


"""simple docstring"""



# 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 _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	torch.FloatTensor
   __UpperCAmelCase			:	Optional[torch.FloatTensor]							  =  None







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Dict=0.999						,							lowercase  :  Dict="cosine"						,							)     ->						Optional[Any]:


    """simple docstring"""





    if alpha_transform_type == "cosine":

        def alpha_bar_fn(lowercase  :  Union[str, Any]			):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

    elif alpha_transform_type == "exp":

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

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

    snake_case   :				List[str]				 =   []
    for i in range(lowercase			):
        snake_case   :				int				 =   i / num_diffusion_timesteps
        snake_case   :				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 _lowerCAmelCase    (				snake_case_   ,							snake_case_     ):
   __UpperCAmelCase			:	Optional[int]							  =  1







   @register_to_config
   def __init__(     self						,     UpperCamelCase__ = 1000						,     UpperCamelCase__ = 0.0001						,     UpperCamelCase__ = 0.02						,     UpperCamelCase__ = "linear"						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     UpperCamelCase__ = True						,     UpperCamelCase__ = 0						,     UpperCamelCase__ = "epsilon"						,     UpperCamelCase__ = 1.0						,     **UpperCamelCase__						,     )     ->		str:

       '''simple docstring'''




       if kwargs.get("set_alpha_to_one"						,     UpperCamelCase__						) is not None:
           snake_case   :				str				 =   (
               "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__						)
           snake_case   :				Optional[int]				 =   kwargs["set_alpha_to_one"]
       if trained_betas is not None:
           snake_case   :				List[Any]				 =   torch.tensor(UpperCamelCase__						,     dtype=torch.floataa						)
       elif beta_schedule == "linear":
           snake_case   :				Union[str, Any]				 =   torch.linspace(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     dtype=torch.floataa						)
       elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           snake_case   :				str				 =   (
               torch.linspace(beta_start**0.5						,     beta_end**0.5						,     UpperCamelCase__						,     dtype=torch.floataa						) ** 2
           )
       elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           snake_case   :				Any				 =   betas_for_alpha_bar(UpperCamelCase__						)
       else:
           raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'						)

       snake_case   :				Optional[Any]				 =   1.0 - self.betas
       snake_case   :				Union[str, 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.
       snake_case   :				List[str]				 =   torch.tensor(0.0						) if set_alpha_to_zero else self.alphas_cumprod[-1]

       # standard deviation of the initial noise distribution
       snake_case   :				Optional[Any]				 =   1.0

       # setable values
       snake_case   :				Optional[Any]				 =   None
       snake_case   :				Optional[int]				 =   torch.from_numpy(np.arange(0						,     UpperCamelCase__						).copy().astype(np.intaa						)						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						)     ->		torch.FloatTensor:

       '''simple docstring'''




       return sample







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						)     ->		str:

       '''simple docstring'''




       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.'						)

       snake_case   :				int				 =   num_inference_steps
       snake_case   :				Union[str, 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
       snake_case   :				List[str]				 =   (np.arange(0						,     UpperCamelCase__						) * step_ratio).round().copy().astype(np.intaa						)
       snake_case   :				Tuple				 =   torch.from_numpy(UpperCamelCase__						).to(UpperCamelCase__						)
       self.timesteps += self.config.steps_offset







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = 0.0						,     UpperCamelCase__ = False						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     )     ->		Union[DDIMSchedulerOutput, Tuple]:

       '''simple docstring'''




       snake_case   :				Dict				 =   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
       snake_case   :				Any				 =   self.alphas_cumprod[timestep]
       snake_case   :				Dict				 =   (
           self.alphas_cumprod[prev_timestep]
           if prev_timestep < self.config.num_train_timesteps
           else self.final_alpha_cumprod
       )

       snake_case   :				str				 =   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":
           snake_case   :				List[str]				 =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           snake_case   :				Union[str, Any]				 =   model_output
       elif self.config.prediction_type == "sample":
           snake_case   :				str				 =   model_output
           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":
           snake_case   :				Any				 =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
           snake_case   :				Optional[int]				 =   (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:
           snake_case   :				int				 =   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
       snake_case   :				List[Any]				 =   (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
       snake_case   :				Dict				 =   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						)     ->		Any:

       '''simple docstring'''




       return self.config.num_train_timesteps









"""simple docstring"""



import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__snake_case             =   data_utils.TransfoXLTokenizer
__snake_case             =   data_utils.TransfoXLCorpus
__snake_case             =   data_utils
__snake_case             =   data_utils







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  int						,							lowercase  :  List[Any]						,							lowercase  :  Union[str, Any]			)     ->						List[Any]:


    """simple docstring"""





    if transfo_xl_dataset_file:
        # Convert a pre-processed corpus (see original TensorFlow repo)
        with open(lowercase						,							"rb"			) as fp:
            snake_case   :				int				 =   pickle.load(lowercase						,							encoding="latin1"			)
        # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
        snake_case   :				int				 =   pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
        print(F'Save vocabulary to {pytorch_vocab_dump_path}'			)
        snake_case   :				str				 =   corpus.vocab.__dict__
        torch.save(lowercase						,							lowercase			)

        snake_case   :				str				 =   corpus.__dict__
        corpus_dict_no_vocab.pop("vocab"						,							lowercase			)
        snake_case   :				Dict				 =   pytorch_dump_folder_path + "/" + CORPUS_NAME
        print(F'Save dataset to {pytorch_dataset_dump_path}'			)
        torch.save(lowercase						,							lowercase			)

    if tf_checkpoint_path:
        # Convert a pre-trained TensorFlow model
        snake_case   :				Union[str, Any]				 =   os.path.abspath(lowercase			)
        snake_case   :				str				 =   os.path.abspath(lowercase			)

        print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'			)
        # Initialise PyTorch model
        if transfo_xl_config_file == "":
            snake_case   :				int				 =   TransfoXLConfig()
        else:
            snake_case   :				Optional[int]				 =   TransfoXLConfig.from_json_file(lowercase			)
        print(F'Building PyTorch model from configuration: {config}'			)
        snake_case   :				str				 =   TransfoXLLMHeadModel(lowercase			)

        snake_case   :				str				 =   load_tf_weights_in_transfo_xl(lowercase						,							lowercase						,							lowercase			)
        # Save pytorch-model
        snake_case   :				Union[str, Any]				 =   os.path.join(lowercase						,							lowercase			)
        snake_case   :				Optional[Any]				 =   os.path.join(lowercase						,							lowercase			)
        print(F'Save PyTorch model to {os.path.abspath(lowercase			)}'			)
        torch.save(model.state_dict()						,							lowercase			)
        print(F'Save configuration file to {os.path.abspath(lowercase			)}'			)
        with open(lowercase						,							"w"						,							encoding="utf-8"			) as f:
            f.write(config.to_json_string()			)


if __name__ == "__main__":
  __snake_case             =   argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
  )
  parser.add_argument(
      """--tf_checkpoint_path""",
      default="""""",
      type=str,
      help="""An optional path to a TensorFlow checkpoint path to be converted.""",
  )
  parser.add_argument(
      """--transfo_xl_config_file""",
      default="""""",
      type=str,
      help=(
          """An optional config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--transfo_xl_dataset_file""",
      default="""""",
      type=str,
      help="""An optional dataset file to be converted in a vocabulary.""",
  )
  __snake_case             =   parser.parse_args()
  convert_transfo_xl_checkpoint_to_pytorch(
      args.tf_checkpoint_path,
      args.transfo_xl_config_file,
      args.pytorch_dump_folder_path,
      args.transfo_xl_dataset_file,
  )





