




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   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






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

  # Load in the weights from the checkpoint_path
  SCREAMING_SNAKE_CASE       :      int							=    torch.load(lowercase						,     map_location="cpu"				)

  # Load the entity vocab file
  SCREAMING_SNAKE_CASE       :      Any							=    load_entity_vocab(lowercase				)

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]				)

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

  SCREAMING_SNAKE_CASE       :      Dict							=    LukeTokenizer.from_pretrained(lowercase				)

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

  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    LukeModel(config=lowercase				).eval()

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

  SCREAMING_SNAKE_CASE       :      int							=    (
      "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 ."
  )
  SCREAMING_SNAKE_CASE       :      List[str]							=    (39, 42)
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    tokenizer(lowercase						,     entity_spans=[span]						,     add_prefix_space=lowercase						,     return_tensors="pt"				)

  SCREAMING_SNAKE_CASE       :      str							=    model(**lowercase				)

  # Verify word hidden states
  if model_size == "large":
    SCREAMING_SNAKE_CASE       :      Any							=    torch.Size((1, 42, 1024)				)
    SCREAMING_SNAKE_CASE       :      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
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.Size((1, 42, 768)				)
    SCREAMING_SNAKE_CASE       :      List[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]						,     lowercase						,     atol=1E-4				):
    raise ValueError

  # Verify entity hidden states
  if model_size == "large":
    SCREAMING_SNAKE_CASE       :      List[str]							=    torch.Size((1, 1, 1024)				)
    SCREAMING_SNAKE_CASE       :      str							=    torch.tensor([[0.0466, -0.0106, -0.0179]]				)
  else:  # base
    SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.Size((1, 1, 768)				)
    SCREAMING_SNAKE_CASE       :      int							=    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]						,     lowercase						,     atol=1E-4				):
    raise ValueError

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


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    {}
  with open(lowercase						,     "r"						,     encoding="utf-8"				) as f:
    for index, line in enumerate(lowercase				):
      SCREAMING_SNAKE_CASE       :      Tuple							=    line.rstrip().split("\t"				)
      SCREAMING_SNAKE_CASE       :      int							=    index

  return entity_vocab


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,
   )














# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		torch.FloatTensor
     UpperCamelCase_							:		Optional[torch.FloatTensor]			=	None





def   lowerCamelCase__			(     lowercase						,     lowercase=0.999						,     lowercase="cosine"						,     ):

  """simple docstring"""






  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase				):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(lowercase				):
      return math.exp(t * -12.0				)

  else:
    raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
  for i in range(lowercase				):
    SCREAMING_SNAKE_CASE       :      str							=    i / num_diffusion_timesteps
    SCREAMING_SNAKE_CASE       :      List[str]							=    (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(lowercase				) / alpha_bar_fn(lowercase				)						,     lowercase				)				)
  return torch.tensor(lowercase						,     dtype=torch.floataa				)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		List[Any]			=	1


     @register_to_config
     def __init__(				self       :	Union[str, Any]					, UpperCAmelCase_       :	int = 1000					, UpperCAmelCase_       :	float = 0.0_001					, UpperCAmelCase_       :	float = 0.02					, UpperCAmelCase_       :	str = "linear"					, UpperCAmelCase_       :	Optional[Union[np.ndarray, List[float]]] = None					, UpperCAmelCase_       :	bool = True					, UpperCAmelCase_       :	bool = True					, UpperCAmelCase_       :	int = 0					, UpperCAmelCase_       :	str = "epsilon"					, UpperCAmelCase_       :	float = 1.0					, **UpperCAmelCase_       :	Dict					, ):
       if kwargs.get("set_alpha_to_one"					, UpperCAmelCase_      ) is not None:
         SCREAMING_SNAKE_CASE       :      int							=    (
             "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
         )
         deprecate("set_alpha_to_one"					, "1.0.0"					, UpperCAmelCase_					, standard_warn=UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      int							=    kwargs["set_alpha_to_one"]
       if trained_betas is not None:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.tensor(UpperCAmelCase_					, dtype=torch.floataa      )
       elif beta_schedule == "linear":
         SCREAMING_SNAKE_CASE       :      List[str]							=    torch.linspace(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, dtype=torch.floataa      )
       elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (
             torch.linspace(beta_start**0.5					, beta_end**0.5					, UpperCAmelCase_					, dtype=torch.floataa      ) ** 2
         )
       elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         SCREAMING_SNAKE_CASE       :      int							=    betas_for_alpha_bar(UpperCAmelCase_      )
       else:
         raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    1.0 - self.betas
       SCREAMING_SNAKE_CASE       :      Any							=    torch.cumprod(self.alphas					, dim=0      )

       # At every step in inverted ddim, we are looking into the next alphas_cumprod
       # For the final step, there is no next alphas_cumprod, and the index is out of bounds
       # `set_alpha_to_zero` decides whether we set this parameter simply to zero
       # in this case, self.step() just output the predicted noise
       # or whether we use the final alpha of the "non-previous" one.
       SCREAMING_SNAKE_CASE       :      Dict							=    torch.tensor(0.0      ) if set_alpha_to_zero else self.alphas_cumprod[-1]

       # standard deviation of the initial noise distribution
       SCREAMING_SNAKE_CASE       :      List[str]							=    1.0

       # setable values
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    None
       SCREAMING_SNAKE_CASE       :      List[str]							=    torch.from_numpy(np.arange(0					, UpperCAmelCase_      ).copy().astype(np.intaa      )      )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	Optional[int] = None      ):
       return sample


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Union[str, torch.device] = None      ):

       if num_inference_steps > self.config.num_train_timesteps:
         raise ValueError(
             f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
             f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
             f''' maximal {self.config.num_train_timesteps} timesteps.'''      )

       SCREAMING_SNAKE_CASE       :      Dict							=    num_inference_steps
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.config.num_train_timesteps // self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       SCREAMING_SNAKE_CASE       :      str							=    (np.arange(0					, UpperCAmelCase_      ) * step_ratio).round().copy().astype(np.intaa      )
       SCREAMING_SNAKE_CASE       :      str							=    torch.from_numpy(UpperCAmelCase_      ).to(UpperCAmelCase_      )
       self.timesteps += self.config.steps_offset


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	float = 0.0					, UpperCAmelCase_       :	bool = False					, UpperCAmelCase_       :	Optional[torch.FloatTensor] = None					, UpperCAmelCase_       :	bool = True					, ):
       # 1. get previous step value (=t+1)
       SCREAMING_SNAKE_CASE       :      Tuple							=    timestep + self.config.num_train_timesteps // self.num_inference_steps

       # 2. compute alphas, betas
       # change original implementation to exactly match noise levels for analogous forward process
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.alphas_cumprod[timestep]
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    (
           self.alphas_cumprod[prev_timestep]
           if prev_timestep < self.config.num_train_timesteps
           else self.final_alpha_cumprod
       )

       SCREAMING_SNAKE_CASE       :      Tuple							=    1 - alpha_prod_t

       # 3. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       if self.config.prediction_type == "epsilon":
         SCREAMING_SNAKE_CASE       :      Any							=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         SCREAMING_SNAKE_CASE       :      List[Any]							=    model_output
       elif self.config.prediction_type == "sample":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model_output
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         SCREAMING_SNAKE_CASE       :      Tuple							=    (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
       else:
         raise ValueError(
             f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
             " `v_prediction`"      )

       # 4. Clip or threshold "predicted x_0"
       if self.config.clip_sample:
         SCREAMING_SNAKE_CASE       :      List[str]							=    pred_original_sample.clamp(
             -self.config.clip_sample_range					, self.config.clip_sample_range      )

       # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       SCREAMING_SNAKE_CASE       :      Dict							=    (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

       # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       SCREAMING_SNAKE_CASE       :      str							=    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

       if not return_dict:
         return (prev_sample, pred_original_sample)
       return DDIMSchedulerOutput(prev_sample=UpperCAmelCase_					, pred_original_sample=UpperCAmelCase_      )






     def __len__(				self       :	Dict      ):
       return self.config.num_train_timesteps






