


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()
snake_case    :   int	    =					logging.get_logger(__name__)
def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Dict						):




      """simple docstring"""





      a						:str										=	[]
      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						(			UpperCAmelCase_						:		Dict			,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





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

            a						:Dict										=	in_proj_weight[
                : encoder_config.hidden_size, :
            ]
            a						:Tuple										=	in_proj_weight[
                encoder_config.hidden_size : encoder_config.hidden_size * 2, :
            ]
            a						:Union[str, Any]										=	in_proj_weight[
                -encoder_config.hidden_size :, :
            ]
def     __lowerCamelCase						(			UpperCAmelCase_						:		List[Any]			,  UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      a						:Union[str, Any]										=	dct.pop(UpperCAmelCase_						)
      a						:Dict										=	val
def     __lowerCamelCase						(			UpperCAmelCase_						:		Any						):




      """simple docstring"""





      if "handwritten" in checkpoint_url:
            a						:int										=	'''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:
            a						:List[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'''
      a						:Union[str, Any]										=	Image.open(requests.get(UpperCAmelCase_			,  stream=UpperCAmelCase_						).raw						).convert('''RGB'''						)
      return im
@torch.no_grad()
def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Optional[Any]						):




      """simple docstring"""





      a						:Any										=	ViTConfig(image_size=384			,  qkv_bias=UpperCAmelCase_						)
      a						:List[str]										=	TrOCRConfig()

      # size of the architecture
      if "base" in checkpoint_url:
            a						:Any										=	768
      elif "large" in checkpoint_url:
            # use ViT-large encoder
            a						:List[Any]										=	1024
            a						:Tuple										=	4096
            a						:Tuple										=	24
            a						:int										=	16
            a						:Any										=	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:
            a						:Any										=	False
            a						:int										=	'''relu'''
            a						:Dict										=	1024
            a						:int										=	True
            a						:int										=	False
            a						:Optional[int]										=	False

      # load HuggingFace model
      a						:int										=	ViTModel(UpperCAmelCase_			,  add_pooling_layer=UpperCAmelCase_						)
      a						:List[str]										=	TrOCRForCausalLM(UpperCAmelCase_						)
      a						:Tuple										=	VisionEncoderDecoderModel(encoder=UpperCAmelCase_			,  decoder=UpperCAmelCase_						)
      model.eval()

      # load state_dict of original model, rename some keys
      a						:List[Any]										=	torch.hub.load_state_dict_from_url(UpperCAmelCase_			,  map_location='''cpu'''			,  check_hash=UpperCAmelCase_						)['''model''']

      a						:Optional[int]										=	create_rename_keys(UpperCAmelCase_			,  UpperCAmelCase_						)
      for src, dest in rename_keys:
            rename_key(UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_						)
      read_in_q_k_v(UpperCAmelCase_			,  UpperCAmelCase_						)

      # 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():
            a						:Any										=	state_dict.pop(UpperCAmelCase_						)
            if key.startswith('''decoder'''						) and "output_projection" not in key:
                  a						:Optional[Any]										=	val
            else:
                  a						:List[Any]										=	val

    # load state dict
      model.load_state_dict(UpperCAmelCase_						)

      # Check outputs on an image
      a						:List[Any]										=	ViTImageProcessor(size=encoder_config.image_size						)
      a						:Optional[Any]										=	RobertaTokenizer.from_pretrained('''roberta-large'''						)
      a						:int										=	TrOCRProcessor(UpperCAmelCase_			,  UpperCAmelCase_						)

      a						:Union[str, Any]										=	processor(images=prepare_img(UpperCAmelCase_						)			,  return_tensors='''pt'''						).pixel_values

      # verify logits
      a						:List[str]										=	torch.tensor([[model.config.decoder.decoder_start_token_id]]						)
      a						:List[Any]										=	model(pixel_values=UpperCAmelCase_			,  decoder_input_ids=UpperCAmelCase_						)
      a						:Any										=	outputs.logits

      a						:int										=	torch.Size([1, 1, 5_0265]						)
      if "trocr-base-handwritten" in checkpoint_url:
            a						:str										=	torch.tensor(
                [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]						)
      elif "trocr-large-handwritten" in checkpoint_url:
            a						:int										=	torch.tensor(
                [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]						)
      elif "trocr-base-printed" in checkpoint_url:
            a						:List[Any]										=	torch.tensor(
                [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]						)
      elif "trocr-large-printed" in checkpoint_url:
            a						:List[str]										=	torch.tensor(
                [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]						)

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

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


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









import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case    :   List[str]	    =					logging.get_logger(__name__)

snake_case    :   Optional[Any]	    =					{
    '''vocab_file''': '''vocab.txt''',
    '''merges_file''': '''bpe.codes''',
}

snake_case    :   str	    =					{
    '''vocab_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt''',
    },
    '''merges_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes''',
    },
}

snake_case    :   List[Any]	    =					{
    '''vinai/phobert-base''': 2_56,
    '''vinai/phobert-large''': 2_56,
}
def     __lowerCamelCase						(			UpperCAmelCase_						:		List[str]						):




      """simple docstring"""





      a						:Union[str, Any]										=	set()
      a						:str										=	word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)						)
            a						:Optional[int]										=	char

      a						:Optional[int]										=	set(UpperCAmelCase_						)
      return pairs







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase="<s>" ,  _lowerCamelCase="</s>" ,  _lowerCamelCase="</s>" ,  _lowerCamelCase="<s>" ,  _lowerCamelCase="<unk>" ,  _lowerCamelCase="<pad>" ,  _lowerCamelCase="<mask>" ,  **_lowerCamelCase ,  ):
       super().__init__(
           bos_token=_lowerCamelCase ,  eos_token=_lowerCamelCase ,  unk_token=_lowerCamelCase ,  sep_token=_lowerCamelCase ,  cls_token=_lowerCamelCase ,  pad_token=_lowerCamelCase ,  mask_token=_lowerCamelCase ,  **_lowerCamelCase ,  )

       a						:Optional[Any]										=	vocab_file
       a						:Optional[Any]										=	merges_file

       a						:Any										=	{}
       a						:Any										=	0
       a						:int										=	1
       a						:Union[str, Any]										=	2
       a						:List[Any]										=	3

       self.add_from_file(_lowerCamelCase   )

       a						:List[str]										=	{v: k for k, v in self.encoder.items()}

       with open(_lowerCamelCase ,  encoding='''utf-8'''   ) as merges_handle:
             a						:List[str]										=	merges_handle.read().split('''\n'''   )[:-1]
       a						:Any										=	[tuple(merge.split()[:-1]   ) for merge in merges]
       a						:str										=	dict(zip(_lowerCamelCase ,  range(len(_lowerCamelCase   )   )   )   )
       a						:str										=	{}




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):

       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       a						:Union[str, Any]										=	[self.cls_token_id]
       a						:Tuple										=	[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase = False   ):

       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_lowerCamelCase ,  token_ids_a=_lowerCamelCase ,  already_has_special_tokens=_lowerCamelCase   )

       if token_ids_a is None:
             return [1] + ([0] * len(_lowerCamelCase   )) + [1]
       return [1] + ([0] * len(_lowerCamelCase   )) + [1, 1] + ([0] * len(_lowerCamelCase   )) + [1]




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       a						:Optional[int]										=	[self.sep_token_id]
       a						:Optional[Any]										=	[self.cls_token_id]

       if token_ids_a is None:
             return len(cls + token_ids_a + sep   ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return len(self.encoder   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return dict(self.encoder ,  **self.added_tokens_encoder   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       if token in self.cache:
             return self.cache[token]
       a						:Optional[int]										=	tuple(_lowerCamelCase   )
       a						:List[str]										=	tuple(list(word[:-1]   ) + [word[-1] + '''</w>''']   )
       a						:Union[str, Any]										=	get_pairs(_lowerCamelCase   )

       if not pairs:
             return token

       while True:
             a						:Optional[Any]										=	min(_lowerCamelCase ,  key=lambda _lowerCamelCase   : self.bpe_ranks.get(_lowerCamelCase ,  float('''inf'''   )   )   )
             if bigram not in self.bpe_ranks:
                   break
             a							,    a						:Dict										=	bigram
             a						:Union[str, Any]										=	[]
             a						:int										=	0
             while i < len(_lowerCamelCase   ):
                   try:
                         a						:Optional[Any]										=	word.index(_lowerCamelCase ,  _lowerCamelCase   )
                   except ValueError:
                         new_word.extend(word[i:]   )
                         break
                   else:
                         new_word.extend(word[i:j]   )
                         a						:Union[str, Any]										=	j

                   if word[i] == first and i < len(_lowerCamelCase   ) - 1 and word[i + 1] == second:
                         new_word.append(first + second   )
                         i += 2
                   else:
                         new_word.append(word[i]   )
                         i += 1
             a						:Union[str, Any]										=	tuple(_lowerCamelCase   )
             a						:int										=	new_word
             if len(_lowerCamelCase   ) == 1:
                   break
             else:
                   a						:List[str]										=	get_pairs(_lowerCamelCase   )
       a						:Union[str, Any]										=	'''@@ '''.join(_lowerCamelCase   )
       a						:Dict										=	word[:-4]
       a						:Any										=	word
       return word




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:Union[str, Any]										=	[]

       a						:str										=	re.findall(R'''\S+\n?''' ,  _lowerCamelCase   )

       for token in words:
             split_tokens.extend(list(self.bpe(_lowerCamelCase   ).split(''' '''   )   )   )
       return split_tokens




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       return self.encoder.get(_lowerCamelCase ,  self.encoder.get(self.unk_token   )   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       return self.decoder.get(_lowerCamelCase ,  self.unk_token   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:Optional[int]										=	''' '''.join(_lowerCamelCase   ).replace('''@@ ''' ,  ''''''   ).strip()
       return out_string




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       if not os.path.isdir(_lowerCamelCase   ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
             return
       a						:Tuple										=	os.path.join(
           _lowerCamelCase ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
       a						:Optional[int]										=	os.path.join(
           _lowerCamelCase ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

       if os.path.abspath(self.vocab_file   ) != os.path.abspath(_lowerCamelCase   ):
             copyfile(self.vocab_file ,  _lowerCamelCase   )

       if os.path.abspath(self.merges_file   ) != os.path.abspath(_lowerCamelCase   ):
             copyfile(self.merges_file ,  _lowerCamelCase   )

       return out_vocab_file, out_merge_file




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       if isinstance(_lowerCamelCase ,  _lowerCamelCase   ):
             try:
                   with open(_lowerCamelCase ,  '''r''' ,  encoding='''utf-8'''   ) as fd:
                         self.add_from_file(_lowerCamelCase   )
             except FileNotFoundError as fnfe:
                   raise fnfe
             except UnicodeError:
                   raise Exception(F'''Incorrect encoding detected in {f}, please rebuild the dataset'''   )
             return

       a						:str										=	f.readlines()
       for lineTmp in lines:
             a						:Tuple										=	lineTmp.strip()
             a						:int										=	line.rfind(''' '''   )
             if idx == -1:
                   raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\''''   )
             a						:Tuple										=	line[:idx]
             a						:Tuple										=	len(self.encoder   )





