






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  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Any,	UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      with open(UpperCamelCase__							) as metadata_file:
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=json.load(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=LukeConfig(use_entity_aware_attention=UpperCamelCase__,	**metadata['''model_config''']							)

      # Load in the weights from the checkpoint_path
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch.load(UpperCamelCase__,	map_location='''cpu'''							)['''module''']

      # Load the entity vocab file
      SCREAMING_SNAKE_CASE__     :				List[str]									=load_original_entity_vocab(UpperCamelCase__							)
      # add an entry for [MASK2]
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=max(entity_vocab.values()							) + 1
      config.entity_vocab_size += 1

      SCREAMING_SNAKE_CASE__     :				Optional[int]									=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=UpperCamelCase__,	rstrip=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Any									=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__,	'''tokenizer_config.json'''							),	'''r'''							) as f:
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=json.load(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				int									='''MLukeTokenizer'''
      with open(os.path.join(UpperCamelCase__,	'''tokenizer_config.json'''							),	'''w'''							) as f:
            json.dump(UpperCamelCase__,	UpperCamelCase__							)

      with open(os.path.join(UpperCamelCase__,	MLukeTokenizer.vocab_files_names['''entity_vocab_file''']							),	'''w'''							) as f:
            json.dump(UpperCamelCase__,	UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=MLukeTokenizer.from_pretrained(UpperCamelCase__							)

      # Initialize the embeddings of the special tokens
      SCREAMING_SNAKE_CASE__     :				str									=tokenizer.convert_tokens_to_ids(['''@''']							)[0]
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=tokenizer.convert_tokens_to_ids(['''#''']							)[0]

      SCREAMING_SNAKE_CASE__     :				Dict									=state_dict['''embeddings.word_embeddings.weight''']
      SCREAMING_SNAKE_CASE__     :				List[str]									=word_emb[ent_init_index].unsqueeze(0							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=word_emb[enta_init_index].unsqueeze(0							)
      SCREAMING_SNAKE_CASE__     :				str									=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__     :				Optional[Any]									=state_dict[bias_name]
            SCREAMING_SNAKE_CASE__     :				List[Any]									=decoder_bias[ent_init_index].unsqueeze(0							)
            SCREAMING_SNAKE_CASE__     :				str									=decoder_bias[enta_init_index].unsqueeze(0							)
            SCREAMING_SNAKE_CASE__     :				List[str]									=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__     :				Tuple									=f"encoder.layer.{layer_index}.attention.self."
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=state_dict[prefix + matrix_name]
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=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__     :				Any									=state_dict['''entity_embeddings.entity_embeddings.weight''']
      SCREAMING_SNAKE_CASE__     :				Any									=entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0							)
      SCREAMING_SNAKE_CASE__     :				Any									=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__     :				Tuple									=entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0							)
      SCREAMING_SNAKE_CASE__     :				Any									=torch.cat([entity_prediction_bias, entity_mask_bias]							)

      SCREAMING_SNAKE_CASE__     :				int									=LukeForMaskedLM(config=UpperCamelCase__							).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__     :				Tuple									=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__     :				Any									=state_dict[key]

      SCREAMING_SNAKE_CASE__     :				List[str]									=model.load_state_dict(UpperCamelCase__,	strict=UpperCamelCase__							)

      if set(UpperCamelCase__							) != {"luke.embeddings.position_ids"}:
            raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}"							)
      if set(UpperCamelCase__							) != {
          "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__     :				Any									=MLukeTokenizer.from_pretrained(UpperCamelCase__,	task='''entity_classification'''							)

      SCREAMING_SNAKE_CASE__     :				str									='''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__     :				Union[str, Any]									=(0, 9)
      SCREAMING_SNAKE_CASE__     :				str									=tokenizer(UpperCamelCase__,	entity_spans=[span],	return_tensors='''pt'''							)

      SCREAMING_SNAKE_CASE__     :				List[Any]									=model(**UpperCamelCase__							)

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            SCREAMING_SNAKE_CASE__     :				str									=torch.Size((1, 3_3, 7_6_8)							)
            SCREAMING_SNAKE_CASE__     :				int									=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],	UpperCamelCase__,	atol=1e-4							):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            SCREAMING_SNAKE_CASE__     :				Any									=torch.Size((1, 1, 7_6_8)							)
            SCREAMING_SNAKE_CASE__     :				int									=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],	UpperCamelCase__,	atol=1e-4							):
            raise ValueError

      # Verify masked word/entity prediction
      SCREAMING_SNAKE_CASE__     :				str									=MLukeTokenizer.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									='''Tokyo is the capital of <mask>.'''
      SCREAMING_SNAKE_CASE__     :				Dict									=(2_4, 3_0)
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=tokenizer(UpperCamelCase__,	entity_spans=[span],	return_tensors='''pt'''							)

      SCREAMING_SNAKE_CASE__     :				List[str]									=model(**UpperCamelCase__							)

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

      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=outputs.entity_logits[0][0].argmax().item()
      SCREAMING_SNAKE_CASE__     :				Dict									=[
          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(UpperCamelCase__							)							)
      model.save_pretrained(UpperCamelCase__							)





def  _a(  UpperCamelCase__				:						Optional[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[Any]									=['''[MASK]''', '''[PAD]''', '''[UNK]''']

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

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


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,
  )







'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							["""vqvae"""]




 def __init__(      self	:							int					,      __lowercase	:							AutoencoderKL					,      __lowercase	:							UNetaDConditionModel					,      __lowercase	:							Mel					,      __lowercase	:							Union[DDIMScheduler, DDPMScheduler]					,      )	->						int:
       super().__init__()
       self.register_modules(unet=__lowercase					,      scheduler=__lowercase					,      mel=__lowercase					,      vqvae=__lowercase      )




 def      __magic_name__	(      self	:							List[str]      )	->						int:
       return 50 if isinstance(self.scheduler					,      __lowercase      ) else 10_00




 @torch.no_grad()
 def __call__(      self	:							Dict					,      __lowercase	:							int = 1					,      __lowercase	:							str = None					,      __lowercase	:							np.ndarray = None					,      __lowercase	:							int = 0					,      __lowercase	:							int = 0					,      __lowercase	:							int = None					,      __lowercase	:							torch.Generator = None					,      __lowercase	:							float = 0					,      __lowercase	:							float = 0					,      __lowercase	:							torch.Generator = None					,      __lowercase	:							float = 0					,      __lowercase	:							torch.Tensor = None					,      __lowercase	:							torch.Tensor = None					,      __lowercase	:							Dict=True					,      )	->						Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=steps or self.get_default_steps()
       self.scheduler.set_timesteps(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size      ) == int:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=randn_tensor(
                 (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size[0],
                     self.unet.config.sample_size[1],
                 )					,      generator=__lowercase					,      device=self.device					,      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=noise
       SCREAMING_SNAKE_CASE__     :				List[str]									=None

       if audio_file is not None or raw_audio is not None:
             self.mel.load_audio(__lowercase					,      __lowercase      )
             SCREAMING_SNAKE_CASE__     :				Dict									=self.mel.audio_slice_to_image(__lowercase      )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=np.frombuffer(input_image.tobytes()					,      dtype='''uint8'''      ).reshape(
                 (input_image.height, input_image.width)      )
             SCREAMING_SNAKE_CASE__     :				int									=(input_image / 2_55) * 2 - 1
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=torch.tensor(input_image[np.newaxis, :, :]					,      dtype=torch.float      ).to(self.device      )

             if self.vqvae is not None:
                   SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.vqvae.encode(torch.unsqueeze(__lowercase					,      0      )      ).latent_dist.sample(
                       generator=__lowercase      )[0]
                   SCREAMING_SNAKE_CASE__     :				int									=self.vqvae.config.scaling_factor * input_images

             if start_step > 0:
                   SCREAMING_SNAKE_CASE__     :				int									=self.scheduler.add_noise(__lowercase					,      __lowercase					,      self.scheduler.timesteps[start_step - 1]      )

             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(
                 self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
             )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=int(mask_start_secs * pixels_per_second      )
             SCREAMING_SNAKE_CASE__     :				Tuple									=int(mask_end_secs * pixels_per_second      )
             SCREAMING_SNAKE_CASE__     :				int									=self.scheduler.add_noise(__lowercase					,      __lowercase					,      torch.tensor(self.scheduler.timesteps[start_step:]      )      )

       for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]      )      ):
             if isinstance(self.unet					,      __lowercase      ):
                   SCREAMING_SNAKE_CASE__     :				str									=self.unet(__lowercase					,      __lowercase					,      __lowercase      )['''sample''']
             else:
                   SCREAMING_SNAKE_CASE__     :				str									=self.unet(__lowercase					,      __lowercase      )['''sample''']

             if isinstance(self.scheduler					,      __lowercase      ):
                   SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.scheduler.step(
                       model_output=__lowercase					,      timestep=__lowercase					,      sample=__lowercase					,      eta=__lowercase					,      generator=__lowercase					,      )['''prev_sample''']
             else:
                   SCREAMING_SNAKE_CASE__     :				Any									=self.scheduler.step(
                       model_output=__lowercase					,      timestep=__lowercase					,      sample=__lowercase					,      generator=__lowercase					,      )['''prev_sample''']

             if mask is not None:
                   if mask_start > 0:
                         SCREAMING_SNAKE_CASE__     :				Optional[Any]									=mask[:, step, :, :mask_start]
                   if mask_end > 0:
                         SCREAMING_SNAKE_CASE__     :				List[str]									=mask[:, step, :, -mask_end:]

       if self.vqvae is not None:
             # 0.18215 was scaling factor used in training to ensure unit variance
             SCREAMING_SNAKE_CASE__     :				str									=1 / self.vqvae.config.scaling_factor * images
             SCREAMING_SNAKE_CASE__     :				int									=self.vqvae.decode(__lowercase      )['''sample''']

       SCREAMING_SNAKE_CASE__     :				List[str]									=(images / 2 + 0.5).clamp(0					,      1      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=images.cpu().permute(0					,      2					,      3					,      1      ).numpy()
       SCREAMING_SNAKE_CASE__     :				Dict									=(images * 2_55).round().astype('''uint8'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=list(
           (Image.fromarray(_[:, :, 0]      ) for _ in images)
           if images.shape[3] == 1
           else (Image.fromarray(__lowercase					,      mode='''RGB'''      ).convert('''L'''      ) for _ in images)      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=[self.mel.image_to_audio(__lowercase      ) for _ in images]
       if not return_dict:
             return images, (self.mel.get_sample_rate(), audios)

       return BaseOutput(**AudioPipelineOutput(np.array(__lowercase      )[:, np.newaxis, :]      )					,      **ImagePipelineOutput(__lowercase      )      )




 @torch.no_grad()
 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							List[Image.Image]					,      __lowercase	:							int = 50      )	->						np.ndarray:
       assert isinstance(self.scheduler					,      __lowercase      )
       self.scheduler.set_timesteps(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=np.array(
           [np.frombuffer(image.tobytes()					,      dtype='''uint8'''      ).reshape((1, image.height, image.width)      ) for image in images]      )
       SCREAMING_SNAKE_CASE__     :				str									=(sample / 2_55) * 2 - 1
       SCREAMING_SNAKE_CASE__     :				str									=torch.Tensor(__lowercase      ).to(self.device      )

       for t in self.progress_bar(torch.flip(self.scheduler.timesteps					,      (0,)      )      ):
             SCREAMING_SNAKE_CASE__     :				Tuple									=t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
             SCREAMING_SNAKE_CASE__     :				Any									=self.scheduler.alphas_cumprod[t]
             SCREAMING_SNAKE_CASE__     :				int									=(
                 self.scheduler.alphas_cumprod[prev_timestep]
                 if prev_timestep >= 0
                 else self.scheduler.final_alpha_cumprod
             )
             SCREAMING_SNAKE_CASE__     :				int									=1 - alpha_prod_t
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.unet(__lowercase					,      __lowercase      )['''sample''']
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(1 - alpha_prod_t_prev) ** 0.5 * model_output
             SCREAMING_SNAKE_CASE__     :				str									=(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
             SCREAMING_SNAKE_CASE__     :				Any									=sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

       return sample




 @staticmethod
 def      __magic_name__	(      __lowercase	:							torch.Tensor					,      __lowercase	:							torch.Tensor					,      __lowercase	:							float      )	->						torch.Tensor:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=acos(torch.dot(torch.flatten(__lowercase      )					,      torch.flatten(__lowercase      )      ) / torch.norm(__lowercase      ) / torch.norm(__lowercase      )      )
       return sin((1 - alpha) * theta      ) * xa / sin(__lowercase      ) + sin(alpha * theta      ) * xa / sin(__lowercase      )