'''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 				snake_case__							(			_A:					int )    ->	Any:

		'''simple docstring'''


		lowerCAmelCase   					=  model.config

		lowerCAmelCase   					=  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      ,   )
		lowerCAmelCase   					=  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 				snake_case__							(			_A:					Tuple )    ->	int:

		'''simple docstring'''


		if "encoder.model" in name:
				lowerCAmelCase   					=  name.replace("""encoder.model"""      ,   """encoder""" )
		if "decoder.model" in name:
				lowerCAmelCase   					=  name.replace("""decoder.model"""      ,   """decoder""" )
		if "patch_embed.proj" in name:
				lowerCAmelCase   					=  name.replace("""patch_embed.proj"""      ,   """embeddings.patch_embeddings.projection""" )
		if "patch_embed.norm" in name:
				lowerCAmelCase   					=  name.replace("""patch_embed.norm"""      ,   """embeddings.norm""" )
		if name.startswith("""encoder""" ):
				if "layers" in name:
						lowerCAmelCase   					=  """encoder.""" + name
				if "attn.proj" in name:
						lowerCAmelCase   					=  name.replace("""attn.proj"""      ,   """attention.output.dense""" )
				if "attn" in name and "mask" not in name:
						lowerCAmelCase   					=  name.replace("""attn"""      ,   """attention.self""" )
				if "norm1" in name:
						lowerCAmelCase   					=  name.replace("""norm1"""      ,   """layernorm_before""" )
				if "norm2" in name:
						lowerCAmelCase   					=  name.replace("""norm2"""      ,   """layernorm_after""" )
				if "mlp.fc1" in name:
						lowerCAmelCase   					=  name.replace("""mlp.fc1"""      ,   """intermediate.dense""" )
				if "mlp.fc2" in name:
						lowerCAmelCase   					=  name.replace("""mlp.fc2"""      ,   """output.dense""" )

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

		return name






def 				snake_case__							(			_A:					Optional[Any]      ,   _A:					List[Any] )    ->	Union[str, Any]:

		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				lowerCAmelCase   					=  orig_state_dict.pop(_A )

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

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

		return orig_state_dict






def 				snake_case__							(			_A:					Union[str, Any]      ,   _A:					Optional[int]=None      ,   _A:					str=False )    ->	Any:

		'''simple docstring'''


		lowerCAmelCase   					=  DonutModel.from_pretrained(_A ).eval()

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

		lowerCAmelCase   					=  original_model.state_dict()
		lowerCAmelCase   					=  convert_state_dict(_A      ,   _A )
		model.load_state_dict(_A )

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

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

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

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

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

		# verify decoder hidden states
		lowerCAmelCase   					=  original_model(_A      ,   _A      ,   _A ).logits
		lowerCAmelCase   					=  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'''




from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__lowercase          =  {
    '''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
    '''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[str]      =       '''ernie_m'''
			UpperCAmelCase_			:     Dict[str, str]      =       {"dropout": "classifier_dropout", "num_classes": "num_labels"}



			def __init__(   self       ,    __lowerCAmelCase = 250002       ,    __lowerCAmelCase = 768       ,    __lowerCAmelCase = 12       ,    __lowerCAmelCase = 12       ,    __lowerCAmelCase = 3072       ,    __lowerCAmelCase = "gelu"       ,    __lowerCAmelCase = 0.1       ,    __lowerCAmelCase = 0.1       ,    __lowerCAmelCase = 514       ,    __lowerCAmelCase = 0.02       ,    __lowerCAmelCase = 1       ,    __lowerCAmelCase = 1E-0_5       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase=0.0       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					super().__init__(pad_token_id=__lowerCAmelCase       ,    **__lowerCAmelCase)
					lowerCAmelCase   					=  vocab_size
					lowerCAmelCase   					=  hidden_size
					lowerCAmelCase   					=  num_hidden_layers
					lowerCAmelCase   					=  num_attention_heads
					lowerCAmelCase   					=  intermediate_size
					lowerCAmelCase   					=  hidden_act
					lowerCAmelCase   					=  hidden_dropout_prob
					lowerCAmelCase   					=  attention_probs_dropout_prob
					lowerCAmelCase   					=  max_position_embeddings
					lowerCAmelCase   					=  initializer_range
					lowerCAmelCase   					=  layer_norm_eps
					lowerCAmelCase   					=  classifier_dropout
					lowerCAmelCase   					=  is_decoder
					lowerCAmelCase   					=  act_dropout




