
"""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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=False,	SCREAMING_SNAKE_CASE_=False,	SCREAMING_SNAKE_CASE_="data2vec."     ):
					SCREAMING_SNAKE_CASE           =      'backbone.' if is_semantic else ''

					SCREAMING_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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=False,	SCREAMING_SNAKE_CASE_=False,	SCREAMING_SNAKE_CASE_="data2vec_vision."     ):
					for i in range(config.num_hidden_layers     ):
										SCREAMING_SNAKE_CASE           =      'backbone.' if is_semantic else ''
										# queries, keys and values
										SCREAMING_SNAKE_CASE           =      state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''     )
										SCREAMING_SNAKE_CASE           =      state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''     )
										SCREAMING_SNAKE_CASE           =      state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''     )

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

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

										SCREAMING_SNAKE_CASE           =      gamma_a
										SCREAMING_SNAKE_CASE           =      gamma_a

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

															SCREAMING_SNAKE_CASE           =      table
															SCREAMING_SNAKE_CASE           =      index

def     UpperCamelCase_    (						):
					SCREAMING_SNAKE_CASE           =      argparse.ArgumentParser(
					    'Convert Data2VecVision to HF for image classification and pretraining',	add_help=a_     )
					parser.add_argument('--hf_checkpoint_name',	type=a_     )
					parser.add_argument('--input_size',	default=2_2_4,	type=a_,	help='images input size'     )
					parser.add_argument('--beit_checkpoint',	default='',	help='beit checkpoint'     )

					return parser.parse_args()

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					def load_state_dict(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_="",	SCREAMING_SNAKE_CASE_="relative_position_index"     ):
										SCREAMING_SNAKE_CASE           =      []
										SCREAMING_SNAKE_CASE           =      []
										SCREAMING_SNAKE_CASE           =      []
										# copy state_dict so _load_from_state_dict can modify it
										SCREAMING_SNAKE_CASE           =      getattr(a_,	'_metadata',	a_     )
										SCREAMING_SNAKE_CASE           =      state_dict.copy()
										if metadata is not None:
															SCREAMING_SNAKE_CASE           =      metadata

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

										load(a_,	prefix=a_     )

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

										SCREAMING_SNAKE_CASE           =      warn_missing_keys

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

					SCREAMING_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_0_0_0,
										        'use_mean_pooling': True,
										        'init_scale': 0.001,
										        'use_rel_pos_bias': True,
										    }     )

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

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

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

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

					load_state_dict(a_,	a_,	prefix=''     )

					return model

def     UpperCamelCase_    (						):
					SCREAMING_SNAKE_CASE           =      get_args()

					SCREAMING_SNAKE_CASE           =      'ft1k' in args.hf_checkpoint_name
					SCREAMING_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
					SCREAMING_SNAKE_CASE           =      DataaVecVisionConfig()
					if is_finetuned:
										SCREAMING_SNAKE_CASE           =      True
										SCREAMING_SNAKE_CASE           =      False
										SCREAMING_SNAKE_CASE           =      True
										SCREAMING_SNAKE_CASE           =      1_0_0_0

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

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

					# 2. Load Beit model
					SCREAMING_SNAKE_CASE           =      load_beit_model(a_,	a_,	a_     )
					orig_model.eval()

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

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

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

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

					read_in_q_k_v(a_,	a_,	hf_prefix=a_,	has_lm_head=a_     )
					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      hf_model.load_state_dict(a_,	strict=a_     )
					print('HF missing',	a_     )
					print('HF unexpected_keys',	a_     )

					# 5. Forward HF Data2VecVision model
					with torch.no_grad():
										SCREAMING_SNAKE_CASE           =      hf_model(a_     )

					SCREAMING_SNAKE_CASE           =      hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

					print(f'''max_absolute_diff = {max_absolute_diff}'''     )
					SCREAMING_SNAKE_CASE           =      torch.allclose(a_,	a_,	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 warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)






class      __a      (       lowerCAmelCase_      ):







       def __init__(       self				,*lowerCamelCase_				,**lowerCamelCase_		):



          '''simple docstring'''

          warnings.warn(
              "The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV1ImageProcessor instead."				,lowerCamelCase_				,)
          super().__init__(*lowerCamelCase_				,**lowerCamelCase_		)

