





"""simple docstring"""
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 					_SCREAMING_SNAKE_CASE(   _snake_case  ):
       SCREAMING_SNAKE_CASE_   :      List[str]							=  (PNDMScheduler,)
       SCREAMING_SNAKE_CASE_   :      Any							=  (("""num_inference_steps""", 50),)




       def     _UpperCamelCase     (						self		,**SCREAMING_SNAKE_CASE__							)			->      Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Dict						=  {
                 """num_train_timesteps""": 10_00,
                 """beta_start""": 0.0_0_0_1,
                 """beta_end""": 0.0_2,
                 """beta_schedule""": """linear""",
             }

             config.update(**__snake_case							)
             return config




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__=0		,**SCREAMING_SNAKE_CASE__							)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  dict(self.forward_default_kwargs							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  kwargs.pop('''num_inference_steps'''		,__snake_case							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  self.dummy_sample
             __SCREAMING_SNAKE_CASE   :List[str]						=  0.1 * sample
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

                   __SCREAMING_SNAKE_CASE   :Dict						=  scheduler.step_prk(__snake_case		,__snake_case		,__snake_case		,**__snake_case							).prev_sample
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  new_scheduler.step_prk(__snake_case		,__snake_case		,__snake_case		,**__snake_case							).prev_sample

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

                   __SCREAMING_SNAKE_CASE   :Dict						=  scheduler.step_plms(__snake_case		,__snake_case		,__snake_case		,**__snake_case							).prev_sample
                   __SCREAMING_SNAKE_CASE   :List[str]						=  new_scheduler.step_plms(__snake_case		,__snake_case		,__snake_case		,**__snake_case							).prev_sample

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




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





             """simple docstring"""






             pass




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__=0		,**SCREAMING_SNAKE_CASE__							)			->      str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Dict						=  dict(self.forward_default_kwargs							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  kwargs.pop('''num_inference_steps'''		,__snake_case							)
             __SCREAMING_SNAKE_CASE   :Any						=  self.dummy_sample
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  0.1 * sample
             __SCREAMING_SNAKE_CASE   :Tuple						=  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

             for scheduler_class in self.scheduler_classes:
                   __SCREAMING_SNAKE_CASE   :int						=  self.get_scheduler_config()
                   __SCREAMING_SNAKE_CASE   :Dict						=  scheduler_class(**__snake_case							)
                   scheduler.set_timesteps(__snake_case							)

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

                   with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(__snake_case							)
                         __SCREAMING_SNAKE_CASE   :str						=  scheduler_class.from_pretrained(__snake_case							)
                         # copy over dummy past residuals
                         new_scheduler.set_timesteps(__snake_case							)

                         # copy over dummy past residual (must be after setting timesteps)
                         __SCREAMING_SNAKE_CASE   :Tuple						=  dummy_past_residuals[:]

                   __SCREAMING_SNAKE_CASE   :Tuple						=  scheduler.step_prk(__snake_case		,__snake_case		,__snake_case		,**__snake_case							).prev_sample
                   __SCREAMING_SNAKE_CASE   :Any						=  new_scheduler.step_prk(__snake_case		,__snake_case		,__snake_case		,**__snake_case							).prev_sample

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

                   __SCREAMING_SNAKE_CASE   :List[str]						=  scheduler.step_plms(__snake_case		,__snake_case		,__snake_case		,**__snake_case							).prev_sample
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  new_scheduler.step_plms(__snake_case		,__snake_case		,__snake_case		,**__snake_case							).prev_sample

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




       def     _UpperCamelCase     (						self		,**SCREAMING_SNAKE_CASE__							)			->      List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  self.scheduler_classes[0]
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.get_scheduler_config(**__snake_case							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  scheduler_class(**__snake_case							)

             __SCREAMING_SNAKE_CASE   :List[Any]						=  10
             __SCREAMING_SNAKE_CASE   :Dict						=  self.dummy_model()
             __SCREAMING_SNAKE_CASE   :int						=  self.dummy_sample_deter
             scheduler.set_timesteps(__snake_case							)

             for i, t in enumerate(scheduler.prk_timesteps							):
                   __SCREAMING_SNAKE_CASE   :List[Any]						=  model(__snake_case		,__snake_case							)
                   __SCREAMING_SNAKE_CASE   :List[Any]						=  scheduler.step_prk(__snake_case		,__snake_case		,__snake_case							).prev_sample

             for i, t in enumerate(scheduler.plms_timesteps							):
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  model(__snake_case		,__snake_case							)
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  scheduler.step_plms(__snake_case		,__snake_case		,__snake_case							).prev_sample

             return sample




       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  dict(self.forward_default_kwargs							)

             __SCREAMING_SNAKE_CASE   :str						=  kwargs.pop('''num_inference_steps'''		,__snake_case							)

             for scheduler_class in self.scheduler_classes:
                   __SCREAMING_SNAKE_CASE   :Any						=  self.get_scheduler_config()
                   __SCREAMING_SNAKE_CASE   :List[Any]						=  scheduler_class(**__snake_case							)

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

                   if num_inference_steps is not None and hasattr(__snake_case		,'''set_timesteps'''							):
                         scheduler.set_timesteps(__snake_case							)
                   elif num_inference_steps is not None and not hasattr(__snake_case		,'''set_timesteps'''							):
                         __SCREAMING_SNAKE_CASE   :Any						=  num_inference_steps

                   # copy over dummy past residuals (must be done after set_timesteps)
                   __SCREAMING_SNAKE_CASE   :List[str]						=  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
                   __SCREAMING_SNAKE_CASE   :Any						=  dummy_past_residuals[:]

                   __SCREAMING_SNAKE_CASE   :Any						=  scheduler.step_prk(__snake_case		,0		,__snake_case		,**__snake_case							).prev_sample
                   __SCREAMING_SNAKE_CASE   :Any						=  scheduler.step_prk(__snake_case		,1		,__snake_case		,**__snake_case							).prev_sample

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

                   __SCREAMING_SNAKE_CASE   :List[Any]						=  scheduler.step_plms(__snake_case		,0		,__snake_case		,**__snake_case							).prev_sample
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  scheduler.step_plms(__snake_case		,1		,__snake_case		,**__snake_case							).prev_sample

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




       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             for timesteps in [1_00, 10_00]:
                   self.check_over_configs(num_train_timesteps=__snake_case							)




       def     _UpperCamelCase     (						self							)			->      Optional[Any]:





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE   :List[Any]						=  self.scheduler_classes[0]
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  self.get_scheduler_config(steps_offset=1							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  scheduler_class(**__snake_case							)
             scheduler.set_timesteps(10							)
             assert torch.equal(
                 scheduler.timesteps		,torch.LongTensor(
                     [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]							)		,)




       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






             for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]		,[0.0_0_2, 0.0_2]							):
                   self.check_over_configs(beta_start=__snake_case		,beta_end=__snake_case							)




       def     _UpperCamelCase     (						self							)			->      List[str]:





             """simple docstring"""






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




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





             """simple docstring"""






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




       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






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




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





             """simple docstring"""






             for t, num_inference_steps in zip([1, 5, 10]		,[10, 50, 1_00]							):
                   self.check_over_forward(num_inference_steps=__snake_case							)




       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  27

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

                   __SCREAMING_SNAKE_CASE   :Tuple						=  self.get_scheduler_config()
                   __SCREAMING_SNAKE_CASE   :Any						=  scheduler_class(**__snake_case							)

                   scheduler.set_timesteps(__snake_case							)

                   # 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]							):
                         __SCREAMING_SNAKE_CASE   :Optional[Any]						=  scheduler.step_prk(__snake_case		,__snake_case		,__snake_case							).prev_sample




       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             with self.assertRaises(__snake_case							):
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  self.scheduler_classes[0]
                   __SCREAMING_SNAKE_CASE   :Tuple						=  self.get_scheduler_config()
                   __SCREAMING_SNAKE_CASE   :Any						=  scheduler_class(**__snake_case							)

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




       def     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[Any]						=  self.full_loop()
             __SCREAMING_SNAKE_CASE   :List[str]						=  torch.sum(torch.abs(__snake_case							)							)
             __SCREAMING_SNAKE_CASE   :str						=  torch.mean(torch.abs(__snake_case							)							)

             assert abs(result_sum.item() - 1_9_8.1_3_1_8							) < 1E-2
             assert abs(result_mean.item() - 0.2_5_8_0							) < 1E-3




       def     _UpperCamelCase     (						self							)			->      Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  self.full_loop(prediction_type='''v_prediction'''							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  torch.sum(torch.abs(__snake_case							)							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  torch.mean(torch.abs(__snake_case							)							)

             assert abs(result_sum.item() - 6_7.3_9_8_6							) < 1E-2
             assert abs(result_mean.item() - 0.0_8_7_8							) < 1E-3




       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  self.full_loop(set_alpha_to_one=__snake_case		,beta_start=0.0_1							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  torch.sum(torch.abs(__snake_case							)							)
             __SCREAMING_SNAKE_CASE   :str						=  torch.mean(torch.abs(__snake_case							)							)

             assert abs(result_sum.item() - 2_3_0.0_3_9_9							) < 1E-2
             assert abs(result_mean.item() - 0.2_9_9_5							) < 1E-3




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  self.full_loop(set_alpha_to_one=__snake_case		,beta_start=0.0_1							)
             __SCREAMING_SNAKE_CASE   :Dict						=  torch.sum(torch.abs(__snake_case							)							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  torch.mean(torch.abs(__snake_case							)							)

             assert abs(result_sum.item() - 1_8_6.9_4_8_2							) < 1E-2
             assert abs(result_mean.item() - 0.2_4_3_4							) < 1E-3



'''simple docstring'''


from __future__ import annotations

import os
from typing import Any

import requests

UpperCAmelCase_		:   List[Any]							   =		'https://api.github.com'

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCAmelCase_		:   Optional[Any]							   =		BASE_URL + '/user'

# https://github.com/settings/tokens
UpperCAmelCase_		:   str							   =		os.environ.get('USER_TOKEN', '')







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      {
				    """Authorization""": f"""token {auth_token}""",
				    """Accept""": """application/vnd.github.v3+json""",
				}
				return requests.get(SCREAMING_SNAKE_CASE__							,      headers=SCREAMING_SNAKE_CASE__     ).json()


if __name__ == "__main__":  # pragma: no cover
				if USER_TOKEN:
								for key, value in fetch_github_info(USER_TOKEN).items():
												print(F"{key}: {value}")
				else:
								raise ValueError('\'USER_TOKEN\' field cannot be empty.')




