


"""simple docstring"""



import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case             =   logging.get_logger()







@dataclass
class _lowerCAmelCase    :
   __UpperCAmelCase			:	nn.Module
   __UpperCAmelCase			:	List[nn.Module]							  =  field(default_factory=snake_case_     )
   __UpperCAmelCase			:	list							  =  field(default_factory=snake_case_     )







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				Any				 =   len(list(m.modules()						)						) == 1 or isinstance(UpperCamelCase__						,     nn.Convad						) or isinstance(UpperCamelCase__						,     nn.BatchNormad						)
       if has_not_submodules:
           self.traced.append(UpperCamelCase__						)







   def __call__(     self						,     UpperCamelCase__						)     ->		Any:

       '''simple docstring'''




       for m in self.module.modules():
           self.handles.append(m.register_forward_hook(self._forward_hook						)						)
       self.module(UpperCamelCase__						)
       [x.remove() for x in self.handles]
       return self







   @property
   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       return list(filter(lambda UpperCamelCase__						: len(list(x.state_dict().keys()						)						) > 0						,     self.traced						)						)







@dataclass
class _lowerCAmelCase    :
   __UpperCAmelCase			:	nn.Module
   __UpperCAmelCase			:	nn.Module
   __UpperCAmelCase			:	int							  =  0
   __UpperCAmelCase			:	List							  =  field(default_factory=snake_case_     )
   __UpperCAmelCase			:	List							  =  field(default_factory=snake_case_     )







   def __call__(     self						,     UpperCamelCase__						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				int				 =   Tracker(self.dest						)(UpperCamelCase__						).parametrized
       snake_case   :				Union[str, Any]				 =   Tracker(self.src						)(UpperCamelCase__						).parametrized

       snake_case   :				List[str]				 =   list(filter(lambda UpperCamelCase__						: type(UpperCamelCase__						) not in self.src_skip						,     UpperCamelCase__						)						)
       snake_case   :				List[str]				 =   list(filter(lambda UpperCamelCase__						: type(UpperCamelCase__						) not in self.dest_skip						,     UpperCamelCase__						)						)

       if len(UpperCamelCase__						) != len(UpperCamelCase__						):
           raise Exception(
               F'Numbers of operations are different. Source module has {len(UpperCamelCase__						)} operations while'
               F' destination module has {len(UpperCamelCase__						)}.'						)

       for dest_m, src_m in zip(UpperCamelCase__						,     UpperCamelCase__						):
           dest_m.load_state_dict(src_m.state_dict()						)
           if self.verbose == 1:
               print(F'Transfered from={src_m} to={dest_m}'						)







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  ResNetConfig						,							lowercase  :  Path						,							lowercase  :  bool = True			)     ->						Dict:


    """simple docstring"""





    print(F'Converting {name}...'			)
    with torch.no_grad():
        snake_case   :				Union[str, Any]				 =   timm.create_model(lowercase						,							pretrained=lowercase			).eval()
        snake_case   :				str				 =   ResNetForImageClassification(lowercase			).eval()
        snake_case   :				Union[str, Any]				 =   ModuleTransfer(src=lowercase						,							dest=lowercase			)
        snake_case   :				Optional[int]				 =   torch.randn((1, 3, 224, 224)			)
        module_transfer(lowercase			)

    assert torch.allclose(from_model(lowercase			)						,							our_model(lowercase			).logits			), "The model logits don't match the original one."

    snake_case   :				Tuple				 =   F'resnet{"-".join(name.split("resnet"			)			)}'
    print(lowercase			)

    if push_to_hub:
        our_model.push_to_hub(
            repo_path_or_name=save_directory / checkpoint_name						,							commit_message="Add model"						,							use_temp_dir=lowercase						,							)

        # we can use the convnext one
        snake_case   :				str				 =   AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"			)
        image_processor.push_to_hub(
            repo_path_or_name=save_directory / checkpoint_name						,							commit_message="Add image processor"						,							use_temp_dir=lowercase						,							)

        print(F'Pushed {checkpoint_name}'			)







def   __lowerCAmelCase						(						lowercase  :  Path						,							lowercase  :  str = None						,							lowercase  :  bool = True			)     ->						int:


    """simple docstring"""





    snake_case   :				List[str]				 =   "imagenet-1k-id2label.json"
    snake_case   :				List[str]				 =   1000
    snake_case   :				Tuple				 =   (1, num_labels)

    snake_case   :				List[str]				 =   "huggingface/label-files"
    snake_case   :				Dict				 =   num_labels
    snake_case   :				int				 =   json.load(open(hf_hub_download(lowercase						,							lowercase						,							repo_type="dataset"			)						,							"r"			)			)
    snake_case   :				str				 =   {int(lowercase			): v for k, v in idalabel.items()}

    snake_case   :				Dict				 =   idalabel
    snake_case   :				Any				 =   {v: k for k, v in idalabel.items()}

    snake_case   :				Union[str, Any]				 =   partial(lowercase						,							num_labels=lowercase						,							idalabel=lowercase						,							labelaid=lowercase			)

    snake_case   :				Dict				 =   {
        "resnet18": ImageNetPreTrainedConfig(
            depths=[2, 2, 2, 2]						,							hidden_sizes=[64, 128, 256, 512]						,							layer_type="basic"			),
        "resnet26": ImageNetPreTrainedConfig(
            depths=[2, 2, 2, 2]						,							hidden_sizes=[256, 512, 1024, 2048]						,							layer_type="bottleneck"			),
        "resnet34": ImageNetPreTrainedConfig(
            depths=[3, 4, 6, 3]						,							hidden_sizes=[64, 128, 256, 512]						,							layer_type="basic"			),
        "resnet50": ImageNetPreTrainedConfig(
            depths=[3, 4, 6, 3]						,							hidden_sizes=[256, 512, 1024, 2048]						,							layer_type="bottleneck"			),
        "resnet101": ImageNetPreTrainedConfig(
            depths=[3, 4, 23, 3]						,							hidden_sizes=[256, 512, 1024, 2048]						,							layer_type="bottleneck"			),
        "resnet152": ImageNetPreTrainedConfig(
            depths=[3, 8, 36, 3]						,							hidden_sizes=[256, 512, 1024, 2048]						,							layer_type="bottleneck"			),
    }

    if model_name:
        convert_weight_and_push(lowercase						,							names_to_config[model_name]						,							lowercase						,							lowercase			)
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(lowercase						,							lowercase						,							lowercase						,							lowercase			)
    return config, expected_shape


if __name__ == "__main__":
  __snake_case             =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default=None,
      type=str,
      help=(
          """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
          """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=Path,
      required=True,
      help="""Path to the output PyTorch model directory.""",
  )
  parser.add_argument(
      """--push_to_hub""",
      default=True,
      type=bool,
      required=False,
      help="""If True, push model and image processor to the hub.""",
  )

  __snake_case             =   parser.parse_args()
  __snake_case             =   args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









"""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   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  int						,							lowercase  :  Dict						,							lowercase  :  Dict						,							lowercase  :  int			)     ->						int:


    """simple docstring"""





    with open(lowercase			) as metadata_file:
        snake_case   :				str				 =   json.load(lowercase			)
    snake_case   :				Optional[Any]				 =   LukeConfig(use_entity_aware_attention=lowercase						,							**metadata["model_config"]			)

    # Load in the weights from the checkpoint_path
    snake_case   :				Tuple				 =   torch.load(lowercase						,							map_location="cpu"			)["module"]

    # Load the entity vocab file
    snake_case   :				Optional[Any]				 =   load_original_entity_vocab(lowercase			)
    # add an entry for [MASK2]
    snake_case   :				Dict				 =   max(entity_vocab.values()			) + 1
    config.entity_vocab_size += 1

    snake_case   :				Union[str, Any]				 =   XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

    # Add special tokens to the token vocabulary for downstream tasks
    snake_case   :				Tuple				 =   AddedToken("<ent>"						,							lstrip=lowercase						,							rstrip=lowercase			)
    snake_case   :				str				 =   AddedToken("<ent2>"						,							lstrip=lowercase						,							rstrip=lowercase			)
    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(lowercase			)
    with open(os.path.join(lowercase						,							"tokenizer_config.json"			)						,							"r"			) as f:
        snake_case   :				str				 =   json.load(lowercase			)
    snake_case   :				List[str]				 =   "MLukeTokenizer"
    with open(os.path.join(lowercase						,							"tokenizer_config.json"			)						,							"w"			) as f:
        json.dump(lowercase						,							lowercase			)

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

    snake_case   :				Dict				 =   MLukeTokenizer.from_pretrained(lowercase			)

    # Initialize the embeddings of the special tokens
    snake_case   :				Tuple				 =   tokenizer.convert_tokens_to_ids(["@"]			)[0]
    snake_case   :				str				 =   tokenizer.convert_tokens_to_ids(["#"]			)[0]

    snake_case   :				Union[str, Any]				 =   state_dict["embeddings.word_embeddings.weight"]
    snake_case   :				str				 =   word_emb[ent_init_index].unsqueeze(0			)
    snake_case   :				Union[str, Any]				 =   word_emb[enta_init_index].unsqueeze(0			)
    snake_case   :				Union[str, Any]				 =   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"]:
        snake_case   :				Tuple				 =   state_dict[bias_name]
        snake_case   :				Optional[Any]				 =   decoder_bias[ent_init_index].unsqueeze(0			)
        snake_case   :				Optional[Any]				 =   decoder_bias[enta_init_index].unsqueeze(0			)
        snake_case   :				Any				 =   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"]:
            snake_case   :				Optional[int]				 =   F'encoder.layer.{layer_index}.attention.self.'
            snake_case   :				Optional[Any]				 =   state_dict[prefix + matrix_name]
            snake_case   :				Optional[Any]				 =   state_dict[prefix + matrix_name]
            snake_case   :				Any				 =   state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    snake_case   :				List[Any]				 =   state_dict["entity_embeddings.entity_embeddings.weight"]
    snake_case   :				str				 =   entity_emb[entity_vocab["[MASK]"]].unsqueeze(0			)
    snake_case   :				Tuple				 =   torch.cat([entity_emb, entity_mask_emb]			)
    # add [MASK2] for 'entity_predictions.bias'
    snake_case   :				Optional[int]				 =   state_dict["entity_predictions.bias"]
    snake_case   :				Optional[int]				 =   entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0			)
    snake_case   :				Union[str, Any]				 =   torch.cat([entity_prediction_bias, entity_mask_bias]			)

    snake_case   :				Union[str, Any]				 =   LukeForMaskedLM(config=lowercase			).eval()

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

    snake_case ,snake_case   :				Optional[Any]				 =   model.load_state_dict(lowercase						,							strict=lowercase			)

    if set(lowercase			) != {"luke.embeddings.position_ids"}:
        raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}'			)
    if set(lowercase			) != {
        "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
    snake_case   :				Optional[Any]				 =   MLukeTokenizer.from_pretrained(lowercase						,							task="entity_classification"			)

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

    snake_case   :				int				 =   model(**lowercase			)

    # Verify word hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        snake_case   :				int				 =   torch.Size((1, 33, 768)			)
        snake_case   :				str				 =   torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]			)

    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]						,							lowercase						,							atol=1e-4			):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        snake_case   :				Any				 =   torch.Size((1, 1, 768)			)
        snake_case   :				List[Any]				 =   torch.tensor([[-0.1482, 0.0609, 0.0322]]			)

    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]						,							lowercase						,							atol=1e-4			):
        raise ValueError

    # Verify masked word/entity prediction
    snake_case   :				List[str]				 =   MLukeTokenizer.from_pretrained(lowercase			)
    snake_case   :				List[Any]				 =   "Tokyo is the capital of <mask>."
    snake_case   :				Optional[Any]				 =   (24, 30)
    snake_case   :				List[str]				 =   tokenizer(lowercase						,							entity_spans=[span]						,							return_tensors="pt"			)

    snake_case   :				Any				 =   model(**lowercase			)

    snake_case   :				int				 =   encoding["input_ids"][0].tolist()
    snake_case   :				str				 =   input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"			)			)
    snake_case   :				Tuple				 =   outputs.logits[0][mask_position_id].argmax(dim=-1			)
    assert "Japan" == tokenizer.decode(lowercase			)

    snake_case   :				Tuple				 =   outputs.entity_logits[0][0].argmax().item()
    snake_case   :				Optional[int]				 =   [
        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(lowercase			)			)
    model.save_pretrained(lowercase			)







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]			)     ->						Dict:


    """simple docstring"""





    snake_case   :				Tuple				 =   ["[MASK]", "[PAD]", "[UNK]"]

    snake_case   :				Optional[Any]				 =   [json.loads(lowercase			) for line in open(lowercase			)]

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


if __name__ == "__main__":
  __snake_case             =   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."""
  )
  __snake_case             =   parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )





