



'''simple docstring'''





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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)



@flax.struct.dataclass
class       snake_case__		:
 A__							=      42
 A__							=      42

 # setable values
 A__							=      42
 A__							=      42
 A__							=      None
 @classmethod
 def A_					(					cls		:				Tuple	,			__a		:				CommonSchedulerState	,			__a		:				jnp.ndarray	,			__a		:				jnp.ndarray	,			__a		:				jnp.ndarray	,			)	->					Optional[int]:







       '''simple docstring'''
       return cls(
           common=_lowerCamelCase	,			final_alpha_cumprod=_lowerCamelCase	,			init_noise_sigma=_lowerCamelCase	,			timesteps=_lowerCamelCase	,			)



@dataclass
class       snake_case__		(  __UpperCAmelCase							):
 A__							=      42



class       snake_case__		(  __UpperCAmelCase   ,			__UpperCAmelCase							):
 A__							=      [e.name for e in FlaxKarrasDiffusionSchedulers]

 A__							=      42
 @property
 def A_					(					self		:				int   )	->					Optional[Any]:







       '''simple docstring'''
       return True
 @register_to_config
 def __init__(					self		:				List[Any]	,			__a		:				int = 1000	,			__a		:				float = 0.0_0_0_1	,			__a		:				float = 0.0_2	,			__a		:				str = "linear"	,			__a		:				Optional[jnp.ndarray] = None	,			__a		:				bool = True	,			__a		:				int = 0	,			__a		:				str = "epsilon"	,			__a		:				jnp.dtype = jnp.floataa	,			)	->					Any:







       '''simple docstring'''
       __snake_case      :					Dict						=  dtype
 def A_					(					self		:				List[str]	,			__a		:				Optional[CommonSchedulerState] = None   )	->					Any:







       '''simple docstring'''
       if common is None:
             __snake_case      :					int						=  CommonSchedulerState.create(self   )

       # At every step in ddim, we are looking into the previous alphas_cumprod
       # For the final step, there is no previous alphas_cumprod because we are already at 0
       # `set_alpha_to_one` decides whether we set this parameter simply to one or
       # whether we use the final alpha of the "non-previous" one.
       __snake_case      :					int						=  (
           jnp.array(1.0	,			dtype=self.dtype   ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
       )

       # standard deviation of the initial noise distribution
       __snake_case      :					int						=  jnp.array(1.0	,			dtype=self.dtype   )

       __snake_case      :					Optional[Any]						=  jnp.arange(0	,			self.config.num_train_timesteps   ).round()[::-1]

       return DDIMSchedulerState.create(
           common=_lowerCamelCase	,			final_alpha_cumprod=_lowerCamelCase	,			init_noise_sigma=_lowerCamelCase	,			timesteps=_lowerCamelCase	,			)
 def A_					(					self		:				Union[str, Any]	,			__a		:				DDIMSchedulerState	,			__a		:				jnp.ndarray	,			__a		:				Optional[int] = None   )	->					Tuple:







       '''simple docstring'''
       return sample
 def A_					(					self		:				Optional[int]	,			__a		:				DDIMSchedulerState	,			__a		:				int	,			__a		:				Tuple = ()   )	->					List[str]:







       '''simple docstring'''
       __snake_case      :					int						=  self.config.num_train_timesteps // num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # rounding to avoid issues when num_inference_step is power of 3
       __snake_case      :					List[Any]						=  (jnp.arange(0	,			_lowerCamelCase   ) * step_ratio).round()[::-1] + self.config.steps_offset

       return state.replace(
           num_inference_steps=_lowerCamelCase	,			timesteps=_lowerCamelCase	,			)
 def A_					(					self		:				Tuple	,			__a		:				DDIMSchedulerState	,			__a		:				Tuple	,			__a		:				int   )	->					int:







       '''simple docstring'''
       __snake_case      :					int						=  state.common.alphas_cumprod[timestep]
       __snake_case      :					int						=  jnp.where(
           prev_timestep >= 0	,			state.common.alphas_cumprod[prev_timestep]	,			state.final_alpha_cumprod   )
       __snake_case      :					Any						=  1 - alpha_prod_t
       __snake_case      :					Any						=  1 - alpha_prod_t_prev

       __snake_case      :					Union[str, Any]						=  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

       return variance
 def A_					(					self		:				str	,			__a		:				DDIMSchedulerState	,			__a		:				jnp.ndarray	,			__a		:				int	,			__a		:				jnp.ndarray	,			__a		:				float = 0.0	,			__a		:				bool = True	,			)	->					Tuple:







       '''simple docstring'''
       if state.num_inference_steps is None:
             raise ValueError(
                 'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'   )

       # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
       # Ideally, read DDIM paper in-detail understanding

       # Notation (<variable name> -> <name in paper>
       # - pred_noise_t -> e_theta(x_t, t)
       # - pred_original_sample -> f_theta(x_t, t) or x_0
       # - std_dev_t -> sigma_t
       # - eta -> η
       # - pred_sample_direction -> "direction pointing to x_t"
       # - pred_prev_sample -> "x_t-1"

       # 1. get previous step value (=t-1)
       __snake_case      :					Any						=  timestep - self.config.num_train_timesteps // state.num_inference_steps

       __snake_case      :					int						=  state.common.alphas_cumprod
       __snake_case      :					List[str]						=  state.final_alpha_cumprod

       # 2. compute alphas, betas
       __snake_case      :					Dict						=  alphas_cumprod[timestep]
       __snake_case      :					Tuple						=  jnp.where(prev_timestep >= 0	,			alphas_cumprod[prev_timestep]	,			_lowerCamelCase   )

       __snake_case      :					int						=  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      :					int						=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
             __snake_case      :					Dict						=  model_output
       elif self.config.prediction_type == "sample":
             __snake_case      :					List[Any]						=  model_output
             __snake_case      :					Union[str, Any]						=  (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      :					List[Any]						=  (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. compute variance: "sigma_t(η)" -> see formula (16)
       # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
       __snake_case      :					str						=  self._get_variance(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase   )
       __snake_case      :					Optional[Any]						=  eta * variance ** 0.5

       # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       __snake_case      :					Any						=  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

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

       if not return_dict:
             return (prev_sample, state)

       return FlaxDDIMSchedulerOutput(prev_sample=_lowerCamelCase	,			state=_lowerCamelCase   )
 def A_					(					self		:				Dict	,			__a		:				DDIMSchedulerState	,			__a		:				jnp.ndarray	,			__a		:				jnp.ndarray	,			__a		:				jnp.ndarray	,			)	->					Tuple:







       '''simple docstring'''
       return add_noise_common(state.common	,			_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase   )
 def A_					(					self		:				List[str]	,			__a		:				DDIMSchedulerState	,			__a		:				jnp.ndarray	,			__a		:				jnp.ndarray	,			__a		:				jnp.ndarray	,			)	->					Optional[Any]:







       '''simple docstring'''
       return get_velocity_common(state.common	,			_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase   )





 def __len__(					self		:				Tuple   )	->					Optional[Any]:







       '''simple docstring'''
       return self.config.num_train_timesteps










"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(lowercase_ ,      lowercase_ ,      lowercase_		)

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
 model.save_pretrained(lowercase_		)


if __name__ == "__main__":
    _lowerCamelCase       =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    _lowerCamelCase       =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

