


'''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()
__lowerCamelCase												=						logging.get_logger(__name__)


def        a__     (					UpperCamelCase_						:      Optional[Any],   UpperCamelCase_						:      int						):
   UpperCAmelCase__	:Any      =      []
   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__     (					UpperCamelCase_						:      List[Any],   UpperCamelCase_						:      str						):
   for i in range(encoder_config.num_hidden_layers						):
      # queries, keys and values (only weights, no biases)
      UpperCAmelCase__	:Union[str, Any]      =      state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight'''						)

      UpperCAmelCase__	:Optional[int]      =      in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      UpperCAmelCase__	:Optional[int]      =      in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      UpperCAmelCase__	:Dict      =      in_proj_weight[
          -encoder_config.hidden_size :, :
      ]


def        a__     (					UpperCamelCase_						:      List[Any],   UpperCamelCase_						:      Tuple,   UpperCamelCase_						:      List[Any]						):
   UpperCAmelCase__	:int      =      dct.pop(UpperCamelCase_						)
   UpperCAmelCase__	:List[Any]      =      val


def        a__     (					UpperCamelCase_						:      Dict						):
   if "handwritten" in checkpoint_url:
      UpperCAmelCase__	:Optional[Any]      =      '''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__	:int      =      '''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__	:Union[str, Any]      =      Image.open(requests.get(UpperCamelCase_,   stream=UpperCamelCase_						).raw						).convert('''RGB'''						)
   return im


@torch.no_grad()
def        a__     (					UpperCamelCase_						:      Optional[Any],   UpperCamelCase_						:      Optional[int]						):
   UpperCAmelCase__	:Optional[int]      =      ViTConfig(image_size=384,   qkv_bias=UpperCamelCase_						)
   UpperCAmelCase__	:Union[str, Any]      =      TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      UpperCAmelCase__	:Optional[int]      =      768
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      UpperCAmelCase__	:Dict      =      1_024
      UpperCAmelCase__	:Optional[int]      =      4_096
      UpperCAmelCase__	:str      =      24
      UpperCAmelCase__	:List[Any]      =      16
      UpperCAmelCase__	:List[str]      =      1_024
   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__	:str      =      False
      UpperCAmelCase__	:List[str]      =      '''relu'''
      UpperCAmelCase__	:Optional[Any]      =      1_024
      UpperCAmelCase__	:Optional[Any]      =      True
      UpperCAmelCase__	:Tuple      =      False
      UpperCAmelCase__	:Union[str, Any]      =      False

   # load HuggingFace model
   UpperCAmelCase__	:Any      =      ViTModel(UpperCamelCase_,   add_pooling_layer=UpperCamelCase_						)
   UpperCAmelCase__	:Dict      =      TrOCRForCausalLM(UpperCamelCase_						)
   UpperCAmelCase__	:List[Any]      =      VisionEncoderDecoderModel(encoder=UpperCamelCase_,   decoder=UpperCamelCase_						)
   model.eval()

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

   UpperCAmelCase__	:Tuple      =      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():
      UpperCAmelCase__	:Dict      =      state_dict.pop(UpperCamelCase_						)
      if key.startswith('''decoder'''						) and "output_projection" not in key:
         UpperCAmelCase__	:Optional[Any]      =      val
      else:
         UpperCAmelCase__	:str      =      val

    # load state dict
   model.load_state_dict(UpperCamelCase_						)

   # Check outputs on an image
   UpperCAmelCase__	:Any      =      ViTImageProcessor(size=encoder_config.image_size						)
   UpperCAmelCase__	:str      =      RobertaTokenizer.from_pretrained('''roberta-large'''						)
   UpperCAmelCase__	:int      =      TrOCRProcessor(UpperCamelCase_,   UpperCamelCase_						)

   UpperCAmelCase__	:Dict      =      processor(images=prepare_img(UpperCamelCase_						),   return_tensors='''pt'''						).pixel_values

   # verify logits
   UpperCAmelCase__	:List[Any]      =      torch.tensor([[model.config.decoder.decoder_start_token_id]]						)
   UpperCAmelCase__	:Optional[int]      =      model(pixel_values=UpperCamelCase_,   decoder_input_ids=UpperCamelCase_						)
   UpperCAmelCase__	:List[Any]      =      outputs.logits

   UpperCAmelCase__	:Union[str, Any]      =      torch.Size([1, 1, 50_265]						)
   if "trocr-base-handwritten" in checkpoint_url:
      UpperCAmelCase__	:List[str]      =      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:
      UpperCAmelCase__	: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:
      UpperCAmelCase__	:List[str]      =      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:
      UpperCAmelCase__	:Dict      =      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],   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__":
    __lowerCamelCase												=						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.'''
    )
    __lowerCamelCase												=						parser.parse_args()
    convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''





from ..utils import DummyObject, requires_backends




class 				UpperCAmelCase			(			metaclass=_snake_case						):
 UpperCAmelCase				=					["speech"]


 def __init__(      self							:	List[Any]			,       *__lowerCamelCase							:	List[Any]			,       **__lowerCamelCase							:	List[str] ):
    requires_backends(self			,       ['''speech'''] )




class 				UpperCAmelCase			(			metaclass=_snake_case						):
 UpperCAmelCase				=					["speech"]


 def __init__(      self							:	int			,       *__lowerCamelCase							:	List[Any]			,       **__lowerCamelCase							:	List[str] ):
    requires_backends(self			,       ['''speech'''] )



