



import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      Optional[Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				Tuple=False    )->       Any:





						"""simple docstring"""





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

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        ("cls_token", "deit.embeddings.cls_token"),
						        ("dist_token", "deit.embeddings.distillation_token"),
						        ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
						        ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
						        ("pos_embed", "deit.embeddings.position_embeddings"),
						    ]    )

						if base_model:
												# layernorm + pooler
												rename_keys.extend(
												    [
												        ("norm.weight", "layernorm.weight"),
												        ("norm.bias", "layernorm.bias"),
												        ("pre_logits.fc.weight", "pooler.dense.weight"),
												        ("pre_logits.fc.bias", "pooler.dense.bias"),
												    ]    )

												# if just the base model, we should remove "deit" from all keys that start with "deit"
												A__							=       [(pair[0], pair[1][4:]) if pair[1].startswith("deit"    ) else pair for pair in rename_keys]
						else:
												# layernorm + classification heads
												rename_keys.extend(
												    [
												        ("norm.weight", "deit.layernorm.weight"),
												        ("norm.bias", "deit.layernorm.bias"),
												        ("head.weight", "cls_classifier.weight"),
												        ("head.bias", "cls_classifier.bias"),
												        ("head_dist.weight", "distillation_classifier.weight"),
												        ("head_dist.bias", "distillation_classifier.bias"),
												    ]    )

						return rename_keys
def  UpperCamelCase						(       _A			:				Union[str, Any]			,							_A			:				Any			,							_A			:				Dict=False    )->       Any:





						"""simple docstring"""





						for i in range(config.num_hidden_layers    ):
												if base_model:
																		A__							=       ""
												else:
																		A__							=       "deit."
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												A__							=       state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""    )
												A__							=       state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""    )
												# next, add query, keys and values (in that order) to the state dict
												A__							=       in_proj_weight[
												    : config.hidden_size, :
												]
												A__							=       in_proj_bias[: config.hidden_size]
												A__							=       in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												A__							=       in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												A__							=       in_proj_weight[
												    -config.hidden_size :, :
												]
												A__							=       in_proj_bias[-config.hidden_size :]
def  UpperCamelCase						(       _A			:				Union[str, Any]			,							_A			:				List[Any]			,							_A			:				int    )->       Optional[int]:





						"""simple docstring"""





						A__							=       dct.pop(_A    )
						A__							=       val
def  UpperCamelCase						(       )->       List[str]:





						"""simple docstring"""





						A__							=       "http://images.cocodataset.org/val2017/000000039769.jpg"
						A__							=       Image.open(requests.get(_A			,							stream=_A    ).raw    )
						return im
@torch.no_grad()
def  UpperCamelCase						(       _A			:				Any			,							_A			:				List[Any]    )->       List[str]:





						"""simple docstring"""





						A__							=       DeiTConfig()
						# all deit models have fine-tuned heads
						A__							=       False
						# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
						A__							=       1000
						A__							=       "huggingface/label-files"
						A__							=       "imagenet-1k-id2label.json"
						A__							=       json.load(open(hf_hub_download(_A			,							_A			,							repo_type="dataset"    )			,							"r"    )    )
						A__							=       {int(_A    ): v for k, v in idalabel.items()}
						A__							=       idalabel
						A__							=       {v: k for k, v in idalabel.items()}
						A__							=       int(deit_name[-6:-4]    )
						A__							=       int(deit_name[-3:]    )
						# size of the architecture
						if deit_name[9:].startswith("tiny"    ):
												A__							=       192
												A__							=       768
												A__							=       12
												A__							=       3
						elif deit_name[9:].startswith("small"    ):
												A__							=       384
												A__							=       1536
												A__							=       12
												A__							=       6
						if deit_name[9:].startswith("base"    ):
												pass
						elif deit_name[4:].startswith("large"    ):
												A__							=       1024
												A__							=       4096
												A__							=       24
												A__							=       16

						# load original model from timm
						A__							=       timm.create_model(_A			,							pretrained=_A    )
						timm_model.eval()

						# load state_dict of original model, remove and rename some keys
						A__							=       timm_model.state_dict()
						A__							=       create_rename_keys(_A			,							_A    )
						for src, dest in rename_keys:
												rename_key(_A			,							_A			,							_A    )
						read_in_q_k_v(_A			,							_A			,							_A    )

						# load HuggingFace model
						A__							=       DeiTForImageClassificationWithTeacher(_A    ).eval()
						model.load_state_dict(_A    )

						# Check outputs on an image, prepared by DeiTImageProcessor
						A__							=       int(
						    (256 / 224) * config.image_size    )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
						A__							=       DeiTImageProcessor(size=_A			,							crop_size=config.image_size    )
						A__							=       image_processor(images=prepare_img()			,							return_tensors="pt"    )
						A__							=       encoding["pixel_values"]
						A__							=       model(_A    )

						A__							=       timm_model(_A    )
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(_A			,							outputs.logits			,							atol=1E-3    )

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


if __name__ == "__main__":
						UpperCAmelCase_							:      Optional[Any]      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--deit_name",
						    default="vit_deit_base_distilled_patch16_224",
						    type=str,
						    help="Name of the DeiT timm model you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
						)

						UpperCAmelCase_							:      Union[str, Any]      =  parser.parse_args()
						convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       RobertaPreLayerNormConfig.from_pretrained(
						    _A			,							architectures=["RobertaPreLayerNormForMaskedLM"]    )

						# convert state_dict
						A__							=       torch.load(hf_hub_download(repo_id=_A			,							filename="pytorch_model.bin"    )    )
						A__							=       {}
						for tensor_key, tensor_value in original_state_dict.items():
												# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
												if tensor_key.startswith("roberta."    ):
																		A__							=       "roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

												# The original implementation contains weights which are not used, remove them from the state_dict
												if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
																		continue

												A__							=       tensor_value

						A__							=       RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=_A			,							config=_A			,							state_dict=_A    )
						model.save_pretrained(_A    )

						# convert tokenizer
						A__							=       AutoTokenizer.from_pretrained(_A    )
						tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint-repo",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase_							:      List[Any]      =  parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





