



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       __magic_name__(  __UpperCAmelCase		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				model.config

      _lowerCamelCase			    =				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 ,						)
      _lowerCamelCase			    =				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       __magic_name__(  __UpperCAmelCase		)						->    Tuple:



      '''simple docstring'''

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

            if name == "encoder.norm.weight":
                  _lowerCamelCase			    =				'''encoder.layernorm.weight'''
            if name == "encoder.norm.bias":
                  _lowerCamelCase			    =				'''encoder.layernorm.bias'''

      return name
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      for key in orig_state_dict.copy().keys():
            _lowerCamelCase			    =				orig_state_dict.pop(__UpperCAmelCase		)

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

                  if "weight" in key:
                        _lowerCamelCase			    =				val[:dim, :]
                        _lowerCamelCase			    =				val[dim : dim * 2, :]
                        _lowerCamelCase			    =				val[-dim:, :]
                  else:
                        _lowerCamelCase			    =				val[:dim]
                        _lowerCamelCase			    =				val[dim : dim * 2]
                        _lowerCamelCase			    =				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:
                  _lowerCamelCase			    =				val

      return orig_state_dict
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=False		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				DonutModel.from_pretrained(__UpperCAmelCase		).eval()

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

      _lowerCamelCase			    =				original_model.state_dict()
      _lowerCamelCase			    =				convert_state_dict(__UpperCAmelCase ,						__UpperCAmelCase		)
      model.load_state_dict(__UpperCAmelCase		)

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

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

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

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

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

      # verify decoder hidden states
      _lowerCamelCase			    =				original_model(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		).logits
      _lowerCamelCase			    =				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__":
     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.',
     )

     snake_case__				= parser.parse_args()
     convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)