


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_   :		Dict	):





			"""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=_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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""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_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




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

						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_   :		Optional[int]       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]=None       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]=False	):





			"""simple docstring"""




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

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

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

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

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

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

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

			# verify decoder hidden states
			UpperCamelCase         =					original_model(_lowerCAmelCase       ,  _lowerCAmelCase       ,  _lowerCAmelCase	).logits
			UpperCamelCase         =					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__":
			__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)








from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




