





'''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__				:   Any      =logging.get_logger(__name__)
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							[]
      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    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


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

            _lowerCAmelCase							     =							in_proj_weight[
                : encoder_config.hidden_size, :
            ]
            _lowerCAmelCase							     =							in_proj_weight[
                encoder_config.hidden_size : encoder_config.hidden_size * 2, :
            ]
            _lowerCAmelCase							     =							in_proj_weight[
                -encoder_config.hidden_size :, :
            ]
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							dct.pop(_lowerCAmelCase  )
      _lowerCAmelCase							     =							val
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      if "handwritten" in checkpoint_url:
            _lowerCAmelCase							     =							'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							     =							'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							     =							Image.open(requests.get(_lowerCAmelCase							,	stream=_lowerCAmelCase  ).raw  ).convert("""RGB"""  )
      return im
@torch.no_grad()
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							ViTConfig(image_size=3_84							,	qkv_bias=_lowerCAmelCase  )
      _lowerCAmelCase							     =							TrOCRConfig()

      # size of the architecture
      if "base" in checkpoint_url:
            _lowerCAmelCase							     =							7_68
      elif "large" in checkpoint_url:
            # use ViT-large encoder
            _lowerCAmelCase							     =							10_24
            _lowerCAmelCase							     =							40_96
            _lowerCAmelCase							     =							24
            _lowerCAmelCase							     =							16
            _lowerCAmelCase							     =							10_24
      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							     =							False
            _lowerCAmelCase							     =							'relu'
            _lowerCAmelCase							     =							10_24
            _lowerCAmelCase							     =							True
            _lowerCAmelCase							     =							False
            _lowerCAmelCase							     =							False

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

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

      _lowerCAmelCase							     =							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():
            _lowerCAmelCase							     =							state_dict.pop(_lowerCAmelCase  )
            if key.startswith("""decoder"""  ) and "output_projection" not in key:
                  _lowerCAmelCase							     =							val
            else:
                  _lowerCAmelCase							     =							val

            # load state dict
      model.load_state_dict(_lowerCAmelCase  )

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

      _lowerCAmelCase							     =							processor(images=prepare_img(_lowerCAmelCase  )							,	return_tensors="""pt"""  ).pixel_values

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

      _lowerCAmelCase							     =							torch.Size([1, 1, 5_02_65]  )
      if "trocr-base-handwritten" in checkpoint_url:
            _lowerCAmelCase							     =							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:
            _lowerCAmelCase							     =							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:
            _lowerCAmelCase							     =							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:
            _lowerCAmelCase							     =							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]							,	_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__":
      A__				:   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.'''
      )
      A__				:   List[Any]      =parser.parse_args()
      convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











'''simple docstring'''





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_UpperCamelCase											=						logging.get_logger(__name__)
logging.set_verbosity_info()

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[int]:
  if "xprophetnet" in prophetnet_checkpoint_path:
    __lowerCamelCase       :       Union[str, Any]           = XLMProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase       )
    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = XLMProphetNetForConditionalGeneration.from_pretrained(
        _lowerCAmelCase       ,output_loading_info=_lowerCAmelCase       )
  else:
    __lowerCamelCase       :       Optional[int]           = ProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase       )
    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = ProphetNetForConditionalGeneration.from_pretrained(
        _lowerCAmelCase       ,output_loading_info=_lowerCAmelCase       )

  __lowerCamelCase       :       Union[str, Any]           = ['key_proj', 'value_proj', 'query_proj']

  __lowerCamelCase       :       Optional[Any]           = {
      'self_attn': 'ngram_self_attn',
      'cross_attn': 'encoder_attn',
      'cross_attn_layer_norm': 'encoder_attn_layer_norm',
      'feed_forward_layer_norm': 'final_layer_norm',
      'feed_forward': '',
      'intermediate': 'fc1',
      'output': 'fc2',
      'key_proj': 'k_proj',
      'query_proj': 'q_proj',
      'value_proj': 'v_proj',
      'word_embeddings': 'embed_tokens',
      'embeddings_layer_norm': 'emb_layer_norm',
      'relative_pos_embeddings': 'relative_linear',
      'ngram_embeddings': 'ngram_input_embed',
      'position_embeddings': 'embed_positions',
  }

  for key in loading_info["missing_keys"]:
    __lowerCamelCase       :       Optional[int]           = key.split('.'       )

    if attributes[0] == "lm_head":
      __lowerCamelCase       :       Dict           = prophet
      __lowerCamelCase       :       List[Any]           = prophet_old
    else:
      __lowerCamelCase       :       Any           = prophet.prophetnet
      __lowerCamelCase       :       Any           = prophet_old.model

    __lowerCamelCase       :       Optional[Any]           = False
    for attribute in attributes:
      if attribute in mapping:
        __lowerCamelCase       :       Any           = mapping[attribute]
        if not hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ) and len(_lowerCAmelCase       ) > 0:
          __lowerCamelCase       :       int           = attribute
      elif hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
        __lowerCamelCase       :       Optional[int]           = attribute

      if attribute == "weight":
        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
        __lowerCamelCase       :       List[Any]           = old_model.weight
        logger.info(F'{attribute} is initialized.'       )
        __lowerCamelCase       :       List[Any]           = True
        break
      elif attribute == "bias":
        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
        __lowerCamelCase       :       List[Any]           = old_model.bias
        logger.info(F'{attribute} is initialized'       )
        __lowerCamelCase       :       Dict           = True
        break
      elif attribute in special_keys and hasattr(_lowerCAmelCase       ,'in_proj_weight'       ):
        __lowerCamelCase       :       Optional[Any]           = old_model.in_proj_weight.shape[0] // 3
        __lowerCamelCase       :       Optional[Any]           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )
        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
        if attribute == "query_proj":
          __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
          __lowerCamelCase       :       Dict           = nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

        elif attribute == "key_proj":
          __lowerCamelCase       :       List[str]           = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
          __lowerCamelCase       :       Dict           = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
        elif attribute == "value_proj":
          __lowerCamelCase       :       str           = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
          __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
        __lowerCamelCase       :       Optional[int]           = True
        break
      elif attribute == "position_embeddings":
        assert (
            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
        ), "Hidden size has to match"
        assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
        __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.embed_positions.weight[:512, :]       )
        __lowerCamelCase       :       Dict           = True
        break

      if attribute.isdigit():
        __lowerCamelCase       :       List[str]           = model[int(_lowerCAmelCase       )]
        __lowerCamelCase       :       Union[str, Any]           = old_model[int(_lowerCAmelCase       )]
      else:
        __lowerCamelCase       :       Union[str, Any]           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )

        if old_attribute == "":
          __lowerCamelCase       :       str           = old_model
        else:
          if not hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
            raise ValueError(F'{old_model} does not have {old_attribute}'       )
          __lowerCamelCase       :       str           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )

    if not is_key_init:
      raise ValueError(F'{key} was not correctly initialized!'       )

  print(F'Saving model to {pytorch_dump_folder_path}'       )
  prophet.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _UpperCamelCase											=						parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)






