"""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        a_  (   _lowerCAmelCase						:				List[str]  ):





       '''simple docstring'''

       lowercase__    :	str     					=			model.config

       lowercase__    :	Optional[int]     					=			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__    :	Optional[int]     					=			MBartConfig(
           is_decoder=_lowerCAmelCase     ,				is_encoder_decoder=_lowerCAmelCase     ,				add_cross_attention=_lowerCAmelCase     ,				decoder_layers=original_config.decoder_layer     ,				max_position_embeddings=original_config.max_position_embeddings     ,				vocab_size=len(
               model.decoder.tokenizer  )     ,				scale_embedding=_lowerCAmelCase     ,				add_final_layer_norm=_lowerCAmelCase     ,				)

       return encoder_config, decoder_config





def        a_  (   _lowerCAmelCase						:				int  ):





       '''simple docstring'''

       if "encoder.model" in name:
              lowercase__    :	str     					=			name.replace('encoder.model'     ,				'encoder'  )
       if "decoder.model" in name:
              lowercase__    :	Any     					=			name.replace('decoder.model'     ,				'decoder'  )
       if "patch_embed.proj" in name:
              lowercase__    :	Optional[Any]     					=			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__    :	str     					=			'encoder.' + name
              if "attn.proj" in name:
                     lowercase__    :	List[Any]     					=			name.replace('attn.proj'     ,				'attention.output.dense'  )
              if "attn" in name and "mask" not in name:
                     lowercase__    :	str     					=			name.replace('attn'     ,				'attention.self'  )
              if "norm1" in name:
                     lowercase__    :	Optional[Any]     					=			name.replace('norm1'     ,				'layernorm_before'  )
              if "norm2" in name:
                     lowercase__    :	int     					=			name.replace('norm2'     ,				'layernorm_after'  )
              if "mlp.fc1" in name:
                     lowercase__    :	List[str]     					=			name.replace('mlp.fc1'     ,				'intermediate.dense'  )
              if "mlp.fc2" in name:
                     lowercase__    :	Any     					=			name.replace('mlp.fc2'     ,				'output.dense'  )

              if name == "encoder.norm.weight":
                     lowercase__    :	List[str]     					=			'encoder.layernorm.weight'
              if name == "encoder.norm.bias":
                     lowercase__    :	List[Any]     					=			'encoder.layernorm.bias'

       return name





def        a_  (   _lowerCAmelCase						:				Tuple     ,				_lowerCAmelCase						:				Optional[int]  ):





       '''simple docstring'''

       for key in orig_state_dict.copy().keys():
              lowercase__    :	Dict     					=			orig_state_dict.pop(_lowerCAmelCase  )

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

                     if "weight" in key:
                            lowercase__    :	Optional[Any]     					=			val[:dim, :]
                            lowercase__    :	List[Any]     					=			val[dim : dim * 2, :]
                            lowercase__    :	Tuple     					=			val[-dim:, :]
                     else:
                            lowercase__    :	Union[str, Any]     					=			val[:dim]
                            lowercase__    :	List[str]     					=			val[dim : dim * 2]
                            lowercase__    :	Tuple     					=			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__    :	List[Any]     					=			val

       return orig_state_dict





def        a_  (   _lowerCAmelCase						:				List[str]     ,				_lowerCAmelCase						:				Union[str, Any]=None     ,				_lowerCAmelCase						:				List[Any]=False  ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			DonutModel.from_pretrained(_lowerCAmelCase  ).eval()

       # load HuggingFace model
       lowercase__					,   lowercase__    :	Dict     					=			get_configs(_lowerCAmelCase  )
       lowercase__    :	Dict     					=			DonutSwinModel(_lowerCAmelCase  )
       lowercase__    :	str     					=			MBartForCausalLM(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			VisionEncoderDecoderModel(encoder=_lowerCAmelCase     ,				decoder=_lowerCAmelCase  )
       model.eval()

       lowercase__    :	int     					=			original_model.state_dict()
       lowercase__    :	Optional[int]     					=			convert_state_dict(_lowerCAmelCase     ,				_lowerCAmelCase  )
       model.load_state_dict(_lowerCAmelCase  )

       # verify results on scanned document
       lowercase__    :	Union[str, Any]     					=			load_dataset('hf-internal-testing/example-documents'  )
       lowercase__    :	str     					=			dataset['test'][0]['image'].convert('RGB'  )

       lowercase__    :	List[Any]     					=			XLMRobertaTokenizerFast.from_pretrained(_lowerCAmelCase     ,				from_slow=_lowerCAmelCase  )
       lowercase__    :	Any     					=			DonutImageProcessor(
           do_align_long_axis=original_model.config.align_long_axis     ,				size=original_model.config.input_size[::-1]  )
       lowercase__    :	List[str]     					=			DonutProcessor(_lowerCAmelCase     ,				_lowerCAmelCase  )
       lowercase__    :	int     					=			processor(_lowerCAmelCase     ,				return_tensors='pt'  ).pixel_values

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

       lowercase__    :	Optional[int]     					=			original_model.encoder.model.patch_embed(_lowerCAmelCase  )
       lowercase__					,   lowercase__    :	str     					=			model.encoder.embeddings(_lowerCAmelCase  )
       assert torch.allclose(_lowerCAmelCase     ,				_lowerCAmelCase     ,				atol=1E-3  )

       # verify encoder hidden states
       lowercase__    :	Union[str, Any]     					=			original_model.encoder(_lowerCAmelCase  )
       lowercase__    :	Optional[Any]     					=			model.encoder(_lowerCAmelCase  ).last_hidden_state
       assert torch.allclose(_lowerCAmelCase     ,				_lowerCAmelCase     ,				atol=1E-2  )

       # verify decoder hidden states
       lowercase__    :	Tuple     					=			original_model(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  ).logits
       lowercase__    :	str     					=			model(_lowerCAmelCase     ,				decoder_input_ids=_lowerCAmelCase  ).logits
       assert torch.allclose(_lowerCAmelCase     ,				_lowerCAmelCase     ,				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(_lowerCAmelCase  )
              processor.save_pretrained(_lowerCAmelCase  )

       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     :		List[str]											=					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     :		int											=					parser.parse_args()
       convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor


@require_torch
@require_vision
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[Any]     					=			AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             lowercase__    :	Union[str, Any]     					=			AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             model.to(a							)

             from datasets import load_dataset

             lowercase__    :	str     					=			load_dataset('nielsr/rvlcdip-demo'							)

             lowercase__    :	Tuple     					=			dataset['train'][0]['image'].convert('RGB'							)

             lowercase__    :	int     					=			image_processor(a	,    return_tensors='pt'							).to(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__    :	List[str]     					=			model(**a							)
                    lowercase__    :	List[Any]     					=			outputs.logits

             lowercase__    :	Union[str, Any]     					=			torch.Size((1, 1_6)							)
             self.assertEqual(logits.shape	,    a							)

             lowercase__    :	Tuple     					=			torch.tensor(
                 [-0.4_158, -0.4_092, -0.4_347]	,    device=a	,    dtype=torch.float	,    )
             self.assertTrue(torch.allclose(logits[0, :3]	,    a	,    atol=1e-4							)							)

