
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__    ):
				lowerCamelCase_						=			model.config

				lowerCamelCase_						=			DonutSwinConfig(
				    image_size=original_config.input_size	, patch_size=4	, depths=original_config.encoder_layer	, num_heads=[4, 8, 1_6, 3_2]	, window_size=original_config.window_size	, embed_dim=1_2_8	, )
				lowerCamelCase_						=			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__    ):
				if "encoder.model" in name:
								lowerCamelCase_						=			name.replace("encoder.model"	, "encoder"    )
				if "decoder.model" in name:
								lowerCamelCase_						=			name.replace("decoder.model"	, "decoder"    )
				if "patch_embed.proj" in name:
								lowerCamelCase_						=			name.replace("patch_embed.proj"	, "embeddings.patch_embeddings.projection"    )
				if "patch_embed.norm" in name:
								lowerCamelCase_						=			name.replace("patch_embed.norm"	, "embeddings.norm"    )
				if name.startswith("encoder"    ):
								if "layers" in name:
												lowerCamelCase_						=			"encoder." + name
								if "attn.proj" in name:
												lowerCamelCase_						=			name.replace("attn.proj"	, "attention.output.dense"    )
								if "attn" in name and "mask" not in name:
												lowerCamelCase_						=			name.replace("attn"	, "attention.self"    )
								if "norm1" in name:
												lowerCamelCase_						=			name.replace("norm1"	, "layernorm_before"    )
								if "norm2" in name:
												lowerCamelCase_						=			name.replace("norm2"	, "layernorm_after"    )
								if "mlp.fc1" in name:
												lowerCamelCase_						=			name.replace("mlp.fc1"	, "intermediate.dense"    )
								if "mlp.fc2" in name:
												lowerCamelCase_						=			name.replace("mlp.fc2"	, "output.dense"    )

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

				return name





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				for key in orig_state_dict.copy().keys():
								lowerCamelCase_						=			orig_state_dict.pop(lowerCamelCase__    )

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

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

				return orig_state_dict





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__=None	, lowerCamelCase__=False    ):
				# load original model
				lowerCamelCase_						=			DonutModel.from_pretrained(lowerCamelCase__    ).eval()

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

				lowerCamelCase_						=			original_model.state_dict()
				lowerCamelCase_						=			convert_state_dict(lowerCamelCase__	, lowerCamelCase__    )
				model.load_state_dict(lowerCamelCase__    )

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

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

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

				lowerCamelCase_						=			original_model.encoder.model.patch_embed(lowerCamelCase__    )
				lowerCamelCase_	,    lowerCamelCase_						=			model.encoder.embeddings(lowerCamelCase__    )
				assert torch.allclose(lowerCamelCase__	, lowerCamelCase__	, atol=1e-3    )

				# verify encoder hidden states
				lowerCamelCase_						=			original_model.encoder(lowerCamelCase__    )
				lowerCamelCase_						=			model.encoder(lowerCamelCase__    ).last_hidden_state
				assert torch.allclose(lowerCamelCase__	, lowerCamelCase__	, atol=1e-2    )

				# verify decoder hidden states
				lowerCamelCase_						=			original_model(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ).logits
				lowerCamelCase_						=			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				=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				=parser.parse_args()
			convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[0 for i in range(r + 1    )]
				# nc0 = 1
				lowerCamelCase_						=			1
				for i in range(1	, n + 1    ):
								# to compute current row from previous row.
								lowerCamelCase_						=			min(lowerCamelCase__	, lowerCamelCase__    )
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]





print(binomial_coefficient(n=1_0, r=5))


