

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 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[int]									= model.config

	UpperCAmelCase__:	int									= 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	,)
	UpperCAmelCase__:	Any									= MBartConfig(
	    is_decoder=SCREAMING_SNAKE_CASE	,is_encoder_decoder=SCREAMING_SNAKE_CASE	,add_cross_attention=SCREAMING_SNAKE_CASE	,decoder_layers=original_config.decoder_layer	,max_position_embeddings=original_config.max_position_embeddings	,vocab_size=len(
	        model.decoder.tokenizer      )	,scale_embedding=SCREAMING_SNAKE_CASE	,add_final_layer_norm=SCREAMING_SNAKE_CASE	,)

	return encoder_config, decoder_config



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

		if name == "encoder.norm.weight":
			UpperCAmelCase__:	List[str]									= "encoder.layernorm.weight"
		if name == "encoder.norm.bias":
			UpperCAmelCase__:	Tuple									= "encoder.layernorm.bias"

	return name



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	for key in orig_state_dict.copy().keys():
		UpperCAmelCase__:	Optional[int]									= orig_state_dict.pop(SCREAMING_SNAKE_CASE      )

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

			if "weight" in key:
				UpperCAmelCase__:	Optional[int]									= val[:dim, :]
				UpperCAmelCase__:	Any									= val[dim : dim * 2, :]
				UpperCAmelCase__:	List[Any]									= val[-dim:, :]
			else:
				UpperCAmelCase__:	Optional[Any]									= val[:dim]
				UpperCAmelCase__:	List[str]									= val[dim : dim * 2]
				UpperCAmelCase__:	List[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:
			UpperCAmelCase__:	Tuple									= val

	return orig_state_dict



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=None	,SCREAMING_SNAKE_CASE=False      ):
	# load original model
	UpperCAmelCase__:	Optional[int]									= DonutModel.from_pretrained(SCREAMING_SNAKE_CASE      ).eval()

	# load HuggingFace model
	UpperCAmelCase__							,						UpperCAmelCase__:	List[str]									= get_configs(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Any									= DonutSwinModel(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	List[Any]									= MBartForCausalLM(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Optional[int]									= VisionEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE	,decoder=SCREAMING_SNAKE_CASE      )
	model.eval()

	UpperCAmelCase__:	Dict									= original_model.state_dict()
	UpperCAmelCase__:	Tuple									= convert_state_dict(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	model.load_state_dict(SCREAMING_SNAKE_CASE      )

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

	UpperCAmelCase__:	Optional[int]									= XLMRobertaTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE	,from_slow=SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Tuple									= DonutImageProcessor(
	    do_align_long_axis=original_model.config.align_long_axis	,size=original_model.config.input_size[::-1]      )
	UpperCAmelCase__:	int									= DonutProcessor(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	List[str]									= processor(SCREAMING_SNAKE_CASE	,return_tensors="pt"      ).pixel_values

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

	UpperCAmelCase__:	Optional[int]									= original_model.encoder.model.patch_embed(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__							,						UpperCAmelCase__:	Dict									= model.encoder.embeddings(SCREAMING_SNAKE_CASE      )
	assert torch.allclose(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,atol=1e-3      )

	# verify encoder hidden states
	UpperCAmelCase__:	Union[str, Any]									= original_model.encoder(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Dict									= model.encoder(SCREAMING_SNAKE_CASE      ).last_hidden_state
	assert torch.allclose(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,atol=1e-2      )

	# verify decoder hidden states
	UpperCAmelCase__:	Any									= original_model(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ).logits
	UpperCAmelCase__:	Optional[int]									= model(SCREAMING_SNAKE_CASE	,decoder_input_ids=SCREAMING_SNAKE_CASE      ).logits
	assert torch.allclose(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,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(SCREAMING_SNAKE_CASE      )
		processor.save_pretrained(SCREAMING_SNAKE_CASE      )

	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__":
					_lowerCAmelCase  :      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.""",
					)

					_lowerCAmelCase  :      Dict			 =parser.parse_args()
					convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase  :      Tuple			 ={
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Tuple			 =["""MobileNetV2FeatureExtractor"""]
					_lowerCAmelCase  :      Optional[int]			 =["""MobileNetV2ImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      List[str]			 =[
					    """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileNetV2ForImageClassification""",
					    """MobileNetV2ForSemanticSegmentation""",
					    """MobileNetV2Model""",
					    """MobileNetV2PreTrainedModel""",
					    """load_tf_weights_in_mobilenet_v2""",
					]


if TYPE_CHECKING:
					from .configuration_mobilenet_va import (
					    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileNetVaConfig,
					    MobileNetVaOnnxConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
										from .image_processing_mobilenet_va import MobileNetVaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilenet_va import (
										    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileNetVaForImageClassification,
										    MobileNetVaForSemanticSegmentation,
										    MobileNetVaModel,
										    MobileNetVaPreTrainedModel,
										    load_tf_weights_in_mobilenet_va,
										)


else:
					import sys

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