






"""simple docstring"""






import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


UpperCAmelCase_   : Any   			= """scheduler_config.json"""




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  1
      __UpperCamelCase	 =  2
      __UpperCamelCase	 =  3
      __UpperCamelCase	 =  4
      __UpperCamelCase	 =  5




@dataclass
class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  42




class 							lowerCAmelCase__				:






      '''simple docstring'''

      __UpperCamelCase	 =  SCHEDULER_CONFIG_NAME
      __UpperCamelCase	 =  ["dtype"]
      __UpperCamelCase	 =  []
      __UpperCamelCase	 =  True
      @classmethod
      def _SCREAMING_SNAKE_CASE (						cls     :  Union[str, Any]						,	lowercase_     :  Dict[str, Any] = None						,	lowercase_     :  Optional[str] = None						,	lowercase_     :  List[str]=False						,	**lowercase_     :  List[Any]						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Any    						=		cls.load_config(
              pretrained_model_name_or_path=lowercase_						,	subfolder=lowercase_						,	return_unused_kwargs=lowercase_						,	**lowercase_						,	)
          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		cls.from_config(lowercase_						,	return_unused_kwargs=lowercase_						,	**lowercase_)

          if hasattr(lowercase_						,	'''create_state''') and getattr(lowercase_						,	'''has_state'''						,	lowercase_):
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		scheduler.create_state()

          if return_unused_kwargs:
              return scheduler, state, unused_kwargs

          return scheduler, state
      def _SCREAMING_SNAKE_CASE (						self     :  Dict						,	lowercase_     :  Union[str, os.PathLike]						,	lowercase_     :  bool = False						,	**lowercase_     :  Any):




          '''simple docstring'''


          self.save_config(save_directory=lowercase_						,	push_to_hub=lowercase_						,	**lowercase_)
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          return self._get_compatibles()



      @classmethod
      def _SCREAMING_SNAKE_CASE (						cls     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		list(set([cls.__name__] + cls._compatibles))
          SCREAMING_SNAKE_CASE_      :  Dict    						=		importlib.import_module(__name__.split('''.''')[0])
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		[
              getattr(lowercase_						,	lowercase_) for c in compatible_classes_str if hasattr(lowercase_						,	lowercase_)
          ]
          return compatible_classes



def 					_A			(__a    ,				__a							) ->       jnp.ndarray:
    """simple docstring"""






    assert len(__a							) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__a							) - x.ndim)							)    ,				__a							)



def 					_A			(__a    ,				__a=0.9_99    ,				__a=jnp.floataa							) ->       jnp.ndarray:
    """simple docstring"""






    def alpha_bar(__a							):
        return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2							) ** 2

    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[]
    for i in range(__a							):
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		i / num_diffusion_timesteps
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(__a							) / alpha_bar(__a							)    ,				__a							)							)
    return jnp.array(__a    ,				dtype=__a							)




@flax.struct.dataclass
class 							lowerCAmelCase__				:






      '''simple docstring'''

      __UpperCamelCase	 =  42
      __UpperCamelCase	 =  42
      __UpperCamelCase	 =  42
      @classmethod
      def _SCREAMING_SNAKE_CASE (						cls     :  int						,	lowercase_     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		scheduler.config

          if config.trained_betas is not None:
              SCREAMING_SNAKE_CASE_      :  str    						=		jnp.asarray(config.trained_betas						,	dtype=scheduler.dtype)
          elif config.beta_schedule == "linear":
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		jnp.linspace(config.beta_start						,	config.beta_end						,	config.num_train_timesteps						,	dtype=scheduler.dtype)
          elif config.beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              SCREAMING_SNAKE_CASE_      :  str    						=		(
                  jnp.linspace(
                      config.beta_start**0.5						,	config.beta_end**0.5						,	config.num_train_timesteps						,	dtype=scheduler.dtype)
                  ** 2
              )
          elif config.beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		betas_for_alpha_bar(config.num_train_timesteps						,	dtype=scheduler.dtype)
          else:
              raise NotImplementedError(
                  F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}')

          SCREAMING_SNAKE_CASE_      :  int    						=		1.0 - betas

          SCREAMING_SNAKE_CASE_      :  Dict    						=		jnp.cumprod(lowercase_						,	axis=0)

          return cls(
              alphas=lowercase_						,	betas=lowercase_						,	alphas_cumprod=lowercase_						,	)



def 					_A			(__a    ,				__a    ,				__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[str]    						=		state.alphas_cumprod

    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		alphas_cumprod[timesteps] ** 0.5
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		sqrt_alpha_prod.flatten()
    SCREAMING_SNAKE_CASE_      :  Any    						=		broadcast_to_shape_from_left(__a    ,				original_samples.shape							)

    SCREAMING_SNAKE_CASE_      :  str    						=		(1 - alphas_cumprod[timesteps]) ** 0.5
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		sqrt_one_minus_alpha_prod.flatten()
    SCREAMING_SNAKE_CASE_      :  str    						=		broadcast_to_shape_from_left(__a    ,				original_samples.shape							)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 					_A			(__a    ,				__a    ,				__a    ,				__a							) ->       List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		get_sqrt_alpha_prod(__a    ,				__a    ,				__a    ,				__a							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples





def 					_A			(__a    ,				__a    ,				__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  str    						=		get_sqrt_alpha_prod(__a    ,				__a    ,				__a    ,				__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity










"""simple docstring"""






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 					_A			(__a    ,				__a    ,				__a							) ->       Dict:
    """simple docstring"""






    if gpta_config_file == "":
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		GPTaConfig()
    else:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		GPTaConfig.from_json_file(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		GPTaModel(__a							)

    # Load weights from numpy
    load_tf_weights_in_gpta(__a    ,				__a    ,				__a							)

    # Save pytorch-model
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		pytorch_dump_folder_path + '''/''' + CONFIG_NAME
    print(f'Save PyTorch model to {pytorch_weights_dump_path}'							)
    torch.save(model.state_dict()    ,				__a							)
    print(f'Save configuration file to {pytorch_config_dump_path}'							)
    with open(__a    ,				'''w'''    ,				encoding='''utf-8'''							) as f:
        f.write(config.to_json_string()							)


if __name__ == "__main__":
  UpperCAmelCase_   : str   			= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--gpt2_config_file""",
      default="""""",
      type=str,
      help=(
          """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
          """This specifies the model architecture."""
      ),
  )
  UpperCAmelCase_   : Union[str, Any]   			= parser.parse_args()
  convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


