






"""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__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=False       ,	_SCREAMING_SNAKE_CASE=False       ,	_SCREAMING_SNAKE_CASE="data2vec."			)    ->Any:
 UpperCAmelCase__ 						=				"""backbone.""" if is_semantic else """"""

 UpperCAmelCase__ 						=				[]
 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__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=False       ,	_SCREAMING_SNAKE_CASE=False       ,	_SCREAMING_SNAKE_CASE="data2vec_vision."			)    ->Union[str, Any]:







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

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

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

  UpperCAmelCase__ 						=				gamma_a
  UpperCAmelCase__ 						=				gamma_a

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

   UpperCAmelCase__ 						=				table
   UpperCAmelCase__ 						=				index




def       snake_case__		(   )    ->Dict:
 UpperCAmelCase__ 						=				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       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->List[str]:







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

  def load(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=""			):
   UpperCAmelCase__ 						=				{} 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_			)

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

  UpperCAmelCase__ 						=				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_			)			)

 UpperCAmelCase__ 						=				{
     """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,
      }			)

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

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

 UpperCAmelCase__ 						=				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:
   UpperCAmelCase__ 						=				checkpoint_model[key]
   UpperCAmelCase__						,	UpperCAmelCase__ 						=				rel_pos_bias.size()
   UpperCAmelCase__						,	UpperCAmelCase__ 						=				model.state_dict()[key].size()
   UpperCAmelCase__ 						=				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       snake_case__		(   )    ->List[str]:
 UpperCAmelCase__ 						=				get_args()

 UpperCAmelCase__ 						=				"""ft1k""" in args.hf_checkpoint_name
 UpperCAmelCase__ 						=				"""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
 UpperCAmelCase__ 						=				DataaVecVisionConfig()
 if is_finetuned:
  UpperCAmelCase__ 						=				True
  UpperCAmelCase__ 						=				False
  UpperCAmelCase__ 						=				True
  UpperCAmelCase__ 						=				1_0_0_0

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

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

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

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

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

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

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

 read_in_q_k_v(a_       ,	a_       ,	hf_prefix=a_       ,	has_lm_head=a_			)
 UpperCAmelCase__						,	UpperCAmelCase__ 						=				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():
  UpperCAmelCase__ 						=				hf_model(a_			)

 UpperCAmelCase__ 						=				hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__							=				logging.get_logger(__name__)

A__							=				{
    "nghuyong/ernie-1.0-base-zh": "https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-2.0-base-en": "https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json",
    "nghuyong/ernie-2.0-large-en": "https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json",
    "nghuyong/ernie-3.0-base-zh": "https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-medium-zh": "https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-mini-zh": "https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-micro-zh": "https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-nano-zh": "https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json",
    "nghuyong/ernie-gram-zh": "https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json",
    "nghuyong/ernie-health-zh": "https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json",
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: Dict												=	"ernie"







	def __init__( self:			List[Any]	,			__UpperCamelCase:			Dict=3_05_22	,			__UpperCamelCase:			str=7_68	,			__UpperCamelCase:			Tuple=12	,			__UpperCamelCase:			int=12	,			__UpperCamelCase:			Optional[Any]=30_72	,			__UpperCamelCase:			str="gelu"	,			__UpperCamelCase:			Any=0.1	,			__UpperCamelCase:			int=0.1	,			__UpperCamelCase:			int=5_12	,			__UpperCamelCase:			Any=2	,			__UpperCamelCase:			Tuple=3	,			__UpperCamelCase:			Any=False	,			__UpperCamelCase:			Optional[int]=0.02	,			__UpperCamelCase:			List[Any]=1E-12	,			__UpperCamelCase:			Union[str, Any]=0	,			__UpperCamelCase:			Tuple="absolute"	,			__UpperCamelCase:			Union[str, Any]=True	,			__UpperCamelCase:			Optional[Any]=None	,			**__UpperCamelCase:			List[Any]	,			):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase	,			**__UpperCamelCase   )

						__magic_name__				=     vocab_size
						__magic_name__				=     hidden_size
						__magic_name__				=     num_hidden_layers
						__magic_name__				=     num_attention_heads
						__magic_name__				=     hidden_act
						__magic_name__				=     intermediate_size
						__magic_name__				=     hidden_dropout_prob
						__magic_name__				=     attention_probs_dropout_prob
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     type_vocab_size
						__magic_name__				=     task_type_vocab_size
						__magic_name__				=     use_task_id
						__magic_name__				=     initializer_range
						__magic_name__				=     layer_norm_eps
						__magic_name__				=     position_embedding_type
						__magic_name__				=     use_cache
						__magic_name__				=     classifier_dropout




class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						if self.task == "multiple-choice":
											__magic_name__				=     {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											__magic_name__				=     {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						        ('task_type_ids', dynamic_axis),
						    ]   )




