




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__							(		SCREAMING_SNAKE_CASE_:							Dict		,    SCREAMING_SNAKE_CASE_:							List[str]=False		,    SCREAMING_SNAKE_CASE_:							Optional[Any]=False		,    SCREAMING_SNAKE_CASE_:							Optional[Any]="data2vec." )     -> Dict:




				'''simple docstring'''





				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'{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__							(		SCREAMING_SNAKE_CASE_:							List[Any]		,    SCREAMING_SNAKE_CASE_:							Union[str, Any]		,    SCREAMING_SNAKE_CASE_:							Union[str, Any]=False		,    SCREAMING_SNAKE_CASE_:							Optional[int]=False		,    SCREAMING_SNAKE_CASE_:							Any="data2vec_vision." )     -> List[str]:




				'''simple docstring'''





				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

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

												A__            =     table
												A__            =     index



def    lowerCAmelCase__							(		)     -> Any:




				'''simple docstring'''





				A__            =     argparse.ArgumentParser(
				    "Convert Data2VecVision to HF for image classification and pretraining"		,    add_help=snake_case__ )
				parser.add_argument("--hf_checkpoint_name"		,    type=snake_case__ )
				parser.add_argument("--input_size"		,    default=2_2_4		,    type=snake_case__		,    help="images input size" )
				parser.add_argument("--beit_checkpoint"		,    default=""		,    help="beit checkpoint" )

				return parser.parse_args()



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]		,    SCREAMING_SNAKE_CASE_:							Dict		,    SCREAMING_SNAKE_CASE_:							str )     -> Union[str, Any]:




				'''simple docstring'''





				def load_state_dict(SCREAMING_SNAKE_CASE_:							Optional[int]		,    SCREAMING_SNAKE_CASE_:							Tuple		,    SCREAMING_SNAKE_CASE_:							str=""		,    SCREAMING_SNAKE_CASE_:							Dict="relative_position_index" ):
								A__            =     []
								A__            =     []
								A__            =     []
								# copy state_dict so _load_from_state_dict can modify it
								A__            =     getattr(snake_case__		,    "_metadata"		,    snake_case__ )
								A__            =     state_dict.copy()
								if metadata is not None:
												A__            =     metadata

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

								load(snake_case__		,    prefix=snake_case__ )

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

								A__            =     warn_missing_keys

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

				A__            =     {
				    "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": 1_0_0_0,
								        "use_mean_pooling": True,
								        "init_scale": 0.001,
								        "use_rel_pos_bias": True,
								    } )

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

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

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

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

				load_state_dict(snake_case__		,    snake_case__		,    prefix="" )

				return model



def    lowerCAmelCase__							(		)     -> List[str]:




				'''simple docstring'''





				A__            =     get_args()

				A__            =     "ft1k" in args.hf_checkpoint_name
				A__            =     "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
				A__            =     DataaVecVisionConfig()
				if is_finetuned:
								A__            =     True
								A__            =     False
								A__            =     True
								A__            =     1_0_0_0

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

				if is_large:
								A__            =     1_0_2_4
								A__            =     4_0_9_6
								A__            =     2_4
								A__            =     1_6

				# 2. Load Beit model
				A__            =     load_beit_model(snake_case__		,    snake_case__		,    snake_case__ )
				orig_model.eval()

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

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

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

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

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

				# 5. Forward HF Data2VecVision model
				with torch.no_grad():
								A__            =     hf_model(snake_case__ )

				A__            =     hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

				print(F'max_absolute_diff = {max_absolute_diff}' )
				A__            =     torch.allclose(snake_case__		,    snake_case__		,    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"




def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							list		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							int )     -> int:




				'''simple docstring'''





				A__            =     int((lower_bound + upper_bound) // 2 )
				if array[r] == value:
								return r
				if lower_bound >= upper_bound:
								return -1
				if array[r] < value:
								return binary_search(SCREAMING_SNAKE_CASE_		,    r + 1		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ )
				else:
								return binary_search(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    r - 1		,    SCREAMING_SNAKE_CASE_ )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							list )     -> list:




				'''simple docstring'''





				A__            =     0
				if matrix[index][0] == value:
								return [index, 0]
				while index < len(SCREAMING_SNAKE_CASE_ ) and matrix[index][0] < value:
								A__            =     binary_search(matrix[index]		,    0		,    len(matrix[index] ) - 1		,    SCREAMING_SNAKE_CASE_ )
								if r != -1:
												return [index, r]
								index += 1
				return [-1, -1]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

