





'''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    (							_lowerCamelCase : Any						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Optional[Any]	):
    # Load configuration defined in the metadata file
    with open(_lowerCamelCase	) as metadata_file:
        A__    =      json.load(_lowerCamelCase	)
    A__    =      LukeConfig(use_entity_aware_attention=_lowerCamelCase						,	**metadata["model_config"]	)

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

    # Load the entity vocab file
    A__    =      load_original_entity_vocab(_lowerCamelCase	)
    # 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=_lowerCamelCase						,	rstrip=_lowerCamelCase	)
    A__    =      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:
        A__    =      json.load(_lowerCamelCase	)
    A__    =      "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	)

    A__    =      MLukeTokenizer.from_pretrained(_lowerCamelCase	)

    # 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=_lowerCamelCase	).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(_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
    A__    =      MLukeTokenizer.from_pretrained(_lowerCamelCase						,	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(_lowerCamelCase						,	entity_spans=[span]						,	return_tensors="pt"	)

    A__    =      model(**_lowerCamelCase	)

    # Verify word hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        A__    =      torch.Size((1, 33, 7_68)	)
        A__    =      torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]	)

    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
        A__    =      torch.Size((1, 1, 7_68)	)
        A__    =      torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]	)

    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
    A__    =      MLukeTokenizer.from_pretrained(_lowerCamelCase	)
    A__    =      "Tokyo is the capital of <mask>."
    A__    =      (24, 30)
    A__    =      tokenizer(_lowerCamelCase						,	entity_spans=[span]						,	return_tensors="pt"	)

    A__    =      model(**_lowerCamelCase	)

    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(_lowerCamelCase	)

    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(_lowerCamelCase	)	)
    model.save_pretrained(_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : Union[str, Any]	):
    A__    =      ["[MASK]", "[PAD]", "[UNK]"]

    A__    =      [json.loads(_lowerCamelCase	) for line in open(_lowerCamelCase	)]

    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							:     List[Any]		     =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							:     Union[str, Any]		     =parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )










'''simple docstring'''

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    (PNDMScheduler,)
 __lowercase       		=    (("""num_inference_steps""", 50),)
 def 					UpperCAmelCase_							(    self				:List[str]						,	**lowercase_				:Optional[int]							)->							List[str]:
     A__    =      {
         "num_train_timesteps": 10_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
     }

     config.update(**lowercase_							)
     return config
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Optional[int]=0						,	**lowercase_				:Any							)->							int:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config(**lowercase_							)
         A__    =      scheduler_class(**lowercase_							)
         scheduler.set_timesteps(lowercase_							)
         # copy over dummy past residuals
         A__    =      dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(lowercase_							)
             A__    =      scheduler_class.from_pretrained(lowercase_							)
             new_scheduler.set_timesteps(lowercase_							)
             # copy over dummy past residuals
             A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     pass
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Optional[Any]=0						,	**lowercase_				:List[str]							)->							str:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)
         scheduler.set_timesteps(lowercase_							)

         # copy over dummy past residuals (must be after setting timesteps)
         A__    =      dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(lowercase_							)
             A__    =      scheduler_class.from_pretrained(lowercase_							)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(lowercase_							)

             # copy over dummy past residual (must be after setting timesteps)
             A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	**lowercase_				:Dict							)->							Optional[int]:
     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(**lowercase_							)
     A__    =      scheduler_class(**lowercase_							)

     A__    =      10
     A__    =      self.dummy_model()
     A__    =      self.dummy_sample_deter
     scheduler.set_timesteps(lowercase_							)

     for i, t in enumerate(scheduler.prk_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     for i, t in enumerate(scheduler.plms_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     return sample
 def 					UpperCAmelCase_							(    self				:int							)->							Union[str, Any]:
     A__    =      dict(self.forward_default_kwargs							)

     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         A__    =      self.dummy_sample
         A__    =      0.1 * sample

         if num_inference_steps is not None and hasattr(lowercase_						,	"set_timesteps"							):
             scheduler.set_timesteps(lowercase_							)
         elif num_inference_steps is not None and not hasattr(lowercase_						,	"set_timesteps"							):
             A__    =      num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
         A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_prk(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

         self.assertEqual(output_a.shape						,	sample.shape							)
         self.assertEqual(output_a.shape						,	output_a.shape							)

         A__    =      scheduler.step_plms(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_plms(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

         self.assertEqual(output_a.shape						,	sample.shape							)
         self.assertEqual(output_a.shape						,	output_a.shape							)
 def 					UpperCAmelCase_							(    self				:Any							)->							Dict:
     for timesteps in [1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							int:
     for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=lowercase_							)

     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(steps_offset=1							)
     A__    =      scheduler_class(**lowercase_							)
     scheduler.set_timesteps(10							)
     assert torch.equal(
         scheduler.timesteps						,	torch.LongTensor(
             [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]							)						,	)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Any:
     for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]						,	[0.0_0_2, 0.0_2]							):
         self.check_over_configs(beta_start=lowercase_						,	beta_end=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Tuple:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Tuple:
     for t in [1, 5, 10]:
         self.check_over_forward(time_step=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Optional[int]:
     for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 1_00]							):
         self.check_over_forward(num_inference_steps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
     A__    =      27

     for scheduler_class in self.scheduler_classes:
         A__    =      self.dummy_sample
         A__    =      0.1 * sample

         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.set_timesteps(lowercase_							)

         # before power of 3 fix, would error on first step, so we only need to do two
         for i, t in enumerate(scheduler.prk_timesteps[:2]							):
             A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     with self.assertRaises(lowercase_							):
         A__    =      self.scheduler_classes[0]
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.step_plms(self.dummy_sample						,	1						,	self.dummy_sample							).prev_sample
 def 					UpperCAmelCase_							(    self				:int							)->							str:
     A__    =      self.full_loop()
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_9_8.1_3_1_8							) < 1E-2
     assert abs(result_mean.item() - 0.2_5_8_0							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
     A__    =      self.full_loop(prediction_type="v_prediction"							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 6_7.3_9_8_6							) < 1E-2
     assert abs(result_mean.item() - 0.0_8_7_8							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 2_3_0.0_3_9_9							) < 1E-2
     assert abs(result_mean.item() - 0.2_9_9_5							) < 1E-3



 def 					UpperCAmelCase_							(    self				:Dict							)->							Any:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_8_6.9_4_8_2							) < 1E-2
     assert abs(result_mean.item() - 0.2_4_3_4							) < 1E-3



