





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_       ,		UpperCamelCase_=False       ,		UpperCamelCase_=False       ,		UpperCamelCase_="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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=False       ,		UpperCamelCase_=False       ,		UpperCamelCase_="data2vec_vision."	)  ->		int:
       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 			lowerCAmelCase_			(				)  ->		List[str]:
       UpperCamelCase_ 							=	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_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Dict:
       def load_state_dict(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=""       ,		UpperCamelCase_="relative_position_index"	):
              UpperCamelCase_ 							=	[]
              UpperCamelCase_ 							=	[]
              UpperCamelCase_ 							=	[]
              # copy state_dict so _load_from_state_dict can modify it
              UpperCamelCase_ 							=	getattr(UpperCamelCase_       ,		"_metadata"       ,		UpperCamelCase_	)
              UpperCamelCase_ 							=	state_dict.copy()
              if metadata is not None:
                     UpperCamelCase_ 							=	metadata

              def load(UpperCamelCase_       ,		UpperCamelCase_=""	):
                     UpperCamelCase_ 							=	{} 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_	)

              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(UpperCamelCase_	)
                     else:
                            ignore_missing_keys.append(UpperCamelCase_	)

              UpperCamelCase_ 							=	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_	)	)

       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": 1000,
                      "use_mean_pooling": True,
                      "init_scale": 0.0_01,
                      "use_rel_pos_bias": True,
                  }	)

       UpperCamelCase_ 							=	create_model(
           "beit_large_patch16_224" if is_large else "beit_base_patch16_224"       ,		**UpperCamelCase_       ,		)
       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(UpperCamelCase_	)

              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(UpperCamelCase_       ,		UpperCamelCase_       ,		prefix=""	)

       return model




def 			lowerCAmelCase_			(				)  ->		Union[str, Any]:
       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_ 							=	1000

              UpperCamelCase_ 							=	"huggingface/label-files"
              UpperCamelCase_ 							=	"imagenet-1k-id2label.json"
              UpperCamelCase_ 							=	json.load(open(hf_hub_download(UpperCamelCase_       ,		UpperCamelCase_       ,		repo_type="dataset"	)       ,		"r"	)	)
              UpperCamelCase_ 							=	{int(UpperCamelCase_	): 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_ 							=	1024
              UpperCamelCase_ 							=	4096
              UpperCamelCase_ 							=	24
              UpperCamelCase_ 							=	16

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

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

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

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

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

       read_in_q_k_v(UpperCamelCase_       ,		UpperCamelCase_       ,		hf_prefix=UpperCamelCase_       ,		has_lm_head=UpperCamelCase_	)
       UpperCamelCase_							,			UpperCamelCase_ 							=	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():
              UpperCamelCase_ 							=	hf_model(UpperCamelCase_	)

       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(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"







import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
_UpperCAmelCase						:    Dict      =  logging.getLogger(__name__)



def    UpperCAmelCase__	(  ):
  lowercase     :int   						=    argparse.ArgumentParser(description="PyTorch Transformer Language Model" )
  parser.add_argument("--model_name",      type=lowerCamelCase,      default="transfo-xl-wt103",      help="pretrained model name" )
  parser.add_argument(
      "--split",      type=lowerCamelCase,      default="test",      choices=["all", "valid", "test"],      help="which split to evaluate" )
  parser.add_argument("--batch_size",      type=lowerCamelCase,      default=10,      help="batch size" )
  parser.add_argument("--tgt_len",      type=lowerCamelCase,      default=128,      help="number of tokens to predict" )
  parser.add_argument("--ext_len",      type=lowerCamelCase,      default=0,      help="length of the extended context" )
  parser.add_argument("--mem_len",      type=lowerCamelCase,      default=1600,      help="length of the retained previous heads" )
  parser.add_argument("--clamp_len",      type=lowerCamelCase,      default=1000,      help="max positional embedding index" )
  parser.add_argument("--no_cuda",      action="store_true",      help="Do not use CUDA even though CUA is available" )
  parser.add_argument("--work_dir",      type=lowerCamelCase,      required=lowerCamelCase,      help="path to the work_dir" )
  parser.add_argument("--no_log",      action="store_true",      help="do not log the eval result" )
  parser.add_argument("--same_length",      action="store_true",      help="set same length attention with masking" )
  parser.add_argument("--server_ip",      type=lowerCamelCase,      default="",      help="Can be used for distant debugging." )
  parser.add_argument("--server_port",      type=lowerCamelCase,      default="",      help="Can be used for distant debugging." )
  lowercase     :Any   						=    parser.parse_args()
  assert args.ext_len >= 0, "extended context length must be non-negative"

  if args.server_ip and args.server_port:
    # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd

    print("Waiting for debugger attach" )
    ptvsd.enable_attach(address=(args.server_ip, args.server_port),      redirect_output=lowerCamelCase )
    ptvsd.wait_for_attach()

  lowercase     :Any   						=    torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
  logger.info("device: {}".format(lowerCamelCase ) )

  # Load a pre-processed dataset
  # You can also build the corpus yourself using TransfoXLCorpus methods
  # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
  # and tokenizing the dataset
  # The pre-processed corpus is a convertion (using the conversion script )
  lowercase     :Dict   						=    TransfoXLCorpus.from_pretrained(args.model_name )

  lowercase     :Optional[int]   						=    corpus.get_iterator("valid",      args.batch_size,      args.tgt_len,      device=lowerCamelCase,      ext_len=args.ext_len )
  lowercase     :Union[str, Any]   						=    corpus.get_iterator("test",      args.batch_size,      args.tgt_len,      device=lowerCamelCase,      ext_len=args.ext_len )

  # Load a pre-trained model
  lowercase     :List[Any]   						=    TransfoXLLMHeadModel.from_pretrained(args.model_name )
  model.to(lowerCamelCase )

  logger.info(
      "Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}".format(
          args.batch_size,      args.tgt_len,      args.ext_len,      args.mem_len,      args.clamp_len ) )

  model.reset_memory_length(args.mem_len )
  if args.clamp_len > 0:
    lowercase     :Tuple   						=    args.clamp_len
  if args.same_length:
    lowercase     :Tuple   						=    True

  ###############################################################################
  # Evaluation code
  ###############################################################################
  def evaluate(lowerCamelCase ):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    lowercase						,     lowercase     :Optional[Any]   						=    0, 0.0
    lowercase     :Optional[Any]   						=    time.time()
    with torch.no_grad():
      lowercase     :Dict   						=    None
      for idx, (data, target, seq_len) in enumerate(lowerCamelCase ):
        lowercase     :Optional[Any]   						=    model(lowerCamelCase,      lm_labels=lowerCamelCase,      mems=lowerCamelCase )
        lowercase						,     lowercase						,     lowercase     :Optional[int]   						=    ret
        lowercase     :Optional[Any]   						=    loss.mean()
        total_loss += seq_len * loss.item()
        total_len += seq_len
      lowercase     :List[Any]   						=    time.time() - start_time
    logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(lowerCamelCase,      1000 * total_time / (idx + 1) ) )
    return total_loss / total_len

  # Run on test data.
  if args.split == "all":
    lowercase     :Union[str, Any]   						=    evaluate(lowerCamelCase )
    lowercase     :List[str]   						=    evaluate(lowerCamelCase )
  elif args.split == "valid":
    lowercase     :Optional[int]   						=    evaluate(lowerCamelCase )
    lowercase     :Dict   						=    None
  elif args.split == "test":
    lowercase     :int   						=    evaluate(lowerCamelCase )
    lowercase     :Optional[Any]   						=    None

  def format_log(lowerCamelCase,      lowerCamelCase ):
    lowercase     :List[str]   						=    "| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(lowerCamelCase,      lowerCamelCase,      math.exp(lowerCamelCase ) )
    return log_str

  lowercase     :Optional[int]   						=    ""
  if valid_loss is not None:
    log_str += format_log(lowerCamelCase,      "valid" )
  if test_loss is not None:
    log_str += format_log(lowerCamelCase,      "test" )

  logger.info("=" * 100 )
  logger.info(lowerCamelCase )
  logger.info("=" * 100 )


if __name__ == "__main__":
     main()



