


from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					{"tokenization_byt5": ["ByT5Tokenizer"]}


if TYPE_CHECKING:
 from .tokenization_byta import ByTaTokenizer
else:
 import sys

 SCREAMING_SNAKE_CASE			: int				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    List[str] =(PNDMScheduler,)
      UpperCamelCase__				:    Dict =(('num_inference_steps', 50),)






      def 			lowerCamelCase			(   self		:	Dict     ,  **lowercase_		:	Dict					)   ->		str:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            ={
                 'num_train_timesteps': 1000,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
             }

             config.update(**lowercase_					)
             return config






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	str=0     ,  **lowercase_		:	str					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =dict(self.forward_default_kwargs					)
             _lowerCamelCase			:						Optional[Any]            =kwargs.pop('num_inference_steps'     ,  lowercase_					)
             _lowerCamelCase			:						Tuple            =self.dummy_sample
             _lowerCamelCase			:						int            =0.1 * sample
             _lowerCamelCase			:						int            =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						str            =self.get_scheduler_config(**lowercase_					)
                    _lowerCamelCase			:						str            =scheduler_class(**lowercase_					)
                    scheduler.set_timesteps(lowercase_					)
                    # copy over dummy past residuals
                    _lowerCamelCase			:						Any            =dummy_past_residuals[:]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_					)
                           _lowerCamelCase			:						Optional[Any]            =scheduler_class.from_pretrained(lowercase_					)
                           new_scheduler.set_timesteps(lowercase_					)
                           # copy over dummy past residuals
                           _lowerCamelCase			:						Any            =dummy_past_residuals[:]

                    _lowerCamelCase			:						Union[str, Any]            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						List[str]            =new_scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

                    assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

                    _lowerCamelCase			:						Optional[int]            =scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						Optional[Any]            =new_scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

                    assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"






      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	List[str]=0     ,  **lowercase_		:	int					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Any            =dict(self.forward_default_kwargs					)
             _lowerCamelCase			:						Dict            =kwargs.pop('num_inference_steps'     ,  lowercase_					)
             _lowerCamelCase			:						Union[str, Any]            =self.dummy_sample
             _lowerCamelCase			:						Optional[int]            =0.1 * sample
             _lowerCamelCase			:						List[str]            =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						Any            =self.get_scheduler_config()
                    _lowerCamelCase			:						int            =scheduler_class(**lowercase_					)
                    scheduler.set_timesteps(lowercase_					)

                    # copy over dummy past residuals (must be after setting timesteps)
                    _lowerCamelCase			:						Any            =dummy_past_residuals[:]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_					)
                           _lowerCamelCase			:						Optional[Any]            =scheduler_class.from_pretrained(lowercase_					)
                           # copy over dummy past residuals
                           new_scheduler.set_timesteps(lowercase_					)

                           # copy over dummy past residual (must be after setting timesteps)
                           _lowerCamelCase			:						Optional[int]            =dummy_past_residuals[:]

                    _lowerCamelCase			:						List[str]            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						str            =new_scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

                    assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

                    _lowerCamelCase			:						Optional[Any]            =scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						List[str]            =new_scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

                    assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  **lowercase_		:	Any					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =self.scheduler_classes[0]
             _lowerCamelCase			:						Optional[Any]            =self.get_scheduler_config(**lowercase_					)
             _lowerCamelCase			:						Any            =scheduler_class(**lowercase_					)

             _lowerCamelCase			:						Union[str, Any]            =10
             _lowerCamelCase			:						str            =self.dummy_model()
             _lowerCamelCase			:						Union[str, Any]            =self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_					)

             for i, t in enumerate(scheduler.prk_timesteps					):
                    _lowerCamelCase			:						Union[str, Any]            =model(lowercase_     ,  lowercase_					)
                    _lowerCamelCase			:						Any            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_					).prev_sample

             for i, t in enumerate(scheduler.plms_timesteps					):
                    _lowerCamelCase			:						List[Any]            =model(lowercase_     ,  lowercase_					)
                    _lowerCamelCase			:						int            =scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_					).prev_sample

             return sample






      def 			lowerCamelCase			(   self		:	Dict					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =dict(self.forward_default_kwargs					)

             _lowerCamelCase			:						Optional[int]            =kwargs.pop('num_inference_steps'     ,  lowercase_					)

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						List[str]            =self.get_scheduler_config()
                    _lowerCamelCase			:						List[Any]            =scheduler_class(**lowercase_					)

                    _lowerCamelCase			:						Union[str, Any]            =self.dummy_sample
                    _lowerCamelCase			:						Optional[int]            =0.1 * sample

                    if num_inference_steps is not None and hasattr(lowercase_     ,  'set_timesteps'					):
                           scheduler.set_timesteps(lowercase_					)
                    elif num_inference_steps is not None and not hasattr(lowercase_     ,  'set_timesteps'					):
                           _lowerCamelCase			:						Tuple            =num_inference_steps

                    # copy over dummy past residuals (must be done after set_timesteps)
                    _lowerCamelCase			:						List[Any]            =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                    _lowerCamelCase			:						Union[str, Any]            =dummy_past_residuals[:]

                    _lowerCamelCase			:						List[Any]            =scheduler.step_prk(lowercase_     ,  0     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						str            =scheduler.step_prk(lowercase_     ,  1     ,  lowercase_     ,  **lowercase_					).prev_sample

                    self.assertEqual(output_a.shape     ,  sample.shape					)
                    self.assertEqual(output_a.shape     ,  output_a.shape					)

                    _lowerCamelCase			:						Tuple            =scheduler.step_plms(lowercase_     ,  0     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						Optional[Any]            =scheduler.step_plms(lowercase_     ,  1     ,  lowercase_     ,  **lowercase_					).prev_sample

                    self.assertEqual(output_a.shape     ,  sample.shape					)
                    self.assertEqual(output_a.shape     ,  output_a.shape					)






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Any:
             """simple docstring"""




             for timesteps in [100, 1000]:
                    self.check_over_configs(num_train_timesteps=lowercase_					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[Any]:
             """simple docstring"""




             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=lowercase_					)

             _lowerCamelCase			:						Optional[int]            =self.scheduler_classes[0]
             _lowerCamelCase			:						Dict            =self.get_scheduler_config(steps_offset=1					)
             _lowerCamelCase			:						List[Any]            =scheduler_class(**lowercase_					)
             scheduler.set_timesteps(10					)
             assert torch.equal(
                 scheduler.timesteps     ,  torch.LongTensor(
                     [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					)     ,  )






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		str:
             """simple docstring"""




             for beta_start, beta_end in zip([0.0001, 0.001]     ,  [0.002, 0.02]					):
                    self.check_over_configs(beta_start=lowercase_     ,  beta_end=lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Union[str, Any]:
             """simple docstring"""




             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Any:
             """simple docstring"""




             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=lowercase_					)






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             for t in [1, 5, 10]:
                    self.check_over_forward(time_step=lowercase_					)






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		List[Any]:
             """simple docstring"""




             for t, num_inference_steps in zip([1, 5, 10]     ,  [10, 50, 100]					):
                    self.check_over_forward(num_inference_steps=lowercase_					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =27

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						Dict            =self.dummy_sample
                    _lowerCamelCase			:						List[Any]            =0.1 * sample

                    _lowerCamelCase			:						List[Any]            =self.get_scheduler_config()
                    _lowerCamelCase			:						List[str]            =scheduler_class(**lowercase_					)

                    scheduler.set_timesteps(lowercase_					)

                    # before power of 3 fix, would error on first step, so we only need to do two
                    for i, t in enumerate(scheduler.prk_timesteps[:2]					):
                           _lowerCamelCase			:						Tuple            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_					).prev_sample






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		Optional[int]:
             """simple docstring"""




             with self.assertRaises(lowercase_					):
                    _lowerCamelCase			:						Dict            =self.scheduler_classes[0]
                    _lowerCamelCase			:						Optional[Any]            =self.get_scheduler_config()
                    _lowerCamelCase			:						List[Any]            =scheduler_class(**lowercase_					)

                    scheduler.step_plms(self.dummy_sample     ,  1     ,  self.dummy_sample					).prev_sample






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =self.full_loop()
             _lowerCamelCase			:						Optional[Any]            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						Union[str, Any]            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 198.1318					) < 1E-2
             assert abs(result_mean.item() - 0.2580					) < 1E-3






      def 			lowerCamelCase			(   self		:	Any					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.full_loop(prediction_type='v_prediction'					)
             _lowerCamelCase			:						Dict            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						str            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 67.3986					) < 1E-2
             assert abs(result_mean.item() - 0.0878					) < 1E-3






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =self.full_loop(set_alpha_to_one=lowercase_     ,  beta_start=0.01					)
             _lowerCamelCase			:						Optional[Any]            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						Union[str, Any]            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 230.0399					) < 1E-2
             assert abs(result_mean.item() - 0.2995					) < 1E-3






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						str            =self.full_loop(set_alpha_to_one=lowercase_     ,  beta_start=0.01					)
             _lowerCamelCase			:						Union[str, Any]            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						str            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 186.9482					) < 1E-2
             assert abs(result_mean.item() - 0.2434					) < 1E-3


