

"""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  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Union[str, Any]:
      # Load configuration defined in the metadata file
      with open(__lowerCAmelCase							) as metadata_file:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	json.load(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	LukeConfig(use_entity_aware_attention=__lowerCAmelCase  ,					**metadata["""model_config"""]							)

      # Load in the weights from the checkpoint_path
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.load(__lowerCAmelCase  ,					map_location="""cpu"""							)["""module"""]

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

      SCREAMING_SNAKE_CASE__   :      Dict      		=	XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]							)

      # Add special tokens to the token vocabulary for downstream tasks
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	AddedToken("""<ent>"""  ,					lstrip=__lowerCAmelCase  ,					rstrip=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	json.load(__lowerCAmelCase							)
      SCREAMING_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							)

      SCREAMING_SNAKE_CASE__   :      int      		=	MLukeTokenizer.from_pretrained(__lowerCAmelCase							)

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

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

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

      SCREAMING_SNAKE_CASE__   :      Optional[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"""							)
      SCREAMING_SNAKE_CASE__   :      str      		=	OrderedDict()
      for key, value in state_dict.items():
            if not (key.startswith("""lm_head"""							) or key.startswith("""entity_predictions"""							)):
                  SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	state_dict[key]
            else:
                  SCREAMING_SNAKE_CASE__   :      Dict      		=	state_dict[key]

      SCREAMING_SNAKE_CASE__   :      int      		=	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
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	MLukeTokenizer.from_pretrained(__lowerCAmelCase  ,					task="""entity_classification"""							)

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

      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(**__lowerCAmelCase							)

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	torch.Size((1, 33, 768)							)
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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]  ,					__lowerCAmelCase  ,					atol=1E-4							):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.Size((1, 1, 768)							)
            SCREAMING_SNAKE_CASE__   :      Any      		=	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]  ,					__lowerCAmelCase  ,					atol=1E-4							):
            raise ValueError

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

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	model(**__lowerCAmelCase							)

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

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	outputs.entity_logits[0][0].argmax().item()
      SCREAMING_SNAKE_CASE__   :      List[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  _lowercase					(    __lowerCAmelCase							)       ->				str:
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	["""[MASK]""", """[PAD]""", """[UNK]"""]

      SCREAMING_SNAKE_CASE__   :      List[str]      		=	[json.loads(__lowerCAmelCase							) for line in open(__lowerCAmelCase							)]

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


if __name__ == "__main__":
  a					:str    =       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."
  )
  a					:int    =       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 torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = (DDPMScheduler,)

     def 					_a			(						self	,    **_a  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """num_train_timesteps""": 1_000,
               """beta_start""": 0.0_001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """variance_type""": """fixed_small""",
               """clip_sample""": True,
           }

           config.update(**_a  )
           return config

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]	,    [0.002, 0.02, 0.2, 2]  ):
                 self.check_over_configs(beta_start=_a	,    beta_end=_a  )

     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_a  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=_a  )

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a  )

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           self.check_over_configs(thresholding=_a  )
           for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=_a	,    prediction_type=_a	,    sample_max_value=_a	,    )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for t in [0, 500, 999]:
                 self.check_over_forward(time_step=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )

           assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.00_979  )  ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.02  )  ) < 1E-5

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      int      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	len(_a  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.dummy_sample_deter
           SCREAMING_SNAKE_CASE__   :      str      		=	torch.manual_seed(0  )

           for t in reversed(range(_a  )  ):
                 # 1. predict noise residual
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(_a	,    _a  )

                 # 2. predict previous mean of sample x_t-1
                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  ).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 SCREAMING_SNAKE_CASE__   :      str      		=	pred_prev_sample

           SCREAMING_SNAKE_CASE__   :      str      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 258.9_606  ) < 1E-2
           assert abs(result_mean.item() - 0.3_372  ) < 1E-3

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_scheduler_config(prediction_type="""v_prediction"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	len(_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_sample_deter
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.manual_seed(0  )

           for t in reversed(range(_a  )  ):
                 # 1. predict noise residual
                 SCREAMING_SNAKE_CASE__   :      int      		=	model(_a	,    _a  )

                 # 2. predict previous mean of sample x_t-1
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  ).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	pred_prev_sample

           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      int      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 202.0_296  ) < 1E-2
           assert abs(result_mean.item() - 0.2_631  ) < 1E-3

     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[100, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scheduler.timesteps

           for i, timestep in enumerate(_a  ):
                 if i == len(_a  ) - 1:
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	-1
                 else:
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	timesteps[i + 1]

                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.previous_timestep(_a  )
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	prev_t.item()

                 self.assertEqual(_a	,    _a  )

     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[100, 87, 50, 51, 0]

           with self.assertRaises(_a	,    msg="""`custom_timesteps` must be in descending order."""  ):
                 scheduler.set_timesteps(timesteps=_a  )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	[100, 87, 50, 1, 0]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	len(_a  )

           with self.assertRaises(_a	,    msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""  ):
                 scheduler.set_timesteps(num_inference_steps=_a	,    timesteps=_a  )





     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[scheduler.config.num_train_timesteps]

           with self.assertRaises(
               _a	,    msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""	,    ):
                 scheduler.set_timesteps(timesteps=_a  )




