





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__   (   UpperCamelCase__	:				List[str]				,			UpperCamelCase__	:				Any=False				,			UpperCamelCase__	:				Union[str, Any]=False				,			UpperCamelCase__	:				List[str]="data2vec."  )	->       List[Any]:



					'''simple docstring'''




					_snake_case													= '''backbone.''' if is_semantic else ''''''

					_snake_case													= []
					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__   (   UpperCamelCase__	:				int				,			UpperCamelCase__	:				int				,			UpperCamelCase__	:				Optional[int]=False				,			UpperCamelCase__	:				Any=False				,			UpperCamelCase__	:				Dict="data2vec_vision."  )	->       List[Any]:



					'''simple docstring'''




					for i in range(config.num_hidden_layers  ):
										_snake_case													= '''backbone.''' if is_semantic else ''''''
										# queries, keys and values
										_snake_case													= state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''  )
										_snake_case													= state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''  )
										_snake_case													= state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''  )

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

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

										_snake_case													= gamma_a
										_snake_case													= gamma_a

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

															_snake_case													= table
															_snake_case													= index





def   lowerCamelCase__   (   )	->       List[str]:



					'''simple docstring'''




					_snake_case													= 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   lowerCamelCase__   (   UpperCamelCase__	:				List[Any]				,			UpperCamelCase__	:				Optional[int]				,			UpperCamelCase__	:				str  )	->       List[str]:



					'''simple docstring'''




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

										def load(UpperCamelCase__	:				Any				,			UpperCamelCase__	:				Any=""  ):
															_snake_case													= {} 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													= []
										_snake_case													= []
										for key in missing_keys:
															_snake_case													= True
															for ignore_key in ignore_missing.split('|'  ):
																				if ignore_key in key:
																									_snake_case													= False
																									break
															if keep_flag:
																				warn_missing_keys.append(lowercase_  )
															else:
																				ignore_missing_keys.append(lowercase_  )

										_snake_case													= 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													= {
					    '''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_000,
										        'use_mean_pooling': True,
										        'init_scale': 0.001,
										        'use_rel_pos_bias': True,
										    }  )

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

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

					_snake_case													= 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													= checkpoint_model[key]
															_snake_case													= rel_pos_bias.size()
															_snake_case													= model.state_dict()[key].size()
															_snake_case													= 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   lowerCamelCase__   (   )	->       Optional[Any]:



					'''simple docstring'''




					_snake_case													= get_args()

					_snake_case													= '''ft1k''' in args.hf_checkpoint_name
					_snake_case													= '''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													= DataaVecVisionConfig()
					if is_finetuned:
										_snake_case													= True
										_snake_case													= False
										_snake_case													= True
										_snake_case													= 1_000

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

					if is_large:
										_snake_case													= 1_024
										_snake_case													= 4_096
										_snake_case													= 24
										_snake_case													= 16

					# 2. Load Beit model
					_snake_case													= load_beit_model(lowercase_				,			lowercase_				,			lowercase_  )
					orig_model.eval()

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

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

					# 4. Load HF Data2VecVision model
					if is_finetuned:
										_snake_case													= DataaVecVisionForImageClassification(lowercase_  )
										hf_model.eval()
										_snake_case													= False
										_snake_case													= '''data2vec_vision.'''
					else:
										_snake_case													= DataaVecVisionModel(lowercase_  )
										hf_model.eval()
										_snake_case													= True
										_snake_case													= ''''''

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

					read_in_q_k_v(lowercase_				,			lowercase_				,			hf_prefix=lowercase_				,			has_lm_head=lowercase_  )
					_snake_case													= 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													= hf_model(lowercase_  )

					_snake_case													= hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

					print(F'''max_absolute_diff = {max_absolute_diff}'''  )
					_snake_case													= 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"""





from typing import TYPE_CHECKING

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


_lowerCamelCase       =	{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	['''MobileNetV1FeatureExtractor''']
    _lowerCamelCase       =	['''MobileNetV1ImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	[
        '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MobileNetV1ForImageClassification''',
        '''MobileNetV1Model''',
        '''MobileNetV1PreTrainedModel''',
        '''load_tf_weights_in_mobilenet_v1''',
    ]


if TYPE_CHECKING:
    from .configuration_mobilenet_va import (
        MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
        MobileNetVaConfig,
        MobileNetVaOnnxConfig,
    )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
        from .image_processing_mobilenet_va import MobileNetVaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mobilenet_va import (
            MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileNetVaForImageClassification,
            MobileNetVaModel,
            MobileNetVaPreTrainedModel,
            load_tf_weights_in_mobilenet_va,
        )


else:
    import sys

    _lowerCamelCase       =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

