

'''simple docstring'''






import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax

 from transformers.models.big_bird.modeling_flax_big_bird import (
     FlaxBigBirdForCausalLM,
     FlaxBigBirdForMaskedLM,
     FlaxBigBirdForMultipleChoice,
     FlaxBigBirdForPreTraining,
     FlaxBigBirdForQuestionAnswering,
     FlaxBigBirdForSequenceClassification,
     FlaxBigBirdForTokenClassification,
     FlaxBigBirdModel,
 )






class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  def __init__( self  :	Tuple   ,lowercase__  :	Tuple   ,lowercase__  :	List[Any]=2   ,lowercase__  :	List[str]=5_6   ,lowercase__  :	Optional[int]=True   ,lowercase__  :	Optional[Any]=True   ,lowercase__  :	Tuple=True   ,lowercase__  :	Any=True   ,lowercase__  :	Union[str, Any]=9_9   ,lowercase__  :	int=3_2   ,lowercase__  :	Optional[int]=2   ,lowercase__  :	Optional[int]=2   ,lowercase__  :	Any=7   ,lowercase__  :	Dict="gelu_new"   ,lowercase__  :	str=0.1   ,lowercase__  :	str=0.1   ,lowercase__  :	List[str]=5_1_2   ,lowercase__  :	int=1_6   ,lowercase__  :	str=2   ,lowercase__  :	str=0.0_2   ,lowercase__  :	int=4   ,lowercase__  :	List[str]="block_sparse"   ,lowercase__  :	str=True   ,lowercase__  :	List[str]=False   ,lowercase__  :	List[str]=2   ,lowercase__  :	Optional[int]=3   ,):
   __lowercase				    =					parent
   __lowercase				    =					batch_size
   __lowercase				    =					seq_length
   __lowercase				    =					is_training
   __lowercase				    =					use_attention_mask
   __lowercase				    =					use_token_type_ids
   __lowercase				    =					use_labels
   __lowercase				    =					vocab_size
   __lowercase				    =					hidden_size
   __lowercase				    =					num_hidden_layers
   __lowercase				    =					num_attention_heads
   __lowercase				    =					intermediate_size
   __lowercase				    =					hidden_act
   __lowercase				    =					hidden_dropout_prob
   __lowercase				    =					attention_probs_dropout_prob
   __lowercase				    =					max_position_embeddings
   __lowercase				    =					type_vocab_size
   __lowercase				    =					type_sequence_label_size
   __lowercase				    =					initializer_range
   __lowercase				    =					num_choices

   __lowercase				    =					rescale_embeddings
   __lowercase				    =					attention_type
   __lowercase				    =					use_bias
   __lowercase				    =					block_size
   __lowercase				    =					num_random_blocks






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   __lowercase				    =					ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size     )

   __lowercase				    =					None
   if self.use_attention_mask:
    __lowercase				    =					random_attention_mask([self.batch_size, self.seq_length]     )

   __lowercase				    =					None
   if self.use_token_type_ids:
    __lowercase				    =					ids_tensor([self.batch_size, self.seq_length]   ,self.type_vocab_size     )

   __lowercase				    =					BigBirdConfig(
       vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,max_position_embeddings=self.max_position_embeddings   ,type_vocab_size=self.type_vocab_size   ,is_decoder=__a   ,initializer_range=self.initializer_range   ,attention_type=self.attention_type   ,block_size=self.block_size   ,num_random_blocks=self.num_random_blocks   ,use_bias=self.use_bias   ,rescale_embeddings=self.rescale_embeddings   ,)

   return config, input_ids, token_type_ids, attention_mask






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   __lowercase				    =					self.prepare_config_and_inputs()
   __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase				    =					config_and_inputs
   __lowercase				    =					{
       '''input_ids''': input_ids,
       '''token_type_ids''': token_type_ids,
       '''attention_mask''': attention_mask,
   }
   return config, inputs_dict






@require_flax
class      lowercase_      (UpperCAmelCase_							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    List[str]							     =     (
      (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdModel,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
      )
      if is_flax_available()
      else ()
  )

  SCREAMING_SNAKE_CASE       :    str							     =     False
  SCREAMING_SNAKE_CASE       :    Optional[int]							     =     False






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					FlaxBigBirdModelTester(self     )






  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   super().test_from_pretrained_save_pretrained()






  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   super().test_from_pretrained_with_no_automatic_init()






  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   super().test_no_automatic_init()






  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   super().test_hidden_states_output()






  @slow
  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):



   for model_class_name in self.all_model_classes:
    __lowercase				    =					model_class_name.from_pretrained('''google/bigbird-roberta-base'''     )
    self.assertIsNotNone(__a     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):



   if self.test_attn_probs:
    super().test_attention_outputs()






  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase    ,      __lowercase				    =					self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__     ):
     __lowercase				    =					self._prepare_for_class(__a   ,__a     )
     __lowercase				    =					model_class(__a     )

     @jax.jit
     def model_jitted(lowercase__  :	List[str]   ,lowercase__  :	Tuple=None   ,**lowercase__  :	List[Any]     ):
      return model(input_ids=__a   ,attention_mask=__a   ,**__a     )

     with self.subTest('''JIT Enabled'''     ):
      __lowercase				    =					model_jitted(**__a     ).to_tuple()

     with self.subTest('''JIT Disabled'''     ):
      with jax.disable_jit():
       __lowercase				    =					model_jitted(**__a     ).to_tuple()

     self.assertEqual(len(__a     )   ,len(__a     )     )
     for jitted_output, output in zip(__a   ,__a     ):
      self.assertEqual(jitted_output.shape   ,output.shape     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]   ,lowercase__  :	Any   ,lowercase__  :	Dict   ,lowercase__  :	Tuple   ,lowercase__  :	List[Any]=1e-5   ,lowercase__  :	int="outputs"   ,lowercase__  :	str=None     ):



   if name.startswith('''outputs.attentions'''     ):
    return
   else:
    super().check_pt_flax_outputs(__a   ,__a   ,__a   ,__a   ,__a   ,__a     )






import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken

@torch.no_grad()
def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      with open(UpperCAmelCase__			) as metadata_file:
            lowerCamelCase             =		json.load(UpperCAmelCase__			)
      lowerCamelCase             =		LukeConfig(use_entity_aware_attention=UpperCAmelCase__ , **metadata["model_config"]			)

      # Load in the weights from the checkpoint_path
      lowerCamelCase             =		torch.load(UpperCAmelCase__ , map_location="cpu"			)

      # Load the entity vocab file
      lowerCamelCase             =		load_entity_vocab(UpperCAmelCase__			)

      lowerCamelCase             =		RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

      # Add special tokens to the token vocabulary for downstream tasks
      lowerCamelCase             =		AddedToken("<ent>" , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__			)
      lowerCamelCase             =		AddedToken("<ent2>" , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__			)
      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(UpperCAmelCase__			)
      with open(os.path.join(UpperCAmelCase__ , LukeTokenizer.vocab_files_names["entity_vocab_file"]			) , "w"			) as f:
            json.dump(UpperCAmelCase__ , UpperCAmelCase__			)

      lowerCamelCase             =		LukeTokenizer.from_pretrained(UpperCAmelCase__			)

      # Initialize the embeddings of the special tokens
      lowerCamelCase             =		state_dict["embeddings.word_embeddings.weight"]
      lowerCamelCase             =		word_emb[tokenizer.convert_tokens_to_ids(["@"]			)[0]].unsqueeze(0			)
      lowerCamelCase             =		word_emb[tokenizer.convert_tokens_to_ids(["#"]			)[0]].unsqueeze(0			)
      lowerCamelCase             =		torch.cat([word_emb, ent_emb, enta_emb]			)

      # 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"]:
                  lowerCamelCase             =		F"""encoder.layer.{layer_index}.attention.self."""
                  lowerCamelCase             =		state_dict[prefix + matrix_name]
                  lowerCamelCase             =		state_dict[prefix + matrix_name]
                  lowerCamelCase             =		state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      lowerCamelCase             =		state_dict["entity_embeddings.entity_embeddings.weight"]
      lowerCamelCase             =		entity_emb[entity_vocab["[MASK]"]]

      lowerCamelCase             =		LukeModel(config=UpperCAmelCase__			).eval()

      lowerCamelCase      ,				lowerCamelCase             =		model.load_state_dict(UpperCAmelCase__ , strict=UpperCAmelCase__			)
      if not (len(UpperCAmelCase__			) == 1 and missing_keys[0] == "embeddings.position_ids"):
            raise ValueError(F"""Missing keys {", ".join(UpperCAmelCase__			)}. Expected only missing embeddings.position_ids"""			)
      if not (all(key.startswith("entity_predictions"			) or key.startswith("lm_head"			) for key in unexpected_keys			)):
            raise ValueError(
                "Unexpected keys"
                F""" {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"			) or key.startswith("lm_head"			))]			)}"""			)

      # Check outputs
      lowerCamelCase             =		LukeTokenizer.from_pretrained(UpperCAmelCase__ , task="entity_classification"			)

      lowerCamelCase             =		(
          "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
          " new world number one avoid a humiliating second- round exit at Wimbledon ."
      )
      lowerCamelCase             =		(39, 42)
      lowerCamelCase             =		tokenizer(UpperCAmelCase__ , entity_spans=[span] , add_prefix_space=UpperCAmelCase__ , return_tensors="pt"			)

      lowerCamelCase             =		model(**UpperCAmelCase__			)

      # Verify word hidden states
      if model_size == "large":
            lowerCamelCase             =		torch.Size((1, 42, 1024)			)
            lowerCamelCase             =		torch.tensor(
                [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]			)
      else:  # base
            lowerCamelCase             =		torch.Size((1, 42, 768)			)
            lowerCamelCase             =		torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]			)

      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] , UpperCAmelCase__ , atol=1E-4			):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            lowerCamelCase             =		torch.Size((1, 1, 1024)			)
            lowerCamelCase             =		torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]			)
      else:  # base
            lowerCamelCase             =		torch.Size((1, 1, 768)			)
            lowerCamelCase             =		torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]			)

      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] , UpperCAmelCase__ , atol=1E-4			):
            raise ValueError

      # Finally, save our PyTorch model and tokenizer
      print("Saving PyTorch model to {}".format(UpperCAmelCase__			)			)
      model.save_pretrained(UpperCAmelCase__			)

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		{}
      with open(UpperCAmelCase__ , "r" , encoding="utf-8"			) as f:
            for index, line in enumerate(UpperCAmelCase__			):
                  lowerCamelCase      ,				lowerCamelCase             =		line.rstrip().split("\t"			)
                  lowerCamelCase             =		index

      return entity_vocab


if __name__ == "__main__":
     a_    :  Tuple						  =		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_    :  Dict						  =		parser.parse_args()
     convert_luke_checkpoint(
         args.checkpoint_path,
         args.metadata_path,
         args.entity_vocab_path,
         args.pytorch_dump_folder_path,
         args.model_size,
     )