


"""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			(				A_       )->      Dict:
  '''simple docstring'''







  a						:		Union[str, Any]      							=			model.config

  a						:		List[str]      							=			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  ,   )
  a						:		str      							=			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      lowercase			(				A_       )->      Dict:
  '''simple docstring'''







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

    if name == "encoder.norm.weight":
      a						:		Tuple      							=			"encoder.layernorm.weight"
    if name == "encoder.norm.bias":
      a						:		Union[str, Any]      							=			"encoder.layernorm.bias"

  return name


def      lowercase			(				A_  ,   A_       )->      Tuple:
  '''simple docstring'''







  for key in orig_state_dict.copy().keys():
    a						:		Tuple      							=			orig_state_dict.pop(A_       )

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

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

  return orig_state_dict


def      lowercase			(				A_  ,   A_=None  ,   A_=False       )->      List[str]:
  '''simple docstring'''







  a						:		Dict      							=			DonutModel.from_pretrained(A_       ).eval()

  # load HuggingFace model
  a							,		a						:		Optional[int]      							=			get_configs(A_       )
  a						:		Union[str, Any]      							=			DonutSwinModel(A_       )
  a						:		Union[str, Any]      							=			MBartForCausalLM(A_       )
  a						:		str      							=			VisionEncoderDecoderModel(encoder=A_  ,   decoder=A_       )
  model.eval()

  a						:		Optional[Any]      							=			original_model.state_dict()
  a						:		Union[str, Any]      							=			convert_state_dict(A_  ,   A_       )
  model.load_state_dict(A_       )

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

  a						:		Any      							=			XLMRobertaTokenizerFast.from_pretrained(A_  ,   from_slow=A_       )
  a						:		Dict      							=			DonutImageProcessor(
      do_align_long_axis=original_model.config.align_long_axis  ,   size=original_model.config.input_size[::-1]       )
  a						:		Tuple      							=			DonutProcessor(A_  ,   A_       )
  a						:		Union[str, Any]      							=			processor(A_  ,   return_tensors="pt"       ).pixel_values

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

  a						:		Optional[int]      							=			original_model.encoder.model.patch_embed(A_       )
  a							,		a						:		int      							=			model.encoder.embeddings(A_       )
  assert torch.allclose(A_  ,   A_  ,   atol=1e-3       )

  # verify encoder hidden states
  a						:		List[str]      							=			original_model.encoder(A_       )
  a						:		Dict      							=			model.encoder(A_       ).last_hidden_state
  assert torch.allclose(A_  ,   A_  ,   atol=1e-2       )

  # verify decoder hidden states
  a						:		Union[str, Any]      							=			original_model(A_  ,   A_  ,   A_       ).logits
  a						:		List[str]      							=			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__":
 __lowercase    					=      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.""",
 )

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














"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      super().__init__()
      self.register_modules(unet=_lowerCamelCase							,		scheduler=_lowerCamelCase       )







    @torch.no_grad()
    def __call__(			self							,		_lowerCamelCase = 1							,		_lowerCamelCase = None							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      lowercase							    =       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,		generator=_lowerCamelCase							,		)
      lowercase							    =       image.to(self.device       )

      # set step values
      self.scheduler.set_timesteps(_lowerCamelCase       )

      for t in self.progress_bar(self.scheduler.timesteps       ):
        # 1. predict noise model_output
        lowercase							    =       self.unet(_lowerCamelCase							,		_lowerCamelCase       ).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        lowercase							    =       self.scheduler.step(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ).prev_sample

      lowercase							    =       (image / 2 + 0.5).clamp(0							,		1       )
      lowercase							    =       image.cpu().permute(0							,		2							,		3							,		1       ).numpy()
      if output_type == "pil":
        lowercase							    =       self.numpy_to_pil(_lowerCamelCase       )

      if not return_dict:
        return (image,), "This is a local test"

      return ImagePipelineOutput(images=_lowerCamelCase       ), "This is a local test"


