






'''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       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     List[Any]							     =  model.config

   lowercase_						:     Optional[Any]							     =  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=128    ,		)
   lowercase_						:     List[str]							     =  MBartConfig(
       is_decoder=_UpperCamelCase    ,		is_encoder_decoder=_UpperCamelCase    ,		add_cross_attention=_UpperCamelCase    ,		decoder_layers=original_config.decoder_layer    ,		max_position_embeddings=original_config.max_position_embeddings    ,		vocab_size=len(
           model.decoder.tokenizer				)    ,		scale_embedding=_UpperCamelCase    ,		add_final_layer_norm=_UpperCamelCase    ,		)

   return encoder_config, decoder_config




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   if "encoder.model" in name:
      lowercase_						:     Dict							     =  name.replace("encoder.model"    ,		"encoder"				)
   if "decoder.model" in name:
      lowercase_						:     Dict							     =  name.replace("decoder.model"    ,		"decoder"				)
   if "patch_embed.proj" in name:
      lowercase_						:     Dict							     =  name.replace("patch_embed.proj"    ,		"embeddings.patch_embeddings.projection"				)
   if "patch_embed.norm" in name:
      lowercase_						:     List[str]							     =  name.replace("patch_embed.norm"    ,		"embeddings.norm"				)
   if name.startswith("encoder"				):
      if "layers" in name:
         lowercase_						:     Optional[int]							     =  "encoder." + name
      if "attn.proj" in name:
         lowercase_						:     Union[str, Any]							     =  name.replace("attn.proj"    ,		"attention.output.dense"				)
      if "attn" in name and "mask" not in name:
         lowercase_						:     Dict							     =  name.replace("attn"    ,		"attention.self"				)
      if "norm1" in name:
         lowercase_						:     Optional[Any]							     =  name.replace("norm1"    ,		"layernorm_before"				)
      if "norm2" in name:
         lowercase_						:     Optional[Any]							     =  name.replace("norm2"    ,		"layernorm_after"				)
      if "mlp.fc1" in name:
         lowercase_						:     int							     =  name.replace("mlp.fc1"    ,		"intermediate.dense"				)
      if "mlp.fc2" in name:
         lowercase_						:     Dict							     =  name.replace("mlp.fc2"    ,		"output.dense"				)

      if name == "encoder.norm.weight":
         lowercase_						:     List[str]							     =  "encoder.layernorm.weight"
      if name == "encoder.norm.bias":
         lowercase_						:     Tuple							     =  "encoder.layernorm.bias"

   return name




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   for key in orig_state_dict.copy().keys():
      lowercase_						:     Tuple							     =  orig_state_dict.pop(_UpperCamelCase				)

      if "qkv" in key:
         lowercase_						:     List[str]							     =  key.split("."				)
         lowercase_						:     int							     =  int(key_split[3]				)
         lowercase_						:     Tuple							     =  int(key_split[5]				)
         lowercase_						:     Optional[Any]							     =  model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            lowercase_						:     Any							     =  val[:dim, :]
            lowercase_						:     str							     =  val[dim : dim * 2, :]
            lowercase_						:     List[Any]							     =  val[-dim:, :]
         else:
            lowercase_						:     int							     =  val[:dim]
            lowercase_						:     Dict							     =  val[dim : dim * 2]
            lowercase_						:     int							     =  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:
         lowercase_						:     int							     =  val

   return orig_state_dict




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase=None    ,		_UpperCamelCase=False				):


   """simple docstring"""




   lowercase_						:     int							     =  DonutModel.from_pretrained(_UpperCamelCase				).eval()

   # load HuggingFace model
   lowercase_     ,		lowercase_						:     List[str]							     =  get_configs(_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  DonutSwinModel(_UpperCamelCase				)
   lowercase_						:     Optional[Any]							     =  MBartForCausalLM(_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  VisionEncoderDecoderModel(encoder=_UpperCamelCase    ,		decoder=_UpperCamelCase				)
   model.eval()

   lowercase_						:     int							     =  original_model.state_dict()
   lowercase_						:     Dict							     =  convert_state_dict(_UpperCamelCase    ,		_UpperCamelCase				)
   model.load_state_dict(_UpperCamelCase				)

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

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

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

   lowercase_						:     Optional[Any]							     =  original_model.encoder.model.patch_embed(_UpperCamelCase				)
   lowercase_     ,		lowercase_						:     Dict							     =  model.encoder.embeddings(_UpperCamelCase				)
   assert torch.allclose(_UpperCamelCase    ,		_UpperCamelCase    ,		atol=1e-3				)

   # verify encoder hidden states
   lowercase_						:     List[Any]							     =  original_model.encoder(_UpperCamelCase				)
   lowercase_						:     Optional[Any]							     =  model.encoder(_UpperCamelCase				).last_hidden_state
   assert torch.allclose(_UpperCamelCase    ,		_UpperCamelCase    ,		atol=1e-2				)

   # verify decoder hidden states
   lowercase_						:     Optional[int]							     =  original_model(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				).logits
   lowercase_						:     int							     =  model(_UpperCamelCase    ,		decoder_input_ids=_UpperCamelCase				).logits
   assert torch.allclose(_UpperCamelCase    ,		_UpperCamelCase    ,		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(_UpperCamelCase				)
      processor.save_pretrained(_UpperCamelCase				)

   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__":
 UpperCamelCase__			=      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.',
 )

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












'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase__			=      {
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      ['LayoutLMv3TokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
     'LayoutLMv3ForQuestionAnswering',
     'LayoutLMv3ForSequenceClassification',
     'LayoutLMv3ForTokenClassification',
     'LayoutLMv3Model',
     'LayoutLMv3PreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFLayoutLMv3ForQuestionAnswering',
     'TFLayoutLMv3ForSequenceClassification',
     'TFLayoutLMv3ForTokenClassification',
     'TFLayoutLMv3Model',
     'TFLayoutLMv3PreTrainedModel',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      ['LayoutLMv3FeatureExtractor']
 UpperCamelCase__			=      ['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
 from .configuration_layoutlmva import (
     LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LayoutLMvaConfig,
     LayoutLMvaOnnxConfig,
 )
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlmva import (
      TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMvaForQuestionAnswering,
      TFLayoutLMvaForSequenceClassification,
      TFLayoutLMvaForTokenClassification,
      TFLayoutLMvaModel,
      TFLayoutLMvaPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
  from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
 import sys

 UpperCamelCase__			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




