



'''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     __lowercase(	UpperCAmelCase__ ):


      """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     __lowercase(	UpperCAmelCase__ ):


      """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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=None   ,   UpperCAmelCase__=False ):


      """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__":
     a_						:     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.',
     )

     a_						:     Tuple	     =		parser.parse_args()
     convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     @property
     def 						_a      (self    ):






           '''simple docstring'''



           torch.manual_seed(0    )
           lowerCamelCase								=		UNetaDModel(
               block_out_channels=(32, 64)				,			layers_per_block=2				,			sample_size=32				,			in_channels=3				,			out_channels=3				,			down_block_types=("DownBlock2D", "AttnDownBlock2D")				,			up_block_types=("AttnUpBlock2D", "UpBlock2D")				,			)
           return model







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.dummy_uncond_unet
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"				,			return_dict=__a    )[0]

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           lowerCamelCase								=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowerCamelCase								=		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2





@slow
@require_torch
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"google/ncsnpp-celebahq-256"
           lowerCamelCase								=		UNetaDModel.from_pretrained(__a    )
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=20				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           assert image.shape == (1, 2_56, 2_56, 3)
           lowerCamelCase								=		np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2