
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_   (      UpperCAmelCase_				:					Union[str, Any]							):
						lowercase							:     Dict									=	model.config

						lowercase							:     Dict									=	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				,      )
						lowercase							:     Optional[int]									=	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 							lowerCamelCase_   (      UpperCAmelCase_				:					List[str]							):




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

												if name == "encoder.norm.weight":
																		lowercase							:     Dict									=	'encoder.layernorm.weight'
												if name == "encoder.norm.bias":
																		lowercase							:     Any									=	'encoder.layernorm.bias'

						return name

def 							lowerCamelCase_   (      UpperCAmelCase_				:					List[str]				,      UpperCAmelCase_				:					Optional[Any]							):




						for key in orig_state_dict.copy().keys():
												lowercase							:     Any									=	orig_state_dict.pop(__lowerCamelCase							)

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

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

						return orig_state_dict

def 							lowerCamelCase_   (      UpperCAmelCase_				:					str				,      UpperCAmelCase_				:					int=None				,      UpperCAmelCase_				:					str=False							):
						lowercase							:     Union[str, Any]									=	DonutModel.from_pretrained(__lowerCamelCase							).eval()

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

						lowercase							:     str									=	original_model.state_dict()
						lowercase							:     Union[str, Any]									=	convert_state_dict(__lowerCamelCase				,      __lowerCamelCase							)
						model.load_state_dict(__lowerCamelCase							)

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

						lowercase							:     Optional[int]									=	XLMRobertaTokenizerFast.from_pretrained(__lowerCamelCase				,      from_slow=__lowerCamelCase							)
						lowercase							:     List[str]									=	DonutImageProcessor(
						    do_align_long_axis=original_model.config.align_long_axis				,      size=original_model.config.input_size[::-1]							)
						lowercase							:     str									=	DonutProcessor(__lowerCamelCase				,      __lowerCamelCase							)
						lowercase							:     Tuple									=	processor(__lowerCamelCase				,      return_tensors='''pt'''							).pixel_values

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

						lowercase							:     List[Any]									=	original_model.encoder.model.patch_embed(__lowerCamelCase							)
						lowercase							:     Union[str, Any]									=	model.encoder.embeddings(__lowerCamelCase							)
						assert torch.allclose(__lowerCamelCase				,      __lowerCamelCase				,      atol=1E-3							)

						# verify encoder hidden states
						lowercase							:     Optional[int]									=	original_model.encoder(__lowerCamelCase							)
						lowercase							:     List[str]									=	model.encoder(__lowerCamelCase							).last_hidden_state
						assert torch.allclose(__lowerCamelCase				,      __lowerCamelCase				,      atol=1E-2							)

						# verify decoder hidden states
						lowercase							:     Dict									=	original_model(__lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase							).logits
						lowercase							:     Dict									=	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)






'''simple docstring'''






import argparse


lowercase				='docs/source/_static/js/custom.js'





def lowerCamelCase__     (		__lowerCamelCase			:				List[Any]				):







   '''simple docstring'''




   with open(__lowerCamelCase			,       encoding='utf-8'			,       newline='\n'				) as f:
      _UpperCAmelCase  :  Optional[Any]				       =f.readlines()
   _UpperCAmelCase  :  Dict				       =0

   # First let's put the right version
   while not lines[index].startswith('const stableVersion ='				):
      index += 1
   _UpperCAmelCase  :  Tuple				       =f"const stableVersion = \"v{version}\"\n"

   # Then update the dictionary
   while not lines[index].startswith('const versionMapping = {'				):
      index += 1

   # We go until the end
   while not lines[index].startswith('}'				):
      index += 1
   # We add the new version at the end
   lines[index - 1] += f"    \"v{version}\": \"v{version}\",\n"

   with open(__lowerCamelCase			,       'w'			,       encoding='utf-8'			,       newline='\n'				) as f:
      f.writelines(__lowerCamelCase				)


if __name__ == "__main__":
 lowercase				=argparse.ArgumentParser()
 parser.add_argument('--version', help='Release version.')
 lowercase				=parser.parse_args()
 update_custom_js(args.version)

