





'''simple docstring'''






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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left




@flax.struct.dataclass
class    _A   :
		# setable values
		lowercase__:      Optional[jnp.ndarray]							    =      None
		lowercase__:      Optional[jnp.ndarray]							    =      None
		lowercase__:      Optional[jnp.ndarray]							    =      None

		@classmethod
		def   lowercase__    (      cls				:      Any )				->						Optional[int]:


					"""simple docstring"""

					return cls()





@dataclass
class    _A   (				__lowercase		):
		lowercase__:      ScoreSdeVeSchedulerState
		lowercase__:      jnp.ndarray
		lowercase__:      Optional[jnp.ndarray]							    =      None





class    _A   (				__lowercase	,				__lowercase		):

		@property
		def   lowercase__    (      self				:      int )				->						Union[str, Any]:


					"""simple docstring"""

					return True

		@register_to_config
		def __init__(      self				:      Optional[int]     ,			__magic_name__				:      int = 20_00     ,			__magic_name__				:      float = 0.15     ,			__magic_name__				:      float = 0.01     ,			__magic_name__				:      float = 1348.0     ,			__magic_name__				:      float = 1E-5     ,			__magic_name__				:      int = 1     ,			)				->						Union[str, Any]:


					"""simple docstring"""

					pass

		def   lowercase__    (      self				:      Union[str, Any] )				->						List[Any]:


					"""simple docstring"""

					__snake_case				:			Any       			=    ScoreSdeVeSchedulerState.create()
					return self.set_sigmas(
					    __magic_name__     ,			self.config.num_train_timesteps     ,			self.config.sigma_min     ,			self.config.sigma_max     ,			self.config.sampling_eps     ,			)

		def   lowercase__    (      self				:      List[str]     ,			__magic_name__				:      ScoreSdeVeSchedulerState     ,			__magic_name__				:      int     ,			__magic_name__				:      Tuple = ()     ,			__magic_name__				:      float = None )				->						ScoreSdeVeSchedulerState:


					"""simple docstring"""

					__snake_case				:			Union[str, Any]       			=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

					__snake_case				:			Union[str, Any]       			=    jnp.linspace(1     ,			__magic_name__     ,			__magic_name__ )
					return state.replace(timesteps=__magic_name__ )

		def   lowercase__    (      self				:      Any     ,			__magic_name__				:      ScoreSdeVeSchedulerState     ,			__magic_name__				:      int     ,			__magic_name__				:      float = None     ,			__magic_name__				:      float = None     ,			__magic_name__				:      float = None     ,			)				->						ScoreSdeVeSchedulerState:


					"""simple docstring"""

					__snake_case				:			int       			=    sigma_min if sigma_min is not None else self.config.sigma_min
					__snake_case				:			Optional[int]       			=    sigma_max if sigma_max is not None else self.config.sigma_max
					__snake_case				:			Optional[Any]       			=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
					if state.timesteps is None:
								__snake_case				:			Any       			=    self.set_timesteps(__magic_name__     ,			__magic_name__     ,			__magic_name__ )

					__snake_case				:			Tuple       			=    jnp.exp(jnp.linspace(jnp.log(__magic_name__ )     ,			jnp.log(__magic_name__ )     ,			__magic_name__ ) )
					__snake_case				:			Optional[Any]       			=    jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps] )

					return state.replace(discrete_sigmas=__magic_name__     ,			sigmas=__magic_name__ )

		def   lowercase__    (      self				:      str     ,			__magic_name__				:      Optional[Any]     ,			__magic_name__				:      List[str]     ,			__magic_name__				:      Union[str, Any] )				->						Union[str, Any]:


					"""simple docstring"""

					return jnp.where(timesteps == 0     ,			jnp.zeros_like(__magic_name__ )     ,			state.discrete_sigmas[timesteps - 1] )

		def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      ScoreSdeVeSchedulerState     ,			__magic_name__				:      jnp.ndarray     ,			__magic_name__				:      int     ,			__magic_name__				:      jnp.ndarray     ,			__magic_name__				:      random.KeyArray     ,			__magic_name__				:      bool = True     ,			)				->						Union[FlaxSdeVeOutput, Tuple]:


					"""simple docstring"""

					if state.timesteps is None:
								raise ValueError(
								    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )

					__snake_case				:			str       			=    timestep * jnp.ones(
					    sample.shape[0]     ,			)
					__snake_case				:			List[Any]       			=    (timestep * (len(state.timesteps ) - 1)).long()

					__snake_case				:			int       			=    state.discrete_sigmas[timesteps]
					__snake_case				:			List[str]       			=    self.get_adjacent_sigma(__magic_name__     ,			__magic_name__     ,			__magic_name__ )
					__snake_case				:			int       			=    jnp.zeros_like(__magic_name__ )
					__snake_case				:			Tuple       			=    (sigma**2 - adjacent_sigma**2) ** 0.5

					# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
					# also equation 47 shows the analog from SDE models to ancestral sampling methods
					__snake_case				:			str       			=    diffusion.flatten()
					__snake_case				:			Optional[int]       			=    broadcast_to_shape_from_left(__magic_name__     ,			sample.shape )
					__snake_case				:			int       			=    drift - diffusion**2 * model_output

					#  equation 6: sample noise for the diffusion term of
					__snake_case				:			Optional[Any]       			=    random.split(__magic_name__     ,			num=1 )
					__snake_case				:			str       			=    random.normal(key=__magic_name__     ,			shape=sample.shape )
					__snake_case				:			List[Any]       			=    sample - drift  # subtract because `dt` is a small negative timestep
					# TODO is the variable diffusion the correct scaling term for the noise?
					__snake_case				:			Tuple       			=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

					if not return_dict:
								return (prev_sample, prev_sample_mean, state)

					return FlaxSdeVeOutput(prev_sample=__magic_name__     ,			prev_sample_mean=__magic_name__     ,			state=__magic_name__ )

		def   lowercase__    (      self				:      Tuple     ,			__magic_name__				:      ScoreSdeVeSchedulerState     ,			__magic_name__				:      jnp.ndarray     ,			__magic_name__				:      jnp.ndarray     ,			__magic_name__				:      random.KeyArray     ,			__magic_name__				:      bool = True     ,			)				->						Union[FlaxSdeVeOutput, Tuple]:


					"""simple docstring"""

					if state.timesteps is None:
								raise ValueError(
								    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )

					# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
					# sample noise for correction
					__snake_case				:			Any       			=    random.split(__magic_name__     ,			num=1 )
					__snake_case				:			Optional[Any]       			=    random.normal(key=__magic_name__     ,			shape=sample.shape )

					# compute step size from the model_output, the noise, and the snr
					__snake_case				:			Dict       			=    jnp.linalg.norm(__magic_name__ )
					__snake_case				:			Any       			=    jnp.linalg.norm(__magic_name__ )
					__snake_case				:			Optional[int]       			=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
					__snake_case				:			str       			=    step_size * jnp.ones(sample.shape[0] )

					# compute corrected sample: model_output term and noise term
					__snake_case				:			str       			=    step_size.flatten()
					__snake_case				:			Dict       			=    broadcast_to_shape_from_left(__magic_name__     ,			sample.shape )
					__snake_case				:			Any       			=    sample + step_size * model_output
					__snake_case				:			str       			=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

					if not return_dict:
								return (prev_sample, state)

					return FlaxSdeVeOutput(prev_sample=__magic_name__     ,			state=__magic_name__ )

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


					"""simple docstring"""

					return self.config.num_train_timesteps









'''simple docstring'''






def 				_a	(					_lowerCamelCase   )   ->							bool:



			"""simple docstring"""


			__snake_case				:			Optional[Any]       			=    [list(_lowerCamelCase   ) for row in matrix]
			if matrix and isinstance(_lowerCamelCase						,  _lowerCamelCase   ):
						if isinstance(matrix[0]						,  _lowerCamelCase   ):
									__snake_case				:			str       			=    0
									for row in matrix:
												if prev_len == 0:
															__snake_case				:			Dict       			=    len(_lowerCamelCase   )
															__snake_case				:			List[Any]       			=    True
												else:
															__snake_case				:			Union[str, Any]       			=    prev_len == len(_lowerCamelCase   )
						else:
									__snake_case				:			List[Any]       			=    True
			else:
						__snake_case				:			List[str]       			=    False

			return result







def 				_a	(					_lowerCamelCase   )   ->							None:



			"""simple docstring"""


			if check_matrix(_lowerCamelCase   ) and len(_lowerCamelCase   ) > 0:
						__snake_case				:			List[str]       			=    [list(_lowerCamelCase   ) for row in a]
						__snake_case				:			Optional[Any]       			=    len(_lowerCamelCase   )
						if isinstance(a[0]						,  _lowerCamelCase   ):
									__snake_case				:			Tuple       			=    len(a[0]   )
						else:
									for dat in a:
												print(_lowerCamelCase   )
									return

						# horizotal printing increasing
						for i in range(0						,  _lowerCamelCase   ):
									print(a[0][i]   )
						# vertical printing down
						for i in range(1						,  _lowerCamelCase   ):
									print(a[i][mat_col - 1]   )
						# horizotal printing decreasing
						if mat_row > 1:
									for i in range(mat_col - 2						,  -1						,  -1   ):
												print(a[mat_row - 1][i]   )
        # vertical printing up
						for i in range(mat_row - 2						,  0						,  -1   ):
									print(a[i][0]   )
						__snake_case				:			Union[str, Any]       			=    [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
						if len(_lowerCamelCase   ) > 0:
									spiral_print_clockwise(_lowerCamelCase   )
						else:
									return
			else:
						print("""Not a valid matrix"""   )
						return







def 				_a	(					_lowerCamelCase   )   ->							list[int]:



			"""simple docstring"""


			if matrix:
						return list(matrix.pop(0   )   ) + spiral_traversal(list(zip(*_lowerCamelCase   )   )[::-1]   )
			else:
						return []


# driver code
if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__UpperCamelCase										=  [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
	spiral_print_clockwise(a)


