
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					(    __lowerCamelCase     : Dict				,					__lowerCamelCase     : List[str]				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Optional[int]    ):



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

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

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

       snake_case       :   int							=      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=__lowerCamelCase				,					rstrip=__lowerCamelCase    )
       snake_case       :   Tuple							=      AddedToken("<ent2>"				,					lstrip=__lowerCamelCase				,					rstrip=__lowerCamelCase    )
       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(__lowerCamelCase    )
       with open(os.path.join(__lowerCamelCase				,					"tokenizer_config.json"    )				,					"r"    ) as f:
              snake_case       :   Any							=      json.load(__lowerCamelCase    )
       snake_case       :   Tuple							=      "MLukeTokenizer"
       with open(os.path.join(__lowerCamelCase				,					"tokenizer_config.json"    )				,					"w"    ) as f:
              json.dump(__lowerCamelCase				,					__lowerCamelCase    )

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

       snake_case       :   Optional[Any]							=      MLukeTokenizer.from_pretrained(__lowerCamelCase    )

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

       snake_case       :   Union[str, Any]							=      state_dict["embeddings.word_embeddings.weight"]
       snake_case       :   int							=      word_emb[ent_init_index].unsqueeze(0    )
       snake_case       :   Optional[int]							=      word_emb[enta_init_index].unsqueeze(0    )
       snake_case       :   List[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       :   Dict							=      state_dict[bias_name]
              snake_case       :   Any							=      decoder_bias[ent_init_index].unsqueeze(0    )
              snake_case       :   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[Any]							=      f"""encoder.layer.{layer_index}.attention.self."""
                     snake_case       :   Optional[int]							=      state_dict[prefix + matrix_name]
                     snake_case       :   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       :   Optional[int]							=      state_dict["entity_embeddings.entity_embeddings.weight"]
       snake_case       :   int							=      entity_emb[entity_vocab["[MASK]"]].unsqueeze(0    )
       snake_case       :   Union[str, Any]							=      torch.cat([entity_emb, entity_mask_emb]    )
       # add [MASK2] for 'entity_predictions.bias'
       snake_case       :   Optional[int]							=      state_dict["entity_predictions.bias"]
       snake_case       :   Dict							=      entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0    )
       snake_case       :   Union[str, Any]							=      torch.cat([entity_prediction_bias, entity_mask_bias]    )

       snake_case       :   int							=      LukeForMaskedLM(config=__lowerCamelCase    ).eval()

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

       snake_case				,					snake_case       :   Tuple							=      model.load_state_dict(__lowerCamelCase				,					strict=__lowerCamelCase    )

       if set(__lowerCamelCase    ) != {"luke.embeddings.position_ids"}:
              raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""    )
       if set(__lowerCamelCase    ) != {
           "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       :   Dict							=      MLukeTokenizer.from_pretrained(__lowerCamelCase				,					task="entity_classification"    )

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

       snake_case       :   Tuple							=      model(**__lowerCamelCase    )

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              snake_case       :   int							=      torch.Size((1, 33, 768)    )
              snake_case       :   int							=      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]				,					__lowerCamelCase				,					atol=1E-4    ):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              snake_case       :   List[str]							=      torch.Size((1, 1, 768)    )
              snake_case       :   Dict							=      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]				,					__lowerCamelCase				,					atol=1E-4    ):
              raise ValueError

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

       snake_case       :   Dict							=      model(**__lowerCamelCase    )

       snake_case       :   Dict							=      encoding["input_ids"][0].tolist()
       snake_case       :   Union[str, Any]							=      input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"    )    )
       snake_case       :   Any							=      outputs.logits[0][mask_position_id].argmax(dim=-1    )
       assert "Japan" == tokenizer.decode(__lowerCamelCase    )

       snake_case       :   str							=      outputs.entity_logits[0][0].argmax().item()
       snake_case       :   str							=      [
           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(__lowerCamelCase    )    )
       model.save_pretrained(__lowerCamelCase    )


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       snake_case       :   int							=      ["[MASK]", "[PAD]", "[UNK]"]

       snake_case       :   Tuple							=      [json.loads(__lowerCamelCase    ) for line in open(__lowerCamelCase    )]

       snake_case       :   str							=      {}
       for entry in data:
              snake_case       :   List[Any]							=      entry["id"]
              for entity_name, language in entry["entities"]:
                     if entity_name in SPECIAL_TOKENS:
                            snake_case       :   Tuple							=      entity_id
                            break
                     snake_case       :   Optional[int]							=      f"""{language}:{entity_name}"""
                     snake_case       :   Tuple							=      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,
   )











import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


lowerCamelCase				:		Any     =   logging.get_logger(__name__)







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  Dict						,							*A_					:  Optional[int]						,							**A_					:  int  )   ->	None:

       """simple docstring"""






       warnings.warn(
           'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
           ' use DonutImageProcessor instead.'						,							A_						,							)
       super().__init__(*A_						,							**A_  )



