


# 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 							a						:




							"""simple docstring"""


							__UpperCAmelCase       :				CommonSchedulerState

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

							@classmethod
							def        __snake_case   (				cls			:    str       ,      lowerCamelCase			:    CommonSchedulerState       ,      lowerCamelCase			:    jnp.ndarray       ,      lowerCamelCase			:    jnp.ndarray							)      ->       int:
														return cls(common=lowerCamelCase       ,      init_noise_sigma=lowerCamelCase       ,      timesteps=lowerCamelCase							)

@dataclass
class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				DDPMSchedulerState

class 							a						(_lowerCAmelCase	,							_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[Any]	    =    [e.name for e in FlaxKarrasDiffusionSchedulers]

							__UpperCAmelCase       :				jnp.dtype

							@property
							def        __snake_case   (				self			:    str							)      ->       Optional[int]:
														return True

							@register_to_config
							def __init__(				self			:    int       ,      lowerCamelCase			:    int = 1000       ,      lowerCamelCase			:    float = 0.00_01       ,      lowerCamelCase			:    float = 0.02       ,      lowerCamelCase			:    str = "linear"       ,      lowerCamelCase			:    Optional[jnp.ndarray] = None       ,      lowerCamelCase			:    str = "fixed_small"       ,      lowerCamelCase			:    bool = True       ,      lowerCamelCase			:    str = "epsilon"       ,      lowerCamelCase			:    jnp.dtype = jnp.floataa       ,      )      ->       Any:
														__snake_case						:     List[str]									=  dtype

							def        __snake_case   (				self			:    Dict       ,      lowerCamelCase			:    Optional[CommonSchedulerState] = None							)      ->       DDPMSchedulerState:
														if common is None:
																					__snake_case						:     Optional[int]									=  CommonSchedulerState.create(self							)

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

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

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

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

							def        __snake_case   (				self			:    Dict       ,      lowerCamelCase			:    DDPMSchedulerState       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Tuple = ()							)      ->       DDPMSchedulerState:
														__snake_case						:     List[Any]									=  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						:     Tuple									=  (jnp.arange(0       ,      lowerCamelCase							) * step_ratio).round()[::-1]

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

							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    DDPMSchedulerState       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    List[str]=None       ,      lowerCamelCase			:    Optional[int]=None							)      ->       Optional[int]:
														__snake_case						:     Union[str, Any]									=  state.common.alphas_cumprod[t]
														__snake_case						:     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
														__snake_case						:     List[Any]									=  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

														return variance

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

														if key is None:
																					__snake_case						:     int									=  jax.random.PRNGKey(0							)

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

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

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

														__snake_case						:     Union[str, Any]									=  jnp.where(t > 0       ,      random_variance()       ,      jnp.zeros(model_output.shape       ,      dtype=self.dtype							)							)

														__snake_case						:     int									=  pred_prev_sample + variance

														if not return_dict:
																					return (pred_prev_sample, state)

														return FlaxDDPMSchedulerOutput(prev_sample=lowerCamelCase       ,      state=lowerCamelCase							)

							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    DDPMSchedulerState       ,      lowerCamelCase			:    jnp.ndarray       ,      lowerCamelCase			:    jnp.ndarray       ,      lowerCamelCase			:    jnp.ndarray       ,      )      ->       jnp.ndarray:
														return add_noise_common(state.common       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    Union[str, Any]       ,      lowerCamelCase			:    DDPMSchedulerState       ,      lowerCamelCase			:    jnp.ndarray       ,      lowerCamelCase			:    jnp.ndarray       ,      lowerCamelCase			:    jnp.ndarray       ,      )      ->       jnp.ndarray:
														return get_velocity_common(state.common       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)






							def __len__(				self			:    Dict							)      ->       Dict:
														return self.config.num_train_timesteps



'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase     :							Any       						=     logging.get_logger(__name__)

lowercase     :							int       						=     {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''timesformer'''





     def __init__(				self		,     SCREAMING_SNAKE_CASE=224		,     SCREAMING_SNAKE_CASE=16		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=768		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3072		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1e-6		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="divided_space_time"		,     SCREAMING_SNAKE_CASE=0		,     **SCREAMING_SNAKE_CASE		,     )					->							Tuple:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE      )

         A			:  List[Any]   =		image_size
         A			:  List[str]   =		patch_size
         A			:  List[str]   =		num_channels
         A			:  str   =		num_frames

         A			:  Any   =		hidden_size
         A			:  Dict   =		num_hidden_layers
         A			:  Any   =		num_attention_heads
         A			:  Optional[Any]   =		intermediate_size
         A			:  Union[str, Any]   =		hidden_act
         A			:  Tuple   =		hidden_dropout_prob
         A			:  Optional[int]   =		attention_probs_dropout_prob
         A			:  Tuple   =		initializer_range
         A			:  List[Any]   =		layer_norm_eps
         A			:  Union[str, Any]   =		qkv_bias

         A			:  List[str]   =		attention_type
         A			:  Union[str, Any]   =		drop_path_rate




