





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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_=False      ,					lowerCAmelCase_=False      ,					lowerCAmelCase_="data2vec."):
	'''simple docstring'''


	lowerCamelCase_			:							Optional[int]      =       "backbone." if is_semantic else ""

	lowerCamelCase_			:							Tuple      =       []
	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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_=False      ,					lowerCAmelCase_=False      ,					lowerCAmelCase_="data2vec_vision."):
	'''simple docstring'''


	for i in range(config.num_hidden_layers):
		lowerCamelCase_			:							Optional[Any]      =       "backbone." if is_semantic else ""
		# queries, keys and values
		lowerCamelCase_			:							Optional[int]      =       state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight""")
		lowerCamelCase_			:							Tuple      =       state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias""")
		lowerCamelCase_			:							Tuple      =       state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias""")

		lowerCamelCase_			:							Tuple      =       in_proj_weight[
		    : config.hidden_size, :
		]
		lowerCamelCase_			:							Tuple      =       q_bias
		lowerCamelCase_			:							Union[str, Any]      =       in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		lowerCamelCase_			:							List[str]      =       in_proj_weight[
		    -config.hidden_size :, :
		]
		lowerCamelCase_			:							Any      =       v_bias

		# gamma_1 and gamma_2
		# we call them lambda because otherwise they are renamed when using .from_pretrained
		lowerCamelCase_			:							List[str]      =       state_dict.pop(F"""{prefix}blocks.{i}.gamma_1""")
		lowerCamelCase_			:							Tuple      =       state_dict.pop(F"""{prefix}blocks.{i}.gamma_2""")

		lowerCamelCase_			:							Union[str, Any]      =       gamma_a
		lowerCamelCase_			:							int      =       gamma_a

		# relative_position bias table + index
		if not has_lm_head:
			# each layer has its own relative position bias
			lowerCamelCase_			:							Union[str, Any]      =       state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_bias_table""")
			lowerCamelCase_			:							int      =       state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_index""")

			lowerCamelCase_			:							str      =       table
			lowerCamelCase_			:							int      =       index







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							str      =       argparse.ArgumentParser(
	    "Convert Data2VecVision to HF for image classification and pretraining"      ,					add_help=_A)
	parser.add_argument("--hf_checkpoint_name"      ,					type=_A)
	parser.add_argument("--input_size"      ,					default=224      ,					type=_A      ,					help="images input size")
	parser.add_argument("--beit_checkpoint"      ,					default=""      ,					help="beit checkpoint")

	return parser.parse_args()







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	def load_state_dict(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_=""      ,					lowerCAmelCase_="relative_position_index"):
		lowerCamelCase_			:							int      =       []
		lowerCamelCase_			:							Optional[Any]      =       []
		lowerCamelCase_			:							Dict      =       []
		# copy state_dict so _load_from_state_dict can modify it
		lowerCamelCase_			:							Union[str, Any]      =       getattr(_A      ,					"_metadata"      ,					_A)
		lowerCamelCase_			:							Any      =       state_dict.copy()
		if metadata is not None:
			lowerCamelCase_			:							Optional[int]      =       metadata

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

		load(_A      ,					prefix=_A)

		lowerCamelCase_			:							List[str]      =       []
		lowerCamelCase_			:							Tuple      =       []
		for key in missing_keys:
			lowerCamelCase_			:							str      =       True
			for ignore_key in ignore_missing.split("|"):
				if ignore_key in key:
					lowerCamelCase_			:							Dict      =       False
					break
			if keep_flag:
				warn_missing_keys.append(_A)
			else:
				ignore_missing_keys.append(_A)

		lowerCamelCase_			:							Union[str, Any]      =       warn_missing_keys

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

	lowerCamelCase_			:							Tuple      =       {
	    "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": 1000,
		        "use_mean_pooling": True,
		        "init_scale": 0.0_01,
		        "use_rel_pos_bias": True,
		    })

	lowerCamelCase_			:							Any      =       create_model(
	    "beit_large_patch16_224" if is_large else "beit_base_patch16_224"      ,					**_A      ,					)
	lowerCamelCase_			:							Optional[int]      =       model.patch_embed.patch_size
	lowerCamelCase_			:							List[Any]      =       (args.input_size // patch_size[0], args.input_size // patch_size[1])
	lowerCamelCase_			:							Optional[Any]      =       torch.load(args.beit_checkpoint      ,					map_location="cpu")

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

	lowerCamelCase_			:							Union[str, Any]      =       list(checkpoint_model.keys())
	for key in all_keys:
		if "relative_position_index" in key:
			checkpoint_model.pop(_A)

		if "relative_position_bias_table" in key:
			lowerCamelCase_			:							Union[str, Any]      =       checkpoint_model[key]
			lowerCamelCase_   ,lowerCamelCase_			:							Any      =       rel_pos_bias.size()
			lowerCamelCase_   ,lowerCamelCase_			:							int      =       model.state_dict()[key].size()
			lowerCamelCase_			:							Optional[int]      =       model.patch_embed.patch_shape
			if dst_patch_shape[0] != dst_patch_shape[1]:
				raise NotImplementedError()

	load_state_dict(_A      ,					_A      ,					prefix="")

	return model







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							str      =       get_args()

	lowerCamelCase_			:							Any      =       "ft1k" in args.hf_checkpoint_name
	lowerCamelCase_			:							List[str]      =       "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
	lowerCamelCase_			:							Optional[Any]      =       DataaVecVisionConfig()
	if is_finetuned:
		lowerCamelCase_			:							Optional[Any]      =       True
		lowerCamelCase_			:							Tuple      =       False
		lowerCamelCase_			:							Any      =       True
		lowerCamelCase_			:							Optional[Any]      =       1000

		lowerCamelCase_			:							Optional[Any]      =       "huggingface/label-files"
		lowerCamelCase_			:							Dict      =       "imagenet-1k-id2label.json"
		lowerCamelCase_			:							List[Any]      =       json.load(open(hf_hub_download(_A      ,					_A      ,					repo_type="dataset")      ,					"r"))
		lowerCamelCase_			:							List[Any]      =       {int(_A): v for k, v in idalabel.items()}
		lowerCamelCase_			:							Optional[Any]      =       idalabel
		lowerCamelCase_			:							Any      =       {v: k for k, v in idalabel.items()}
	else:
		lowerCamelCase_			:							Union[str, Any]      =       False
		lowerCamelCase_			:							Union[str, Any]      =       True
		lowerCamelCase_			:							Dict      =       False

	if is_large:
		lowerCamelCase_			:							Any      =       1024
		lowerCamelCase_			:							List[str]      =       4096
		lowerCamelCase_			:							List[str]      =       24
		lowerCamelCase_			:							Union[str, Any]      =       16

	# 2. Load Beit model
	lowerCamelCase_			:							Optional[Any]      =       load_beit_model(_A      ,					_A      ,					_A)
	orig_model.eval()

	# 3. Forward Beit model
	lowerCamelCase_			:							Optional[Any]      =       BeitImageProcessor(size=config.image_size      ,					do_center_crop=_A)
	lowerCamelCase_			:							str      =       Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png")
	lowerCamelCase_			:							Tuple      =       image_processor(images=_A      ,					return_tensors="pt")
	lowerCamelCase_			:							str      =       encoding["pixel_values"]

	lowerCamelCase_			:							List[str]      =       (pixel_values,) if is_finetuned else (pixel_values, None)
	with torch.no_grad():
		lowerCamelCase_			:							Any      =       orig_model(*_A)

	# 4. Load HF Data2VecVision model
	if is_finetuned:
		lowerCamelCase_			:							Tuple      =       DataaVecVisionForImageClassification(_A)
		hf_model.eval()
		lowerCamelCase_			:							Optional[int]      =       False
		lowerCamelCase_			:							List[str]      =       "data2vec_vision."
	else:
		lowerCamelCase_			:							int      =       DataaVecVisionModel(_A)
		hf_model.eval()
		lowerCamelCase_			:							int      =       True
		lowerCamelCase_			:							List[str]      =       ""

	lowerCamelCase_			:							Tuple      =       create_rename_keys(_A      ,					hf_prefix=_A      ,					has_lm_head=_A)
	lowerCamelCase_			:							Optional[int]      =       orig_model.state_dict()
	for src, dest in rename_keys:
		lowerCamelCase_			:							Optional[Any]      =       state_dict.pop(_A)
		lowerCamelCase_			:							Dict      =       val

	read_in_q_k_v(_A      ,					_A      ,					hf_prefix=_A      ,					has_lm_head=_A)
	lowerCamelCase_   ,lowerCamelCase_			:							Tuple      =       hf_model.load_state_dict(_A      ,					strict=_A)
	print("HF missing"      ,					_A)
	print("HF unexpected_keys"      ,					_A)

	# 5. Forward HF Data2VecVision model
	with torch.no_grad():
		lowerCamelCase_			:							List[str]      =       hf_model(_A)

	lowerCamelCase_			:							Union[str, Any]      =       hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

	# 6. Compare
	lowerCamelCase_			:							Tuple      =       torch.max(torch.abs(hf_output - orig_model_output)).item()

	print(F"""max_absolute_diff = {max_absolute_diff}""")
	lowerCamelCase_			:							List[Any]      =       torch.allclose(_A      ,					_A      ,					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"





import math
import os

import cva
import numpy as np

__magic_name__       					=      2_55.0







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							List[Any]      =       np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

	return 20 * math.logaa(PIXEL_MAX / math.sqrt(lowerCAmelCase_))







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       os.path.dirname(os.path.realpath(lowerCAmelCase_))
	# Loading images (original image and compressed image)
	lowerCamelCase_			:							Optional[int]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/original_image.png"))
	lowerCamelCase_			:							List[str]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/compressed_image.png")      ,					1)

	lowerCamelCase_			:							str      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-base.png"))
	lowerCamelCase_			:							int      =       cva.imread(
	    os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-comp-10.jpg")      ,					1)

	# Value expected: 29.73dB
	print("-- First Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")

	# # Value expected: 31.53dB (Wikipedia Example)
	print("\n-- Second Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")


if __name__ == "__main__":
							main()

