



import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase=False				,		_lowerCAmelCase=False				,		_lowerCAmelCase="data2vec."):
					UpperCamelCase_											=				"backbone." if is_semantic else ""

					UpperCamelCase_											=				[]
					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"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight"""))
										rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias"""))
										rename_keys.append(
										    (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight"""))
										rename_keys.append(
										    (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias"""))
										rename_keys.append(
										    (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight"""))
										rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias"""))
										rename_keys.append(
										    (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight"""))
										rename_keys.append(
										    (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias"""))
										rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight"""))
										rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias"""))

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

					if has_lm_head:
										# mask token + shared relative position bias + layernorm
										rename_keys.extend(
										    [
										        ("mask_token", f"""{hf_prefix}embeddings.mask_token"""),
										        (
										            "rel_pos_bias.relative_position_bias_table",
										            f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
										        ),
										        (
										            "rel_pos_bias.relative_position_index",
										            f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
										        ),
										        ("norm.weight", "layernorm.weight"),
										        ("norm.bias", "layernorm.bias"),
										    ])
					elif is_semantic:
										# semantic segmentation classification heads
										rename_keys.extend(
										    [
										        ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
										        ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
										        ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
										        ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
										    ])
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ("fc_norm.weight", f"""{hf_prefix}pooler.layernorm.weight"""),
										        ("fc_norm.bias", f"""{hf_prefix}pooler.layernorm.bias"""),
										        ("head.weight", "classifier.weight"),
										        ("head.bias", "classifier.bias"),
										    ])

					return rename_keys



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase=False				,		_lowerCAmelCase=False				,		_lowerCAmelCase="data2vec_vision."):
					for i in range(config.num_hidden_layers):
										UpperCamelCase_											=				"backbone." if is_semantic else ""
										# queries, keys and values
										UpperCamelCase_											=				state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight""")
										UpperCamelCase_											=				state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias""")
										UpperCamelCase_											=				state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias""")

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

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

										UpperCamelCase_											=				gamma_a
										UpperCamelCase_											=				gamma_a

										# relative_position bias table + index
										if not has_lm_head:
															# each layer has its own relative position bias
															UpperCamelCase_											=				state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table""")
															UpperCamelCase_											=				state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index""")

															UpperCamelCase_											=				table
															UpperCamelCase_											=				index



def 				_lowerCAmelCase      ():
					UpperCamelCase_											=				argparse.ArgumentParser(
					    "Convert Data2VecVision to HF for image classification and pretraining"				,		add_help=_lowerCAmelCase)
					parser.add_argument("--hf_checkpoint_name"				,		type=_lowerCAmelCase)
					parser.add_argument("--input_size"				,		default=2_24				,		type=_lowerCAmelCase				,		help="images input size")
					parser.add_argument("--beit_checkpoint"				,		default=""				,		help="beit checkpoint")

					return parser.parse_args()



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase):
					def load_state_dict(_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase=""				,		_lowerCAmelCase="relative_position_index"):
										UpperCamelCase_											=				[]
										UpperCamelCase_											=				[]
										UpperCamelCase_											=				[]
										# copy state_dict so _load_from_state_dict can modify it
										UpperCamelCase_											=				getattr(_lowerCAmelCase				,		"_metadata"				,		_lowerCAmelCase)
										UpperCamelCase_											=				state_dict.copy()
										if metadata is not None:
															UpperCamelCase_											=				metadata

										def load(_lowerCAmelCase				,		_lowerCAmelCase=""):
															UpperCamelCase_											=				{} if metadata is None else metadata.get(prefix[:-1]				,		{})
															module._load_from_state_dict(
															    _lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase)
															for name, child in module._modules.items():
																				if child is not None:
																									load(_lowerCAmelCase				,		prefix + name + ".")

										load(_lowerCAmelCase				,		prefix=_lowerCAmelCase)

										UpperCamelCase_											=				[]
										UpperCamelCase_											=				[]
										for key in missing_keys:
															UpperCamelCase_											=				True
															for ignore_key in ignore_missing.split("|"):
																				if ignore_key in key:
																									UpperCamelCase_											=				False
																									break
															if keep_flag:
																				warn_missing_keys.append(_lowerCAmelCase)
															else:
																				ignore_missing_keys.append(_lowerCAmelCase)

										UpperCamelCase_											=				warn_missing_keys

										if len(_lowerCAmelCase) > 0:
															print(
															    "Weights of {} not initialized from pretrained model: {}".format(
															        model.__class__.__name__				,		_lowerCAmelCase))
										if len(_lowerCAmelCase) > 0:
															print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__				,		_lowerCAmelCase))
										if len(_lowerCAmelCase) > 0:
															print(
															    "Ignored weights of {} not initialized from pretrained model: {}".format(
															        model.__class__.__name__				,		_lowerCAmelCase))
										if len(_lowerCAmelCase) > 0:
															print("\n".join(_lowerCAmelCase))

					UpperCamelCase_											=				{
					    "pretrained": False,
					    "use_shared_rel_pos_bias": True,
					    "use_abs_pos_emb": False,
					    "init_values": 0.1,
					}

					if is_finetuned:
										model_kwargs.update(
										    {
										        "num_classes": 10_00,
										        "use_mean_pooling": True,
										        "init_scale": 0.001,
										        "use_rel_pos_bias": True,
										    })

					UpperCamelCase_											=				create_model(
					    "beit_large_patch16_224" if is_large else "beit_base_patch16_224"				,		**_lowerCAmelCase				,		)
					UpperCamelCase_											=				model.patch_embed.patch_size
					UpperCamelCase_											=				(args.input_size // patch_size[0], args.input_size // patch_size[1])
					UpperCamelCase_											=				torch.load(args.beit_checkpoint				,		map_location="cpu")

					print(f"""Load ckpt from {args.beit_checkpoint}""")
					UpperCamelCase_											=				None
					for model_key in ("model", "module"):
										if model_key in checkpoint:
															UpperCamelCase_											=				checkpoint[model_key]
															print(f"""Load state_dict by model_key = {model_key}""")
															break

					UpperCamelCase_											=				list(checkpoint_model.keys())
					for key in all_keys:
										if "relative_position_index" in key:
															checkpoint_model.pop(_lowerCAmelCase)

										if "relative_position_bias_table" in key:
															UpperCamelCase_											=				checkpoint_model[key]
															UpperCamelCase_					,      UpperCamelCase_											=				rel_pos_bias.size()
															UpperCamelCase_					,      UpperCamelCase_											=				model.state_dict()[key].size()
															UpperCamelCase_											=				model.patch_embed.patch_shape
															if dst_patch_shape[0] != dst_patch_shape[1]:
																				raise NotImplementedError()

					load_state_dict(_lowerCAmelCase				,		_lowerCAmelCase				,		prefix="")

					return model



def 				_lowerCAmelCase      ():
					UpperCamelCase_											=				get_args()

					UpperCamelCase_											=				"ft1k" in args.hf_checkpoint_name
					UpperCamelCase_											=				"large" in args.hf_checkpoint_name

					if is_finetuned:
										# To convert Beit's data2vec_vision to HF you need to copy
										# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
										# into this folder.
										import modeling_finetune  # noqa: F401
					else:
										# To convert Beit's data2vec_vision to HF you need to copy
										# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
										# into this folder
										# IMPORTANT: Note that for now we've only converted the down-stream
										# model and not the full pretrained model. This means for the integration
										# test you need to add a `return x` after the following line:
										# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
										# to make the integration test pass.
										import modeling_cyclical  # noqa: F401

					# 1. Create model config
					UpperCamelCase_											=				DataaVecVisionConfig()
					if is_finetuned:
										UpperCamelCase_											=				True
										UpperCamelCase_											=				False
										UpperCamelCase_											=				True
										UpperCamelCase_											=				10_00

										UpperCamelCase_											=				"huggingface/label-files"
										UpperCamelCase_											=				"imagenet-1k-id2label.json"
										UpperCamelCase_											=				json.load(open(hf_hub_download(_lowerCAmelCase				,		_lowerCAmelCase				,		repo_type="dataset")				,		"r"))
										UpperCamelCase_											=				{int(_lowerCAmelCase): v for k, v in idalabel.items()}
										UpperCamelCase_											=				idalabel
										UpperCamelCase_											=				{v: k for k, v in idalabel.items()}
					else:
										UpperCamelCase_											=				False
										UpperCamelCase_											=				True
										UpperCamelCase_											=				False

					if is_large:
										UpperCamelCase_											=				10_24
										UpperCamelCase_											=				40_96
										UpperCamelCase_											=				24
										UpperCamelCase_											=				16

					# 2. Load Beit model
					UpperCamelCase_											=				load_beit_model(_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase)
					orig_model.eval()

					# 3. Forward Beit model
					UpperCamelCase_											=				BeitImageProcessor(size=config.image_size				,		do_center_crop=_lowerCAmelCase)
					UpperCamelCase_											=				Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png")
					UpperCamelCase_											=				image_processor(images=_lowerCAmelCase				,		return_tensors="pt")
					UpperCamelCase_											=				encoding["pixel_values"]

					UpperCamelCase_											=				(pixel_values,) if is_finetuned else (pixel_values, None)
					with torch.no_grad():
										UpperCamelCase_											=				orig_model(*_lowerCAmelCase)

					# 4. Load HF Data2VecVision model
					if is_finetuned:
										UpperCamelCase_											=				DataaVecVisionForImageClassification(_lowerCAmelCase)
										hf_model.eval()
										UpperCamelCase_											=				False
										UpperCamelCase_											=				"data2vec_vision."
					else:
										UpperCamelCase_											=				DataaVecVisionModel(_lowerCAmelCase)
										hf_model.eval()
										UpperCamelCase_											=				True
										UpperCamelCase_											=				""

					UpperCamelCase_											=				create_rename_keys(_lowerCAmelCase				,		hf_prefix=_lowerCAmelCase				,		has_lm_head=_lowerCAmelCase)
					UpperCamelCase_											=				orig_model.state_dict()
					for src, dest in rename_keys:
										UpperCamelCase_											=				state_dict.pop(_lowerCAmelCase)
										UpperCamelCase_											=				val

					read_in_q_k_v(_lowerCAmelCase				,		_lowerCAmelCase				,		hf_prefix=_lowerCAmelCase				,		has_lm_head=_lowerCAmelCase)
					UpperCamelCase_					,      UpperCamelCase_											=				hf_model.load_state_dict(_lowerCAmelCase				,		strict=_lowerCAmelCase)
					print("HF missing"				,		_lowerCAmelCase)
					print("HF unexpected_keys"				,		_lowerCAmelCase)

					# 5. Forward HF Data2VecVision model
					with torch.no_grad():
										UpperCamelCase_											=				hf_model(_lowerCAmelCase)

					UpperCamelCase_											=				hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

					# 6. Compare
					UpperCamelCase_											=				torch.max(torch.abs(hf_output - orig_model_output)).item()

					print(f"""max_absolute_diff = {max_absolute_diff}""")
					UpperCamelCase_											=				torch.allclose(_lowerCAmelCase				,		_lowerCAmelCase				,		atol=1e-3)
					print("Do both models output the same tensors?"				,		"🔥" if success else "💩")
					if not success:
										raise Exception("Something went wRoNg")

					# 7. Save
					print(f"""Saving to {args.hf_checkpoint_name}""")
					hf_model.save_pretrained(args.hf_checkpoint_name)
					image_processor.save_pretrained(args.hf_checkpoint_name)


if __name__ == "__main__":
	main()
	# Run the following to convert checkpoints
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./pretrained_base.pt \
	#          --hf_checkpoint_name "./data2vec-vision-base"
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./finetuned_base.pt \
	#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./pretrained_large.pt \
	#          --hf_checkpoint_name "./data2vec-vision-large"
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./finetuned_large.pt \
	#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"





from typing import TYPE_CHECKING

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


UpperCAmelCase				:							Optional[int]								={
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase				:							Optional[int]								=["""RealmTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase				:							Union[str, Any]								=[
	    """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RealmEmbedder""",
	    """RealmForOpenQA""",
	    """RealmKnowledgeAugEncoder""",
	    """RealmPreTrainedModel""",
	    """RealmReader""",
	    """RealmScorer""",
	    """load_tf_weights_in_realm""",
	]
	UpperCAmelCase				:							List[str]								=["""RealmRetriever"""]


if TYPE_CHECKING:
	from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
	from .tokenization_realm import RealmTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_realm import RealmTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_realm import (
		    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RealmEmbedder,
		    RealmForOpenQA,
		    RealmKnowledgeAugEncoder,
		    RealmPreTrainedModel,
		    RealmReader,
		    RealmScorer,
		    load_tf_weights_in_realm,
		)
		from .retrieval_realm import RealmRetriever


else:
	import sys

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