


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     (		SCREAMING_SNAKE_CASE_ :		Union[str, Any]						):





			"""simple docstring"""




			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=128							,	)
			UpperCamelCase								=       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     (		SCREAMING_SNAKE_CASE_ :		Any						):





			"""simple docstring"""




			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 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		int							,	SCREAMING_SNAKE_CASE_ :		str						):





			"""simple docstring"""




			for key in orig_state_dict.copy().keys():
						UpperCamelCase								=       orig_state_dict.pop(UpperCamelCase__						)

						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 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Tuple							,	SCREAMING_SNAKE_CASE_ :		Union[str, Any]=None							,	SCREAMING_SNAKE_CASE_ :		List[Any]=False						):





			"""simple docstring"""




			UpperCamelCase								=       DonutModel.from_pretrained(UpperCamelCase__						).eval()

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

			UpperCamelCase								=       original_model.state_dict()
			UpperCamelCase								=       convert_state_dict(UpperCamelCase__							,	UpperCamelCase__						)
			model.load_state_dict(UpperCamelCase__						)

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

			UpperCamelCase								=       XLMRobertaTokenizerFast.from_pretrained(UpperCamelCase__							,	from_slow=UpperCamelCase__						)
			UpperCamelCase								=       DonutImageProcessor(
			    do_align_long_axis=original_model.config.align_long_axis							,	size=original_model.config.input_size[::-1]						)
			UpperCamelCase								=       DonutProcessor(UpperCamelCase__							,	UpperCamelCase__						)
			UpperCamelCase								=       processor(UpperCamelCase__							,	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}"""							,	UpperCamelCase__						)
			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(UpperCamelCase__							,	add_special_tokens=UpperCamelCase__							,	return_tensors="""pt"""						)[
			    'input_ids'
			]

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

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

			# verify decoder hidden states
			UpperCamelCase								=       original_model(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__						).logits
			UpperCamelCase								=       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)








'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase = True   ,   __lowercase = False    ):

  """simple docstring"""







  __A	:		List[str]	  =      scheduler
  __A	:		Dict	  =      optimizers if isinstance(__lowercase   ,   (list, tuple)    ) else [optimizers]
  __A	:		List[Any]	  =      split_batches
  __A	:		Any	  =      step_with_optimizer
  __A	:		List[Any]	  =      GradientState()


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  if not self.step_with_optimizer:
   # No link between scheduler and optimizer -> just step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
   return

  # Otherwise, first make sure the optimizer was stepped.
  if not self.gradient_state.sync_gradients:
   if self.gradient_state.adjust_scheduler:
    self.scheduler._step_count += 1
   return

  for opt in self.optimizers:
   if opt.step_was_skipped:
    return
  if self.split_batches:
   # Split batches -> the training dataloader batch size is not changed so one step per training step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
  else:
   # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
   # num_processes steps per training step
   __A	:		Tuple	  =      AcceleratorState().num_processes
   for _ in range(__lowercase    ):
    # Special case when using OneCycle and `drop_last` was not used
    if hasattr(self.scheduler   ,   'total_steps'    ):
     if self.scheduler._step_count <= self.scheduler.total_steps:
      self.scheduler.step(*__lowercase   ,   **__lowercase    )
    else:
     self.scheduler.step(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_last_lr()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.state_dict()


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  self.scheduler.load_state_dict(__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_lr()







 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  return self.scheduler.print_lr(*__lowercase   ,   **__lowercase    )






