




'''simple docstring'''




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 						snake_case__    ( lowerCamelCase__				:   Any			,		lowerCamelCase__				:   int=False			,		lowerCamelCase__				:   Tuple=False			,		lowerCamelCase__				:   Union[str, Any]="data2vec."			)   ->   Optional[Any]:
					A_ :     Optional[int] =							'''backbone.''' if is_semantic else ''''''

					A_ :     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 						snake_case__    ( lowerCamelCase__				:   List[str]			,		lowerCamelCase__				:   Dict			,		lowerCamelCase__				:   str=False			,		lowerCamelCase__				:   Dict=False			,		lowerCamelCase__				:   Optional[Any]="data2vec_vision."			)   ->   Any:
					for i in range(config.num_hidden_layers			):
										A_ :     List[Any] =							'''backbone.''' if is_semantic else ''''''
										# queries, keys and values
										A_ :     Tuple =							state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'			)
										A_ :     Optional[Any] =							state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'			)
										A_ :     Any =							state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'			)

										A_ :     Dict =							in_proj_weight[
										    : config.hidden_size, :
										]
										A_ :     Tuple =							q_bias
										A_ :     Tuple =							in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										A_ :     Optional[int] =							in_proj_weight[
										    -config.hidden_size :, :
										]
										A_ :     Union[str, Any] =							v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										A_ :     List[Any] =							state_dict.pop(f'{prefix}blocks.{i}.gamma_1'			)
										A_ :     Optional[Any] =							state_dict.pop(f'{prefix}blocks.{i}.gamma_2'			)

										A_ :     Union[str, Any] =							gamma_a
										A_ :     str =							gamma_a

										# relative_position bias table + index
										if not has_lm_head:
															# each layer has its own relative position bias
															A_ :     Tuple =							state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table'			)
															A_ :     Optional[Any] =							state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index'			)

															A_ :     Tuple =							table
															A_ :     List[Any] =							index




def 						snake_case__    ( )   ->   Union[str, Any]:
					A_ :     str =							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_2_4			,		type=lowerCamelCase__			,		help='''images input size'''			)
					parser.add_argument('''--beit_checkpoint'''			,		default=''''''			,		help='''beit checkpoint'''			)

					return parser.parse_args()




def 						snake_case__    ( lowerCamelCase__				:   int			,		lowerCamelCase__				:   Any			,		lowerCamelCase__				:   str			)   ->   List[str]:
					def load_state_dict(lowerCamelCase__				:   Union[str, Any]			,		lowerCamelCase__				:   Dict			,		lowerCamelCase__				:   int=""			,		lowerCamelCase__				:   List[Any]="relative_position_index"			):
										A_ :     str =							[]
										A_ :     int =							[]
										A_ :     Dict =							[]
										# copy state_dict so _load_from_state_dict can modify it
										A_ :     int =							getattr(lowerCamelCase__			,		'''_metadata'''			,		lowerCamelCase__			)
										A_ :     List[str] =							state_dict.copy()
										if metadata is not None:
															A_ :     Tuple =							metadata

										def load(lowerCamelCase__				:   Any			,		lowerCamelCase__				:   str=""			):
															A_ :     Union[str, Any] =							{} 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__			)

										A_ :     Dict =							[]
										A_ :     str =							[]
										for key in missing_keys:
															A_ :     int =							True
															for ignore_key in ignore_missing.split('''|'''			):
																				if ignore_key in key:
																									A_ :     Any =							False
																									break
															if keep_flag:
																				warn_missing_keys.append(lowerCamelCase__			)
															else:
																				ignore_missing_keys.append(lowerCamelCase__			)

										A_ :     Any =							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__			)			)

					A_ :     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''': 1_0_0_0,
										        '''use_mean_pooling''': True,
										        '''init_scale''': 0.001,
										        '''use_rel_pos_bias''': True,
										    }			)

					A_ :     Optional[Any] =							create_model(
					    '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''			,		**lowerCamelCase__			,		)
					A_ :     int =							model.patch_embed.patch_size
					A_ :     int =							(args.input_size // patch_size[0], args.input_size // patch_size[1])
					A_ :     Union[str, Any] =							torch.load(args.beit_checkpoint			,		map_location='''cpu'''			)

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

					A_ :     List[str] =							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:
															A_ :     Optional[int] =							checkpoint_model[key]
															A_ :     Union[str, Any] =							rel_pos_bias.size()
															A_ :     List[Any] =							model.state_dict()[key].size()
															A_ :     Any =							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 						snake_case__    ( )   ->   Tuple:
					A_ :     int =							get_args()

					A_ :     Union[str, Any] =							'''ft1k''' in args.hf_checkpoint_name
					A_ :     Any =							'''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_ :     Tuple =							DataaVecVisionConfig()
					if is_finetuned:
										A_ :     Tuple =							True
										A_ :     List[Any] =							False
										A_ :     List[Any] =							True
										A_ :     Optional[Any] =							1_0_0_0

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

					if is_large:
										A_ :     Optional[Any] =							1_0_2_4
										A_ :     Tuple =							4_0_9_6
										A_ :     str =							2_4
										A_ :     Optional[Any] =							1_6

					# 2. Load Beit model
					A_ :     Optional[int] =							load_beit_model(lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__			)
					orig_model.eval()

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

					A_ :     List[Any] =							(pixel_values,) if is_finetuned else (pixel_values, None)
					with torch.no_grad():
										A_ :     Tuple =							orig_model(*lowerCamelCase__			)

					# 4. Load HF Data2VecVision model
					if is_finetuned:
										A_ :     Dict =							DataaVecVisionForImageClassification(lowerCamelCase__			)
										hf_model.eval()
										A_ :     Any =							False
										A_ :     Any =							'''data2vec_vision.'''
					else:
										A_ :     Any =							DataaVecVisionModel(lowerCamelCase__			)
										hf_model.eval()
										A_ :     str =							True
										A_ :     List[str] =							''''''

					A_ :     int =							create_rename_keys(lowerCamelCase__			,		hf_prefix=lowerCamelCase__			,		has_lm_head=lowerCamelCase__			)
					A_ :     Tuple =							orig_model.state_dict()
					for src, dest in rename_keys:
										A_ :     Dict =							state_dict.pop(lowerCamelCase__			)
										A_ :     Any =							val

					read_in_q_k_v(lowerCamelCase__			,		lowerCamelCase__			,		hf_prefix=lowerCamelCase__			,		has_lm_head=lowerCamelCase__			)
					A_ :     Tuple =							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():
										A_ :     Dict =							hf_model(lowerCamelCase__			)

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

					# 6. Compare
					A_ :     List[str] =							torch.max(torch.abs(hf_output - orig_model_output			)			).item()

					print(f'max_absolute_diff = {max_absolute_diff}'			)
					A_ :     Any =							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"









'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__          =						logging.get_logger(__name__)

snake_case__          =						{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}






class     UpperCamelCase_     (a__     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					'qdqbert'







						def __init__(     self      :       Union[str, Any]							, _lowerCamelCase      :       List[Any]=30522							, _lowerCamelCase      :       str=768							, _lowerCamelCase      :       Optional[Any]=12							, _lowerCamelCase      :       str=12							, _lowerCamelCase      :       List[Any]=3072							, _lowerCamelCase      :       Union[str, Any]="gelu"							, _lowerCamelCase      :       Dict=0.1							, _lowerCamelCase      :       Union[str, Any]=0.1							, _lowerCamelCase      :       Optional[int]=512							, _lowerCamelCase      :       Dict=2							, _lowerCamelCase      :       Optional[Any]=0.02							, _lowerCamelCase      :       List[Any]=1E-12							, _lowerCamelCase      :       Tuple=True							, _lowerCamelCase      :       Tuple=1							, _lowerCamelCase      :       Optional[Any]=0							, _lowerCamelCase      :       Optional[int]=2							, **_lowerCamelCase      :       List[str]							, ):





											"""simple docstring"""







											super().__init__(pad_token_id=_lowerCamelCase							, bos_token_id=_lowerCamelCase							, eos_token_id=_lowerCamelCase							, **_lowerCamelCase    )

											A_ :     Optional[Any] =							vocab_size
											A_ :     str =							max_position_embeddings
											A_ :     List[str] =							hidden_size
											A_ :     str =							num_hidden_layers
											A_ :     List[str] =							num_attention_heads
											A_ :     List[str] =							intermediate_size
											A_ :     int =							hidden_act
											A_ :     Tuple =							hidden_dropout_prob
											A_ :     Optional[Any] =							attention_probs_dropout_prob
											A_ :     Dict =							initializer_range
											A_ :     Optional[int] =							type_vocab_size
											A_ :     Any =							layer_norm_eps
											A_ :     Optional[int] =							use_cache



