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       __lowerCAmelCase      (						A_   : Any					)    ->  Dict:
  __UpperCAmelCase						    =       model.config

  __UpperCAmelCase						    =       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						,	)
  __UpperCAmelCase						    =       MBartConfig(
      is_decoder=A_						,	is_encoder_decoder=A_						,	add_cross_attention=A_						,	decoder_layers=original_config.decoder_layer						,	max_position_embeddings=original_config.max_position_embeddings						,	vocab_size=len(
          model.decoder.tokenizer					)						,	scale_embedding=A_						,	add_final_layer_norm=A_						,	)

  return encoder_config, decoder_config




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

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

  return name




def       __lowerCAmelCase      (						A_   : Optional[int]						,	A_   : str					)    ->  Union[str, Any]:
  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase						    =       orig_state_dict.pop(A_					)

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

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

  return orig_state_dict




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : List[str]=None						,	A_   : Tuple=False					)    ->  Optional[Any]:
  # load original model
  __UpperCAmelCase						    =       DonutModel.from_pretrained(A_					).eval()

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

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

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

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

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

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

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

  # verify decoder hidden states
  __UpperCAmelCase						    =       original_model(A_						,	A_						,	A_					).logits
  __UpperCAmelCase						    =       model(A_						,	decoder_input_ids=A_					).logits
  assert torch.allclose(A_						,	A_						,	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(A_					)
    processor.save_pretrained(A_					)

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





import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor




def       __lowerCAmelCase      (						A_   : int					)    ->  Optional[Any]:
  __UpperCAmelCase						    =       SwinaSRConfig()

  if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
    __UpperCAmelCase						    =       4
  elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
    __UpperCAmelCase						    =       4
    __UpperCAmelCase						    =       48
    __UpperCAmelCase						    =       "pixelshuffle_aux"
  elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
    __UpperCAmelCase						    =       [6, 6, 6, 6]
    __UpperCAmelCase						    =       60
    __UpperCAmelCase						    =       [6, 6, 6, 6]
    __UpperCAmelCase						    =       "pixelshuffledirect"
  elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
    __UpperCAmelCase						    =       4
    __UpperCAmelCase						    =       "nearest+conv"
  elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
    __UpperCAmelCase						    =       1
    __UpperCAmelCase						    =       1
    __UpperCAmelCase						    =       1_26
    __UpperCAmelCase						    =       7
    __UpperCAmelCase						    =       2_55.0
    __UpperCAmelCase						    =       ""

  return config




def       __lowerCAmelCase      (						A_   : Optional[int]						,	A_   : str					)    ->  Dict:
  if "patch_embed.proj" in name and "layers" not in name:
    __UpperCAmelCase						    =       name.replace("patch_embed.proj"						,	"embeddings.patch_embeddings.projection"					)
  if "patch_embed.norm" in name:
    __UpperCAmelCase						    =       name.replace("patch_embed.norm"						,	"embeddings.patch_embeddings.layernorm"					)
  if "layers" in name:
    __UpperCAmelCase						    =       name.replace("layers"						,	"encoder.stages"					)
  if "residual_group.blocks" in name:
    __UpperCAmelCase						    =       name.replace("residual_group.blocks"						,	"layers"					)
  if "attn.proj" in name:
    __UpperCAmelCase						    =       name.replace("attn.proj"						,	"attention.output.dense"					)
  if "attn" in name:
    __UpperCAmelCase						    =       name.replace("attn"						,	"attention.self"					)
  if "norm1" in name:
    __UpperCAmelCase						    =       name.replace("norm1"						,	"layernorm_before"					)
  if "norm2" in name:
    __UpperCAmelCase						    =       name.replace("norm2"						,	"layernorm_after"					)
  if "mlp.fc1" in name:
    __UpperCAmelCase						    =       name.replace("mlp.fc1"						,	"intermediate.dense"					)
  if "mlp.fc2" in name:
    __UpperCAmelCase						    =       name.replace("mlp.fc2"						,	"output.dense"					)
  if "q_bias" in name:
    __UpperCAmelCase						    =       name.replace("q_bias"						,	"query.bias"					)
  if "k_bias" in name:
    __UpperCAmelCase						    =       name.replace("k_bias"						,	"key.bias"					)
  if "v_bias" in name:
    __UpperCAmelCase						    =       name.replace("v_bias"						,	"value.bias"					)
  if "cpb_mlp" in name:
    __UpperCAmelCase						    =       name.replace("cpb_mlp"						,	"continuous_position_bias_mlp"					)
  if "patch_embed.proj" in name:
    __UpperCAmelCase						    =       name.replace("patch_embed.proj"						,	"patch_embed.projection"					)

  if name == "norm.weight":
    __UpperCAmelCase						    =       "layernorm.weight"
  if name == "norm.bias":
    __UpperCAmelCase						    =       "layernorm.bias"

  if "conv_first" in name:
    __UpperCAmelCase						    =       name.replace("conv_first"						,	"first_convolution"					)

  if (
      "upsample" in name
      or "conv_before_upsample" in name
      or "conv_bicubic" in name
      or "conv_up" in name
      or "conv_hr" in name
      or "conv_last" in name
      or "aux" in name
  ):
    # heads
    if "conv_last" in name:
      __UpperCAmelCase						    =       name.replace("conv_last"						,	"final_convolution"					)
    if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
      if "conv_before_upsample.0" in name:
        __UpperCAmelCase						    =       name.replace("conv_before_upsample.0"						,	"conv_before_upsample"					)
      if "upsample.0" in name:
        __UpperCAmelCase						    =       name.replace("upsample.0"						,	"upsample.convolution_0"					)
      if "upsample.2" in name:
        __UpperCAmelCase						    =       name.replace("upsample.2"						,	"upsample.convolution_1"					)
      __UpperCAmelCase						    =       "upsample." + name
    elif config.upsampler == "pixelshuffledirect":
      __UpperCAmelCase						    =       name.replace("upsample.0.weight"						,	"upsample.conv.weight"					)
      __UpperCAmelCase						    =       name.replace("upsample.0.bias"						,	"upsample.conv.bias"					)
    else:
      pass
  else:
    __UpperCAmelCase						    =       "swin2sr." + name

  return name




def       __lowerCAmelCase      (						A_   : Dict						,	A_   : Tuple					)    ->  Optional[Any]:
  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase						    =       orig_state_dict.pop(A_					)

    if "qkv" in key:
      __UpperCAmelCase						    =       key.split("."					)
      __UpperCAmelCase						    =       int(key_split[1]					)
      __UpperCAmelCase						    =       int(key_split[4]					)
      __UpperCAmelCase						    =       config.embed_dim

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

  return orig_state_dict




def       __lowerCAmelCase      (						A_   : Any						,	A_   : str						,	A_   : Optional[int]					)    ->  Optional[Any]:
  __UpperCAmelCase						    =       get_config(A_					)
  __UpperCAmelCase						    =       SwinaSRForImageSuperResolution(A_					)
  model.eval()

  __UpperCAmelCase						    =       torch.hub.load_state_dict_from_url(A_						,	map_location="cpu"					)
  __UpperCAmelCase						    =       convert_state_dict(A_						,	A_					)
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       model.load_state_dict(A_						,	strict=A_					)

  if len(A_					) > 0:
    raise ValueError("Missing keys when converting: {}".format(A_					)					)
  for key in unexpected_keys:
    if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
      raise ValueError(F'''Unexpected key {key} in state_dict'''					)

    # verify values
  __UpperCAmelCase						    =       "https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
  __UpperCAmelCase						    =       Image.open(requests.get(A_						,	stream=A_					).raw					).convert("RGB"					)
  __UpperCAmelCase						    =       SwinaSRImageProcessor()
  # pixel_values = processor(image, return_tensors="pt").pixel_values

  __UpperCAmelCase						    =       1_26 if "Jpeg" in checkpoint_url else 2_56
  __UpperCAmelCase						    =       Compose(
      [
          Resize((image_size, image_size)					),
          ToTensor(),
          Normalize(mean=[0.4_85, 0.4_56, 0.4_06]						,	std=[0.2_29, 0.2_24, 0.2_25]					),
      ]					)
  __UpperCAmelCase						    =       transforms(A_					).unsqueeze(0					)

  if config.num_channels == 1:
    __UpperCAmelCase						    =       pixel_values[:, 0, :, :].unsqueeze(1					)

  __UpperCAmelCase						    =       model(A_					)

  # assert values
  if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
    __UpperCAmelCase						    =       torch.Size([1, 3, 5_12, 5_12]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]]					)
  elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
    __UpperCAmelCase						    =       torch.Size([1, 3, 10_24, 10_24]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]]					)
  elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
    # TODO values didn't match exactly here
    __UpperCAmelCase						    =       torch.Size([1, 3, 10_24, 10_24]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]]					)
  elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
    __UpperCAmelCase						    =       torch.Size([1, 3, 5_12, 5_12]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]]					)
  elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
    __UpperCAmelCase						    =       torch.Size([1, 3, 10_24, 10_24]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]]					)

  assert (
      outputs.reconstruction.shape == expected_shape
  ), F'''Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'''
  assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]						,	A_						,	atol=1e-3					)
  print("Looks ok!"					)

  __UpperCAmelCase						    =       {
      "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth": (
          "swin2SR-classical-sr-x2-64"
      ),
      "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth": (
          "swin2SR-classical-sr-x4-64"
      ),
      "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth": (
          "swin2SR-compressed-sr-x4-48"
      ),
      "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth": (
          "swin2SR-lightweight-x2-64"
      ),
      "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth": (
          "swin2SR-realworld-sr-x4-64-bsrgan-psnr"
      ),
  }
  __UpperCAmelCase						    =       url_to_name[checkpoint_url]

  if pytorch_dump_folder_path is not None:
    print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''					)
    model.save_pretrained(A_					)
    print(F'''Saving image processor to {pytorch_dump_folder_path}'''					)
    processor.save_pretrained(A_					)

  if push_to_hub:
    model.push_to_hub(F'''caidas/{model_name}'''					)
    processor.push_to_hub(F'''caidas/{model_name}'''					)


if __name__ == "__main__":
       a_							=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--checkpoint_url""",
           default="""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""",
           type=str,
           help="""URL of the original Swin2SR checkpoint you'd like to convert.""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
       )
       parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the converted model to the hub.""")

       a_							=      parser.parse_args()
       convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



