
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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 							lowercase   :
		__lowercase			:			Optional[jnp.ndarray]      	=							None
		__lowercase			:			Optional[jnp.ndarray]      	=							None
		__lowercase			:			Optional[jnp.ndarray]      	=							None



		@classmethod
		def __UpperCamelCase					(					cls			)    ->   List[Any]:
				"""simple docstring"""
				return cls()






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






class 							lowercase   (						UpperCAmelCase__    ,  UpperCAmelCase__  ):



		@property
		def __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				return True



		@register_to_config
		def __init__(					self							, A_ = 2_000							, A_ = 0.15							, A_ = 0.01							, A_ = 1348.0							, A_ = 1e-5							, A_ = 1							, )    ->   int:
				"""simple docstring"""
				pass



		def __UpperCamelCase					(					self			)    ->   List[Any]:
				"""simple docstring"""
				UpperCamelCase            =	ScoreSdeVeSchedulerState.create()
				return self.set_sigmas(
				    A_							, self.config.num_train_timesteps							, self.config.sigma_min							, self.config.sigma_max							, self.config.sampling_eps							, )



		def __UpperCamelCase					(					self							, A_							, A_							, A_ = ()							, A_ = None			)    ->   ScoreSdeVeSchedulerState:
				"""simple docstring"""
				UpperCamelCase            =	sampling_eps if sampling_eps is not None else self.config.sampling_eps

				UpperCamelCase            =	jnp.linspace(1							, A_							, A_			)
				return state.replace(timesteps=A_			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_ = None							, A_ = None							, A_ = None							, )    ->   ScoreSdeVeSchedulerState:
				"""simple docstring"""
				UpperCamelCase            =	sigma_min if sigma_min is not None else self.config.sigma_min
				UpperCamelCase            =	sigma_max if sigma_max is not None else self.config.sigma_max
				UpperCamelCase            =	sampling_eps if sampling_eps is not None else self.config.sampling_eps
				if state.timesteps is None:
						UpperCamelCase            =	self.set_timesteps(A_							, A_							, A_			)

				UpperCamelCase            =	jnp.exp(jnp.linspace(jnp.log(A_			)							, jnp.log(A_			)							, A_			)			)
				UpperCamelCase            =	jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]			)

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



		def __UpperCamelCase					(					self							, A_							, A_							, A_			)    ->   List[Any]:
				"""simple docstring"""
				return jnp.where(timesteps == 0							, jnp.zeros_like(A_			)							, state.discrete_sigmas[timesteps - 1]			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, A_							, A_							, A_ = 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'			)

				UpperCamelCase            =	timestep * jnp.ones(
				    sample.shape[0]							, )
				UpperCamelCase            =	(timestep * (len(state.timesteps			) - 1)).long()

				UpperCamelCase            =	state.discrete_sigmas[timesteps]
				UpperCamelCase            =	self.get_adjacent_sigma(A_							, A_							, A_			)
				UpperCamelCase            =	jnp.zeros_like(A_			)
				UpperCamelCase            =	(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
				UpperCamelCase            =	diffusion.flatten()
				UpperCamelCase            =	broadcast_to_shape_from_left(A_							, sample.shape			)
				UpperCamelCase            =	drift - diffusion**2 * model_output

				#  equation 6: sample noise for the diffusion term of
				UpperCamelCase            =	random.split(A_							, num=1			)
				UpperCamelCase            =	random.normal(key=A_							, shape=sample.shape			)
				UpperCamelCase            =	sample - drift  # subtract because `dt` is a small negative timestep
				# TODO is the variable diffusion the correct scaling term for the noise?
				UpperCamelCase            =	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=A_							, prev_sample_mean=A_							, state=A_			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, A_							, A_ = 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
				UpperCamelCase            =	random.split(A_							, num=1			)
				UpperCamelCase            =	random.normal(key=A_							, shape=sample.shape			)

				# compute step size from the model_output, the noise, and the snr
				UpperCamelCase            =	jnp.linalg.norm(A_			)
				UpperCamelCase            =	jnp.linalg.norm(A_			)
				UpperCamelCase            =	(self.config.snr * noise_norm / grad_norm) ** 2 * 2
				UpperCamelCase            =	step_size * jnp.ones(sample.shape[0]			)

				# compute corrected sample: model_output term and noise term
				UpperCamelCase            =	step_size.flatten()
				UpperCamelCase            =	broadcast_to_shape_from_left(A_							, sample.shape			)
				UpperCamelCase            =	sample + step_size * model_output
				UpperCamelCase            =	prev_sample_mean + ((step_size * 2) ** 0.5) * noise

				if not return_dict:
						return (prev_sample, state)

				return FlaxSdeVeOutput(prev_sample=A_							, state=A_			)



		def __len__(					self			)    ->   Tuple:
				"""simple docstring"""
				return self.config.num_train_timesteps


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__snake_case					:							List[Any]           =							logging.getLogger()




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Any 		=    argparse.ArgumentParser()
			parser.add_argument('-f'							)
			UpperCAmelCase__		:List[Any] 		=    parser.parse_args()
			return args.f




class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Optional[int] 		=    logging.StreamHandler(sys.stdout	)
									logger.addHandler(A	)

									UpperCAmelCase__		:List[str] 		=    self.get_auto_remove_tmp_dir()
									UpperCAmelCase__		:List[str] 		=    F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

									with patch.object(A  , 'argv'  , A	):
												UpperCAmelCase__		:Any 		=    run_glue_with_pabee.main()
												for value in result.values():
															self.assertGreaterEqual(A  , 0.75	)





