


import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				A       (			UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					LxmertTokenizer
					A__     =					LxmertTokenizerFast
					A__     =					True
					A__     =					True

					def     lowerCamelCase__	(self       :		List[Any]						)		->  List[Any]:




							"""simple docstring"""


							super().setUp()

							lowercase__	     =						[
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							lowercase__	     =						os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES["""vocab_file"""]						)
							with open(self.vocab_file ,   """w""" ,   encoding="""utf-8"""						) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)

					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		List[Any]						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						"""UNwant\u00E9d,running"""
							lowercase__	     =						"""unwanted, running"""
							return input_text, output_text

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						self.tokenizer_class(self.vocab_file						)

							lowercase__	     =						tokenizer.tokenize("""UNwant\u00E9d,running"""						)
							self.assertListEqual(_UpperCAmelCase ,   ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]						)
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						) ,   [7, 4, 5, 10, 8, 9]						)


					def     lowerCamelCase__	(self       :		str						)		->  List[Any]:




							"""simple docstring"""


							if not self.test_rust_tokenizer:
									return

							lowercase__	     =						self.get_tokenizer()
							lowercase__	     =						self.get_rust_tokenizer()

							lowercase__	     =						"""I was born in 92000, and this is falsé."""

							lowercase__	     =						tokenizer.tokenize(_UpperCAmelCase						)
							lowercase__	     =						rust_tokenizer.tokenize(_UpperCAmelCase						)
							self.assertListEqual(_UpperCAmelCase ,   _UpperCAmelCase						)

							lowercase__	     =						tokenizer.encode(_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase						)
							lowercase__	     =						rust_tokenizer.encode(_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase						)
							self.assertListEqual(_UpperCAmelCase ,   _UpperCAmelCase						)

							lowercase__	     =						self.get_rust_tokenizer()
							lowercase__	     =						tokenizer.encode(_UpperCAmelCase						)
							lowercase__	     =						rust_tokenizer.encode(_UpperCAmelCase						)
							self.assertListEqual(_UpperCAmelCase ,   _UpperCAmelCase						)





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   (      __magic_name__	:				Any	)  ->    int:
		"""simple docstring"""




		lowercase__	     =						model.config

		lowercase__	     =						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       ,       )
		lowercase__	     =						MBartConfig(
		    is_decoder=__magic_name__       ,       is_encoder_decoder=__magic_name__       ,       add_cross_attention=__magic_name__       ,       decoder_layers=original_config.decoder_layer       ,       max_position_embeddings=original_config.max_position_embeddings       ,       vocab_size=len(
		        model.decoder.tokenizer	)       ,       scale_embedding=__magic_name__       ,       add_final_layer_norm=__magic_name__       ,       )

		return encoder_config, decoder_config






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




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

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

		return name






def 					UpperCamelCase   (      __magic_name__	:				Any       ,       __magic_name__	:				str	)  ->    Optional[int]:
		"""simple docstring"""




		for key in orig_state_dict.copy().keys():
				lowercase__	     =						orig_state_dict.pop(__magic_name__	)

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

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

		return orig_state_dict






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				List[Any]=None       ,       __magic_name__	:				Dict=False	)  ->    int:
		"""simple docstring"""




		lowercase__	     =						DonutModel.from_pretrained(__magic_name__	).eval()

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

		lowercase__	     =						original_model.state_dict()
		lowercase__	     =						convert_state_dict(__magic_name__       ,       __magic_name__	)
		model.load_state_dict(__magic_name__	)

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

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

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

		lowercase__	     =						original_model.encoder.model.patch_embed(__magic_name__	)
		lowercase__     ,      lowercase__	     =						model.encoder.embeddings(__magic_name__	)
		assert torch.allclose(__magic_name__       ,       __magic_name__       ,       atol=1E-3	)

		# verify encoder hidden states
		lowercase__	     =						original_model.encoder(__magic_name__	)
		lowercase__	     =						model.encoder(__magic_name__	).last_hidden_state
		assert torch.allclose(__magic_name__       ,       __magic_name__       ,       atol=1E-2	)

		# verify decoder hidden states
		lowercase__	     =						original_model(__magic_name__       ,       __magic_name__       ,       __magic_name__	).logits
		lowercase__	     =						model(__magic_name__       ,       decoder_input_ids=__magic_name__	).logits
		assert torch.allclose(__magic_name__       ,       __magic_name__       ,       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(__magic_name__	)
				processor.save_pretrained(__magic_name__	)

		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			:						Dict        =  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[int]        =  parser.parse_args()
	convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

