

"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel

@require_tf
class       lowerCamelCase	:


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  PegasusConfig
  SCREAMING_SNAKE_CASE					      =  {}
  SCREAMING_SNAKE_CASE					      =  'gelu'



  def __init__(self							,       _lowerCamelCase							,       _lowerCamelCase=13							,       _lowerCamelCase=7							,       _lowerCamelCase=True							,       _lowerCamelCase=False							,       _lowerCamelCase=99							,       _lowerCamelCase=32							,       _lowerCamelCase=2							,       _lowerCamelCase=4							,       _lowerCamelCase=37							,       _lowerCamelCase=0.1							,       _lowerCamelCase=0.1							,       _lowerCamelCase=40							,       _lowerCamelCase=2							,       _lowerCamelCase=1							,       _lowerCamelCase=0							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    parent
        UpperCAmelCase__			:							int							=    batch_size
        UpperCAmelCase__			:							Dict							=    seq_length
        UpperCAmelCase__			:							Tuple							=    is_training
        UpperCAmelCase__			:							Dict							=    use_labels
        UpperCAmelCase__			:							Dict							=    vocab_size
        UpperCAmelCase__			:							Optional[Any]							=    hidden_size
        UpperCAmelCase__			:							Union[str, Any]							=    num_hidden_layers
        UpperCAmelCase__			:							Union[str, Any]							=    num_attention_heads
        UpperCAmelCase__			:							int							=    intermediate_size

        UpperCAmelCase__			:							str							=    hidden_dropout_prob
        UpperCAmelCase__			:							List[Any]							=    attention_probs_dropout_prob
        UpperCAmelCase__			:							Optional[int]							=    max_position_embeddings
        UpperCAmelCase__			:							Optional[Any]							=    eos_token_id
        UpperCAmelCase__			:							Any							=    pad_token_id
        UpperCAmelCase__			:							Any							=    bos_token_id



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    ids_tensor([self.batch_size, self.seq_length - 1]							,       self.vocab_size	)
        UpperCAmelCase__			:							Tuple							=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)							,       1	)
        UpperCAmelCase__			:							Tuple							=    tf.concat([input_ids, eos_tensor]							,       axis=1	)

        UpperCAmelCase__			:							str							=    ids_tensor([self.batch_size, self.seq_length]							,       self.vocab_size	)

        UpperCAmelCase__			:							Any							=    self.config_cls(
            vocab_size=self.vocab_size							,       d_model=self.hidden_size							,       encoder_layers=self.num_hidden_layers							,       decoder_layers=self.num_hidden_layers							,       encoder_attention_heads=self.num_attention_heads							,       decoder_attention_heads=self.num_attention_heads							,       encoder_ffn_dim=self.intermediate_size							,       decoder_ffn_dim=self.intermediate_size							,       dropout=self.hidden_dropout_prob							,       attention_dropout=self.attention_probs_dropout_prob							,       max_position_embeddings=self.max_position_embeddings							,       eos_token_ids=[2]							,       bos_token_id=self.bos_token_id							,       pad_token_id=self.pad_token_id							,       decoder_start_token_id=self.pad_token_id							,       **self.config_updates							,       )
        UpperCAmelCase__			:							int							=    prepare_pegasus_inputs_dict(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        return config, inputs_dict




  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    TFPegasusModel(config=_lowerCamelCase	).get_decoder()
        UpperCAmelCase__			:							Dict							=    inputs_dict["""input_ids"""]

        UpperCAmelCase__			:							Union[str, Any]							=    input_ids[:1, :]
        UpperCAmelCase__			:							List[Any]							=    inputs_dict["""attention_mask"""][:1, :]
        UpperCAmelCase__			:							int							=    inputs_dict["""head_mask"""]
        UpperCAmelCase__			:							List[Any]							=    1

        # first forward pass
        UpperCAmelCase__			:							List[Any]							=    model(_lowerCamelCase							,       attention_mask=_lowerCamelCase							,       head_mask=_lowerCamelCase							,       use_cache=_lowerCamelCase	)

        UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        UpperCAmelCase__			:							Any							=    ids_tensor((self.batch_size, 3)							,       config.vocab_size	)
        UpperCAmelCase__			:							List[str]							=    tf.cast(ids_tensor((self.batch_size, 3)							,       2	)							,       tf.inta	)

        # append to next input_ids and
        UpperCAmelCase__			:							Tuple							=    tf.concat([input_ids, next_tokens]							,       axis=-1	)
        UpperCAmelCase__			:							Any							=    tf.concat([attention_mask, next_attn_mask]							,       axis=-1	)

        UpperCAmelCase__			:							List[str]							=    model(_lowerCamelCase							,       attention_mask=_lowerCamelCase	)[0]
        UpperCAmelCase__			:							List[str]							=    model(_lowerCamelCase							,       attention_mask=_lowerCamelCase							,       past_key_values=_lowerCamelCase	)[0]

        self.parent.assertEqual(next_tokens.shape[1]							,       output_from_past.shape[1]	)

        # select random slice
        UpperCAmelCase__			:							int							=    int(ids_tensor((1,)							,       output_from_past.shape[-1]	)	)
        UpperCAmelCase__			:							str							=    output_from_no_past[:, -3:, random_slice_idx]
        UpperCAmelCase__			:							Tuple							=    output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(_lowerCamelCase							,       _lowerCamelCase							,       rtol=1e-3	)




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase=None			,	lowerCAmelCase=None			,	lowerCAmelCase=None			,	lowerCAmelCase=None			,	lowerCAmelCase=None			,	)	-> str:
      if attention_mask is None:
            UpperCAmelCase__			:							Optional[int]							=    tf.cast(tf.math.not_equal(lowerCAmelCase			,	config.pad_token_id    )			,	tf.inta    )
      if decoder_attention_mask is None:
            UpperCAmelCase__			:							Optional[int]							=    tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape			,	dtype=tf.inta    ),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			,	config.pad_token_id    )			,	tf.inta    ),
                ]			,	axis=-1			,	)
      if head_mask is None:
            UpperCAmelCase__			:							Tuple							=    tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
      if decoder_head_mask is None:
            UpperCAmelCase__			:							List[str]							=    tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
      if cross_attn_head_mask is None:
            UpperCAmelCase__			:							int							=    tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }

@require_tf
class       lowerCamelCase	(       lowerCAmelCase__					,					lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE					      =  (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE					      =  (
      {
          'conversational': TFPegasusForConditionalGeneration,
          'feature-extraction': TFPegasusModel,
          'summarization': TFPegasusForConditionalGeneration,
          'text2text-generation': TFPegasusForConditionalGeneration,
          'translation': TFPegasusForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE					      =  True
  SCREAMING_SNAKE_CASE					      =  False
  SCREAMING_SNAKE_CASE					      =  False



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    TFPegasusModelTester(self	)
        UpperCAmelCase__			:							List[str]							=    ConfigTester(self							,       config_class=_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        self.config_tester.run_common_tests()




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*_lowerCamelCase	)




@require_sentencepiece
@require_tokenizers
@require_tf
class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  [
      ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
      ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
  ]
  SCREAMING_SNAKE_CASE					      =  [
      'California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'
      ' reduce the risk of wildfires.',
      'N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.',
  ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
  SCREAMING_SNAKE_CASE					      =  'google/pegasus-xsum'



  @cached_property
  def  _a      (self	):


        """simple docstring"""

        return AutoTokenizer.from_pretrained(self.model_name	)



  @cached_property
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
        return model



  def  _a      (self							,       **_lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    self.translate_src_text(**_lowerCamelCase	)
        assert self.expected_text == generated_words



  def  _a      (self							,       **_lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    self.tokenizer(self.src_text							,       **_lowerCamelCase							,       padding=_lowerCamelCase							,       return_tensors="""tf"""	)
        UpperCAmelCase__			:							Dict							=    self.model.generate(
            model_inputs.input_ids							,       attention_mask=model_inputs.attention_mask							,       num_beams=2							,       use_cache=_lowerCamelCase							,       )
        UpperCAmelCase__			:							Optional[int]							=    self.tokenizer.batch_decode(generated_ids.numpy()							,       skip_special_tokens=_lowerCamelCase	)
        return generated_words




  @slow
  def  _a      (self	):


        """simple docstring"""

        self._assert_generated_batch_equal_expected()




"""simple docstring"""
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 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> Tuple:
      # Load configuration defined in the metadata file
      with open(lowerCAmelCase    ) as metadata_file:
            UpperCAmelCase__			:							Optional[int]							=    json.load(lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[int]							=    LukeConfig(use_entity_aware_attention=lowerCAmelCase			,	**metadata["""model_config"""]    )

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

      # Load the entity vocab file
      UpperCAmelCase__			:							List[str]							=    load_entity_vocab(lowerCAmelCase    )

      UpperCAmelCase__			:							Union[str, Any]							=    RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]    )

      # Add special tokens to the token vocabulary for downstream tasks
      UpperCAmelCase__			:							Any							=    AddedToken("""<ent>"""			,	lstrip=lowerCAmelCase			,	rstrip=lowerCAmelCase    )
      UpperCAmelCase__			:							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			,	LukeTokenizer.vocab_files_names["""entity_vocab_file"""]    )			,	"""w"""    ) as f:
            json.dump(lowerCAmelCase			,	lowerCAmelCase    )

      UpperCAmelCase__			:							int							=    LukeTokenizer.from_pretrained(lowerCAmelCase    )

      # Initialize the embeddings of the special tokens
      UpperCAmelCase__			:							Any							=    state_dict["""embeddings.word_embeddings.weight"""]
      UpperCAmelCase__			:							Any							=    word_emb[tokenizer.convert_tokens_to_ids(["""@"""]    )[0]].unsqueeze(0    )
      UpperCAmelCase__			:							List[Any]							=    word_emb[tokenizer.convert_tokens_to_ids(["""#"""]    )[0]].unsqueeze(0    )
      UpperCAmelCase__			:							List[Any]							=    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"]:
                  UpperCAmelCase__			:							Any							=    F"""encoder.layer.{layer_index}.attention.self."""
                  UpperCAmelCase__			:							List[Any]							=    state_dict[prefix + matrix_name]
                  UpperCAmelCase__			:							Dict							=    state_dict[prefix + matrix_name]
                  UpperCAmelCase__			:							Optional[int]							=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      UpperCAmelCase__			:							List[str]							=    state_dict["""entity_embeddings.entity_embeddings.weight"""]
      UpperCAmelCase__			:							int							=    entity_emb[entity_vocab["""[MASK]"""]]

      UpperCAmelCase__			:							Tuple							=    LukeModel(config=lowerCAmelCase    ).eval()

      UpperCAmelCase__					,		UpperCAmelCase__			:							Optional[Any]							=    model.load_state_dict(lowerCAmelCase			,	strict=lowerCAmelCase    )
      if not (len(lowerCAmelCase    ) == 1 and missing_keys[0] == "embeddings.position_ids"):
            raise ValueError(F"""Missing keys {', '.join(lowerCAmelCase    )}. 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
      UpperCAmelCase__			:							Optional[Any]							=    LukeTokenizer.from_pretrained(lowerCAmelCase			,	task="""entity_classification"""    )

      UpperCAmelCase__			:							Dict							=    (
          """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 ."""
      )
      UpperCAmelCase__			:							List[Any]							=    (39, 42)
      UpperCAmelCase__			:							str							=    tokenizer(lowerCAmelCase			,	entity_spans=[span]			,	add_prefix_space=lowerCAmelCase			,	return_tensors="""pt"""    )

      UpperCAmelCase__			:							List[str]							=    model(**lowerCAmelCase    )

      # Verify word hidden states
      if model_size == "large":
            UpperCAmelCase__			:							Optional[Any]							=    torch.Size((1, 42, 10_24)    )
            UpperCAmelCase__			:							Union[str, Any]							=    torch.tensor(
                [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]    )
      else:  # base
            UpperCAmelCase__			:							Optional[int]							=    torch.Size((1, 42, 7_68)    )
            UpperCAmelCase__			:							Optional[Any]							=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]    )

      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":
            UpperCAmelCase__			:							Union[str, Any]							=    torch.Size((1, 1, 10_24)    )
            UpperCAmelCase__			:							str							=    torch.tensor([[0.0466, -0.0106, -0.0179]]    )
      else:  # base
            UpperCAmelCase__			:							Tuple							=    torch.Size((1, 1, 7_68)    )
            UpperCAmelCase__			:							List[str]							=    torch.tensor([[0.1457, 0.1044, 0.0174]]    )

      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

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




def 					a__						(						lowerCAmelCase    )	-> Union[str, Any]:
      UpperCAmelCase__			:							List[str]							=    {}
      with open(lowerCAmelCase			,	"""r"""			,	encoding="""utf-8"""    ) as f:
            for index, line in enumerate(lowerCAmelCase    ):
                  UpperCAmelCase__					,		UpperCAmelCase__			:							Tuple							=    line.rstrip().split("""\t"""    )
                  UpperCAmelCase__			:							int							=    index

      return entity_vocab


if __name__ == "__main__":
    _A							=  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							=  parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )

