


"""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 				A_       ( snake_case__		)					->  str:
			_UpperCamelCase					:List[Any]					     =	model.config

			_UpperCamelCase					:Union[str, Any]					     =	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=1_28       ,					)
			_UpperCamelCase					:Tuple					     =	MBartConfig(
			    is_decoder=snake_case__       ,					is_encoder_decoder=snake_case__       ,					add_cross_attention=snake_case__       ,					decoder_layers=original_config.decoder_layer       ,					max_position_embeddings=original_config.max_position_embeddings       ,					vocab_size=len(
			        model.decoder.tokenizer		)       ,					scale_embedding=snake_case__       ,					add_final_layer_norm=snake_case__       ,					)

			return encoder_config, decoder_config





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

						if name == "encoder.norm.weight":
									_UpperCamelCase					:Tuple					     =	'''encoder.layernorm.weight'''
						if name == "encoder.norm.bias":
									_UpperCamelCase					:int					     =	'''encoder.layernorm.bias'''

			return name





def 				A_       ( snake_case__       ,					snake_case__		)					->  Any:
			for key in orig_state_dict.copy().keys():
						_UpperCamelCase					:str					     =	orig_state_dict.pop(snake_case__		)

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

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

			return orig_state_dict





def 				A_       ( snake_case__       ,					snake_case__=None       ,					snake_case__=False		)					->  int:
			# load original model
			_UpperCamelCase					:Tuple					     =	DonutModel.from_pretrained(snake_case__		).eval()

			# load HuggingFace model
			_UpperCamelCase     ,      _UpperCamelCase					:Optional[Any]					     =	get_configs(snake_case__		)
			_UpperCamelCase					:int					     =	DonutSwinModel(snake_case__		)
			_UpperCamelCase					:int					     =	MBartForCausalLM(snake_case__		)
			_UpperCamelCase					:List[str]					     =	VisionEncoderDecoderModel(encoder=snake_case__       ,					decoder=snake_case__		)
			model.eval()

			_UpperCamelCase					:Optional[int]					     =	original_model.state_dict()
			_UpperCamelCase					:List[str]					     =	convert_state_dict(snake_case__       ,					snake_case__		)
			model.load_state_dict(snake_case__		)

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

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

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

			_UpperCamelCase					:int					     =	original_model.encoder.model.patch_embed(snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:Union[str, Any]					     =	model.encoder.embeddings(snake_case__		)
			assert torch.allclose(snake_case__       ,					snake_case__       ,					atol=1E-3		)

			# verify encoder hidden states
			_UpperCamelCase					:List[str]					     =	original_model.encoder(snake_case__		)
			_UpperCamelCase					:Optional[int]					     =	model.encoder(snake_case__		).last_hidden_state
			assert torch.allclose(snake_case__       ,					snake_case__       ,					atol=1E-2		)

			# verify decoder hidden states
			_UpperCamelCase					:List[Any]					     =	original_model(snake_case__       ,					snake_case__       ,					snake_case__		).logits
			_UpperCamelCase					:Dict					     =	model(snake_case__       ,					decoder_input_ids=snake_case__		).logits
			assert torch.allclose(snake_case__       ,					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(snake_case__		)
						processor.save_pretrained(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__":
					UpperCamelCase__				:Optional[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.""",
					)

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





"""simple docstring"""






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin






@require_jieba
class      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							CpmAntTokenizer
		A    =							False
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					super().setUp()

					_UpperCamelCase					:str					     =	[
					    '''<d>''',
					    '''</d>''',
					    '''<s>''',
					    '''</s>''',
					    '''</_>''',
					    '''<unk>''',
					    '''<pad>''',
					    '''</n>''',
					    '''我''',
					    '''是''',
					    '''C''',
					    '''P''',
					    '''M''',
					    '''A''',
					    '''n''',
					    '''t''',
					]
					_UpperCamelCase					:List[str]					     =	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]   )   )





		@tooslow
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b'''   )
					_UpperCamelCase					:List[str]					     =	'''今天天气真好！'''
					_UpperCamelCase					:Tuple					     =	['''今天''', '''天气''', '''真''', '''好''', '''！''']
					_UpperCamelCase					:Tuple					     =	tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Dict					     =	'''今天天气真好！'''
					_UpperCamelCase					:Optional[int]					     =	[tokenizer.bos_token] + tokens

					_UpperCamelCase					:List[str]					     =	[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	tokenizer.decode(SCREAMING_SNAKE_CASE__   )
					self.assertEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

