





'''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    lowercase_   (		lowercase__					,	lowercase__=False					,	lowercase__=False					,	lowercase__="data2vec."    ) ->Tuple:
						_snake_case: List[Any]	       =		'backbone.' if is_semantic else ''

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

												_snake_case: Optional[int]	       =		in_proj_weight[
												    : config.hidden_size, :
												]
												_snake_case: List[Any]	       =		q_bias
												_snake_case: List[Any]	       =		in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_snake_case: Union[str, Any]	       =		in_proj_weight[
												    -config.hidden_size :, :
												]
												_snake_case: Optional[int]	       =		v_bias

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

												_snake_case: Tuple	       =		gamma_a
												_snake_case: Optional[Any]	       =		gamma_a

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

																		_snake_case: Optional[Any]	       =		table
																		_snake_case: Optional[Any]	       =		index





def    lowercase_   (		) ->Any:
						_snake_case: int	       =		argparse.ArgumentParser(
						    'Convert Data2VecVision to HF for image classification and pretraining'					,	add_help=lowercase__    )
						parser.add_argument('--hf_checkpoint_name'					,	type=lowercase__    )
						parser.add_argument('--input_size'					,	default=224					,	type=lowercase__					,	help='images input size'    )
						parser.add_argument('--beit_checkpoint'					,	default=''					,	help='beit checkpoint'    )

						return parser.parse_args()





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->str:
						def load_state_dict(lowercase__					,	lowercase__					,	lowercase__=""					,	lowercase__="relative_position_index"    ):
												_snake_case: List[Any]	       =		[]
												_snake_case: Any	       =		[]
												_snake_case: Tuple	       =		[]
												# copy state_dict so _load_from_state_dict can modify it
												_snake_case: Any	       =		getattr(lowercase__					,	'_metadata'					,	lowercase__    )
												_snake_case: Any	       =		state_dict.copy()
												if metadata is not None:
																		_snake_case: Tuple	       =		metadata

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

												load(lowercase__					,	prefix=lowercase__    )

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

												_snake_case: int	       =		warn_missing_keys

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

						_snake_case: Dict	       =		{
						    '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_0_1,
												        'use_rel_pos_bias': True,
												    }    )

						_snake_case: str	       =		create_model(
						    'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'					,	**lowercase__					,	)
						_snake_case: Tuple	       =		model.patch_embed.patch_size
						_snake_case: str	       =		(args.input_size // patch_size[0], args.input_size // patch_size[1])
						_snake_case: Tuple	       =		torch.load(args.beit_checkpoint					,	map_location='cpu'    )

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

						_snake_case: Optional[Any]	       =		list(checkpoint_model.keys()    )
						for key in all_keys:
												if "relative_position_index" in key:
																		checkpoint_model.pop(lowercase__    )

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

						load_state_dict(lowercase__					,	lowercase__					,	prefix=''    )

						return model





def    lowercase_   (		) ->Optional[int]:
						_snake_case: int	       =		get_args()

						_snake_case: Dict	       =		'ft1k' in args.hf_checkpoint_name
						_snake_case: 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
						_snake_case: List[str]	       =		DataaVecVisionConfig()
						if is_finetuned:
												_snake_case: Dict	       =		True
												_snake_case: Union[str, Any]	       =		False
												_snake_case: List[str]	       =		True
												_snake_case: List[Any]	       =		1000

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

						if is_large:
												_snake_case: Optional[int]	       =		1024
												_snake_case: Optional[int]	       =		4096
												_snake_case: Optional[Any]	       =		24
												_snake_case: List[Any]	       =		16

						# 2. Load Beit model
						_snake_case: Any	       =		load_beit_model(lowercase__					,	lowercase__					,	lowercase__    )
						orig_model.eval()

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

						_snake_case: Tuple	       =		(pixel_values,) if is_finetuned else (pixel_values, None)
						with torch.no_grad():
												_snake_case: Union[str, Any]	       =		orig_model(*lowercase__    )

						# 4. Load HF Data2VecVision model
						if is_finetuned:
												_snake_case: Optional[Any]	       =		DataaVecVisionForImageClassification(lowercase__    )
												hf_model.eval()
												_snake_case: Tuple	       =		False
												_snake_case: Dict	       =		'data2vec_vision.'
						else:
												_snake_case: Union[str, Any]	       =		DataaVecVisionModel(lowercase__    )
												hf_model.eval()
												_snake_case: Union[str, Any]	       =		True
												_snake_case: List[str]	       =		''

						_snake_case: Optional[Any]	       =		create_rename_keys(lowercase__					,	hf_prefix=lowercase__					,	has_lm_head=lowercase__    )
						_snake_case: Any	       =		orig_model.state_dict()
						for src, dest in rename_keys:
												_snake_case: Dict	       =		state_dict.pop(lowercase__    )
												_snake_case: Any	       =		val

						read_in_q_k_v(lowercase__					,	lowercase__					,	hf_prefix=lowercase__					,	has_lm_head=lowercase__    )
						_snake_case       ,    _snake_case: Optional[Any]	       =		hf_model.load_state_dict(lowercase__					,	strict=lowercase__    )
						print('HF missing'					,	lowercase__    )
						print('HF unexpected_keys'					,	lowercase__    )

						# 5. Forward HF Data2VecVision model
						with torch.no_grad():
												_snake_case: Union[str, Any]	       =		hf_model(lowercase__    )

						_snake_case: str	       =		hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

						print(F'''max_absolute_diff = {max_absolute_diff}'''    )
						_snake_case: str	       =		torch.allclose(lowercase__					,	lowercase__					,	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'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
		A :			Optional[Any]							   =      None


A :			Tuple							   =      logging.get_logger(__name__)

A :			Optional[int]							   =      {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A :			Tuple							   =      {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

A :			int							   =      {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}







class       lowerCamelCase       (    __UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE				 =     ["input_ids", "attention_mask"]
					_SCREAMING_SNAKE_CASE				 =     XLMRobertaTokenizer


					def __init__(       self						:	Any							,  __snake_case						:	Tuple=None							,  __snake_case						:	List[Any]=None							,  __snake_case						:	List[str]="<s>"							,  __snake_case						:	int="</s>"							,  __snake_case						:	str="</s>"							,  __snake_case						:	Any="<s>"							,  __snake_case						:	int="<unk>"							,  __snake_case						:	Optional[Any]="<pad>"							,  __snake_case						:	Optional[int]="<mask>"							,  **__snake_case						:	List[str]							,  ):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		AddedToken(__snake_case							,  lstrip=__snake_case							,  rstrip=__snake_case							) if isinstance(__snake_case							,  __snake_case							) else mask_token

											super().__init__(
											    __snake_case							,  tokenizer_file=__snake_case							,  bos_token=__snake_case							,  eos_token=__snake_case							,  sep_token=__snake_case							,  cls_token=__snake_case							,  unk_token=__snake_case							,  pad_token=__snake_case							,  mask_token=__snake_case							,  **__snake_case							,  )

											_snake_case: int	       =		vocab_file
											_snake_case: List[Any]	       =		False if not self.vocab_file else True


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	List[int]							,  __snake_case						:	Optional[List[int]] = None							):





											'''simple docstring'''





											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_snake_case: Union[str, Any]	       =		[self.cls_token_id]
											_snake_case: int	       =		[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	List[int]							,  __snake_case						:	Optional[List[int]] = None							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		[self.sep_token_id]
											_snake_case: List[str]	       =		[self.cls_token_id]

											if token_ids_a is None:
																	return len(cls + token_ids_a + sep							) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	str							,  __snake_case						:	Optional[str] = None							):





											'''simple docstring'''





											if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																	    'tokenizer.'							)

											if not os.path.isdir(__snake_case							):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''							)
																	return
											_snake_case: Optional[int]	       =		os.path.join(
											    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

											if os.path.abspath(self.vocab_file							) != os.path.abspath(__snake_case							):
																	copyfile(self.vocab_file							,  __snake_case							)

											return (out_vocab_file,)





