






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		torch.FloatTensor
       a    :		torch.FloatTensor







class     _lowerCAmelCase(	UpperCAmelCase_							,     UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Tuple									=1





       @register_to_config
       def __init__( self  ,      _lowerCamelCase = 2_0_0_0  ,      _lowerCamelCase = 0.1_5  ,      _lowerCamelCase = 0.0_1  ,      _lowerCamelCase = 1_3_4_8.0  ,      _lowerCamelCase = 1e-5  ,      _lowerCamelCase = 1  ,      ):
         # standard deviation of the initial noise distribution
         UpperCamelCase_: Any   				=    sigma_max

         # setable values
         UpperCamelCase_: Union[str, Any]   				=    None

         self.set_sigmas(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None							):
         return sample





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None							):
         UpperCamelCase_: Any   				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

         UpperCamelCase_: List[Any]   				=    torch.linspace(1  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      device=_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None							):
         UpperCamelCase_: int   				=    sigma_min if sigma_min is not None else self.config.sigma_min
         UpperCamelCase_: Any   				=    sigma_max if sigma_max is not None else self.config.sigma_max
         UpperCamelCase_: str   				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
         if self.timesteps is None:
           self.set_timesteps(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: Tuple   				=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
         UpperCamelCase_: List[str]   				=    torch.exp(torch.linspace(math.log(_lowerCamelCase							)  ,      math.log(_lowerCamelCase							)  ,      _lowerCamelCase							)							)
         UpperCamelCase_: Optional[int]   				=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         return torch.where(
             timesteps == 0  ,      torch.zeros_like(t.to(timesteps.device							)							)  ,      self.discrete_sigmas[timesteps - 1].to(timesteps.device							)  ,      )





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      ):
         if self.timesteps is None:
           raise ValueError(
               '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'							)

         UpperCamelCase_: Optional[Any]   				=    timestep * torch.ones(
             sample.shape[0]  ,      device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
         UpperCamelCase_: Tuple   				=    (timestep * (len(self.timesteps							) - 1)).long()

         # mps requires indices to be in the same device, so we use cpu as is the default with cuda
         UpperCamelCase_: List[str]   				=    timesteps.to(self.discrete_sigmas.device							)

         UpperCamelCase_: int   				=    self.discrete_sigmas[timesteps].to(sample.device							)
         UpperCamelCase_: Any   				=    self.get_adjacent_sigma(_lowerCamelCase  ,      _lowerCamelCase							).to(sample.device							)
         UpperCamelCase_: Any   				=    torch.zeros_like(_lowerCamelCase							)
         UpperCamelCase_: Union[str, Any]   				=    (sigma**2 - adjacent_sigma**2) ** 0.5

         # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
         # also equation 47 shows the analog from SDE models to ancestral sampling methods
         UpperCamelCase_: Dict   				=    diffusion.flatten()
         while len(diffusion.shape							) < len(sample.shape							):
           UpperCamelCase_: List[str]   				=    diffusion.unsqueeze(-1							)
         UpperCamelCase_: List[Any]   				=    drift - diffusion**2 * model_output

         #  equation 6: sample noise for the diffusion term of
         UpperCamelCase_: List[Any]   				=    randn_tensor(
             sample.shape  ,      layout=sample.layout  ,      generator=_lowerCamelCase  ,      device=sample.device  ,      dtype=sample.dtype							)
         UpperCamelCase_: Dict   				=    sample - drift  # subtract because `dt` is a small negative timestep
         # TODO is the variable diffusion the correct scaling term for the noise?
         UpperCamelCase_: str   				=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

         if not return_dict:
           return (prev_sample, prev_sample_mean)

         return SdeVeOutput(prev_sample=_lowerCamelCase  ,      prev_sample_mean=_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      ):
         if self.timesteps is None:
           raise ValueError(
               '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'							)

         # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
         # sample noise for correction
         UpperCamelCase_: Any   				=    randn_tensor(sample.shape  ,      layout=sample.layout  ,      generator=_lowerCamelCase							).to(sample.device							)

         # compute step size from the model_output, the noise, and the snr
         UpperCamelCase_: List[str]   				=    torch.norm(model_output.reshape(model_output.shape[0]  ,      -1							)  ,      dim=-1							).mean()
         UpperCamelCase_: str   				=    torch.norm(noise.reshape(noise.shape[0]  ,      -1							)  ,      dim=-1							).mean()
         UpperCamelCase_: Tuple   				=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
         UpperCamelCase_: List[Any]   				=    step_size * torch.ones(sample.shape[0]							).to(sample.device							)
         # self.repeat_scalar(step_size, sample.shape[0])

         # compute corrected sample: model_output term and noise term
         UpperCamelCase_: int   				=    step_size.flatten()
         while len(step_size.shape							) < len(sample.shape							):
           UpperCamelCase_: Union[str, Any]   				=    step_size.unsqueeze(-1							)
         UpperCamelCase_: Optional[Any]   				=    sample + step_size * model_output
         UpperCamelCase_: Any   				=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

         if not return_dict:
           return (prev_sample,)

         return SchedulerOutput(prev_sample=_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         UpperCamelCase_: Tuple   				=    timesteps.to(original_samples.device							)
         UpperCamelCase_: List[str]   				=    self.discrete_sigmas.to(original_samples.device							)[timesteps]
         UpperCamelCase_: Tuple   				=    (
             noise * sigmas[:, None, None, None]
             if noise is not None
             else torch.randn_like(_lowerCamelCase							) * sigmas[:, None, None, None]
         )
         UpperCamelCase_: Union[str, Any]   				=    noise + original_samples
         return noisy_samples





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







import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


A_		:		Tuple		     =			[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]

def   snake_case (UpperCAmelCase__  )	->    str:
  for pegasus_name, hf_name in PATTERNS:
    UpperCamelCase_: List[str]   				=    k.replace(UpperCAmelCase__     ,   UpperCAmelCase__  )
  return k

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    PegasusForConditionalGeneration:
  UpperCamelCase_: List[str]   				=    DEFAULTS.copy()
  cfg_kwargs.update(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    PegasusConfig(**UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    PegasusForConditionalGeneration(UpperCAmelCase__  )
  UpperCamelCase_: List[Any]   				=    torch_model.model.state_dict()
  UpperCamelCase_: str   				=    {}
  for k, v in tf_weights.items():
    UpperCamelCase_: Dict   				=    rename_state_dict_key(UpperCAmelCase__  )
    if new_k not in sd:
      raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''  )

    if "dense" in k or "proj" in new_k:
      UpperCamelCase_: int   				=    v.T
    UpperCamelCase_: Union[str, Any]   				=    torch.tensor(UpperCAmelCase__     ,   dtype=sd[new_k].dtype  )
    assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
  # make sure embedding.padding_idx is respected
  UpperCamelCase_: Tuple   				=    torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]  )
  UpperCamelCase_: int   				=    mapping['shared.weight']
  UpperCamelCase_: Union[str, Any]   				=    mapping['shared.weight']
  UpperCamelCase_: Dict   				=    {k: torch.zeros_like(UpperCAmelCase__  ) for k, v in sd.items() if k.endswith('bias'  ) and k not in mapping}
  mapping.update(**UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    torch_model.model.load_state_dict(UpperCAmelCase__     ,   strict=UpperCAmelCase__  )
  UpperCamelCase_: List[str]   				=    [
      k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
  ]
  assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
  assert extra == [], F'''no matches found for the following tf keys {extra}'''
  return torch_model

def   snake_case (UpperCAmelCase__="./ckpt/aeslc/model.ckpt-32000"  )	->    Dict:
  UpperCamelCase_: Union[str, Any]   				=    tf.train.list_variables(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    {}
  UpperCamelCase_: Dict   				=    ['Adafactor', 'global_step']
  for name, shape in tqdm(UpperCAmelCase__     ,   desc='converting tf checkpoint to dict'  ):
    UpperCamelCase_: Union[str, Any]   				=    any(pat in name for pat in ignore_name  )
    if skip_key:
      continue
    UpperCamelCase_: Dict   				=    tf.train.load_variable(UpperCAmelCase__     ,   UpperCAmelCase__  )
    UpperCamelCase_: Optional[Any]   				=    array
  return tf_weights

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  # save tokenizer first
  UpperCamelCase_: Any   				=    Path(UpperCAmelCase__  ).parent.name
  UpperCamelCase_: Tuple   				=    task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
  UpperCamelCase_: Optional[Any]   				=    PegasusTokenizer.from_pretrained('sshleifer/pegasus'     ,   model_max_length=UpperCAmelCase__  )
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(UpperCAmelCase__  )

  # convert model
  UpperCamelCase_: Optional[Any]   				=    get_tf_weights_as_numpy(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    task_specific_params[F'''summarization_{dataset}''']
  if dataset == "large":
    UpperCamelCase_: Union[str, Any]   				=    task_specific_params
  UpperCamelCase_: Tuple   				=    convert_pegasus(UpperCAmelCase__     ,   UpperCAmelCase__  )
  torch_model.save_pretrained(UpperCAmelCase__  )
  UpperCamelCase_: int   				=    torch_model.state_dict()
  sd.pop('model.decoder.embed_positions.weight'  )
  sd.pop('model.encoder.embed_positions.weight'  )
  torch.save(UpperCAmelCase__     ,   Path(UpperCAmelCase__  ) / 'pytorch_model.bin'  )


if __name__ == "__main__":
   A_		:		str		     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
   parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
   A_		:		Optional[Any]		     =			parser.parse_args()
   if args.save_dir is None:
      A_		:		Union[str, Any]		     =			Path(args.tf_ckpt_path).parent.name
      A_		:		Optional[Any]		     =			os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)