


"""simple docstring"""



import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import DecisionTransformerModel
  from transformers.models.decision_transformer.modeling_decision_transformer import (
      DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  )







class _lowerCAmelCase    :







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=13						,     UpperCamelCase__=7						,     UpperCamelCase__=6						,     UpperCamelCase__=17						,     UpperCamelCase__=23						,     UpperCamelCase__=11						,     UpperCamelCase__=True						,     )     ->		Any:

       '''simple docstring'''




       snake_case   :				Any				 =   parent
       snake_case   :				Union[str, Any]				 =   batch_size
       snake_case   :				Dict				 =   seq_length
       snake_case   :				Optional[int]				 =   act_dim
       snake_case   :				Any				 =   state_dim
       snake_case   :				int				 =   hidden_size
       snake_case   :				List[Any]				 =   max_length
       snake_case   :				Tuple				 =   is_training







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

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   floats_tensor((self.batch_size, self.seq_length, self.state_dim)						)
       snake_case   :				List[Any]				 =   floats_tensor((self.batch_size, self.seq_length, self.act_dim)						)
       snake_case   :				Union[str, Any]				 =   floats_tensor((self.batch_size, self.seq_length, 1)						)
       snake_case   :				str				 =   floats_tensor((self.batch_size, self.seq_length, 1)						)
       snake_case   :				Optional[Any]				 =   ids_tensor((self.batch_size, self.seq_length)						,     vocab_size=1000						)
       snake_case   :				str				 =   random_attention_mask((self.batch_size, self.seq_length)						)

       snake_case   :				Optional[int]				 =   self.get_config()

       return (
           config,
           states,
           actions,
           rewards,
           returns_to_go,
           timesteps,
           attention_mask,
       )







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

       '''simple docstring'''




       return DecisionTransformerConfig(
           batch_size=self.batch_size						,     seq_length=self.seq_length						,     act_dim=self.act_dim						,     state_dim=self.state_dim						,     hidden_size=self.hidden_size						,     max_length=self.max_length						,     )







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

       '''simple docstring'''




       snake_case   :				str				 =   DecisionTransformerModel(config=lowerCAmelCase__						)
       model.to(lowerCAmelCase__						)
       model.eval()
       snake_case   :				Optional[int]				 =   model(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						)

       self.parent.assertEqual(result.state_preds.shape						,     states.shape						)
       self.parent.assertEqual(result.action_preds.shape						,     actions.shape						)
       self.parent.assertEqual(result.return_preds.shape						,     returns_to_go.shape						)
       self.parent.assertEqual(
           result.last_hidden_state.shape						,     (self.batch_size, self.seq_length * 3, self.hidden_size)						)  # seq length *3 as there are 3 modelities: states, returns and actions







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

       '''simple docstring'''




       snake_case   :				str				 =   self.prepare_config_and_inputs()
       (
           snake_case	
       )   :				Optional[int]				 =   config_and_inputs
       snake_case   :				str				 =   {
           "states": states,
           "actions": actions,
           "rewards": rewards,
           "returns_to_go": returns_to_go,
           "timesteps": timesteps,
           "attention_mask": attention_mask,
       }
       return config, inputs_dict







@require_torch
class _lowerCAmelCase    (				snake_case_   ,							snake_case_   ,							snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	Union[str, Any]							  =  (DecisionTransformerModel,) if is_torch_available() else ()
   __UpperCAmelCase			:	List[str]							  =  ()
   __UpperCAmelCase			:	Any							  =  {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

   # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
   __UpperCAmelCase			:	Dict							  =  False

   # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
   __UpperCAmelCase			:	List[Any]							  =  False
   __UpperCAmelCase			:	Optional[Any]							  =  False
   __UpperCAmelCase			:	int							  =  False
   __UpperCAmelCase			:	str							  =  False
   __UpperCAmelCase			:	int							  =  False
   __UpperCAmelCase			:	Optional[int]							  =  False
   __UpperCAmelCase			:	Any							  =  False
   __UpperCAmelCase			:	List[Any]							  =  False
   __UpperCAmelCase			:	Optional[Any]							  =  False







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Tuple				 =   DecisionTransformerModelTester(self						)
       snake_case   :				List[Any]				 =   ConfigTester(self						,     config_class=lowerCAmelCase__						,     hidden_size=37						)







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

       '''simple docstring'''




       self.config_tester.run_common_tests()







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Any				 =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCAmelCase__						)







   @slow
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           snake_case   :				Dict				 =   DecisionTransformerModel.from_pretrained(lowerCAmelCase__						)
           self.assertIsNotNone(lowerCAmelCase__						)







   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				Any				 =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           snake_case   :				int				 =   model_class(lowerCAmelCase__						)
           snake_case   :				Tuple				 =   inspect.signature(model.forward						)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           snake_case   :				str				 =   [*signature.parameters.keys()]

           snake_case   :				Tuple				 =   [
               "states",
               "actions",
               "rewards",
               "returns_to_go",
               "timesteps",
               "attention_mask",
           ]

           self.assertListEqual(arg_names[: len(lowerCAmelCase__						)]						,     lowerCAmelCase__						)







@require_torch
class _lowerCAmelCase    (				unittest.TestCase     ):







   @slow
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				List[str]				 =   2  # number of steps of autoregressive prediction we will perform
       snake_case   :				List[str]				 =   10  # defined by the RL environment, may be normalized
       snake_case   :				Optional[Any]				 =   DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert"						)
       snake_case   :				Optional[Any]				 =   model.to(lowerCAmelCase__						)
       snake_case   :				Any				 =   model.config
       torch.manual_seed(0						)
       snake_case   :				Dict				 =   torch.randn(1						,     1						,     config.state_dim						).to(device=lowerCAmelCase__						,     dtype=torch.floataa						)  # env.reset()

       snake_case   :				Union[str, Any]				 =   torch.tensor(
           [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]						,     device=lowerCAmelCase__						)

       snake_case   :				Any				 =   torch.tensor(lowerCAmelCase__						,     device=lowerCAmelCase__						,     dtype=torch.floataa						).reshape(1						,     1						,     1						)
       snake_case   :				str				 =   state
       snake_case   :				Union[str, Any]				 =   torch.zeros(1						,     0						,     config.act_dim						,     device=lowerCAmelCase__						,     dtype=torch.floataa						)
       snake_case   :				Dict				 =   torch.zeros(1						,     0						,     device=lowerCAmelCase__						,     dtype=torch.floataa						)
       snake_case   :				Optional[Any]				 =   torch.tensor(0						,     device=lowerCAmelCase__						,     dtype=torch.long						).reshape(1						,     1						)

       for step in range(lowerCAmelCase__						):
           snake_case   :				Optional[Any]				 =   torch.cat([actions, torch.zeros(1						,     1						,     config.act_dim						,     device=lowerCAmelCase__						)]						,     dim=1						)
           snake_case   :				Tuple				 =   torch.cat([rewards, torch.zeros(1						,     1						,     device=lowerCAmelCase__						)]						,     dim=1						)

           snake_case   :				List[str]				 =   torch.ones(1						,     states.shape[1]						).to(dtype=torch.long						,     device=states.device						)

           with torch.no_grad():
               snake_case   :				Union[str, Any]				 =   model(
                   states=lowerCAmelCase__						,     actions=lowerCAmelCase__						,     rewards=lowerCAmelCase__						,     returns_to_go=lowerCAmelCase__						,     timesteps=lowerCAmelCase__						,     attention_mask=lowerCAmelCase__						,     return_dict=lowerCAmelCase__						,     )

           self.assertEqual(action_pred.shape						,     actions.shape						)
           self.assertTrue(torch.allclose(action_pred[0, -1]						,     expected_outputs[step]						,     atol=1e-4						)						)
           snake_case   :				Any				 =   (  # env.step(action)
               torch.randn(1						,     1						,     config.state_dim						).to(device=lowerCAmelCase__						,     dtype=torch.floataa						),
               1.0,
               False,
               {},
           )

           snake_case   :				List[str]				 =   action_pred[0, -1]
           snake_case   :				Union[str, Any]				 =   torch.cat([states, state]						,     dim=1						)
           snake_case   :				Any				 =   returns_to_go[0, -1] - reward
           snake_case   :				List[Any]				 =   torch.cat([returns_to_go, pred_return.reshape(1						,     1						,     1						)]						,     dim=1						)
           snake_case   :				Dict				 =   torch.cat(
               [timesteps, torch.ones((1, 1)						,     device=lowerCAmelCase__						,     dtype=torch.long						) * (step + 1)]						,     dim=1						)









"""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,
  )





