

'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase		       =    logging.get_logger(__name__)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__=False				)			->			Tuple:
       A_						=      []
       for i in range(config.num_hidden_layers				):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''')				)
              rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')				)
              rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')				)
              rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')				)
              rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')				)
              rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')				)
              rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')				)
              rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')				)
              rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')				)
              rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')				)

       # projection layer + position embeddings
       rename_keys.extend(
           [
               ("""cls_token""", """deit.embeddings.cls_token"""),
               ("""dist_token""", """deit.embeddings.distillation_token"""),
               ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
               ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
               ("""pos_embed""", """deit.embeddings.position_embeddings"""),
           ]				)

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ("""norm.weight""", """layernorm.weight"""),
                      ("""norm.bias""", """layernorm.bias"""),
                      ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
                      ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
                  ]				)

              # if just the base model, we should remove "deit" from all keys that start with "deit"
              A_						=      [(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""				) else pair for pair in rename_keys]
       else:
              # layernorm + classification heads
              rename_keys.extend(
                  [
                      ("""norm.weight""", """deit.layernorm.weight"""),
                      ("""norm.bias""", """deit.layernorm.bias"""),
                      ("""head.weight""", """cls_classifier.weight"""),
                      ("""head.bias""", """cls_classifier.bias"""),
                      ("""head_dist.weight""", """distillation_classifier.weight"""),
                      ("""head_dist.bias""", """distillation_classifier.bias"""),
                  ]				)

       return rename_keys
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__=False				)			->			int:
       for i in range(config.num_hidden_layers				):
              if base_model:
                     A_						=      """"""
              else:
                     A_						=      """deit."""
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              A_						=      state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''				)
              A_						=      state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''				)
              # next, add query, keys and values (in that order) to the state dict
              A_						=      in_proj_weight[
                  : config.hidden_size, :
              ]
              A_						=      in_proj_bias[: config.hidden_size]
              A_						=      in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              A_						=      in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              A_						=      in_proj_weight[
                  -config.hidden_size :, :
              ]
              A_						=      in_proj_bias[-config.hidden_size :]
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Any:
       A_						=      dct.pop(UpperCAmelCase__				)
       A_						=      val
def       UpperCAmelCase__    (      )			->			Optional[Any]:
       A_						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
       A_						=      Image.open(requests.get(UpperCAmelCase__,       stream=UpperCAmelCase__				).raw				)
       return im
@torch.no_grad()
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			str:
       A_						=      DeiTConfig()
       # all deit models have fine-tuned heads
       A_						=      False
       # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
       A_						=      10_00
       A_						=      """huggingface/label-files"""
       A_						=      """imagenet-1k-id2label.json"""
       A_						=      json.load(open(hf_hub_download(UpperCAmelCase__,       UpperCAmelCase__,       repo_type="""dataset"""				),       """r"""				)				)
       A_						=      {int(UpperCAmelCase__				): v for k, v in idalabel.items()}
       A_						=      idalabel
       A_						=      {v: k for k, v in idalabel.items()}
       A_						=      int(deit_name[-6:-4]				)
       A_						=      int(deit_name[-3:]				)
       # size of the architecture
       if deit_name[9:].startswith("""tiny"""				):
              A_						=      1_92
              A_						=      7_68
              A_						=      12
              A_						=      3
       elif deit_name[9:].startswith("""small"""				):
              A_						=      3_84
              A_						=      15_36
              A_						=      12
              A_						=      6
       if deit_name[9:].startswith("""base"""				):
              pass
       elif deit_name[4:].startswith("""large"""				):
              A_						=      10_24
              A_						=      40_96
              A_						=      24
              A_						=      16

       # load original model from timm
       A_						=      timm.create_model(UpperCAmelCase__,       pretrained=UpperCAmelCase__				)
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       A_						=      timm_model.state_dict()
       A_						=      create_rename_keys(UpperCAmelCase__,       UpperCAmelCase__				)
       for src, dest in rename_keys:
              rename_key(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)
       read_in_q_k_v(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       # load HuggingFace model
       A_						=      DeiTForImageClassificationWithTeacher(UpperCAmelCase__				).eval()
       model.load_state_dict(UpperCAmelCase__				)

       # Check outputs on an image, prepared by DeiTImageProcessor
       A_						=      int(
           (2_56 / 2_24) * config.image_size				)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
       A_						=      DeiTImageProcessor(size=UpperCAmelCase__,       crop_size=config.image_size				)
       A_						=      image_processor(images=prepare_img(),       return_tensors="""pt"""				)
       A_						=      encoding["""pixel_values"""]
       A_						=      model(UpperCAmelCase__				)

       A_						=      timm_model(UpperCAmelCase__				)
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(UpperCAmelCase__,       outputs.logits,       atol=1e-3				)

       Path(UpperCAmelCase__				).mkdir(exist_ok=UpperCAmelCase__				)
       print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}'''				)
       model.save_pretrained(UpperCAmelCase__				)
       print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
       image_processor.save_pretrained(UpperCAmelCase__				)


if __name__ == "__main__":
      __lowerCamelCase		       =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--deit_name''',
          default='''vit_deit_base_distilled_patch16_224''',
          type=str,
          help='''Name of the DeiT timm model you\'d like to convert.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )

      __lowerCamelCase		       =    parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



'''simple docstring'''







import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			List[Any]:
       # Load configuration defined in the metadata file
       with open(UpperCAmelCase__				) as metadata_file:
              A_						=      json.load(UpperCAmelCase__				)
       A_						=      LukeConfig(use_entity_aware_attention=UpperCAmelCase__,       **metadata["""model_config"""]				)

       # Load in the weights from the checkpoint_path
       A_						=      torch.load(UpperCAmelCase__,       map_location="""cpu"""				)["""module"""]

       # Load the entity vocab file
       A_						=      load_original_entity_vocab(UpperCAmelCase__				)
       # add an entry for [MASK2]
       A_						=      max(entity_vocab.values()				) + 1
       config.entity_vocab_size += 1

       A_						=      XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]				)

       # Add special tokens to the token vocabulary for downstream tasks
       A_						=      AddedToken("""<ent>""",       lstrip=UpperCAmelCase__,       rstrip=UpperCAmelCase__				)
       A_						=      AddedToken("""<ent2>""",       lstrip=UpperCAmelCase__,       rstrip=UpperCAmelCase__				)
       tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}				)
       config.vocab_size += 2

       print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''				)
       tokenizer.save_pretrained(UpperCAmelCase__				)
       with open(os.path.join(UpperCAmelCase__,       """tokenizer_config.json"""				),       """r"""				) as f:
              A_						=      json.load(UpperCAmelCase__				)
       A_						=      """MLukeTokenizer"""
       with open(os.path.join(UpperCAmelCase__,       """tokenizer_config.json"""				),       """w"""				) as f:
              json.dump(UpperCAmelCase__,       UpperCAmelCase__				)

       with open(os.path.join(UpperCAmelCase__,       MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]				),       """w"""				) as f:
              json.dump(UpperCAmelCase__,       UpperCAmelCase__				)

       A_						=      MLukeTokenizer.from_pretrained(UpperCAmelCase__				)

       # Initialize the embeddings of the special tokens
       A_						=      tokenizer.convert_tokens_to_ids(["""@"""]				)[0]
       A_						=      tokenizer.convert_tokens_to_ids(["""#"""]				)[0]

       A_						=      state_dict["""embeddings.word_embeddings.weight"""]
       A_						=      word_emb[ent_init_index].unsqueeze(0				)
       A_						=      word_emb[enta_init_index].unsqueeze(0				)
       A_						=      torch.cat([word_emb, ent_emb, enta_emb]				)
       # add special tokens for 'entity_predictions.bias'
       for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
              A_						=      state_dict[bias_name]
              A_						=      decoder_bias[ent_init_index].unsqueeze(0				)
              A_						=      decoder_bias[enta_init_index].unsqueeze(0				)
              A_						=      torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]				)

       # Initialize the query layers of the entity-aware self-attention mechanism
       for layer_index in range(config.num_hidden_layers				):
              for matrix_name in ["query.weight", "query.bias"]:
                     A_						=      F'''encoder.layer.{layer_index}.attention.self.'''
                     A_						=      state_dict[prefix + matrix_name]
                     A_						=      state_dict[prefix + matrix_name]
                     A_						=      state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
       A_						=      state_dict["""entity_embeddings.entity_embeddings.weight"""]
       A_						=      entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0				)
       A_						=      torch.cat([entity_emb, entity_mask_emb]				)
       # add [MASK2] for 'entity_predictions.bias'
       A_						=      state_dict["""entity_predictions.bias"""]
       A_						=      entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0				)
       A_						=      torch.cat([entity_prediction_bias, entity_mask_bias]				)

       A_						=      LukeForMaskedLM(config=UpperCAmelCase__				).eval()

       state_dict.pop("""entity_predictions.decoder.weight"""				)
       state_dict.pop("""lm_head.decoder.weight"""				)
       state_dict.pop("""lm_head.decoder.bias"""				)
       A_						=      OrderedDict()
       for key, value in state_dict.items():
              if not (key.startswith("""lm_head"""				) or key.startswith("""entity_predictions"""				)):
                     A_						=      state_dict[key]
              else:
                     A_						=      state_dict[key]

       A_					,			A_						=      model.load_state_dict(UpperCAmelCase__,       strict=UpperCAmelCase__				)

       if set(UpperCAmelCase__				) != {"luke.embeddings.position_ids"}:
              raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''				)
       if set(UpperCAmelCase__				) != {
           "lm_head.decoder.weight",
           "lm_head.decoder.bias",
           "entity_predictions.decoder.weight",
       }:
              raise ValueError(F'''Unexpected missing_keys: {missing_keys}'''				)

       model.tie_weights()
       assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
       assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

       # Check outputs
       A_						=      MLukeTokenizer.from_pretrained(UpperCAmelCase__,       task="""entity_classification"""				)

       A_						=      """ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
       A_						=      (0, 9)
       A_						=      tokenizer(UpperCAmelCase__,       entity_spans=[span],       return_tensors="""pt"""				)

       A_						=      model(**UpperCAmelCase__				)

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              A_						=      torch.Size((1, 33, 7_68)				)
              A_						=      torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]				)

       if not (outputs.last_hidden_state.shape == expected_shape):
              raise ValueError(
                  F'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''				)
       if not torch.allclose(outputs.last_hidden_state[0, :3, :3],       UpperCAmelCase__,       atol=1e-4				):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              A_						=      torch.Size((1, 1, 7_68)				)
              A_						=      torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]				)

       if not (outputs.entity_last_hidden_state.shape == expected_shape):
              raise ValueError(
                  F'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
                  F''' {expected_shape}'''				)
       if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3],       UpperCAmelCase__,       atol=1e-4				):
              raise ValueError

       # Verify masked word/entity prediction
       A_						=      MLukeTokenizer.from_pretrained(UpperCAmelCase__				)
       A_						=      """Tokyo is the capital of <mask>."""
       A_						=      (24, 30)
       A_						=      tokenizer(UpperCAmelCase__,       entity_spans=[span],       return_tensors="""pt"""				)

       A_						=      model(**UpperCAmelCase__				)

       A_						=      encoding["""input_ids"""][0].tolist()
       A_						=      input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""				)				)
       A_						=      outputs.logits[0][mask_position_id].argmax(dim=-1				)
       assert "Japan" == tokenizer.decode(UpperCAmelCase__				)

       A_						=      outputs.entity_logits[0][0].argmax().item()
       A_						=      [
           entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
       ]
       assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""				)][0] == "en:Japan"

       # Finally, save our PyTorch model and tokenizer
       print("""Saving PyTorch model to {}""".format(UpperCAmelCase__				)				)
       model.save_pretrained(UpperCAmelCase__				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      ["""[MASK]""", """[PAD]""", """[UNK]"""]

       A_						=      [json.loads(UpperCAmelCase__				) for line in open(UpperCAmelCase__				)]

       A_						=      {}
       for entry in data:
              A_						=      entry["""id"""]
              for entity_name, language in entry["entities"]:
                     if entity_name in SPECIAL_TOKENS:
                            A_						=      entity_id
                            break
                     A_						=      F'''{language}:{entity_name}'''
                     A_						=      entity_id
       return new_mapping


if __name__ == "__main__":
      __lowerCamelCase		       =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
      parser.add_argument(
          '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
      )
      parser.add_argument(
          '''--entity_vocab_path''',
          default=None,
          type=str,
          help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
      )
      parser.add_argument(
          '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
      )
      __lowerCamelCase		       =    parser.parse_args()
      convert_luke_checkpoint(
          args.checkpoint_path,
          args.metadata_path,
          args.entity_vocab_path,
          args.pytorch_dump_folder_path,
          args.model_size,
      )
