


"""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 _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    model.config

			__lowerCAmelCase       					=    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					,				)
			__lowerCAmelCase       					=    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 _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			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			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			for key in orig_state_dict.copy().keys():
						__lowerCAmelCase       					=    orig_state_dict.pop(_UpperCamelCase   )

						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			(	_UpperCamelCase					,				_UpperCamelCase=None					,				_UpperCamelCase=False   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    DonutModel.from_pretrained(_UpperCamelCase   ).eval()

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

			__lowerCAmelCase       					=    original_model.state_dict()
			__lowerCAmelCase       					=    convert_state_dict(_UpperCamelCase					,				_UpperCamelCase   )
			model.load_state_dict(_UpperCamelCase   )

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

			__lowerCAmelCase       					=    XLMRobertaTokenizerFast.from_pretrained(_UpperCamelCase					,				from_slow=_UpperCamelCase   )
			__lowerCAmelCase       					=    DonutImageProcessor(
			    do_align_long_axis=original_model.config.align_long_axis					,				size=original_model.config.input_size[::-1]   )
			__lowerCAmelCase       					=    DonutProcessor(_UpperCamelCase					,				_UpperCamelCase   )
			__lowerCAmelCase       					=    processor(_UpperCamelCase					,				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}"					,				_UpperCamelCase   )
			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(_UpperCamelCase					,				add_special_tokens=_UpperCamelCase					,				return_tensors="pt"   )[
			    "input_ids"
			]

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

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

			# verify decoder hidden states
			__lowerCAmelCase       					=    original_model(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ).logits
			__lowerCAmelCase       					=    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  :     Tuple    					=    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  :     Tuple    					=    parser.parse_args()
	convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A  :     Union[str, Any]    					=    {
    "configuration_x_clip": [
        "XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XCLIPConfig",
        "XCLIPTextConfig",
        "XCLIPVisionConfig",
    ],
    "processing_x_clip": ["XCLIPProcessor"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A  :     Optional[int]    					=    [
	    "XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "XCLIPModel",
	    "XCLIPPreTrainedModel",
	    "XCLIPTextModel",
	    "XCLIPVisionModel",
	]

if TYPE_CHECKING:
	from .configuration_x_clip import (
	    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XCLIPConfig,
	    XCLIPTextConfig,
	    XCLIPVisionConfig,
	)
	from .processing_x_clip import XCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_x_clip import (
		    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XCLIPModel,
		    XCLIPPreTrainedModel,
		    XCLIPTextModel,
		    XCLIPVisionModel,
		)

else:
	import sys

	A  :     Union[str, Any]    					=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




