


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 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Optional[Any]:


						'''simple docstring'''






						__snake_case				=		model.config

						__snake_case				=		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				=		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 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Any:


						'''simple docstring'''






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

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

						return name





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	int:


						'''simple docstring'''






						for key in orig_state_dict.copy().keys():
												__snake_case				=		orig_state_dict.pop(_lowerCAmelCase )

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

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

						return orig_state_dict





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase=None      ,	_lowerCAmelCase=False )						->	Dict:


						'''simple docstring'''






						__snake_case				=		DonutModel.from_pretrained(_lowerCAmelCase ).eval()

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

						__snake_case				=		original_model.state_dict()
						__snake_case				=		convert_state_dict(_lowerCAmelCase      ,	_lowerCAmelCase )
						model.load_state_dict(_lowerCAmelCase )

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

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

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

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

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

						# verify decoder hidden states
						__snake_case				=		original_model(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase ).logits
						__snake_case				=		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							:					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[int]     						=     parser.parse_args()
	convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf

if is_vision_available():
	from PIL import Image





def 	_lowerCAmelCase       (    _lowerCAmelCase="" )						->	str:


						'''simple docstring'''






						__snake_case				=		tempfile.mkdtemp()
						return os.path.join(_lowerCAmelCase      ,	str(uuid.uuida() ) + suffix )






@require_soundfile
@require_torch
class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

											# Ensure that the tensor and the agent_type's tensor are the same
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()     ,    atol=1e-4						)						)

											del agent_type

											# Ensure the path remains even after the object deletion
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)

											# Ensure that the file contains the same value as the original tensor
											__snake_case		,					__snake_case				=		sf.read(SCREAMING_SNAKE_CASE						)
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    torch.tensor(SCREAMING_SNAKE_CASE						)     ,    atol=1e-4						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		get_new_path(suffix=".wav"						)
											sf.write(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    1_6_0_0_0						)

											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)

											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()     ,    atol=1e-4						)						)
											self.assertEqual(agent_type.to_string()     ,    SCREAMING_SNAKE_CASE						)






@require_vision
@require_torch
class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		torch.randint(0     ,    2_5_6     ,    (6_4, 6_4, 3)						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

											# Ensure that the tensor and the agent_type's tensor are the same
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type._tensor     ,    atol=1e-4						)						)

											self.assertIsInstance(agent_type.to_raw()     ,    Image.Image						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Any:




											'''simple docstring'''

											__snake_case				=		Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"
											__snake_case				=		Image.open(SCREAMING_SNAKE_CASE						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)

											self.assertTrue(path.samefile(agent_type.to_string()						)						)
											self.assertTrue(image == agent_type.to_raw()						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)






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




											'''simple docstring'''

											__snake_case				=		Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"
											__snake_case				=		Image.open(SCREAMING_SNAKE_CASE						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)

											self.assertFalse(path.samefile(agent_type.to_string()						)						)
											self.assertTrue(image == agent_type.to_raw()						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)






class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Any:




											'''simple docstring'''

											__snake_case				=		"Hey!"
											__snake_case				=		AgentText(SCREAMING_SNAKE_CASE						)

											self.assertEqual(SCREAMING_SNAKE_CASE     ,    agent_type.to_string()						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)





