
'''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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							List[Any]:


		'''simple docstring'''


		snake_case  :       Optional[int]     			=						model.config

		snake_case  :       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 ,       )
		snake_case  :       List[Any]     			=						MBartConfig(
		    is_decoder=SCREAMING_SNAKE_CASE__ ,       is_encoder_decoder=SCREAMING_SNAKE_CASE__ ,       add_cross_attention=SCREAMING_SNAKE_CASE__ ,       decoder_layers=original_config.decoder_layer ,       max_position_embeddings=original_config.max_position_embeddings ,       vocab_size=len(
		        model.decoder.tokenizer			) ,       scale_embedding=SCREAMING_SNAKE_CASE__ ,       add_final_layer_norm=SCREAMING_SNAKE_CASE__ ,       )

		return encoder_config, decoder_config




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


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

				if name == "encoder.norm.weight":
						snake_case  :       Any     			=						'''encoder.layernorm.weight'''
				if name == "encoder.norm.bias":
						snake_case  :       Tuple     			=						'''encoder.layernorm.bias'''

		return name




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Union[str, Any]:


		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				snake_case  :       Any     			=						orig_state_dict.pop(SCREAMING_SNAKE_CASE__			)

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

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

		return orig_state_dict




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=None ,       SCREAMING_SNAKE_CASE__=False			)  ->							List[str]:


		'''simple docstring'''


		snake_case  :       Any     			=						DonutModel.from_pretrained(SCREAMING_SNAKE_CASE__			).eval()

		# load HuggingFace model
		snake_case			,snake_case  :       int     			=						get_configs(SCREAMING_SNAKE_CASE__			)
		snake_case  :       str     			=						DonutSwinModel(SCREAMING_SNAKE_CASE__			)
		snake_case  :       List[str]     			=						MBartForCausalLM(SCREAMING_SNAKE_CASE__			)
		snake_case  :       Union[str, Any]     			=						VisionEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__ ,       decoder=SCREAMING_SNAKE_CASE__			)
		model.eval()

		snake_case  :       Dict     			=						original_model.state_dict()
		snake_case  :       int     			=						convert_state_dict(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		model.load_state_dict(SCREAMING_SNAKE_CASE__			)

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

		snake_case  :       Dict     			=						XLMRobertaTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE__ ,       from_slow=SCREAMING_SNAKE_CASE__			)
		snake_case  :       List[Any]     			=						DonutImageProcessor(
		    do_align_long_axis=original_model.config.align_long_axis ,       size=original_model.config.input_size[::-1]			)
		snake_case  :       str     			=						DonutProcessor(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		snake_case  :       int     			=						processor(SCREAMING_SNAKE_CASE__ ,       return_tensors='''pt'''			).pixel_values

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

		snake_case  :       str     			=						original_model.encoder.model.patch_embed(SCREAMING_SNAKE_CASE__			)
		snake_case			,snake_case  :       Optional[int]     			=						model.encoder.embeddings(SCREAMING_SNAKE_CASE__			)
		assert torch.allclose(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       atol=1E-3			)

		# verify encoder hidden states
		snake_case  :       str     			=						original_model.encoder(SCREAMING_SNAKE_CASE__			)
		snake_case  :       int     			=						model.encoder(SCREAMING_SNAKE_CASE__			).last_hidden_state
		assert torch.allclose(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       atol=1E-2			)

		# verify decoder hidden states
		snake_case  :       Any     			=						original_model(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			).logits
		snake_case  :       List[str]     			=						model(SCREAMING_SNAKE_CASE__ ,       decoder_input_ids=SCREAMING_SNAKE_CASE__			).logits
		assert torch.allclose(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       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(SCREAMING_SNAKE_CASE__			)
				processor.save_pretrained(SCREAMING_SNAKE_CASE__			)

		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'''
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


lowercase__						       =      get_logger(__name__)






class 	snake_case__       (      enum.Enum					):







		"""simple docstring"""

		lowerCamelCase     							=						"""all_checks"""
		lowerCamelCase     							=						"""basic_checks"""
		lowerCamelCase     							=						"""no_checks"""







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=None			)  ->							str:


		'''simple docstring'''


		if expected_checksums is None:
				logger.info('''Unable to verify checksums.'''			)
				return
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise ExpectedMoreDownloadedFiles(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise UnexpectedDownloadedFile(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		snake_case  :       Any     			=						[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
		snake_case  :       Union[str, Any]     			=						''' for ''' + verification_name if verification_name is not None else ''''''
		if len(SCREAMING_SNAKE_CASE__			) > 0:
				raise NonMatchingChecksumError(
				    F'Checksums didn\'t match{for_verification_name}:\n'
				    F'{bad_urls}\n'
				    '''Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'''			)
		logger.info('''All the checksums matched successfully''' + for_verification_name			)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		if expected_splits is None:
				logger.info('''Unable to verify splits sizes.'''			)
				return
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise ExpectedMoreSplits(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise UnexpectedSplits(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		snake_case  :       Dict     			=						[
		    {'''expected''': expected_splits[name], '''recorded''': recorded_splits[name]}
		    for name in expected_splits
		    if expected_splits[name].num_examples != recorded_splits[name].num_examples
		]
		if len(SCREAMING_SNAKE_CASE__			) > 0:
				raise NonMatchingSplitsSizesError(str(SCREAMING_SNAKE_CASE__			)			)
		logger.info('''All the splits matched successfully.'''			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ = True			)  ->							dict:


		'''simple docstring'''


		if record_checksum:
				snake_case  :       Any     			=						shaaaa()
				with open(SCREAMING_SNAKE_CASE__ ,       '''rb'''			) as f:
						for chunk in iter(lambda: f.read(1 << 20			) ,       B''''''			):
								m.update(SCREAMING_SNAKE_CASE__			)
						snake_case  :       Any     			=						m.hexdigest()
		else:
				snake_case  :       Tuple     			=						None
		return {"num_bytes": os.path.getsize(SCREAMING_SNAKE_CASE__			), "checksum": checksum}




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Dict:


		'''simple docstring'''


		if dataset_size and config.IN_MEMORY_MAX_SIZE:
				return dataset_size < config.IN_MEMORY_MAX_SIZE
		else:
				return False


