# 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   snake_case_       :
   __A	:		CommonSchedulerState

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






   @classmethod
   def       __UpperCamelCase     (      cls				:     str	,						lowercase_				:     CommonSchedulerState	,						lowercase_				:     jnp.ndarray	,						lowercase_				:     jnp.ndarray  )					->						Optional[int]:
       return cls(common=lowercase_	,						init_noise_sigma=lowercase_	,						timesteps=lowercase_  )



@dataclass
class   snake_case_       (		__A       ):
   __A	:		DDPMSchedulerState



class   snake_case_       (		__A  ,__A       ):
   __A	:		List[Any]         =				[e.name for e in FlaxKarrasDiffusionSchedulers]

   __A	:		jnp.dtype






   @property
   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						List[Any]:
       return True






   @register_to_config
   def __init__(      self				:     Optional[Any]	,						lowercase_				:     int = 10_00	,						lowercase_				:     float = 0.00_01	,						lowercase_				:     float = 0.02	,						lowercase_				:     str = "linear"	,						lowercase_				:     Optional[jnp.ndarray] = None	,						lowercase_				:     str = "fixed_small"	,						lowercase_				:     bool = True	,						lowercase_				:     str = "epsilon"	,						lowercase_				:     jnp.dtype = jnp.floataa	,						)					->						List[str]:
       lowercase__ :   Any    					=				dtype






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     Optional[CommonSchedulerState] = None  )					->						DDPMSchedulerState:
       if common is None:
           lowercase__ :   str    					=				CommonSchedulerState.create(self  )

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

       lowercase__ :   Union[str, Any]    					=				jnp.arange(0	,						self.config.num_train_timesteps  ).round()[::-1]

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






   def       __UpperCamelCase     (      self				:     Optional[int]	,						lowercase_				:     DDPMSchedulerState	,						lowercase_				:     jnp.ndarray	,						lowercase_				:     Optional[int] = None  )					->						jnp.ndarray:
       return sample






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     DDPMSchedulerState	,						lowercase_				:     int	,						lowercase_				:     Tuple = ()  )					->						DDPMSchedulerState:
       lowercase__ :   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
       lowercase__ :   List[str]    					=				(jnp.arange(0	,						lowercase_  ) * step_ratio).round()[::-1]

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






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     DDPMSchedulerState	,						lowercase_				:     Optional[Any]	,						lowercase_				:     Any=None	,						lowercase_				:     Tuple=None  )					->						Optional[int]:
       lowercase__ :   Dict    					=				state.common.alphas_cumprod[t]
       lowercase__ :   List[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
       lowercase__ :   List[str]    					=				(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

       if variance_type is None:
           lowercase__ :   Dict    					=				self.config.variance_type

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

       return variance






   def       __UpperCamelCase     (      self				:     Any	,						lowercase_				:     DDPMSchedulerState	,						lowercase_				:     jnp.ndarray	,						lowercase_				:     int	,						lowercase_				:     jnp.ndarray	,						lowercase_				:     Optional[jax.random.KeyArray] = None	,						lowercase_				:     bool = True	,						)					->						Union[FlaxDDPMSchedulerOutput, Tuple]:
       lowercase__ :   str    					=				timestep

       if key is None:
           lowercase__ :   Any    					=				jax.random.PRNGKey(0  )

       if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
           lowercase__					,   lowercase__ :   Union[str, Any]    					=				jnp.split(lowercase_	,						sample.shape[1]	,						axis=1  )
       else:
           lowercase__ :   Any    					=				None

       # 1. compute alphas, betas
       lowercase__ :   str    					=				state.common.alphas_cumprod[t]
       lowercase__ :   Optional[Any]    					=				jnp.where(t > 0	,						state.common.alphas_cumprod[t - 1]	,						jnp.array(1.0	,						dtype=self.dtype  )  )
       lowercase__ :   Optional[int]    					=				1 - alpha_prod_t
       lowercase__ :   List[str]    					=				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":
           lowercase__ :   Any    					=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
           lowercase__ :   Any    					=				model_output
       elif self.config.prediction_type == "v_prediction":
           lowercase__ :   List[str]    					=				(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:
           lowercase__ :   str    					=				jnp.clip(lowercase_	,						-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
       lowercase__ :   List[str]    					=				(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
       lowercase__ :   List[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
       lowercase__ :   Any    					=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       def random_variance():
           lowercase__ :   Optional[int]    					=				jax.random.split(lowercase_	,						num=1  )
           lowercase__ :   List[str]    					=				jax.random.normal(lowercase_	,						shape=model_output.shape	,						dtype=self.dtype  )
           return (self._get_variance(lowercase_	,						lowercase_	,						predicted_variance=lowercase_  ) ** 0.5) * noise

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

       lowercase__ :   Any    					=				pred_prev_sample + variance

       if not return_dict:
           return (pred_prev_sample, state)

       return FlaxDDPMSchedulerOutput(prev_sample=lowercase_	,						state=lowercase_  )






   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     DDPMSchedulerState	,						lowercase_				:     jnp.ndarray	,						lowercase_				:     jnp.ndarray	,						lowercase_				:     jnp.ndarray	,						)					->						jnp.ndarray:
       return add_noise_common(state.common	,						lowercase_	,						lowercase_	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     str	,						lowercase_				:     DDPMSchedulerState	,						lowercase_				:     jnp.ndarray	,						lowercase_				:     jnp.ndarray	,						lowercase_				:     jnp.ndarray	,						)					->						jnp.ndarray:
       return get_velocity_common(state.common	,						lowercase_	,						lowercase_	,						lowercase_  )







   def __len__(      self				:     Any  )					->						Optional[Any]:
       return self.config.num_train_timesteps





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase			:     Any           =   {
    'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
    'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
    'processing_mctct': ['MCTCTProcessor'],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     int           =   [
        'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MCTCTForCTC',
        'MCTCTModel',
        'MCTCTPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
    from .feature_extraction_mctct import MCTCTFeatureExtractor
    from .processing_mctct import MCTCTProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
    import sys

    __lowerCAmelCase			:     List[Any]           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

