





'''simple docstring'''


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Tuple         =					(UnCLIPScheduler,)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      {
         "num_train_timesteps": 1000,
         "variance_type": "fixed_small_log",
         "clip_sample": True,
         "clip_sample_range": 1.0,
         "prediction_type": "epsilon",
     }

     config.update(**_lowerCAmelCase		)
     return config




 def 							_snake_case   (	self		)    ->							List[str]:
     for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Any:
     for variance in ["fixed_small_log", "learned_range"]:
         self.check_over_configs(variance_type=_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Tuple:
     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     for clip_sample_range in [1, 5, 10, 20]:
         self.check_over_configs(clip_sample_range=_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Dict:
     for prediction_type in ["epsilon", "sample"]:
         self.check_over_configs(prediction_type=_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     for time_step in [0, 500, 999]:
         for prev_timestep in [None, 5, 100, 250, 500, 750]:
             if prev_timestep is not None and prev_timestep >= time_step:
                 continue

             self.check_over_forward(time_step=_lowerCAmelCase			,     prev_timestep=_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      self.scheduler_classes[0]
     _lowerCAmelCase				=      self.get_scheduler_config(variance_type="fixed_small_log"		)
     _lowerCAmelCase				=      scheduler_class(**_lowerCAmelCase		)

     assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.0000E-10		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0549625		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.9994987		)		) < 1E-5




 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      self.scheduler_classes[0]
     _lowerCAmelCase				=      self.get_scheduler_config(variance_type="learned_range"		)
     _lowerCAmelCase				=      scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase				=      0.5

     assert scheduler._get_variance(1			,     predicted_variance=_lowerCAmelCase		) - -10.1712790 < 1E-5
     assert scheduler._get_variance(487			,     predicted_variance=_lowerCAmelCase		) - -5.7998052 < 1E-5
     assert scheduler._get_variance(999			,     predicted_variance=_lowerCAmelCase		) - -0.0010011 < 1E-5




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      self.scheduler_classes[0]
     _lowerCAmelCase				=      self.get_scheduler_config()
     _lowerCAmelCase				=      scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase				=      scheduler.timesteps

     _lowerCAmelCase				=      self.dummy_model()
     _lowerCAmelCase				=      self.dummy_sample_deter
     _lowerCAmelCase				=      torch.manual_seed(0		)

     for i, t in enumerate(_lowerCAmelCase		):
         # 1. predict noise residual
         _lowerCAmelCase				=      model(_lowerCAmelCase			,     _lowerCAmelCase		)

         # 2. predict previous mean of sample x_t-1
         _lowerCAmelCase				=      scheduler.step(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     generator=_lowerCAmelCase		).prev_sample

         _lowerCAmelCase				=      pred_prev_sample

     _lowerCAmelCase				=      torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase				=      torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 252.2682495		) < 1E-2
     assert abs(result_mean.item() - 0.3284743		) < 1E-3




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.scheduler_classes[0]
     _lowerCAmelCase				=      self.get_scheduler_config()
     _lowerCAmelCase				=      scheduler_class(**_lowerCAmelCase		)

     scheduler.set_timesteps(25		)

     _lowerCAmelCase				=      scheduler.timesteps

     _lowerCAmelCase				=      self.dummy_model()
     _lowerCAmelCase				=      self.dummy_sample_deter
     _lowerCAmelCase				=      torch.manual_seed(0		)

     for i, t in enumerate(_lowerCAmelCase		):
         # 1. predict noise residual
         _lowerCAmelCase				=      model(_lowerCAmelCase			,     _lowerCAmelCase		)

         if i + 1 == timesteps.shape[0]:
             _lowerCAmelCase				=      None
         else:
             _lowerCAmelCase				=      timesteps[i + 1]

         # 2. predict previous mean of sample x_t-1
         _lowerCAmelCase				=      scheduler.step(
             _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     prev_timestep=_lowerCAmelCase			,     generator=_lowerCAmelCase		).prev_sample

         _lowerCAmelCase				=      pred_prev_sample

     _lowerCAmelCase				=      torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase				=      torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 258.2044983		) < 1E-2
     assert abs(result_mean.item() - 0.3362038		) < 1E-3




 def 							_snake_case   (	self		)    ->							Dict:
     pass





 def 							_snake_case   (	self		)    ->							Optional[Any]:
     pass









'''simple docstring'''


import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							"T5Config"


def 	__a(SCREAMING_SNAKE_CASE_  :					jnp.array		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      jnp.zeros_like(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      jnp.where(shifted_input_ids == -100		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )
    return shifted_input_ids

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Any         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Dict         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Optional[Any]         =					"mt5"
 __lowerCamelCase						:							str         =					MTaConfig


