




"""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 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase=False   ,      lowerCamelCase=False   ,      lowerCamelCase="data2vec."		):
						'''simple docstring'''


						snake_case__							:       int				      =       'backbone.' if is_semantic else ''

						snake_case__							:       Optional[Any]				      =       []
						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				(			lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase=False   ,      lowerCamelCase=False   ,      lowerCamelCase="data2vec_vision."		):
						'''simple docstring'''


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

												snake_case__							:       Tuple				      =       in_proj_weight[
												    : config.hidden_size, :
												]
												snake_case__							:       Tuple				      =       q_bias
												snake_case__							:       Optional[int]				      =       in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												snake_case__							:       Union[str, Any]				      =       in_proj_weight[
												    -config.hidden_size :, :
												]
												snake_case__							:       Any				      =       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__							:       int				      =       state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''		)

												snake_case__							:       Union[str, Any]				      =       gamma_a
												snake_case__							:       Optional[int]				      =       gamma_a

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

																		snake_case__							:       str				      =       table
																		snake_case__							:       str				      =       index

def 		lowerCAmelCase				(			):
						'''simple docstring'''


						snake_case__							:       Optional[Any]				      =       argparse.ArgumentParser(
						    '''Convert Data2VecVision to HF for image classification and pretraining'''   ,      add_help=UpperCAmelCase_		)
						parser.add_argument('''--hf_checkpoint_name'''   ,      type=UpperCAmelCase_		)
						parser.add_argument('''--input_size'''   ,      default=224   ,      type=UpperCAmelCase_   ,      help='''images input size'''		)
						parser.add_argument('''--beit_checkpoint'''   ,      default=''''''   ,      help='''beit checkpoint'''		)

						return parser.parse_args()

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						def load_state_dict(lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase=""   ,      lowerCamelCase="relative_position_index"		):
												snake_case__							:       Optional[Any]				      =       []
												snake_case__							:       Any				      =       []
												snake_case__							:       List[Any]				      =       []
												# copy state_dict so _load_from_state_dict can modify it
												snake_case__							:       int				      =       getattr(UpperCAmelCase_   ,      '''_metadata'''   ,      UpperCAmelCase_		)
												snake_case__							:       int				      =       state_dict.copy()
												if metadata is not None:
																		snake_case__							:       str				      =       metadata

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

												load(UpperCAmelCase_   ,      prefix=UpperCAmelCase_		)

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

												snake_case__							:       Optional[Any]				      =       warn_missing_keys

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

						snake_case__							:       List[Any]				      =       {
						    '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''': 0.001,
												        '''use_rel_pos_bias''': True,
												    }		)

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

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

						snake_case__							:       List[Any]				      =       list(checkpoint_model.keys()		)
						for key in all_keys:
												if "relative_position_index" in key:
																		checkpoint_model.pop(UpperCAmelCase_		)

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

						load_state_dict(UpperCAmelCase_   ,      UpperCAmelCase_   ,      prefix=''''''		)

						return model

def 		lowerCAmelCase				(			):
						'''simple docstring'''


						snake_case__							:       Dict				      =       get_args()

						snake_case__							:       str				      =       'ft1k' in args.hf_checkpoint_name
						snake_case__							:       Union[str, 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__							:       Tuple				      =       DataaVecVisionConfig()
						if is_finetuned:
												snake_case__							:       int				      =       True
												snake_case__							:       Any				      =       False
												snake_case__							:       Tuple				      =       True
												snake_case__							:       List[str]				      =       1000

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

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

						# 2. Load Beit model
						snake_case__							:       List[str]				      =       load_beit_model(UpperCAmelCase_   ,      UpperCAmelCase_   ,      UpperCAmelCase_		)
						orig_model.eval()

						# 3. Forward Beit model
						snake_case__							:       List[str]				      =       BeitImageProcessor(size=config.image_size   ,      do_center_crop=UpperCAmelCase_		)
						snake_case__							:       Tuple				      =       Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						snake_case__							:       Optional[int]				      =       image_processor(images=UpperCAmelCase_   ,      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__							:       List[str]				      =       orig_model(*UpperCAmelCase_		)

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

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

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

						# 5. Forward HF Data2VecVision model
						with torch.no_grad():
												snake_case__							:       Optional[int]				      =       hf_model(UpperCAmelCase_		)

						snake_case__							:       List[str]				      =       hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

						# 6. Compare
						snake_case__							:       str				      =       torch.max(torch.abs(hf_output - orig_model_output		)		).item()

						print(f'''max_absolute_diff = {max_absolute_diff}'''		)
						snake_case__							:       str				      =       torch.allclose(UpperCAmelCase_   ,      UpperCAmelCase_   ,      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 __future__ import annotations




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 assert n > 1 and a > 0 and greatest_common_divisor(UpperCAmelCase_    ,			UpperCAmelCase_      ) == 1
 ((UpperCAmelCase)   ,					(UpperCAmelCase)   ,					(UpperCAmelCase))			:      Dict		   =					extended_gcd(UpperCAmelCase_    ,			UpperCAmelCase_      )  # Implemented below
 UpperCAmelCase			:      int		   =					(b * s) % n
 return x




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 ((UpperCAmelCase)   ,					(UpperCAmelCase))			:      int		   =					extended_euclid(UpperCAmelCase_    ,			UpperCAmelCase_      )  # Implemented below
 if b < 0:
  UpperCAmelCase			:      Optional[int]		   =					(b % n + n) % n
 return b




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      Tuple		   =					invert_modulo(UpperCAmelCase_    ,			UpperCAmelCase_      )
 UpperCAmelCase			:      List[Any]		   =					(b * s) % n
 return x




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 assert a >= 0 and b >= 0

 if b == 0:
  UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      Dict		   =					a, 1, 0
 else:
  ((UpperCAmelCase)   ,					(UpperCAmelCase)   ,					(UpperCAmelCase))			:      Optional[int]		   =					extended_gcd(UpperCAmelCase_    ,			a % b      )
  UpperCAmelCase			:      Dict		   =					q
  UpperCAmelCase			:      int		   =					p - q * (a // b)

 assert a % d == 0 and b % d == 0
 assert d == a * x + b * y

 return (d, x, y)




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 if b == 0:
  return (1, 0)
 ((UpperCAmelCase)   ,					(UpperCAmelCase))			:      Tuple		   =					extended_euclid(UpperCAmelCase_    ,			a % b      )
 UpperCAmelCase			:      Tuple		   =					a // b
 return (y, x - k * y)




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 if a < b:
  UpperCAmelCase   ,					UpperCAmelCase			:      Union[str, Any]		   =					b, a

 while a % b != 0:
  UpperCAmelCase   ,					UpperCAmelCase			:      int		   =					b, a % b

 return b


if __name__ == "__main__":
 from doctest import testmod

 testmod(name="modular_division", verbose=True)
 testmod(name="modular_division2", verbose=True)
 testmod(name="invert_modulo", verbose=True)
 testmod(name="extended_gcd", verbose=True)
 testmod(name="extended_euclid", verbose=True)
 testmod(name="greatest_common_divisor", verbose=True)




