





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()
__UpperCAmelCase								=			logging.get_logger(__name__)





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

      __lowerCAmelCase  :      Tuple    =						in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      __lowerCAmelCase  :      str    =						in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      __lowerCAmelCase  :      str    =						in_proj_weight[
          -encoder_config.hidden_size :, :
      ]





def      snake_case_				(__A					:						Union[str, Any] ,       __A					:						str ,       __A					:						Optional[Any]					)      ->     Optional[Any]:
   __lowerCAmelCase  :      Any    =						dct.pop(__A					)
   __lowerCAmelCase  :      str    =						val





def      snake_case_				(__A					:						int					)      ->     Tuple:
   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  :      Optional[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  :      Dict    =						Image.open(requests.get(__A ,       stream=__A					).raw					).convert("""RGB"""					)
   return im





@torch.no_grad()
def      snake_case_				(__A					:						Any ,       __A					:						Union[str, Any]					)      ->     Optional[int]:
   __lowerCAmelCase  :      List[Any]    =						ViTConfig(image_size=3_8_4 ,       qkv_bias=__A					)
   __lowerCAmelCase  :      List[Any]    =						TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      __lowerCAmelCase  :      Union[str, Any]    =						7_6_8
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      __lowerCAmelCase  :      Any    =						1_0_2_4
      __lowerCAmelCase  :      Any    =						4_0_9_6
      __lowerCAmelCase  :      Optional[int]    =						2_4
      __lowerCAmelCase  :      str    =						1_6
      __lowerCAmelCase  :      List[Any]    =						1_0_2_4
   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  :      Tuple    =						False
      __lowerCAmelCase  :      Union[str, Any]    =						"""relu"""
      __lowerCAmelCase  :      List[Any]    =						1_0_2_4
      __lowerCAmelCase  :      Any    =						True
      __lowerCAmelCase  :      List[Any]    =						False
      __lowerCAmelCase  :      Dict    =						False

   # load HuggingFace model
   __lowerCAmelCase  :      Dict    =						ViTModel(__A ,       add_pooling_layer=__A					)
   __lowerCAmelCase  :      Union[str, Any]    =						TrOCRForCausalLM(__A					)
   __lowerCAmelCase  :      Any    =						VisionEncoderDecoderModel(encoder=__A ,       decoder=__A					)
   model.eval()

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

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

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

    # load state dict
   model.load_state_dict(__A					)

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

   __lowerCAmelCase  :      List[str]    =						processor(images=prepare_img(__A					) ,       return_tensors="""pt"""					).pixel_values

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

   __lowerCAmelCase  :      Union[str, Any]    =						torch.Size([1, 1, 5_0_2_6_5]					)
   if "trocr-base-handwritten" in checkpoint_url:
      __lowerCAmelCase  :      Dict    =						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:
      __lowerCAmelCase  :      List[Any]    =						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:
      __lowerCAmelCase  :      Tuple    =						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:
      __lowerCAmelCase  :      List[Any]    =						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, :1_0] ,       __A ,       atol=1e-3					), "First elements of logits not as expected"

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


if __name__ == "__main__":
   __UpperCAmelCase								=			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."""
   )
   __UpperCAmelCase								=			parser.parse_args()
   convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





from pathlib import Path

import fire





def      snake_case_				(__A					:						str ,       __A					:						str ,       __A					:						int					)      ->     Any:
   __lowerCAmelCase  :      Tuple    =						Path(__A					)
   __lowerCAmelCase  :      Tuple    =						Path(__A					)
   dest_dir.mkdir(exist_ok=__A					)
   for path in src_dir.iterdir():
      __lowerCAmelCase  :      str    =						[x.rstrip() for x in list(path.open().readlines()					)][:n]
      __lowerCAmelCase  :      Dict    =						dest_dir.joinpath(path.name					)
      print(__A					)
      dest_path.open("""w"""					).write("""\n""".join(__A					)					)


if __name__ == "__main__":
   fire.Fire(minify)


