpperCamelCase       =  load_dataset(args.dataset_name				,  split='train'				,  **__lowercase     )
       __UpperCamelCase       =  ConstantLengthDataset(__lowercase				,  __lowercase				,  seq_length=args.seq_length     )
       __UpperCamelCase       =  DataLoader(__lowercase				,  batch_size=args.batch_size     )
       return eval_dataloader
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      Optional[Any]:



       """simple docstring"""






       model.eval()
       __UpperCamelCase       =  []
       for step, batch in enumerate(__lowercase     ):
              with torch.no_grad():
                     __UpperCamelCase       =  model(__lowercase				,  labels=__lowercase     )
              __UpperCamelCase       =  outputs.loss.repeat(args.batch_size     )
              losses.append(accelerator.gather(__lowercase     )     )

              if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                     break
       __UpperCamelCase       =  torch.mean(torch.cat(__lowercase     )     )
       try:
              __UpperCamelCase       =  torch.exp(__lowercase     )
       except OverflowError:
              __UpperCamelCase       =  float('inf'     )
       return loss.item(), perplexity.item()


# Setup Accelerator
a__							:							int =Accelerator()

# Parse configuration
a__							:							Dict =HfArgumentParser(EvaluationArguments)
a__							:							Union[str, Any] =parser.parse_args()
set_seed(args.seed)

# Logging
a__							:							List[Any] =logging.getLogger(__name__)
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)

# Load model and tokenizer
a__							:							Union[str, Any] =AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a__							:							List[Any] =AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a__							:							Union[str, Any] =create_dataloader(args)

# Prepare everything with our `accelerator`.
a__ , a__							:							List[str] =accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
a__ , a__							:							Any =evaluate(args)


logger.info(f'loss/eval: {eval_loss}, perplexity: {perplexity}')
