


"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a_       							=    logging.get_logger(__name__)






def      a__ (				__lowercase     ,			__lowercase=False     ,			__lowercase=False   )	->	str:
				_A					  =			"backbone." if is_semantic else ""

				_A					  =			[]
				for i in range(config.num_hidden_layers   ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight""")   )
								rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""")   )
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""")   )
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""")   )
								rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""")   )
								rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""")   )
								rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""")   )
								rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""")   )
								rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""")   )
								rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""")   )

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (f"""{prefix}cls_token""", "beit.embeddings.cls_token"),
				        (f"""{prefix}patch_embed.proj.weight""", "beit.embeddings.patch_embeddings.projection.weight"),
				        (f"""{prefix}patch_embed.proj.bias""", "beit.embeddings.patch_embeddings.projection.bias"),
				        (f"""{prefix}pos_embed""", "beit.embeddings.position_embeddings"),
				    ]   )

				if has_lm_head:
								# mask token + layernorm
								rename_keys.extend(
								    [
								        ("mask_token", "beit.embeddings.mask_token"),
								        ("norm.weight", "layernorm.weight"),
								        ("norm.bias", "layernorm.bias"),
								    ]   )
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
								        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
								        ("head.weight", "classifier.weight"),
								        ("head.bias", "classifier.bias"),
								    ]   )

				return rename_keys






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase=False     ,			__lowercase=False   )	->	Optional[int]:
				for i in range(config.num_hidden_layers   ):
								_A					  =			"backbone." if is_semantic else ""
								# queries, keys and values
								_A					  =			state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""   )
								_A					  =			state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""   )
								_A					  =			state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""   )

								_A					  =			in_proj_weight[
								    : config.hidden_size, :
								]
								_A					  =			q_bias
								_A					  =			in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_A					  =			in_proj_weight[
								    -config.hidden_size :, :
								]
								_A					  =			v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								_A					  =			state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""   )
								_A					  =			state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""   )

								_A					  =			gamma_a
								_A					  =			gamma_a






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	Tuple:
				_A					  =			dct.pop(__lowercase   )
				_A					  =			val






def      a__ (				)	->	List[str]:
				_A					  =			"http://images.cocodataset.org/val2017/000000039769.jpg"
				_A					  =			Image.open(requests.get(__lowercase     ,			stream=__lowercase   ).raw   )
				return im






@torch.no_grad()
def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase=False   )	->	Tuple:
				_A					  =			False if "rvlcdip" in checkpoint_url else True
				_A					  =			BeitConfig(use_absolute_position_embeddings=__lowercase     ,			use_mask_token=__lowercase   )

				# size of the architecture
				if "large" in checkpoint_url or "dit-l" in checkpoint_url:
								_A					  =			1024
								_A					  =			4096
								_A					  =			24
								_A					  =			16

				# labels
				if "rvlcdip" in checkpoint_url:
								_A					  =			16
								_A					  =			"huggingface/label-files"
								_A					  =			"rvlcdip-id2label.json"
								_A					  =			json.load(open(hf_hub_download(__lowercase     ,			__lowercase     ,			repo_type="dataset"   )     ,			"r"   )   )
								_A					  =			{int(__lowercase   ): v for k, v in idalabel.items()}
								_A					  =			idalabel
								_A					  =			{v: k for k, v in idalabel.items()}

				# load state_dict of original model, remove and rename some keys
				_A					  =			torch.hub.load_state_dict_from_url(__lowercase     ,			map_location="cpu"   )["model"]

				_A					  =			create_rename_keys(__lowercase     ,			has_lm_head=__lowercase   )
				for src, dest in rename_keys:
								rename_key(__lowercase     ,			__lowercase     ,			__lowercase   )
				read_in_q_k_v(__lowercase     ,			__lowercase     ,			has_lm_head=__lowercase   )

				# load HuggingFace model
				_A					  =			BeitForMaskedImageModeling(__lowercase   ) if has_lm_head else BeitForImageClassification(__lowercase   )
				model.eval()
				model.load_state_dict(__lowercase   )

				# Check outputs on an image
				_A					  =			BeitImageProcessor(
				    size=config.image_size     ,			resample=PILImageResampling.BILINEAR     ,			do_center_crop=__lowercase   )
				_A					  =			prepare_img()

				_A					  =			image_processor(images=__lowercase     ,			return_tensors="pt"   )
				_A					  =			encoding["pixel_values"]

				_A					  =			model(__lowercase   )
				_A					  =			outputs.logits

				# verify logits
				_A					  =			[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
				assert logits.shape == torch.Size(__lowercase   ), "Shape of logits not as expected"

				Path(__lowercase   ).mkdir(exist_ok=__lowercase   )
				print(f"""Saving model to {pytorch_dump_folder_path}"""   )
				model.save_pretrained(__lowercase   )
				print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
				image_processor.save_pretrained(__lowercase   )

				if push_to_hub:
								if has_lm_head:
												_A					  =			"dit-base" if "base" in checkpoint_url else "dit-large"
								else:
												_A					  =			"dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
								image_processor.push_to_hub(
								    repo_path_or_name=Path(__lowercase     ,			__lowercase   )     ,			organization="nielsr"     ,			commit_message="Add image processor"     ,			use_temp_dir=__lowercase     ,			)
								model.push_to_hub(
								    repo_path_or_name=Path(__lowercase     ,			__lowercase   )     ,			organization="nielsr"     ,			commit_message="Add model"     ,			use_temp_dir=__lowercase     ,			)


if __name__ == "__main__":
		a_       							=    argparse.ArgumentParser()

		parser.add_argument(
		    "--checkpoint_url",
		    default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
		    type=str,
		    help="URL to the original PyTorch checkpoint (.pth file).",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
		)
		parser.add_argument(
		    "--push_to_hub",
		    action="store_true",
		)
		a_       							=    parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def      a__ (				__lowercase   )	->	Optional[int]:
				_A					  =			[
				    "encoder.version",
				    "decoder.version",
				    "model.encoder.version",
				    "model.decoder.version",
				    "_float_tensor",
				    "decoder.output_projection.weight",
				]
				for k in ignore_keys:
								state_dict.pop(__lowercase     ,			__lowercase   )






def      a__ (				__lowercase   )	->	List[Any]:
				_A  ,		_A					  =			emb.weight.shape
				_A					  =			nn.Linear(__lowercase     ,			__lowercase     ,			bias=__lowercase   )
				_A					  =			emb.weight.data
				return lin_layer






def      a__ (				__lowercase     ,			__lowercase="facebook/mbart-large-en-ro"     ,			__lowercase=False     ,			__lowercase=False   )	->	List[str]:
				_A					  =			torch.load(__lowercase     ,			map_location="cpu"   )["model"]
				remove_ignore_keys_(__lowercase   )
				_A					  =			state_dict["encoder.embed_tokens.weight"].shape[0]

				_A					  =			MBartConfig.from_pretrained(__lowercase     ,			vocab_size=__lowercase   )
				if mbart_aa and finetuned:
								_A					  =			"relu"

				_A					  =			state_dict["decoder.embed_tokens.weight"]
				_A					  =			MBartForConditionalGeneration(__lowercase   )
				model.model.load_state_dict(__lowercase   )

				if finetuned:
								_A					  =			make_linear_from_emb(model.model.shared   )

				return model


if __name__ == "__main__":
		a_       							=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
		)
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument(
		    "--hf_config",
		    default="facebook/mbart-large-cc25",
		    type=str,
		    help="Which huggingface architecture to use: mbart-large",
		)
		parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
		parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
		a_       							=    parser.parse_args()
		a_       							=    convert_fairseq_mbart_checkpoint_from_disk(
		    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
		)
		model.save_pretrained(args.pytorch_dump_folder_path)