"""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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Optional[int]:
				SCREAMING_SNAKE_CASE__		  =				model.config

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

				return encoder_config, decoder_config




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

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

				return name




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   int:
				for key in orig_state_dict.copy().keys():
								SCREAMING_SNAKE_CASE__		  =				orig_state_dict.pop(__UpperCAmelCase       )

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

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

				return orig_state_dict




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=False       )							->   List[Any]:
				# load original model
				SCREAMING_SNAKE_CASE__		  =				DonutModel.from_pretrained(__UpperCAmelCase       ).eval()

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

				SCREAMING_SNAKE_CASE__		  =				original_model.state_dict()
				SCREAMING_SNAKE_CASE__		  =				convert_state_dict(__UpperCAmelCase  ,					__UpperCAmelCase       )
				model.load_state_dict(__UpperCAmelCase       )

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

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

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

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

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

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

				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)





"""simple docstring"""






import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


_A   					=    HUGGINGFACE_HUB_CACHE


_A   					=    'config.json'
_A   					=    'diffusion_pytorch_model.bin'
_A   					=    'diffusion_flax_model.msgpack'
_A   					=    'model.onnx'
_A   					=    'diffusion_pytorch_model.safetensors'
_A   					=    'weights.pb'
_A   					=    'https://huggingface.co'
_A   					=    default_cache_path
_A   					=    'diffusers_modules'
_A   					=    os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))
_A   					=    ['fp16', 'non-ema']
_A   					=    '.self_attn'




