
"""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				(			A__:    List[Any]     )  ->     Union[str, Any]:
			__lowerCamelCase					:	Optional[int]	       =				model.config

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

						if name == "encoder.norm.weight":
									__lowerCamelCase					:	Optional[Any]	       =				'encoder.layernorm.weight'
						if name == "encoder.norm.bias":
									__lowerCamelCase					:	Tuple	       =				'encoder.layernorm.bias'

			return name





def 						UpperCAmelCase				(			A__:    Tuple							,				A__:    Any     )  ->     List[Any]:
			for key in orig_state_dict.copy().keys():
						__lowerCamelCase					:	Optional[int]	       =				orig_state_dict.pop(_lowerCamelCase     )

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

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

			return orig_state_dict





def 						UpperCAmelCase				(			A__:    Union[str, Any]							,				A__:    Dict=None							,				A__:    str=False     )  ->     Tuple:
			# load original model
			__lowerCamelCase					:	int	       =				DonutModel.from_pretrained(_lowerCamelCase     ).eval()

			# load HuggingFace model
			__lowerCamelCase       ,      __lowerCamelCase					:	str	       =				get_configs(_lowerCamelCase     )
			__lowerCamelCase					:	str	       =				DonutSwinModel(_lowerCamelCase     )
			__lowerCamelCase					:	str	       =				MBartForCausalLM(_lowerCamelCase     )
			__lowerCamelCase					:	Tuple	       =				VisionEncoderDecoderModel(encoder=_lowerCamelCase							,				decoder=_lowerCamelCase     )
			model.eval()

			__lowerCamelCase					:	Optional[Any]	       =				original_model.state_dict()
			__lowerCamelCase					:	int	       =				convert_state_dict(_lowerCamelCase							,				_lowerCamelCase     )
			model.load_state_dict(_lowerCamelCase     )

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

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

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

			__lowerCamelCase					:	Optional[Any]	       =				original_model.encoder.model.patch_embed(_lowerCamelCase     )
			__lowerCamelCase       ,      __lowerCamelCase					:	Any	       =				model.encoder.embeddings(_lowerCamelCase     )
			assert torch.allclose(_lowerCamelCase							,				_lowerCamelCase							,				atol=1E-3     )

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

			# verify decoder hidden states
			__lowerCamelCase					:	Optional[int]	       =				original_model(_lowerCamelCase							,				_lowerCamelCase							,				_lowerCamelCase     ).logits
			__lowerCamelCase					:	List[Any]	       =				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__":
					a_				:    Tuple										=     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_				:    str										=     parser.parse_args()
					convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""
import pytest

import datasets


# Import fixture modules as plugins
a_				:    Tuple										=     ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    Optional[Any]     )  ->     List[str]:
			# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
			for item in items:
						if any(marker in item.keywords for marker in ['integration', 'unit']     ):
									continue
						item.add_marker(pytest.mark.unit     )





def 						UpperCAmelCase				(			A__:    int     )  ->     Optional[int]:
			config.addinivalue_line('markers'							,				'torchaudio_latest: mark test to run with torchaudio>=0.12'     )





@pytest.fixture(autouse=A__     )
def 						UpperCAmelCase				(			A__:    Optional[Any]							,				A__:    List[Any]     )  ->     Any:
			# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
			__lowerCamelCase					:	str	       =				tmp_path_factory.getbasetemp() / 'cache'
			__lowerCamelCase					:	Dict	       =				test_hf_cache_home / 'datasets'
			__lowerCamelCase					:	int	       =				test_hf_cache_home / 'metrics'
			__lowerCamelCase					:	List[str]	       =				test_hf_cache_home / 'modules'
			monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'							,				str(A__     )     )
			monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'							,				str(A__     )     )
			monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'							,				str(A__     )     )
			__lowerCamelCase					:	List[Any]	       =				test_hf_datasets_cache / 'downloads'
			monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'							,				str(A__     )     )
			__lowerCamelCase					:	Optional[int]	       =				test_hf_datasets_cache / 'downloads' / 'extracted'
			monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'							,				str(A__     )     )





@pytest.fixture(autouse=A__							,				scope='session'     )
def 						UpperCAmelCase				(			)  ->     Any:
			datasets.disable_progress_bar()





@pytest.fixture(autouse=A__     )
def 						UpperCAmelCase				(			A__:    Optional[Any]     )  ->     Dict:
			# don't take tests into account when counting downloads
			monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'							,				A__     )






@pytest.fixture
def 						UpperCAmelCase				(			A__:    Union[str, Any]     )  ->     Optional[Any]:
			# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
			# To be removed once SQLAlchemy 2.0 supported
			monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'							,				A__     )



