




"""simple docstring"""



import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger




class      UpperCAmelCase_  (						A_							):




    def __init__(   self						:	Union[str, Any]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Optional[Any]				)				->							str:




     '''simple docstring'''

     A__	=    params

     A__	=    np.array(snake_case_				)
     A__	=    np.array([len(snake_case_				) for t in data]				)

     self.check()
     self.remove_long_sequences()
     self.remove_empty_sequences()
     self.remove_unknown_sequences()
     self.check()
     self.print_statistics()




    def __getitem__(   self						:	int     ,			snake_case_						:	Dict				)				->							Union[str, Any]:




     '''simple docstring'''

     return (self.token_ids[index], self.lengths[index])




    def __len__(   self						:	List[Any]				)				->							Any:




     '''simple docstring'''

     return len(self.lengths				)




    def 							__magic_name__							(   self						:	Tuple				)				->							Any:




     '''simple docstring'''

     assert len(self.token_ids				) == len(self.lengths				)
     assert all(self.lengths[i] == len(self.token_ids[i]				) for i in range(len(self.lengths				)				)				)




    def 							__magic_name__							(   self						:	Dict				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    self.params.max_model_input_size
     A__	=    self.lengths > max_len
     logger.info(F"""Splitting {sum(snake_case_				)} too long sequences."""				)

     def divide_chunks(snake_case_						:	Tuple     ,			snake_case_						:	Tuple				):
      return [l[i : i + n] for i in range(0     ,			len(snake_case_				)     ,			snake_case_				)]

     A__	=    []
     A__	=    []
     if self.params.mlm:
      A__,				A__	=    self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
     else:
      A__,				A__	=    self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

     for seq_, len_ in zip(self.token_ids     ,			self.lengths				):
      assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
      if len_ <= max_len:
       new_tok_ids.append(seq_				)
       new_lengths.append(len_				)
      else:
       A__	=    []
       for sub_s in divide_chunks(seq_     ,			max_len - 2				):
        if sub_s[0] != cls_id:
         A__	=    np.insert(snake_case_     ,			0     ,			snake_case_				)
        if sub_s[-1] != sep_id:
         A__	=    np.insert(snake_case_     ,			len(snake_case_				)     ,			snake_case_				)
        assert len(snake_case_				) <= max_len
        assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
        sub_seqs.append(snake_case_				)

       new_tok_ids.extend(snake_case_				)
       new_lengths.extend([len(snake_case_				) for l in sub_seqs]				)

     A__	=    np.array(snake_case_				)
     A__	=    np.array(snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							List[str]:




     '''simple docstring'''

     A__	=    len(self				)
     A__	=    self.lengths > 11
     A__	=    self.token_ids[indices]
     A__	=    self.lengths[indices]
     A__	=    len(self				)
     logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""				)




    def 							__magic_name__							(   self						:	Optional[int]				)				->							str:




     '''simple docstring'''

     if "unk_token" not in self.params.special_tok_ids:
      return
     else:
      A__	=    self.params.special_tok_ids["unk_token"]
     A__	=    len(self				)
     A__	=    np.array([np.count_nonzero(a == unk_token_id				) for a in self.token_ids]				)
     A__	=    (unk_occs / self.lengths) < 0.5
     A__	=    self.token_ids[indices]
     A__	=    self.lengths[indices]
     A__	=    len(self				)
     logger.info(F"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."""				)




    def 							__magic_name__							(   self						:	int				)				->							Dict:




     '''simple docstring'''

     if not self.params.is_master:
      return
     logger.info(F"""{len(self				)} sequences"""				)
     # data_len = sum(self.lengths)
     # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
     # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

     # unk_idx = self.params.special_tok_ids['unk_token']
     # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
     # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	str				)				->							Tuple:




     '''simple docstring'''

     A__	=    [t[0] for t in batch]
     A__	=    [t[1] for t in batch]
     assert len(snake_case_				) == len(snake_case_				)

     # Max for paddings
     A__	=    max(snake_case_				)

     # Pad token ids
     if self.params.mlm:
      A__	=    self.params.special_tok_ids["pad_token"]
     else:
      A__	=    self.params.special_tok_ids["unk_token"]
     A__	=    [list(t.astype(snake_case_				)				) + [pad_idx] * (max_seq_len_ - len(snake_case_				)) for t in token_ids]
     assert len(tk_				) == len(snake_case_				)
     assert all(len(snake_case_				) == max_seq_len_ for t in tk_				)

     A__	=    torch.tensor(tk_				)  # (bs, max_seq_len_)
     A__	=    torch.tensor(snake_case_				)  # (bs)
     return tk_t, lg_t











"""simple docstring"""



import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		List[str]:
 A__	=    model.config

 A__	=    DonutSwinConfig(
     image_size=original_config.input_size      ,			patch_size=4      ,			depths=original_config.encoder_layer      ,			num_heads=[4, 8, 16, 32]      ,			window_size=original_config.window_size      ,			embed_dim=1_28      ,			)
 A__	=    MBartConfig(
     is_decoder=lowercase_      ,			is_encoder_decoder=lowercase_      ,			add_cross_attention=lowercase_      ,			decoder_layers=original_config.decoder_layer      ,			max_position_embeddings=original_config.max_position_embeddings      ,			vocab_size=len(
         model.decoder.tokenizer						)      ,			scale_embedding=lowercase_      ,			add_final_layer_norm=lowercase_      ,			)

 return encoder_config, decoder_config







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 if "encoder.model" in name:
  A__	=    name.replace("encoder.model"      ,			"encoder"						)
 if "decoder.model" in name:
  A__	=    name.replace("decoder.model"      ,			"decoder"						)
 if "patch_embed.proj" in name:
  A__	=    name.replace("patch_embed.proj"      ,			"embeddings.patch_embeddings.projection"						)
 if "patch_embed.norm" in name:
  A__	=    name.replace("patch_embed.norm"      ,			"embeddings.norm"						)
 if name.startswith("encoder"						):
  if "layers" in name:
   A__	=    "encoder." + name
  if "attn.proj" in name:
   A__	=    name.replace("attn.proj"      ,			"attention.output.dense"						)
  if "attn" in name and "mask" not in name:
   A__	=    name.replace("attn"      ,			"attention.self"						)
  if "norm1" in name:
   A__	=    name.replace("norm1"      ,			"layernorm_before"						)
  if "norm2" in name:
   A__	=    name.replace("norm2"      ,			"layernorm_after"						)
  if "mlp.fc1" in name:
   A__	=    name.replace("mlp.fc1"      ,			"intermediate.dense"						)
  if "mlp.fc2" in name:
   A__	=    name.replace("mlp.fc2"      ,			"output.dense"						)

  if name == "encoder.norm.weight":
   A__	=    "encoder.layernorm.weight"
  if name == "encoder.norm.bias":
   A__	=    "encoder.layernorm.bias"

 return name







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Any:
 for key in orig_state_dict.copy().keys():
  A__	=    orig_state_dict.pop(lowercase_						)

  if "qkv" in key:
   A__	=    key.split("."						)
   A__	=    int(key_split[3]						)
   A__	=    int(key_split[5]						)
   A__	=    model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

   if "weight" in key:
    A__	=    val[:dim, :]
    A__	=    val[dim : dim * 2, :]
    A__	=    val[-dim:, :]
   else:
    A__	=    val[:dim]
    A__	=    val[dim : dim * 2]
    A__	=    val[-dim:]
  elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
   # HuggingFace implementation doesn't use attn_mask buffer
   # and model doesn't use final LayerNorms for the encoder
   pass
  else:
   A__	=    val

 return orig_state_dict







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_=None      ,			lowercase_=False						)						->		Dict:
 # load original model
 A__	=    DonutModel.from_pretrained(lowercase_						).eval()

 # load HuggingFace model
 A__,				A__	=    get_configs(lowercase_						)
 A__	=    DonutSwinModel(lowercase_						)
 A__	=    MBartForCausalLM(lowercase_						)
 A__	=    VisionEncoderDecoderModel(encoder=lowercase_      ,			decoder=lowercase_						)
 model.eval()

 A__	=    original_model.state_dict()
 A__	=    convert_state_dict(lowercase_      ,			lowercase_						)
 model.load_state_dict(lowercase_						)

 # verify results on scanned document
 A__	=    load_dataset("hf-internal-testing/example-documents"						)
 A__	=    dataset["test"][0]["image"].convert("RGB"						)

 A__	=    XLMRobertaTokenizerFast.from_pretrained(lowercase_      ,			from_slow=lowercase_						)
 A__	=    DonutImageProcessor(
     do_align_long_axis=original_model.config.align_long_axis      ,			size=original_model.config.input_size[::-1]						)
 A__	=    DonutProcessor(lowercase_      ,			lowercase_						)
 A__	=    processor(lowercase_      ,			return_tensors="pt"						).pixel_values

 if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
  A__	=    "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
  A__	=    "When is the coffee break?"
  A__	=    task_prompt.replace("{user_input}"      ,			lowercase_						)
 elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
  A__	=    "<s_rvlcdip>"
 elif model_name in [
     "naver-clova-ix/donut-base-finetuned-cord-v1",
     "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
 ]:
  A__	=    "<s_cord>"
 elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
  A__	=    "s_cord-v2>"
 elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
  A__	=    "<s_zhtrainticket>"
 elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
  # use a random prompt
  A__	=    "hello world"
 else:
  raise ValueError("Model name not supported"						)
 A__	=    original_model.decoder.tokenizer(lowercase_      ,			add_special_tokens=lowercase_      ,			return_tensors="pt"						)[
     "input_ids"
 ]

 A__	=    original_model.encoder.model.patch_embed(lowercase_						)
 A__,				A__	=    model.encoder.embeddings(lowercase_						)
 assert torch.allclose(lowercase_      ,			lowercase_      ,			atol=1E-3						)

 # verify encoder hidden states
 A__	=    original_model.encoder(lowercase_						)
 A__	=    model.encoder(lowercase_						).last_hidden_state
 assert torch.allclose(lowercase_      ,			lowercase_      ,			atol=1E-2						)

 # verify decoder hidden states
 A__	=    original_model(lowercase_      ,			lowercase_      ,			lowercase_						).logits
 A__	=    model(lowercase_      ,			decoder_input_ids=lowercase_						).logits
 assert torch.allclose(lowercase_      ,			lowercase_      ,			atol=1E-3						)
 print("Looks ok!"						)

 if pytorch_dump_folder_path is not None:
  print(f"""Saving model and processor to {pytorch_dump_folder_path}"""						)
  model.save_pretrained(lowercase_						)
  processor.save_pretrained(lowercase_						)

 if push_to_hub:
  model.push_to_hub("nielsr/" + model_name.split("/"						)[-1]      ,			commit_message="Update model"						)
  processor.push_to_hub("nielsr/" + model_name.split("/"						)[-1]      ,			commit_message="Update model"						)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default="naver-clova-ix/donut-base-finetuned-docvqa",
          required=False,
          type=str,
          help="Name of the original model you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          required=False,
          type=str,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
          help="Whether or not to push the converted model and processor to the 🤗 hub.",
      )

      SCREAMING_SNAKE_CASE    =	parser.parse_args()
      convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





