





'''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		( SCREAMING_SNAKE_CASE_ :			int )							->     int:

       """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=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       __snake_case		( SCREAMING_SNAKE_CASE_ :			Union[str, Any] )							->     List[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       __snake_case		( SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			Union[str, Any] )							->     Tuple:

       """simple docstring"""


       for key in orig_state_dict.copy().keys():
              UpperCAmelCase  =						orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )

              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       __snake_case		( SCREAMING_SNAKE_CASE_ :			Union[str, Any]			,	SCREAMING_SNAKE_CASE_ :			List[Any]=None			,	SCREAMING_SNAKE_CASE_ :			int=False )							->     List[Any]:

       """simple docstring"""


       UpperCAmelCase  =						DonutModel.from_pretrained(SCREAMING_SNAKE_CASE_ ).eval()

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

       UpperCAmelCase  =						original_model.state_dict()
       UpperCAmelCase  =						convert_state_dict(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )
       model.load_state_dict(SCREAMING_SNAKE_CASE_ )

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

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

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

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

       # verify decoder hidden states
       UpperCAmelCase  =						original_model(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ).logits
       UpperCAmelCase  =						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__":
    a__			:					List[Any]       						=						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.',
    )

    a__			:					Optional[Any]       						=						parser.parse_args()
    convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


UpperCAmelCase_							:					Optional[Any]		 =   [
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
    {'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
    {'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
    {'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
    {'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str=True       )					->				List[Any]:
	if with_config:
		return [
		    {
		        "testcase_name": d["dataset"] + "/" + d["config_name"],
		        "dataset": d["dataset"],
		        "config_name": d["config_name"],
		    }
		    for d in DATASETS_ON_HF_GCP
		]
	else:
		return [
		    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
		]






@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_lowercase	)	)
class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Optional[int]     					=				None
	_lowercase		:   str     					=				None




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Optional[int]					,      __A						:     Optional[Any]							):
		with TemporaryDirectory() as tmp_dir:
			__A :			List[Any]		    =			dataset_module_factory(__A					,      cache_dir=__A							)

			__A :			Tuple		    =			import_main_class(dataset_module.module_path					,      dataset=__A							)

			__A :			DatasetBuilder		    =			builder_cls(
			    cache_dir=__A					,      config_name=__A					,      hash=dataset_module.hash					,      )

			__A :			List[Any]		    =			"""/""".join(
			    [
			        HF_GCP_BASE_URL,
			        builder_instance._relative_data_dir(with_hash=__A							).replace(os.sep					,      """/"""							),
			        config.DATASET_INFO_FILENAME,
			    ]							)
			__A :			Union[str, Any]		    =			cached_path(__A					,      cache_dir=__A							)
			self.assertTrue(os.path.exists(__A							)							)






@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict       )					->				Optional[Any]:
	__A :			Optional[Any]		    =			tmp_path_factory.mktemp("""test_hf_gcp"""       ) / """test_wikipedia_simple"""
	__A :			Union[str, Any]		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			List[Any]		    =			import_main_class(dataset_module.module_path       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
	__A :			Any		    =			None
	builder_instance.download_and_prepare()
	__A :			Union[str, Any]		    =			builder_instance.as_dataset()
	assert ds







@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				List[str]:
	__A :			Tuple		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			str		    =			import_main_class(dataset_module.module_path			,dataset=a__       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	__A :			Optional[int]		    =			builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(a__			,a__       )
	assert "train" in ds
	assert isinstance(ds["""train"""]			,a__       )
	assert next(iter(ds["""train"""]       )       )





