


# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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 	UpperCAmelCase_					:

   '''simple docstring'''




   __A :      CommonSchedulerState

   # setable values
   __A :      jnp.ndarray
   __A :      jnp.ndarray
   __A :      Optional[int]     		=		None






   @classmethod
   def _snake_case    (		cls						,				__A						,				__A						,				__A ):






     """simple docstring"""





     return cls(common=__A						,				init_noise_sigma=__A						,				timesteps=__A )



@dataclass
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      DDPMSchedulerState



class 	UpperCAmelCase_					(					UpperCamelCase		, UpperCamelCase    ):

   '''simple docstring'''




   __A :      Union[str, Any]     		=		[e.name for e in FlaxKarrasDiffusionSchedulers]

   __A :      jnp.dtype






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return True






   @register_to_config
   def __init__(		self						,				__A = 1000						,				__A = 0.0001						,				__A = 0.02						,				__A = "linear"						,				__A = None						,				__A = "fixed_small"						,				__A = True						,				__A = "epsilon"						,				__A = jnp.floataa						,				):






     """simple docstring"""





     lowerCamelCase :					Any    =				dtype






   def _snake_case    (		self						,				__A = None ):






     """simple docstring"""





     if common is None:
       lowerCamelCase :					Dict    =				CommonSchedulerState.create(self )

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

     lowerCamelCase :					Dict    =				jnp.arange(0						,				self.config.num_train_timesteps ).round()[::-1]

     return DDPMSchedulerState.create(
         common=__A						,				init_noise_sigma=__A						,				timesteps=__A						,				)






   def _snake_case    (		self						,				__A						,				__A						,				__A = None ):






     """simple docstring"""





     return sample






   def _snake_case    (		self						,				__A						,				__A						,				__A = () ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				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
     lowerCamelCase :					str    =				(jnp.arange(0						,				__A ) * step_ratio).round()[::-1]

     return state.replace(
         num_inference_steps=__A						,				timesteps=__A						,				)






   def _snake_case    (		self						,				__A						,				__A						,				__A=None						,				__A=None ):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				state.common.alphas_cumprod[t]
     lowerCamelCase :					Union[str, Any]    =				jnp.where(t > 0						,				state.common.alphas_cumprod[t - 1]						,				jnp.array(1.0						,				dtype=self.dtype ) )

     # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
     # and sample from it to get previous sample
     # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
     lowerCamelCase :					List[Any]    =				(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

     if variance_type is None:
       lowerCamelCase :					int    =				self.config.variance_type

     # hacks - were probably added for training stability
     if variance_type == "fixed_small":
       lowerCamelCase :					List[Any]    =				jnp.clip(__A						,				a_min=1e-20 )
     # for rl-diffuser https://arxiv.org/abs/2205.09991
     elif variance_type == "fixed_small_log":
       lowerCamelCase :					Tuple    =				jnp.log(jnp.clip(__A						,				a_min=1e-20 ) )
     elif variance_type == "fixed_large":
       lowerCamelCase :					str    =				state.common.betas[t]
     elif variance_type == "fixed_large_log":
       # Glide max_log
       lowerCamelCase :					List[str]    =				jnp.log(state.common.betas[t] )
     elif variance_type == "learned":
       return predicted_variance
     elif variance_type == "learned_range":
       lowerCamelCase :					str    =				variance
       lowerCamelCase :					str    =				state.common.betas[t]
       lowerCamelCase :					Union[str, Any]    =				(predicted_variance + 1) / 2
       lowerCamelCase :					Any    =				frac * max_log + (1 - frac) * min_log

     return variance






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A = None						,				__A = True						,				):






     """simple docstring"""





     lowerCamelCase :					int    =				timestep

     if key is None:
       lowerCamelCase :					str    =				jax.random.PRNGKey(0 )

     if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
       lowerCamelCase    ,	lowerCamelCase :					int    =				jnp.split(__A						,				sample.shape[1]						,				axis=1 )
     else:
       lowerCamelCase :					int    =				None

     # 1. compute alphas, betas
     lowerCamelCase :					List[str]    =				state.common.alphas_cumprod[t]
     lowerCamelCase :					Tuple    =				jnp.where(t > 0						,				state.common.alphas_cumprod[t - 1]						,				jnp.array(1.0						,				dtype=self.dtype ) )
     lowerCamelCase :					Optional[int]    =				1 - alpha_prod_t
     lowerCamelCase :					Tuple    =				1 - alpha_prod_t_prev

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     if self.config.prediction_type == "epsilon":
       lowerCamelCase :					Tuple    =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
       lowerCamelCase :					Dict    =				model_output
     elif self.config.prediction_type == "v_prediction":
       lowerCamelCase :					Any    =				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
     else:
       raise ValueError(
           F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
           " for the FlaxDDPMScheduler." )

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
       lowerCamelCase :					Dict    =				jnp.clip(__A						,				-1						,				1 )

     # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     lowerCamelCase :					Any    =				(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
     lowerCamelCase :					Union[str, Any]    =				state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

     # 5. Compute predicted previous sample µ_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     lowerCamelCase :					List[str]    =				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     def random_variance():
       lowerCamelCase :					Dict    =				jax.random.split(__A						,				num=1 )
       lowerCamelCase :					int    =				jax.random.normal(__A						,				shape=model_output.shape						,				dtype=self.dtype )
       return (self._get_variance(__A						,				__A						,				predicted_variance=__A ) ** 0.5) * noise

     lowerCamelCase :					Any    =				jnp.where(t > 0						,				random_variance()						,				jnp.zeros(model_output.shape						,				dtype=self.dtype ) )

     lowerCamelCase :					Tuple    =				pred_prev_sample + variance

     if not return_dict:
       return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=__A						,				state=__A )






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				):






     """simple docstring"""





     return add_noise_common(state.common						,				__A						,				__A						,				__A )






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				):






     """simple docstring"""





     return get_velocity_common(state.common						,				__A						,				__A						,				__A )






   def __len__(		self ):






     """simple docstring"""





     return self.config.num_train_timesteps







from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		["image_processor", "tokenizer"]
   __A :      Dict     		=		"BridgeTowerImageProcessor"
   __A :      Optional[int]     		=		("RobertaTokenizer", "RobertaTokenizerFast")






   def __init__(		self						,				__A						,				__A ):






     """simple docstring"""





     super().__init__(__A						,				__A )






   def __call__(		self						,				__A						,				__A = None						,				__A = True						,				__A = False						,				__A = None						,				__A = None						,				__A = 0						,				__A = None						,				__A = None						,				__A = None						,				__A = False						,				__A = False						,				__A = False						,				__A = False						,				__A = True						,				__A = None						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					str    =				self.tokenizer(
         text=__A						,				add_special_tokens=__A						,				padding=__A						,				truncation=__A						,				max_length=__A						,				stride=__A						,				pad_to_multiple_of=__A						,				return_token_type_ids=__A						,				return_attention_mask=__A						,				return_overflowing_tokens=__A						,				return_special_tokens_mask=__A						,				return_offsets_mapping=__A						,				return_length=__A						,				verbose=__A						,				return_tensors=__A						,				**__A						,				)
     # add pixel_values + pixel_mask
     lowerCamelCase :					int    =				self.image_processor(
         __A						,				return_tensors=__A						,				do_normalize=__A						,				do_center_crop=__A						,				**__A )
     encoding.update(__A )

     return encoding






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





     return self.tokenizer.batch_decode(*__A						,				**__A )






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





     return self.tokenizer.decode(*__A						,				**__A )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				self.tokenizer.model_input_names
     lowerCamelCase :					int    =				self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )



