


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__	(      a	:   int				)    ->	Any:





			"""simple docstring"""
			a__ :str						  =    model.config

			a__ :List[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=128	,  )
			a__ :List[str]						  =    MBartConfig(
			    is_decoder=a	,  is_encoder_decoder=a	,  add_cross_attention=a	,  decoder_layers=original_config.decoder_layer	,  max_position_embeddings=original_config.max_position_embeddings	,  vocab_size=len(
			        model.decoder.tokenizer				)	,  scale_embedding=a	,  add_final_layer_norm=a	,  )

			return encoder_config, decoder_config


def 				lowerCamelCase__	(      a	:   List[str]				)    ->	List[str]:





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

						if name == "encoder.norm.weight":
									a__ :Optional[Any]						  =    "encoder.layernorm.weight"
						if name == "encoder.norm.bias":
									a__ :Any						  =    "encoder.layernorm.bias"

			return name


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





			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						a__ :Tuple						  =    orig_state_dict.pop(a				)

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

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

			return orig_state_dict


def 				lowerCamelCase__	(      a	:   str	,  a	:   str=None	,  a	:   str=False				)    ->	Union[str, Any]:





			"""simple docstring"""
			# load original model
			a__ :Dict						  =    DonutModel.from_pretrained(a				).eval()

			# load HuggingFace model
			a__  ,  a__ :List[Any]						  =    get_configs(a				)
			a__ :Union[str, Any]						  =    DonutSwinModel(a				)
			a__ :Optional[Any]						  =    MBartForCausalLM(a				)
			a__ :Union[str, Any]						  =    VisionEncoderDecoderModel(encoder=a	,  decoder=a				)
			model.eval()

			a__ :Optional[Any]						  =    original_model.state_dict()
			a__ :Dict						  =    convert_state_dict(a	,  a				)
			model.load_state_dict(a				)

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

			a__ :Dict						  =    XLMRobertaTokenizerFast.from_pretrained(a	,  from_slow=a				)
			a__ :List[Any]						  =    DonutImageProcessor(
			    do_align_long_axis=original_model.config.align_long_axis	,  size=original_model.config.input_size[::-1]				)
			a__ :Optional[Any]						  =    DonutProcessor(a	,  a				)
			a__ :str						  =    processor(a	,  return_tensors="pt"				).pixel_values

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

			a__ :Union[str, Any]						  =    original_model.encoder.model.patch_embed(a				)
			a__  ,  a__ :List[str]						  =    model.encoder.embeddings(a				)
			assert torch.allclose(a	,  a	,  atol=1e-3				)

			# verify encoder hidden states
			a__ :str						  =    original_model.encoder(a				)
			a__ :Any						  =    model.encoder(a				).last_hidden_state
			assert torch.allclose(a	,  a	,  atol=1e-2				)

			# verify decoder hidden states
			a__ :str						  =    original_model(a	,  a	,  a				).logits
			a__ :str						  =    model(a	,  decoder_input_ids=a				).logits
			assert torch.allclose(a	,  a	,  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(a				)
						processor.save_pretrained(a				)

			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__":
		snake_case__      =						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.''',
		)

		snake_case__      =						parser.parse_args()
		convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import os
import time

import numpy as np
import onnxruntime as ort


snake_case__			 =						'''1'''
snake_case__			 =						'''0'''
snake_case__			 =						'''1'''

snake_case__			 =						ort.SessionOptions()
snake_case__			 =						ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
snake_case__			 =						['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
snake_case__			 =						ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
snake_case__			 =						ort.RunOptions()

snake_case__			 =						128
snake_case__			 =						1
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
snake_case__			 =						time.time()
snake_case__			 =						2000
snake_case__			 =						{}
for iter in range(max_iters):
		snake_case__			 =						sess.run(
		    None,
		    {
		        sess.get_inputs()[0].name: input_ids,
		        sess.get_inputs()[1].name: attention_mask,
		        sess.get_inputs()[2].name: token_type_ids,
		    },
		    run_options=run_opt,
		)
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1000 / max_iters))


